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Dec 9, 2010 - The common carp (Cyprinus carpio carpio) is a fresh- water fish and one ...... Conrardy, S. Tong , I.V. Kuzmin, M. Niezgoda, C.E. Rupprecht, J.R.. Gentsch, M.D. ...... Dalton C, Durrheim D, Fejsa J, Francis L, Carlson S, Tursan.
Peer-Reviewed Journal Tracking and Analyzing Disease Trends

pages 1827–2040

EDITOR-IN-CHIEF D. Peter Drotman Managing Senior Editor Polyxeni Potter, Atlanta, Georgia, USA Senior Associate Editor Brian W.J. Mahy, Bury St. Edmunds, Suffolk, UK Associate Editors Paul Arguin, Atlanta, Georgia, USA Charles Ben Beard, Ft. Collins, Colorado, USA Ermias Belay, Atlanta, GA, USA David Bell, Atlanta, Georgia, USA Corrie Brown, Athens, Georgia, USA Charles H. Calisher, Ft. Collins, Colorado, USA Michel Drancourt, Marseille, France Paul V. Effler, Perth, Australia David Freedman, Birmingham, AL, USA Peter Gerner-Smidt, Atlanta, GA, USA Stephen Hadler, Atlanta, GA, USA K. Mills McNeill, Madison, MS, USA Nina Marano, Atlanta, Georgia, USA Martin I. Meltzer, Atlanta, Georgia, USA David Morens, Bethesda, Maryland, USA J. Glenn Morris, Gainesville, Florida, USA Patrice Nordmann, Paris, France Tanja Popovic, Atlanta, Georgia, USA Didier Raoult, Marseille, France Pierre Rollin, Atlanta, Georgia, USA Ronald M. Rosenberg, Fort Collins, Colorado, USA Dixie E. Snider, Atlanta, Georgia, USA Frank Sorvillo, Los Angeles, California, USA David Walker, Galveston, Texas, USA David Warnock, Atlanta, Georgia, USA J. Todd Weber, Stockholm, Sweden Henrik C. Wegener, Copenhagen, Denmark Founding Editor Joseph E. McDade, Rome, Georgia, USA Copy Editors Karen Foster, Thomas Gryczan, Nancy Mannikko, Beverly Merritt, Carol Snarey, P. Lynne Stockton Production Ann Jordan, Carole Liston, Shannon O’Connor, Reginald Tucker Editorial Assistant Carrie Huntington

www.cdc.gov/eid Emerging Infectious Diseases

Emerging Infectious Diseases is published monthly by the Centers for Disease Control and Prevention, 1600 Clifton Road, Mailstop D61, Atlanta, GA 30333, USA. Telephone 404-639-1960, fax 404-639-1954, email [email protected] The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the Centers for Disease Control and Prevention or the institutions with which the authors are affiliated. All material published in Emerging Infectious Diseases is in the public domain and may be used and reprinted without special permission; proper citation, however, is required. Use of trade names is for identification only and does not imply endorsement by the Public Health Service or by the U.S. Department of Health and Human Services.

EDITORIAL BOARD Dennis Alexander, Addlestone Surrey, United Kingdom Timothy Barrett, Atlanta, GA, USA Barry J. Beaty, Ft. Collins, Colorado, USA Martin J. Blaser, New York, New York, USA Christopher Braden, Atlanta, GA, USA Carolyn Bridges, Atlanta, GA, USA Arturo Casadevall, New York, New York, USA Kenneth C. Castro, Atlanta, Georgia, USA Louisa Chapman, Atlanta, GA, USA Thomas Cleary, Houston, Texas, USA Vincent Deubel, Shanghai, China Ed Eitzen, Washington, DC, USA Daniel Feikin, Baltimore, MD, USA Kathleen Gensheimer, Cambridge, MA, USA Duane J. Gubler, Singapore Richard L. Guerrant, Charlottesville, Virginia, USA Scott Halstead, Arlington, Virginia, USA David L. Heymann, London, UK Charles King, Cleveland, Ohio, USA Keith Klugman, Atlanta, Georgia, USA Takeshi Kurata, Tokyo, Japan S.K. Lam, Kuala Lumpur, Malaysia Stuart Levy, Boston, Massachusetts, USA John S. MacKenzie, Perth, Australia Marian McDonald, Atlanta, Georgia, USA John E. McGowan, Jr., Atlanta, Georgia, USA Tom Marrie, Halifax, Nova Scotia, Canada Philip P. Mortimer, London, United Kingdom Fred A. Murphy, Galveston, Texas, USA Barbara E. Murray, Houston, Texas, USA P. Keith Murray, Geelong, Australia Stephen M. Ostroff, Harrisburg, Pennsylvania, USA David H. Persing, Seattle, Washington, USA Richard Platt, Boston, Massachusetts, USA Gabriel Rabinovich, Buenos Aires, Argentina Mario Raviglione, Geneva, Switzerland David Relman, Palo Alto, California, USA Connie Schmaljohn, Frederick, Maryland, USA Tom Schwan, Hamilton, Montana, USA Ira Schwartz, Valhalla, New York, USA Tom Shinnick, Atlanta, Georgia, USA Bonnie Smoak, Bethesda, Maryland, USA Rosemary Soave, New York, New York, USA P. Frederick Sparling, Chapel Hill, North Carolina, USA Robert Swanepoel, Johannesburg, South Africa Phillip Tarr, St. Louis, Missouri, USA Timothy Tucker, Cape Town, South Africa Elaine Tuomanen, Memphis, Tennessee, USA John Ward, Atlanta, Georgia, USA Mary E. Wilson, Cambridge, Massachusetts, USA ∞ Emerging Infectious Diseases is printed on acid-free paper that meets the requirements of ANSI/NISO 239.48-1992 (Permanence of Paper)

Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 16, No. 12, December 2010

December 2010 On the Cover

Bartonella spp. in Bats, Kenya ................1875

Jacopo Bassano (c. 1510–1592) Allegory of Water (16th century) Oil on canvas (139.7 cm × 180.3 cm) Bequest of John Ringling, 1936, Collection of The John and Mable Ringling Museum of Art, the State Art Museum of Florida, a division of Florida State University About the Cover p. 2025

Perspective

Surveillance and Analysis of Avian Influenza Viruses, Australia ..........1896

Synopsis

P.M. Hansbro et al. A third lineage unique to Australia has been identified.

Cyprinid Herpesvirus 3 ............................1835 B. Michel et al. This virus is useful for fundamental and applied research.

p. 1841

Research M.D. Esona et al. Bats may be reservoirs of zoonotic viruses that threaten human health.

B. Durand et al. Environmental seropositivity risk factors indicate that natural ecosystems could have played a role.

Eastern Equine Encephalitis Virus in Mosquitoes and Their Role as Bridge Vectors ..........................................1869 P.M. Armstrong and T.G. Andreadis Virus titers are useful for assessing which mosquito species may transmit virus.

Freshwater Aquaculture Nurseries and Infection of Fish with Zoonotic Trematodes, Vietnam ...............................1905 V.T. Phan et al. Nurseries should be included in control programs to produce parasite-free fish for human consumption.

Reassortant Group A Rotavirus from Straw-colored Fruit Bat ...................1844

Bluetongue Virus Serotype 8 Epizootic Wave, France, 2007–2008 .......1861

A.G. Alzahrani et al. Infection was associated with tick bites and contact with farm animals.

R. Indriani et al. This method is time and labor efficient and minimizes potential risk for virus aerosolization.

B.J. Hoye et al. Hypothesis-driven surveillance through strategic compilation of standardized, local surveys over broad geographic areas is needed.

V.P. Martinez et al. Limited person-to-person transmission is suggested.

Alkhurma Hemorrhagic Fever in Humans, Najran, Saudi Arabia ................1882

Environmental Sampling for Avian Influenza Virus A (H5N1) in Live-Bird Markets, Indonesia ...................................1889

Surveillance of Wild Birds for Avian Influenza Virus ...............................1827

Hantavirus Pulmonary Syndrome in Argentina, 1995–2008 ...............................1853

M. Kosoy et al. These bacteria might be responsible for human illnesses.

Pandemic (H1N1) 2009 Infection in Patients with Hematologic Malignancy ...1910 p. 1947

C. Liu et al. Infection control, improved diagnostics, and early therapy can prevent nosocomial transmission and optimize patient care.

Yellow Fever Virus in Haemagogus leucocelaenus and Aedes serratus Mosquitoes, Southern Brazil, 2008 .........1918 J. da C. Cardoso et al. Hg. leucocelaenus is the main vector; Ae. serratus may be a secondary vector.

Reemergence of Rabies in Chhukha District, Bhutan, 2008 ...............................1925 Tenzin et al. A major outbreak affected dogs, domestic livestock, and humans.

Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 16, No. 12, December 2010

Historical Review Mortality Risk Factors for Pandemic Influenza on New Zealand Troop Ship, 1918 ..................................................1931

December 2010

J.A. Summers et al. Crowding and ventilation problems contributed to an increased risk for death.

1994

Increasing Contact with Hepatitis E Virus in Red Deer, Spain M. Boadella et al.

1997

Henipavirus and Rubulavirus Antibodies in Pteropid Bats, Papua New Guinea A.C. Breed et al.

Dispatches 1938

1943 1946

1949

Ocular Thelaziosis in Dogs, France P. Ruytoor et al. Emergence of African Swine Fever Virus, Northwestern Iran P. Rahimi et al.

p. 1964

Mycobacterium tuberculosis Infection of Domesticated Asian Elephants, Thailand T. Angkawanish et al.

1952

Hantaviruses and Hantavirus Pulmonary Syndrome, Maranhão, Brazil E.S. Travassos da Rosa et al.

1956

Wild Chimpanzees Infected with 5 Plasmodium Species M. Kaiser et al.

1960

Letters

Oseltamivir-Resistant Pandemic (H1N1) 2009 Virus, South Korea H. Yi et al.

Online Flutracking Survey of Influenzalike Illness during Pandemic (H1N1) 2009, Australia S.J. Carlson et al.

p. 2015

2000

Fatal 1918 Pneumonia Complicated by Erythrocyte Sickling

2001

Human Brucellosis, Inner Mongolia, China

2003

Multiple Serotypes of Bluetongue Virus in Sheep and Cattle, Israel

2004

Rabies Virus RNA in Naturally Infected Vampire Bats, Northeastern Brazil

2006

Wildlife-associated Cryptosporidium fayeri Infection in Human, Australia

2007

Canine Distemper Epizootic among Red Foxes, Italy, 2009

2009

Ribavirin for Lassa Fever Postexposure Prophylaxis

2011

Transmission of Ovine Herpesvirus 2 from Asymptomatic Boars to Sows

2013

Molecular Detection of Bartonella alsatica in Rabbit Fleas, France

2014

Cutaneous Myiasis and Chrysomya bezziana Larvae, Mexico

2016

Rabbit Tularemia and Hepatic Coccidiosis in Wild Rabbit

1963

Bartonella henselae in Skin Biopsy Specimens of Patients with Cat-Scratch Disease E. Angelakis et al.

2017

1966

Brucella ceti Infection in Harbor Porpoise T.P. Jauniaux et al.

Imported Leishmaniasis in Dogs, US Military Bases, Japan

2019

1969

Bundibugyo Ebola Virus Infection, Uganda A. MacNeil et al.

Pandemic (H1N1) 2009 Infection in Dogs, Italy

2021

Brucellosis Reactivation after 28 Years

1973

Leishmania tropica Infection in Golden Jackals and Red Foxes, Israel D. Talmi-Frank et al.

2023

In Memoriam Jocelyn Anne Rankin (1946–2010)

1976

Co-detection of Pandemic (H1N1) 2009 Virus and Other Respiratory Pathogens K. Koon et al.

1979

Alkhurma Hemorrhagic Fever in Travelers Returning from Egypt F. Carletti et al.

1983

Multispacer Typing of Bartonella henselae Isolates from Humans and Cats, Japan M. Yanigahara et al.

1986

Pandemic (H1N1) 2009 Outbreak at Canadian Forces Cadet Camp R.Y. Kropp et al.

1990

Characterization of Nipah Virus from Naturally Infected Bats, Malaysia S.A. Rahman et al.

