1932

Abstract

Conducting real-world public health experiments is often costly, time-consuming, and ethically challenging, so mathematical models have a long-standing history of being used to inform policy. Applications include estimating disease burden, performing economic evaluation of interventions, and responding to health emergencies such as pandemics. Models played a pivotal role during the COVID-19 pandemic, providing early detection of SARS-CoV-2’s pandemic potential and informing subsequent public health measures. While models offer valuable policy insights, they often carry limitations, especially when they depend on assumptions and incomplete data. Striking a balance between accuracy and timely decision-making in rapidly evolving situations such as disease outbreaks is challenging. Modelers need to explore the extent to which their models deviate from representing the real world. The uncertainties inherent in models must be effectively communicated to policy makers and the public. As the field becomes increasingly influential, it needs to develop reporting standards that enable rigorous external scrutiny.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-publhealth-060222-025149
2024-05-20
2024-12-02
Loading full text...

Full text loading...

/deliver/fulltext/publhealth/45/1/annurev-publhealth-060222-025149.html?itemId=/content/journals/10.1146/annurev-publhealth-060222-025149&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Adam D. 2020.. Special report: the simulations driving the world's response to COVID-19. . Nature 580:(7803):31618
    [Crossref] [Google Scholar]
  2. 2.
    Adams S, Rhodes T, Lancaster K. 2022.. New directions for participatory modelling in health: redistributing expertise in relation to localised matters of concern. . Glob. Public Health 17:(9):182741
    [Crossref] [Google Scholar]
  3. 3.
    Anderson RM, May RM. 1992.. Infectious Diseases of Humans: Dynamics and Control. Oxford, UK/New York:: Oxford Univ. Press
    [Google Scholar]
  4. 4.
    Andronis L, Barton P, Bryan S. 2009.. Sensitivity analysis in economic evaluation: an audit of NICE current practice and a review of its use and value in decision-making. . Health Technol. Assess. 13:(29):iii, ix–xi, 1–61
    [Crossref] [Google Scholar]
  5. 5.
    Atkinson J-A, O'Donnell E, Wiggers J, McDonnell G, Mitchell J, et al. 2017.. Dynamic simulation modelling of policy responses to reduce alcohol-related harms: rationale and procedure for a participatory approach. . Public Health Res. Pract. 27:(1):2711707
    [Crossref] [Google Scholar]
  6. 6.
    Baguelin M, Flasche S, Camacho A, Demiris N, Miller E, Edmunds WJ. 2013.. Assessing optimal target populations for influenza vaccination programmes: an evidence synthesis and modelling study. . PLOS Med. 10:(10):e1001527
    [Crossref] [Google Scholar]
  7. 7.
    Baio G, Dawid AP. 2015.. Probabilistic sensitivity analysis in health economics. . Stat. Methods Med. Res. 24:(6):61534
    [Crossref] [Google Scholar]
  8. 8.
    Begley S. 2020.. Influential Covid-19 model uses flawed methods and shouldn't guide U.S. policies, critics say. . STAT, April 17. https://www.statnews.com/2020/04/17/influential-covid-19-model-uses-flawed-methods-shouldnt-guide-policies-critics-say/
    [Google Scholar]
  9. 9.
    Bhalerao A, Sivandzade F, Archie SR, Cucullo L. 2019.. Public health policies on e-cigarettes. . Curr. Cardiol. Rep. 21:(10):111
    [Crossref] [Google Scholar]
  10. 10.
    Bilcke J, Beutels P, Brisson M, Jit M. 2011.. Accounting for methodological, structural, and parameter uncertainty in decision-analytic models: a practical guide. . Med. Decis. Mak. 31:(4):67592
    [Crossref] [Google Scholar]
  11. 11.
    Box GEP. 1979.. Robustness in the strategy of scientific model building. . In Robustness in Statistics, ed. RL Launer, GN Wilkinson , pp. 20136. Cambridge, MA:: Academic Press
    [Google Scholar]
  12. 12.
    Brooks-Pollock E, Danon L, Jombart T, Pellis L. 2021.. Modelling that shaped the early COVID-19 pandemic response in the UK. . Philos. Trans. R. Soc. B 376:(1829):20210001
    [Crossref] [Google Scholar]
  13. 13.
    Cabore JW, Karamagi HC, Kipruto H, Asamani JA, Droti B, et al. 2020.. The potential effects of widespread community transmission of SARS-CoV-2 infection in the World Health Organization African Region: a predictive model. . BMJ Glob. Health 5:(5):e002647
    [Crossref] [Google Scholar]
  14. 14.
