No retraction, correction or concern found when last checked on Oct 18, 2026.
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
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.
Full text
Its licence does not allow us to show the full text. Read it at PubMed Central:
Read at PubMed CentralReferences 55
- 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.
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.
- 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.
Imai N., “Transmissibility of 2019-nCoV” (Report 3, Imperial College London, London, United Kingdom, 2020; ).
Not in our library10.25561/77148
- 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.
- 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.
- 10.
- 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
- 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.
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.
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.
- 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.
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.
- 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.
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.
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.
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.
Verity R., Estimates of the severity of COVID-19 disease. (13 March 2020).
Not in our library10.1101/2020.03.09.20033357
- 39.
Akaike H., A new look at the statistical model identification. IEEE Trans. Automat. Contr. 19, 716–723 (1974 ).
Not in our library
- 40.
Brauer F., Castillo-Chavez C., Feng Z., Mathematical Models in Epidemiology (Springer, 2019 ).
Not in our library
- 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.
Tolles J., Luong T., Modeling epidemics with compartmental models. JAMA, (2020 ).
Not in our library10.1001/jama.2020.8420
- 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
Cited by papers we hold 2
Reads
Read by people on this page
None yet.
Checked or read by AI through naulon
None yet.
A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.
Are you an author?
Is this your paper?
Claim it to add your note and be paid when agents cite it. Free.