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Background: Several statistical models are built to associate climate sensitive variables such as temperature, rainfall, relative humidity and wind speed with selected water-borne (WB) diseases namely enteric fever, diarrhea and dysentery and vector-borne (VB) diseases namely malaria, encephalitis and leishmaniasis separately based upon daily time series data. Objective: The objective of the paper is to associate climate sensitive variables with WB and VB diseases in the context of Nepal through building statistical models. Materials and Methods : Analysis is based upon the ecological study design with five years of daily hospital inpatient data collected from 22 leading hospitals of 10 districts spread across all the three ecobelts of Nepal and corresponding meteorological data covering 16 stations of the selected districts collected from Department of Hydrology and Meteorology for the period 2009-2014. Negative binomial model is found suitable and used to account the over-dispersed count response variables. Results: Temperature is found to be consistently and positively associated with the accounted disease hospitalizations with around 3-9% (95% CI: 1.3-10.1% combined) and 2-12% (95% CI: 0.3-15.1% combined) estimated increase in WB and VB diseases per 10C increase in average temperature, respectively. However, the same type and level of consistency are not detected in the remaining meteorological variables in the presence of confounders like annual trend, holiday effect and seasonality. Conclusion: Under the climate change scenario of Nepal, WB and VB diseases that can be attributed to rise in temperature is expected to increase in future with substantial attributable burden of diseases. Consequently, Nepal needs to face the challenges of climate change by improving health facilities, reducing poverty among Nepalese people, implementing suitable adaptation and vulnerability related plans and policies, widespread use of eco-friendly energy and technology, etc. along with achieving the targeted sustainable development goals in years to come. Nepalese Journal of Statistics, Vol. 2, 11-26

Negative Binomial Model in Linking Water-borne and Vector-borne Disease Hospitalizations with Climate Sensitive Variables in Nepal