Book Review 2024

Rinderpest and Peste des Petits Ruminants: Virus Plagues of Large and Small Ruminants

About the Cover 2025

Abundant Harvest and Fishing for Trouble

1843

Etymologia Cyprinid Herpesvirus

2024 2024

Corrections Vol. 16, No. 8 Vol. 16, No. 9

Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 16, No. 12, December 2010

Surveillance of Wild Birds for Avian Influenza Virus Bethany J. Hoye, Vincent J. Munster, Hiroshi Nishiura, Marcel Klaassen, and Ron A.M. Fouchier

Recent demand for increased understanding of avian influenza virus in its natural hosts, together with the development of high-throughput diagnostics, has heralded a new era in wildlife disease surveillance. However, survey design, sampling, and interpretation in the context of host populations still present major challenges. We critically reviewed current surveillance to distill a series of considerations pertinent to avian influenza virus surveillance in wild birds, including consideration of what, when, where, and how many to sample in the context of survey objectives. Recognizing that wildlife disease surveillance is logistically and financially constrained, we discuss pragmatic alternatives for achieving probability-based sampling schemes that capture this host–pathogen system. We recommend hypothesis-driven surveillance through standardized, local surveys that are, in turn, strategically compiled over broad geographic areas. Rethinking the use of existing surveillance infrastructure can thereby greatly enhance our global understanding of avian influenza and other zoonotic diseases.

A

vian influenza virus (AIV) gained a high profile after the unprecedented bird-to-human transmission of highly pathogenic AIV (HPAIV) subtype H5N1 in 1997. Originating in Asia, HPAIV (H5N1) subsequently caused widespread deaths among wild and domestic birds in Southeast Asia and westward throughout Europe and Africa in

Author affiliations: Netherlands Institute for Ecology, Nieuwersluis, the Netherlands (B.J. Hoye, M. Klaassen); Erasmus Medical Centre, Rotterdam, the Netherlands (V.J. Munster, R.A.M. Fouchier); National Institute of Health, Hamilton, Montana, USA (V.J. Munster); University of Utrecht, Utrecht, the Netherlands (H. Nishiura); Japan Science and Technology Agency, Saitama, Japan (H. Nishiura); and Deakin University, Waurn Ponds, Victoria, Australia (M. Klaassen) DOI: 10.3201/eid1612.100589

2005 and 2006. After ≈50 years of research in wild birds, a wide range of low-pathogenicity AIV (LPAIV) subtypes is known to circulate in numerous species (1,2–5), and LPAIVs are believed to perpetuate in aquatic bird populations (6). In contrast, outbreaks of HPAIV are extremely rare in wild birds (7). Although the role of wild birds in HPAIV maintenance remains controversial (8), the magnitude of the subtype H5N1 epidemics increased the demand for early recognition of potential threats to humans and poultry and an understanding of the natural history of AIV in wild birds. Consequently, surveillance of aquatic bird populations surged (9). Although surveillance for AIV often uses state-ofthe-art storage, transport and diagnostics, these must be underpinned by appropriate survey design, sampling, and interpretation in the context of the host population. In the wake of such rapid growth in surveillance, we reviewed the literature to determine a scientifically and statistically sound approach to the design, conduct, and interpretation of surveillance for AIV and other wildlife diseases. Current Surveillance We reviewed 191 published reports of surveillance in wild birds (online Technical Appendix, http://www.cdc. gov/EID/content/16/12/1827-Techapp.pdf). The number of studies initiated per year rapidly increased after the first reports of HPAIV (H5N1) in Asia (Figure 1). All studies addressed 4 major lines of investigation: 1) early detection of HPAIVs; 2) ecology and epidemiology of LPAIV in host populations; 3) diversity and evolution of viral strains within wild birds; and 4) identification of the pathogens that infect individual birds or populations, often as part of multipathogen surveillance. Multiple aims can, and often are, addressed within the same surveillance program, albeit in a post hoc manner. However, identifying the aims in

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tion and the rate and direction of genetic alterations could become a powerful tool for identifying transmission parameters, reservoir populations (14), viral maintenance in the face of host immunity (12,15), and factors promoting disease emergence (10). Such information also facilitates compilation of comprehensive diagnostic reference panels and generation of potential vaccines (13). Investigation of variation in the viral population requires isolates that represent the entire circulating virus pool. Host Health

Figure 1. Average number of surveys of avian influenza in wild birds initiated per year in different awareness periods: each decade from the first discovery in 1961 until the outbreak of highly pathogenic avian influenza virus (HPAIV) (H5N1) in Asia in 1997; the period after the first outbreak, 1997–2004; and the period after mass deaths of wild birds from HPAIV (H5N1) (2005–2007). Black bar sections indicate studies citing the detection of contemporary HPAIV strains as one of the main aims of their survey are indicated in black; white bar sections indicate studies investigating other aspects of the wild bird–avian influenza system without mention of monitoring HPAIV.

advance is vital, because what, when, and where to sample will critically depend on the purpose of the survey (10,11).

Almost 15% of the studies reviewed aimed to ascertain whether certain individuals or populations had been infected with AIV as part of broader health surveys within the context of conservation programs, or in an attempt to understand causes of death. Although these studies often have a predefined host population of interest, they are likely to be sensitive to the underlying spatial and temporal patterns of disease. Critical Assessment To characterize the specific features required for rigorous wildlife disease surveillance, it is critical to highlight methods that encumber our current approach. Our assessment therefore aims to foster the development of more objective and scientifically sound disease surveillance networks. Maximizing Viral Yield

Greater understanding of transmission cycles, reservoirs, and the role of wildlife in the dynamics of AIV invoke questions related to the epidemiology and ecology of the virus, including host range and spatial and temporal variation in infection (12,13). Elucidating such questions requires investigating not just presence or absence of infection in a specific host, but also prevalence over space and time.

A successful surveillance program is often perceived as one that identifies a high number of positive samples. Moreover, exploitation of spatial, temporal, phylogenetic, and demographic differences in viral prevalence have been advocated to maximize the proportion of positive samples collected (12,16). Minimizing the number of negative samples is expedient from a laboratory perspective, particularly when labor-intensive virus isolation techniques are being used. However, a key tenet of surveillance is that the sampling scheme is representative: infection characteristics of the host population and genetic diversity of the viral population are sufficiently captured, and results can be interpreted on the basis of statistical probability (11,17). A study designed to maximize the number of positive samples by sampling historically high cohorts, populations, times, and locations can confirm the presence of the disease in the sampled cohort. However, such samples cannot be used to conclude the absence of AIV in the population or to estimate prevalence or diversity of circulating viral strains (17).

Viral Diversity

Host Range

Early Detection of HPAIV

More than half of the studies reviewed, and all but a handful initiated since the mass bird deaths in 2005–2006, cited early detection of HPAIV as one of the main goals of conducting the research (Figure 1). Such early warning systems question whether HPAIV exists in a population at a given location and point in time. The global rarity of HPAIV in wild birds and apparent clustering of such cases (7) present additional challenges to addressing this aim. Ecology and Epidemiology

Influenza viruses are highly diverse and capable of rapid genetic alteration. Understanding the pathogenic and antigenic properties of AIVs circulating in the host popula1828

Although AIVs have been isolated from >100 species, several species from the orders Anseriformes (ducks, geese, and swans) and Charadriiformes (shorebirds) are thought to

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Surveillance of Wild Birds for Avian Influenza Virus

act as the reservoir community for AIV (6), primarily because AIVs have been most frequently isolated from these groups (9). Yet, surveillance is rarely representative of the diversity of wild birds or their relative abundance at the time and location of sampling. Considerable bias exists toward species that are easily caught or are present in accessible areas at high concentrations (9,13). Surveys that have included a wide range of species often obtained samples in a highly opportunistic manner, resulting in few species being sampled in reasonable numbers (12,13). For instance, despite sampling >56,000 birds in the Netherlands from 1998 to 2009, only 20 of the 174 species were sampled >300 times. Moreover, prevalence in a given species may vary over space and time. Although passerines have often been found negative for AIV, recent evidence suggests that, when sampled in or near waterfowl-rich bodies of water, a high proportion of individuals from 8 different passerine families show infection (18,19). Current surveillance may, therefore, overlook many potential reservoir or transient host species and their role in the introduction, transmission, maintenance and diversity of AIV. Temporal and Spatial Patterns

The prevalence of AIV infection has long been recognized to vary over time and space. Viruses have been most frequently isolated from duck populations in North America and Europe in late summer and early autumn (5,15,20), a pattern attributed to high concentrations of susceptible juvenile birds on premigratory staging grounds (4,6). Less frequent isolations from wintering populations have prompted suggestions that prevalence rapidly decreases over the course of autumn migration (21,22); thus, premigratory staging grounds in late summer and early autumn are considered the optimal time and location for conducting surveillance among waterfowl (16,23). Yet when samples have been collected elsewhere, high numbers of AIVs have been isolated in winter (21,24), spring (20), and summer (25). Several positive samples from birds in the tropics (26) have also been found, including unexpectedly high numbers in tropical Africa (27). The temporal and spatial bias in existing surveillance may therefore result in delayed detection of novel strains or an incomplete understanding of AIV transmission, maintenance, diversity, and evolution.

composed of >80% juvenile birds, and numerous infected adults have also been found (4,24). Given that recent experimental results indicate that age at the time of infection might also affect the extent of viral shedding (28), different age cohorts may play different roles in the introduction, transmission, maintenance, and diversity of AIVs. Site of Infection

AIVs replicate in the gastrointestinal tract (sampled by swabbing the cloaca or collecting droppings) and in the respiratory tract (sampled by swabbing the oropharynx) (16). Individual mallards (Anas platyrhynchos) have historically shown higher detection probability from cloacal c.f. oropharyngeal swabs (29; Figure 2). Accordingly, 61% of studies investigating contemporary infection sampled the gastrointestinal tract alone. However, the site of infection may differ between species. As part of ongoing surveillance (21,29), free-living Eurasian wigeons (Anas penelope) showed no difference in detection probability between the cloacal and oropharyngeal swabs (p>0.05, McNemar test; Figure 2). In contrast, white-fronted geese (Anser albifrons) were roughly 2× as likely to have infection detected in the oropharynx (6.58%; 95% confidence interval 6.57–6.59) than in the cloaca (3.13%; 95% confidence interval 3.13–3.14; p 10,000 species of birds worldwide, careful selection of a local target population is critical to the design of any surveillance program. Because the prevalence of infection is generally low (requiring large sample sizes) and can vary over time and between locations within a species, it is difficult to make an initial assessment of the most important species to target on the basis of virus detection alone. Each of the surveillance aims outlined above may be most appropriately addressed by considering 1) populations with evidence of previous infection, or ecologic potential for infection (32), on the basis of not only existing literature and conventional monitoring but also serosurveillance in a large number of locally and regionally abundant species; and 2) Evidence of contemporary AIV infection in populations that were identified in step 1, and species in which AIV has historically been detected (for comparative purposes). Surveillance for emergent HPAIV may also benefit from targeting species displaying natural histories of interest, including species that link wild and human/agricultural populations or disparate locations. Serologic studies have great potential for enhancing wildlife disease surveillance and understanding. However, in isolation, cross-sectional observations of seroprevalence provide insufficient information to interpret the degree to which a population has been infected with AIV. Without age specificity, high seroprevalence may indicate a recent outbreak of infection or long-term antibody maintenance rather than persistence of AIV infection in the population (14,16). Moreover, low seroprevalence may result from a high mortality rate among infected birds, a long time interval between infection and sampling, or species-specific differences in the sensitivity or specificity of the antibody

diagnostics. Explicit interpretation of seroprevalence calls for age-specific sampling, longitudinal observations, understanding of the underlying epidemiologic dynamics, and experimental validation of antibody diagnostics. Individual Birds within Populations

Within each species, infection may depend on multiple factors, including age and prior exposure to AIV (4), gender (33), and even nutrition or social status (8). Given that most capture methods inherently result in biases within these cohorts, a population should ideally be sampled to account for these differences. Experimental validation of such interindividual differences in infection could greatly enhance the design and interpretation of surveillance. When, Where, and How Often to Sample? When and where sampling is conducted will critically depend on the question at hand and should be representative of the biology of the hosts of interest. Single time or location studies may be sufficient to inform of novel incursions of HPAIV (Table) and may therefore be best matched to times/locations with a high risk for wild bird–poultry interaction. Changes in climatic conditions, host population dynamics, and host population immunity are likely relevant to understanding the ecology, epidemiology, and evolution of AIV in its natural host(s) (34). Enhancing our knowledge in these areas will require information from before, during, and after infection from ecologically connected populations (35), often over longer periods and across large spatial scales when studying migratory birds (36). Coordinated local surveys, both along flyways and over time, will greatly enhance these efforts. How Many Individual Birds Should Be Sampled? As prevalence decreases, an increasingly large number of birds need to be sampled to detect contemporary infection (Figures 3, 4). Deciding just how many is critically dependent on the study aim, with a clear distinction between surveys that aim to substantiate freedom from infection (presence or absence), and those that are designed to provide an estimate of disease prevalence.

