Infectious disease pandemic planning and response: Incorporating decision analysis

English version

Autoři: Freya M. Shearer ^aff001; Robert Moss ^aff001; Jodie McVernon ^aff001; Joshua V. Ross ^aff004; James M. McCaw ^aff001
Působiště autorů: Modelling and Simulation Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia ^aff001; Peter Doherty Institute for Infection and Immunity, The Royal Melbourne Hospital and The University of Melbourne, Australia ^aff002; Murdoch Children’s Research Institute, The Royal Children’s Hospital, Melbourne, Australia ^aff003; School of Mathematical Sciences, The University of Adelaide, Adelaide, Australia ^aff004; School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia ^aff005
Vyšlo v časopise: Infectious disease pandemic planning and response: Incorporating decision analysis. PLoS Med 17(1): e1003018. doi:10.1371/journal.pmed.1003018
Kategorie: Policy Forum
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pmed.1003018

Souhrn

Freya Shearer and co-authors discuss the use of decision analysis in planning for infectious disease pandemics.

Klíčová slova:

Pathogens – Infectious diseases – Antivirals – Health education and awareness – Influenza – Decision making – Infectious disease surveillance – Forecasting

Zdroje

1. Sands P, Mundaca-Shah C, Dzau VJ. The neglected dimension of global security—A framework for countering infectious-disease crises. N Engl J Med. 2016;374(13):1281–1287. doi: 10.1056/NEJMsr1600236 26761419

2. Mills CE, Robins JM, Lipsitch M. Transmissibility of 1918 pandemic influenza. Nature. 2004;432:904. doi: 10.1038/nature03063 15602562

3. World Health Organization. Disease Outbreak News 11 June 2009. Influenza A(H1N1)–update 47; 2009 June [cited 2019 Dec 5]. http://www.who.int/csr/don/2009_06_11/en/.

4. Holmes EC, Rambaut A, Andersen KG. Pandemics: Spend on surveillance, not prediction. Nature. 2018;558(7709):180–182. doi: 10.1038/d41586-018-05373-w 29880819

5. McVernon J, McCaw CT, Mathews JD. Model answers or trivial pursuits? The role of mathematical models in influenza pandemic preparedness planning. Influenza Other Resp. 2007;1(2):43–54.

6. Kerkhove MDV, Ferguson NM. Epidemic and intervention modelling–a scientific rationale for policy decisions? Lessons from the 2009 influenza pandemic. Bull World Health Organ. 2012;90(4):306–310. doi: 10.2471/BLT.11.097949 22511828

7. Fox JP, Kilbourne ED. Epidemiology of Influenza: Summary of Influenza Workshop IV. J Infect Dis. 1973;128:361–386.

8. Elveback LR, Fox JP, Ackerman E, Langworthy A, Boyd M, Gatewood L. An influenza simulation model for immunization studies. Am J Epidemiol. 1976;103(2):52–65.

9. Fraser C, Donnelly CA, Cauchemez S, Hanage WP, Van Kerkhove MD, Hollingsworth TD, et al. Pandemic potential of a strain of influenza A (H1N1): Early findings. Science. 2009;324(5934):1557–1561. doi: 10.1126/science.1176062 19433588

10. Yang Y, Sugimoto JD, Halloran ME, Basta NE, Chao DL, Matrajt L, et al. The transmissibility and control of pandemic influenza A (H1N1) virus. Science. 2009;326(5953):729–733. doi: 10.1126/science.1177373 19745114

11. Ferguson NM, Cummings DAT, Cauchemez S, Fraser C, Riley S, Meeyai A, et al. Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature. 2005;437:209. doi: 10.1038/nature04017 16079797

12. Longini IM, Nizam A, Xu S, Ungchusak K, Hanshaoworakul W, Cummings DAT, et al. Containing pandemic influenza at the source. Science. 2005;309(5737):1083–1087. doi: 10.1126/science.1115717 16079251

13. McCaw JM, Wood JG, McCaw CT, McVernon J. Impact of emerging antiviral drug resistance on influenza containment and spread: influence of subclinical infection and strategic use of a stockpile containing one or two drugs. PLoS ONE. 2008;3(6):1–10.

