Understanding the spatial dynamics of populations at grid level can be important for emergency response and resource allocation. High and low risk regions were identified by integrating GIS grid based and multi-criteria analysis to provide information about high risk Covid19 populations across the country. Individual and Transmission risk factors namely; population density, household size, smoking population, mode of transport, access to water and handwashing and the elderly population. A spatially weighted index model was then implemented for developing a risk assessment model.
The map displays the risk profile of Nairobi’s population most at risk to severe complications if they contract covid19 due to the socioeconomic factors considered in the model.
For a disease impacting people globally, hyper-local information on risk factors, vulnerable populations and the best means to reach them may radically transform the pandemic response. The risk profile further exposes the breadth of social and economic inequities. The map clearly displays slum areas such as Kwa Njenga, Kwa Reuben, Viwandani, Imara Daima as the highest risk areas in Nairobi County. Social distancing, self-isolation and handwashing are impossible luxuries in these areas. Strategic partnerships and well-coordinated efforts by the public, private and non-governmental will play a key role in providing safety nets to the most vulnerable populations during this pandemic.
View Kenya’s population risk profile on this interactive dashboard at ward level.
Data source: Fraym (https://go.fraym.io/coviddatafraym)