Mathematical Modeling and Simulation of SIR Model for COVID-2019 Epidemic Outbreak: A Case Study of India

Yadav, Ramjeet Singh

doi:10.1101/2020.05.15.20103077

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…The description of the SIR model for Recover returning to Susceptible because the world outbreak of COVID-2019 has developed that patients who recover can be re-infected. 18 S in this study is based on the estimated number of people who should be examined according to WHO standards of 1/1000 population per week. 19 During the period of March 16 to June 14, 2020 (13 weeks), the population of Banyumas District was 1,840,152 people, so 1,840 x 13 = 23,922 people as Suspected (S).…”

Section: Effective Reproduction Number (Rt)mentioning

confidence: 99%

Characteristics, Effective Reproduction Number (Rt), and Prediction SIR Model of Covid-19 at Banyumas District, Central Java, Indonesia

2021

MLU

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Background:The Covid-19 pandemic has spread to 215 countries and territories since the outbreak first occurred in Wuhan, China. Indonesia has also declared the Covid-19 national disaster since 2 positive patients were found on March 2, 2020 and there has been a rapid spread to various regions in Indonesia including Banyumas District. This study aims to determine the risk factors, effective reproduction number and predictions SIR of Covid-19 in the Banyumas District for the period of 16 March -14 June 2020.Method: This study is a cross-sectional design. Cases are patients with comfirmed covid-19 in the area. Minimum sample size was 43 and total sample size was 70 people. Univariate analysis was used to describe of characteristics, epiestim to determine effective reproduction number, and SIR model to determine prediction of covid-19.Result: As of June 14, 2020, in Banyumas District there were 392 suspected cases, 70 confirmed cases, 58 cases were recovered and 4 people died. The characteristics of the confirmed cases were the age group of 15-24 years as much as 21.1%, males 62%, 52.1% have a history of travel to/from out of town, and 91.5% showed symptoms especially fever symptoms as much as 81.7%. The effective reproduction number (Rt) for the period March 16, 2020-June 14, 2020 was 1.81 (0.83-3.35; 95% CrI). SIR model predictions, that the peak of the outbreak will occurred on June 21, 2020 with the number of confirmed cases of 4,312 people. Conclusions:The covid-19 outbreak in Banyumas District was still out of control and still ongoing. It is recommended to continue to increase discipline of health protocols, get used to wearing masks, keep a distance and wash hands with soap and strengthen tests, traces and treatments and also protect vulnerable groups.

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Section: Effective Reproduction Number (Rt)mentioning

confidence: 99%

Characteristics, Effective Reproduction Number (Rt), and Prediction SIR Model of Covid-19 at Banyumas District, Central Java, Indonesia

2021

MLU

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“…The survival time of coronavirus differs on different surfaces, increasing the spread of the disease [5,6,7]. The airborne transmission may affect the spread of COVID-19 [8]. The attention of the public domain on preventive strategies for the disease should be increased [9].…”

Section: Introductionmentioning

confidence: 99%

Vaccination Coverage for COVID-19 in Sri Lanka: With and Without Age Stratification on Susceptible-Infectious-Recovered Simulation

Attanayake

2022

J Occup Health Epidemiol

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Background: Vaccination against COVID-19 is as a key solution to interrupt its spread. This study aimed to describe the vaccination coverage required to stop the spread of COVID-19 in Sri Lanka using a mathematical modeling strategy. Materials & Methods: This longitudinal study used age-stratified and unstratified Susceptible-Infectious-Recovered (SIR) models. Data on the population's age distribution were acquired from the census report of the Census and Statistics Center of Sri Lanka, consisting of groups: below 30, between 30-59, and over 60. Models with differential equations forecasted the spread of COVID-19 with vaccination based on parameter estimates and numerical simulation, assuming fixed population, infection, and recovery rates.Results: Simulations investigated how the susceptible, infected, and recovered populations varied according to the different vaccination coverages. According to the results, 75% vaccination coverage was required in the entire population of Sri Lanka to interrupt the transmission of COVID-19 completely. The age-stratified SIR model showed that over 90% of vaccination coverage in each age group (below 30, between 30-59, and over 60) was required to interrupt the transmission of COVID-19 in the country altogether. Conclusions: The number of COVID-19 infections in each age group of Sri Lanka reduces with the increase in vaccination coverage. As 75% vaccination coverage is required in Sri Lanka to interrupt the transmission of the disease, precise vaccination coverage measurement is essential to assess the successfulness of a vaccine campaign and control COVID-19.

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“…The goal of our research is to show that mathematical models, which are used frequently in investigating disease spread, epidemiology and even COVID-19 [15], can be applied to the training of neural network models as a pretraining step, resulting in a more accurate model. In our paper we show that if we train a neural network first on an SIR model, then train it further on real data, this method outperforms not only the original mathematical model, but also a neural network trained only on real data.…”

Section: Introductionmentioning

confidence: 99%

Replacing the SIR epidemic model with a neural network and training it further to increase prediction accuracy

Bogacsovics¹,

Hajdú²,

Lakatos³

et al. 2021

AMI

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Researchers often use theoretical models which provide a relatively simple, yet concise and effective way of modelling various phenomena. However, it is a well-known fact that the more complex the model, the more complex the mathematical description is. For this reason, theoretical models generally avoid large complexity and aim for the simplest possible definition, which although makes models mathematically more manageable, in practice it also often leads to sub-optimal performance. Furthermore, the data collected during the observations usually contain confounding factors, for which a simple theoretical model can not be prepared. Overall, mathematical models are usually too rigid and sophisticated, and therefore cannot really deal with

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Mathematical Modeling and Simulation of SIR Model for COVID-2019 Epidemic Outbreak: A Case Study of India

Cited by 6 publications

References 12 publications

Characteristics, Effective Reproduction Number (Rt), and Prediction SIR Model of Covid-19 at Banyumas District, Central Java, Indonesia

Characteristics, Effective Reproduction Number (Rt), and Prediction SIR Model of Covid-19 at Banyumas District, Central Java, Indonesia

Vaccination Coverage for COVID-19 in Sri Lanka: With and Without Age Stratification on Susceptible-Infectious-Recovered Simulation

Replacing the SIR epidemic model with a neural network and training it further to increase prediction accuracy

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