Comparative spatial-temporal analysis and predictive modeling of climate change-induced malaria vectors' invasion in new hotspots in Kenya

Climate change/variability is a major driving factor among others that contribute to the spread of suitable malaria vectors' geographical extent. The current study employed comparative spatial-temporal analysis using bioclimatic envelope modeling to predict and quantify the possible surge of suitable malaria vectors' habitats in new hotspots in Kenya. BIOCLIM and BIOCLIM True/False models were run with model data from HADCM3, CCCMA and SCIRO IPCC future climatic projections under A2a scenario. Prediction and projection of the malaria vectors' prevalence and distribution were done for the whole country. Spatial-temporal models were generated for the baseline climate, and projections were done to depict how the vectors are likely to be distributed by the years 2020, 2050 and 2080 under the influence of climate change. The results showed that the highest suitable malaria vectors' habitats by area was 227, 092 km(2) obtained when prediction was done with HADCM3 future climate by the year 2050. The least suitable habitats by area was 80, 060 km(2) which was predicted with CCCMA projection by the year 2050. Ecological niche prediction from HADCM3 and CSIRO showed a similar trend although at different magnitudes. The prediction results portrayed high likelihood of shift in some suitable habitats that could turn unsuitable, while new hotspots are likely to emerge. The BIOCLIM prediction with the three future climate models showed that the current endemic zones of the lake region and south coastal strip of the Indian Ocean will still remain suitable habitats but with a decline shift in spatial extent by the year 2020, then start to expand by the years 2050-2080. Predictions from HADCM3 by the year 2050 has shown possible wide spread of malaria spatial extents in counties like Narok, Kajiado, Kitui, Makueni, Machakos, Meru, Marsabit, Isiolo, Samburu, Baringo, West Pokot, Turkana and Mandera, while a few others of lower extent might have some emerging isolated hotspots. Laikipia County might become unsuitable habitat for malaria vectors by the year 2050, and the case may remain the same by the year 2080. The malaria burden is likely to shift from Laikipia to the neighboring counties of Baringo, Isiolo, Meru and Turkana. In conclusion, malaria vectors are likely to spread in new continuous and isolated hotspots with future likely increase in malaria prevalence hence possible epidemic upsurge by the year 2050. Consequently, evidence-based scientific research can be utilized to guide policy for sustainable development in the health agenda.