Evaluation to prevent influenza Evaluation of Control Measures Against Influenza Epidemic by Multiagent Simulation
Main Article Content
Abstract
Background: At the campus of Japan Coast Academy in January 2017, 37 of 150 students developed influenza (situation A). Besides, 18 of 56 students at the training ship and 13 of 109 students at the campus of the academy developed influenza in January 2019 at same time (situation B and C). The rare data of the three situations including behavioral history data of students, daily clinical data of influenza-positive students and social contact data of students were collected and directly analyzed. Then, using a proposed spatio-temporal Susceptible – Exposed – Pre-infectious – Infectious – Recovered (SEPIR) model, the incubation period and the infectivity rate from asymptomatic people were calculated. And multiagent simulation (MAS) of influenza epidemic has been performed using spatio-temporal SEPIR model and has been validated.
Aim: Effective non-pharmaceutical interventions to prevent influenza epidemic is described.
Methods: The epidemic is defined using ratio of daily patients in the community. Then, situation A and B are classified as epidemics, but situation C is not. Using the validated MAS, three measures of infection control (preventive measure such as wearing mask, school closure when 20 % of patients are discovered and that when first patients are discovered) against influenza in the two epidemic situations A and B are evaluated. The criteria for evaluation are the frequency of epidemic and the ratio of total patients.
Results: The frequency of epidemic by the school closures as infection control measure is 100 %, whereas, those by the preventive measure are low (4.5 % and 22.9 %) in two epidemic situations. The ratio of total patients by the school closures are above 80 % and those by the preventive measure are low (7.6 % and 23.0 %) in two epidemic situations.
Conclusions: MAS suggests that the preventive measure such as wearing masks, washing hands and better ventilating rooms is useful. Then, the preventive measure is recommended after winter vacation or three-day weekend in January not school closure.
Article Details
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References
2. Iwanaga S, Kawaguchi K. Analysis of epidemic of seasonal influenza closed space. Proc 22nd IES. 2018; 37-44.
3. Iwanaga S. Super-spreading is possible by the day the first patients are discovered in the community. Journal of Advances in Artificial Life Robotics. 2022; 3(1): 24-31.
4. Iwanaga S. A research on multi-agent simulation of seasonal influenza from pre-infectious people in closed spaces. Vietnam Journal of Computer Science. 2023. in press.
5. ECDC. Factsheet about seasonal influenza. https: //www.ecdc.europa.eu/en/seasonal-influenza/facts/factsheet. Apr 12, 2022.
6. OECD stat. Health Status (Mortality). https: //stats.oecd.org/Index.aspx?DatasetCode=HEALTH_STAT. July 4, 2022.
7. Reichert TA, Sugaya N, Fedson DS et al. The Japanese experience with vaccinating schoolchildren against influenza. N Engl J Med. 2001; 344: 889-896.
8. Monto AS, Davenport FM, Napier JA, Francis T Jr. Effect of vaccination of a school-age population upon the course of an A2-Hong Kong influenza epidemic. Bull World Health Organ. 1969; 41(3): 537-542.
9. Taya K. Influenza vaccination rate. Infectious Agents Surveillance Report. 2002; 23(12): 311-313. in Japanese.
10. Brownstein JS, Kleinman KP, Mandl KD. Identifying pediatric age groups for influenza vaccination using a real-time regional surveillance system. Am J Epidemiol. 2005; 162(7): 686-93.
11. Sullivan KM, Monto AS, Longini IM Jr. Estimates of the US health impact of influenza. Am J Public Health. 1993 Dec; 83(12): 1712-6.
12. Fox JP, Hall CE, Cooney MK, Foy HM. Influenzavirus infections in Seattle families, 1975-1979. I. Study design, methods and the occurrence of infections by time and age. Am J Epidemiol. 1982; 116(2): 212-227.
13. Longini IM Jr, Koopman JS, Haber M, et al. Statistical influence for infectious diseases: risk-specific household and community transmission parameters. Am J Epidemiol 1988; 128: 845—859
14. Longini IM Jr, Halloran ME. Strategy for distribution of influenza vaccine to high-risk groups and children. Am J Epidemiol. 2005 Feb 15; 161(4): 303-6.
15. Takeuchi H, Kawashima R. Disappearance and re-emergence of influenza during the COVID-19 pandemic: Association with Infection Control Measures. Viruses. 2023; 15(1): 223.
16. NIID. Annual surveillance data (Sentinel-reporting diseases). https: //www.niid.go.jp/niid/ja/ydata/11532-report-jb2021.html. Feb. 3, 2023. in Japanese.
17. MHLW. Influenza outbreak. https: //www.mhlw.go.jp/stf/seisakunitsuite/bunya/kenkou_iryou/kenkou/kekkaku-kansenshou01/houdou_00010.html. Mar. 31, 2023. in Japanese.
18. Eto T, Nakahara T. Handbook for school medical needs for physicians and teachers. Tokyo: Bunkodo; 2006. in Japanese.
19. Epidemiology. Influenza in a boarding school. BMJ. Mar. 4, 1978; 587.
20. Kermack WO, McKendrick AG. A contribution to the mathematical theory of epidemics. Proc. R. Soc. Lond. 1927; A115700–721.