Table. Data requirements for assessment of major questions regarding avian influenza in wild birds* Aim Type of question Geographic range Temporal range Early detection Presence/absence Local/regional Period when birds present of HPAIV Ecology and Comparative prevalence Local to flyway, depending 1 to many epidemic epidemiology on the process in question seasons (multiple times/year) Diversity and evolution

Comparative prevalence (of viral strains)

Flyway to global

Frequency Approximately weekly (average infection duration) Weekly to monthly (multiple times before, during, and after an epidemic)

Decades (multiple times/year repeated for multiple years)

Monthly to seasonally

*Larger-scale studies can be compiled over large geographic areas from relevant local surveys that are methodologically comparable and over long periods from relevant annual surveys that are likewise methodologically comparable. HPAIV, highly pathogenic avian influenza virus.

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Figure 3. Probability of detecting >1 individual bird infected with avian influenza virus from a given number of samples selected at random from an extremely large population in which individual birds are infected at random at different prevalence levels. Although this nominal minimum detectable prevalence assumes binomial sampling, it can also be used for gaining a rough quantitative estimate of the minimum number of samples required before embarking on a surveillance program.

Presence/Absence

In practice, it is not possible to confirm disease freedom in a large population by any direct observational method. Instead, appropriate sampling and analysis can demonstrate that at that time and location, prevalence was below a nominal detection threshold (online Technical Appendix) (17). Although this nominal minimum detectable prevalence assumes binomial sampling, it can also be used for gaining a rough quantitative estimate of the minimum number of samples required before embarking on a surveillance program (Figure 3; online Technical Appendix). Given that information on the absence of pathogens is crucial to understanding disease dynamics (10), postsurveillance reporting of such maximum undetected prevalence is highly desirable for all studies with negative findings.

demographic, and phylogenetic variation in the wild bird population, often requiring detailed information on host population size, density, demographic structure, rates of recruitment and attrition, habitat utilization, and species composition. However, wildlife surveillance is also faced with substantial logistical and financial constraints. Effective surveillance, therefore, requires a compromise between sampling that is based on probability and the constraints of sample collection, transport and analysis, the details of which will depend on the specific objectives of the survey. To this end, it is critical to have active, investigator-defined surveillance designs based on probability on a larger scale while using convenience sampling within these units (11). For instance, probability methods could be used to plan the species, locations, and months of the year to sample, and a certain number of individual birds within these units could be sampled by ornithologists and hunters, with additional top-up sampling where necessary. Such convenience-within-probability surveillance could provide statistically valid estimates of disease absence and prevalence by reducing the effect of bias generated by sampling on a first-comefirst-served basis. It facilitates stipulation of an upper limit to the use of convenience samples, allowing targeted allocation of limited sampling, diagnostic, and financial resources. To employ such convenience-within-probability surveillance, samples will often need to be collected from times, places, and species that are not currently covered by ornithologists and hunters. Preferably, individual birds should be sampled to confirm species, gender, age, and body mass, and sampling of digestive and respiratory tracts. However, when it is logistically and/or financially difficult to capture live birds several alternatives exist. Swabbing

Prevalence

The proportion of positive findings among a given number of samples is rarely sufficiently precise to inform population prevalence. Thus, the confidence intervals of any observed proportion should be calculated and reported alongside any prevalence estimates when reporting surveillance results. Such confidence limits depend on the number of samples taken and the underlying true (unbiased) prevalence of infection (Figure 4). Achieving Effective Surveillance Each of the points above highlight the need for surveillance that captures the underlying temporal, spatial, 1832

Figure 4. The 95% confidence intervals for prevalence in an independent population for a given number of samples, derived from the binomial distribution. Confidence intervals depend on the number of samples taken and unbiased prevalence of infection; they should be calculated and reported along with prevalence estimates when reporting surveillance results.

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Surveillance of Wild Birds for Avian Influenza Virus

of fresh, species-specific feces is 1 method for collecting a regulated number of samples (16). Species should be identified through careful presampling observation of flocks, or, when sampling mixed-species flocks, through DNA barcoding of the fecal samples (37). Given that AIV can be detected from the same nucleic acid extract used in species identification (37), and substantially more samples can be collected at a much higher frequency than traditional trapping methods, dropping samples may greatly enhance our capacity to detect AIV in the population. Other, more proximate surveillance methods include sampling surface water that is, has been, or is about to be inhabited by wild birds (16), as well as regular sampling of sentinel species (38). Both methods are likely to yield insight into infection in the broader host population (16), although their usefulness for understanding infection in specific populations must be carefully assessed. Conclusions Surveillance for wildlife diseases is an inherently arduous task. However, as the vanguard of our understanding of these diseases, surveillance warrants a scientific approach. To make major inroads into the broader understanding of AIV ecology, epidemiology, and evolution, as well as risks associated with HPAIV, an integrated sampling strategy with clearly defined aims and appropriate methods is required. The financial and logistical constraints of covering vast spatial and temporal scales call for concerted efforts among our combined virologic, ecologic, and genetic expertise. Acknowledgments We thank our collaborators in the field and the laboratories for continuous support and the 2 anonymous referees for valuable comments on an earlier version of this manuscript.

References 1. 2. 3.

4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.

This study was supported through the Bird Health programme within the International Polar Year by the Netherlands Organisation for Scientific Research (grant nos. 851.40.073 and 851.40.074), European Union Framework six program NewFluBird (044490), Japan Science and Technology Precursory Research for Embryonic Science and Technology program, and the Intramural Research Program of the National Institutes of Health, contract NIAIDNIH HHSN266200700010C. This is publication 4876 of the Netherlands Institute of Ecology (NIOO-KNAW).

15.

Ms Hoye is a graduate student in the Department of Animal Ecology at the Netherlands Institute for Ecology. In collaboration with the Department of Virology at Erasmus Medical Centre, she studies ecological interactions between migratory waterfowl and avian influenza viruses.

19.

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Address for correspondence: Bethany J. Hoye, Netherlands Institute for Ecology (NIOO-KNAW), Rijksstraatweg 6, 3631 AC Nieuwersluis, the Netherlands; email: [email protected] All material published in Emerging Infectious Diseases is in the public domain and may be used and reprinted without special permission; proper citation, however, is required.

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Cyprinid Herpesvirus 3 Benjamin Michel, Guillaume Fournier, François Lieffrig, Bérénice Costes, and Alain Vanderplasschen

The recently designated cyprinid herpesvirus 3 (CyHV-3) is an emerging agent that causes fatal disease in common and koi carp. Since its emergence in the late 1990s, this highly contagious pathogen has caused severe financial losses in common and koi carp culture industries worldwide. In addition to its economic role, recent studies suggest that CyHV-3 may have a role in fundamental research. CyHV-3 has the largest genome among viruses in the order Herpesvirales and serves as a model for mutagenesis of large DNA viruses. Other studies suggest that the skin of teleost fish represents an efficient portal of entry for certain viruses. The effect of temperature on viral replication suggests that the body temperature of its poikilotherm host could regulate the outcome of the infection (replicative vs. nonreplicative). Recent advances with regard to CyHV-3 provide a role for this virus in fundamental and applied research.

T

he common carp (Cyprinus carpio carpio) is a freshwater fish and one of the most economically valuable species in aquaculture; worldwide, 2.9 million metric tons are produced each year (1). Common carp are usually cultivated for human consumption. Koi (C. carpio koi) are an often-colorful subspecies of carp, usually grown for personal pleasure and competitive exhibitions. In the late 1990s, a highly contagious and virulent disease began to cause severe economic losses in these 2 carp industries worldwide (2) (Figure 1). The rapid spread was attributed to international fish trade and koi shows around the world (3). The causative agent of the disease was initially called koi herpesvirus because of its morphologic resemblance to viruses of the order Herpesvirales (3). The virus was subsequently called carp interstitial nephritis and gill necrosis virus because of the associated lesions (4). Recently, on the basis of homology of its genome with previously de-

Author affiliations: University of Liège, Liège, Belgium (B. Michel, G. Fournier, B. Costes, A. Vanderplasschen); and Centre d’Economie Rurale Groupe, Marloie, Belgium (F. Lieffrig) DOI: 10.3201/eid1612.100593

scribed cyprinid herpesviruses (5), the virus was assigned to family Alloherpesviridae, genus Cyprinivirus, species Cyprinid herpesvirus 3 and renamed cyprinid herpesvirus 3 (CyHV-3). Because of the economic losses caused by this virus, CyHV-3 rapidly became a subject for applied research. However, recent studies have demonstrated that CyHV-3 is also useful for fundamental research. We therefore summarized recent advances in CyHV-3 applied and fundamental research. Characterization of CyHV-3 Classification

CyHV-3 is a member of the order Herpesvirales and newly designated family Alloherpesviridae (5,6) (Figure 2, panel A). Alloherpesviridae viruses infect fish and amphibians. The common ancestor of this family is thought to have diverged from the common ancestor of the family Herpesviridae (herpesviruses that infect reptiles, birds, and mammals) (6). According to phylogenetic analysis of specific genes, the family Alloherpesviridae seems to be subdivided into 2 clades (6) (Figure 2, panel B). The first clade comprises anguillid and cyprinid herpesviruses, which possess the largest genomes in the order Herpesvirales (245–295 kb). The second clade comprises ictalurid, salmonid, acipenserid, and ranid herpesviruses, which have smaller DNA genomes (134–235 kb). Structure

The CyHV-3 structure is typical of viruses of the order Herpesvirales. An icosahedral capsid contains the genome, which consists of a single, linear, double-stranded DNA molecule. The capsid is covered by a proteinaceous matrix called the tegument, which is surrounded by a lipid envelope derived from host cell trans-golgi membrane (7) (Figure 3). The envelope contains viral glycoproteins (3). The diameter of the entire CyHV-3 particle is 170–200 nm (3,8).

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potential membrane glycoproteins. The expression products of 4 of the sequences were detected in mature virions (ORF25, ORF65, ORF148, and ORF149) (10). CyHV-3

Figure 1. Mass deaths of common carp caused by cyprinid herpesvirus 3 infection in Lake Biwa, Japan, 2004. A) Dead wild common carp; deaths occurred throughout the lake. B) Dead carp (>100,000) collected from the lake in 2004. An estimated 2–3× more carp died but were not collected from the lake. Reproduced with permission from Matsui et al. (2).

Molecular Structure Genome

The genome of CyHV-3 is a 295-kb, linear, doublestranded DNA molecule consisting of a large central portion flanked by two 22-kb repeat regions, called the left and right repeats (9). The genome size is similar to that of CyHV-1 but larger than that of other members of the order Herpesvirales, which are generally 125–240 kb. The CyHV-3 genome encodes 156 potential proteincoding open reading frames (ORFs), including 8 ORFs encoded by the repeat regions. These 8 ORFs are consequently present as 2 copies in the genome (9). Five families of related genes have been described: ORF2, tumor necrosis factor receptor, ORF22, ORF25, and RING families. The ORF25 family consists of 6 ORFs (ORF25, ORF26, ORF27, ORF65, ORF148, and ORF149) encoding related, 1836

Figure 2. A) Cladogram depicting relationships among viruses in the order Herpesvirales, based on the conserved regions of the terminase gene. The Bayesian maximum-likelihood tree was rooted by using bacteriophages T4 and RB69. Numbers at each node represent the posterior probabilities (values >90 shown) of the Bayesian analysis. B) Phylogenetic tree depicting the evolution of fish and amphibian herpesviruses, based on sequences of the DNA polymerase and terminase genes. The maximum-likelihood tree was rooted with 2 mammalian herpesviruses (human herpesviruses 1 and 8). Maximum-likelihood values >80 and Bayesian values >90 are indicated above and below each node, respectively. Scale bar indicates branch lengths, which are based on the number of inferred substitutions. AlHV-1, alcelaphine herpesvirus 1; AtHV-3, ateline herpesvirus 3; BoHV-1, -4, -5, bovine herpesviruses 1, 4, 5; CeHV-2, -9, cercopithecine herpesviruses 2, 9; CyHV-1, -2, cyprinid herpesviruses 1, 2; EHV-1, -4, equid herpesvirus 1, 4; GaHV-1, -2, -3, gallid herpesvirus 1, 2, 3; HHV-1, -2, -3, -4, -5, -6, -7, -8, human herpesvirus 1, 2, 3, 4, 5, 6, 7, 8; IcHV-1, ictalurid herpesvirus 1; McHV-1, -4, -8, macacine herpesvirus 1, 4, 8; MeHV-1, meleagrid herpesvirus 1; MuHV-2, -4, murid herpesvirus 2, 4; OsHV-1, ostreid herpesvirus 1; OvHV-2, ovine herpesvirus 2; PaHV-1, panine herpesvirus 1; PsHV-1, psittacid herpesvirus 1; RaHV-1, -2, ranid herpesvirus 1, 2; SaHV-2, saimiriine herpesvirus 2; SuHV-1, suid herpesvirus 1; and TuHV-1, tupaiid herpesvirus 1. Adapted with permission from Waltzek et al. (6).

