Open AccessEstimating the herd immunity threshold by accounting for the hidden asymptomatics using a COVID-19 specific model
Author(s) -
Shaurya Kaushal,
Abhineet Singh Rajput,
S. Bhattacharya,
M. Vidyasagar,
Aloke Kumar,
Meher K. Prakash,
Santosh Ansumali
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0242132
Subject(s) - herd immunity , population , covid-19 , asymptomatic , statistics , epidemiology , medicine , mathematics , environmental health , disease , infectious disease (medical specialty)
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 lock-down and resulting spatial migration of population due to announcement of lock-down. A method is presented for estimating the model parameters from real-world data, and it is shown that the various phases in the observed epidemiological data are captured well. It is shown that increase of infections slows down and herd immunity is achieved when active symptomatic patients are 10-25% of the population for the four countries we studied. 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.