1932
0

Annual Review of Statistics and Its Application

Volume 12, 2025

Infectious Disease Modeling

  • Jing Huang1 and Jeffrey S. Morris1
  • Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA; email: [email protected]
  • Vol. 12:19-44 (Volume publication date March 2025)
  • First published as a Review in Advance on November 12, 2024
  • Copyright © 2025 by the author(s).
    This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See credit lines of images or other third-party material in this article for license information.

Abstract

Infectious diseases pose a persistent challenge to public health worldwide. Recent global health crises, such as the COVID-19 pandemic and Ebola outbreaks, have underscored the vital role of infectious disease modeling in guiding public health policy and response. Infectious disease modeling is a critical tool for society, informing risk mitigation measures, prompting timely interventions, and aiding preparedness for healthcare delivery systems. This article synthesizes the current landscape of infectious disease modeling, emphasizing the integration of statistical methods in understanding and predicting the spread of infectious diseases. We begin by examining the historical context and the foundational models that have shaped the field, such as the SIR (susceptible, infectious, recovered) and SEIR (susceptible, exposed, infectious, recovered) models. Subsequently, we delve into the methodological innovations that have arisen, including stochastic modeling, network-based approaches, and the use of big data analytics. We also explore the integration of machine learning techniques in enhancing model accuracy and responsiveness. The review identifies the challenges of parameter estimation, model validation, and the incorporation of real-time data streams. Moreover, we discuss the ethical implications of modeling, such as privacy concerns and the communication of risk. The article concludes by discussing future directions for research, highlighting the need for data integration and interdisciplinary collaboration for advancing infectious disease modeling.

Keyword(s): agent-based models, compartmental models, data integration, spatial-temporal correlation, stochastic modeling, time-since-infection models
Loading

Article metrics loading...

/content/journals/10.1146/annurev-statistics-112723-034351
2025-03-07
2025-06-19
Loading full text...

Full text loading...

/deliver/fulltext/statistics/12/1/annurev-statistics-112723-034351.html?itemId=/content/journals/10.1146/annurev-statistics-112723-034351&mimeType=html&fmt=ahah

