Infectious disease modelling for SARS-CoV-2 in Africa to guide policy: A systematic review
Teresia Njoki Kimani,
Mutono Nyamai,
Lillian Owino,
Anita Makori,
Loice Achieng Ombajo,
MaryBeth Maritim,
Omu Anzala,
S.M. Thumbi
Affiliations
Teresia Njoki Kimani
KAVI-Institute of Clinical Research, University of Nairobi, Kenya; Center for Epidemiological Modelling and Analysis, University of Nairobi, Kenya; Paul G Allen School for Global Animal Health, Washington State University, United States; Ministry of Health Kenya, Kiambu County, Kenya; Corresponding author at: KAVI-Institute of Clinical Research, University of Nairobi, Kenya.
Mutono Nyamai
Center for Epidemiological Modelling and Analysis, University of Nairobi, Kenya; Paul G Allen School for Global Animal Health, Washington State University, United States; Institute of Tropical and Infectious Diseases, University of Nairobi, Kenya
Lillian Owino
Center for Epidemiological Modelling and Analysis, University of Nairobi, Kenya; Institute of Tropical and Infectious Diseases, University of Nairobi, Kenya
Anita Makori
Center for Epidemiological Modelling and Analysis, University of Nairobi, Kenya; Paul G Allen School for Global Animal Health, Washington State University, United States; Institute of Tropical and Infectious Diseases, University of Nairobi, Kenya
Loice Achieng Ombajo
Center for Epidemiological Modelling and Analysis, University of Nairobi, Kenya; Department of Clinical Medicine and Therapeutics, University of Nairobi, Kenya
MaryBeth Maritim
Department of Clinical Medicine and Therapeutics, University of Nairobi, Kenya
Omu Anzala
KAVI-Institute of Clinical Research, University of Nairobi, Kenya
S.M. Thumbi
Center for Epidemiological Modelling and Analysis, University of Nairobi, Kenya; Paul G Allen School for Global Animal Health, Washington State University, United States; Institute of Tropical and Infectious Diseases, University of Nairobi, Kenya; Department of Clinical Medicine and Therapeutics, University of Nairobi, Kenya; South African Center for Epidemiological Modelling and Analysis, South Africa; Institute of Immunology and Infection Research, University of Edinburgh, Scotland
Applied epidemiological models have played a critical role in understanding the transmission and control of disease outbreaks. Their utility and accuracy in decision-making on appropriate responses during public health emergencies is however a factor of their calibration to local data, evidence informing model assumptions, speed of obtaining and communicating their results, ease of understanding and willingness by policymakers to use their insights. We conducted a systematic review of infectious disease models focused on SARS-CoV-2 in Africa to determine: a) spatial and temporal patterns of SARS-CoV-2 modelling in Africa, b) use of local data to calibrate the models and local expertise in modelling activities, and c) key modelling questions and policy insights. We searched PubMed, Embase, Web of Science and MedRxiv databases following the PRISMA guidelines to obtain all SARS-CoV-2 dynamic modelling papers for one or multiple African countries. We extracted data on countries studied, authors and their affiliations, modelling questions addressed, type of models used, use of local data to calibrate the models, and model insights for guiding policy decisions. A total of 74 papers met the inclusion criteria, with nearly two-thirds of these coming from 6% (3) of the African countries. Initial papers were published 2 months after the first cases were reported in Africa, with most papers published after the first wave. More than half of all papers (53, 78%) and (48, 65%) had a first and last author affiliated to an African institution respectively, and only 12% (9) used local data for model calibration. A total of 60% (46) of the papers modelled assessment of control interventions. The transmission rate parameter was found to drive the most uncertainty in the sensitivity analysis for majority of the models. The use of dynamic models to draw policy insights was crucial and therefore there is need to increase modelling capacity in the continent.
