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Jung Kjær L, Ward MP, Boklund AE, Larsen LE, Hjulsager CK, Kirkeby CT. Using surveillance data for early warning modelling of highly pathogenic avian influenza in Europe reveals a seasonal shift in transmission, 2016-2022. Sci Rep 2023; 13:15396. [PMID: 37717056 PMCID: PMC10505205 DOI: 10.1038/s41598-023-42660-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 09/13/2023] [Indexed: 09/18/2023] Open
Abstract
Avian influenza in wild birds and poultry flocks constitutes a problem for animal welfare, food security and public health. In recent years there have been increasing numbers of outbreaks in Europe, with many poultry flocks culled after being infected with highly pathogenic avian influenza (HPAI). Continuous monitoring is crucial to enable timely implementation of control to prevent HPAI spread from wild birds to poultry and between poultry flocks within a country. We here utilize readily available public surveillance data and time-series models to predict HPAI detections within European countries and show a seasonal shift that happened during 2021-2022. The output is models capable of monitoring the weekly risk of HPAI outbreaks, to support decision making.
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Affiliation(s)
- Lene Jung Kjær
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Michael P Ward
- Faculty of Science, Sydney School of Veterinary Science, University of Sydney, Camden, NSW, Australia
| | - Anette Ella Boklund
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lars Erik Larsen
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Carsten Thure Kirkeby
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Ssematimba A, Malladi S, Bonney PJ, Flores-Figueroa C, Muñoz-Aguayo J, Halvorson DA, Cardona CJ. Quantifying the effect of swab pool size on the detection of influenza A viruses in broiler chickens and its implications for surveillance. BMC Vet Res 2018; 14:265. [PMID: 30176867 PMCID: PMC6122460 DOI: 10.1186/s12917-018-1602-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Accepted: 08/28/2018] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Timely diagnosis of influenza A virus infections is critical for outbreak control. Due to their rapidity and other logistical advantages, lateral flow immunoassays can support influenza A virus surveillance programs and here, their field performance was proactively assessed. The performance of real-time polymerase chain reaction and two lateral flow immunoassay kits (FluDETECT and VetScan) in detecting low pathogenicity influenza A virus in oropharyngeal swab samples from experimentally inoculated broiler chickens was evaluated and at a flock-level, different testing scenarios were analyzed. RESULTS For real-time polymerase chain reaction positive individual-swabs, FluDETECT respectively detected 37% and 58% for the H5 and H7 LPAIV compared to 28% and 42% for VetScan. The mean virus titer in H7 samples was higher than for H5 samples. For real-time polymerase chain reaction positive pooled swabs (containing one positive), detections by FluDETECT were significantly higher in the combined 5- and 6-swab samples compared to 11-swab samples. FluDETECT detected 58%, 55.1% and 44.9% for the H7 subtype and 28.3%, 34.0% and 24.6% for the H5 in pools of 5, 6 and 11 respectively. In our testing scenario analysis, at low flock-level LPAIV infection prevalence, testing pools of 11 detected slightly more infections while at higher prevalence, testing pools of 5 or 6 performed better. For highly pathogenic avian influenza virus, testing pools of 11 (versus 5 or 6) detected up to 5% more infections under the assumption of similar sensitivity across pools and detected less by 3% when its sensitivity was assumed to be lower. CONCLUSIONS Much as pooling a bigger number of swab samples increases the chances of having a positive swab included in the sample to be tested, this study's outcomes indicate that this practice may actually reduce the chances of detecting the virus since it may result into lowering the virus titer of the pooled sample. Further analysis on whether having more than one positive swab in a pooled sample would result in increased sensitivity for low pathogenicity avian influenza virus is needed.