Literature Cited

  1. Aguilar J, García BA, Toral R, Meloni S, Ramasco JJ. 2023.. Endemic infectious states below the epidemic threshold and beyond herd immunity. . Commun. Phys. 6:(1):187
     [Crossref] [Google Scholar]
  2. Almotairi KH, Hussein AM, Abualigah L, Abujayyab SK, Mahmoud EH, et al. 2023.. Impact of artificial intelligence on COVID-19 pandemic: a survey of image processing, tracking of disease, prediction of outcomes, and computational medicine. . Big Data Cogn. Comput. 7:(1):11
     [Crossref] [Google Scholar]
  3. Ameri K, Cooper KD. 2019.. A network-based compartmental model for the spread of whooping cough in Nebraska. . In Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2224–25. Piscataway, NJ:: IEEE
     [Google Scholar]
  4. Amman F, Markt R, Endler L, Hupfauf S, Agerer B, et al. 2022.. Viral variant-resolved wastewater surveillance of SARS-CoV-2 at national scale. . Nat. Biotechnol. 40:(12):1814–22
     [Crossref] [Google Scholar]
  5. Anderson RM, May RM. 1985.. Age-related changes in the rate of disease transmission: implications for the design of vaccination programmes. . Epidemiol. Infect. 94:(3):365–436
     [Google Scholar]
  6. Anderson RM, May RM. 1991.. Infectious Diseases of Humans: Dynamics and Control. Oxford, UK:: Oxford Univ. Press
     [Google Scholar]
  7. Aramaki E, Maskawa S, Morita M. 2011.. Twitter catches the flu: detecting influenza epidemics using Twitter. . In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 1568–76. Stroudsburg, PA:: Assoc. Comput. Linguist.
     [Google Scholar]
  8. Arino J, Jordan R, Van den Driessche P. 2007.. Quarantine in a multi-species epidemic model with spatial dynamics. . Math. Biosci. 206:(1):46–60
     [Crossref] [Google Scholar]
  9. Assaf D, Gutman Y, Neuman Y, Segal G, Amit S, et al. 2020.. Utilization of machine-learning models to accurately predict the risk for critical COVID-19. . Intern. Emerg. Med. 15::1435–43
     [Crossref] [Google Scholar]
  10. Bartlett MS. 1956.. Deterministic and stochastic models for recurrent epidemics. . In Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, Vol. 4, ed. J Neyman , pp. 81–109. Berkeley:: Univ. Calif. Press
     [Google Scholar]
  11. Bernoulli D. 1766.. Essai d'une nouvelle analyse de la mortalité causée par la petite vérole, et des avantages de l'inoculation pour la prévenir. . In Histoire de l'Académie Royale des Sciences, 1766, Vol. 1: Avec les Mémoires de Physique pour la Mme Année, Tirés des Registres de cette Académie, pp. 1–45. Paris:: Impr. R.
     [Google Scholar]
  12. Boatto S, Bonnet C, Cazelles B, Mazenc F. 2018.. SIR model with time dependent infectivity parameter: approximating the epidemic attractor and the importance of the initial phase. HAL Open Sci. Work. Pap. hal-01677886, version 1 , CNRS, Paris:
     [Google Scholar]
  13. Brauer F, Castillo-Chavez C, Feng Z, Brauer F, Castillo-Chavez C, Feng Z. 2019.. Spatial structure in disease transmission models. . Math. Models Epidemiol. 69::457–76
     [Crossref] [Google Scholar]
  14. Cator LJ, Johnson LR, Mordecai EA, El Moustaid F, Smallwood TR, et al. 2020.. The role of vector trait variation in vector-borne disease dynamics. . Front. Ecol. Evol. 8::189
     [Crossref] [Google Scholar]
  15. Chaix C, Durand-Zaleski I, Alberti C, Brun-Buisson C. 1999.. Control of endemic methicillin-resistant staphylococcus aureus: a cost-benefit analysis in an intensive care unit. . JAMA 282:(18):1745–51
     [Crossref] [Google Scholar]
  16. Chen YC, Lu PE, Chang CS, Liu TH. 2020.. A time-dependent SIR model for COVID-19 with undetectable infected persons. . IEEE Trans. Netw. Sci. Eng. 7:(4):3279–94
     [Crossref] [Google Scholar]
  17. Cheng X, Wang Y, Huang G. 2023.. Edge-based compartmental modeling for the spread of cholera on random networks: a case study in Somalia. . Math. Biosci. 366::109092
     [Crossref] [Google Scholar]
  18. Cori A. 2021.. EpiEstim: estimate time varying reproduction numbers from epidemic curves. . R Package, version 2.2-4. https://cran.r-project.org/web/packages/EpiEstim/index.html
    [Google Scholar]
