Determining the Association between Climatic Variables and Malaria Incidence in Kokrajhar District of Assam, India
The Kokrajhar region’s weather is favourable for malaria transmission all year round. Due to the district’s extensive forest coverage, the dynamics of malaria transmission in forested and non-forested areas differ. The temporal link between malaria incidence and climatic factors was investigated using observed malaria incidence rates in the forest, non-forest, and total district from 2001 to 2010. To seek for connections between the two, Pearson correlation analysis was utilised. Cross-correlation testing was used to compare pre-whitened series of meteorological variables with data related to malaria. While linear regressions were employed to discover linear relationships between climatic conditions and malaria incidence, weighted least squares regression was used to construct models for explaining and estimating malaria incidence rates. Using the Markham approach, the seasonal index was utilised to look at the yearly concentration of malaria incidence. There are several malaria seasons in locations with and without forests. While the frequency of non-forest malaria was negatively linked with temperature series, the incidence of malaria in forests was favourably correlated with relative humidity. Comparatively to non-forest areas, the forest region has a greater seasonality of malaria concentration. Annual variations in malaria cases in the forest were significantly correlated with temperature (coeff =0.689, p=0.040). A substantial portion of the observed variability in the incidence rates for all may be explained by separate, credible models that were built for forecasting malaria incidence rates based on the combined effect of meteorological factors on malaria incidence in different sections of the district. The occurrence of malaria in the region is complicated by the local climate. In both forests and non-forest settings, the incidence of malaria is impacted differently by climate factors. The amount of rainfall has a significant impact on the district’s malaria prevalence. Parasites that cause malaria have evolved to a relative humidity level that was greater above the district’s usual range for transmission. The combined impacts of the climatic conditions were utilised to develop models rather than the individual effects.
Dimacha Dwibrang Mwchahary,
Department of Mathematics, Kokrajhar Govt. College, India.
Dilip C. Nath,
Former VC, Assam University, Assam, India.
Please see the link here: https://stm.bookpi.org/RDASS-V5/article/view/7287
Keywords: Monthly malaria incidence rate, climatic variables, forest area, non-forest area, modeling.