A comparison of SIR and SEIR epidemiological models for COVID-19 transmission in India with emphasis on non-pharmaceutical intervention
Abstract
The models on disease transmission are useful in planning decisions on pandemic, resource allocation and implementation of non-pharmaceutical intervention. The SEIR differs from SIR model with an additional exposure period due to the incubation period of COVID-19 during which individuals are not yet infectious. I have applied Bayesian approach with Monte Carlo Markov Chain (MCMC) sampling on SEIR and SIR epidemiological models using python code PymC3 to study the dynamics of COVID-19 pandemic in India, assess the effectiveness of non-pharmaceutical measures from March to October 2020, and generate predictions on daily new and cumulative infected cases. The accuracy of prediction was computed by symmetric mean absolute prediction error (SMAPE) and mean squared relative prediction error (MSRPE
Keywords: COVID-19
Biography:
Manisha Mandal is currently working as a Professor in the MGM Medical College, Kishanganj, India
Speaker Publications:
1. “Indigenous Probiotic Lactobacillus Isolates Presenting Antibiotic like Activity against Human Pathogenic Bacteria V (5(2):31.
11th International Conference on Emerging Infectious Diseases; Webinar- November 23-24, 2020.
Abstract Citation:
Manisha Mandal, A comparison of SIR and SEIR epidemiological models for COVID-19 transmission in India with emphasis on non-pharmaceutical intervention, Emerging Diseases 2020, 11th International Conference on Emerging Infectious Diseases; Webinar- November 23-24, 2020 (https://emerging-diseases.infectiousconferences.com/abstract/2020/a-comparison-of-sir-and-seir-epidemiological-models-for-covid-19-transmission-in-india-with-emphasis-on-non-pharmaceutical-intervention)
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