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Affiliation(s)
- Amos Ssematimba
- Secure Food Systems Team, University of Minnesota, Veterinary and Biomedical Sciences, 301C Veterinary Science Building, 1971 Commonwealth Avenue, Saint Paul, MN 55108 USA
- Department of Mathematics, Faculty of Science, Gulu University, P.O. Box 166, Gulu, Uganda
| | - Sasidhar Malladi
- Secure Food Systems Team, University of Minnesota, Veterinary and Biomedical Sciences, 301C Veterinary Science Building, 1971 Commonwealth Avenue, Saint Paul, MN 55108 USA
| | - Peter J. Bonney
- Secure Food Systems Team, University of Minnesota, Veterinary and Biomedical Sciences, 301C Veterinary Science Building, 1971 Commonwealth Avenue, Saint Paul, MN 55108 USA
| | | | | | - David A. Halvorson
- Secure Food Systems Team, University of Minnesota, Veterinary and Biomedical Sciences, 301C Veterinary Science Building, 1971 Commonwealth Avenue, Saint Paul, MN 55108 USA
| | - Carol J. Cardona
- Secure Food Systems Team, University of Minnesota, Veterinary and Biomedical Sciences, 301C Veterinary Science Building, 1971 Commonwealth Avenue, Saint Paul, MN 55108 USA
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Yuan XY, Wang YL, Yu KX, Zhang YX, Xu HY, Yang JX, Li F, Song MX. Isolation and genetic characterization of avian influenza virus H4N6 from ducks in China. Arch Virol 2014; 160:55-9. [DOI: 10.1007/s00705-014-2229-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2014] [Accepted: 09/02/2014] [Indexed: 11/29/2022]
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Systematic review of surveillance systems and methods for early detection of exotic, new and re-emerging diseases in animal populations. Epidemiol Infect 2014; 143:2018-42. [PMID: 25353252 DOI: 10.1017/s095026881400212x] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
In this globalized world, the spread of new, exotic and re-emerging diseases has become one of the most important threats to animal production and public health. This systematic review analyses conventional and novel early detection methods applied to surveillance. In all, 125 scientific documents were considered for this study. Exotic (n = 49) and re-emerging (n = 27) diseases constituted the most frequently represented health threats. In addition, the majority of studies were related to zoonoses (n = 66). The approaches found in the review could be divided in surveillance modalities, both active (n = 23) and passive (n = 5); and tools and methodologies that support surveillance activities (n = 57). Combinations of surveillance modalities and tools (n = 40) were also found. Risk-based approaches were very common (n = 60), especially in the papers describing tools and methodologies (n = 50). The main applications, benefits and limitations of each approach were extracted from the papers. This information will be very useful for informing the development of tools to facilitate the design of cost-effective surveillance strategies. Thus, the current literature review provides key information about the advantages, disadvantages, limitations and potential application of methodologies for the early detection of new, exotic and re-emerging diseases.
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Corley CD, Pullum LL, Hartley DM, Benedum C, Noonan C, Rabinowitz PM, Lancaster MJ. Disease prediction models and operational readiness. PLoS One 2014; 9:e91989. [PMID: 24647562 PMCID: PMC3960139 DOI: 10.1371/journal.pone.0091989] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2012] [Accepted: 02/19/2014] [Indexed: 11/18/2022] Open
Abstract
The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4), spatial (26), ecological niche (28), diagnostic or clinical (6), spread or response (9), and reviews (3). The model parameters (e.g., etiology, climatic, spatial, cultural) and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological) were recorded and reviewed. A component of this review is the identification of verification and validation (V&V) methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology Readiness Level definitions.
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Affiliation(s)
- Courtney D. Corley
- Pacific Northwest National Laboratory, Richland, Washington, United States of America
- * E-mail:
| | - Laura L. Pullum
- Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America
| | - David M. Hartley
- Georgetown University Medical Center, Washington, DC, United States of America
| | - Corey Benedum
- Pacific Northwest National Laboratory, Richland, Washington, United States of America
| | - Christine Noonan
- Pacific Northwest National Laboratory, Richland, Washington, United States of America
| | - Peter M. Rabinowitz
- Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Mary J. Lancaster
- Pacific Northwest National Laboratory, Richland, Washington, United States of America
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Carpenter TE, Cardona C. Assessing alternative low pathogenic avian influenza virus surveillance strategies in a live bird market. Avian Dis 2013; 56:880-3. [PMID: 23402107 DOI: 10.1637/10205-041512-reg.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Surveillance, comprised of sampling and testing, of low pathogenic avian influenza virus (LPAIV) in a live bird market (LBM) may enable the detection of the virus, reducing its spread within the market to humans and birds and to other markets within the LBM system. In addition, detection of infected birds would also reduce the probability of reassortment and possible change from a LPAIV to a highly pathogenic avian influenza virus, which would have a devastating impact on the economy, trade, and society. In this paper we present results from a computer simulation model based on previously collected survey and experimental transmission data. Once we validated the model with experimental transmission data, we applied it to address some of the questions that need to be answered in order to create an efficient surveillance system in an LBM. We have identified effective sampling times, patterns, and sizes that would enhance the probability of an early detection of LPAIV if present and minimize the associated labor and cost. The model may be modified to evaluate different sized and structured LBMs. It also provides the basis to evaluate an entire LBM system for the United States or other countries.
