A COVID-19 Epidemiological Model for Community and Policy Maker Use

TL;DR

This paper presents a COVID-19 epidemiological model designed for use by public health officials and policymakers, highlighting the importance of reducing R0 below 1 through effective social distancing to control the pandemic.

Contribution

It introduces a new epidemiological model that distinguishes four disease stages and provides insights into effective strategies, challenging common misconceptions like herd immunity and flattening the curve.

Findings

Herd immunity and flattening the curve are misleading for COVID-19.

Reducing R0 below 1 is essential for effective control.

Social distancing must be over 65-75% effective to succeed.

Abstract

An epidemiological model for COVID-19 was developed and implemented in MATLAB/GNU Octave for use by public health practitioners, policy makers and the general public. The model distinguishes four stages in the disease: infected, sick, seriously sick, and better. The model was preliminarily parameterized based on observations of the spread of the disease. The model is consistent with a mortality rate of 1.5 %. Preliminary simulations with the model indicate that concepts such as "herd immunity" and "flattening the curve" are highly misleading in the context of this virus. Public policies based on these concepts are inadequate to protect the population. Only reducing the R0 of the virus below 1 is an effective strategy for maintaining the death burden of COVID-19 within the normal range of seasonal flu. As R0 values estimated with the model range from 2.82 worldwide outside of China and 3.83 in the Western world in late February - early March 2020, this means social distancing with effectiveness greater than 65 % (worldwide) or 75 % (Western world) are needed to combat the virus successfully.