Adaptive Multi-Factor Quantitative Analysis and Prediction Models: Vaccination, Virus Mutation and Social Isolation on COVID-19

Abstract

Different countries have adopted various control measures for the COVID-19 pandemic in different periods, and as the virus continues to mutate, the progression of the pandemic and preventive measures adopted have varied dynamically over time. Thus, quantitative analysis of the dynamic impact of different factors such as vaccination, mutant virus, social isolation, etc., on transmission and predicting pandemic progress has become a difficult task. To overcome the challenges above and enable governments to formulate reasonable countermeasures against the ongoing COVID-19 pandemic, we integrate several mathematical methods and propose a new adaptive multifactorial and geographically diverse epidemiological model based on a modified version of the classical susceptible-exposed-infectious-recovered (SEIR) model. Based on public datasets, a multi-center study was carried out considering 21 regions. First, a retrospective study was conducted to predict the number of infections over the next 30 days in 13 representative pandemic areas worldwide with an accuracy of 87.53%, confirming the robustness of the proposed model. Second, the impact of three scenarios on COVID-19 was quantified based on the scalability of the model: two different vaccination regimens were analyzed, and it was found that the number of infections would progressively decrease over time after vaccination; variant virus caused a 301.55% increase in infections in the United Kingdom; and 3-tier social lockdown in the United Kingdom reduced the infections by 47.01%. Third, we made short-term prospective predictions for the next 15 and 30 days for six countries with severe COVID-19 transmission and the predicted trend is accurate. This study is expected to inform public health responses. Code and data are publicly available at https://github.com/yuanyuanpei7/covid-19.