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Affiliation(s)
- Tim E Carpenter
- EpiCentre, Institute for Veterinary and Biomedical Sciences, Massey University, Private Bag 11222, Palmerston North, 4442 New Zealand.
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Mathematical Models of Infectious Diseases in Livestock: Concepts and Application to the Spread of Highly Pathogenic Avian Influenza Virus Strain Type H5N1. HEALTH AND ANIMAL AGRICULTURE IN DEVELOPING COUNTRIES 2012. [PMCID: PMC7120485 DOI: 10.1007/978-1-4419-7077-0_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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De Leo GA, Bolzoni L. Getting a free ride on poultry farms: how highly pathogenic avian influenza may persist in spite of its virulence. THEOR ECOL-NETH 2011. [DOI: 10.1007/s12080-011-0136-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Lv J, Wei B, Chai T, Xia X, Miao Z, Yao M, Gao Y, Huang R, Yang H, Roesler U. Development of a real-time RT-PCR method for rapid detection of H9 avian influenza virus in the air. Arch Virol 2011; 156:1795-801. [PMID: 21735211 DOI: 10.1007/s00705-011-1054-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2011] [Accepted: 06/18/2011] [Indexed: 11/26/2022]
Abstract
Avian influenza virus (AIV) has caused serious epidemics all over the world. Notably, the low-pathogenic AIV H9N2 has been spreading widely, leading to enormous economic losses to the poultry industry. To rapidly monitor airborne H9 AIVs in chicken houses, a real-time RT-PCR method was established and used to detect virus in air samples, and it was also compared with the traditional RT-PCR. The results showed that the real-time RT-PCR possessed high specificity and sensitivity for H9 AIVs, and the sensitivity reached 100 copies/reaction, much higher than the traditional RT-PCR; airborne H9 AIVs were found in the six chicken houses by real-time RT-PCR, and their mean concentrations ranged from 1.25×10(4) to 6.92×10(4) copies/m(3) air. Overall, the real-time PCR is a valuable tool for detecting airborne H9 AIVs.
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Affiliation(s)
- Jing Lv
- College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an 271018, Shandong, China
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Stegeman A, Bouma A, de Jong MCM. Use of epidemiologic models in the control of highly pathogenic avian influenza. Avian Dis 2010; 54:707-12. [PMID: 20521719 DOI: 10.1637/8821-040209-review.1] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
In the past decades, mathematical models have become more and more accepted as a tool to develop surveillance programs and to evaluate the efficacy of intervention measures for the control of infectious diseases such as highly pathogenic avian influenza. Predictive models are used to simulate the effect of various control measures on the course of an epidemic; analytical models are used to analyze data from outbreaks or from experiments. A key parameter in both types of models is the reproductive ratio, which indicates whether virus can be transmitted in the population, resulting in an epidemic, or not. Parameters obtained from real data using the analytical models can subsequently be used in predictive models to evaluate control strategies or surveillance programs. Examples of the use of these models are described here.
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Affiliation(s)
- Arjan Stegeman
- Faculty of Veterinary Medicine, Department of Farm Animal Health, Utrecht University, Yalelaan 7, 3584 CL, Utrecht, The Netherlands.