    Canfell K, Kim JJ, Kulasingam S, Berkhof J, Barnabas R, et al. 2019.. HPV-FRAME: a consensus statement and quality framework for modelled evaluations of HPV-related cancer control. . Papillomavirus Res. 8::100184
    [Crossref] [Google Scholar]
  15. 15.
    Caro JJ, Briggs AH, Siebert U, Kuntz KM, ISPOR-SMDM Model. Good Res. Pract. Task Force. 2012. Modeling good research practices–overview: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force–1. . Value Health 15:(6):796803
    [Crossref] [Google Scholar]
  16. 16.
    Caselli F, Grigoli F, Lian W, Sandri D. 2020.. Protecting lives and livelihoods with early and tight lockdowns. WP/20/234 , IMF, Washington, DC:. https://www.imf.org/en/Publications/WP/Issues/2020/11/08/Protecting-Lives-and-Livelihoods-with-Early-and-Tight-Lockdowns-49866
    [Google Scholar]
  17. 17.
    Chao DL, Halloran ME, Obenchain VJ, Longini IM Jr. 2010.. FluTE, a publicly available stochastic influenza epidemic simulation model. . PLOS Comput. Biol. 6:(1):e1000656
    [Crossref] [Google Scholar]
  18. 18.
    Chowell G, Nishiura H. 2014.. Transmission dynamics and control of Ebola virus disease (EVD): a review. . BMC Med. 12:(1):196
    [Crossref] [Google Scholar]
  19. 19.
    Chretien J-P, Riley S, George DB. 2015.. Mathematical modeling of the West Africa Ebola epidemic. . eLife 4::e09186
    [Crossref] [Google Scholar]
  20. 20.
    Cookson R, Robson M, Skarda I, Doran T. 2021.. Equity-informative methods of health services research. . J. Health Organ. Manag. 35::66581
    [Crossref] [Google Scholar]
  21. 21.
    Crane M, Bohn-Goldbaum E, Grunseit A, Bauman A. 2020.. Using natural experiments to improve public health evidence: a review of context and utility for obesity prevention. . Health Res. Policy Syst. 18:(1):48
    [Crossref] [Google Scholar]
  22. 22.
    Davies NG, Klepac P, Liu Y, Prem K, Jit M, Eggo RM. 2020.. Age-dependent effects in the transmission and control of COVID-19 epidemics. . Nat. Med. 26:(8):120511
    [Crossref] [Google Scholar]
  23. 23.
    den Boon S, Jit M, Brisson M, Medley G, Beutels P, et al. 2019.. Guidelines for multi-model comparisons of the impact of infectious disease interventions. . BMC Med. 17:(1):163
    [Crossref] [Google Scholar]
  24. 24.
    Dickens BL, Koo JR, Lim JT, Park M, Sun H, et al. 2021.. Determining quarantine length and testing frequency for international border opening during the COVID-19 pandemic. . J. Travel Med. 28:(7):taab088
    [Crossref] [Google Scholar]
  25. 25.
    Doan TTT, Tan KW, Dickens BSL, Lean YA, Yang Q, Cook AR. 2020.. Evaluating smoking control policies in the e-cigarette era: a modelling study. . Tob. Control 29:(5):52230
    [Google Scholar]
  26. 26.
    Doerre A, Doblhammer G. 2022.. The influence of gender on COVID-19 infections and mortality in Germany: insights from age- and gender-specific modeling of contact rates, infections, and deaths in the early phase of the pandemic. . PLOS ONE 17:(5):e0268119
    [Crossref] [Google Scholar]
  27. 27.
    Drolet M, Bénard É, Jit M, Hutubessy R, Brisson M. 2018.. Model comparisons of the effectiveness and cost-effectiveness of vaccination: a systematic review of the literature. . Value Health 21:(10):125058
    [Crossref] [Google Scholar]
  28. 28.
    Drummond MF, Sculpher MJ, Claxton K, Stoddart GL, Torrance GW. 2015.. Methods for the Economic Evaluation of Health Care Programmes. Oxford, UK:: Oxford Univ. Press
    [Google Scholar]
  29. 29.
    Dyson F. 2004.. A meeting with Enrico Fermi. . Nature 427:(6972):297
    [Crossref] [Google Scholar]
  30. 30.
    Eker S. 2020.. Validity and usefulness of COVID-19 models. . Humanit. Soc. Sci. Commun. 7:(1):54
    [Crossref] [Google Scholar]
  31. 31.
