Data-driven deep learning algorithms for time-varying infection rates of COVID-19 and mitigation measures

TL;DR

This paper introduces an Epidemiology-Informed Neural Network to learn time-varying COVID-19 transmission rates, accounting for mitigation measures and asymptomatic cases, validated with data from multiple countries.

Contribution

It presents a novel neural network approach to estimate dynamic infection rates and asymptomatic proportions during a pandemic, improving modeling accuracy.

Findings

Effective modeling of mitigation measures impacts

Accurate estimation of asymptomatic infectives

Validated across multiple countries

Abstract

Epidemiological models with constant parameters may not capture satisfactory infection patterns in the presence of pharmaceutical and non-pharmaceutical mitigation measures during a pandemic, since infectiousness is a function of time. In this paper, an Epidemiology-Informed Neural Network algorithm is introduced to learn the time-varying transmission rate for the COVID-19 pandemic in the presence of various mitigation scenarios. There are asymptomatic infectives, mostly unreported, and the proposed algorithm learns the proportion of the total infective individuals that are asymptomatic infectives. Using cumulative and daily reported cases of the symptomatic infectives, we simulate the impact of non-pharmaceutical mitigation measures such as early detection of infectives, contact tracing, and social distancing on the basic reproduction number. We demonstrate the effectiveness of vaccination on the transmission of COVID-19. The accuracy of the proposed algorithm is demonstrated using error metrics in the data-driven simulation for COVID-19 data of Italy, South Korea, the United Kingdom, and the United States.