An attempt is made to use the simplest epidemic models: SIR and SEIR to predict the spread of COVID-19 in Russia. Simplicity and a small number of parameters are very significant advantages of SIR and SEIR models in conditions of a lack of numerical initial data and structural incompleteness of models. The forecast of distribution of COVID-19 in Russia is carried out according to public data sets from March 10 to April 20, 2020. Comparison of forecast results by SIR and SEIR models are given. In both cases, the peak number of infected persons while maintaining the current level of quarantine measures is forecasted at the end of May 2020.
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http://dx.doi.org/10.1016/j.ifacol.2021.04.209 | DOI Listing |
Bull Math Biol
January 2025
Department of Mathematics, University of Trento, Via Sommarive 14, Povo, 38123, Trento, Italy.
One of the strategies used in some countries to contain the COVID-19 epidemic has been the test-and-isolate policy, generally coupled with contact tracing. Such strategies have been examined in several simulation models, but a theoretical analysis of their effectiveness in simple epidemic model is, to our knowledge, missing. In this paper, we present four epidemic models of either SIR or SEIR type, in which it is assumed that at fixed times the whole population (or a part of the population) is tested and, if positive, isolated.
View Article and Find Full Text PDFComput Biol Med
December 2024
Department of Mathematics, Hacettepe University, 06532 Beytepe, Ankara, Türkiye; Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan. Electronic address:
This study revisits the mathematical SIR/SEIR epidemic models, aiming to introduce novel exponential-type series solutions. Beyond standard non-dimensionalization, we implement a successful rescaling technique that reduces the parameter count in classical epidemiology. Consequently, solutions for the SIR model are determined solely by the basic reproduction number and initial infected fractions.
View Article and Find Full Text PDFMath Med Biol
December 2024
Instituto de Matemática Multidisciplinar, Universitat Politècnica de València, Valencia, Spain.
Influenza and influenza-like illnesses pose significant challenges to healthcare systems globally. Mathematical models play a crucial role in understanding their dynamics, calibrating them to specific scenarios and making projections about their evolution over time. This study proposes a calibration process for three different but well-known compartmental models-SIR, SEIR/SLIR and SLAIR-using influenza data from the 2016-2017 season in the Valencian Community, Spain.
View Article and Find Full Text PDFMath Biosci
September 2024
Division of Biostatistics, College of Public Health, The Ohio State University, 1841 Neil Avenue, Cunz Hall, Columbus, 43210, OH, United States of America. Electronic address:
In epidemiology, realistic disease dynamics often require Susceptible-Exposed-Infected-Recovered (SEIR)-like models because they account for incubation periods before individuals become infectious. However, for the sake of analytical tractability, simpler Susceptible-Infected-Recovered (SIR) models are commonly used, despite their lack of biological realism. Bridging these models is crucial for accurately estimating parameters and fitting models to observed data, particularly in population-level studies of infectious diseases.
View Article and Find Full Text PDFFront Epidemiol
June 2024
Salem Center for Policy, Department of Finance, & Department of Information, Risk, and Operations Management, Austin, TX, United States.
During the COVID-19 pandemic, several forecasting models were released to predict the spread of the virus along variables vital for public health policymaking. Of these, the susceptible-infected-recovered (SIR) compartmental model was the most common. In this paper, we investigated the forecasting performance of The University of Texas COVID-19 Modeling Consortium SIR model.
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