    Endo A, van Leeuwen E, Baguelin M. 2019.. Introduction to particle Markov-chain Monte Carlo for disease dynamics modellers. . Epidemics 29::100363
    [Crossref] [Google Scholar]
  32. 32.
    Fang L-Q, Yang Y, Jiang J-F, Yao H-W, Kargbo D, et al. 2016.. Transmission dynamics of Ebola virus disease and intervention effectiveness in Sierra Leone. . PNAS 113:(16):448893
    [Crossref] [Google Scholar]
  33. 33.
    Ferguson NM, Cummings DAT, Fraser C, Cajka JC, Cooley PC, Burke DS. 2006.. Strategies for mitigating an influenza pandemic. . Nature 442:(7101):44852
    [Crossref] [Google Scholar]
  34. 34.
    Ferguson NM, Laydon D, Nedjati Gilani G, Imai N, Ainslie K, et al. 2020.. Report 9: impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand. Rep. , Imp. Coll. London, London, UK:. https://doi.org/10.25561/77482
    [Google Scholar]
  35. 35.
    Fernández-Villaverde J, Jones CI. 2020.. Macroeconomic outcomes and COVID-19: a progress report. NBER Work. Pap. 28004. https://www.nber.org/papers/w28004
    [Google Scholar]
  36. 36.
    Ferretti L, Wymant C, Kendall M, Zhao L, Nurtay A, et al. 2020.. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. . Science 368:(6491):eabb6936
    [Crossref] [Google Scholar]
  37. 37.
    Fetzer T, Graeber T. 2021.. Measuring the scientific effectiveness of contact tracing: evidence from a natural experiment. . PNAS 118:(33):e2100814118
    [Crossref] [Google Scholar]
  38. 38.
    Flasche S, Jit M, Rodríguez-Barraquer I, Coudeville L, Recker M, et al. 2016.. The long-term safety, public health impact, and cost-effectiveness of routine vaccination with a recombinant, live-attenuated dengue vaccine (Dengvaxia): a model comparison study. . PLOS Med. 13:(11):e1002181
    [Crossref] [Google Scholar]
  39. 39.
    Fumanelli L, Ajelli M, Merler S, Ferguson NM, Cauchemez S. 2016.. Model-based comprehensive analysis of school closure policies for mitigating influenza epidemics and pandemics. . PLOS Comput. Biol. 12:(1):e1004681
    [Crossref] [Google Scholar]
  40. 40.
    Funk S, Camacho A, Kucharski AJ, Lowe R, Eggo RM, Edmunds WJ. 2019.. Assessing the performance of real-time epidemic forecasts: a case study of Ebola in the Western Area region of Sierra Leone, 2014–15. . PLOS Comput. Biol. 15:(2):e1006785
    [Crossref] [Google Scholar]
  41. 41.
    Gail MH, Brinton LA, Byar DP, Corle DK, Green SB, et al. 1989.. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. . J. Natl. Cancer Inst. 81:(24):187986
    [Crossref] [Google Scholar]
  42. 42.
    Garnett GP. 2002.. An introduction to mathematical models in sexually transmitted disease epidemiology. . Sex. Transm. Infect. 78:(1):712
    [Crossref] [Google Scholar]
  43. 43.
    Gerland P, Raftery AE, Ševčíková H, Li N, Gu D, et al. 2014.. World population stabilization unlikely this century. . Science 346:(6206):23437
    [Crossref] [Google Scholar]
  44. 44.
    Goeyvaerts N, Willem L, Van Kerckhove K, Vandendijck Y, Hanquet G, et al. 2015.. Estimating dynamic transmission model parameters for seasonal influenza by fitting to age and season-specific influenza-like illness incidence. . Epidemics 13::19
    [Crossref] [Google Scholar]
  45. 45.
    Hall AJ. 2010.. The United Kingdom Joint Committee on Vaccination and Immunisation. . Vaccine 28:(Suppl. 1):A5457
    [Crossref] [Google Scholar]
  46. 46.
    Han E, Tan MMJ, Turk E, Sridhar D, Leung GM, et al. 2020.. Lessons learnt from easing COVID-19 restrictions: an analysis of countries and regions in Asia Pacific and Europe. . Lancet 396:(10261):152534
    [Crossref] [Google Scholar]
  47. 47.