  19. Cori A, Ferguson NM, Fraser C, Cauchemez S. 2013.. A new framework and software to estimate time-varying reproduction numbers during epidemics. . Am. J. Epidemiol. 178:(9):1505–12
     [Crossref] [Google Scholar]
  20. Das S, Bose I, Sarkar UK. 2023.. Predicting the outbreak of epidemics using a network-based approach. . Eur. J. Oper. Res. 309:(2):819–31
     [Crossref] [Google Scholar]
  21. Dashtbali M, Mirzaie M. 2021.. A compartmental model that predicts the effect of social distancing and vaccination on controlling COVID-19. . Sci. Rep. 11:(1):8191
     [Crossref] [Google Scholar]
  22. de Miguel-Arribas A, Aleta A, Moreno Y. 2022.. Impact of vaccine hesitancy on secondary COVID-19 outbreaks in the US: an age-structured SIR model. . BMC Infect. Dis. 22:(1):511
     [Crossref] [Google Scholar]
  23. Dugas AF, Jalalpour M, Gel Y, Levin S, Torcaso F, et al. 2013.. Influenza forecasting with Google Flu Trends. . PLOS ONE 8:(2):e56176
     [Crossref] [Google Scholar]
  24. Elaziz MA, Hosny KM, Salah A, Darwish MM, Lu S, Sahlol AT. 2020.. New machine learning method for image-based diagnosis of COVID-19. . PLOS ONE 15:(6):e0235187
     [Crossref] [Google Scholar]
  25. Epstein JM, Axtell R. 1996.. Growing Artificial Societies: Social Science from the Bottom Up. Washington, DC:: Brookings Inst. Press
     [Google Scholar]
  26. Epstein JM, Parker J, Cummings D, Hammond RA. 2008.. Coupled contagion dynamics of fear and disease: mathematical and computational explorations. . PLOS ONE 3:(12):e3955
     [Crossref] [Google Scholar]
  27. Eubank S, Guclu H, Anil Kumar V, Marathe MV, Srinivasan A, et al. 2004.. Modelling disease outbreaks in realistic urban social networks. . Nature 429:(6988):180–84
     [Crossref] [Google Scholar]
  28. Ferguson NM, Cummings DA, Cauchemez S, Fraser C, Riley S, et al. 2005.. Strategies for containing an emerging influenza pandemic in Southeast Asia. . Nature 437:(7056):209–14
     [Crossref] [Google Scholar]
  29. Finger F, Genolet T, Mari L, de Magny GC, Manga NM, et al. 2016.. Mobile phone data highlights the role of mass gatherings in the spreading of cholera outbreaks. . PNAS 113:(23):6421–26
     [Crossref] [Google Scholar]
  30. Fraser C. 2007.. Estimating individual and household reproduction numbers in an emerging epidemic. . PLOS ONE 2:(8):e758
     [Crossref] [Google Scholar]
  31. Ganyani T, Faes C, Hens N. 2021.. Simulation and analysis methods for stochastic compartmental epidemic models. . Annu. Rev. Stat. Appl. 8::69–88
     [Crossref] [Google Scholar]
  32. Ge Y, Wu X, Zhang W, Wang X, Zhang D, et al. 2023.. Effects of public-health measures for zeroing out different SARS-CoV-2 variants. . Nat. Commun. 14:(1):5270
     [Crossref] [Google Scholar]
  33. Hao X, Cheng S, Wu D, Wu T, Lin X, Wang C. 2020.. Reconstruction of the full transmission dynamics of COVID-19 in Wuhan. . Nature 584:(7821):420–24
     [Crossref] [Google Scholar]
  34. Harko T, Lobo FS, Mak MK. 2014.. Exact analytical solutions of the susceptible-infected-recovered (SIR) epidemic model and of the SIR model with equal death and birth rates. . Appl. Math. Comput. 236::184–94
     [Google Scholar]
  35. He X, Lau EH, Wu P, Deng X, Wang J, et al. 2020.. Temporal dynamics in viral shedding and transmissibility of COVID-19. . Nat. Med. 26:(5):672–75
     [Crossref] [Google Scholar]
  36. Held L, Höhle M, Hofmann M. 2005.. A statistical framework for the analysis of multivariate infectious disease surveillance counts. . Stat. Model. 5:(3):187–99
     [Crossref] [Google Scholar]
  37. Hooker G, Ellner SP, Roditi LDV, Earn DJ. 2011.. Parameterizing state–space models for infectious disease dynamics by generalized profiling: measles in Ontario. . J. R. Soc. Interface 8:(60):961–74
     [Crossref] [Google Scholar]
  38. Horton S. 2017.. Cost-effectiveness analysis in disease control priorities. . In Disease Control Priorities: Improving Health and Reducing Poverty, ed. DT Jamison, H Gelband, S Horton, P Jha, R Laxminarayan, et al. , pp. 147–56. Washington, DC:: World Bank. , 3rd ed..
     [Google Scholar]