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Cyprinid Herpesvirus 3

Because CyHV-3 possesses the largest genome among members of the order Herpesvirales, it provides a model for mutagenesis of large DNA viruses. Recently, the CyHV-3 genome was cloned as a stable and infectious bacterial artificial chromosome, which could be used to produce CyHV-3 recombinants (13). Structural Proteome

The structural proteome of CyHV-3 was recently characterized by using liquid chromatography tandem mass spectrometry (10). A total of 40 structural proteins, comprising 3 capsid, 13 envelope, 2 tegument, and 22 unclassified proteins, were described. The genome of CyHV-3 possesses 30 potential transmembrane-coding ORFs (9). With the exception of ORF81, which encodes a type 3 membrane protein expressed on the CyHV-3 envelope (10,14), no CyHV-3 structural proteins have been studied. ORF81 is thought to be one of the most immunogenic (major) membrane proteins of CyHV-3 (14). Figure 3. Electron micrograph image of cyprinid herpesvirus 3 virion. Scale bar = 100 nm. Adapted with permission from Mettenleiter et al. (7).

encodes several genes that could be involved in immune evasion processes, such as ORF16, which codes for a potential G-protein coupled receptor; ORF134, which codes for an IL-10 homolog; and ORF12, which codes for a tumor necrosis factor receptor homolog. Within the family Alloherpesviridae, anguillid herpesvirus 1 is the closest relative of CyHV-3 that has been sequenced (11). Each of these viruses possesses 40 ORFs exhibiting similarity. Sequencing of CyHV-1 and CyHV-2 will probably identify more CyHV-3 gene homologs. The putative products of most ORFs in the CyHV-3 genome lack obvious relatives in other organisms; 110 ORFs fall into this class. Six ORFs encode proteins with closest relatives in virus families such as Poxviridae and Iridoviridae (9). For example, CyHV-3 genes such as B22R (ORF139), thymidylate kinase (ORF140), thymidine kinase (ORF55), and subunits of ribonucleotide reductase (ORF23 and ORF141) appear to have evolved from poxvirus genes (9). Neither thymidylate kinase nor B22R has been identified previously in a member of the order Herpesvirales. Three unrelated strains of CyHV-3, isolated in Israel (CyHV-3 I), Japan (CyHV-3 J), and the United States (CyHV-3 U), have been fully sequenced (9). Despite their distant geographic origins, these strains exhibit high sequence identity. Low diversity of sequences among strains seems to be a characteristic of the CyHV-3 species. Despite this low diversity, molecular markers enabling discrimination among 9 genotypes (7 from Europe and 2 from Asia) have been identified (12).

In Vitro Replication

CyHV-3 is widely cultivated in cell lines derived from koi fin, C. carpio carp brain, and C. carpio carp gill (3,4,8,15–17) (Table 1). Other cell lines have been tested, but few have been found to be permissive for CyHV-3 infection (Table 1). The CyHV-3 replication cycle was recently studied by use of electron microscopy (7). Its morphologic stages suggested that it replicates in a manner similar to that of members of the family Herpesviridae. Capsids leave the nucleus by budding at the inner nuclear membrane, resulting in formation of primary enveloped virions in the perinuclear space. The primary envelope then fuses with the outer leaflet of the nuclear membrane, thereby releasing nucleocapsids into the cytoplasm. Final envelopment occurs by budding into trans-golgi vesicles. Because CyHV-3 glycoproteins have little or no similarity with those of members of the family Herpesviridae, identification of the CyHV-3 glycoproteins involved in entry and egress will require further study. Table 1. Cyprinid herpesvirus 3–susceptible cell lines Cytopathic effect Cell type (cell line) (reference) Cyprinus carpio brain (CCB) Yes (8,15) C. carpio gill (CCG) Yes (8) Epithelioma papulosum cyprinid (EPC) No (3,4,15,16); Yes (8) Koi fin (KFC, KF-1) Yes (3,4,15,17) Carp fin (CFC, CaF-2) Yes (8) Fathead minnow (FHM) No (3,15); Yes (16) Chinook salmon embryo (CHSE-214) No (16) Rainbow trout gonad (RTG-2) No (16) Goldfish fin (Au) Yes (15) Channel catfish ovary (CCO) No (15) Silver carp fin (Tol/FL) Yes (15)

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Because fish are poikilotherms and because CyHV-3 only affects fish when the water temperature is 18°C–28°C, the effect of temperature on CyHV-3 replication growth in vitro has been investigated. Replication in cell culture is restricted by temperature; optimal viral growth is at 15°C– 25°C. Virus propagation and virus gene transcription are turned off when cells are moved to a nonpermissive temperature of 30°C (18). Despite the absence of detectable virus replication, infected cells maintained for 30 days at 30°C preserve infectious virus, as demonstrated by viral replication when the cells are returned to permissive temperatures (18) (Figure 4). These results suggest that CyHV-3 can persist asymptomatically for long periods in the fish body when the temperature prevents virus replication; bursts of new infection occur after exposure to permissive temperatures.

in the world except South America, Australia, and northern Africa (20). Worldwide, CyHV-3 has caused severe financial and economic losses in the koi and common carp culture industries. Host Range

Common and koi carp are the only species known to be affected by CyHV-3 infection (21). Numerous fish species, cyprinid and noncyprinid, were tested for their ability to carry CyHV-3 asymptomatically and to spread it to unexposed carp (21–23) (Table 2). CyHV-3 DNA was recovered from only 2 other fish species: goldfish and crucian carp. Cohabitation experiments suggest that goldfish, grass carp, and tench can carry CyHV-3 asymptomatically and spread it to unexposed common carp. Hybrids (koi–goldfish and koi–crucian carp) die of CyHV-3 infection (24).

Disease Caused by CyHV-3 Susceptibity History

In 1998, the first mass deaths of common and koi carp were reported in Israel and the United States (3). However, analyses of samples from archives determined that the virus had been in wild common carp since 1996 in the United Kingdom (19). Soon after the first report, outbreaks of CyHV-3 were identified in countries in Europe, Asia, and Africa. Currently, CyHV-3 has been identified everywhere

CyHV-3 affects carp of all ages, but younger fish (1–3 months, 2.5–6 g) seem to be more susceptible to infection than mature fish (1 year, ≈230 g) (16,21). Recently, the susceptibility of young carp to CyHV-3 infection was analyzed by experimental infection (25). Most infected juveniles (>13 days posthatching) died of the disease, but the larvae (3 days posthatching) were not susceptible.

Figure 4. Effects of temperature on cyprinid herpesvirus 3 replication in Cyprinus carpio carp brain cells. After infection, cells were kept at 22°C (A) or shifted to 30°C (B–D); some cells were returned to 22°C at 24 hours (C) or 48 hours (D) postinfection. Uninfected control cells (E) and infected cells at 9 days postinfection were fixed, stained, and photographed. Viral replication was highest in cells maintained at 22°C and lowest in those maintained at 30°C. Original magnification ×20. Adapted with permission from Dishon et al. (18). 1838

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Table 2. Fish tested for cyprinid herpesvirus 3 infection* Species (common name) Carassius auratus (goldfish) Ctenopharyngodon idella (grass carp) Carassius carassius (crucian carp) Hypophthalmichthys molitrix (silver carp) Aristichtys nobilis (bighead carp) Bidyanus bidyanus (silver perch) Oreochromis niloticus (Nile tilapia) Tinca tinca (tench) Silurus glanis (sheatfish) Vimba vimba (vimba) Acipenser ruthenus (sterlet) Acipenser gueldenstaedtii (Russian sturgeon) Acipenser oxyrinchus (Atlantic sturgeon)

DNA Yes (23) NT NT NT NT NT NT NT NT NT NT NT NT

Inoculated fish (reference) Protein Clinical signs Yes (23) No (21); Yes (23) NT No (21) NT NT NT No (21) NT NT NT No (21) NT No (21) NT NT NT NT NT NT NT NT NT NT NT NT

Carp deaths during cohabitation (reference) No (21); Yes (22) No (21); Yes (22) No (22) No (21,22) No (22) No (21) No (21) Yes (22) No (22) No (22) No (22) No (22) No (22)

*NT, not tested.

Pathogenesis

Several researchers have postulated that the gills might be the portal of entry for CyHV-3 (17, 26–28); however, this hypothesis was recently refuted (29). Bioluminescent imaging and an original system for performing percutaneous infection restricted to the posterior part of the fish showed that the skin covering the fin and body mediated entry of CyHV-3 into carp (29) (Figure 5). This study, together with an earlier study of the portal of entry of a rhabdovirus (infectious hematopoietic necrosis virus) in salmonids (30), suggests that the skin of teleost fish represents an efficient portal of entry for certain viruses. The skin of teleost fish is a stratified squamous epithelium that, unlike its mammalian counterpart, is living and capable of mitotic division at all levels, even the outermost squamous layer. The scales are dermal structures. More extensive studies are needed to demonstrate that the skin is the only portal of entry of CyHV-3 into carp. After initial replication in the epidermis (29), the virus is postulated to spread rapidly in infected fish, as indicated by detection of CyHV-3 DNA in fish tissues (27). As early as 24 hours postinfection, CyHV-3 DNA was recovered from almost all internal tissues (including liver, kidney, gut, spleen, and brain) (27), where viral replication occurs at later stages of infection and causes lesions. One hypothesis regarding the rapid and systemic dissemination indicated by PCR is that CyHV-3 secondarily infects blood cells. Virus replication in organs such as the gills, skin, and gut at the later stages of infection represents sources of viral excretion into the environment. After natural infection under permissive temperatures (18°C–28°C), the highest mortality rates occur 8–12 days postinfection (dpi) (21). Gilad et al. suggest that death is due to loss of the osmoregulatory functions of the gills, kidneys, and gut (27). All members of the family Herpesviridae exhibit 2 distinct life-cycle phases: lytic replication and latency. Laten-

cy is characterized by maintenance of the viral genome as a nonintegrated episome and expression of a limited number of viral genes and microRNAs. At the time of reactivation, latency is replaced by lytic replication. Latency has not been demonstrated conclusively in members of the family Alloherpesviridae. However, some evidence supports existence of a latent phase. CyHV-3 DNA has been detected by real-time PCR at 65 dpi in clinically healthy fish (27). Furthermore, the virus persisted in a wild population of common carp for at least 2 years after the initial outbreak (31). Finally, St-Hilaire et al. demonstrated the possibility of a temperature-dependent reactivation of CyHV-3 lytic infection several months after initial exposure to the virus (32). This finding suggests that the temperature of the water could control the outcome of the infection (replicative/ nonreplicative). Whether the observations described above reflect latent infection, as described for the family Herpesviridae, or some type of chronic infection, remains to be determined. Similarly, the carp organs that support this latent or chronic infection still need to be identified. Transmission

Horizontal transmission of CyHV-3 in feces (26) and secretion of viral particles into water (21) have been demonstrated. The skin of carp acts as the portal of entry of CyHV-3 and the site of early replication (29). The early replication of the virus at the portal of entry could contribute not only to the spread of the virus within infected fish but also to the spread of the virus throughout the fish population. As early as 2–3 dpi, infected fish rubbed against other fish or against objects. This behavior could contribute to a skin-to-skin mode of transmission. Later during infection, this mode of transmission could also occur when uninfected fish pick at the macroscopic herpetic skin lesions on infected fish. To date, no evidence of vertical transmission of CyHV-3 has been found.

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al cells of the gill filaments. These cells exhibit hyperplasia, hypertrophy, and/or nuclear degeneration (3,17,21,28). Severe inflammation leads to the fusion of respiratory epithelial cells with cells of the neighboring lamellae, resulting in lamellar fusion (17,28). In the kidney, a weak peritubular inflammatory infiltrate is evident as early as 2 dpi and, along with blood vessel congestion and degeneration of the tubular epithelium in many nephrons, increases with time (17). In the spleen and liver, splenocytes and hepatocytes, respectively, are the most obviously infected cells (28). In brain of fish that showed neurologic signs, congestion of capillaries and small veins are apparent in the valvula cerebelli and medulla oblongata, associated with edematous dissociation of nerve fibers (28). Diagnosis

Figure 5. Skin of carp as a portal of entry for cyprinid herpesvirus 3. A schematic representation of the system used to restrict viral inoculation to the fish skin is shown on the left. The lower drawing shows the conditions under which 6 fish were inoculated by restricted contact of the virus with the skin located posterior to the anterior part of the dorsal fin. The upper drawing shows control conditions under which 6 fish were inoculated in the system but without the latex diaphragm dividing the fish body into 2 isolated parts, enabling virus to reach the entire fish body. The fish were infected by bathing them for 24 h in water containing 2 × 103 PFU/ mL of a recombinant cyprinid herpesvirus 3 strain able to emit bioluminescence. All fish were analyzed 24 h postinfection (hpi) by bioluminescence imaging. After an additional incubation period of 24 h in individual tanks containing fresh water, they were reanalyzed by bioluminescence imaging at 48 hpi. Three representative fish are shown. The images are shown with standardized minimum and maximum threshold values for photon flux. Adapted with permission from Costes et al. (29).