14. McCaw JM, McVernon J. Prophylaxis or treatment? Optimal use of an antiviral stockpile during an influenza pandemic. Math Biosci. 2007;209(2):336–360. doi: 10.1016/j.mbs.2007.02.003 17416393

15. Moss R, McCaw JM, McVernon J. Diagnosis and antiviral intervention strategies for mitigating an influenza epidemic. PLoS ONE. 2011;6(2):1–10.

16. Moss R, McCaw JM, Cheng AC, Hurt AC, McVernon J. Reducing disease burden in an influenza pandemic by targeted delivery of neuraminidase inhibitors: mathematical models in the Australian context. BMC Infect Dis. 2016;16(1):552. doi: 10.1186/s12879-016-1866-7 27724915

17. McCaw JM, Moss R, McVernon J. A decision support tool for evaluating the impact of a diagnostic capacity and antiviral-delivery constrained intervention strategy on an influenza pandemic. Influenza Other Resp. 2011;5(Suppl. 1):202–229.

18. Bauch C, Lloyd-Smith J, Coffee M, Galvani A. Dynamically modeling SARS and other newly emerging respiratory illnesses: past, present, and future. Epidemiology. 2005;16(6):791–801. doi: 10.1097/01.ede.0000181633.80269.4c 16222170

19. Chretien JP, Riley S, George DB. Mathematical modeling of the West Africa Ebola epidemic. eLife. 2015;4:e09186. doi: 10.7554/eLife.09186 26646185

20. Nicoll A, Brown C, Karcher F, Penttinen P, Hegermann-Lindencrone M, Villanueva S, et al. Developing pandemic preparedness in Europe in the 21st century: experience, evolution and next steps. Bull World Health Organ. 2012;90:311–317. doi: 10.2471/BLT.11.097972 22511829

21. Parada LV. Public health: Life lessons. Nature. 2011;480:S11. doi: 10.1038/480S11a 22158294

22. Bennett B, Carney T. Public health emergencies of international concern: global, regional, and local responses to risk. Med Law Rev. 2017;25(2):223–239. doi: 10.1093/medlaw/fwx004 28379440

23. News Nature. Pandemic flu: from the front lines. Nature. 2009;461:20–21. doi: 10.1038/461020a 19727174

24. World Health Organization. Pandemic Influenza Risk Management: A WHO guide to inform and harmonize national and international pandemic preparedness and response; Geneva, 2017 May. https://apps.who.int/iris/handle/10665/259893

25. US Department of Health and Human Services. Pandemic Influenza Plan 2017 Update; 2017 June [cited 2019 Dec 5]. https://www.cdc.gov/flu/pandemic-resources

26. Public Health England. Pandemic Influenza Response Plan; London, 2014 Aug [cited 2019 Dec 5]. https://www.gov.uk/government/publications/pandemic-influenza-response-plan

27. Australian Government Department of Health. Australian Health Management Plan for Pandemic Influenza. Canberra; 2014 Aug [cited 2019 Dec 5]. https://www1.health.gov.au/internet/main/publishing.nsf/Content/ohp-ahmppi.htm

28. McCaw JM, Glass K, Mercer GN, McVernon J. Pandemic controllability: a concept to guide a proportionate and flexible operational response to future influenza pandemics. J Public Health. 2014;36(1):5–12.

29. Lipsitch M, Finelli L, Heffernan RT, Leung GM, Redd SC. Improving the Evidence Base for Decision Making During a Pandemic: The Example of 2009 Influenza A/H1N1. Biosecur Bioterror. 2011;9(2):89–115. doi: 10.1089/bsp.2011.0007 21612363

30. Lipsitch M, Santillana M. Enhancing Situational Awareness to Prevent Infectious Disease Outbreaks from Becoming Catastrophic. In: Inglesby T, Adalja A, editors. Global Catastrophic Biological Risks. Current Topics in Microbiology and Immunology. Berlin, Heidelberg: Springer; 2019.

