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The challenges of modeling and forecasting the spread of COVID-19

Andrea L. Bertozzi, Elisa Franco, George Mohler and 2 more

Proceedings of the National Academy of Sciences · 2020 · CC BY

doi:10.1073/pnas.2006520117

Abstract

Significance The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remain a challenge. Here, we present and detail three regional-scale models for forecasting and assessing the course of the pandemic. This work is intended to demonstrate the utility of parsimonious models for understanding the pandemic and to provide an accessible framework for generating policy-relevant insights into its course. We show how these models can be connected to each other and to time series data for a particular region. Capable of measuring and forecasting the impacts of social distancing, these models highlight the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or antiviral therapies.

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References 55

  1. 1.

    Phelan A. L., Katz R., Gostin L. O., The novel coronavirus originating in Wuhan, China: Challenges for global health governance. JAMA 323, 709–710 (2020 ).

    Not in our library10.1001/jama.2020.1097

  2. 2.

    Pan A., Association of public health interventions with the Epidemiology of the COVID-19 outbreak in Wuhan, China. JAMA 323, 1–9 (2020 ).

    Not in our library10.1001/jama.2020.6130

  3. 3.
  4. 4.

    Landler M., Castle S., Behind the virus report that jarred the U.S. and the U.K. to action. NY Times, 17 March 2020. https://www.nytimes.com/2020/03/17/world/europe/coronavirus-imperial-college-johnson.html. Accessed 24 March 2020.

    Not in our library

  5. 5.

    Imai N., “Transmissibility of 2019-nCoV” (Report 3, Imperial College London, London, United Kingdom, 2020; ).

    Not in our library10.25561/77148

  6. 6.

    Li T., Simulating the spread of epidemics in China on the multi-layer transportation network: Beyond the coronavirus in Wuhan. arXiv:2002.12280 (27 February 2020).

    Not in our library

  7. 7.
  8. 8.

    Perkins T. A., Estimating unobserved SARS-CoV-2 infections in the United States. (26 March 2020).

    Not in our library10.1101/2020.03.15.20036582

  9. 9.
  10. 10.
  11. 11.

    Hellewell J., Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. Lancet Global Health 8, 488–496 (2020 ).

    Not in our library10.1016/s2214-109x(20)30074-7

  12. 12.

    Wu J. T., Leung K., Leung P. G. M., Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: A modelling study Lancet 395, 689–697 (2020 ).

    Not in our library10.1016/s0140-6736(20)30260-9

  13. 13.

    Arenas A., A mathematical model for the spatiotemporal epidemic spreading of COVID19. (23 March 2020).

    Not in our library10.1101/2020.03.21.20040022

  14. 14.

    Altieri N., Curating a COVID-19 data repository and forecasting county-level death counts in the United States. arXiv:2005.07882 (16 May 2020).

    Not in our library

  15. 15.

    IHME COVID-19 Health Service Utilization Forecasting Team, Murray C. J. L., Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator-days and deaths by US state in the next 4 months. https://www.medrxiv.org/content/early/2020/03/30/2020.03.27.20043752 (30 March 2020).

    Not in our library

  16. 16.

    Lewis R. J., Projections of hospital based healthcare demand due to COVID-19 in Los Angeles county. County DHS COVID 19 Predictive Modeling Team: May 6 update. http://dhs.lacounty.gov/wps/portal/dhs/covid19/mp. Accessed 10 April 2020.

    Not in our library

  17. 17.

    Dong E., Du H., Gardner L., An interactive web-based dashboard to track COVID-19 in real time. Lancet 20, 533–534 (2020 ).

    Not in our library10.1016/s1473-3099(20)30120-1

  18. 18.

    Mohler G., Schoenberg F., Short M. B., Sledge D., Analyzing the world-wide impact of public health interventions on the transmission dynamics of COVID-19. arXiv:2004.01714 (3 April 2020).

    Not in our library

  19. 19.

    Liu Y., Gayle A. A., Wilder-Smith A., Rocklöv J., The reproductive number of COVID-19 is higher compared to SARS coronavirus. J. Trav. Med. 27, taaa021 (2020 ).

    Not in our library10.1093/jtm/taaa021

  20. 20.

    Pan F., Time course of lung changes on chest CT during recovery from coronavirus disease 2019 (COVID-19). Radiology, 295, 715–721 (2020 ).

    Not in our library10.1148/radiol.2020200370

  21. 21.

    Zhou F., Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet 395, 1054–1062 (2020 ).

    Not in our library10.1016/s0140-6736(20)30566-3

  22. 22.

    Li Q., Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. N. Engl. J. Med. 382, 1199–1207 (2020 ).

    Not in our library10.1056/nejmoa2001316

  23. 23.

    Meyer S., Held L., Höhle M., Spatio-temporal analysis of epidemic phenomena using the R package surveillance. J. Stat. Softw. 77, 6981 (2017 ).

    Not in our library

  24. 24.

    Farrington C., Kanaan M., Gay N., Branching process models for surveillance of infectious diseases controlled by mass vaccination. Biostatistics 4, 279–295 (2003 ).

    Not in our library10.1093/biostatistics/4.2.279

  25. 25.

    Schoenberg F., Hoffmann M., Harrigan R., A recursive point process model for infectious diseases. Ann. Inst. Stat. Math. 71, 1271–1287 (2019 ).

    Not in our library

  26. 26.
  27. 27.

    Stomakhin A., Short M. B., Bertozzi A. L., Reconstruction of missing data in social networks based on temporal patterns of interactions. Inverse Probl. 27, 115013 (2011 ).

    Not in our library

  28. 28.

