Modelling COVID-19 Pandemic with Asymptomatic Patients for the State Andhra Pradesh, India

Abstract

Andhra Pradesh is one of most covid affected state during first and second waves in India. This state contains 13 districts. This paper proposes Susceptible, Asymptomatic, Infected and Recovered – Regression and Grid Search (SAIRRGS) model for analyzing COVID-19 pandemic with asymptomatic patients for the state Andhra Pradesh during second wave from 1-2-2021 to 30-09-2021. SAIR-RGS initially collects daily covid cases information from Department of Health, medical and family welfare, AP and estimates the model parameters using regression and grid search methods. To calculate recovery rate (γ) the proposed model uses least square method between daily active cases and recoveries. This method estimates remaining two parameters i.e., contact rate (β) and symptomatic rate (δ) using grid search method in two phases. After that the proposed method estimates the average total number of asymptomatic cases during second wave for all 13 districts of AP. Also, the proposed SAIR-RGS model calculates infectious period and basic reproduction number (R₀) for all districts. Finally proposed model predicts the chances of third wave in each district.