Diagnosis of the relationship between weather variables and occurrences of measles in soroti and serere districts

Abstract

It is commonly accepted that climate plays a role in the transmission of many infectious diseases, some of which are among the most important causes of mortality and morbidity in developing countries. Often these diseases occur in epidemics which may be triggered by variations in climatic conditions that favour higher transmission rates. This study aims to investigate the relationship between weather variables and the occurrence of measles in Soroti and Serere districts in the Teso sub-region of North -Eastern Uganda. Monthly data of patients with measles from 1995 to 20 I0 was collected from the District Health Office, Soroti. Monthly data for rainfall, maximum temperature, minimum temperature, relative humidity (at 0600 and 1200 UTC), and wind run for the same years was collected from the Department of Meteorology, Kampala. The methodologies included assessing the seasonal pattern of measles outbreak as well as relating the weather parameters with the occurrences of measles at a lag on one season. It was found out that an outbreak of measles occurs every two years on average. Analysis showed that these weather variables have significant correlation with the occurrence of measles. There was correlation of minimum temperatures in the November-February season with measles occurrences in March-May season. There was also correlation of rainfall in March - May season with measles occurrences in the July-October season. There was correlation of maximum temperatures and relative humidity at 0600 UTe in the July-October season with measles occurrences in the November-February season. Simple and multiple linear regression models developed particularly with minimum temperatures, maximum temperatures, rainfall, a combination of minimum temperatures and wind run, and a combination of maximum and relative humidity at 0600 gave good estimates of measles incidences at one season's lag. The relationship was taken to be significant at 0.05. This enabled the construction of a model to predict measles occurrences.