Mapping urban physical distancing constraints, sub-Saharan Africa: a case study from Kenya

With the onset of the coronavirus disease 2019 (COVID-19) pandemic, public health measures such as physical distancing were recommended to reduce transmission of the virus causing the disease. However, the same approach in all areas, regardless of context, may lead to measures being of limited effectiveness and having unforeseen negative consequences, such as loss of livelihoods and food insecurity. A prerequisite to planning and implementing effective, context-appropriate measures to slow community transmission is an understanding of any constraints, such as the locations where physical distancing would not be possible. Focusing on sub-Saharan Africa, we outline and discuss challenges that are faced by residents of urban informal settlements in the ongoing COVID-19 pandemic. We describe how new geospatial data sets can be integrated to provide more detailed information about local constraints on physical distancing and can inform planning of alternative ways to reduce transmission of COVID-19 between people. We include a case study for Nairobi County, Kenya, with mapped outputs which illustrate the intra-urban variation in the feasibility of physical distancing and the expected difficulty for residents of many informal settlement areas. Our examples demonstrate the potential of new geospatial data sets to provide insights and support to policy-making for public health measures, including COVID-19

Estimating the malaria risk of African mosquito movement by air travel

Background: The expansion of global travel has resulted in the importation of African Anopheles mosquitoes; giving rise to cases of local malaria transmission. Here; cases of airport malaria are used to quantify; using a combination of global climate and air traffic volume; where and when are the greatest risks of a Plasmodium falciparum-carrying mosquito being importated by air. This prioritises areas at risk of further airport malaria and possible importation or reemergence of the disease. Methods: Monthly data on climate at the Worlds' major airports were combined with air traffic information and African malaria seasonality maps to identify; month-by-month; those existing and future air routes at greatest risk of African malaria-carrying mosquito importation and temporary establishment. Results: The location and timing of recorded airport malaria cases proved predictable using a combination of climate and air traffic data. Extending the analysis beyond the current air network architecture enabled identification of the airports and months with greatest climatic similarity to Plasmodium falciparum endemic regions of Africa within their principal transmission seasons; and therefore at risk should new aviation routes become operational. Conclusions: With the growth of long haul air travel from Africa; the identification of the seasonality and routes of mosquito importation is important in guiding effective aircraft disinsection and vector control. The recent and continued addition of air routes from Africa to more climatically similar regions than Europe will increase movement risks. The approach outlined here is capable of identifying when and where these risks are greatest