Now, with an unprecedented level of accuracy, an international team of researchers, led by Dr. Moritz Kramer at the University of Oxford’s Department of Zoology, have used statistical mapping techniques to predict where the species will spread over an immediate, medium and long-term time-scale. Published today in Nature Microbiology they pinpoint this information with the precision of 5×5 km.
The researchers have used 35 years of historic data, together with 17 of the highest-regarded and accepted climate change models to create a tool for public health officials which will allow them to target resource most efficiently and effectively to combat disease outbreak.
Dr. Moritz Kramer said: ‘By combining data on the history of mosquito species spread, human population movements and climatic factors we have been able to reconstruct and predict the future of these disease-carrying mosquitoes. We hope that these high resolution maps will be used to target specific geographic areas for surveillance, control and elimination of these harmful mosquito populations.’
Despite the picture they paint being not quite as bleak as previous studies have suggested, the researchers’ results show that areas of particular concern are large urban areas in the southern United States and southern China.
However, with the precision now afforded in these high resolution maps, it is hoped that timely, focused and targeted interventions can be mobilised to prevent future outbreaks of disease while simultaneously being most resource efficient.
Dr. Simon I. Hay, Director of Geospatial Science at IHME and Professor of Health Metrics Sciences at the University of Washington said: ‘With this new work, we can start to anticipate how the transmission of diseases like dengue and Zika might be influenced by a variety of environmental changes. Incorporating this information into future scenarios of risk can help policymakers prepare for and predict health impacts, to help guide strategies to limit the spread of these mosquito species, an essential step to reduce the disease burden.’
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