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Ofori SK, Dankwa EA, Estrada EH, Hua X, Kimani TN, Wade CG, Buckee CO, Murray MB, Hedt-Gauthier BL. COVID-19 vaccination strategies in Africa: A scoping review of the use of mathematical models to inform policy. Trop Med Int Health 2024; 29:466-476. [PMID: 38740040 DOI: 10.1111/tmi.13994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
OBJECTIVE Mathematical models are vital tools to understand transmission dynamics and assess the impact of interventions to mitigate COVID-19. However, historically, their use in Africa has been limited. In this scoping review, we assess how mathematical models were used to study COVID-19 vaccination to potentially inform pandemic planning and response in Africa. METHODS We searched six electronic databases: MEDLINE, Embase, Web of Science, Global Health, MathSciNet and Africa-Wide NiPAD, using keywords to identify articles focused on the use of mathematical modelling studies of COVID-19 vaccination in Africa that were published as of October 2022. We extracted the details on the country, author affiliation, characteristics of models, policy intent and heterogeneity factors. We assessed quality using 21-point scale criteria on model characteristics and content of the studies. RESULTS The literature search yielded 462 articles, of which 32 were included based on the eligibility criteria. Nineteen (59%) studies had a first author affiliated with an African country. Of the 32 included studies, 30 (94%) were compartmental models. By country, most studies were about or included South Africa (n = 12, 37%), followed by Morocco (n = 6, 19%) and Ethiopia (n = 5, 16%). Most studies (n = 19, 59%) assessed the impact of increasing vaccination coverage on COVID-19 burden. Half (n = 16, 50%) had policy intent: prioritising or selecting interventions, pandemic planning and response, vaccine distribution and optimisation strategies and understanding transmission dynamics of COVID-19. Fourteen studies (44%) were of medium quality and eight (25%) were of high quality. CONCLUSIONS While decision-makers could draw vital insights from the evidence generated from mathematical modelling to inform policy, we found that there was limited use of such models exploring vaccination impacts for COVID-19 in Africa. The disparity can be addressed by scaling up mathematical modelling training, increasing collaborative opportunities between modellers and policymakers, and increasing access to funding.
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Affiliation(s)
- Sylvia K Ofori
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Emmanuelle A Dankwa
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Eve Hiyori Estrada
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Xinyi Hua
- Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, Georgia, USA
| | - Teresia N Kimani
- KAVI-Institute of Clinical Research, University of Nairobi, Nairobi, Kenya
- Center for Epidemiological Modelling and Analysis, University of Nairobi, Nairobi, Kenya
- Paul G Allen School for Global Animal Health, Washington State University, Pullman, Washington, USA
- Department of Health Services, Kiambu County, Ministry of Health Kenya, Kiambu County, Kenya
| | - Carrie G Wade
- Countway Library, Harvard School of Medicine, Boston, Massachusetts, USA
| | - Caroline O Buckee
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Megan B Murray
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Bethany L Hedt-Gauthier
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, USA
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Mpinganzima L, Ntaganda JM, Banzi W, Muhirwa JP, Nannyonga BK, Niyobuhungiro J, Rutaganda E, Ngaruye I, Ndanguza D, Nzabanita J, Masabo E, Gahamanyi M, Dushimirimana J, Nyandwi B, Kurujyibwami C, Ruganzu LFU, Nyagahakwa V, Mukeshimana S, Ngendahayo JP, Nsabimana JP, Niyigena JDD, Uwonkunda J, Mbalawata IS. Compartmental mathematical modelling of dynamic transmission of COVID-19 in Rwanda. IJID REGIONS 2023; 6:99-107. [PMID: 36644499 PMCID: PMC9827742 DOI: 10.1016/j.ijregi.2023.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 01/04/2023] [Accepted: 01/04/2023] [Indexed: 06/17/2023]
Abstract
OBJECTIVES Mathematical modelling is of interest to study the dynamics of coronavirus disease 2019 (COVID-19), and models such as SEIR (Susceptible-Exposed-Infected-Recovered) have been considered. This article describes the development of a compartmental transmission network model - Susceptible-Exposed-Quarantine-Infectious-Infectious, undetected-Infectious, home-based care-Hospitalized-Vaccinated-Recovered-Dead - to simulate the dynamics of COVID-19 in order to account for specific measures put into place by the Government of Rwanda to prevent further spread of the disease. METHODS The compartments of this model are connected by parameters, some of which are known from the literature, and others are estimated from available data using the least squares method. For the stability of the model, equilibrium points were determined and the basic reproduction number R 0 was studied; R 0 is an indicator for contagiousness. RESULTS The model showed that secondary infections are generated from the exposed group, the asymptomatic group, the infected (symptomatic) group, the infected (undetected) group, the infected (home-based care) group and the hospitalized group. The formulated model was reliable and fit the data. Furthermore, the estimated R 0 of 2.16 shows that COVID-19 will persist without the application of control measures. CONCLUSIONS This article presents results regarding predicted spread of COVID-19 in Rwanda.