    Hine DD. 2010.. The 2009 influenza pandemic. Rep. 400208/0710 , Cabinet Off., London, UK:. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/61252/the2009influenzapandemic-review.pdf
    [Google Scholar]
  48. 48.
    Holmdahl I, Buckee C. 2020.. Wrong but useful—what Covid-19 epidemiologic models can and cannot tell us. . N. Engl. J. Med. 383:(4):3035
    [Crossref] [Google Scholar]
  49. 49.
    Homer JB, Hirsch GB. 2006.. System dynamics modeling for public health: background and opportunities. . Am. J. Public Health 96:(3):45258
    [Crossref] [Google Scholar]
  50. 50.
    Husereau D, Drummond M, Augustovski F, de Bekker-Grob E, Briggs AH, et al. 2022.. Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations. . Value Health 25:(1):39
    [Crossref] [Google Scholar]
  51. 51.
    Husereau D, Drummond M, Petrou S, Carswell C, Moher D, et al. 2013.. Consolidated Health Economic Evaluation Reporting Standards (CHEERS)–explanation and elaboration: a report of the ISPOR Health Economic Evaluation Publication Guidelines Good Reporting Practices Task Force. . Value Health 16:(2):23150
    [Crossref] [Google Scholar]
  52. 52.
    Imai N, Dorigatti I, Cori A, Donnelly C, Riley S, Ferguson NM. 2020.. Estimating the potential total number of novel Coronavirus cases in Wuhan City, China. Rep. , Imp. Coll. London, London, UK:. https://doi.org/10.25561/77150
    [Crossref] [Google Scholar]
  53. 53.
    Jackson C, Mangtani P, Hawker J, Olowokure B, Vynnycky E. 2014.. The effects of school closures on influenza outbreaks and pandemics: systematic review of simulation studies. . PLOS ONE 9:(5):e97297
    [Crossref] [Google Scholar]
  54. 54.
    Jeong YD, Ejima K, Kim KS, Joohyeon W, Iwanami S, et al. 2022.. Designing isolation guidelines for COVID-19 patients with rapid antigen tests. . Nat. Commun. 13:(1):4910
    [Crossref] [Google Scholar]
  55. 55.
    Jewell NP, Lewnard JA, Jewell BL. 2020.. Caution warranted: using the Institute for Health Metrics and Evaluation model for predicting the course of the COVID-19 pandemic. . Ann. Intern. Med. 173:(3):22627
    [Crossref] [Google Scholar]
  56. 56.
    Jin S, Dickens BL, Quek AM, Hartman M, Tambyah PA, et al. 2022.. Estimating transmission dynamics of SARS-CoV-2 at different intraspatial levels in an institutional outbreak. . Epidemics 40::100617
    [Crossref] [Google Scholar]
  57. 57.
    Johansson MA, Apfeldorf KM, Dobson S, Devita J, Buczak AL, et al. 2019.. An open challenge to advance probabilistic forecasting for dengue epidemics. . PNAS 116:(48):2426874
    [Crossref] [Google Scholar]
  58. 58.
    Kecht V, Van Dijcke D, Brzezinski A. 2020.. The cost of staying open: voluntary social distancing and lockdowns in the US. . VoxEU, June 12. https://cepr.org/voxeu/columns/cost-staying-open-voluntary-social-distancing-and-lockdowns-us#
    [Google Scholar]
  59. 59.
    Keeling MJ, Rohani P. 2008.. Modeling Infectious Diseases in Humans and Animals. Princeton, NJ:: Princeton Univ. Press
    [Google Scholar]
  60. 60.
    Khullar D, Jena AB. 2021.. “Natural experiments” in health care research. . JAMA Health Forum 2:(6):e210290
    [Crossref] [Google Scholar]
  61. 61.
    Kim S-Y, Goldie SJ. 2008.. Cost-effectiveness analyses of vaccination programmes: a focused review of modelling approaches. . Pharmacoeconomics 26:(3):191215
    [Crossref] [Google Scholar]
  62. 62.
    Knight GM, Davies NG, Colijn C, Coll F, Donker T, et al. 2019.. Mathematical modelling for antibiotic resistance control policy: Do we know enough?. BMC Infect. Dis. 19:(1):1011
    [Crossref] [Google Scholar]
  63. 63.
    Koo JR, Cook AR, Park M, Sun Y, Sun H, et al. 2020.. Interventions to mitigate early spread of SARS-CoV-2 in Singapore: a modelling study. . Lancet Infect. Dis. 20:(6):67888
    [Crossref] [Google Scholar]
  64. 64.
