COVID-19 and Malaria Co-Infection: Do Stigmatization and Self-Medication Matter? A Mathematical Modelling Study for Nigeria

30 Pages Posted: 11 May 2022

See all articles by Wisdom Avusuglo

Wisdom Avusuglo

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

Qing Han

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

Woldegebriel Assefa Woldegerima

York University

Nicola Luigi Bragazzi

York University - Laboratory for Industrial and Applied Mathematics

Ali Ahmadi

Africa-Canada Artificial Intelligence and Data Innovation Consortium; K.N. Toosi University, Faculty of Computer Engineering

Ali Asgary

York University

Jianhong Wu

York University - Laboratory for Industrial and Applied Mathematics; Africa-Canada Artificial Intelligence and Data Innovation Consortium

James Orbinski

York University

Jude Dzevela Kong

Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University

Date Written: April 21, 2022

Abstract

Self-medication and the use of complementary medicine are common among people in the Global South for social, economic, and psychological reasons. Governments in these countries are generally faced with several challenges, including limited resources and poor infrastructure, and patient health literacy. For COVID-19, this is fueled by the rapid spread of rumors in favour of these modalities on social media. Also common in the Global South is the stigmatization of people with COVID-19. Because of the stigma attached to having COVID-19, most COVID-19 patients prefer to test instead for malaria, since malaria (which is very common in the Global South) and COVID-19 share several symptoms leading to misdiagnosis. Thus, to efficiently predict the dynamics of COVID-19 in the Global South, the role of the self-medicated population, the dynamics of malaria, and the impact of stigmatization need to be taken into account. In this paper, we formulate and analyze a mathematical model for the co-dynamics of COVID-19 and malaria in Nigeria. The model is represented by a system of compartmental ODEs that take into account the self-medicated population and the impact of COVID-19 stigmatization. Our findings reveal that COVID-19 stigmatization and misdiagnosis contribute to self-medication, which, in turn, increases the prevalence of COVID-19. The basic and invasion reproduction numbers for these diseases and quantification of model parameters uncertainties and sensitivities are presented.

Note:
Funding Information: This research is funded by Canada’s International Development Research Centre (IDRC) and the Swedish International Development Coop- eration Agency (SIDA) (Grant No. 109559-001). JDK acknowledges support from NSERC Discovery Grant (Grant No. RGPIN-2022-04559).

Conflict of Interests: The authors declare no conflict of interest.

Keywords: Epidemiology, COVID-19, Malaria, Self-medication

Suggested Citation

Avusuglo, Wisdom and Han, Qing and Woldegerima, Woldegebriel Assefa and Bragazzi, Nicola Luigi and Ahmadi, Ali and Asgary, Ali and Wu, Jianhong and Orbinski, James and Kong, Jude Dzevela, COVID-19 and Malaria Co-Infection: Do Stigmatization and Self-Medication Matter? A Mathematical Modelling Study for Nigeria (April 21, 2022). Available at SSRN: https://ssrn.com/abstract=4090040 or http://dx.doi.org/10.2139/ssrn.4090040

Wisdom Avusuglo (Contact Author)

Africa-Canada Artificial Intelligence and Data Innovation Consortium ( email )

Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Laboratory for Industrial and Applied Mathematics (LIAM), York University ( email )

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

Qing Han

Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Laboratory for Industrial and Applied Mathematics (LIAM), York University ( email )

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

Woldegebriel Assefa Woldegerima

York University

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

Nicola Luigi Bragazzi

York University - Laboratory for Industrial and Applied Mathematics

Canada

Ali Ahmadi

Africa-Canada Artificial Intelligence and Data Innovation Consortium ( email )

K.N. Toosi University, Faculty of Computer Engineering ( email )

Tehran
Iran

Ali Asgary

York University ( email )

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

Jianhong Wu

York University - Laboratory for Industrial and Applied Mathematics ( email )

Canada

Africa-Canada Artificial Intelligence and Data Innovation Consortium ( email )

James Orbinski

York University

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

Jude Dzevela Kong

Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University ( email )

4700 Keele St
Toronto, ON M3J 1P3
Canada

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