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Infectious Diseases: preparing for the future

Author: Dr Simon I. Hay, Dr Andrew J. Tatem, Dr Carlos A. Guerra and Professor Robert W. Snow
Organisation: Centre for Geographic Medicine, KEMRI/Wellcome Trust Collaborative Programme, Kenya University of Ox
Publish Date: 2006
Country: Africa
Sector: Health
Method: Forecasting
Theme: Infectious Diseases
Type: Other publication
Language: English
Tags: Africa, Infectious Diseases, Malaria

We have simulated the combined effects of climate change, population growth and urbanisation on the population at risk (PAR) of Plasmodium falciparum malaria in Africa. PAR is defined as the number of people living in areas of climatic suitability for stable P. falciparum malaria transmission. The results suggest that the PAR will change from approximately 0.63 billion in 2005, to 0.87 billion in 2015 and 1.15 billion in 2030. These PAR numbers are presented sequentially for each of these influences, so that the magnitude of each effect and its direction could be established before they were integrated.

The majority of this future PAR change can be attributed to the massive rates of population growth expected on the continent. These PAR changes are reduced slightly because populations in large urban areas suffer reduced malaria risk. Climate change is likely to further increase the numbers at risk. These increases were small, however, when compared with demographic changes. There also remain considerable difficulties in disentangling the effects of real climate change from artefacts introduced by comparing our detailed spatial knowledge of climate today with the poor spatial resolution models of the future. These results are discussed against a background of existing work and a previous review (Snow et al., 2006) that outlined the suite of additional drivers that will affect the evolution of malaria’s epidemiology in the next quarter of a century.

Some of the difficulties in using PAR to estimate future morbidity and mortality rates are also discussed. Research avenues are suggested to improve this work by: (i) understanding better assumptions made in demographic change; (ii) incorporating additional land-use change influences; and, (iii), most importantly, moving to a probabilistic treatment of uncertainty so that the true confidence of such estimates can be conveyed unambiguously to policymakers.
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