Open AccessDetection of transmission change points during unlock-3 and unlock-4 measures controlling COVID-19 in India
Author(s) -
Manisha Mandal,
Shyamapada Mandal
Publication year - 2021
Publication title -
journal of drug delivery and therapeutics
Language(s) - English
Resource type - Journals
ISSN - 2250-1177
DOI - 10.22270/jddt.v11i2.4600
Subject(s) - transmission (telecommunications) , covid-19 , epidemic model , markov chain monte carlo , statistics , basic reproduction number , bayesian probability , computer science , population , econometrics , mathematics , medicine , environmental health , telecommunications , disease , pathology , infectious disease (medical specialty)
Objective: To evaluate the efficiency of unlock-3 and unlock-4 measure related to COVID-19 transmission change points in India, for projecting the infected population, to help in prospective planning of suitable measures related to future interventions and lifting of restrictions so that the economic settings are not damaged beyond repair.
Methods: The SIR model and Bayesian approach combined with Monte Carlo Markov algorithms were applied on the Indian COVID-19 daily new infected cases from 1 August 2020 to 30 September 2020. The effectiveness of unlock-3 and unlock-4 measure were quantified as the change in both effective transmission rates and the basic reproduction number (R0).
Results: The study demonstrated that the COVID-19 epidemic declined after implementing unlock-4 measure and the identified change-points were consistent with the timelines of announced unlock-3 and unlock-4 measure, on 1 August 2020 and 1 September 2020, respectively.
Conclusions: Changes in the transmission rates with 100% reduction as well as the R0 attaining 1 during unlock-3 and unlock-4 indicated that the measures adopted to control and mitigate the COVID-19 epidemic in India were effective in flattening and receding the epidemic curve.
Keywords: COVID-19 in India, epidemiological parameters, unlock-3 and unlock-4, SIR model, Bayesian inference, Monte Carlo Markov sampling