  39. Hussain A, Tahir A, Hussain Z, Sheikh Z, Gogate M, et al. 2021.. Artificial intelligence–enabled analysis of public attitudes on Facebook and Twitter toward COVID-19 vaccines in the United Kingdom and the United States: observational study. . J. Med. Internet Res. 23:(4):e26627
     [Crossref] [Google Scholar]
  40. Iyaniwura SA, Falcão RC, Ringa N, Adu PA, Spencer M, et al. 2022.. Mathematical modeling of COVID-19 in British Columbia: an age-structured model with time-dependent contact rates. . Epidemics 39::100559
     [Crossref] [Google Scholar]
  41. Jewell NP. 2021.. Statistical models for COVID-19 incidence, cumulative prevalence, and Rt. . J. Am. Stat. Assoc. 116:(536):1578–82
     [Crossref] [Google Scholar]
  42. Källén A, Arcuri P, Murray J. 1985.. A simple model for the spatial spread and control of rabies. . J. Theor. Biol. 116:(3):377–93
     [Crossref] [Google Scholar]
  43. Kermack WO, McKendrick AG. 1927.. A contribution to the mathematical theory of epidemics. . Proc. R. Soc. Lond. Ser. A 115:(772):700–21
     [Crossref] [Google Scholar]
  44. Kerr CC, Stuart RM, Mistry D, Abeysuriya RG, Rosenfeld K, et al. 2021.. Covasim: an agent-based model of COVID-19 dynamics and interventions. . PLOS Comput. Biol. 17:(7):e1009149
     [Crossref] [Google Scholar]
  45. Khyar O, Allali K. 2020.. Global dynamics of a multi-strain SEIR epidemic model with general incidence rates: application to COVID-19 pandemic. . Nonlinear Dyn. 102:(1):489–509
     [Crossref] [Google Scholar]
  46. Lee EK, Liu Y, Pietz FH. 2016.. A compartmental model for zika virus with dynamic human and vector populations. . AMIA Annu. Symp. Proc. 2016::743–52
     [Google Scholar]
  47. 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:(1):1501
     [Crossref] [Google Scholar]
  48. Levy N, Iv M, Yom-Tov E. 2018.. Modeling influenza-like illnesses through composite compartmental models. . Physica A 494::288–93
     [Crossref] [Google Scholar]
  49. Li MY, Muldowney JS. 1995.. Global stability for the seir model in epidemiology. . Math. Biosci. 125:(2):155–64
     [Crossref] [Google Scholar]
  50. Liu Y, Mao C, Leiva V, Liu S, Silva Neto WA. 2022.. Asymmetric autoregressive models: statistical aspects and a financial application under COVID-19 pandemic. . J. Appl. Stat. 49:(5):1323–47
     [Crossref] [Google Scholar]
  51. Macdonald G. 1957.. The Epidemiology and Control of Malaria. Oxford, UK:: Oxford Univ. Press
     [Google Scholar]
  52. Maleki M, Mahmoudi MR, Wraith D, Pho KH. 2020.. Time series modelling to forecast the confirmed and recovered cases of COVID-19. . Travel Med. Infect. Dis. 37::101742
     [Crossref] [Google Scholar]
  53. Maliyoni M, Gaff HD, Govinder KS, Chirove F. 2023.. Multipatch stochastic epidemic model for the dynamics of a tick-borne disease. . Front. Appl. Math. Stat. 9::1122410
     [Crossref] [Google Scholar]
  54. Massard M, Eftimie R, Perasso A, Saussereau B. 2022.. A multi-strain epidemic model for COVID-19 with infected and asymptomatic cases: application to French data. . J. Theor. Biol. 545::111117
     [Crossref] [Google Scholar]
  55. Matis J, Wehrly T. 1979.. Stochastic models of compartmental systems. . Biometrics 35:(1):199–220
     [Crossref] [Google Scholar]
  56. Miller JC, Slim AC, Volz EM. 2012.. Edge-based compartmental modelling for infectious disease spread. . J. R. Soc. Interface 9:(70):890–906
     [Crossref] [Google Scholar]
  57. Minayev P, Ferguson N. 2009.. Improving the realism of deterministic multi-strain models: implications for modelling influenza A. . J. R. Soc. Interface 6:(35):509–18
     [Crossref] [Google Scholar]
  58. Mohle-Boetani JC, Miller B, Halpern M, Trivedi A, Lessler J, et al. 1995.. School-based screening for tuberculous infection: a cost-benefit analysis. . JAMA 274:(8):613–19
     [Crossref] [Google Scholar]
  59. Nash RK, Nouvellet P, Cori A. 2022.. Real-time estimation of the epidemic reproduction number: scoping review of the applications and challenges. . PLOS Digital Health 1:(6):e0000052
     [Crossref] [Google Scholar]
  60. Ng PC, Spachos P, Gregori S, Plataniotis KN. 2022.. Epidemic exposure tracking with wearables: a machine learning approach to contact tracing. . IEEE Access 10::14134–48
