ArticleBMC public health2021
Investigating the effectiveness of re-opening policies before vaccination during a pandemic: SD modelling research based on COVID-19 in Wuhan.
Article in BMC public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effective factors in people's preventive behaviors during covid-19 pandemic: a systematic review and meta-synthesis.BMC public health · 2022Pooled it
- Addressing Hospital Overwhelm During the COVID-19 Pandemic by Using a Primary Health Care-Based Integrated Health System: Modeling Study.JMIR medical informatics · 2024Article
- Determinants of COVID-19 Infection Among Employees of an Italian Financial Institution.La Medicina del lavoro · 2024Article
- Using simulation modelling and systems science to help contain COVID-19: A systematic review.Systems research and behavioral science · 2022Article
- Optimizing national border reopening policies in the COVID-19 pandemic: A modeling study.Frontiers in public health · 2022Article
- Which Matters More in Fighting COVID-19-Government Policy or Community Participation?Frontiers in public health · 2022Article
- Policy choices for Shanghai responding to challenges of Omicron.Frontiers in public health · 2022Article
- The association between first and second wave COVID-19 mortality in Italy.BMC public health · 2021Article
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Authors and funding
9 authors.
Funding
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. KEYORDS: COVID-19; Risk of re-opening; Effectiveness of re-opening policies; IPC measures; SD modelling.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.