Calibrated Intervention and Containment of the COVID-19 Pandemic

TL;DR

This paper develops a calibrated epidemiological model focusing on pre-symptomatic transmission of COVID-19, providing explicit expressions for latent populations and analyzing the combined effects of control measures on reducing the reproduction number.

Contribution

It introduces a new model centered on transmission around symptom onset, calibrated with early outbreak data, to evaluate intervention strategies' effectiveness.

Findings

Combined interventions multiply their effect on R_0 reduction.

Explicit formulas for latent and pre-symptomatic populations during exponential growth.

Model aligns with early COVID-19 wave data across regions.

Abstract

Within a short period of time, COVID-19 grew into a world-wide pandemic. Transmission by pre-symptomatic and asymptomatic viral carriers rendered intervention and containment of the disease extremely challenging. Based on reported infection case studies, we construct an epidemiological model that focuses on transmission around the symptom onset. The model is calibrated against incubation period and pairwise transmission statistics during the initial outbreaks of the pandemic outside Wuhan with minimal non-pharmaceutical interventions. Mathematical treatment of the model yields explicit expressions for the size of latent and pre-symptomatic subpopulations during the exponential growth phase, with the local epidemic growth rate as input. We then explore reduction of the basic reproduction number R_0 through specific disease control measures such as contact tracing, testing, social distancing, wearing masks and sheltering in place. When these measures are implemented in combination, their effects on R_0 multiply. We also compare our model behaviour to the first wave of the COVID-19 spreading in various affected regions and highlight generic and less generic features of the pandemic development.