Clinical Signs

The first signs appear at 2–3 dpi. The fish exhibit appetite loss and lethargy and lie at the bottom of the tank with the dorsal fin folded. Depending on the stage of the infection, the skin exhibits different clinical signs, such as hyperemia, particularly at the base of the fins and on the abdomen; mucus hypersecretion; and herpetic lesions (Figure 6). The gills frequently become necrotic and hypersecrete mucus, which suffocates the fish. Bilateral enophthalmia is observed in the later stages of infection. Some fish show neurologic signs in the final stage of the disease, when they become disoriented and lose equilibrium (3,19,21). Histopathologic Findings

In CyHV-3 infected fish, prominent pathologic changes occur in the gill, skin, kidney, liver, spleen, gastrointestinal system, and brain (3,17,21,28). Histopathologic changes appear in the gills as early as 2 dpi and involve the epitheli1840

Diagnosis of CyHV-3 infection is described elsewhere (20). Suspicion of CyHV-3 infection is based on clinical signs and histopathologic findings. Since initial isolation of CyHV-3 in 1999, complementary diagnostic methods have been developed. Virus isolation from infected fish tissues in cell culture (C. carpio carp brain and koi fin cells) was the first method to be developed (3). This time-consuming approach is still the most effective method for detecting infectious particles during an outbreak of CyHV-3 infection. A complete set of techniques for detecting viral genes— including PCR (20), nested PCR (33), TaqMan PCR (27), and loop-mediated isothermal amplification (34)—has been developed. Real-time TaqMan PCR has been used to detect CyHV-3 in freshwater environments after concentration of viral particles (2). Finally, ELISAs have been developed to detect specific anti-CyHV-3 antibodies in the blood of carp (35) and to detect CyHV-3 antigens in samples (17,26). Immune Response Immunity in ectothermic vertebrates differs in several ways from that of their mammalian counterparts. Environmental temperature has drastic effects on the fish immune system. In carp, for example, at 50% of the municipalities): C1, C2, C4, C5, and C6 (Table 3). Variables associated with seroprevalence were identified through a univariate analysis by using logistic models in which the department was systematically included. Variables for which the associated p value was 1 other pathogen was co-detected for 28% (Figure). The most commonly co-detected pathogens were S. aureus (458; 14.73%), S. pneumoniae (316; 10.16%), and H. influenzae (110; 3.54%) (Table 1). A significant difference (t = 25.6, p = 0.01) was found for the age distribution between patients with positive and negative pandemic (H1N1) 2009 virus results. The mean ± SD age was 19.64 (±14.45) years for those who were pandemic (H1N1) 2009 positive and 29.67 (±19.74) years for those who were negative. The median age of the 5 patients for whom 3 other pathogens were co-detected with pandemic (H1N1) 2009 virus (Figure) was 15.5 years. S. pneumoniae was detected in all 5 of these samples. For most,

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Pandemic (H1N1) 2009 Virus and Other Respiratory Pathogens

Table 1. Results of screening of clinical samples from 23 US states for pandemic (H1N1) 2009 virus and bacterial respiratory targets, May–October 2009* Bacteria detected, no. (%) samples H. S. S. C. A. P. S. N. K. M. aureus aeruginosa pyogenes meningitidis influenzae pneum pneum pneum pneum baumannii No. (%) samples Pandemic (H1N1) 458 3 4 7 110 1 22 1 316 34 (14.727) (0.096) (0.129) (0.225) (3.537) (0.032) (0.707) (0.032) (10.161) (1.093) 2009 positive, 3,110 (29.270) Pandemic (H1N1) 875 28 34 16 411 8 44 8 573 152 (11.645) (0.373) (0.452) (0.213) (5.470) (0.106) (0.586) (0.106) (7.626) (2.023) 2009 negative, 7,514 (70.730) Total, 10,624 1,333 31 38 23 521 9 66 9 889 186 (100.000) (12.547) (0.292) (0.358) (0.216) (4.904) (0.085) (0.621) (0.085) (8.367) (1.751) *Screening for Legionella pneumophilia detected no bacteria in any samples. S. aureus, Staphylococcus aureus; P. aeruginosa, Pseudomonas aeruginosa; S. pyogenes, Streptococcus pyogenes; N. meningitidis, Neisseria meningitidis; H. influenzae, Haemophilus influenzae; C. pneum, Chlamydophila pneumoniae; K. pneum, Klebsiella pneumoniae; M. pneum, Mycoplasma pneumoniae; S. pneum, Streptococcus pneumoniae; A. baumannii, Acinetobacter baumannii. Boldface indicates predominant pathogens.

the other 2 pathogens were bacteria; for only 1, a virus (parainfluenza) was detected. Of the 96 samples in which pandemic (H1N1) 2009 virus and 2 other pathogens were co-detected (Figure), 30 (31.25%) contained S. pneumoniae and H. influenzae. The median age of these 30 patients was 4.25 years, whereas the median age of all 96 patients was 8.2 years. The median age for the 28% of patients for whom >1 other target was detected was 11.8 years. Conclusions The main finding of this large-scale clinical study was the co-detection of multiple pathogens with the pandemic influenza virus strain. In 44% of samples, no pathogens were detected, which may represent infection with common pathogens not detected by the assay. For example, bocavirus and all coronavirus groups not detected by the assay account for ≈12% and 5%–10%, respectively (8,9), of respiratory infections. An expanded test menu may improve the detection rate for such pathogens. This study raises 2 questions. First, does co-detection equal co-infection? Second, and more practical, does codetection change the clinical outcome? We chose the word co-detection rather than co-infection or co-colonization because co-infection means all identified microorganisms contributed to the pathogenic effect, and co-colonization may not indicate the causative agent. Co-detection indicates that >1 other pathogen was detected in a sample. The

differences among the definitions have etiologic meaning, but the data presented here cannot be used directly to address etiology. Most samples in this study were nasal swabs rather than upper or lower respiratory tract samples. Nasal swab samples have greater potential for contamination with normal flora, particularly S. aureus. No data on asymptomatic carriers were available because these persons rarely seek healthcare. However, these findings raise questions about the effectiveness of the single-agent etiology approach toward infectious diseases. Pandemic (H1N1) 2009 virus and multiple other pathogens are often detected during autopsy (1,2), indicating that co-infection may play a major role in the disease process. In addition, detection of multiple pathogens is associated with increased critical illness in children (7). The Centers for Disease Control and Prevention identified “the need for early recognition of bacterial pneumonia in persons with influenza” (2). However, no suggestions were provided for meeting this need. Furthermore, the Centers “underscore the importance of managing patients with influenza who also might have bacterial pneumonia with both empiric antibacterial therapy and antiviral medications” (2) without identifying measures that would make this task tangible. Current practices of clinical diagnosis based on signs and symptoms inherently lack this type of information.

Table 2. Results of screening for of clinical samples from 23 US states for pandemic (H1N1) 2009 virus and respiratory pathogen gene targets, May–October 2009* Viruses detected, no. (%) samples No. (%) samples Adeno Coxackie/echo Metapneumo Influenza A Influenza B Parainfluenza RS Rhino Pandemic (H1N1) 1 (0.032) 13 (0.418) 0 0 0 3 (0.096) 0 7 (0.225) 2009 positive, 3,110 (29.270) Pandemic (H1N1) 17 (0.226) 650 (8.651) 14 (0.186) 3 (0.040) 2 (0.027) 173 (2.302) 3 (0.040) 449 (5.976) 2009 negative, 7,514 (70.730) Total, 10,624 18 (0.169) 663 (6.240) 14 (0.132) 3 (0.028) 2 (0.019) 176 (1.656) 3 0.(028) 456 (4.292) (100.000) *RS, respiratory syncytial. Boldface indicates predominant pathogens.

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Mrs Koon is pursuing a doctorate degree in public health at Walden University, Minneapolis, MN, USA. Her research interests include developing multiplex amplification assays for respiratory pathogens and infectious disease surveillance. References 1.

Figure. Respiratory pathogens co-detected with pandemic (H1N1) 2009 virus in clinical samples from 23 US states, May–October 2009.

The true value of a multiplex molecular method of screening for infectious respiratory agents depends on the clinical relevance. Among the samples with >1 positive results, 53% had positive results for viral pathogens without co-detection of bacterial pathogens. For these patients, prescription of antimicrobial drugs on the basis of clinical findings alone could serve to spread drug resistance through selective pressure on normal flora. Furthermore, limited secondary treatment resources, such as oseltamivir administration during a pandemic, could be prioritized on the basis of screening results. Of the 10,624 samples studied, 70.7% were negative for the pandemic (H1N1) 2009 virus strain. Our findings suggest that multiplex screening for respiratory pathogens is useful for providing rapid surveillance information to inform physicians who would otherwise base decisions on clinical signs and symptoms alone. Electronic reporting of empirical laboratory respiratory pathogen detection provided by a Clinical Laboratory Improvement Amendments–approved laboratory can greatly enhance surveillance data collection (10). Because most states have the authority to collect data of public health relevance (10), the screening service provided by the Diatherix Laboratories could facilitate reporting of notifiable diseases.

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Morens DM, Taubenberger JK, Fauci AS. Predominant role of bacterial pneumonia as a cause of death in pandemic influenza: implications for pandemic influenza preparedness. J Infect Dis. 2008;198:962–70. DOI: 10.1086/591708 2. Centers for Diseases Control and Prevention. Bacterial coinfections in lung tissue specimens from fatal cases of 2009 pandemic influenza A (H1N1)—United States, May–August 2009. MMWR Morb Mortal Wkly Rep. 2009;58:1–4. 3. Brunstein J, Thomas E. Direct screening of clinical specimens for multiple respiratory pathogens using the Genaco respiratory panels 1 and 2. Diagn Mol Pathol. 2006;15:169–73. DOI: 10.1097/01. pdm.0000210430.35340.53 4. Li H, McCormac MA, Estes RW, Sefers SE, Dare RK, Chapell JD, et al. Simultaneous detection and high-throughput identification of a panel of RNA viruses causing respiratory tract infections. J Clin Microbiol. 2007;45:2105–9. DOI: 10.1128/JCM.00210-07 5. Zou S, Han J, Wen L, Liu Y, Cronin K, Lum SH, et al. Human influenza A virus (H5N1) detection by a novel multiplex PCR typing method. J Clin Microbiol. 2007;45:1889-92. 6. Benson R, Tondella ML, Bhatnagar J, Carvalho MS, Sampson JS, Talkington DF, et al. Development and evaluation of a novel multiplex PCR technology for molecular differential detection of bacterial respiratory disease pathogens. J Clin Microbiol. 2008;46:2074–7. DOI: 10.1128/JCM.01858-07 7. Brunstein JD, Cline CL, McKinney S, Thomas E. Evidence from multiplex molecular assays for complex multipathogen interactions in acute respiratory infections. J Clin Microbiol. 2008;46:97–102. DOI: 10.1128/JCM.01117-07 8. Kleines M, Scheithauer S, Rackowitz A, Ritter K, Hausler M. High prevalence of human bocavirus detected in young children with severe acute lower respiratory tract disease by use of a standard PCR protocol and a novel real-time PCR protocol. J Clin Microbiol. 2007;45:1032–4. DOI: 10.1128/JCM.01884-06 9. Kuypers J, Martin ET, Heugel J, Wright N, Morrow R, Englund JA. Clinical disease in children associated with newly described coronavirus subtypes. Pediatrics. 2007;119:e70–6. DOI: 10.1542/ peds.2006-1406 10. Silk BJ, Berkelman RL. A review of strategies for enhancing the completeness of notifiable disease reporting. J Public Health Manag Pract. 2005;11:191–200. Address for correspondence: Catherine M. Sanders, HudsonAlpha Institute of Biotechnology–Han Lab, 601 Genome Way, Huntsville, AL 35806, USA; email: [email protected]

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Alkhurma Hemorrhagic Fever in Travelers Returning from Egypt, 2010 Fabrizio Carletti, Concetta Castilletti, Antonino Di Caro, Maria R. Capobianchi, Carla Nisii, Fredy Suter, Marco Rizzi, Alessandra Tebaldi, Antonio Goglio, Cristiana Passerini Tosi, and Giuseppe Ippolito Two travelers returning to Italy from southern Egypt were hospitalized with a fever of unknown origin. Test results showed infection with Alkhurma virus. The geographic distribution of this virus could be broader than previously thought.

