Data-Driven Deep-Learning Algorithm for Asymptomatic COVID-19 Model with Varying Mitigation Measures and Transmission Rate

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.

Multi-variant COVID-19 model with heterogeneous transmission rates using deep neural networks

“…The model parameters are summarized in Table 1. Time-varying transmission rates have been shown to efficiently model the spread of COVID-19 [4,11]. Next, will discuss the form of the time-varying transmission rates for each variant.…”

“…We present a data-driven deep learning algorithm for a model consisting of time-varying transmission rates for each active variant. Using infected daily cases data, we learn the form of the time-varying transmission rates, to reveal a timeline of the impact of mitigation measures on the transmission of COVID-19 [4,5]. It can also be demonstrated that this algorithm shows improvement on short-term forecasting when combined with a recurrent neural network and an adaptive neuro-fuzzy inference system.…”

Learning differential equations from data

“…Data-driven approaches including neural networks and nonlinear regression have been applied to dynamical systems [7,9,8]. Data-driven approaches are not replacing good intuition and reasoning but instead are unlocking new possibilities [1]. In [12], the authors used Gaussian processes to discover differential equations from data.…”

“…The equilibrium solution of ( 2) is (u e , v e ) as shown in Figure (1). Where the nullclines of the FHN equations are a line and a cubic that intersect in a single rest point.…”

“…Where the nullclines of the FHN equations are a line and a cubic that intersect in a single rest point. We consider the following initial conditions u(x, 0) and v(x, 0) for system (1) given as follows:…”