Analytical Support for COVID-19 Tracking Survey in Africa


Project Overview


To produce usable, representative data from repeated phone surveys on COVID-19 in seven countries across Sub-Saharan Africa, so policymakers can understand the effects on subgroups like women, rural populations and the very poor.

Project Motivation

FinMark Trust, a non-profit organization based in South Africa that focuses on financial inclusion in low and middle income countries, asked Mathematica to develop a statistical modelling technique to improve the accuracy of phone survey data so that policymakers can understand the effects of COVID-19 on African populations.

Prepared For

FinMark Trust

In this project, Mathematica provides analytical support for FinMark Trust's COVID-19 tracking survey.

FinMark Trust, with their subcontractor GeoPoll, is undertaking a phone-based survey on the effects of COVID-19 in seven countries across Sub-Saharan Africa: Ghana, Kenya, Nigeria, Rwanda, South Africa, Uganda and Zambia. The survey themes include health and risk behaviors, food security, income, work and job security, personal safety concerns, and access to government and community support.

Mathematica uses an innovative Multi-level regression with post-stratification (MrP) technique to create usable sub-population and nationally-weighted datasets. MrP is a model-based poststratification method that is used to adjust results from a nonrepresentative sample to a target population. It allows to create more representative results and more robust estimations for groups with low mobile phone survey coverage (for example, rural women).

To support dissemination of the survey results and promote data-based approaches to decision making, Mathematica developed a publicly shared blog focusing on the effects of COVID-19 on African populations over time. 

Related Staff

Sarah Hughes

Sarah Hughes

Senior Fellow

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Jonathan Gellar

Jonathan Gellar

Senior Statistician

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