Abstract
This paper develops an agent-based model of the spread of highly pathogenic avian influenza in a regional poultry sector. Spatially located flocks and flock owners interact with each other within a realistic regional geography. A specific regional poultry industry in Thailand shapes the model details: production practices that affect bio-containment, key transportation routes and methods, commercial and social networks, and an unusually detailed tabulation of flock types, flock owners, and human behaviors. Model simulations follow disease spread from an initial infection through both direct and indirect transmission pathways. This demonstrates how to realistically calibrate a model of disease spread to a regional poultry industry, while attending to the relative importance of different pathways. Despite its region-specific calibration, the model design facilitates easy adaptation to similar settings. This supports public policy by providing a modeling framework that may be used to simulate interventions to reduce or prevent the regional transmission of avian influenza.
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Notes
Known H5 strains do not transmit efficiently between humans. The 2017 outbreaks in the Philippines among ducks and chickens are not currently known to have caused any human infections. Still, in 2022 the World Health Organization reported 862 human cases of H5N1 since 2003, with 455 deaths.
Reported cases of H7N1 in Thai quail farms do not appear to highly pathogenic (Wongphatcharachai et al. 2014).
Livestock officers at the district level estimated that there may be up to 29% more flocks in the district than are registered. In addition, our model includes nine flocks of free-grazing ducks (FGDs), because Beaudoin (2012) was able to find and interview nine flock owners. See the online appendix for additional details.
The baseline simulation has no egg cooperative, since DKY did not have one. However, since they are common in similar settings, the model allows for the existence of an egg cooperative for free-grazing duck eggs.
It is uncommon for backyard chicken owners to sell chicken eggs, so these agents are not included in the egg-trading component of the model.
The rai is a measure of area, included in the International System of Units and commonly used in Thailand. It is about a third of an acre.
For example, for each grazing site, a single patch serves an access point. This paper loosely refers to a grazing site for a free-grazing duck flock as a field; however, this is a slight misnomer. Individually owned fields in Central Thailand average only 3 hectares, or approximately 20 rai, but a single grazing site may comprise many such fields.
Using GIS land-cover data (obtained from the Royal Thai Survey Department (RTSD) in Bangkok, Thailand), we determined the proportion to be 73%. Similar proportions apply across the Muang District, which is characterized by intensive rice production.
This close match depends on idiosyncratic modeling choices, such as the number of patches to associate with a model feature. It is by no means necessary for model adequacy; we would have been happy with anything from 65% to 85%.
The maximum field size of 610 rai can roughly feed one 2,000-duck flock for two months. Interviews by Beaudoin (2012) found that interviewees report staying in one place for 4–60 days. While a few interviewees reported staying longer periods, their flocks tended to graze just around their homes, allowing the ducks to wander year-round.
The road types were defined in our land-cover data as hard surface two lane with median (road type A), hard surface two lane (road type B), loose surface one lane (road type C), and nonspecific road (road type D).
Since DKY did not have an egg cooperative, none is not included in the baseline simulation. However, the model optionally includes one.
No non-field patches are placed along highways (type A roads). Although a highway can nevertheless become contaminated, this cannot lead to contamination of other patches.
In the tables, some numbers are rounded for presentational convenience. Exact values are in the online appendix.
In the simulation literature, discrete and PERT distributions are often used to incorporate estimates provided through expert opinion (Van Hauwermeiren et al. 2012). The PERT distribution is a version of the Beta distribution: It can take any value within the range defined by the minimum and maximum values, weighted by the location of the most likely value.
