Mathematical modeling and analysis of leptospirosis–COVID-19 co-infection with real data

Muhammad Said; Yunil Roh; Il Hyo Jung

doi:10.1140/epjp/s13360-024-05846-0

2024 Impact factor 2.9

EPJ PLUS - Condensed Matter and Complex Systems

Open Access

Eur. Phys. J. Plus (2024) 139: 1030
https://doi.org/10.1140/epjp/s13360-024-05846-0

Regular Article

Mathematical modeling and analysis of leptospirosis–COVID-19 co-infection with real data

Muhammad Said¹, Yunil Roh¹ and Il Hyo Jung¹^,2^,3^c

¹ Department of Mathematics, Pusan National University, 46241, Busan, Republic of Korea
² Institute of Mathematical Science, Pusan National University, 46241, Busan, Republic of Korea
³ Finance Fishery Manufacture Industrial Mathematics Center on BigData, Pusan National University, 46241, Busan, Republic of Korea

^c ilhjung@pusan.ac.kr

Received: 31 October 2024
Accepted: 16 November 2024
Published online: 28 November 2024

Abstract

In this paper, we present a mathematical model to analyze the dynamics of leptospirosis and COVID-19 co-infection. The model used actual data, and estimation of the parameters via the MLE method is performed, which includes the rates of disease transmission, progression of the disease, disease-related death, and recovery rates for each disease and their co-infection. Key parameters: $β_{1}$ $\beta _1$ , $β_{2}$ $\beta _2$ , $ϕ_{1}$ $\phi _1$ , $ϕ_{2}$ $\phi _2$ , and $μ_{c}$ $\mu _c$ are used to characterize the dynamics of the co-infection burden of leptospirosis and COVID-19. The results reveal a notable contrast in transmission dynamics and clinical outcomes between the two diseases, with leptospirosis demonstrating a higher transmission rate and increased morbidity. Co-infections showed more severe clinical outcomes, with higher mortality and delayed recovery than single infections. These findings highlight the importance of targeted public health strategies for managing co-infected populations.

© The Author(s) 2024

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