    Fox E. W., Modeling e-mail networks and inferring leadership using self-exciting point processes. J. Am. Stat. Assoc. 111, 564–584 (2016 ).

    Not in our library

  29. 29.

    Zipkin J. R., Schoenberg F. P., Coronges K., Bertozzi A. L., Point-process models of social network interactions: Parameter estimation and missing data recovery. Eur. J. Appl. Math. 27, 502–529 (2016 ).

    Not in our library

  30. 30.
  31. 31.

    Wallinga J., Teunis P., Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. Am. J. Epidemiol. 160, 509–516 (2004 ).

    Not in our library10.1093/aje/kwh255

  32. 32.

    Forsberg White L., Pagano M., A likelihood-based method for real-time estimation of the serial interval and reproductive number of an epidemic. Stat. Med. 27, 2999–3016 (2008 ).

    Not in our library10.1002/sim.3136

  33. 33.
  34. 34.

    Cowling B. J., The effective reproduction number of pandemic influenza: Prospective estimation. Epidemiology 21, 842–846 (2010 ).

    Not in our library10.1097/ede.0b013e3181f20977

  35. 35.

    Rizoiu M. A., Mishra S., Kong Q., Carman M., Xie L., SIR-Hawkes: Linking epidemic models and Hawkes processes to model diffusions in finite populations. https://arxiv.org/abs/1711.01679 (21 February 2018).

    Not in our library

  36. 36.

    You C., Estimation of the time-varying reproduction number of COVID-19 outbreak in China. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3539694 (19 February 2020).

    Not in our library10.1016/j.ijheh.2020.113555

  37. 37.

    Riley S., Transmission dynamics of the etiological agent of SARS in Hong Kong: Impact of public health interventions. Science 300, 1961–1966 (2003 ).

    Not in our library10.1126/science.1086478

  38. 38.

    Verity R., Estimates of the severity of COVID-19 disease. (13 March 2020).

    Not in our library10.1101/2020.03.09.20033357

  39. 39.

    Akaike H., A new look at the statistical model identification. IEEE Trans. Automat. Contr. 19, 716–723 (1974 ).

    Not in our library

  40. 40.

    Brauer F., Castillo-Chavez C., Feng Z., Mathematical Models in Epidemiology (Springer, 2019 ).

    Not in our library

  41. 41.

    Kermack W. O., McKendrick A. G., A contribution to the mathematical theory of epidemics. Proc. Roy. Soc. A 115, 700–721 (1927 ).

    Not in our library

  42. 42.

    Tolles J., Luong T., Modeling epidemics with compartmental models. JAMA, (2020 ).

    Not in our library10.1001/jama.2020.8420

  43. 43.

    Bootsma M. C. J., Ferguson N. M., The effect of public health measures on the 1918 influenza pandemic in U.S. cities. Proc. Natl. Acad. Sci. U.S.A. 104, 7588–7593 (2007 ).

    Not in our library10.1073/pnas.0611071104

  44. 44.

    Frasso G., Lambert P., Bayesian inference in an extended SEIR model with nonparametric disease transmission rate: An application to the Ebola epidemic in Sierra Leone. Biostatistics 17, 779–792 (2016 ).

    Not in our library10.1093/biostatistics/kxw027

  45. 45.

    Angelov A. G., Slavtchova-Bojkova M., Bayesian estimation of the offspring mean in branching processes: Application to infectious disease data. Comput. Math. Appl. 64, 229–235 (2012 ).

    Not in our library

  46. 46.

    Miller J., A note on the derivation of epidemic final sizes. Bull. Math. Biol. 74, 2125–2141 (2012 ).

    Not in our library10.1007/s11538-012-9749-6

  47. 47.

    Miller J., Mathematical models of SIR disease spread with combined non-sexual and sexual transmission routes. Infect. Dis. Model. 2, 35–55 (2017 ).

    Not in our library10.1016/j.idm.2016.12.003

  48. 48.

    Harko T., Lobo F. S. N., Mak M. K., 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, 184–194 (2014 ).

    Not in our library

  49. 49.

    Sledge D., Health Divided: Public Health and Individual Medicine in the Making of the Modern American State (University Press of Kansas, Lawrence, KS, 2017 ).

    Not in our library

  50. 50.

    Haffajee R. L., Mello M. M., Thinking globally, acting locally—the U.S. response to COVID-19. N. Engl. J. Med. 382, e75 (2020 ).

    Not in our library10.1056/nejmp2006740

  51. 51.

    Yan P., “Distribution theory, stochastic processes and infectious disease modelling” in Mathematical Epidemiology, Brauer F., van den Driessche P., J. Wu, Eds. (Lecture Notes in Mathematics, vol. 1945, Springer, Berlin, 2008 ), pp. 229–293.

    Not in our library

  52. 52.

    Evans N. D., White L. J., Chapman M. J., Godfrey K. R., Chappell M. J., The structural identifiability of the susceptible infected recovered model with seasonal forcing. Math. Biosci. 194, 175–197 (2005 ).

    Not in our library10.1016/j.mbs.2004.10.011

  53. 53.

    Roosa K., Chowell G., Assessing parameter identifiability in compartmental dynamic models using a computational approach: Application to infectious disease transmission models. Theor. Biol. Med. Model. 16, 1 (2019 ).

    Not in our library10.1186/s12976-018-0097-6

  54. 54.

    World Health Organization, Coronavirus disease (COVID-2019) situation reports. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports. Accessed 1 April 2020.

    Not in our library

  55. 55.

    Wu J. T., Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China. Nat. Med. 26, 506–510 (2020 ).

    Not in our library10.1038/s41591-020-0822-7

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