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COVID-19 and dengue co-infection in Brazil: optimal control and cost-effectiveness analysis
A mathematical model for the co-interaction of COVID-19 and dengue transmission dynamics is formulated and analyzed. The sub-models are shown to be locally asymptotically stable when the respective reproduction numbers are below unity. Using available data sets, the model is fitted to the cumulative...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8554757/ https://www.ncbi.nlm.nih.gov/pubmed/34729293 http://dx.doi.org/10.1140/epjp/s13360-021-02030-6 |
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author | Omame, A. Rwezaura, H. Diagne, M. L. Inyama, S. C. Tchuenche, J. M. |
author_facet | Omame, A. Rwezaura, H. Diagne, M. L. Inyama, S. C. Tchuenche, J. M. |
author_sort | Omame, A. |
collection | PubMed |
description | A mathematical model for the co-interaction of COVID-19 and dengue transmission dynamics is formulated and analyzed. The sub-models are shown to be locally asymptotically stable when the respective reproduction numbers are below unity. Using available data sets, the model is fitted to the cumulative confirmed daily COVID-19 cases and deaths for Brazil (a country with high co-endemicity of both diseases) from February 1, 2021 to September 20, 2021. The fitting was done using the fmincon function in the Optimization Toolbox of MATLAB. Parameters denoting the COVID-19 contact rate, death rate and loss of infection acquired immunity to COVID-19 were estimated using the two data sets. The model is then extended to include optimal control strategies. The appropriate conditions for the existence of optimal control and the optimality system for the co-infection model are established using the Pontryagin’s Principle. Different control strategies and their cost-effectiveness analyses were considered and simulated for the model, which include: controls against incident dengue and COVID-19 infections, control against co-infection with a second disease and treatment controls for both dengue and COVID-19. Highlights of the simulation results show that: (1) dengue prevention strategy could avert as much as 870,000 new COVID-19 infections; (2) dengue only control strategy or COVID-19 only control strategy significantly reduces new co-infection cases; (3) the strategy implementing control against incident dengue infection is the most cost-effective in controlling dengue and COVID-19 co-infections. |
format | Online Article Text |
id | pubmed-8554757 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-85547572021-10-29 COVID-19 and dengue co-infection in Brazil: optimal control and cost-effectiveness analysis Omame, A. Rwezaura, H. Diagne, M. L. Inyama, S. C. Tchuenche, J. M. Eur Phys J Plus Regular Article A mathematical model for the co-interaction of COVID-19 and dengue transmission dynamics is formulated and analyzed. The sub-models are shown to be locally asymptotically stable when the respective reproduction numbers are below unity. Using available data sets, the model is fitted to the cumulative confirmed daily COVID-19 cases and deaths for Brazil (a country with high co-endemicity of both diseases) from February 1, 2021 to September 20, 2021. The fitting was done using the fmincon function in the Optimization Toolbox of MATLAB. Parameters denoting the COVID-19 contact rate, death rate and loss of infection acquired immunity to COVID-19 were estimated using the two data sets. The model is then extended to include optimal control strategies. The appropriate conditions for the existence of optimal control and the optimality system for the co-infection model are established using the Pontryagin’s Principle. Different control strategies and their cost-effectiveness analyses were considered and simulated for the model, which include: controls against incident dengue and COVID-19 infections, control against co-infection with a second disease and treatment controls for both dengue and COVID-19. Highlights of the simulation results show that: (1) dengue prevention strategy could avert as much as 870,000 new COVID-19 infections; (2) dengue only control strategy or COVID-19 only control strategy significantly reduces new co-infection cases; (3) the strategy implementing control against incident dengue infection is the most cost-effective in controlling dengue and COVID-19 co-infections. Springer Berlin Heidelberg 2021-10-29 2021 /pmc/articles/PMC8554757/ /pubmed/34729293 http://dx.doi.org/10.1140/epjp/s13360-021-02030-6 Text en © The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Regular Article Omame, A. Rwezaura, H. Diagne, M. L. Inyama, S. C. Tchuenche, J. M. COVID-19 and dengue co-infection in Brazil: optimal control and cost-effectiveness analysis |
title | COVID-19 and dengue co-infection in Brazil: optimal control and cost-effectiveness analysis |
title_full | COVID-19 and dengue co-infection in Brazil: optimal control and cost-effectiveness analysis |
title_fullStr | COVID-19 and dengue co-infection in Brazil: optimal control and cost-effectiveness analysis |
title_full_unstemmed | COVID-19 and dengue co-infection in Brazil: optimal control and cost-effectiveness analysis |
title_short | COVID-19 and dengue co-infection in Brazil: optimal control and cost-effectiveness analysis |
title_sort | covid-19 and dengue co-infection in brazil: optimal control and cost-effectiveness analysis |
topic | Regular Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8554757/ https://www.ncbi.nlm.nih.gov/pubmed/34729293 http://dx.doi.org/10.1140/epjp/s13360-021-02030-6 |
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