Assessment of bidirectional impact of stigmatization induced self-medication on COVID-19 and malaria transmissions using mathematical modeling: Nigeria as a case study.

COVID-19 Co-dynamics Malaria Mathematical modeling Nigeria Self-medication Stigmatization

Journal

Mathematical biosciences
ISSN: 1879-3134
Titre abrégé: Math Biosci
Pays: United States
ID NLM: 0103146

Informations de publication

Date de publication:
24 Jul 2024
Historique:
received: 08 09 2022
revised: 16 01 2024
accepted: 05 06 2024
medline: 27 7 2024
pubmed: 27 7 2024
entrez: 26 7 2024
Statut: aheadofprint

Résumé

The continual social and economic impact of infectious diseases on nations has maintained sustained attention on their control and treatment, of which self-medication has been one of the means employed by some individuals. Self-medication complicates the attempt of their control and treatment as it conflicts with some of the measures implemented by health authorities. Added to these complications is the stigmatization of individuals with some diseases in some jurisdictions. This study investigates the co-infection of COVID-19 and malaria and its related deaths and further highlights how self-medication and stigmatization add to the complexities of the fight against these two diseases using Nigeria as a study case. Using a mathematical model on COVID-19 and malaria co-infection, we address the question: to what degree does the impact of the interaction between COVID-19 and malaria amplify infections and deaths induced by both diseases via self-medication and stigmatization? We demonstrate that COVID-19 related self-medication due to misdiagnoses contributes substantially to the prevalence of disease. The control reproduction numbers for these diseases and quantification of model parameters uncertainties and sensitivities are presented.

Identifiants

pubmed: 39059710
pii: S0025-5564(24)00109-3
doi: 10.1016/j.mbs.2024.109249
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

109249

Informations de copyright

Copyright © 2024. Published by Elsevier Inc.

Déclaration de conflit d'intérêts

Declaration of competing interest The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of their respective institutions. The authors declare no conflict of interest. Ethics and consent: All authors have been personally and actively involved in substantial work leading to the paper, and will take public responsibility for its content.

Auteurs

Wisdom S Avusuglo (WS)

Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Canada; Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Canada.

Qing Han (Q)

Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Canada; Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Canada.

Woldegebriel Assefa Woldegerima (WA)

Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Canada; Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Canada.

Ali Asgary (A)

Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Canada; Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Canada.

Jianhong Wu (J)

Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Canada; Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Canada.

James Orbinski (J)

Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Canada; The Dahdaleh Institute for Global Health Research, York University, Canada.

Nicola Bragazzi (N)

Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Canada; Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Canada.

Ali Ahmadi (A)

Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Canada; K. N. Toosi University of Technology, Faculty of Computer Engineering, Iran.

Jude Dzevela Kong (JD)

Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Canada; Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Canada. Electronic address: jdkong@yorku.ca.

Classifications MeSH