References
Beaudoin AL (2012) Avian influenza in Suphanburi Province, Thailand: Assessment of transmission dynamics and interventions in the local poultry sector. PhD thesis, University of Minnesota. https://conservancy.umn.edu/bitstream/handle/11299/131430/Beaudoin_umn_0130E_12823.pdf
Chen H, Deng G, Li Z, Tian G, Li Y, Jiao P, Zhang L, Liu Z, Webster RG, Yu K (2004) The evolution of H5N1 influenza viruses in ducks in southern China. Proc Natl Acad Sci USA 101(28):10452–10457. https://doi.org/10.1073/pnas.0403212101
FAO (2004) FAO recommendations on the prevention, control and eradication of highly pathogenic avian influenza (HPAI) in Asia. FAO Position Paper, Food and Agriculture Organization, United Nations
FAO (2022) Global aiv with zoonotic potential. Situation update, Food and Agriculture Organization. https://www.fao.org/ag/againfo/programmes/en/empres/Global_AIV_Zoonotic_Update/situation_update.html
FAO-OIE-WHO (2011) Current evolution of avian influenza H5N1 viruses. FAO-OIE-WHO Technical Update, Food and Agriculture Organization and World Organisation for Animal Health and World Health Organization. http://www.who.int/influenza/human_animal_interface/tripartite_notes_H5N1.pdf
Fichtner GJ (2003(1987)) The Pennsylvania/Virginia experience in eradication of avian influenza (H5N2). Avian Diseases 47, Special Issue. In: Proceedings of the Second International Symposium on Avian Influenza, pp 33–38. http://www.jstor.org/stable/3298724
Gilbert M, Chaitaweesub P, Parakamawongsa T, Premashthira S, Tiensin T, Kalpravidh W, Wagner H, Slingenbergh J (2006) Free-grazing ducks and highly pathogenic avian influenza, Thailand. Emerg Infect Dis 12(2):227–234
Gilbert M, Xiao X, Chaitaweesub P, Kalpravidh W, Premashthira S, Boles S, Slingenberghe J (2007) Avian influenza, domestic ducks and rice agriculture in Thailand. Agr Ecosyst Environ 119(3):409–415. https://doi.org/10.1016/j.agee.2006.09.001
Hulse-Post DJ, Sturm-Ramirez KM, Humberd J, Seiler P, Govorkova EA, Krauss S, Scholtissek C, Puthavathana P, Buranathai C, Nguyen TD, Long HT, Naipospos TSP, Chen H, Ellis TM, Guan Y, Peiris JSM, Webster RG (2005) Role of domestic ducks in the propagation and biological evolution of highly pathogenic H5N1 influenza viruses in Asia. Proc Natl Acad Sci USA 102(30):10682–10687. https://doi.org/10.1073/pnas.0504662102
Hunter JD (2007) Matplotlib: a 2D graphics environment. Comput Sci Eng 9:90–95. https://doi.org/10.1109/MCSE.2007.55
John D and Associates, Inc (2020) 2020 poultry and egg economic impact study: Methodology. Technical report, The US Poultry and Egg Association, Tucker, Georgia. https://poultry.guerrillaeconomics.net/res/Methodology.pdf
Keawcharoen J, van den Broek J, Bouma A, Tiensin T, Osterhaus AD, Heesterbeek H (2011) Wild birds and increased transmission of highly pathogenic avian influenza (h5n1) among poultry, Thailand. Emerg Infect Dis 16(6):1016–1022
Kida H, Yanagawa R, Matsuoka Y (1980) Duck influenza lacking evidence of disease signs and immune response. Infect Immun 30(2):547–553. https://doi.org/10.1128/iai.30.2.547-553.1980
Kim HR, Lee YJ, Park CK, Oem JK, Lee OS, Kang HM, Choi JG, Bae YC (2012) Highly pathogenic avian influenza (H5N1) outbreaks in wild birds and poultry, South Korea. Emerg Infect Dis 18(3):480–483
Kim T, Hwang W, Zhang A, Sen S, Ramanathan M (2010) Multi-agent modeling of the South Korean avian influenza epidemic. BMC Infect Dis 10:236. https://doi.org/10.1186/1471-2334-10-236
Li KS, Guan Y, Wang J, Smith GJ, Xu KM, Duan L, Rahardjo AP, Puthavathana P, Buranathai C, Nguyen TD, Estoepangestie AT, Chaisingh A, Auewarakul P, Long HT, Hanh NT, Webby RJ, Poon LL, Chen H, Shortridge KF, Yuen KY, Webster RG, Peiris JS (2004) Genesis of a highly pathogenic and potentially pandemic H5N1 influenza virus in eastern Asia. Nature 430(6996):209–213. https://doi.org/10.1038/nature02746
Nicita A (2008) Avian influenza and the poultry trade. Policy Research Working Paper 4551, The World Bank Development Research Group Trade Team. https://elibrary.worldbank.org/doi/abs/10.1596/1813-9450-4551
Park YR, Lee YN, Lee DH, Baek YG, Si YJ, Meeduangchanh P, Theppangna W, Douangngeun B, Kye SJ, Lee MH, Park CK, Lee YJ (2020) Genetic and pathogenic characteristics of clade 2.3.2.1c H5N1 highly pathogenic avian influenza viruses isolated from poultry outbreaks in Laos during 2015–2018. Transbound Emerg Dis 67(2):947–955. https://doi.org/10.1111/tbed.13430
Paul MC, Gilbert M, Desvaux S, Andriamanivo HR, Peyre M, Khong NV, Thanapongtharm W, Chevalier V (2014) Agro-environmental determinants of avian influenza circulation: a multisite study in Thailand, Vietnam and Madagascar. PLoS ONE 9(7):e101958. https://doi.org/10.1371/journal.pone.0101958
Railsback SF, Grimm V (2019) Agent-based and individual-based modeling: a practical introduction, 2nd edn. Princeton University Press, Princeton
Rao DM, Chernyakhovsky A, Rao V (2009) Modeling and analysis of global epidemiology of avian influenza. Environ Model Softw 24(1):124–134. https://doi.org/10.1016/j.envsoft.2008.06.011
