Temporal Cross-Correlation and Spatial Analysis of Pneumonia Occurrence in Relation to Population and Thermal Environment in Ondo State, Nigeria

This study investigated the temporal cross-correlation and spatial dynamics of occurrence of pneumonia in relation to human population and meteorological/ biometeorological variables in Ondo State, Nigeria over the period 1998–2008 to better understand the role of the thermal environment in the occurrence of pneumonia. The statistical relationship between monthly pneumonia case count data series and monthly mean value of meteorological/ biometeorological parameters series were analyzed over the study period. The lag period between pneumonia and each meteorological/ biometeorological parameter were determined, also stepwise regression analysis was applied in order to determine the meteorological/ biometeorological parameters that lead to statistical significant in reported cases of pneumonia. The logarithm of reported cases of pneumonia in relation to population of each local government area (LGA) showed that 5, 8 and 5 LGAs belongs to high, middle and low classes respectively. There was a significant correlation (P<0.01) between monthly cases of pneumonia and thermal environmental factors. High transmission of pneumonia occurred in the months of June to October with peak period in July during high rainfall and regular surface wetness. A combinations of rainfall, air temperature and relative humidity at lag of zero month each with solar radiation at one month lead-time accounted for 87.6% of pneumonia occurrence in humid forest zone of Ondo State. For derived savannah, combinations of air temperature and rainfall at lag of zero month each with solar radiation at one month lead-time and wind speed at two month lead-time accounted for 86.8% variation in the occurrence of pneumonia. The statistical model of this study would be a valuable tool for early warning system for predicting the magnitude of occurrence of pneumonia up to one month ahead if the total monthly rainfall, mean air temperature and relative humidity for the future month can be predicted with high certainty.

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