COVID-19 Intervention Scenarios for a Long-term Disease Management

Document Type : Original Article


Department of Geoinformatics – Z_GIS, University of Salzburg, Salzburg, Austria


The first outbreak of coronavirus disease 2019 (COVID-19) was successfully restrained in many countries around the world by means of a severe lockdown. Now, we are entering the second phase of the pandemics in which the spread of the virus needs to be contained within the limits that national health systems can cope with. This second phase of the epidemics is expected to last until a vaccination is available or herd immunity is reached. Long-term management strategies thus need to be developed.
In this paper we present a new agent-based simulation model “COVID-19 ABM” with which we simulate 4 alternative scenarios for the second “new normality” phase that can help decision-makers to take adequate control and intervention measures.
The scenarios resulted in distinctly different outcomes. A continued lockdown could regionally eradicate the virus within a few months, whereas a relaxation back to 80% of former activity-levels was followed by a second outbreak. Contact-tracing as well as adaptive response strategies could keep COVID-19 within limits.
The main insights are that low-level voluntary use of tracing apps shows no relevant effects on containing the virus, whereas medium or high-level tracing allows maintaining a considerably higher level of social activity. Adaptive control strategies help in finding the level of least restrictions. A regional approach to adaptive management can further help in fine-tuning the response to regional dynamics and thus minimise negative economic effects.


Main Subjects

  1. Ng Y, Li Z, Chua YX, et al. Evaluation of the effectiveness of surveillance and containment measures for the first 100 patients with COVID-19 in Singapore - January 2-February 29, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(11):307-311. doi:10.15585/mmwr.mm6911e1
  2. Raskar R, Schunemann I, Barbar R, et al. Apps gone rogue: maintaining personal privacy in an epidemic. arXiv preprint arXiv:2003.08567. 2020.
  3. Ferguson N, Laydon D, Nedjati Gilani G, et al. Report 9: Impact of Non-Pharmaceutical Interventions (NPIs) to Reduce COVID19 Mortality and Healthcare Demand. London: Imperial College London; 2020. doi:10.25561/77482
  4. Ferretti L, Wymant C, Kendall M, et al. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science. 2020;368(6491). doi:10.1126/science.abb6936
  5. Roche B, Duboz R. Individual-Based Models for Public Health. In: Handbook of Statistics. Vol 37. Elsevier; 2017:347-365. doi:10.1016/
  6. Gros C, Valenti R, Valenti K, Gros D. Strategies for controlling the medical and socio-economic costs of the Corona pandemic. arXiv preprint arXiv:2004.00493. 2020.
  7. Bauer AL, Beauchemin CA, Perelson AS. Agent-based modeling of host-pathogen systems: the successes and challenges. Inf Sci (N Y). 2009;179(10):1379-1389. doi:10.1016/j.ins.2008.11.012
  8. Sanche S, Lin YT, Xu C, Romero-Severson E, Hengartner NW, Ke R. The novel coronavirus, 2019-nCoV, is highly contagious and more infectious than initially estimated. arXiv preprint arXiv:2002.03268. 2020.
  9. Cecconi F, Barazzetti A. Agent-Based Simulation Model Applied to Social Behaviors Determining the Dynamics of Pandemics. Msida, Malta: United Campus of Malta; 2020.
  10. Chang SL, Harding N, Zachreson C, Cliff OM, Prokopenko M. Modelling transmission and control of the COVID-19 pandemic in Australia. arXiv preprint arXiv:2003.10218. 2020.
  11. Cliff OM, Harding N, Piraveenan M, Erten EY, Gambhir M, Prokopenko M. Investigating spatiotemporal dynamics and synchrony of influenza epidemics in Australia: an agent-based modelling approach. Simul Model Pract Theory. 2018;87:412-431. doi:10.1016/j.simpat.2018.07.005
  12. Huang Y. Modeling the Severe Acute Respiratory Syndrome (SARS) outbreak in Beijing - an agent-based approach. Paper presented at: Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application. Washington, DC, USA: COM Geo; 2010. doi:10.1145/1823854.1823895
  13. Getz WM, Carlson C, Dougherty E, Porco Francis TC 1st, Salter R. An agent-based model of school closing in under-vacccinated communities during measles outbreaks. Agent Dir Simul Symp. 2016;2016:10.
  14. Wallentin G, Loidl M. Agent-based bicycle traffic model for Salzburg city. GI_Forum J Geogr Inf Sci. 2015;2015(1):558-566. doi:10.1553/giscience2015s558
  15. Epidemiology SIR (ABM vs EBM) [computer program]. 2016.
  16. Corona-Virus (SARS-CoV-2). Land Salzburg website. Accessed  April 16, 2020. Published 2020.
  17. Taillandier P, Gaudou B, Grignard A, et al. Building, composing and experimenting complex spatial models with the GAMA platform. GeoInformatica. 2019;23(2):299-322. doi:10.1007/s10707-018-00339-6
  18. COVID-19 ABM [computer program]. Version 1.0.1. CoMSES Computational Model Library; 2020.
  19. Lorscheid I, Heine BO, Meyer M. Opening the ‘black box’ of simulations: increased transparency and effective communication through the systematic design of experiments. Comput Math Organ Theory. 2012;18(1):22-62. doi:10.1007/s10588-011-9097-3
  20. Grimm V, Berger U, DeAngelis DL, Polhill JG, Giske J, Railsback SF. The ODD protocol: a review and first update. Ecol Modell. 2010;221(23):2760-2768. doi:10.1016/j.ecolmodel.2010.08.019
  21. Loidl M. A Very High Resolution Bicycle Flow Model. Barcelona: International Cycling Safety Conference; 2018.
  22. European Centre for Disease Prevention and Control (ECDC). Contact tracing: Public health management of persons, including healthcare workers, having had contact with COVID-19 cases in the European Union - second update. Stockholm: ECDC; 2020.
  23. Kupferschmidt K. The lockdowns worked-but what comes next? Science. 2020;368(6488):218-219. doi:10.1126/science.368.6488.218
  24. He X, Lau EHY, Wu P, et al. Temporal dynamics in viral shedding and transmissibility of COVID-19. Nat Med. 2020;26(5):672-675. doi:10.1038/s41591-020-0869-5
  25. Randolph HE, Barreiro LB. Herd immunity: understanding COVID-19. Immunity. 2020;52(5):737-741. doi:10.1016/j.immuni.2020.04.012
  26. Tian H, Liu Y, Li Y, et al. An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science. 2020;368(6491):638-642. doi:10.1126/science.abb6105
  27. 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). doi:10.1093/jtm/taaa021
  28. Mossong J, Hens N, Jit M, et al. Social contacts and mixing patterns relevant to the spread of infectious diseases. PLoS Med. 2008;5(3):e74. doi:10.1371/journal.pmed.0050074
  29. Ooi PL, Lim S, Chew SK. Use of quarantine in the control of SARS in Singapore. Am J Infect Control. 2005;33(5):252-257. doi:10.1016/j.ajic.2004.08.007
  30. Regan HM, Ben-Haim Y, Langford B, et al. Robust decision‐making under severe uncertainty for conservation management. Ecol Appl. 2005;15(4):1471-1477. doi:10.1890/03-5419
  31. Wynants L, Van Calster B, Collins GS, et al. Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. BMJ. 2020;369:m1328. doi:10.1136/bmj.m1328
Volume 9, Issue 12
December 2020
Pages 508-516
  • Receive Date: 19 April 2020
  • Revise Date: 11 July 2020
  • Accept Date: 11 July 2020
  • First Publish Date: 01 December 2020