Estimating Hidden Asymptomatics, Herd Immunity Threshold and Lockdown Effects using a COVID-19 Specific Model

TL;DR

This paper introduces a COVID-19 model that accounts for hidden asymptomatic cases, analyzes lockdown effects, and estimates herd immunity thresholds and asymptomatic patient numbers using real-world data.

Contribution

It presents a novel quantitative model for COVID-19 that includes asymptomatic patients and provides methods for parameter estimation and herd immunity assessment.

Findings

Herd immunity achieved when symptomatic patients are 4-6% of population

Infection spread slows down as total infected reach 50-56%

Method for estimating hidden asymptomatic cases is proposed

Abstract

A quantitative COVID-19 model that incorporates hidden asymptomatic patients is developed, and an analytic solution in parametric form is given. The model incorporates the impact of lockdown and resulting spatial migration of population due to announcement of lockdown. A method is presented for estimating the model parameters from real-world data. It is shown that increase of infections slows down and herd immunity is achieved when symptomatic patients are 4-6\% of the population for the European countries we studied, when the total infected fraction is between 50-56 \%. Finally, a method for estimating the number of asymptomatic patients, who have been the key hidden link in the spread of the infections, is presented.