Simulation based study for estimation of COVID-19 spread in India using SEIR model

Modi, Kanak; Umate, Laxmikant; Makade, Kiran; Dubey, Ravi Shanker; Agarwal, Pankaj

doi:10.1080/09720502.2020.1838059

Cited by 8 publications

(2 citation statements)

References 18 publications

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“…Kovacevic et al [20], on the other hand, employed a distributed optimal control epidemiological model for understanding the COVID-19 pandemic, emphasizing the importance of coordinated control efforts in disease mitigation. Modi et al [21] focused on the spread of COVID-19 in India using the susceptible-exposed-infected-recovered (SEIR) model, providing crucial insights into the potential dynamics of the virus in dense populations. Kevrekidis et al [22] added a spatial dimension to the modelling of COVID-19, studying the outbreak in Greece and Andalusia using reaction-diffusion models.…”

Section: Related Work On Sars-cov-2 Epidemic Modelsmentioning

confidence: 99%

Parameter estimation for contact tracing in graph-based models

Okolie,

Müller,

Kretzschmar

2023

J. R. Soc. Interface.

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We adopt a maximum-likelihood framework to estimate parameters of a stochastic susceptible–infected–recovered (SIR) model with contact tracing on a rooted random tree. Given the number of detectees per index case, our estimator allows to determine the degree distribution of the random tree as well as the tracing probability. Since we do not discover all infectees via contact tracing, this estimation is non-trivial. To keep things simple and stable, we develop an approximation suited for realistic situations (contract tracing probability small, or the probability for the detection of index cases small). In this approximation, the only epidemiological parameter entering the estimator is R 0 . The estimator is tested in a simulation study and is furthermore applied to COVID-19 contact tracing data from India. The simulation study underlines the efficiency of the method. For the empirical COVID-19 data, we compare different degree distributions and perform a sensitivity analysis. We find that particularly a power-law and a negative binomial degree distribution fit the data well and that the tracing probability is rather large. The sensitivity analysis shows no strong dependency of the estimates on the reproduction number. Finally, we discuss the relevance of our findings.

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Section: Related Work On Sars-cov-2 Epidemic Modelsmentioning

confidence: 99%

Parameter estimation for contact tracing in graph-based models

Okolie,

Müller,

Kretzschmar

2023

J. R. Soc. Interface.

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“…(Modi et al. 2021 ) utilized online data in order to estimate the progression of COVID-19 spread utilizing the SEIR epidemic compartmental model, including the states (Susceptible–Exposed–Infected–Recovered), over various values of . (Duan et al.…”

Section: Introductionmentioning

confidence: 99%

An integrated simulation framework for the prevention and mitigation of pandemics caused by airborne pathogens

Chondros

Nikolopoulos

Polenakis

2022

Netw Model Anal Health Inform Bioinforma

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In this work, we developed an integrated simulation framework for pandemic prevention and mitigation of pandemics caused by airborne pathogens, incorporating three sub-models, namely the spatial model, the mobility model, and the propagation model, to create a realistic simulation environment for the evaluation of the effectiveness of different countermeasures on the epidemic dynamics. The spatial model converts images of real cities obtained from Google Maps into undirected weighted graphs that capture the spatial arrangement of the streets utilized next for the mobility of individuals. The mobility model implements a stochastic agent-based approach, developed to assign specific routes to individuals moving in the city, through the use of stochastic processes, utilizing the weights of the underlying graph to deploy shortest path algorithms. The propagation model implements both the epidemiological model and the physical substance of the transmission of an airborne pathogen (in our approach, we investigate the transmission parameters of SARS-CoV-2). The deployment of a set of countermeasures was investigated in reducing the spread of the pathogen, where, through a series of repetitive simulation experiments, we evaluated the effectiveness of each countermeasure in pandemic prevention.

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The contagion effect of COVID-19 on stock markets

Wang

2022

Journal of Statistics and Management Systems

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Simulation based study for estimation of COVID-19 spread in India using SEIR model

Cited by 8 publications

References 18 publications

Parameter estimation for contact tracing in graph-based models

Parameter estimation for contact tracing in graph-based models

An integrated simulation framework for the prevention and mitigation of pandemics caused by airborne pathogens

The contagion effect of COVID-19 on stock markets

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