31. Polonsky JA, Baidjoe A, Kamvar ZN, Cori A, Durski K, Edmunds WJW, et al. Outbreak analytics: a developing data science for informing the response to emerging pathogens. Phil Trans R Soc B. 2019;374:20180276. doi: 10.1098/rstb.2018.0276 31104603

32. Black AJ, Geard N, McCaw JM, McVernon J, Ross JV. Characterising pandemic severity and transmissibility from data collected during first few hundred studies. Epidemics. 2017;19:61–73. doi: 10.1016/j.epidem.2017.01.004 28189386

33. World Health Organization. Global surveillance during an influenza pandemic; Geneva: 2009 Apr [cited 2019 Dec 5]. https://www.who.int/csr/disease/swineflu/global_pandemic_influenza_surveilance_apr09.pdf

34. Walker JN, Ross JV, Black AJ. Inference of epidemiological parameters from household stratified data. PLoS ONE. 2017;12(10):1–21.

35. Moss R, Fielding JE, Franklin LJ, Stephens N, McVernon J, Dawson P, et al. Epidemic forecasts as a tool for public health: interpretation and (re)calibration. Aust NZ J Publ Heal. 2018;42(1):69–76.

36. Doms C, Kramer SC, Shaman J. Assessing the use of influenza forecasts and epidemiological modeling in public health decision making in the United States. Sci Rep. 2018;8(1):12406. doi: 10.1038/s41598-018-30378-w 30120267

37. Yamana TK, Kandula S, Shaman J. Individual versus superensemble forecasts of seasonal influenza outbreaks in the United States. PLoS Comput Biol. 2017;13(11):1–17.

38. Biggerstaff M, Johansson M, Alper D, Brooks LC, Chakraborty P, Farrow DC, et al. Results from the second year of a collaborative effort to forecast influenza seasons in the United States. Epidemics. 2018;24:26–33. doi: 10.1016/j.epidem.2018.02.003 29506911

39. Viboud C, Sun K, Gaffey R, Ajelli M, Fumanelli L, Merler S, et al. The RAPIDD ebola forecasting challenge: synthesis and lessons learnt. Epidemics. 2018;22:13–21. doi: 10.1016/j.epidem.2017.08.002 28958414

40. Ferguson NM, Cummings DAT, Fraser C, Cajka JC, Cooley PC, Burke DS. Strategies for mitigating an influenza pandemic. Nature. 2006;442(7101):448–452. doi: 10.1038/nature04795 16642006

41. Araz OM, Damien P, Paltiel DA, Burke S, Van De Geijn B, Galvani A, et al. Simulating school closure policies for cost effective pandemic decision making. BMC Public Health. 2012;12(1):1.

42. Khazeni N, Hutton DW, Garber AM, Owens DK. Effectiveness and cost-effectiveness of expanded antiviral prophylaxis and adjuvanted vaccination strategies for the next influenza pandemic. Ann Intern Med. 2009;151(12):840–853. doi: 10.7326/0003-4819-151-12-200912150-00156 20008760

43. Morgan O. How decision makers can use quantitative approaches to guide outbreak responses. Phil Trans R Soc B. 2019;374:20180365. doi: 10.1098/rstb.2018.0365 31104605

44. Rivers C, Chretien JP, Riley S, Pavlin JA, Woodward A, Brett-Major D, et al. Using “outbreak science” to strengthen the use of models during epidemics. Nat Commun. 2019;10(1):3102. doi: 10.1038/s41467-019-11067-2 31308372

45. Boneh T, Weymouth PN, Potts R, Bally J, Nicholson AE, Korb KB. Fog forecasting for Melbourne Airport using a Bayesian decision network. Weather Forecast. 2015;30:1218–1232.

46. Wu S, Cheng MH, Beck JL, Heaton TH. An engineering application of earthquake early warning: ePAD-based decision framework for elevator control. J Struct Eng. 2016;142(1):04015092.

47. Dunn CJ, Thompson MP, Calkin DE. A framework for developing safe and effective large-fire response in a new fire management paradigm. Forest Ecol Manag. 2017;404:184–196.

48. Ge L, Mourits MCM, Kristensen AR, Huirne RBM. A modelling approach to support dynamic decision-making in the control of FMD epidemics. Prev Vet Med. 2010;95(3):167–174.

49. Shea K, Tildesley MJ, Runge MC, Fonnesbeck CJ, Ferrari MJ. Adaptive management and the value of information: learning via intervention in epidemiology. PLoS Biol. 2014;12(10):1–11.

