COVID-19 outbreak in India: an SEIR model-based analysis

Abstract

We present a modelling and analysis of the COVID-19 outbreak in India with an emphasis on the socio-economic composition, based on the progress of the pandemic (during its first phase-from March to August 2020) in 11 federal states where the outbreak is the largest in terms of the total number of infectives. Our model is based on the susceptible-exposedinfectives-removed (SEIR) model, including an asymptomatic transmission rate, time-dependent incubation period and time-dependent transmission rate. We carry out the analysis with the available disease data up to the end of August 2020, with a projection of 42 days into the months of September and October 2020, based on the past data. Overall, we have presented a projection up to 351 days (till February 2021) for India and we have found that our model is able to predict correctly the first phase of the pandemic in India with correct projections of the peak of the pandemic as well as daily new infections. We also find the existence of a critical day, signifying a sudden shift in the transmission pattern of the disease, with interesting relation of the behaviour of the pandemic with demographic and socio-economic parameters. Towards the end, we have modelled the available data with the help of logistic equations and compare this with our model. The results of this work can be used as a future guide to follow in case of similar pandemics in developing countries.

Gauging the Impact of Artificial Intelligence and Mathematical Modeling in Response to the COVID-19 Pandemic: A Systematic Review

“…Furthermore, the mathematical models of [51] assume a homogenously mixing population, which is quite vague as it is implausible that all individuals have an equal probability of getting in contact with other individuals. Also, the model of [55], which has been validated for a large population, might fit well for cities. Still, the deduction of results through different equations will lead to unrealistic results for villages.…”

An Analytical Framework for Understanding Infection Progression Under Social Mitigation Measures

“…The model is a generalization of the compartmental SEIR model -socalled SPEIRD [57,58] -that accounts for the influence of social measures by mathematically representing them as an effect that removes susceptibles from the transmission process (and places them in a newly introduced Protected compartment). A similar approach to introduce the effects of distancing in compartmental models can be found in [32][33][34][35][36], and is in certain aspects advantageous over simply varying transmission coefficients in time, implemented in many models [4,8,22,[37][38][39]. SPEIRD also introduces additional compartments that directly correspond to epidemiological observables: the number of detected cases and the number of fatalities.…”

“…In particular, the attempts to combat the disease led to the introduction of social distancing measures on the scales hardly imaginable before [24]. While it is evident that they have a serious impact on the epidemiological dynamic, their effects are not yet well understood [25][26][27], despite a lot of undergoing effort to incorporate the influence of these measures into the existing epidemiological models [4,8,22,[28][29][30][31][32][33][34][35][36][37][38][39]. Common for all these extensions of epidemiological models (both those related to the effects of mitigation measures and to the inclusion of other relevant factors), is rely-…”

Petri nets in epidemiology

“…Example 16. Now we study another SEIR model used by Saika-Bora for the COVID-19 outbreak in India [SB21]. This model has species {S, E, I, R} and the following ODE,…”