Skip to main navigation menu Skip to main content Skip to site footer
##common.pageHeaderLogo.altText##

Original article

Vol. 151 No. 1718 (2021)

Dynamic modelling to identify mitigation strategies for the COVID-19 pandemic

DOI
https://doi.org/10.4414/smw.2021.20487
Cite this as:
Swiss Med Wkly. 2021;151:w20487
Published
04.05.2021

Summary

Relevant pandemic-spread scenario simulations can provide guiding principles for containment and mitigation policies. We devised a compartmental model to predict the effectiveness of different mitigation strategies with a main focus on mass testing. The model consists of a set of simple differential equations considering the population size, reported and unreported infections, reported and unreported recoveries, and the number of COVID-19-inflicted deaths. We assumed that COVID-19 survivors are immune (e.g., mutations are not considered) and that the virus is primarily passed on by asymptomatic and pre-symptomatic individuals. Moreover, the current version of the model does not account for age-dependent differences in the death rates, but considers higher mortality rates due to temporary shortage of intensive care units. The model parameters have been chosen in a plausible range based on information found in the literature, but it is easily adaptable, i.e., these values can be replaced by updated information any time. We compared infection rates, the total number of people getting infected and the number of deaths in different scenarios. Social distancing or mass testing can contain or drastically reduce the infections and the predicted number of deaths when compared with a situation without mitigation. We found that mass testing alone and subsequent isolation of detected cases can be an effective mitigation strategy, alone and in combination with social distancing. It is of high practical relevance that a relationship between testing frequency and the effective reproduction number of the virus can be provided. However, unless one assumes that the virus can be globally defeated by reducing the number of infected persons to zero, testing must be upheld, albeit at reduced intensity, to prevent subsequent waves of infection. The model suggests that testing strategies can be equally effective as social distancing, though at much lower economic costs. We discuss how our mathematical model may help to devise an optimal mix of mitigation strategies against the COVID-19 pandemic. Moreover, we quantify the theoretical limit of contact tracing and by how much the effect of testing is enhanced, if applied to sub-populations with increased exposure risk or prevalence.

