Estimating the parameters of SIR model of COVID-19 cases in India during lock down periods

Bagal, Dilip Kumar; Rath, Arati; Barua, Abhishek; Patnaik, Dulu

doi:10.1101/2020.06.03.20120899

Cited by 5 publications

(3 citation statements)

References 62 publications

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“…However, the main finding is to focus on illustrating the effects of compliances in a population where superspreaders exist [24]. The model reflects our understanding of the importance of compliance and adherence to government guidelines for a quicker uplift of lockdown measures [25].…”

Section: Limitationsmentioning

confidence: 99%

Computational modelling of COVID-19: A study of compliance and superspreaders

Lee

Pérez-Ortiz

Shawe‐Taylor

2021

Preprint

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Background: The success of social distancing implementations of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) depends heavily on population compliance. Mathematical modelling has been used extensively to assess the rate of viral transmission from behavioural responses. Previous epidemics of SARS-Cov-2 have been characterised by superspreaders, a small number of individuals who transmit a disease to a large group of individuals, who contribute to the stochasticity (or randomness) of transmission compared to other pathogens such as Influenza. This growing evidence proves an urgent matter to understand transmission routes in order to target and combat outbreaks. Objective: To investigate the role of superspreaders in the rate of viral transmission with various levels of compliance. Method: A SEIRS inspired social network model is adapted and calibrated to observe the infected links of a general population with and without superspreaders on four compliance levels. Local and global connection parameters are adjusted to simulate close contact networks and travel restrictions respectively and each performance assessed. The mean and standard deviation of infections with superspreaders and non-superspreaders were calculated for each compliance level. Results: Increased levels of compliance of superspreaders proves a significant reduction in infections. Assuming long-lasting immunity, superspreaders could potentially slow down the spread due to their high connectivity. Discussion: The main advantage of applying the network model is to capture the heterogeneity and locality of social networks, including the role of superspreaders in epidemic dynamics. The main challenge is the immediate attention on social settings with targeted interventions to tackle superspreaders in future empirical work. Conclusion: Superspreaders play a central role in slowing down infection spread following compliance guidelines. It is crucial to adjust social distancing measures to prevent future outbreaks accompanied by population-wide testing and effective tracing.

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Section: Limitationsmentioning

confidence: 99%

Computational modelling of COVID-19: A study of compliance and superspreaders

Lee

Pérez-Ortiz

Shawe‐Taylor

2021

Preprint

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“…It is constructive from the SIR model but adds the latency period (Exposed) compartment as a variable. Model development stages are as follows[12]:…”

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confidence: 99%

Visual Data Analysis and Simulation Prediction for COVID-19 in Saudi Arabia Using SEIR Prediction Model

Khan

2021

Int. J. Onl. Eng.

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<p>The World Health Organization (WHO) reported the COVID-19 epidemic a global health emergency on January 30 and confirmed its transformation into a pandemic on March 11. China has been the hardest hit since the virus's outbreak, which may date back to late November. Saudi Arabia realized the danger of the Coronavirus in March 2020, took the initiative to take a set of pre-emptive decisions that preceded many countries of the world, and worked to harness all capabilities to confront the outbreak of the epidemic. Several researchers are currently using various mathematical and machine learning-based prediction models to estimate this pandemic's future trend. In this work, the SEIR model was applied to predict the epidemic situation in Saudi Arabia and evaluate the effectiveness of some epidemic control measures, and finally, providing some advice on preventive measures.</p>

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“…Nesta direção, a evolução dos modelos matemáticos, têm possibilitado a elucidação dos processos de transmissão de doenças infecciosas em populações humanas (Wang, Andrews, Wu, Wang, & Bauch, 2015), especialmente os modelos diferenciais que incorporam elementos mais básicos onde exista uma população homogênea compartimentada em indivíduos Suscetíveis (S), Infectados (I) e Recuperados (R), podendo a partir dessas características evoluir para modelos mais complexos. Estudos dessa natureza estão sendo amplamente empregados para descrever a evolução de COVID-19 em diversos países (Bagal, Rath, Barua, & Patnaik, 2020;Boudrioua & Boudrioua, 2020;Sinkala et al, 2020;Freitas, Silva, & Sandes, 2020). Wang et al (2015) asseveram que, os modelos diferenciais compartimentados clássicos usados para descrever epidemias que consideram a taxa de transmissão fixa, tornam suas suposições demasiadamente simplificadas, pois, podem não levar em consideração o fato da população mudar o seu comportamento, no decorrer de uma epidemia, com o intuito de mitigar a transmissão da doença.…”

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Modelagem das mudanças comportamentais durante a propagação do sars-cov-2: um estudo de caso considerando o atraso nos testes

Pires

Dias

Silva

et al. 2020

RSD

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This article presents an epidemic-behavior model for the SARS-CoV-2 spreading in Amapá employing a generalization of the SIR model, which captures the behavioral response of the population during the epidemic evolution through a reduction factor δ in the contagion rate. The results of the model's validation step with empirical data show that δ = [0.614,0.638] indicating that the behavioral response was responsible for a reduction of ~[36,39]% in the epidemic transmissibility. Subsequently, we provide an analysis of the scenarios that may emerge if the isolation measures are lifted tR days after the first peak considering a new reduction factor δR > δ. The results indicate that the careful exit (δR ≤ 0.80) from the confinement measures with 3 weeks (tR = 21) after the first peak can still trigger a second peak, but with a smaller size than the first one.

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Estimating the parameters of SIR model of COVID-19 cases in India during lock down periods

Cited by 5 publications

References 62 publications

Computational modelling of COVID-19: A study of compliance and superspreaders

Computational modelling of COVID-19: A study of compliance and superspreaders

Visual Data Analysis and Simulation Prediction for COVID-19 in Saudi Arabia Using SEIR Prediction Model

Modelagem das mudanças comportamentais durante a propagação do sars-cov-2: um estudo de caso considerando o atraso nos testes

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