From Policy to Prediction: Forecasting COVID-19 Dynamics Under Imperfect Vaccination

Abstract

Understanding the joint impact of vaccination and non-pharmaceutical interventions on COVID-19 development is important for making public health decisions that control the pandemic. Recently, we created a method in forecasting the daily number of confirmed cases of infectious diseases by combining a mechanistic ordinary differential equation (ODE) model for infectious classes and a generalized boosting machine learning model (GBM) for predicting how public health policies and mobility data affect the transmission rate in the ODE model (Wang et al. in Bull Math Biol 84:57, 2022). In this paper, we extend the method to the post-vaccination period, accordingly obtain a retrospective forecast of COVID-19 daily confirmed cases in the US, and identify the relative influence of the policies used as the predictor variables. In particular, our ODE model contains both partially and fully vaccinated compartments and accounts for the breakthrough cases, that is, vaccinated individuals can still get infected. Our results indicate that the inclusion of data on non-pharmaceutical interventions can significantly improve the accuracy of the predictions. With the use of policy data, the model predicts the number of daily infected cases up to 35 days in the future, with an average mean absolute percentage error of 20.15%, which is further improved to 14.88% if combined with human mobility data. Moreover, the most influential predictor variables are the policies of restrictions on gatherings, testing B Hao Wang

A novel hybrid supervised and unsupervised hierarchical ensemble for COVID-19 cases and mortality prediction

“…The model described in 26 was able to predict the number of daily infected cases up to 35 days in the future, with an average mean absolute percentage error of 20.15% with further improvement to 14.88% if combined with human mobility data. In our study, we used the MSE metric instead, so the results cannot be compared directly.…”

“…Wang et al 26 determined the policies of restrictions on gatherings, testing and school closing as the most influential predictor variables. In this paper, the most influential predictor variables are virus pressure, social distancing total grade, total population, area, and retail and recreation mobility percent change.…”

“…The forecasting of the COVID-19 dynamics under imperfect vaccination was studied by Wang et al 26 by combining a mechanistic ordinary differential equation model and a generalized boosting machine learning model. The ordinary differential equation model was utilized for infectious class prediction.…”

Analysis of a COVID-19 Epidemic Model with Seasonality

“…From (Wang et al 2022), the COVID-19 virus is infectious in the incubation period. Based on the above population classification, the detailed transmission diagram is given in Fig.…”

“…There are many mathematical models of COVID-19 with the compartmental structure describing the size of populations within different classes (see Xue et al 2020;Yan et al 2020;Liu et al 2021;Musa et al 2022;Wang et al 2022;Zou et al 2022;Zhou et al 2022;Zhang and Li 2021;Tang et al 2020a, b;Yang et al 2020;Zhang et al 2020;Munayco et al 2020;Liu et al 2020;Lauer et al 2020;Kuniya 2020;Hu et al 2020;Gatto et al 2020). Xue et al (2020) presented a datadriven network model to capture the contact heterogeneity between individuals.…”