ArticleChaos, solitons, and fractals2020
Simulation of coronavirus disease 2019 (COVID-19) scenarios with possibility of reinfection.
Article in Chaos, solitons, and fractals, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Investigation of epidemiological and clinical characteristics of people infected with SARS-CoV-2 during the second pandemic of COVID-19 in Chengdu, China.Frontiers in public health · 2024Article
- Bayesian model selection for COVID-19 pandemic state estimation using extended Kalman filters: Case study for Saudi Arabia.PLOS global public health · 2024Article
- Evaluation of possible COVID-19 reinfection in children: A multicenter clinical study.Archives de pediatrie : organe officiel de la Societe francaise de pediatrie · 2023Article
- Data-driven prediction of COVID-19 cases in Germany for decision making.BMC medical research methodology · 2022Article
- The Effect of Varying Interval Definitions on the Prevalence of SARS-CoV-2 Reinfections: A Retrospective Cross-Sectional Cohort Study.Diagnostics (Basel, Switzerland) · 2022Article
- COVID-19: What if immunity wanes?The Canadian journal of economics. Revue canadienne d'economique · 2022Article
- Estimation of COVID-19 recovery and decease periods in Canada using delay model.Scientific reports · 2021Article
- Extended Kalman filter based on stochastic epidemiological model for COVID-19 modelling.Computers in biology and medicine · 2021Article
- Impact of vaccine supplies and delays on optimal control of the COVID-19 pandemic: mapping interventions for the Philippines.Infectious diseases of poverty · 2021Article
- COVID-19 reinfection in a healthcare worker after exposure with high dose of virus: A case report.Clinical case reports · 2021Article
- Using Unstated Cases to Correct for COVID-19 Pandemic Outbreak and Its Impact on Easing the Intervention for Qatar.Biology · 2021Article
- SARS-CoV-2 Reinfection among Healthcare Workers in Mexico: Case Report and Literature Review.Medicina (Kaunas, Lithuania) · 2021Review
- 8806 Russian patients demonstrate T cell count as better marker of COVID-19 clinical course severity than SARS-CoV-2 viral load.Scientific reports · 2021Article
- Antibody tests: They are more important than we thought.Journal of mathematical economics · 2021Article
- COVID-19 reinfection: the role of natural immunity, vaccines, and variants.Journal of community hospital internal medicine perspectives · 2021Review
- Beyond the new normal: Assessing the feasibility of vaccine-based suppression of SARS-CoV-2.PloS one · 2021Article
- Evaluation of Reinfection in COVID-19 Patients in the World: A Narrative Review.Medical journal of the Islamic Republic of Iran · 2021Review
- Towards Dynamic lockdown strategies controlling pandemic spread under healthcare resource budget.Applied network science · 2021Article
- A Peek into the Inner Workings of Pandemic Prediction Models.Missouri medicineArticle
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epidemiological models of COVID-19 transmission assume that recovered individuals have a fully protected immunity. To date, there is no definite answer about whether people who recover from COVID-19 can be reinfected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In the absence of a clear answer about the risk of reinfection, it is instructive to consider the possible scenarios. To study the epidemiological dynamics with the possibility of reinfection, I use a Susceptible-Exposed-Infectious-Resistant-Susceptible model with the time-varying transmission rate. I consider three different ways of modeling reinfection. The crucial feature of this study is that I explore both the difference between the reinfection and no-reinfection scenarios and how the mitigation measures affect this difference. The principal results are the following. First, the dynamics of the reinfection and no-reinfection scenarios are indistinguishable before the infection peak. Second, the mitigation measures delay not only the infection peak, but also the moment when the difference between the reinfection and no-reinfection scenarios becomes prominent. These results are robust to various modeling assumptions.
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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.