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Infectious disease modelling for SARS-CoV-2 in Africa to guide policy: A systematic review.
Kimani, Teresia Njoki; Nyamai, Mutono; Owino, Lillian; Makori, Anita; Ombajo, Loice Achieng; Maritim, MaryBeth; Anzala, Omu; Thumbi, S M.
Affiliation
  • Kimani TN; 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. Electronic add
  • Nyamai M; 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.
  • Owino L; Center for Epidemiological Modelling and Analysis, University of Nairobi, Kenya; Institute of Tropical and Infectious Diseases, University of Nairobi, Kenya.
  • Makori A; 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.
  • Ombajo LA; Center for Epidemiological Modelling and Analysis, University of Nairobi, Kenya; Department of Clinical Medicine and Therapeutics, University of Nairobi, Kenya.
  • Maritim M; Department of Clinical Medicine and Therapeutics, University of Nairobi, Kenya.
  • Anzala O; KAVI-Institute of Clinical Research, University of Nairobi, Kenya.
  • Thumbi SM; 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, Un
Epidemics ; 40: 100610, 2022 09.
Article in En | MEDLINE | ID: mdl-35868211
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.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Communicable Diseases / COVID-19 Type of study: Prognostic_studies / Systematic_reviews Limits: Humans Language: En Journal: Epidemics Year: 2022 Document type: Article Country of publication: Netherlands

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Communicable Diseases / COVID-19 Type of study: Prognostic_studies / Systematic_reviews Limits: Humans Language: En Journal: Epidemics Year: 2022 Document type: Article Country of publication: Netherlands