How control and relaxation interventions and virus mutations influence the resurgence of COVID-19
Cao, L.; Liu, Q.
Show abstract
After a year of the unprecedented COVID-19 pandemic in 2020, the world has been overwhelmed by COVID-19 resurgences and virus mutations up to today. Here we develop a dynamic intervention, vaccination and mutation-driven epidemiological model with sequential interventions influencing epidemiological compartments and their state transition. We quantify epidemiological differences between waves under fatal viral mutations, the impacts of control or relaxation interventions and fatal virus mutations on resurgence under vaccinated or unvaccinated conditions, and estimate potential trends under varying interventions and mutations. Comprehensive analyses - between waves, with or without vaccinations, across representative countries with distinct ethnic and cultural backgrounds, what-if scenario simulations on second waves, and future 30-day trend - in two COVID-19 waves in Germany, France, Italy, Israel and Japan over 2020 and 2021 obtain quantitative empirical indication of the influence of strong vs. weak interventions, various combinations of control vs. relaxation strategies, and different transmissibility levels of coronavirus mutants on the behaviors and patterns of different waves and resurgences and future infection trends. The analyses quantify that (1) virus mutations, intervention fatigue, early relaxations, and lagging interventions, etc. may be common reasons for the resurgences observed in many countries; (2) timely strong interventions such as full lockdown will contain resurgence; (3) some resurgences relating to fatal mutants could have been better contained by either carrying forward the effective interventions from their early waves or implementing better controls and timing; (4) insufficient evidence is found on distinguishing the infection between unvaccinated and vaccinated countries while substantial vaccinations ensure much low mortality rate and high recovery rate; (5) resurgences with substantial vaccination have a much lower mortality rate and a higher recovery rate than those without vaccination; and (6) in the absence of sufficient vaccination, herd immunity and effective antiviral pharmaceutical treatments and with more infectious mutations, the widespread early or fast relaxation of interventions including public activity restrictions likely result in a COVID-19 resurgence. We also find the severity, number and timing of control and relaxation interventions determines a protection-deconfinement tradeoff, which can be used to evaluate the containment effect and the opportunity of resurgence and reopening under vaccination and fatal mutations.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Community structured model for vaccine strategies to control COVID19 spread: a mathematical study 98%
- Is increased mortality by multiple exposures to COVID-19 an overseen factor when aiming for herd immunity? 97%
- Modeling the effect of vaccination strategies in an Excel spreadsheet: The rate of vaccination, and not only the vaccination coverage, is a determinant for containing COVID-19 in urban areas 97%
Similar papers in this journal
- State-specific Projection of COVID-19 Infection in the United States and Evaluation of Three Major Control Measures 97%
- Strategies for COVID-19 vaccination under a shortage scenario: a geo-stochastic modelling approach 97%
- Application and Significance of SIRVB Model in Analyzing COVID-19 Dynamics 96%
Similar papers in this journal
Similar papers in this journal
- An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City 97%
- Estimating behavioural relaxation induced by COVID-19 vaccines in the first months of their rollout 96%
- The importance of non-pharmaceutical interventions during the COVID-19 vaccine rollout 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.