21. Keeling MJ, Rohani P. Modeling infectious diseases in humans and animals. Princeton: Princeton University Press; 2008.
22. Giesecke J. Modern infectious disease epidemiology. Boca Raton. CRC Press; 2017.
23. Witkop CT, Duffy MR, Macias EA et al. Novel influenza A (H1N1) outbreak at the U.S. Air Force Academy- epidemiology and viral shedding duration, AJPM. 2010; 38(2): 121-126.
24. Rothman KJ. Epidemiology: An Introduction (2nd edition). Oxford: Oxford University Press; 2012.
25. Duan W , Cao Z, Wang Y et al. An ACP Approach to public health emergency management: Using a campus outbreak of H1N1 influenza as a case study. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2013; 43(5): 1028-1041.
26. CDC. How influenza Spreads. https: //www.cdc.gov/flu/about/disease/spread.htm. Sep 20, 2022.
27. Daniel Z, Zhidong C, Daniel BN. Artificial intelligence - enabled public health surveillance - from local detection to global epidemic monitoring and control, Artificial Intelligence in Medicine. London: Academic Press; 2021, 437-453.
28. Parker J, Epstein JM. A distributed platform for global-scale agent-based models of disease transmission. ACM Trans on Modeling and Computer Simulation. 2011; 22(1): 2.
29. Cliff OM, Harding N, Piraveenan M et al. Investigating spatiotemporal dynamics and synchrony of influenza epidemics in Australia: an agent-based modelling approach. Simulation Modelling Practice and Theory. 2008; 87: 412-431.
30. Barrett CL, Bisset KR, Eubank SG et al. EpiSimdemics: An efficient algorithm for simulating the spread of infectious disease over large realistic social networks. Proc of SC 08. 2008.
31. Bisset K, Chen J, Feng X et al. Epifast: A fast algorithm for large scale realistic epidemic simulations on distributed memory systems. Proc ICS 09. 2009; 430-439.
32. Chao D, Halloran M, Obenchain V et al. FluTE, a publicly available stochastic influenza epidemic simulation model. PLoS Comput Biol. 2019; 6(1): e1000656.
33. Nagai H, Kurahashi S. Measures to prevent and control the spread of novel coronavirus disease (COVID-19) infection in tourism locations, SICE Journal of Control, Measurement, and System Integration. 2022; 15(2), 1-12.
34. Bonde A. Defining small data, https: //smalldatagroup.com/2013/10/18/defining-small-data. Oct 18, 2013.
35. Heymann DL(edit). Control of communicable diseases manual: An official report of the American public health association (21th). Washington DC, American Public Health Association; 2022.
36. MHLW. Influenza/ Influenza season and peak season (nationwide). https: //www.mhlw.go.jp/content/000620809.pdf. Mar 31, 2023. in Japanese.
37. Russell SJ. Subramanian D. Provably bounded-optimal agents. Journal of Artificial Intelligence Research. 1995; 2: 575-609.
38. Wooldridge M, An Introduction to multiagent systems, 2002. Wiley
39. Epstein JM, Axtell R. Growing artificial societies, Social science from the bottom Up. Washington DC, Brookings Institution Press & MIT Press; 1996
40. Kozo Keikaku Engineering Inc. Artisoc 4. https: //mas.kke.co.jp/en/artisoc4. Mar 31, 2023.
41. Nikkyo web. What you need to know in order not to panic even if a class closure occurs. https: //www.kyoiku-press.com/post-211110/. Jan 3, 2020. in Japanese.
42. Fukumoto K, McClean CT, Nakagawa K. No causal effect of school closures in Japan on the spread of COVID-19 in spring 2020. Nat Med. 2021; 27: 2111–2119.
43. Piovani D, Christodoulou MN, Hadjidemetriou A et al. Effect of early application of social distancing interventions on COVID-19 mortality over the first pandemic wave: An analysis of longitudinal data from 37 countries. J Infect. 2021; 82(1): 133-142.
44. Jackson C, Mangtani P, Hawker J et al. The Effects of School Closures on Influenza Outbreaks and Pandemics: Systematic Review of Simulation Studies. PLoS One. 2014; 9(5): e97297.
45. Milne GJ, Kelso JK, Kelly HA, Huband ST, McVernon J. A small community model for the transmission of infectious diseases: comparison of school closure as an intervention in individual-based models of an influenza pandemic. PLoS One. 2008; 3(12): e4005.
46. Ferguson N, Cummings D, Fraser C et al. Strategies for mitigating an influenza pandemic. Nature. 2006; 442: 448–452.
47. Germann T, Kadau K, Longini I, Macken C. Mitigation strategies for pandemic influenza in the United States. PNAS. 2006;103: 5935–5940.
48. Haber J, Shay D, Davis X, Patel R, Jin X. Effectiveness of interventions to reduce contact rates during a simulated influenza pandemic. Emerg Infect Dis. 2007; 13: 581–589.