A

lkhurma virus (ALKV) is a recently described member of the tick-borne hemorrhagic fever group of the genus Flavivirus. It was initially isolated in the late 1990s (1,2) and is today considered a variant of the Kyasanur Forest disease virus, sharing 89% nt sequence homology (3,4). This emerging pathogen causes signs and symptoms such as fever, headache, joint pain, muscle pain, vomiting, and thrombocytopenia; severe cases may have hemorrhagic manifestations (epistaxis, ecchymoses, petechiae, hematemesis) and encephalitis, which can result in death (reported case-fatality rate as high as 25%) (5–8). Camels and sheep are thought to be the natural hosts of ALKV, but whether other mammals are also involved in its life cycle remains unknown. ALKV RNA was recently detected in an Ornithodoros savignyi tick collected near Jeddah, Saudi Arabia (9); on the Arabian Peninsula, these ticks have been associated with camels and their resting places and can be found where cases of ALKV infection in humans have been reported. These ticks seek multiple hosts, are nocturnal and cryptic, and commonly attack humans and other animals resting under trees (10). The hypothesis that mosquitoes could also be vectors has been suggested by 2 studies (6,7); despite the absence of data to substantiate it, this possibility cannot be excluded.

Author affiliations: “Lazzaro Spallanzani” National Institute for Infectious Diseases, Rome, Italy (F. Carletti, C. Castilletti, A. Di Caro, M.R. Capobianchi, C. Nisii, G. Ippolito); and “Ospedali Riuniti di Bergamo,” Bergamo, Italy (F. Suter, M. Rizzi, A. Tebaldi, A. Goglio, C.P. Tosi) DOI: 10.3201/eid1612101092

Evidence suggests that ALKV infects humans either transcutaneously (by contamination of a skin wound with the blood of an infected vertebrate or through the bite of an infected tick) or orally through consumption of unpasteurized contaminated milk. Transmission to humans has been associated with butchering of sheep and camels. No human-to-human transmission has been reported. ALKV is classified in different countries as a BioSafety Level 3 or 4 agent. ALKV has been detected only in Saudi Arabia, but the closely related Kyasanur Forest disease virus has spread as far as India and the People’s Republic of China (4). We describe 2 cases of Alkhurma hemorrhagic fever in 2 travelers who returned to Italy from Egypt in 2010. The Cases The first patient, a 64-year-old man from Italy, spent 1 week (April 25–May 1, 2010) in a touristic village in southern Egypt, near the Sudan border. While visiting a camel and dromedary market in Shalatin on April 29, he was bitten on the foot by an unidentified arthropod (although not formally identified, was described as tick shaped). Soon after, a small, papular lesion developed. During his return flight to Italy, ≈48 hours after the bite, the patient experienced high fever, shaking chills, anorexia, malaise, nausea and vomiting, and blurred vision. During the next 5 days, these signs and symptoms worsened, and the man was admitted to the “Ospedali Riuniti di Bergamo” in northern Italy. His medical history was unremarkable, but he frequently traveled abroad and had been vaccinated against yellow fever in 1998. Laboratory test results showed leukopenia (2,250 cells/mm3), thrombocytopenia (67,000 platelets/mm3), and increased liver enzymes (aspartate transaminase 469 U/L, reference 3–46 U/L; alanine transaminase 406 U/L, reference 3–46 U/L). The patient was given acetaminophen, and fever and general malaise progressively decreased over the next 5 days. He was discharged 11 days later, on May 17, in good general condition despite persistence of asthenia. Acute-phase and convalescent-phase serum samples (collected on May 10 and 27, respectively) were sent to the virology laboratory of the “Lazzaro Spallanzani” National Institute for Infectious Diseases in Rome to be tested for dengue and West Nile viruses. Immunoglobulins (Ig) G and M for both viruses were detected by immunofluorescence of both samples; for each virus, IgG titer was >640 and IgM titer was >20. No evidence of rising antibody titers was found in the convalescent-phase specimen, raising suspicion of cross-reactivity to a previous Flavivirus infection or yellow fever vaccination. A genus-specific reverse transcription–PCR selective for the nonstructural protein (NS) 5 gene of flaviviruses (11) was positive for the acute-phase and negative for the convalescent-phase

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samples. Sequence analysis of the amplicon (GenBank accession no. HM629507) showed high similarity with ALKV sequences in GenBank (BLAST [www.ncbi.nlm. nih.gov/blast/Blast.cgi] submission showed 97% identity with AF331718). This unexpected result called for further investigations to confirm the diagnosis of an ALKV infection. Thus, an ALKV-specific nested reverse transcription– PCR selective for a wider region of a different gene (E) was designed by using the following primers: outer forward 5′-TGGAACCCCACACGGGTGACT-3′; outer reverse 5′-ATGCCCACTGTCGGTTGGCG-3′; inner forward 5′CCCACAGCAATCGAAAAACGGCATC-3′; inner reverse 5′- GCCCACATCACAGGTGACATGACC-3′. All residual biological samples collected during the patient’s hospital stay were sent to the virology laboratory of the Spallanzani Hospital (Italy’s national reference labora-

tory for viral hemorrhagic fever viruses, BioSafety Level 4) in compliance with biosafety procedures. The new ALKV PCR result was positive, and the sequence of the amplicon (GenBank accession no. HM629508) showed high homology with ALKV (99% identity with AF331718). The phylogenetic trees based on partial sequences of NS5 (Figure 1) and E (Figure 2) genes confirmed the diagnosis of ALKV infection. After submitting this article, we detected ALKV infection in a second patient. This patient had traveled to the same area ≈1 month later, visited the same camel market, and was affected by a milder disease. NS5 (HQ218942) and E (HQ218941) gene sequences obtained from this patient have been included in the phylogenetic tree, showing that they cluster together with those from the first patient (Figures 1, 2).

Figure 1. Phylogenetic tree based on sequences of the amplicon produced by the flavivirus nonstructural protein (NS) 5 gene reverse transcription–PCR (amplicon size, 208 bp; position in reference AF331718, nt 9077–9275), performed on the acute-phase serum samples of 2 travelers returning to Italy from Egypt (open arrow) showing relationship with other flaviviruses. Sequences are identified by name and GenBank accession number. Multiple alignment of other flavivirus sequences available in GenBank was generated by use of the ClustalW 1.7 software (www.clustal.org) included in the Bioedit package (www.mbio.ncsu.edu/BioEdit/BioEdit.html). The phylogenetic tree was constructed by nucleotide alignment, the Kimura 2-parameter algorithm, and the neighbor-joining method implemented in MEGA 4.1 software (www.megasoftware.net). The robustness of branching patterns was tested by 1,000 bootstrap pseudo-replications. Scale bar indicates nucleotide substitutions per site. DFV, dengue fever virus; JEV, Japanese encephalitis virus; WNFV, West Nile fever virus; TBEV, tick-borne encephalitis virus; OHFV, Omsk hemorrhagic fever virus; KFDV, Kyasanur Forest disease virus.

Conclusions The 2 patients had traveled to an area of the world where ALKV had not been previously reported. Although viremia was demonstrated 10 days after symptom onset, and we can reliably suppose that it started when fever and chills appeared, the probability of a susceptible vector in Europe is small, and the infection seems not to be transmissible from human to human. Laboratory diagnosis of this infection is not easy to obtain and requires a specialized laboratory because of antibody cross-reactivity with other members of the family Flaviviridae and because of the absence of commercially available serologic tests and reference biologic materials for their development. However, surveillance of travelers returning from areas where highly dangerous infectious diseases are endemic should be improved and should include ALKV. The finding that the distribution of this virus is wider than previously thought and that it includes the African continent is in line with the hypothesis that tick-borne flaviviruses originated in Africa (12). The low genetic distance between the Egypt and Saudi Arabia sequences supports the hypothesis of a recent divergence from Kyasanur Forest disease virus, i.e., the closest flavivirus (5), and a slow microevolution of ALKV, as for other tick-borne flaviviruses (13). The higher genetic divergence in the NS5 gene than in the E gene of ALKV strains confirms previous observations for viruses isolated from human samples after inoculation of suckling mice (5) and deserves more detailed evolutionary analysis. The detection of 2 independent infection events for travelers who visited the same area in a restricted period strongly supports the hypothesis of sustained local ALKV circulation. Further veterinary and entomologic investigations are needed to expand understanding of the geographic distribution of ALKV and to assess the danger for local populations and visitors. It would be advisable to inform

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Alkhurma Hemorrhagic Fever in Travelers Returning from Egypt

Figure 2. Phylogenetic tree based on the sequences of Alkhurma hemorrhagic fever virus E gene amplicon (amplicon size, 516 bp; position in reference AF331718, nt 1398–1913),obtained from acute-phase serum samples from a patient who had traveled to Egypt (open arrow) with respect to other flaviviruses. Sequences are identified by name and GenBank accession number. The phylogenetic tree was constructed by nucleotide alignment, the Kimura 2-parameter algorithm, and the neighbor-joining method implemented in MEGA 4.1 software (www.megasoftware.net). The robustness of branching patterns was tested by 1,000 bootstrap pseudo-replications. Scale bar indicates nucleotide substitutions per site. DFV, dengue fever virus; JEV, Japanese encephalitis virus; WNFV, West Nile fever virus; TBEV, tick-borne encephalitis virus; OHFV, Omsk hemorrhagic fever virus; KFDV, Kyasanur Forest disease virus. The relevant part of the tree is enlarged at right.

travelers about the danger of coming into contact with infected animals in areas where the virus has been reported. Avoidance of or minimization of exposure to infected ticks should be recommended as the most effective prevention measure. This work was conducted with the support of the following European Union–funded projects: RiViGene, European Network of P4 Laboratories, and the European Network for the Diagnosis of Imported Viral Diseases–Collaborative Network. Dr Carletti is a scientist at the Virology Laboratory of the “Lazzaro Spallanzani” National Institute for Infectious Diseases in Rome. His research interests include emerging and reemerging infections, especially virologic aspects of host–pathogen interactions and development of tools and protocols for diagnosis of emerging viral diseases, including viral hemorrhagic fevers. References 1.

Zaki AM. Isolation of a flavivirus related to the tick-borne encephalitis complex from human cases in Saudi Arabia. Trans R Soc Trop Med Hyg. 1997;91:179–81. DOI: 10.1016/S0035-9203(97)90215-7

2.

Qattan I, Akbar N, Afif H, Abu Azmah S, Al-Khateeb T, Zaki A, et al. A novel flavivirus: Makkah region, 1994–1996. Saudi Epidemiology Bulletin. 1996;3:1–3. 3. Charrel RN, Zaki AM, Attoui H, Fakeeh M, Billoir F, Yousef AI, et al. Complete coding sequence of the Alkhurma virus, a tick-borne flavivirus causing severe hemorrhagic fever in humans in Saudi Arabia. Biochem Biophys Res Commun. 2001;287:455–61. DOI: 10.1006/bbrc.2001.5610 4. Mehla R, Kumar SR, Yadav P, Barde PV, Yergolkar PN, Erickson BR, et al. Recent ancestry of Kyasanur Forest disease virus. Emerg Infect Dis. 2009;15:1431–7. DOI: 10.3201/eid1509.080759 5. Charrel RN, Zaki AM, Fakeeh M, Yousef AI, de Chesse R, Attoui H, et al. Low diversity of Alkhurma hemorrhagic fever virus, Saudi Arabia, 1994–1999. Emerg Infect Dis. 2005;11:683–8. 6. Madani TA. Alkhumra virus infection, a new viral hemorrhagic fever in Saudi Arabia. J Infect. 2005;51:91–7. DOI: 10.1016/j. jinf.2004.11.012 7. Charrel RN, Zaki AM, Fagbo S, de Lamballerie X. Alkhurma hemorrhagic fever virus is an emerging tick-borne flavivirus. J Infect. 2006;52:463–4. DOI: 10.1016/j.jinf.2005.08.011 8. Alkhurma virus–Saudi Arabia: (Makkah). ProMEDmail [cited 2010 Jan 6]. http://www.promedmail.org, archive no. 20100106.0056. 9. Charrel RN, Fagbo S, Moureau G, Alqahtani MH, Temmam S, de Lamballerie X. Alkhurma hemorrhagic fever virus in Ornithodoros savignyi ticks. Emerg Infect Dis. 2007;13:153–5. DOI: 10.3201/ eid1301.061094 10. Hoogstraal H. Argasid and nuttallielid ticks as parasites and vectors. Adv Parasitol. 1985;24:135–238. DOI: 10.1016/S0065-308X(08)60563-1

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11.