     [Crossref] [Google Scholar]
  61. Nouvellet P, Bhatia S, Cori A, Ainslie KE, Baguelin M, et al. 2021.. Reduction in mobility and COVID-19 transmission. . Nat. Commun. 12:(1):1090
     [Crossref] [Google Scholar]
  62. Oidtman RJ, Omodei E, Kraemer MU, Castañeda-Orjuela CA, Cruz-Rivera E, et al. 2021.. Trade-offs between individual and ensemble forecasts of an emerging infectious disease. . Nat. Commun. 12:(1):5379
     [Crossref] [Google Scholar]
  63. Osthus D, Hickmann KS, Caragea PC, Higdon D, Del Valle SY. 2017.. Forecasting seasonal influenza with a state-space SIR model. . Ann. Appl. Stat. 11:(1):202–24
     [Crossref] [Google Scholar]
  64. Pan A, Liu L, Wang C, Guo H, Hao X, et al. 2020.. Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China. . JAMA 323:(19):1915–23
     [Crossref] [Google Scholar]
  65. Paul M. 2010.. Cost-effectiveness analysis in infectious diseases. . Clin. Microbiol. Infect. 16:(12):1705–6
     [Crossref] [Google Scholar]
  66. Pei S, Kandula S, Yang W, Shaman J. 2018.. Forecasting the spatial transmission of influenza in the United States. . PNAS 115:(11):2752–57
     [Crossref] [Google Scholar]
  67. Peiffer-Smadja N, Rawson TM, Ahmad R, Buchard A, Georgiou P, et al. 2020.. Machine learning for clinical decision support in infectious diseases: a narrative review of current applications. . Clin. Microbiol. Infect. 26:(5):584–95
     [Crossref] [Google Scholar]
  68. Polgreen PM, Chen Y, Pennock DM, Nelson FD, Weinstein RA. 2008.. Using internet searches for influenza surveillance. . Clin. Infect. Dis. 47:(11):1443–48
     [Crossref] [Google Scholar]
  69. Quick C, Dey R, Lin X. 2021.. Regression models for understanding COVID-19 epidemic dynamics with incomplete data. . J. Am. Stat. Assoc. 116:(536):1561–77
     [Crossref] [Google Scholar]
  70. Ray EL, Reich NG. 2018.. Prediction of infectious disease epidemics via weighted density ensembles. . PLOS Comput. Biol. 14:(2):e1005910
     [Crossref] [Google Scholar]
  71. Reif J, Heun-Johnson H, Tysinger B, Lakdawalla D. 2021.. Measuring the COVID-19 mortality burden in the United States: a microsimulation study. . Ann. Intern. Med. 174:(12):1700–9
     [Crossref] [Google Scholar]
  72. Richard Q, Alizon S, Choisy M, Sofonea MT, Djidjou-Demasse R. 2021.. Age-structured non-pharmaceutical interventions for optimal control of COVID-19 epidemic. . PLOS Comput. Biol. 17:(3):e1008776
     [Crossref] [Google Scholar]
  73. Ross R. 1897.. On some peculiar pigmented cells found in two mosquitos fed on malarial blood. . Br. Med. J. 2::178688
     [Crossref] [Google Scholar]
  74. Ross R. 1911.. Some quantitative studies in epidemiology. . Nature 87:(2188):466–67
     [Crossref] [Google Scholar]
  75. Ross R. 1916.. An application of the theory of probabilities to the study of a priori pathometry—part I. . Proc. R. Soc. Lond. Ser. A 92:(638):204–30
     [Crossref] [Google Scholar]
  76. Ross R, Hudson HP. 1917a.. An application of the theory of probabilities to the study of a priori pathometry—part II. . Proc. R. Soc. Lond. Ser. A 93:(650):212–25
     [Crossref] [Google Scholar]
  77. Ross R, Hudson HP. 1917b.. An application of the theory of probabilities to the study of a priori pathometry—part III. . Proc. R. Soc. Lond. Ser. A 93:(650):225–40
     [Crossref] [Google Scholar]
  78. Rowthorn R, Maciejowski J. 2020.. A cost–benefit analysis of the COVID-19 disease. . Oxf. Rev. Econ. Policy 36:(Suppl. 1):S38–55
     [Crossref] [Google Scholar]
  79. Salas J. 2021.. Improving the estimation of the COVID-19 effective reproduction number using nowcasting. . Stat. Methods Med. Res. 30:(9):2075–84
     [Crossref] [Google Scholar]
  80. Salathe M, Bengtsson L, Bodnar TJ, Brewer DD, Brownstein JS, et al. 2012.. Digital epidemiology. . PLOS Comput. Biol. 8:(7):e1002616
     [Crossref] [Google Scholar]
  81. Shahid F, Zameer A, Muneeb M. 2020.. Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM. . Chaos Solitons Fractals 140::110212
     [Crossref] [Google Scholar]