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Fuller TL, Saatchi SS, Curd EE, Toffelmier E, Thomassen HA, Buermann W, DeSante DF, Nott MP, Saracco JF, Ralph CJ, Alexander JD, Pollinger JP, Smith TB. Mapping the risk of avian influenza in wild birds in the US. BMC Infect Dis 2010; 10:187. [PMID: 20573228 PMCID: PMC2912310 DOI: 10.1186/1471-2334-10-187] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2010] [Accepted: 06/23/2010] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Avian influenza virus (AIV) is an important public health issue because pandemic influenza viruses in people have contained genes from viruses that infect birds. The H5 and H7 AIV subtypes have periodically mutated from low pathogenicity to high pathogenicity form. Analysis of the geographic distribution of AIV can identify areas where reassortment events might occur and how high pathogenicity influenza might travel if it enters wild bird populations in the US. Modelling the number of AIV cases is important because the rate of co-infection with multiple AIV subtypes increases with the number of cases and co-infection is the source of reassortment events that give rise to new strains of influenza, which occurred before the 1968 pandemic. Aquatic birds in the orders Anseriformes and Charadriiformes have been recognized as reservoirs of AIV since the 1970s. However, little is known about influenza prevalence in terrestrial birds in the order Passeriformes. Since passerines share the same habitat as poultry, they may be more effective transmitters of the disease to humans than aquatic birds. We analyze 152 passerine species including the American Robin (Turdus migratorius) and Swainson's Thrush (Catharus ustulatus). METHODS We formulate a regression model to predict AIV cases throughout the US at the county scale as a function of 12 environmental variables, sampling effort, and proximity to other counties with influenza outbreaks. Our analysis did not distinguish between types of influenza, including low or highly pathogenic forms. RESULTS Analysis of 13,046 cloacal samples collected from 225 bird species in 41 US states between 2005 and 2008 indicates that the average prevalence of influenza in passerines is greater than the prevalence in eight other avian orders. Our regression model identifies the Great Plains and the Pacific Northwest as high-risk areas for AIV. Highly significant predictors of AIV include the amount of harvested cropland and the first day of the year when a county is snow free. CONCLUSIONS Although the prevalence of influenza in waterfowl has long been appreciated, we show that 22 species of song birds and perching birds (order Passeriformes) are influenza reservoirs in the contiguous US.
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Affiliation(s)
- Trevon L Fuller
- Center for Tropical Research, Institute of the Environment, University of California, Los Angeles, La Kretz Hall, Suite 300, Box 951496, Los Angeles, CA 90095-1496, USA
| | - Sassan S Saatchi
- Center for Tropical Research, Institute of the Environment, University of California, Los Angeles, La Kretz Hall, Suite 300, Box 951496, Los Angeles, CA 90095-1496, USA
- Radar Science Technical Group, Radar Science & Engineering Section, Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109-8099, USA
| | - Emily E Curd
- Center for Tropical Research, Institute of the Environment, University of California, Los Angeles, La Kretz Hall, Suite 300, Box 951496, Los Angeles, CA 90095-1496, USA
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, 621 Charles E. Young Drive South, Los Angeles, CA 90095-1606, USA
| | - Erin Toffelmier
- Center for Tropical Research, Institute of the Environment, University of California, Los Angeles, La Kretz Hall, Suite 300, Box 951496, Los Angeles, CA 90095-1496, USA
| | - Henri A Thomassen
- Center for Tropical Research, Institute of the Environment, University of California, Los Angeles, La Kretz Hall, Suite 300, Box 951496, Los Angeles, CA 90095-1496, USA
| | - Wolfgang Buermann
- Center for Tropical Research, Institute of the Environment, University of California, Los Angeles, La Kretz Hall, Suite 300, Box 951496, Los Angeles, CA 90095-1496, USA
- Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA 90095-1565, USA
| | - David F DeSante
- The Institute for Bird Populations, P.O. Box 1346, Point Reyes Station, CA 94956-1346, USA
| | - Mark P Nott
- The Institute for Bird Populations, P.O. Box 1346, Point Reyes Station, CA 94956-1346, USA
| | - James F Saracco
- The Institute for Bird Populations, P.O. Box 1346, Point Reyes Station, CA 94956-1346, USA
| | - CJ Ralph
- U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station, Redwood Sciences Laboratory, 1700 Bayview Drive, Arcata, CA 95521, USA
| | | | - John P Pollinger
- Center for Tropical Research, Institute of the Environment, University of California, Los Angeles, La Kretz Hall, Suite 300, Box 951496, Los Angeles, CA 90095-1496, USA
| | - Thomas B Smith
- Center for Tropical Research, Institute of the Environment, University of California, Los Angeles, La Kretz Hall, Suite 300, Box 951496, Los Angeles, CA 90095-1496, USA
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, 621 Charles E. Young Drive South, Los Angeles, CA 90095-1606, USA
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