Research and Markets (2021) Poultry Global Market. Research and Markets. https://www.researchandmarkets.com/reports/5240275/poultry-global-market-report-2021-covid-19
Sims L, Narrod C (2008) Understanding avian influenza—a review of the emergence, spread, control, prevention and effects of Asian-lineage H5N1 highly pathogenic viruses. United Nations Food and Agriculture Organization (FAO). https://www.fao.org/avianflu/documents/key_ai/key_book_preface.htm
Sims LD, Brown IH (2008) Multicontinental epidemic of h5n1 hpai virus (1996–2007). Chap 11. In: Swayne DE (ed) Avian influenza. Wiley, New York, pp 251–286. https://doi.org/10.1002/9780813818634.ch11
Soares Magalhães RJ, Ortiz-Pelaez A, Thi KLL, Dinh QH, Otte J, Pfeiffer DU (2010) Associations between attributes of live poultry trade and HPAI H5N1 outbreaks: a descriptive and network analysis study in northern Vietnam. BMC Vet Res. https://doi.org/10.1186/1746-6148-6-10
Songserm T, Jam-on R, Sae-Heng N, Meemak N, Hulse-Post DJ, Sturm-Ramirez KM, Webster RG (2006) Domestic ducks and H5N1 influenza epidemic, Thailand. Emerg Infect Dis 12(4):575–581
Swayne DE (2007) Understanding the complex pathobiology of high pathogenicity avian influenza viruses in birds. Avian Dis 51(1, Supplement: Sixth International Symposium on Avian Influenza):242–249
Swayne DE (ed) (2008) Avian influenza. Wiley-Blackwell, London
Swayne DE, Slemons RD (2008) Using mean infectious dose of high- and low-pathogenicity avian influenza viruses originating from wild duck and poultry as one measure of infectivity and adaptation to poultry. Avian Dis 52(3):455–460. https://doi.org/10.1637/8229-012508-Reg.1
Tiensin T, Ahmed SSU, Rojanasthien S, Songserm T, Ratanakorn P, Chaichoun K, Kalpravidh W, Wongkasemjit S, Patchimasiri T, Chanachai K, Thanapongtham W, Chotinan S, Stegeman A, Nielen M (2009) Ecologic risk factor investigation of clusters of avian influenza a (h5n1) virus infection in Thailand. J Infect Dis 199(12):1735–1743. https://doi.org/10.1086/599207
Van Hauwermeiren M, Vose D, Vanden Bossche S (2012) A compendium of distributions used in modelrisk. Vose Software, Ghent, Belgium. https://www.vosesoftware.com/knowledgebase/whitepapers/pdf/ebookdistributions.pdf
Vynnycky E, White R (2010) An introduction to infectious disease modelling. Oxford University Press, Oxford
Walker P, Cauchemez S, Hartemink N, Tiensin T, Ghani AC (2012) Outbreaks of H5N1 in poultry in Thailand: the relative role of poultry production types in sustaining transmission and the impact of active surveillance in control. J R Soc Interface 9(73):1836–1845. https://doi.org/10.1098/rsif.2012.0022
van der Walt S, Colbert SC, Varoquaux G (2011) The NumPy array: a structure for efficient numerical computation. Comput Sci Eng 13:22–30. https://doi.org/10.1109/MCSE.2011.37
Wilensky U (1999) Netlogo. Technical report, Center for connected learning and computer-based modeling. Northwestern University, Evanston, IL. http://ccl.northwestern.edu/netlogo
Wongphatcharachai M, Wisedchanwet T, Nonthabenjawan N, Jairak W, Chaiyawong S, Bunpapong N, Amonsin A (2014) Genetic characterization of Influenza A virus subtype H7N1 isolated from quail, Thailand. Virus Genes 49(3):428–437. https://doi.org/10.1007/s11262-014-1120-6
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Authors are in alphabetical order; authorship is equal. This paper draws on work and data described in Beaudoin’s dissertation. We thank David Castellan, David Halvorson, and David Swayne—three experts in the field of avian influenza, who provided expert opinions used to calibrate the baseline PERT distributions. We also thank Orapin Laosee of the ASEAN Institute for Health Development, Mahidol University, Thailand, whose in-depth interviews produced data used to calibrate our baseline model. This work could not have been conducted without the considerable assistance provided by the Thai Department of Livestock Development, including livestock officers and colleagues.
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Appendix: Software and source code
Appendix: Software and source code
The agent-based computational model introduced in this paper is implemented in NetLogo 6 (Wilensky 1999), which provides substantial facilities for modeling spatially situated agents. Model source code is available upon request. In the model, the spatial environment represents the DKY subdistrict that is the focus of our investigation. We utilized the intersect tool in Arc Toolbox to identify DKY road intersections, from which we developed a stylized regional transportation network. (See figure 1 of the online appendix.) Data analysis was primarily in Python, along with the NumPy and Matplotlib libraries (van der Walt et al. 2011; Hunter 2007).
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Beaudoin, A., Isaac, A.G. Direct and indirect transmission of avian influenza: results from a calibrated agent-based model. J Econ Interact Coord 18, 191–212 (2023). https://doi.org/10.1007/s11403-022-00353-w
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DOI: https://doi.org/10.1007/s11403-022-00353-w