    Koo JR, Dickens BL, Jin S, Lim JT, Sun Y, et al. 2022.. Testing strategies to contain COVID-19 in migrant worker dormitories. . J. Migr. Health 5::100079
    [Crossref] [Google Scholar]
  65. 65.
    Kowal S, Ng CD, Schuldt R, Sheinson D, Cookson R. 2023.. The impact of funding inpatient treatments for COVID-19 on health equity in the United States: a distributional cost-effectiveness analysis. . Value Health 26:(2):21625
    [Crossref] [Google Scholar]
  66. 66.
    Kramer SC, Shaman J. 2019.. Development and validation of influenza forecasting for 64 temperate and tropical countries. . PLOS Comput. Biol. 15:(2):e1006742
    [Crossref] [Google Scholar]
  67. 67.
    Kucharski AJ, Klepac P, Conlan AJK, Kissler SM, Tang ML, et al. 2020.. Effectiveness of isolation, testing, contact tracing, and physical distancing on reducing transmission of SARS-CoV-2 in different settings: a mathematical modelling study. . Lancet Infect. Dis. 20:(10):115160
    [Crossref] [Google Scholar]
  68. 68.
    Leelahavarong P, Doungthipsirikul S, Kumluang S, Poonchai A, Kittiratchakool N, et al. 2019.. Health technology assessment in Thailand: institutionalization and contribution to healthcare decision making: review of literature. . Int. J. Technol. Assess. Health Care 35:(6):46773
    [Crossref] [Google Scholar]
  69. 69.
    Leung K, Wu JT, Leung GM. 2021.. Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing. . Nat. Commun. 12::1501
    [Crossref] [Google Scholar]
  70. 70.
    Levy DT, Bauer JE, Lee H-R. 2006.. Simulation modeling and tobacco control: creating more robust public health policies. . Am. J. Public Health 96:(3):49498
    [Crossref] [Google Scholar]
  71. 71.
    Levy DT, Benjakul S, Ross H, Ritthiphakdee B. 2008.. The role of tobacco control policies in reducing smoking and deaths in a middle income nation: results from the Thailand SimSmoke simulation model. . Tob. Control 17:(1):5359
    [Crossref] [Google Scholar]
  72. 72.
    Levy DT, Cho S-I, Kim Y-M, Park S, Suh M-K, Kam S. 2010.. SimSmoke model evaluation of the effect of tobacco control policies in Korea: the unknown success story. . Am. J. Public Health 100:(7):126773
    [Crossref] [Google Scholar]
  73. 73.
    Levy DT, Nikolayev L, Mumford E. 2005.. Recent trends in smoking and the role of public policies: results from the SimSmoke tobacco control policy simulation model. . Addiction 100:(10):152636
    [Crossref] [Google Scholar]
  74. 74.
    Levy DT, Nikolayev L, Mumford E, Compton C. 2005.. The Healthy People 2010 smoking prevalence and tobacco control objectives: results from the SimSmoke tobacco control policy simulation model (United States). . Cancer Causes Control 16:(4):35971
    [Crossref] [Google Scholar]
  75. 75.
    Levy DT, Sánchez-Romero LM, Travis N, Yuan Z, Li Y, et al. 2021.. US nicotine vaping product SimSmoke simulation model: the effect of vaping and tobacco control policies on smoking prevalence and smoking-attributable deaths. . Int. J. Environ. Res. Public Health 18:(9):4876
    [Crossref] [Google Scholar]
  76. 76.
    Li Q, Guan X, Wu P, Wang X, Zhou L, et al. 2020.. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. New Engl. . J. Med. 382:(13):1199207
    [Google Scholar]
  77. 77.
    Li T, White LF. 2021.. Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic. . PLOS Comput. Biol. 17:(7):e1009210
    [Crossref] [Google Scholar]
  78. 78.
    Loyo HK, Batcher C, Wile K, Huang P, Orenstein D, Milstein B. 2013.. From model to action: using a system dynamics model of chronic disease risks to align community action. . Health Promot. Pract. 14:(1):5361
    [Crossref] [Google Scholar]
  79. 79.
    Ma KC, Menkir TF, Kissler S, Grad YH, Lipsitch M. 2021.. Modeling the impact of racial and ethnic disparities on COVID-19 epidemic dynamics. . eLife 10::e66601
    [Crossref] [Google Scholar]
  80. 80.
    Macbeth D. 2001.. On “reflexivity” in qualitative research: two readings, and a third. . Qual. Inq. 7:(1):3568
    [Crossref] [Google Scholar]
  81. 81.
