Completed Infection & Immunity Public Health & Healthcare

COVID-19 Intervention Modelling for East Africa (CIMEA)

In plain English

AI plain-English summary

East African health authorities are about to get real-time computer projections of how COVID-19 will spread through their populations, based on local phone data and household surveys. Why this matters: Most COVID-19 models were built for wealthy countries with different demographics, contact patterns, and healthcare systems. Uganda and Kenya lack the data to predict how fast the virus will move through densely populated urban slums or remote rural areas, or whether contact tracing can work where many people share a single room. The virus spreads rapidly—each infected person passes it to roughly two others, with about a week between cases—so control will be difficult without locally grounded forecasts. If this succeeds, national emergency plans in East Africa will shift from guesswork to evidence. The project will collect epidemiological, genomic, and behavioural data from health facilities and household follow-up studies, then feed those numbers into models that incorporate age-related contact patterns and real mobile-phone movement data. Results go directly to authorities and are released publicly in near real-time. The lasting impact could be a permanent change in how research informs policy decisions during health emergencies across the region.

View original technical description
COVID-19 is a global threat to health, with many countries reporting extended outbreaks. To date 9 countries in Africa have recorded infection and it seems imminent that East Africa will have introductions and onward transmission. The SARS-CoV-2 virus (the aetiological agent of COVID-19) spreads rapidly (R0~2, serial interval about 1 week), and hence control will be difficult. National plans for dealing with this public health emergency will benefit from predictions of the expected rate, distribution and extent of spread in countries throughout the region, and on the likely impact and feasibility of isolation and contact tracing interventions. We will support the emergency preparations through bespoke modelling, incorporating known demographic population structure, age-related contact patterns and existing mobile phone population movement data. In Uganda and Kenya we will collect epidemiological, genomic and behavioural data through health facility surveillance, household follow-up and contact studies to quantify uncertainties of SARS-CoV-2 virus epidemiology and contact patterns in well and unwell individuals. Results from the study will be rapidly communicated to the relevant authorities, and modelling code and analysis, and data including sequences, placed in the public domain in near real-time. This project could have lasting impact on the role of research in policy decisions.

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Researchers

James Nokes (EPMC Awardee)

Related Research

Grants with similar aims, by meaning.

Virus Genomics for Outbreak Response (ViGOR) in Central/East Africa
Mathematical Modelling for Infectious Disease Dynamics and Control in East Africa (MMIDD-EA)
Cross-AAPs acceleration of genomics for escalating infectious diseases
Mathematical modelling to improve the efficiency of vaccine development pipelines.
Modelling for Decisions in a Dynamic Africa

Original classification

Research Grant

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