Semi-supervised Neural Networks solve an inverse problem for modeling Covid-19 spread

TL;DR

This paper introduces a semi-supervised neural network approach to model COVID-19 spread, incorporating real data to estimate key epidemiological parameters and account for passive populations.

Contribution

It presents a novel semi-supervised neural network method combining unsupervised differential equation solutions with supervised inverse problem solving for COVID-19 modeling.

Findings

Estimated the basic reproduction number for different countries.

Determined the evolution of the passive population during the pandemic.

Provided a data-driven approach to model COVID-19 dynamics.

Abstract

Studying the dynamics of COVID-19 is of paramount importance to understanding the efficiency of restrictive measures and develop strategies to defend against upcoming contagion waves. In this work, we study the spread of COVID-19 using a semi-supervised neural network and assuming a passive part of the population remains isolated from the virus dynamics. We start with an unsupervised neural network that learns solutions of differential equations for different modeling parameters and initial conditions. A supervised method then solves the inverse problem by estimating the optimal conditions that generate functions to fit the data for those infected by, recovered from, and deceased due to COVID-19. This semi-supervised approach incorporates real data to determine the evolution of the spread, the passive population, and the basic reproduction number for different countries.