Infectious disease modelling for SARS-CoV-2 in Africa to guide policy: A systematic review

Kimani, Teresia Njoki; Mutono, Nyamai; Owino, Lillian; Makori, Anita; Ombajo, Loice Achieng; Maritim, Marybeth; Anzala, Omu; Thumbi, S.M

doi:10.1016/j.epidem.2022.100610

Cited by 9 publications

(11 citation statements)

References 62 publications

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“…Moreover, some of the trainees indicated their interest in further training in infectious disease modeling. We acknowledge however that Africa-based faculty-level expertise in infectious disease modeling is currently limited; however, if trainings such as the one we have organized are more frequent, the situation can be expected to improve in a few years [11].…”

Section: In-country Expertisementioning

confidence: 99%

“…These institutions have a long-standing reputation for supporting the government with evidence-based policy. However, there is a geographical disparity in the mathematical modeling outputs in Africa, with South Africa and Morocco having the most capacity and Central and East African countries like Chad, Rwanda, and Uganda having limited outputs [11]. A 2022 report identified training and mentorship as key approaches to strengthening mathematical modeling capacity in Africa [12].…”

Section: Introductionmentioning

confidence: 99%

“…This report recommended that trainings should be structured as short courses, formal training with continuous support as students develop models, and project-driven training. Other researchers have also highlighted the need for infectious disease modeling training in the continent [10,11].…”

Section: Introductionmentioning

confidence: 99%

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Evidence-based Decision Making: Infectious Disease Modeling Training for Policymakers in East Africa

Ofori,

Dankwa,

Ngwakongnwi

et al. 2024

agh

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Background: Mathematical modeling of infectious diseases is an important decision-making tool for outbreak control. However, in Africa, limited expertise reduces the use and impact of these tools on policy. Therefore, there is a need to build capacity in Africa for the use of mathematical modeling to inform policy. Here we describe our experience implementing a mathematical modeling training program for public health professionals in East Africa. Methods: We used a deliverable-driven and learning-by-doing model to introduce trainees to the mathematical modeling of infectious diseases. The training comprised two two-week in-person sessions and a practicum where trainees received intensive mentorship. Trainees evaluated the content and structure of the course at the end of each week, and this feedback informed the strategy for subsequent weeks. Findings: Out of 875 applications from 38 countries, we selected ten trainees from three countries – Rwanda (6), Kenya (2), and Uganda (2) – with guidance from an advisory committee. Nine trainees were based at government institutions and one at an academic organization. Participants gained skills in developing models to answer questions of interest and critically appraising modeling studies. At the end of the training, trainees prepared policy briefs summarizing their modeling study findings. These were presented at a dissemination event to policymakers, researchers, and program managers. All trainees indicated they would recommend the course to colleagues and rated the quality of the training with a median score of 9/10. Conclusions: Mathematical modeling training programs for public health professionals in Africa can be an effective tool for research capacity building and policy support to mitigate infectious disease burden and forecast resources. Overall, the course was successful, owing to a combination of factors, including institutional support, trainees’ commitment, intensive mentorship, a diverse trainee pool, and regular evaluations.

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Section: In-country Expertisementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evidence-based Decision Making: Infectious Disease Modeling Training for Policymakers in East Africa

Ofori,

Dankwa,

Ngwakongnwi

et al. 2024

agh

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“…However, of all 242 papers reviewed, none focused on Brazil, while 18 included Brazil in a general analysis. Kimani et al [ 20 ] thoroughly review infectious disease modeling for SARS-CoV-2 in Africa, which is very enriching to understand the mathematical model’s role in guiding local policy. There are no reviews on Brazil’s mathematical and statistical model approach guiding the pandemic.…”

Section: Introductionmentioning

confidence: 99%

Dynamic transmission modeling of COVID-19 to support decision-making in Brazil: A scoping review in the pre-vaccine era

Berg de Almeida,

Mendes Simon,

Maria Bagattini

et al. 2023

PLOS Glob Public Health

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Brazil was one of the countries most affected during the first year of the COVID-19 pandemic, in a pre-vaccine era, and mathematical and statistical models were used in decision-making and public policies to mitigate and suppress SARS-CoV-2 dispersion. In this article, we intend to overview the modeling for COVID-19 in Brazil, focusing on the first 18 months of the pandemic. We conducted a scoping review and searched for studies on infectious disease modeling methods in peer-reviewed journals and gray literature, published between January 01, 2020, and June 2, 2021, reporting real-world or scenario-based COVID-19 modeling for Brazil. We included 81 studies, most corresponding to published articles produced in Brazilian institutions. The models were dynamic and deterministic in the majority. The predominant model type was compartmental, but other models were also found. The main modeling objectives were to analyze epidemiological scenarios (testing interventions’ effectiveness) and to project short and long-term predictions, while few articles performed economic impact analysis. Estimations of the R0 and transmission rates or projections regarding the course of the epidemic figured as major, especially at the beginning of the crisis. However, several other outputs were forecasted, such as the isolation/quarantine effect on transmission, hospital facilities required, secondary cases caused by infected children, and the economic effects of the pandemic. This study reveals numerous articles with shared objectives and similar methods and data sources. We observed a deficiency in addressing social inequities in the Brazilian context within the utilized models, which may also be expected in several low- and middle-income countries with significant social disparities. We conclude that the models were of great relevance in the pandemic scenario of COVID-19. Nevertheless, efforts could be better planned and executed with improved institutional organization, dialogue among research groups, increased interaction between modelers and epidemiologists, and establishment of a sustainable cooperation network.

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“…The final group of papers focuses on understanding the use of epidemiological modeling in LMICs. The systematic review provides vital information on what was done for modeling COVID19 in Africa ( Kimani et al, 2022 ). The authors highlight that within Africa, a small proportion of countries are producing the majority of papers, with many countries not having any modelling publications.…”

mentioning

confidence: 99%

Epidemic SI COVID-19 modeling in LMICs: Accompanying commentary

Pan‐Ngum¹,

Clapham

Dawa

et al. 2022

Epidemics

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Infectious disease modelling for SARS-CoV-2 in Africa to guide policy: A systematic review

Cited by 9 publications

References 62 publications

Evidence-based Decision Making: Infectious Disease Modeling Training for Policymakers in East Africa

Evidence-based Decision Making: Infectious Disease Modeling Training for Policymakers in East Africa

Dynamic transmission modeling of COVID-19 to support decision-making in Brazil: A scoping review in the pre-vaccine era

Epidemic SI COVID-19 modeling in LMICs: Accompanying commentary

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