Scaramozzino N, Crance JM, Jouan A, DeBriel DA, Stoll F, Garin D. Comparison of flavivirus universal primer pairs and development of a rapid, highly sensitive heminested reverse transcription–PCR assay for detection of flaviviruses targeted to a conserved region of the NS5 gene sequences. J Clin Microbiol. 2001;39:1922–7. DOI: 10.1128/JCM.39.5.1922-1927.2001 12. Grard G, Moureau G, Charrel RN, Lemasson JJ, Gonzalez JP, Gallian P, et al. Genetic characterization of tick-borne flaviviruses: new insights into evolution, pathogenetic determinants and taxonomy. Virology. 2007;361:80–92. DOI: 10.1016/j.virol.2006.09.015

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Zanotto PM, Gould EA, Gao GF, Harvey PH, Holmes EC. Population dynamics of flaviviruses revealed by molecular phylogenies. Proc Natl Acad Sci U S A. 1996; 23;93:548–53.

Address for correspondence: Giuseppe Ippolito, National Institute for Infectious Diseases “L. Spallanzani,” 292 Via Portuense, 00149 Rome, Italy: email: [email protected]

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Multispacer Typing of Bartonella henselae Isolates from Humans and Cats, Japan Masashi Yanagihara, Hidehiro Tsuneoka, Motoki Sugasaki, Junzo Nojima, and Kiyoshi Ichihara To determine genotypic distribution of and relationship between human and cat strains of Bartonella henselae, we characterized 56 specimens using multispacer typing (MST). Of 13 MST genotypes identified, 12 were grouped into cluster 1. In Japan, human infections can be caused by B. henselae strains in cluster 1.

T

he causative agent of cat-scratch disease (CSD), Bartonella henselae, is a gram-negative bacterium associated with cats. Human infection usually occurs through scratches or bites by infected cats and typically is seen with localized lymphadenopathy. Occasionally, the infection may have an atypical manifestation, such as endocarditis, encephalopathy, neuroretinitis, or systemic CSD with hepatic and splenic granuloma (1). B. henselae strains are classified into two 16S rRNA genotypes, 16S type I/Houston-1 and 16S type II/Marseille. Although both genotypes are present worldwide, 16S type II appears to be dominant in the cat population of Europe, whereas 16S type I is more common in Asia, including Japan (2,3). Multispacer typing (MST) is a nucleotide sequencingbased genotyping method that uses highly variable intergenic spacers as typing markers. It is the most suitable genotyping procedure for evaluating the population structure of closely related strains of B. henselae (4). Previously, 50 MST genotypes from 201 B. henselae strains were phylogenetically organized into 4 lineages, and human strains mostly grouped within 2 of these lineages, Houston-1 and Marseille (5,6). Because genotypic data on B. henselae from Asian countries are limited, we applied MST to 56 human and cat specimens to determine the genotypic distribution and relationship of human and cat strains of B. henselae in Japan.

Author affiliation: Yamaguchi University, Ube, Yamaguchi, Japan DOI: 10.3201/eid1612.100962

The Study During 1997 through 2008, we collected 56 B. henselae specimens from western Japan, mainly from Yamaguchi prefecture; the specimens included 1 B. henselae isolate from a patient with endocarditis (7), 24 clinical specimens from CSD patients who had test results positive for B. henselae DNA, and 31 B. henselae isolates from domestic cats (8). The 24 clinical specimens included 5 lymph node specimens and 16 pus specimens from patients with typical CSD, 1 blood specimen from a patient with bacteremia, 1 liver specimen from a patient with hepatic granuloma, and 1 spleen specimen from a patient with splenic granuloma. Total genomic DNA was extracted from the specimens by using the QIAamp DNA Mini Kit (QIAGEN, Hilden, Germany). B. henselae DNA was detected by using PCR with specific primers for the 16S–23S rRNA intergenic spacer (9) and the htrA gene (10), and the 16S rRNA genotype was confirmed by partial sequencing of the 16S rRNA gene (11). In addition, previously described MST primers were used to amplify and sequence the 9 intergenic spacers in B. henselae DNA (6). Locus-specific PCR was performed for spacer S1; direct sequencing was unsuccessful because of an unusual number of variable number tandem repeats (VNTR) (8). Spacer sequences were assigned according to published data (5,6,8). For each of the 9 spacers (S1–S9), we identified 8, 2, 3, 4, 3, 2, 3, 2, and 3 genotypes, respectively (Table). Three novel spacer sequences were deposited in the DNA Data Bank of Japan with these accession numbers: AB558532, S2 genotype 9; AB558533, S4 genotype 7; and AB558534, S4 genotype 8. We identified 13 different MST genotypes among the 56 specimens (Table). Of these 13 MST genotypes, 7 were novel (types 51–57). Six MST genotypes belonged to human and cat strains, including 2 predominant MST genotypes (14 and 35). All MST data were deposited in the MST-Rick database (http:// ifr48.timone.univ-mrs.fr/MST_BHenselae/mst). Subsequently, we analyzed the phylogenetic relationships of the 7 novel MST genotypes identified in this study with the 50 previously identified genotypes. Multiple sequence alignment of the concatenated spacer sequences was performed by using ClustalW (www.ebi.ac.uk/clustalw). Finally, a phylogenetic tree was constructed by using the unweighted pair-group method with arithmetic mean (UPGMA) in MEGA4 (12). This phylogenetic tree is grouped into 4 clusters (Figure). Of the 13 MST genotypes identified in this study, 12 genotypes belonged to cluster 1, but one genotype (MST genotype 52) belonged to cluster 4. Conclusions This study showed that MST genotypes in Japan were mainly grouped into 1 lineage (cluster 1), which was composed of Asian and American strains of B. henselae, and

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Table. Multispacer typing of 56 Bartonella henselae strains isolated from humans and cats, Japan* B. henselae source Genotypes No. human No. cat S1 S2 S3 S4 S5 S6 S7 S8 1 1 7 2 5 4 1 2 1 1 8 12 4 2 5 4 1 2 1 1 1 0 3 2 6 5 2 2 2 1 1 0 8 2 5 4 1 2 2 1 2† 1 4 2 5 4 1 2 2 1 9‡§ 4 5 2 6 5 2 2 2 1 0 1 11 2 6 4 1 2 1 1 0 2 12 9 2 7 5 4 4 3 1 2 5 2 6 8 2 2 2 1 1¶ 1 4 2 5 5 1 2 1 1 0 4 5 2 6 5 1 2 2 1 0 3 7 + 4# 2 5 4 1 2 1 1 1 0 4 2 5 4 2 2 1 1

S9 3 3 1 3 3 1 3 2 1 3 1 3 3

MST 7 14 21 32 33 35 51 52 53 54 55 56 57

16S rRNA genotype I I I I I I I II I I I I I

*MST, multispacer typing; I, 16S type I; II, 16S type II. †Includes a clinical specimen isolated from a patient with hepatic granuloma. ‡Includes B. henselae strain isolated from a patient with endocarditis. §Includes a clinical specimen isolated from a patient with bacteremia. ¶Includes a clinical specimen isolated from a patient with splenic granuloma. #Strain with 2 different copies of intergenic spacer S1 in its genome.

that the genotypic distribution of human strains coincided with that of cat strains. Although only 1 human strain from the West Indies belonged to cluster 1 before this study (6), we discovered that all 25 of our human strains from Japan were grouped into cluster 1. These results demonstrate that human infections can be caused by B. henselae strains in cluster 1, which differed from clusters corresponding to the Houston-1 and Marseille type strains. In a previous study, MST genotype 35 was the most common genotype in cluster 1, and 4/6 (67%) of Japanese cat strains belonged to this genotype (5). In this study, MST genotypes 14 (36%; 20/56) and 35 (23%; 13/56) were predominant genotypes. Additionally, most human strains (88%; 15/17) belonging to these genotypes were isolated from patients with typical CSD; 2 strains with MST genotype 35 were isolated from patients with endocarditis and bacteremia (Table). The genotypic distribution of the human strains in this study differed from that reported by Li et al. (6) because their strains isolated in France were grouped under 2 lineages (Houston-1 and Marseille). However, we found that the lineages of human strains matched those of cat strains in each country. These results are consistent with the role of cats as the major reservoir of B. henselae (13). In this study, we identified 2 cat strains that were classified into cluster 4. These strains belonged to 16S type II, which is rare in Japan (3). In previous MST studies, strains in cluster 4 were isolated from cats and belonged to 16S type II (5,6). Intriguingly, similar lineages consisting of 16S type II isolates from cats were observed in other genotyping studies involving the use of multilocus sequence typing (MLST) (14) and multiple locus variable number tandem

1984

repeat analysis (MLVA) (15). Thus, these lineages may be less pathogenic for humans. However, further studies are needed to investigate this hypothesis. When we characterized the strains in this study by MLST we found that almost all of them shared the same sequence type as Houston-1 (8). In contrast, we identified 13 MST genotypes that belonged to different clusters than Houston-1. The lower resolving power of MLST is mostly likely due to sequence conservation in the 8 housekeeping genes selected for the method. MST has a higher resolving power because the spacers used in this method are more variable than MLST markers. As a result, MST is better suited for evaluating the population structure of closely related B. henselae strains. We conclude that the MST genotypes in Japan are mainly grouped into cluster 1 and that the genotypic distribution of human strains coincides with that of cat strains. In Japan, human infections can be caused by B. henselae strains in cluster 1, distinct from clusters containing the Houston-1 and Marseille type strains. These results improve our understanding of the population structure of and geographic relationship between human and cat strains of B. henselae. This work was supported by Grant-in-Aid for Young Scientists (B) No. 21790538 from the Ministry of Education, Culture, Sports, Science and Technology of Japan. Dr Yanagihara is a medical technologist and research scientist in the Department of Basic Laboratory Sciences, Yamaguchi University Graduate School of Medicine. His research interests focus on B. henselae infections and their molecular epidemiology.

Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 16, No. 12, December 2010

Bartonella henselae Isolates, Japan

MST genotypes MST7 MST56 MST14 MST9 MST8 MST23 MST57 MST54 MST36 MST51 MST6 MST32 MST33 MST19 MST50 MST53 MST35 MST21 MST55 MST38 MST37 MST24 MST10 MST20 MST39 MST22 MST48 MST12 MST27 MST15 MST40 MST29 MST5 MST4 MST3 MST30 MST46 MST49 MST1 MST26 MST25 MST28 MST16 MST47 MST31 MST44 MST42 MST43 MST45 MST41 MST34 MST17 MST52 MST13 MST18 MST11 MST2 0.005

0.004

0.003

0.002

0.001

No. B. henselae originated from Human Cat 1 1 3 8 12

1 1

References Cl t Cluster (lineage)

1. 2. 3.

1 1

1 2

1 9 1

1

1

2 4

4. 5.

4

6. 7. 2 (Houston-1)

8.

9. 3 (Marseille)

10. 11.

12.

2 4

13.

0.000

Total

57

25

31

Figure. Phylogeny and clusters of multispacer typing (MST) genotypes of Bartonella henselae isolates from humans and cats, Japan, based on 9 concatenated intergenic spacer sequences in 57 MST genotypes. The unweighted pair-group method with arithmetic mean method in MEGA4 software (12) was used for phylogenetic analysis. Dotted rectangles show 4 clusters of MST genotypes, 2 of which correspond to the B. henselae Houston-1 and Marseille type strains. Scale bar indicates nucleotide substitutions per site.

14.

15.