50. Probert WJM, Shea K, Fonnesbeck CJ, Runge MC, Carpenter TE, Dürr S, et al. Decision-making for foot-and-mouth disease control: objectives matter. Epidemics. 2016;15:10–19. doi: 10.1016/j.epidem.2015.11.002 27266845

51. Webb CT, Ferrari M, Lindström T, Carpenter T, Dürr S, Garner G, et al. Ensemble modelling and structured decision-making to support emergency disease management. Prev Vet Med. 2017;138:124–133. doi: 10.1016/j.prevetmed.2017.01.003 28237227

52. Yaesoubi R, Cohen T. Identifying cost-effective dynamic policies to control epidemics. Stat Med. 2016;35(28):5189–5209. doi: 10.1002/sim.7047 27449759

53. Australian Government Department of Health and Ageing. Antivirals Evidence Summary; Canberra, 2014 [cited 2019 Dec 5]. https://www1.health.gov.au/internet/main/publishing.nsf/Content/ohp-ahmppi.htm#comm-reports.

54. McVernon J, McCaw JM, Nolan TM. Modelling strategic use of the national antiviral stockpile during the CONTAIN and SUSTAIN phases of an Australian pandemic influenza response. Aust NZ J Publ Heal. 2010;34(2):113–119.

55. Ibuka Y, Chapman GB, Meyers LA, Li M, Galvani AP. The dynamics of risk perceptions and precautionary behavior in response to 2009 (H1N1) pandemic influenza. BMC Infect Dis. 2010;10(1):296.

56. Davis MDM, Stephenson N, Lohm D, Waller E, Flowers P. Beyond resistance: social factors in the general public response to pandemic influenza. BMC Public Health. 2015;15(1):436.

57. Funk S, Bansal S, Bauch CT, Eames KTD, Edmunds WJ, Galvani AP, et al. Nine challenges in incorporating the dynamics of behaviour in infectious diseases models. Epidemics. 2015;10:21–25. doi: 10.1016/j.epidem.2014.09.005 25843377

58. McVernon J, Mason K, Petrony S, Nathan P, LaMontagne AD, Bentley R, et al. Recommendations for and compliance with social restrictions during implementation of school closures in the early phase of the influenza A (H1N1) 2009 outbreak in Melbourne, Australia. BMC Infect Dis. 2011;11:257. doi: 10.1186/1471-2334-11-257 21958428

59. DeBruin D, Liaschenko J, Marshall MF. Social justice in pandemic preparedness. Am J Public Health. 2012;102(4):586–591. doi: 10.2105/AJPH.2011.300483 22397337

60. Gregory R, Failing L, Harstone G, Long T, McDaniels T, Ohlson D. Structured decision making: a practical guide to environmental management choices. Oxford, United Kingdom: Wiley-Blackwell; 2012.

61. Klein CJ, Jupiter SD, Possingham HP. Setting conservation priorities in Fiji: decision science versus additive scoring systems. Mar Policy. 2014;48:204–205.

62. Marcot BG, Thompson MP, Runge MC, Thompson FR, McNulty S, Cleaves D, et al. Recent advances in applying decision science to managing national forests. Forest Ecol Manag. 2012;285:123–132.

63. Moss R, Zarebski AE, Dawson P, Franklin LJ, Birrell FA, McCaw JM. Anatomy of a seasonal influenza epidemic forecast. Commun Dis Intell. 2019;43:1–14.

64. Chowell G, Fenimore PW, Castillo-Garsow MA, Castillo-chavez C. SARS outbreaks in Ontario, Hong Kong and Singapore: the role of diagnosis and isolation as a control mechanism. J Theor Biol. 2003;224:1–8. doi: 10.1016/s0022-5193(03)00228-5 12900200

65. Gumel AB, Ruan S, Day T, Watmough J, Brauer F, Van Den Driessche P, et al. Modelling strategies for controlling SARS outbreaks. Proc Royal Soc B. 2004;271:2223–2232.

66. Day T, Park A, Madras N, Gumel A, Wu J. When is quarantine a useful control strategy for emerging infectious diseases? Am J Epidemiol. 2006;163(5):479–485. doi: 10.1093/aje/kwj056 16421244

67. Wong-Parodi G, Krishnamurti T, Davis A, Schwartz D, Fischhoff B. A decision science approach for integrating social science in climate and energy solutions. Nat Clim Change. 2016;6:563.