Forecasting Malaria Incidence Based on Monthly Case Reports and Climatic Factors in Ubon Ratchathani Province, Thailand, 2000 – 2009

Base on Malaria count data report from 2000-2009 in Ubon Ratchathani province of north-easternThailand, malaria incidence rates are computed by rain, mean temperature, minimum temperature,maximum temperature, humidity and month. Linear regression model, Poisson and Negative binomialGLM containing additive effects associated with the season of the year, climatic factors and themalaria incidence rates in the previous months provides a good fit to the data, and can be used toprovide useful short-term forecasts. Although the season, rain, mean temperature, minimumtemperature, maximum temperature, humidity effects are all highly statistically significant, by far thebest predictor of the number of new cases occurring in any month is the disease incidence rate in thepreceding month. Having a model that provides such forecasts of disease outbreak.