Anderson BE, Neuman MA. Bartonella spp. as emerging human pathogens. Clin Microbiol Rev. 1997;10:203–19. Boulouis HJ, Chang CC, Henn JB, Kasten RW, Chomel BB. Factors associated with the rapid emergence of zoonotic Bartonella infections. Vet Res. 2005;36:383–410. DOI: 10.1051/vetres:2005009 Maruyama S, Nakamura Y, Kabeya H, Tanaka S, Sakai T, Katsube Y. Prevalence of Bartonella henselae, Bartonella clarridgeiae and the 16S rRNA gene types of Bartonella henselae among pet cats in Japan. J Vet Med Sci. 2000;62:273–9. DOI: 10.1292/jvms.62.273 Li W, Raoult D, Fournier PE. Bacterial strain typing in the genomic era. FEMS Microbiol Rev. 2009;33:892–916. DOI: 10.1111/j.15746976.2009.00182.x Li W, Chomel BB, Maruyama S, Guptil L, Sander A, Raoult D, et al. Multispacer typing to study the genotypic distribution of Bartonella henselae populations. J Clin Microbiol. 2006;44:2499–506. DOI: 10.1128/JCM.00498-06 Li W, Raoult D, Fournier PE. Genetic diversity of Bartonella henselae in human infection detected with multispacer typing. Emerg Infect Dis. 2007;13:1178–83. Tsuneoka H, Yanagihara M, Otani S, Katayama Y, Fujinami H, Nagafuji H, et al. A first Japanese case of Bartonella henselae induced endocarditis diagnosed by prolonged culture of a specimen from the excised valve. Diagn Microbiol Infect Dis. 2010;68:174–6. Yanagihara M, Tsuneoka H, Hoshide S, Ishido E, Umeda A, Tsukahara M, et al. Molecular typing of Bartonella henselae DNA extracted from human clinical specimens and cat isolates in Japan. FEMS Immunol Med Microbiol. 2010;60:44–8. DOI: 10.1111/j.1574-695X.2010.00711.x Jensen WA, Fall MZ, Rooney J, Kordick DL, Breitschwerdt EB. Rapid identification and differentiation of Bartonella species using a single-step PCR assay. J Clin Microbiol. 2000;38:1717–22. Anderson B, Sims K, Regnery R, Robinson L, Schmidt MJ, Goral S, et al. Detection of Rochalimaea henselae DNA in specimens from cat scratch disease patients by PCR. J Clin Microbiol. 1994;32:942–8. Bergmans AM, Schellekens JF, van Embden JD, Schouls LM. Predominance of two Bartonella henselae variants among cat-scratch disease patients in the Netherlands. J Clin Microbiol. 1996;34:254– 60. Tamura K, Dudley J, Nei M, Kumar S. MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0. Mol Biol Evol. 2007;24:1596–9. DOI: 10.1093/molbev/msm092 Regnery R, Martin M, Olson J. Naturally occurring “Rochalimaea henselae” infection in domestic cat. Lancet. 1992;340:557–8. DOI: 10.1016/0140-6736(92)91760-6 Arvand M, Feil EJ, Giladi M, Boulouis HJ, Viezens J. Multi-locus sequence typing of Bartonella henselae isolates from three continents reveals hypervirulent and feline-associated clones. PLoS ONE. 2007;2:e1346. DOI: 10.1371/journal.pone.0001346 Bouchouicha R, Durand B, Monteil M, Chomel BB, Berrich M, Arvand M, et al. Molecular epidemiology of feline and human Bartonella henselae isolates. Emerg Infect Dis. 2009;15:813–6. DOI: 10.3201/eid1505.080995

Address for correspondence: Masashi Yanagihara, Department of Basic Laboratory Sciences, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-kogushi, Ube, Yamaguchi 755-8505, Japan; email: [email protected]

Search past issues of EID at www.cdc.gov/eid Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 16, No. 12, December 2010

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Pandemic (H1N1) 2009 Outbreak at Canadian Forces Cadet Camp Rhonda Y. Kropp, Laura E. Bogaert, Robert Barber, Francois-William Tremblay, Robert Ennis, Martin Tepper, Robert Pless, Nathalie Bastien, Yan Li, Carole Beaudoin, James Anderson, Louise Pelletier, and Rachel Rodin We conducted a case–control study to describe the clinical and epidemiologic characteristics of an outbreak of pandemic (H1N1) 2009 at a Canadian military cadet training center. We found that asthma and obesity confer greater risk for infection. Viral shedding was detected by PCR up to 18 days after symptom onset.

O

n July 29, 2009, the Public Health Agency of Canada was notified of an outbreak of pandemic (H1N1) 2009 at the Army Cadet Summer Training Centre Argonaut at Canadian Forces Base, Gagetown, New Brunswick. The Cadet Summer Training Centre camp opened in early July and ran sessions lasting 2–6 weeks. The camp setting was semiclosed, with limited movement on and off camp. A case–control study was conducted to describe transmission, clinical characteristics, viral shedding, and risk factors for infection.

The Study Approximately 506 cadets, 12–18 years of age, and 322 staff cadets, officers, and support staff lived on camp premises. All persons at the camp were invited to participate. This study received expedited approval from the Health Canada Research Ethics Board. Participants were interviewed in person at the camp or by telephone; swab specimens were collected by on-site nurses. Samples were sent to the National Microbiology Laboratory for testing using reverse transcription–PCR and primer sets developed by the US Centers for Disease Control and Prevention (1). Specimens were cultured in primary CMK cells (Viromed Author affiliations: Public Health Agency of Canada, Ottawa, Ontario, Canada (R.Y. Kropp, F-W. Tremblay, R. Pless, L. Pelletier, R. Rodin); Department of National Defence Headquarters, Ottawa (L.E. Bogaert, R. Barber, R. Ennis, M. Tepper, J. Anderson); and Public Health Agency of Canada, Winnipeg, Manitoba, Canada (N. Bastien, Y. Li, C. Beaudoin) DOI: 10.3201/eid1612.100451 1986

Laboratories, Inc., Minnetonka, MN, USA) and the hemagglutinin titer was checked at days 6 and 10. A modified case definition for pandemic (H1N1) 2009 infection was developed based on Canada’s surveillance case definition for influenza-like illness. Symptom onset was defined as earliest date of onset of self-reported history of fever or cough. The case definition is outlined in Table 1. During August 3–27, 2009, we conducted 144 faceto-face and 21 phone interviews. Approximately 20% of cadets and 20% of staff cadets, officers, and support staff participated. Of the 165 participants, 56 were classified as confirmed cases, 24 as suspected cases, and 85 as controls. Participant age ranged from 13 to 43 years; 88% were 13– 18 years of age, and 55% were male. No statistically significant demographic differences (p2 of the following symptoms: sore throat, nausea, nasal congestion, chills OR 2. Reported fever and cough and had negative PCR results for pandemic (H1N1) 2009 within 5 days after symptom onset Controls Persons who 1. Did not report fever or cough OR 2. Reported fever or cough but without >2 of the following symptoms: sore throat, nausea, nasal congestion, chills

Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 16, No. 12, December 2010

Pandemic (H1N1) 2009 at Cadet Camp

Figure. Epidemic curve of 54 confirmed and 21 suspected cases of pandemic (H1N1) 2009 infection and of 27 additional cases of fever and cough identified by the camp Health Care Centre, Army Cadet Summer Training Centre Argonaut at Canadian Forces Base, Gagetown, New Brunswick, Canada, 2009.

was delayed 2 weeks. Mass screening for fever and cough was undertaken on August 6, before the arrival of new cadets; all the new cadets were screened on arrival. No activity or exposure was linked to increased risk for illness (data not shown). All but 1 person with a suspected or confirmed case reported symptoms; 58/85 (68.2%) of controls also reported symptoms during the outbreak period. Odds of experiencing shortness of breath, chest pain, sputum production, vomiting, rhinorrhea, nose bleeds, or change in level of awareness were all >5× higher for those with cases/suspected cases than for controls (Table 2). The mean number of symptoms among those with symptomatic cases/suspected cases was greater than among symptomatic controls (8.7 vs. 3.4; p10 days was reported by 40% of persons with cases/suspected cases whose symptoms had resolved and 47% of those with unresolved symptoms. Median time from symptom onset to illness peak was 2 days (range 1–14 days). With the exception of cough, sputum production, and malaise, symptoms peaked rapidly (24–48 hours) after onset. Overall, 86.1% of persons with cases/suspected cases accessed the HCC; none were hospitalized. Oseltamivir was given to 2 persons with confirmed cases who had comorbid conditions (asthma and kidney disease). Forty-four persons with cases/suspected cases (55.7%) were not isolated because they did not seek treatment at the HCC or not while both fever and cough were present. Eight persons had positive PCR results for pandemic (H1N1) 2009 7–18 days after symptom onset, and live virus was detected up to 14 days after symptom onset. All but 1 of these persons were capable of transmitting virus given upper respiratory symptoms, and 2 reported diarrhea and vomiting on the day the swab sample was obtained. Four

persons had live virus detected after day 7 of illness (up to 14 days); 2 of these reported comorbid conditions. Persons with confirmed and suspected cases did not differ with regard to comorbidity or risk factors, except for seasonal influenza vaccination; 6/48 (12.5%) of persons with confirmed cases reported having received the seasonal influenza vaccine in the year of the study versus 8/22 (36.4%) of those with suspected cases (odds ratio 4.0; p1 comorbidity was >2.7× higher for persons with case/suspected cases than for controls (p3.9× higher (p < 0.05). The odds of being obese were >3× higher for persons with cases/ suspected cases (odds ratio 3.4, 95% confidence interval 1.0–10.9). Conclusions In accordance with national recommendations (2), antiviral drugs were not used for control; transmission appeared to be reduced through nonpharmaceutical measures. Multiple index cases could not be ruled out. No individual activity or exposure was linked to increased risk for illness. High rates of obesity have been noted among hospitalized patients with pandemic (H1N1) 2009 (3–6). This study suggests obesity is a risk factor for infection or clinical illness and given low prevalence of comorbid conditions may stand alone as a risk factor. Consistent with international studies (7), vaccination for seasonal influenza was neither protective nor a risk factor for acquiring pandemic (H1N1) 2009. One third of case-patients reported change in level of awareness, which suggests the potential for mild neurologic sequelae. Neurologic complications of influenza infection have been reported in hospitalized children (8,9). Seven of 8 participants who had positive PCR results for pandemic (H1N1) 2009 >7 days after symptom onset

Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 16, No. 12, December 2010

1987

DISPATCHES

Table 2. Frequency of reported symptoms of pandemic (H1N1) 2009 infection, Army Cadet Summer Training Centre Argonaut at Canadian Forces Base, Gagetown, New Brunswick, Canada, 2009* No. persons reporting symptom/no. persons reporting (%) Persons with confirmed/ suspected cases,† n = 80 Symptoms OR (95% CI)‡ All participants, n = 165 Controls, n = 85 None 28/165 (17.0) 1/80 (1.3) 27/85 (31.8) Systemic Fever 56/164 (34.1) 55/80 (68.8) 1/84 (1.2) 44/165 (26.7) 41/80 (51.2) 3/85 (3.5) Chills Headache 58/165 (35.2) 39/80 (48.8) 19/85 (22.4) 3.3 (1.7–6.5)§ 52/164 (31.7) 37/79 (46.8) 15/85 (17.6) 4.1 (2.0–8.4)¶ Prostration 67/165 (40.6) 47/80 (58.8) 20/85 (23.5) 4.6 (2.4–9.0)¶ Malaise Arthralgia 19/165 (11.5) 13/80 (16.2) 6/85 (7.1) 2.6 (0.9–7.1) 26/165 (15.8) 20/80 (25.0) 6/85 (7.1) 4.4 (1.7–11.6)§ Myalgia Lower respiratory Cough 97/165 (58.8) 76/80 (95.0) 21/85 (24.7) 33/164 (20.1) 26/79 (32.9) 7/85 (8.2) 5.5 (2.2–13.5)¶ Sputum production 30/165 (18.2) 26/80 (32.5) 4/85 (4.7) 9.8 (3.2–29.5)¶ Shortness of breath Chest pain 14/165 (8.5) 13/80 (16.2) 1/85 (1.2) 16.3 (2.1–127.8)§ Upper respiratory Sore throat 76/164 (46.3) 56/80 (70.0) 20/84 (23.8) 76/164 (46.3) 55/79 (69.6) 21/85 (24.7) Nasal congestion Sneezing 27/164 (16.5) 20/79 (25.3) 7/85 (8.2) 3.8 (1.5–9.5)§ 41/159 (25.8) 32/75 (42.7) 9/84 (10.7) 6.2 (2.7–14.2)¶ Runny nose Nosebleeds 10/165 (6.1) 9/80 (11.2) 1/85 (1.2) 10.6 (1.3–86.1)# Gastrointestinal Nausea 50/165 (30.3) 42/80 (52.5) 8/85 (9.4) 28/165 (17.0) 17/80 (21.2) 11/85 (12.9) 1.8 (0.8–4.2) Abdominal pain Diarrhea 26/165 (15.8) 19/80 (23.8) 7/85 (8.2) 3.5 (1.4–8.8)# 24/165 (14.5) 20/80 (25.0) 4/85 (4.7) 6.8 (2.2–20.8)¶ Vomiting Neurologic Seizures 0/164 (0.0) 0/80 (0.0) 0/85 (0.0) NA 29/165 (17.6) 25/80 (31.2) 4/85 (4.7) 9.2 (3.0–27.9)¶ Change in awareness Other Conjunctivitis 2/164 (1.2) 1/79 (1.3) 1/85 (1.2) 1.0 (0.1–17.5) Other** 16/165 (9.7) 14/80 (17.5) 2/85 (2.4) 8.8 (2.0–40.1)§ 6.5 8.7 3.4 Mean no. symptoms *One person reported no symptoms but was PCR and culture positive. OR, odds ratio; CI, confidence interval; NA, not applicable. †Fever and nausea reported by a greater proportion of cases than suspected cases (p