Abstract
BackgroundLockdown policies were widely adopted during the coronavirus disease 2019 (COVID-19) pandemic to control the spread of the virus before vaccines became available. These policies had significant economic impacts and caused social disruptions. Early re-opening is preferable, but it introduces the risk of a resurgence of the epidemic. Although the World Health Organization has outlined criteria for re-opening, decisions on re-opening are mainly based on epidemiologic criteria. To date, the effectiveness of re-opening policies remains unclear.MethodsA system dynamics COVID-19 model, SEIHR(Q), was constructed by integrating infection prevention and control measures implemented in Wuhan into the classic SEIR epidemiological model and was validated with real-world data. The input data were obtained from official websites and the published literature.ResultsThe simulation results showed that track-and-trace measures had significant effects on the level of risk associated with re-opening. In the case of Wuhan, where comprehensive contact tracing was implemented, there would have been almost no risk associated with re-opening. With partial contact tracing, re-opening would have led to a minor second wave of the epidemic. However, if only limited contact tracing had been implemented, a more severe second outbreak of the epidemic would have occurred, overwhelming the available medical resources. If the ability to implement a track-trace-quarantine policy is fixed, the epidemiological criteria need to be further taken into account. The model simulation revealed different levels of risk associated with re-opening under different levels of track-and-trace ability and various epidemiological criteria. A matrix was developed to evaluate the effectiveness of the re-opening policies.ConclusionsThe SEIHR(Q) model designed in this study can quantify the impact of various re-opening policies on the spread of COVID-19. Integrating epidemiologic criteria, the contact tracing policy, and medical resources, the model simulation predicts whether the re-opening policy is likely to lead to a further outbreak of the epidemic and provides evidence-based support for decisions regarding safe re-opening during an ongoing epidemic.KeyordsCOVID-19; Risk of re-opening; Effectiveness of re-opening policies; IPC measures; SD modelling.
Highlights
Lockdown policies were widely adopted during the coronavirus disease 2019 (COVID-19) pandemic to control the spread of the virus before vaccines became available
SD modelling Methods We developed a model of COVID-19 transmission in Wuhan using system dynamics (SD)
Based on the severity of the second outbreak, we classified the risk of reopening into the following categories: (1) no-risk/safe, meaning that there was almost no observed increase in the number of new cases after re-opening; (2) low risk, which corresponds to a slight increase in new confirmed cases, but a lower peak in the second wave than in the first; (3) high risk, which corresponds to a sharp increase in new infected cases, generating a higher peak in the second wave than in the first; and (4) unacceptable risk, in which case would the second wave have a higher peak value but the hospitalized population would exceed the hospital capacity, which implies a collapse of the medical system if new resources were not supplied
Summary
Lockdown policies were widely adopted during the coronavirus disease 2019 (COVID-19) pandemic to control the spread of the virus before vaccines became available. These policies had significant economic impacts and caused social disruptions. In the absence of a vaccine, non-pharmaceutical infection prevention and control (IPC) measures can be reliable “weapons” in the fight against the virus. The highly contagious nature of COVID19 forced a number of jurisdictions around the world to apply the strictest form of movement restriction, complete lockdown. Lai et al [4] found that one-, two- or three-week delays in implementing a lockdown in Wuhan, China, would have led to a 3fold, 7-fold or 18-fold increase in the number of cases, respectively
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