An Extended Susceptible-Exposed-Infected-Recovered (SEIR) Model with Vaccination for Predicting the COVID-19 Pandemic in Sri Lanka

Rajapaksha, Nayani Umesha; Wijesinghe, Millawage Supun Dilara; Jayasooriya, Sujith; Gunawardana, Indika; Weerasinghe, W. M. Prasad Chathuranga

doi:10.1101/2021.06.17.21258837

Cited by 9 publications

(6 citation statements)

References 19 publications

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“…78,79 The main enhancements used for epidemic modeling involve the use of machine learning for epidemic forecasting, 74,75,80 and the use of proxy measurements for the assessment of epidemic parameters such as tracing mobility from social networks, 81,82 assessing vaccination rates, 83 or using simulations to assess different epidemic scenarios while changing relevant parameters. 84,85 The majority of reported models are theoretical, while those that use real data mainly focused on a single parameter, be it lockdown or mobility, 86,87 distancing, 88 patient status, 89 or vaccination rates, 90 among others. When the number of studied parameters increases, and when the SEIR models are applied to multiple regions, given the specifics of each regional epidemic, it becomes difficult to reproduce actual data using the uniform model.…”

Section: Comparison With the Prior Workmentioning

confidence: 99%

Dynamically adjustable SVEIR(MH) model of multiwave epidemics: Estimating the effects of public health measures against COVID‐19

Yin,

Xiao,

McDonald

et al. 2023

Journal of Medical Virology

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The COVID‐19 pandemic was characterized by multiple subsequent, overlapping outbreaks, as well as extremely rapid changes in viral genomes. The information about local epidemics spread and the epidemic control measures was shared on a daily basis (number of cases and deaths) via centralized repositories. The vaccines were developed within the first year of the pandemic. New modes of monitoring and sharing of epidemic data were implemented using Internet resources. We modified the basic SEIR compartmental model to include public health measures, multiwave scenarios, and the variation of viral infectivity and transmissibility reflected by the basic reproduction number R0 of emerging viral variants. SVEIR(MH) model considers the capacity of the medical system, lockdowns, vaccination, and changes in viral reproduction rate on the epidemic spread. The developed model uses daily infection reports for assessing the epidemic dynamics, and daily changes of mobility data from mobile phone networks to assess the lockdown effectiveness. This model was deployed to six European regions Baden‐Württemberg (Germany), Belgium, Czechia, Lombardy (Italy), Sweden, and Switzerland for the first 2 years of the pandemic. The correlation coefficients between observed and reported infection data showed good concordance for both years of the pandemic (ρ = 0.84–0.94 for the raw data and ρ = 0.91–0.98 for smoothed 7‐day averages). The results show stability across the regions and the different epidemic waves. Optimal control of epidemic waves can be achieved by dynamically adjusting epidemic control measures in real‐time. SVEIR(MH) model can simulate different scenarios and inform adjustments to the public health policies to achieve the target outcomes. Because this model is highly representative of actual epidemic situations, it can be used to assess both the public health and socioeconomic effects of the public health measures within the first 7 days of the outbreak.

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Section: Comparison With the Prior Workmentioning

confidence: 99%

Dynamically adjustable SVEIR(MH) model of multiwave epidemics: Estimating the effects of public health measures against COVID‐19

Yin,

Xiao,

McDonald

et al. 2023

Journal of Medical Virology

View full text Add to dashboard Cite

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“…A new SEIRV model was implemented in Sri Lanka by Rajapaksha [ 21 ], in which they used a compartmental model to forecast changes in epidemiological indicators. We simulated various vaccination tactics using a dynamic Susceptible-Exposed-Infected-Recovered-Vaccinated (SEIRV) model under a variety of epidemic scenarios.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced SEIR Model for Prediction of COVID-19 with Vaccination Effect

Poonia

Saudagar

AlTameem

et al. 2022

Life

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Currently, the spread of COVID-19 is running at a constant pace. The current situation is not so alarming, but every pandemic has a history of three waves. Two waves have been seen, and now expecting the third wave. Compartmental models are one of the methods that predict the severity of a pandemic. An enhanced SEIR model is expected to predict the new cases of COVID-19. The proposed model has an additional compartment of vaccination. This proposed model is the SEIRV model that predicts the severity of COVID-19 when the population is vaccinated. The proposed model is simulated with three conditions. The first condition is when social distancing is not incorporated, while the second condition is when social distancing is included. The third one condition is when social distancing is combined when the population is vaccinated. The result shows an epidemic growth rate of about 0.06 per day, and the number of infected people doubles every 10.7 days. Still, with imparting social distancing, the proposed model obtained the value of R0 is 1.3. Vaccination of infants and kids will be considered as future work.

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“…Inspired by the earlier SEIR model from Rajapaksha (2021) [11], We designed the new version of the Susceptible-Exposed-Infected-Recovered-Vaccinated (SEIRV) Model with vaccination. The SEIRV model, named for its 5 compartments (Susceptible, Exposed, Infected, Recovered, Vaccinated), is a basic quantitative model of infectious diseases.…”

Section: Seirv Modelmentioning

confidence: 99%

Analyzing the Economic Impact on COVID-19 with Vaccination Based on the Epidemiological and Economic Models

Huang¹

2022

Atlantis Highlights in Computer Sciences

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In December 2019, COVID-19, a novel coronavirus disease, started in China and spread worldwide. The development of COVID-19 vaccines helped many countries in this world to diminish the disturbance caused by COVID-19. In order to explore the economic influence of COVID-19 with vaccination, this paper sets up 12 different scenarios with various daily vaccination rates and vaccination efficiencies to predict the cumulative fraction of the infective population based on a modified version of the SEVIR model, which is a basic quantitative model of infectious diseases. Inspired by previous research, we utilized the G-cube (G20) Model from the Computable General Equilibrium (CGE) model to estimate the GDP growth rate in the field of microeconomics based on prediction from previous data results of the SEVIR model. According to the results from this paper, we conclude that as the increase of daily vaccination rates and vaccine efficacy, the infection growth rate will decrease inversely.

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An Extended Susceptible-Exposed-Infected-Recovered (SEIR) Model with Vaccination for Predicting the COVID-19 Pandemic in Sri Lanka

Cited by 9 publications

References 19 publications

Dynamically adjustable SVEIR(MH) model of multiwave epidemics: Estimating the effects of public health measures against COVID‐19

Dynamically adjustable SVEIR(MH) model of multiwave epidemics: Estimating the effects of public health measures against COVID‐19

An Enhanced SEIR Model for Prediction of COVID-19 with Vaccination Effect

Analyzing the Economic Impact on COVID-19 with Vaccination Based on the Epidemiological and Economic Models

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