    MacDonald Gibson J, Rodriguez D, Dennerlein T, Mead J, Hasch T, et al. 2015.. Predicting urban design effects on physical activity and public health: a case study. . Health Place 35::7984
    [Crossref] [Google Scholar]
  82. 82.
    Mackintosh DR, Stewart GT. 1979.. A mathematical model of a heroin epidemic: implications for control policies. . J. Epidemiol. Commun. Health 33:(4):299304
    [Crossref] [Google Scholar]
  83. 83.
    Marjoram P, Molitor J, Plagnol V, Tavaré S. 2003.. Markov chain Monte Carlo without likelihoods. . PNAS 100:(26):1532428
    [Crossref] [Google Scholar]
  84. 84.
    Massad E, Burattini MN, Lopez LF, Coutinho FAB. 2005.. Forecasting versus projection models in epidemiology: the case of the SARS epidemics. . Med. Hypotheses 65:(1):1722
    [Crossref] [Google Scholar]
  85. 85.
    McGowan CJ, Biggerstaff M, Johansson M, Apfeldorf KM, Ben-Nun M, et al. 2019.. Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016. . Sci. Rep. 9:(1):683
    [Crossref] [Google Scholar]
  86. 86.
    McKinley T, Cook AR, Deardon R. 2009.. Inference in epidemic models without likelihoods. . Int. J. Biostat. 5:(1):24
    [Crossref] [Google Scholar]
  87. 87.
    Medley GF. 2022.. A consensus of evidence: the role of SPI-M-O in the UK COVID-19 response. . Adv. Biol. Regul. 86::100918
    [Crossref] [Google Scholar]
  88. 88.
    Meltzer MI, Santibanez S, Fischer LS, Merlin TL, Adhikari BB, et al. 2016.. Modeling in real time during the Ebola response. . MMWR Suppl. 65::8589
    [Crossref] [Google Scholar]
  89. 89.
    Miyama T, Jung S-M, Hayashi K, Anzai A, Kinoshita R, et al. 2022.. Phenomenological and mechanistic models for predicting early transmission data of COVID-19. . Math. Biosci. Eng. 19:(2):204355
    [Crossref] [Google Scholar]
  90. 90.
    Murray CJL, Aravkin AY, Zheng P, Abbafati C, Abbas KM, et al. 2020.. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. . Lancet 396:(10258):122349
    [Crossref] [Google Scholar]
  91. 91.
    Murray CJL, Lopez AD. 2013.. Measuring the global burden of disease. . New Engl. J. Med. 369:(5):44857
    [Crossref] [Google Scholar]
  92. 92.
    O'Neill PD, Roberts GO. 1999.. Bayesian inference for partially observed stochastic epidemics. . J. R. Stat. Soc. Ser. A 162:(1):12129
    [Crossref] [Google Scholar]
  93. 93.
    Ouakrim DA, Wilson T, Waa A, Maddox R, Andrabi H, et al. 2023.. Tobacco endgame intervention impacts on health gains and Māori:non-Māori health inequity: a simulation study of the Aotearoa/New Zealand Tobacco Action Plan. . Tob. Control. https://doi.org/10.1136/tc-2022-057655. In press
    [Google Scholar]
  94. 94.
    Owen L, Morgan A, Fischer A, Ellis S, Hoy A, Kelly MP. 2012.. The cost-effectiveness of public health interventions. . J. Public Health 34:(1):3745
    [Crossref] [Google Scholar]
  95. 95.
    Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, et al. 2021.. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. . BMJ 372::n71
    [Crossref] [Google Scholar]
  96. 96.
    Palmer AJ, Mount Hood 5 Model. Group, Clarke P, Gray A, Leal J, et al. 2013.. Computer modeling of diabetes and its complications: a report on the Fifth Mount Hood challenge meeting. . Value Health 16:(4):67085
    [Crossref] [Google Scholar]
  97. 97.
    Pearson AL, Cleghorn CL, van der Deen FS, Cobiac LJ, Kvizhinadze G, et al. 2017.. Tobacco retail outlet restrictions: health and cost impacts from multistate life-table modelling in a national population. . Tob. Control 26:(5):57985
    [Crossref] [Google Scholar]
  98. 98.
    Phan TP, Alkema L, Tai ES, Tan KHX, Yang Q, et al. 2014.. Forecasting the burden of type 2 diabetes in Singapore using a demographic epidemiological model of Singapore. . BMJ Open Diabetes Res. Care 2:(1):e000012
    [Crossref] [Google Scholar]
  99. 99.
