Estimation of Epidemiological Parameters and Ascertainment Rate from Early Transmission of COVID-19 across Africa

17 Pages Posted: 27 Jun 2022

See all articles by Qing Han

Qing Han

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

Nicola Luigi Bragazzi

York University - Laboratory for Industrial and Applied Mathematics

Ali Asgary

York University

James Orbinski

York University

Jianhong Wu

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

Jude Dzevela Kong

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

Multiple version iconThere are 2 versions of this paper

Date Written: May 30, 2022

Abstract

Country reported case counts suggested a slow spread of SARS-CoV-2 in the initial phase of the COVID-19 pandemic in Africa. However, due to inadequate public awareness, unestablished monitoring practices, limited testing, ineffective diagnosis, stigmas attached to being infected with SARS-CoV-2, self-medication, and the use of complementary/alternative medicine that are common among Africans for social, economic, and psychological reasons, there might exist extensive under-ascertainment and therefore an underestimation of the true number of cases, especially at the beginning of the novel epidemic. We developed a compartmentalized epidemiological model based on an augmented susceptible-exposed-infectious-recovered (SEIR) model to track the early epidemics in 54 African countries. Data on the reported cumulative number of cases and daily confirmed cases were used to fit the model for the time period with no or little massive national interventions yet in each country. We estimated that the mean basic reproduction number is 2.02 (SD 0.7), with a range between 1.12 (Zambia) and 3.64 (Nigeria), whereas the mean basic reproduction number for observed cases was estimated to be 0.17 (SD 0.17), with a range between 0 (Sao Tome and Principe, Seychelles, Tanzania, South Sudan, Mozambique, Liberia, Togo) and 0.68 (South Africa). It was estimated that the mean overall report rate is 5.37% (SD 5.71%), with the highest 30.41% in Libya and the lowest 0.02% in Sao Tome and Principe. An average of 5.46% (SD 6.4%) of all infected cases were severe cases and 66.74% (SD 17.28%) were asymptomatic ones, with Libya having the most (39.45%) fraction of severe cases and Togo the most (97.38%) fraction of asymptomatic cases. The estimated low reporting rates in Africa suggested a clear need for improved reporting and surveillance system in these countries.

Note:
Funding Information: This research is funded by Canada’s International Development Research Centre (IDRC) and the Swedish International Development Cooperation Agency(SIDA)(Grant No.109559-001).

Conflict of Interests: The authors declare no competing interests.

Keywords: COVID-19, reporting rate, mathematical model

Suggested Citation

Han, Qing and Bragazzi, Nicola Luigi and Asgary, Ali and Orbinski, James and Wu, Jianhong and Kong, Jude Dzevela, Estimation of Epidemiological Parameters and Ascertainment Rate from Early Transmission of COVID-19 across Africa (May 30, 2022). Available at SSRN: https://ssrn.com/abstract=4131409 or http://dx.doi.org/10.2139/ssrn.4131409

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

Nicola Luigi Bragazzi

York University - Laboratory for Industrial and Applied Mathematics

Canada

Ali Asgary

York University ( email )

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

James Orbinski

York University

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 )

Jude Dzevela Kong (Contact Author)

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

4700 Keele St
Toronto, ON M3J 1P3
Canada

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