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Predictive Analysis of COVID-19 Spread in Sri Lanka using an Adaptive Compartmental Model: Susceptible-Exposed-Infected-Recovered-Dead (SEIRD) Model
Nayani Umesha Rajapaksha; Supun Dilara Wijesinghe; Toms K Thomas; Sujith P. Jayasooriya; Prasad Chathuranga Weerasinghe; Indika Gunawardana; Shalini Bhakta; Yibeltal Assefa.
Afiliação
  • Nayani Umesha Rajapaksha; Health Promotion Bureau, Ministry of Health, Sri Lanka
  • Supun Dilara Wijesinghe; Health Promotion Bureau, Ministry of Health, Sri Lanka
  • Toms K Thomas; Department of Public Health, SRM University, Sikkim, India.
  • Sujith P. Jayasooriya; Innovation for Development Consultants
  • Prasad Chathuranga Weerasinghe; Health Promotion Bureau, Ministry of Health, Sri Lanka
  • Indika Gunawardana; Health Promotion Bureau, Ministry of Health, Sri Lanka
  • Shalini Bhakta; SRM University Sikkim, India
  • Yibeltal Assefa; School of Public Health, The University of Queensland, Australia
Preprint em Inglês | medRxiv | ID: ppmedrxiv-21261819
ABSTRACT
Novel Corona Virus (COVID-19) is still spreading throughout the world despite various degrees of movement restrictions and the availability of multiple safe and effective vaccines. Modelling in predicting the spread of an epidemic is important for health planning and policies. This study aimed to apply a dynamic Susceptible-Exposed-Infected-Recovered-Deaths (SEIRD) model and simulated it under a range of epidemic conditions using python programme language. The predictions were based on different scenarios from without any preventive measures to several different preventive measures under R0 of 4. The model shows that more weight to personal protection can halt the spread of transmission followed by the closure of public places and interprovincial movement restriction. Results after simulating various scenarios indicate that disregarding personal protective measures can have devastating effects on the local population. Strict adherence, maintaining and monitoring of self-preventive measures are vital towards minimizing the death toll from COVID-19.
Licença
cc_by_nc_nd
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Estudo prognóstico Idioma: Inglês Ano de publicação: 2021 Tipo de documento: Preprint
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Estudo prognóstico Idioma: Inglês Ano de publicação: 2021 Tipo de documento: Preprint
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