Control Intervention Strategies for Within-Host, Between-Host and their Efficacy in the Treatment, Spread of COVID-19 : A Multi Scale Modeling Approach

TL;DR

This study develops a multi-scale model linking within-host and between-host COVID-19 dynamics to evaluate intervention strategies, showing combined drug and social measures effectively reduce virus spread and mortality.

Contribution

It introduces a novel multi-scale model integrating within-host and population-level COVID-19 dynamics to assess intervention efficacy.

Findings

Combined drug and social interventions significantly reduce virus transmission.

Immunotherapies and hygiene measures lower the basic reproduction number.

Targeted strategies outperform single interventions in controlling COVID-19.

Abstract

The COVID-19 pandemic has resulted in more than 14.5 million infections and 6,04,917 deaths in 212 countries over the last few months. Different drug intervention acting at multiple stages of pathogenesis of COVID-19 can substantially reduce the infection induced,thereby decreasing the mortality. Also population level control strategies can reduce the spread of the COVID-19 substantially. Motivated by these observations, in this work we propose and study a multi scale model linking both within-host and between-host dynamics of COVID-19. Initially the natural history dealing with the disease dynamics is studied. Later, comparative effectiveness is performed to understand the efficacy of both the within-host and population level interventions. Findings of this study suggest that a combined strategy involving treatment with drugs such as Arbidol, remdesivir, Lopinavir/Ritonavir that inhibits viral replication and immunotherapies like monoclonal antibodies, along with environmental hygiene and generalized social distancing proved to be the best and optimal in reducing the basic reproduction number and environmental spread of the virus at the population level.