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Affiliation(s)
- Lydie Mpinganzima
- Department of Mathematics, School of Science, College of Science and Technology, University of Rwanda, Kigali, Rwanda
| | - Jean Marie Ntaganda
- Department of Mathematics, School of Science, College of Science and Technology, University of Rwanda, Kigali, Rwanda
| | - Wellars Banzi
- Department of Mathematics, School of Science, College of Science and Technology, University of Rwanda, Kigali, Rwanda
| | - Jean Pierre Muhirwa
- Department of Mathematics, School of Science, College of Science and Technology, University of Rwanda, Kigali, Rwanda
| | - Betty Kivumbi Nannyonga
- Department of Mathematics, School of Physical Sciences, College of Natural Sciences, Makerere University, Kampala, Uganda
| | | | - Eric Rutaganda
- Department of Internal Medicine, Kigali University Teaching Hospital, Kigali, Rwanda
| | - Innocent Ngaruye
- Department of Mathematics, School of Science, College of Science and Technology, University of Rwanda, Kigali, Rwanda
| | - Denis Ndanguza
- Department of Mathematics, School of Science, College of Science and Technology, University of Rwanda, Kigali, Rwanda
| | - Joseph Nzabanita
- Department of Mathematics, School of Science, College of Science and Technology, University of Rwanda, Kigali, Rwanda
| | - Emmanuel Masabo
- Department of Mathematics, School of Science, College of Science and Technology, University of Rwanda, Kigali, Rwanda
- African Centre of Excellence in Data Science, Kigali, Rwanda
| | - Marcel Gahamanyi
- Department of Mathematics, School of Science, College of Science and Technology, University of Rwanda, Kigali, Rwanda
| | - Justine Dushimirimana
- Department of Mathematics, School of Science, College of Science and Technology, University of Rwanda, Kigali, Rwanda
| | - Bosco Nyandwi
- Department of Mathematics, School of Science, College of Science and Technology, University of Rwanda, Kigali, Rwanda
| | - Célestin Kurujyibwami
- Department of Mathematics, School of Science, College of Science and Technology, University of Rwanda, Kigali, Rwanda
| | | | - Venuste Nyagahakwa
- Department of Mathematics, School of Science, College of Science and Technology, University of Rwanda, Kigali, Rwanda
| | - Solange Mukeshimana
- Department of Mathematics, School of Science, College of Science and Technology, University of Rwanda, Kigali, Rwanda
| | - Jean Pierre Ngendahayo
- Department of Mathematics, School of Science, College of Science and Technology, University of Rwanda, Kigali, Rwanda
| | - Jean Paul Nsabimana
- Department of Mathematics, School of Science, College of Science and Technology, University of Rwanda, Kigali, Rwanda
| | - Jean De Dieu Niyigena
- Department of Mathematics, School of Science, College of Science and Technology, University of Rwanda, Kigali, Rwanda
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Kilonzo CM, Wamalwa M, Whegang SY, Tonnang HEZ. Assessing the impact of non-pharmaceutical interventions (NPIs) and BCG vaccine cross-protection in the transmission dynamics of SARS-CoV-2 in eastern Africa. BMC Res Notes 2022; 15:283. [PMID: 36059028 PMCID: PMC9440862 DOI: 10.1186/s13104-022-06171-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 08/10/2022] [Indexed: 12/03/2022] Open
Abstract
Objective The outbreak of the novel coronavirus disease 2019 (COVID-19) is still affecting African countries. The pandemic presents challenges on how to measure governmental, and community responses to the crisis. Beyond health risks, the socio-economic implications of the pandemic motivated us to examine the transmission dynamics of COVID-19 and the impact of non-pharmaceutical interventions (NPIs). The main objective of this study was to assess the impact of BCG vaccination and NPIs enforced on COVID-19 case-death-recovery counts weighted by age-structured population in Ethiopia, Kenya, and Rwanda. We applied a semi-mechanistic Bayesian hierarchical model (BHM) combined with Markov Chain Monte Carlo (MCMC) simulation to the age-structured pandemic data obtained from the target countries. Results The estimated mean effective reproductive number (Rt) for COVID-19 was 2.50 (C1: 1.99–5.95), 3.51 (CI: 2.28–7.28) and 3.53 (CI: 2.97–5.60) in Ethiopia, Kenya and Rwanda respectively. Our results indicate that NPIs such as lockdowns, and curfews had a large effect on reducing Rt. Current interventions have been effective in reducing Rt and thereby achieve control of the epidemic. Beyond age-structure and NPIs, we found no significant association between COVID-19 and BCG vaccine-induced protection. Continued interventions should be strengthened to control transmission of SARS-CoV-2. Supplementary Information The online version contains supplementary material available at 10.1186/s13104-022-06171-4.
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Affiliation(s)
- Chelsea Mbeke Kilonzo
- International Centre of Insect Physiology and Ecology (Icipe), P.O. Box 30772-00100, Nairobi, Kenya
| | - Mark Wamalwa
- International Centre of Insect Physiology and Ecology (Icipe), P.O. Box 30772-00100, Nairobi, Kenya. .,Department of Biochemistry, Microbiology and Biotechnology, Kenyatta University, Nairobi, Kenya.
| | - Solange Youdom Whegang
- Department of Public Health, Faculty of Medicine and Pharmaceutical Sciences, University of Dschang, P.O Box: 96, Dschang, Cameroon
| | - Henri E Z Tonnang
- International Centre of Insect Physiology and Ecology (Icipe), P.O. Box 30772-00100, Nairobi, Kenya
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