Determining the Association between Climatic Variables and Malaria Incidence in Kokrajhar District of Assam, India

Throughout the year, the weather conditions of Kokrajhar area are ideal for malaria transmission. Because a large portion of the district is covered with forest, different malaria transmission dynamics arise in forest and non-forest areas. Observed malaria incidence rates in the forest, non-forest, and entire district from 2001 to 2010 were used to examine the temporal relationship between malaria incidence and meteorological variables. Pearson correlation analysis was used to look for links between the two. Pre-whitened series of meteorological variables and malaria data were compared using cross-correlation testing. Weighted least squares regression was used to build models for explaining and estimating malaria incidence rates, whereas linear regressions were utilized to find linear connections between climatic conditions and malaria incidence. The seasonal index was used to examine the annual concentration of malaria incidence using the Markham technique. Malaria seasons differ between forest and non-forest areas. The incidence of forest malaria was positively connected with relative humidity, while the incidence of non-forest malaria was inversely correlated with temperature series. The forest area has a higher seasonality of malaria concentration than the non-forest area. There was a significant relationship between annual fluctuations in malaria cases in the forest and temperature (coeff =0.689, p=0.040). Separate reliable models constructed for forecasting malaria incidence rates based on the combined influence of climatic variables on malaria incidence in different areas of the district were able to explain substantial percentage of observed variability in the incidence rates for all). There is a complex relationship between climate conditions and the district's malaria incidence. Climate variables have varied effects on malaria incidence in forest and non-forest areas. Rainfall is a major factor in determining the prevalence of malaria in the district. Malaria parasites had adapted to a relative humidity level higher than the typical range for transmission in the district. Instead of using the separate effects of the climatic factors, the combined effects were used to build models.