What Can We Estimate From Fatality and Infectious Case Data Using the Susceptible-Infected (sir) Model? a Case Study of Covid-19 Pandemic

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Date

2020

Authors

Ahmetolan, Semra
Bilge, Ayşe Hümeyra
Demirci, Ali
Peker-Dobie, Ayşe
Ergönül, Önder

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Journal ISSN

Volume Title

Publisher

Frontıers Medıa Sa

Open Access Color

GOLD

Green Open Access

Yes

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No
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Abstract

The rapidly spreading Covid-19 that affected almost all countries, was first reported at the end of 2019. As a consequence of its highly infectious nature, countries all over the world have imposed extremely strict measures to control its spread. Since the earliest stages of this major pandemic, academics have done a huge amount of research in order to understand the disease, develop medication, vaccines and tests, and model its spread. Among these studies, a great deal of effort has been invested in the estimation of epidemic parameters in the early stage, for the countries affected by Covid-19, hence to predict the course of the epidemic but the variability of the controls over the course of the epidemic complicated the modeling processes. In this article, the determination of the basic reproduction number, the mean duration of the infectious period, the estimation of the timing of the peak of the epidemic wave is discussed using early phase data. Daily case reports and daily fatalities for China, South Korea, France, Germany, Italy, Spain, Iran, Turkey, the United Kingdom and the United States over the period January 22, 2020-April 18, 2020 are evaluated using the Susceptible-Infected-Removed (SIR) model. For each country, the SIR models fitting cumulative infective case data within 5% error are analyzed. It is observed that the basic reproduction number and the mean duration of the infectious period can be estimated only in cases where the spread of the epidemic is over (for China and South Korea in the present case). Nevertheless, it is shown that the timing of the maximum and timings of the inflection points of the proportion of infected individuals can be robustly estimated from the normalized data. The validation of the estimates by comparing the predictions with actual data has shown that the predictions were realized for all countries except USA, as long as lock-down measures were retained.

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Keywords

COVID-19, SIR model, Parameter estimation, Mathematical models, Epidemiology, Medicine; General and internal medicine, Mathematical models, Medicine (General), Epidemiology, General and internal medicine, Populations and Evolution (q-bio.PE), COVID-19, Quantitative Biology - Quantitative Methods, R5-920, FOS: Biological sciences, Parameter estimation, Medicine, epidemiology, SIR model, parameter estimation, Quantitative Biology - Populations and Evolution, mathematical models, Quantitative Methods (q-bio.QM), COVID-19; Epidemiology; Mathematical models; Parameter estimation; SIR model

Fields of Science

0301 basic medicine, 03 medical and health sciences, 0302 clinical medicine

Citation

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Q1

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Q1
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OpenCitations Citation Count
19

Source

Frontiers in Medicine

Volume

7

Issue

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Scopus : 24

PubMed : 9

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24

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17

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4

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150

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