ArticleInternational journal of environmental research and public health2020
Integer Versus Fractional Order SEIR Deterministic and Stochastic Models of Measles.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- The 2025 measles outbreak in Texas.BMC infectious diseases · 2026Article
- Fractional model for the spread of COVID-19 subject to government intervention and public perception.Applied mathematical modelling · 2021Article
- A fractional-order compartmental model for the spread of the COVID-19 pandemic.Communications in nonlinear science & numerical simulation · 2021Article
- On a Method of Solution of Systems of Fractional Pseudo-Differential Equations.Fractional calculus & applied analysis · 2021Article
- Stability analysis of the hiv model through incommensurate fractional-order nonlinear system.Chaos, solitons, and fractals · 2020Article
- Modeling and Prediction of the Covid-19 Cases With Deep Assessment Methodology and Fractional Calculus.IEEE access : practical innovations, open solutions · 2020Article
- Fractional-Order SEIQRDP Model for Simulating the Dynamics of COVID-19 Epidemic.IEEE open journal of engineering in medicine and biology · 2020Article
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4 authors.
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Abstract
In this paper, we compare the performance between systems of ordinary and (Caputo) fractional differential equations depicting the susceptible-exposed-infectious-recovered (SEIR) models of diseases. In order to understand the origins of both approaches as mean-field approximations of integer and fractional stochastic processes, we introduce the fractional differential equations (FDEs) as approximations of some type of fractional nonlinear birth and death processes. Then, we examine validity of the two approaches against empirical courses of epidemics; we fit both of them to case counts of three measles epidemics that occurred during the pre-vaccination era in three different locations. While ordinary differential equations (ODEs) are commonly used to model epidemics, FDEs are more flexible in fitting empirical data and theoretically offer improved model predictions. The question arises whether, in practice, the benefits of using FDEs over ODEs outweigh the added computational complexities. While important differences in transient dynamics were observed, the FDE only outperformed the ODE in one of out three data sets. In general, FDE modeling approaches may be worth it in situations with large refined data sets and good numerical algorithms.
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