  82. Shi J, Morris JS, Rubin DM, Huang J. 2022.. Robust modeling and inference of disease transmission using error-prone data with application to SARS-CoV-2. . arXiv:2212.08282 [stat.ME]
  83. Song PX, Wang L, Zhou Y, He J, Zhu B, et al. 2020.. An epidemiological forecast model and software assessing interventions on the COVID-19 epidemic in China. . J. Data Sci. 18:(3):409–32
     [Google Scholar]
  84. Spooner F, Abrams JF, Morrissey K, Shaddick G, Batty M, et al. 2021.. A dynamic microsimulation model for epidemics. . Soc. Sci. Med. 291::114461
     [Crossref] [Google Scholar]
  85. Truscott J, Fraser C, Hinsley W, Cauchemez S, Donnelly C, et al. 2009.. Quantifying the transmissibility of human influenza and its seasonal variation in temperate regions. . PLOS Curr. 2009::RRN1125
     [Google Scholar]
  86. Van der Ploeg CP, Van Vliet C, De Vlas SJ, Ndinya-Achola JO, Fransen L, et al. 1998.. STDSIM: a microsimulation model for decision support in STD control. . Interfaces 28:(3):84–100
     [Crossref] [Google Scholar]
  87. Viboud C, Bjørnstad ON, Smith DL, Simonsen L, Miller MA, Grenfell BT. 2006.. Synchrony, waves, and spatial hierarchies in the spread of influenza. . Science 312:(5772):447–51
     [Crossref] [Google Scholar]
  88. Wallinga J, Teunis P. 2004.. Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. . Am. J. Epidemiol. 160:(6):509–16
     [Crossref] [Google Scholar]
  89. Wei HM, Li XZ, Martcheva M. 2008.. An epidemic model of a vector-borne disease with direct transmission and time delay. . J. Math. Anal. Appl. 342:(2):895–908
     [Crossref] [Google Scholar]
  90. WHO Ebola Response Team. 2014.. Ebola virus disease in West Africa—the first 9 months of the epidemic and forward projections. . N. Engl. J. Med. 371:(16):1481–95
     [Crossref] [Google Scholar]
  91. Wilder B, Mina M, Tambe M. 2021.. Tracking disease outbreaks from sparse data with Bayesian inference. . In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, pp. 4883–91. Palo Alto, CA:: AAAI
     [Google Scholar]
  92. Wong F, de la Fuente-Nunez C, Collins JJ. 2023.. Leveraging artificial intelligence in the fight against infectious diseases. . Science 381:(6654):164–70
     [Crossref] [Google Scholar]
  93. Yagin FH, Cicek IB, Alkhateeb A, Yagin B, Colak C, et al. 2023.. Explainable artificial intelligence model for identifying COVID-19 gene biomarkers. . Comput. Biol. Med. 154::106619
     [Crossref] [Google Scholar]
  94. Ye J, Hai J, Wang Z, Wei C, Song J. 2023.. Leveraging natural language processing and geospatial time series model to analyze COVID-19 vaccination sentiment dynamics on tweets. . JAMIA Open 6:(2):ooad023
     [Crossref] [Google Scholar]
  95. Yuan Y, Li N. 2022.. Optimal control and cost-effectiveness analysis for a COVID-19 model with individual protection awareness. . Physica A 603::127804
     [Crossref] [Google Scholar]
  96. Zelenkov Y, Reshettsov I. 2023.. Analysis of the COVID-19 pandemic using a compartmental model with time-varying parameters fitted by a genetic algorithm. . Expert Syst. Appl. 224::120034
     [Crossref] [Google Scholar]
  97. Zhang Q, Yi GY. 2023.. Sensitivity analysis of error-contaminated time series data under autoregressive models with the application of COVID-19 data. . J. Appl. Stat. 50:(7):1611–34
     [Crossref] [Google Scholar]
  98. Zhou T, Ji Y. 2020.. Semiparametric Bayesian inference for the transmission dynamics of COVID-19 with a state-space model. . Contemp. Clin. Trials 97::106146
     [Crossref] [Google Scholar]
  99. Zoabi Y, Deri-Rozov S, Shomron N. 2021.. Machine learning-based prediction of COVID-19 diagnosis based on symptoms. . npj Digit. Med. 4::3
     [Crossref] [Google Scholar]
/content/journals/10.1146/annurev-statistics-112723-034351
Loading
Infectious Disease Modeling
Annual Review of Statistics and Its Application 12, 19 (2025); https://doi.org/10.1146/annurev-statistics-112723-034351
/content/journals/10.1146/annurev-statistics-112723-034351
/content/journals/10.1146/annurev-statistics-112723-034351
Loading

Data & Media loading...

Most Cited Most Cited RSS feed

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