SPATIO- TEMPORAL EFFECT OF CLIMATE VARIABILITY ON THE OCCURRENCE OF MENINGITIS (meningococci) IN THE SUDANO-SAHELIAN AND GUINEA SAVANNA ZONES OF NIGERIA.

Abstract

Climatic alteration is likely to have an influence on the geographic array and seasonal activity of vector species, as well as disease transmission. In view of this, the study examines the spatio-temporal effect of climate variability on the occurrence of meningitis (meningococci) in the Sudano-Sahelian and Guinea Savanna zones of Nigeria. Four specific objectives are addressed by this research, these include: (i) to examine the Spatio-temporal trend of meningitis in the Sudano- Sahelian and Guinea Savanna zones of Nigeria between 2007 and 2019, (ii) to investigate whether there is a relationship between Climatic variables (Relative Humidity, Rainfall, Temperature and wind speed) and meningitis occurrence in the study area, (iii) to analyse the impact of the climatic variables on Meningitis occurrence in the study areas, and (iv) to attempt to generate a model for predicting CSM outbreak in the study areas using climatic variables. A twelve-year period was considered from 2007 to 2019 for twelve states; Sokoto, Katsina, Borno, Jigawa, in the Sudano-Sahelian region; Kaduna, Niger, Adamawa, and Abuja in the northern part of the Guinea Savanna and, Kogi, Enugu, Benue and Kwara state in the southern part of the Guinea Savanna. The climatic parameters in consideration are mean air temperature, maximum temperature, relative humidity, and rainfall and wind speed. Meningitis data was acquired weekly from Nigeria Centre for Disease Control (NCDC), bureau of statistics while weather parameters were sourced from daily satellite data set of the National Oceanic and Atmospheric Administration (NOAA), International Research Institute for Climate and Society (IRI). This daily data was aggregated into weekly data to suit the study. The data was analysed using linear trend analysis, Pearson correlation for relationship, multiple regression analysis and Generalized linear regression model. The linear trend results revealed a decline in Cerebro-Spinal Meningitis (CSM), wind speed, maximum and minimum temperature and an increase in relative humidity and rainfall. Generally, results reveal that the individual states and regions had various explanatory weather variables influencing CSM. Climatic variables such as relative humidity, rainfall amount, maximum air temperature, mean air temperature and wind speed have a great impact on the occurrence of meningitis over the study area. Rainfall and relative humidity are seen to have inverse relationship with meningitis occurrence in the study area. In the Sudano-Sahelian region, about 77% of the observed cases in meningitis prevalence was accounted for by climatic variables at a significant level of. 0.05. In the Northern Savanna zone, these climatic variables accounted for about 79% of meningitis cases in the region, at a significant level of at 0.05. The Southern Savanna had an R2 value of 0.44 which implies that 44% of meningitis cases in Southern Savanna region were accounted for by climatic variables like relative humidity, rainfall amount, maximum temperatures, mean air temperatures and wind speed. Although the impact of these variables are low in the southern Guinea Savanna region, they are however significant at 0.05. Suffice it to add that climatic variables in Benue, Enugu and Kogi are not significant predictors of meningitis. For this reason, it can be established that climatic variables have no impact on meningitis outbreak. On modelling meningitis outbreak, different climatic variables are significant for the different zones in consideration. For northern Savanna, only three variable, mean air temperature, relative humidity and rainfall amount can be used to predict meningitis outbreak using the derived formula. In the Sudano-Sahelian region, rainfall amount and mean air temperature can be used to predict meningitis outbreak while in the Southern Savanna zone, only wind speed because it was the only variable that was statistically significant at 0.000. This study recommends that the model developed be used to forecast meningitis outbreak and also used to create a meningitis prevalence matrix based on behaviour of weather parameters.