    Plamondon KM, Bisung E. 2019.. The CCGHR Principles for Global Health Research: centering equity in research, knowledge translation, and practice. . Soc. Sci. Med. 239::112530
    [Crossref] [Google Scholar]
  100. 100.
    Pollett S, Johansson MA, Reich NG, Brett-Major D, Del Valle SY, et al. 2021.. Recommended reporting items for epidemic forecasting and prediction research: the EPIFORGE 2020 guidelines. . PLOS Med. 18:(10):e1003793
    [Crossref] [Google Scholar]
  101. 101.
    Prem K, Choi YH, Bénard É, Burger EA, Hadley L, et al. 2023.. Global impact and cost-effectiveness of one-dose versus two-dose human papillomavirus vaccination schedules: a comparative modelling analysis. . BMC Med. 21::313
    [Crossref] [Google Scholar]
  102. 102.
    Prem K, Liu Y, Russell TW, Kucharski AJ, Eggo RM, et al. 2020.. The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study. . Lancet Public Health 5:(5):e26170
    [Crossref] [Google Scholar]
  103. 103.
    Puljević C, Morphett K, Hefler M, Edwards R, Walker N, et al. 2022.. Closing the gaps in tobacco endgame evidence: a scoping review. . Tob. Control 31:(2):36575
    [Crossref] [Google Scholar]
  104. 104.
    Reich NG, McGowan CJ, Yamana TK, Tushar A, Ray EL, et al. 2019.. Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S. . PLOS Comput. Biol. 15:(11):e1007486
    [Crossref] [Google Scholar]
  105. 105.
    Ross R. 1916.. An application of the theory of probabilities to the study of a priori pathometry—part I. . Proc. R. Soc. A 92:(638):20430
    [Google Scholar]
  106. 106.
    Ross R, Hudson HP. 1917.. An application of the theory of probabilities to the study of a priori pathometry—part II. . Proc. R. Soc. A 93:(650):21225
    [Google Scholar]
  107. 107.
    Ross R, Hudson HP. 1917.. An application of the theory of probabilities to the study of a priori pathometry—part III. . Proc. R. Soc. A 93:(650):22540
    [Google Scholar]
  108. 108.
    Schulz KF, Altman DG, Moher D, CONSORT Group. 2010.. CONSORT 2010 statement: updated guidelines for reporting parallel group randomized trials. . Ann. Intern. Med. 152:(11):72632
    [Crossref] [Google Scholar]
  109. 109.
    Sci. Med. Cent. 2022.. Expert reaction to Rishi Sunak's interview in the Spectator about the role of scientists in the pandemic. . Science Media Centre, Aug. 25. https://www.sciencemediacentre.org/expert-reaction-to-rishi-sunaks-interview-in-the-spectator-about-the-role-of-scientists-in-the-pandemic/
    [Google Scholar]
  110. 110.
    Shi Y, Liu X, Kok S-Y, Rajarethinam J, Liang S, et al. 2016.. Three-month real-time dengue forecast models: an early warning system for outbreak alerts and policy decision support in Singapore. . Environ. Health Perspect. 124:(9):136975
    [Crossref] [Google Scholar]
  111. 111.
    Singh Chawla D. 2020.. Critiqued coronavirus simulation gets thumbs up from code-checking efforts. . Nature 582:(7812):32324
    [Crossref] [Google Scholar]
  112. 112.
    Soneji S, Barrington-Trimis JL, Wills TA, Leventhal AM, Unger JB, et al. 2017.. Association between initial use of e-cigarettes and subsequent cigarette smoking among adolescents and young adults: a systematic review and meta-analysis. . JAMA Pediatr. 171:(8):78897
    [Crossref] [Google Scholar]
  113. 113.
    Sweileh WM. 2020.. Bibliometric analysis of peer-reviewed literature on climate change and human health with an emphasis on infectious diseases. . Glob. Health 16:(1):44
    [Crossref] [Google Scholar]
  114. 114.
    Tan IB, Tan C, Hsu LY, Dan YY, Aw A, et al. 2021.. Prevalence and outcomes of SARS-CoV-2 infection among migrant workers in Singapore. . JAMA 325:(6):58485
    [Crossref] [Google Scholar]
  115. 115.
    Teerawattananon Y, Kc S, Chi Y-L, Dabak S, Kazibwe J, et al. 2022.. Recalibrating the notion of modelling for policymaking during pandemics. . Epidemics 38::100552
    [Crossref] [Google Scholar]
  116. 116.
