Presentation Information
[MS07-03]Modeling Co-Infection Dynamics of COVID-19 and Dengue: Insights from Mathematical Models and Epidemiological Interactions
*Alexander Jimena Balsomo1, Wyeth Ian C. Blase2 (1. West Visayas State University (Philippines), 2. Aklan State University (Philippines))
Keywords:
Co-infection modeling,Stability analysis,COVID-19,Dengue
The interplay between multiple infectious diseases within a population presents significant challenges in epidemiology, particularly when co-infections or co-endemicity arise. This presentation explores the dynamics of COVID-19 and dengue co-infection through mathematical models, emphasizing epidemiological interactions, equilibrium properties, and numerical simulations.
Our main compartmental model for COVID-19 and dengue integrates elements from both co-endemic and co-infection models, combining key features to analyze disease interactions, equilibrium states, and stability properties. The model incorporates direct human-to-human transmission of COVID-19, mosquito-borne transmission of dengue, and the potential for co-infection within a population. Using the Next Generation Matrix method, we compute the basic reproduction numbers for both diseases and examine their interplay. The resulting equilibrium points—disease-free, COVID-19-only, dengue-only, and co-infection equilibria—are directly linked to these reproduction numbers. Specifically, the disease-free equilibrium remains stable when both reproduction numbers are below one, while exceeding this threshold leads to either a single-disease endemic state or a co-infection equilibrium.
Numerical simulations illustrate multiple epidemiological scenarios, reinforcing analytical results. Elasticity analysis highlights that co-infection outcomes are strongly influenced by recovery rates and disease interaction terms. A phase diagram of equilibrium stability with respect to COVID-19 and dengue transmission parameters provides deeper insights into potential containment strategies, showing that disease dynamics are highly sensitive to intervention measures. Even when extending the model from a co-endemic to a co-infection framework, simulations confirm the presence of oscillatory behavior under specific transmission and recovery conditions. However, these numerical results emphasize the practical importance of accelerating recovery through early diagnosis and treatment to mitigate outbreak severity. When stable equilibria emerge, they settle into predictable patterns that can inform long-term disease management strategies for policymakers.
Moreover, the simulations reveal interspecific competition between COVID-19 and dengue, demonstrating an inverse relationship between their infection rates. This suggests that changes in the prevalence of one disease can indirectly suppress or amplify the transmission of the other. Understanding these interactions provides valuable epidemiological insights, informing targeted public health interventions to optimize resource allocation and containment efforts.
Our main compartmental model for COVID-19 and dengue integrates elements from both co-endemic and co-infection models, combining key features to analyze disease interactions, equilibrium states, and stability properties. The model incorporates direct human-to-human transmission of COVID-19, mosquito-borne transmission of dengue, and the potential for co-infection within a population. Using the Next Generation Matrix method, we compute the basic reproduction numbers for both diseases and examine their interplay. The resulting equilibrium points—disease-free, COVID-19-only, dengue-only, and co-infection equilibria—are directly linked to these reproduction numbers. Specifically, the disease-free equilibrium remains stable when both reproduction numbers are below one, while exceeding this threshold leads to either a single-disease endemic state or a co-infection equilibrium.
Numerical simulations illustrate multiple epidemiological scenarios, reinforcing analytical results. Elasticity analysis highlights that co-infection outcomes are strongly influenced by recovery rates and disease interaction terms. A phase diagram of equilibrium stability with respect to COVID-19 and dengue transmission parameters provides deeper insights into potential containment strategies, showing that disease dynamics are highly sensitive to intervention measures. Even when extending the model from a co-endemic to a co-infection framework, simulations confirm the presence of oscillatory behavior under specific transmission and recovery conditions. However, these numerical results emphasize the practical importance of accelerating recovery through early diagnosis and treatment to mitigate outbreak severity. When stable equilibria emerge, they settle into predictable patterns that can inform long-term disease management strategies for policymakers.
Moreover, the simulations reveal interspecific competition between COVID-19 and dengue, demonstrating an inverse relationship between their infection rates. This suggests that changes in the prevalence of one disease can indirectly suppress or amplify the transmission of the other. Understanding these interactions provides valuable epidemiological insights, informing targeted public health interventions to optimize resource allocation and containment efforts.