An Extended Susceptible-Exposed-Infected-Recovered (SEIR) Model with Vaccination for Predicting the COVID-19 Pandemic in Sri Lanka
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Abstract
The role of modelling in predicting the spread of an epidemic is important for health planning and policies. This study aimed to apply a compartmental model for predicting the variations of epidemiological parameters in Sri Lanka. We used a dynamic Susceptible-Exposed-Infected-Recovered-Vaccinated (SEIRV) model and simulated potential vaccine strategies under a range of epidemic conditions. The predictions were based on different vaccination coverages (5% to 90%), vaccination-rates (1%, 2%, 5%) and vaccine-efficacies (40%, 60%, 80%) under different R 0 (2,4,6). We estimated the duration, exposed, and infected populations. When the R 0 was increased, the days of reduction of susceptibility and the days to reach the peak of the infection were reduced gradually. At least 45% vaccine coverage is required for reducing the infected population to mitigate a disastrous situation in Sri Lanka. The results revealed that when R 0 is increased in the SEIRV model along with the increase of vaccination efficacy and vaccination rate, the population to be vaccinated is reducing. Thus, the vaccination offers greater benefits to the local population by reducing the time to reach the peak, exposed and infected population through flattening the curves.
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SciScore for 10.1101/2021.06.17.21258837: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Python programming language was used for the analysis, and variables in Table 1 included in this model’s development. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:However, their value may be affected by the inadequate explanations of these models’ representations, usefulness and inherent limitations. Importantly, accurate …
SciScore for 10.1101/2021.06.17.21258837: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Python programming language was used for the analysis, and variables in Table 1 included in this model’s development. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:However, their value may be affected by the inadequate explanations of these models’ representations, usefulness and inherent limitations. Importantly, accurate public communications are vital during any disaster situation like the COVID-19 global pandemic. Moreover, explaining the current circumstances, actions, and intended outcomes with clarity are a timely need to gain the support and cooperation of the public and stakeholders to manage the critical situation and prevent spreading of the fake news and minimize civil disobedience [23]. The compartment models were invented during the late 1920s, which are the most commonly used models in epidemiology. Moreover, different approaches using agent-based simulations still based on those [21]. The SEIR model is very frequently used to explain the COVID-19 pandemic, which is basic and a reasonably good fit for this disease [14]. Furthermore, the accuracy of the predictions of the epidemiological models depends critically on the quality of the data feed into the model. If the data quality is good, the model can precisely describe the situations. A fitting example would be when accurately estimating the case fatality rate, which requires all cases of the disease and the number of dead [8]. However, during the COVID-19 pandemic, the number of deaths has often been highly inaccurate for many reasons, and the number of infected have also been incorrect. There can be undiagnosed cases during that period because of limited testing, which...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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