    Topley WWC, Wilson GS. 1923.. The spread of bacterial infection. The problem of herd-immunity. . J. Hyg. 21:(3):24349
    [Crossref] [Google Scholar]
  117. 117.
    Turnnidge S. 2021.. How media reporting of modelling has shaped our understanding of the pandemic. . Full Fact, Aug. 19. https://fullfact.org/health/scientific-modelling-covid/
    [Google Scholar]
  118. 118.
    van Walbeek C. 2010.. A simulation model to predict the fiscal and public health impact of a change in cigarette excise taxes. . Tob. Control 19:(1):3136
    [Crossref] [Google Scholar]
  119. 119.
    van Zandvoort K, Jarvis CI, Pearson CAB, Davies NG, Ratnayake R, et al. 2020.. Response strategies for COVID-19 epidemics in African settings: a mathematical modelling study. . BMC Med. 18:(1):324
    [Crossref] [Google Scholar]
  120. 120.
    Vanni T, Karnon J, Madan J, White RG, Edmunds WJ, et al. 2011.. Calibrating models in economic evaluation: a seven-step approach. . Pharmacoeconomics 29:(1):3549
    [Crossref] [Google Scholar]
  121. 121.
    Verguet S, Kim JJ, Jamison DT. 2016.. Extended cost-effectiveness analysis for health policy assessment: a tutorial. . Pharmacoeconomics 34:(9):91323
    [Crossref] [Google Scholar]
  122. 122.
    von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, et al. 2007.. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. . Lancet 370:(9596):145357
    [Crossref] [Google Scholar]
  123. 123.
    Vos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, et al. 2020.. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. . Lancet 396:(10258):120422
    [Crossref] [Google Scholar]
  124. 124.
    Vynnycky E, White R. 2010.. An Introduction to Infectious Disease Modelling. Oxford, UK/New York:: Oxford Univ. Press
    [Google Scholar]
  125. 125.
    Walker S, Palmer S, Sculpher M. 2007.. The role of NICE technology appraisal in NHS rationing. . Br. Med. Bull. 81–82:(1):5164
    [Crossref] [Google Scholar]
  126. 126.
    Wang H, Abbas KM, Abbasifard M, Abbasi-Kangevari M, Abbastabar H, et al. 2020.. Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950–2019: a comprehensive demographic analysis for the Global Burden of Disease Study 2019. . Lancet 396:(10258):1160203
    [Crossref] [Google Scholar]
  127. 127.
    Watson C. 2022.. Rise of the preprint: how rapid data sharing during COVID-19 has changed science forever. . Nat. Med. 28:(1):25
    [Crossref] [Google Scholar]
  128. 128.
    Wilson EB, Worcester J. 1945.. The law of mass action in epidemiology. . PNAS 31:(1):2434
    [Crossref] [Google Scholar]
  129. 129.
    Wu JT, Leung K, Bushman M, Kishore N, Niehus R, et al. 2020.. Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China. . Nat. Med. 26:(4):50610
    [Crossref] [Google Scholar]
  130. 130.
    Wu JT, Leung K, Leung GM. 2020.. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. . Lancet 395:(10225):68997
    [Crossref] [Google Scholar]
  131. 131.
    Zaman M, Afridi G, Ohly H, McArdle HJ, Lowe NM. 2020.. Equitable partnerships in global health research. . Nat. Food 1:(12):76061
    [Crossref] [Google Scholar]
  132. 132.
    Zeng Z, Cook AR, Chen JI-P, van der Eijk Y. 2022.. Evaluating the public health impact of partial and full tobacco flavour bans: a simulation study. . Lancet Reg. Health West. Pac. 21::100414
    [Google Scholar]
  133. 133.
    Zeng Z, Cook AR, van der Eijk Y. 2023.. What measures are needed to achieve a tobacco endgame target? A Singapore-based simulation study. . Tob. Control. https://doi.org/10.1136/tc-2022-057856. In press
    [Google Scholar]
  134. 134.
    Zimmerman L, Lounsbury DW, Rosen CS, Kimerling R, Trafton JA, Lindley SE. 2016.. Participatory system dynamics modeling: increasing stakeholder engagement and precision to improve implementation planning in systems. . Adm. Policy Ment. Health 43:(6):83449
    [Crossref] [Google Scholar]
/content/journals/10.1146/annurev-publhealth-060222-025149
Loading
  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error