References

  1. Tracy M, Cerdá M, Keyes KM. Agent-Based Modeling in Public Health: Current Applications and Future Directions. Annu Rev Public Health. 2018;39(1):77–94. doi:.https://doi.org/10.1146/annurev-publhealth-040617-014317
  2. Ball F, Sirl D, Trapman P. Analysis of a stochastic SIR epidemic on a random network incorporating household structure. Math Biosci. 2010;224(2):53–73. doi:.https://doi.org/10.1016/j.mbs.2009.12.003
  3. Kiss IZ, Miller JC, Simon PL. Mathematics of epidemics on networks. Vol. 46. Springer; 2017.
  4. Allen LJS, Bauch CT, Castillo-Chavez C, Earn DJD, Feng Z, Lewis MA, et al. Mathematical Epidemiology. Brauer F, van den Driessche P, Wu J, editors. 2008. 415 p.
  5. Siettos CI, Russo L. Mathematical modeling of infectious disease dynamics. Virulence. 2013;4(4):295–306. doi:.https://doi.org/10.4161/viru.24041
  6. Daley DJ, Gani J. Epidemic modeling: an introduction. Cambridge: Cambridge University Press; 1999. 15.
  7. Britton T. Stochastic epidemic models: a survey. Math Biosci. 2010;225(1):24–35. doi:.https://doi.org/10.1016/j.mbs.2010.01.006
  8. Allen LJS. A primer on stochastic epidemic models: Formulation, numerical simulation, and analysis. Infect Dis Model. 2017;2(2):128–42. doi:.https://doi.org/10.1016/j.idm.2017.03.001
  9. Zhou Y, Yuan S, Zhao D. Threshold behavior of a stochastic SIS model with Lévy jumps. Appl Math Comput. 2016;275:255–67. doi:https://doi.org/10.1016/j.amc.2015.11.077
  10. Wu JT, Leung K, Leung GM. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet. 2020;395(10225):689–97. doi:.https://doi.org/10.1016/S0140-6736(20)30260-9
  11. Hou C, Chen J, Zhou Y, Hua L, Yuan J, He S, et al. The effectiveness of quarantine of Wuhan city against the Corona Virus Disease 2019 (COVID-19): A well-mixed SEIR model analysis. J Med Virol. 2020;92(7):841–8. doi:.https://doi.org/10.1002/jmv.25827
  12. Rǎdulescu A, Williams C, Cavanagh K. Management strategies in a SEIR-type model of COVID 19 community spread. Sci Rep. 2020;10(1):21256. doi:.https://doi.org/10.1038/s41598-020-77628-4
  13. He S, Peng Y, Sun K. SEIR modeling of the COVID-19 and its dynamics. Nonlinear Dyn. 2020;101(3):1667–80. doi:.https://doi.org/10.1007/s11071-020-05743-y
  14. Block P, Hoffman M, Raabe IJ, Dowd JB, Rahal C, Kashyap R, et al. Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world. Nat Hum Behav. 2020;4(6):588–96. doi:.https://doi.org/10.1038/s41562-020-0898-6
  15. Rockett RJ, Arnott A, Lam C, Sadsad R, Timms V, Gray K-A, et al. Revealing COVID-19 transmission in Australia by SARS-CoV-2 genome sequencing and agent-based modeling. Nat Med. 2020;26(9):1398–404. doi:.https://doi.org/10.1038/s41591-020-1000-7
  16. Silva PCL, Batista PVC, Lima HS, Alves MA, Guimarães FG, Silva RCP. COVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions. Chaos Solitons Fractals. 2020;139(10223):110088. doi:.https://doi.org/10.1016/j.chaos.2020.110088
  17. BBC. Covid-19: China's Qingdao to test nine million in five days. 2020 Oct 12. Available from: https://www.bbc.com/news/world-asia-54504785
  18. Li R, Pei S, Chen B, Song Y, Zhang T, Yang W, et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2). Science. 2020;368(6490):489–93. doi:.https://doi.org/10.1126/science.abb3221
  19. Ferguson N, Laydon D, Nedjati Gilani G, Imai N, Ainslie K, Baguelin M, et al. Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand. 2020 Mar 16. Available from: http://spiral.imperial.ac.uk/handle/10044/1/77482
  20. Diekmann O, Heesterbeek JA, Metz JA. On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations. J Math Biol. 1990;28(4):365–82. doi:.https://doi.org/10.1007/BF00178324
  21. Virlogeux V, Fang VJ, Wu JT, Ho L-M, Peiris JSM, Leung GM, et al. Brief Report: Incubation Period Duration and Severity of Clinical Disease Following Severe Acute Respiratory Syndrome Coronavirus Infection. Epidemiology. 2015;26(5):666–9. doi:.https://doi.org/10.1097/EDE.0000000000000339
  22. Men K, Wang X, Yihao L, Zhang G, Hu J, Gao Y, et al. Estimate the incubation period of coronavirus 2019 (COVID-19). medRxiv. 2020;24(2):219.
  23. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054–62. Available at: https://linkinghub.elsevier.com/retrieve/pii/S0140673620305663. doi:.https://doi.org/10.1016/S0140-6736(20)30566-3
  24. Ferretti L, Wymant C, Kendall M, Zhao L, Nurtay A, Abeler-Dörner L, et al. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science. 2020;368(6491):eabb6936–9. doi:.https://doi.org/10.1126/science.abb6936
  25. Anastassopoulou C, Russo L, Tsakris A, Siettos C. Data-based analysis, modelling and forecasting of the COVID-19 outbreak. PLoS One. 2020;15(3):e0230405–21. doi:.https://doi.org/10.1371/journal.pone.0230405
  26. Liu Y, Gayle AA, Wilder-Smith A, Rocklöv J. The reproductive number of COVID-19 is higher compared to SARS coronavirus. J Travel Med. 2020;27(2): taaa021. doi:.https://doi.org/10.1093/jtm/taaa021
  27. Liu Z, Bing X, Zhi XZ ; Epidemiology Working Group for NCIP Epidemic Response, Chinese Center for Disease Control and Prevention. [The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China]. Zhonghua Liu Xing Bing Xue Za Zhi. 2020;41(2):145–51. Article in Chinese. doi:10.3760/cma.j.issn.0254-6450.2020.02.003.
  28. Karagiannidis C, Mostert C, Hentschker C, Voshaar T, Malzahn J, Schillinger G, et al. Case characteristics, resource use, and outcomes of 10 021 patients with COVID-19 admitted to 920 German hospitals: an observational study. Lancet Respir Med. 2020;8(9):853–62. doi:.https://doi.org/10.1016/S2213-2600(20)30316-7
  29. Baud D, Qi X, Nielsen-Saines K, Musso D, Pomar L, Favre G. Real estimates of mortality following COVID-19 infection. Lancet Infect Dis. 2020;20(7):773. doi:.https://doi.org/10.1016/S1473-3099(20)30195-X
  30. Mizumoto K, Chowell G. Estimating Risk for Death from Coronavirus Disease, China, January-February 2020. Emerg Infect Dis. 2020;26(6):1251–6. doi:.https://doi.org/10.3201/eid2606.200233
  31. McIntosh K, Hirsch MS, Bloom A. Coronavirus disease 2019 (COVID-19) [Internet]. UpToDate; 2020. Available from: https://www.cmim.org/PDF_covid/Coronavirus_disease2019_COVID-19_UpToDate2.pdf
  32. Corman VM, Landt O, Kaiser M, Molenkamp R, Meijer A, Chu DK, et al. Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Euro Surveill. 2020;25(3):1–8. doi:.https://doi.org/10.2807/1560-7917.ES.2020.25.3.2000045
  33. Hossain A, Reis AC, Rahman S, Salis HM. A massively parallel COVID-19 diagnostic assay for simultaneous testing of 19200 patient samples. Google Docs. 2020. Available from: https://docs.google.com/document/d/1kP2w_uTMSep2UxTCOnUhh1TMCjWvHEY0sUUpkJHPYV4/edit
  34. Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, et al. Estimates of the severity of coronavirus disease 2019: a model-based analysis. Lancet Infect Dis. 2020;20(6):669–77. doi:.https://doi.org/10.1016/S1473-3099(20)30243-7
  35. Hinch R, Probert WJM, Nurtay A, Kendall M, Wymatt C, Hall M, et al. OpenABM-Covid19 - an agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing. medRxiv. 2021 Jan 30:1–23.
  36. Fraser C, Riley S, Anderson RM, Ferguson NM. Factors that make an infectious disease outbreak controllable. Proc Natl Acad Sci USA. 2004;101(16):6146–51. doi:.https://doi.org/10.1073/pnas.0307506101
  37. Lavezzo E, Franchin E, Ciavarella C, Cuomo-Dannenburg G, Barzon L, Del Vecchio C, et al. Suppression of COVID-19 outbreak in the municipality of Vo. Nature. 2020;584(7821):425–9. doi:.https://doi.org/10.1038/s41586-020-2488-1