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

Defining and Detecting Malaria epidemics in the Highlands of Western Kenya

"Epidemic detection algorithms are being increasingly recommended for malaria surveillance in sub-Saharan Africa. We present the results of applying three simple epidemic detection techniques to routinely collected longitudinal pediatric malaria admissions data from three health facilities in the highlands of western Kenya in the late 1980s and 1990s. The algorithms tested were chosen because they could be feasibly implemented at the health facility level in sub-Saharan Africa. Assumptions of these techniques about the normal distribution of admissions data and the confidence intervals used to define normal years were also investigated. All techniques identified two ""epidemic"" years in one of the sites. The untransformed Cullen method with standard confidence intervals detected the two ""epidemic"" years in the remaining two sites but also triggered many false alarms. The performance of these methods is discussed and comments are made about their appropriateness for the highlands of western Kenya."