Predicting Measles Occurrence Using Some Weather Variables in Kano, North western Nigeria

Abstract

The impact of weather variables on some human diseases are now of major concern worldwide. Nigeria cannot be left out because is also home to many infectious diseases. Measles is a highly contagious disease caused by measles virus characterized by fever, fatigue and cough before the onset of rash. This study seeks to clarify the mechanism linking weather and measles occurrence and examine the possibility of predicting the number of expected cases of the disease using some weather variables and the reported cases from standard government hospitals within the study area. Monthly (1997-2012) measles cases in Kano were retrieved from Muhammad Abdullahi Wase Specialist hospital, Kano, a standard government hospital situated at the Centre of Kano city. The weather data during (1997-2012) monthly rainfall, relative humidity, minimum and maximum temperature and wind speed were obtained from Nigerian Meteorological agency. We performed the Spearman rank correlation tests to examine the relationship between monthly incidence and the weather variables, and used the statistically significant variables to develop models. The monthly (1997–2012) measles incidence was modeled using a Poisson regression model combined with Autoregressive moving average model (ARIMA). The results showed a linear effects of maximum and minimum temperature and relative humidity on measles incidence. The relative risk for the measles incidence associated with the 75th percentile of maximum temperature has a temperature window of approximately 38 to 40°C and relative humidity ranging from 19-30% within which the highest risk of measles prevalence is observed in Kano. Low relative humidity is a risk factor of measles morbidity. The months of April and May are month with highest occurrence of measles cases. Of all the models tested, the poison model combinations of all the weather variables used fits the measles incidence data best according to normalized Akaike information criterion (AIC) and goodness-of-fit criteria. Also, ARIMA (0, 0, 1) is observed to be the best fits for measles incidence data according to normalized Bayesian information criterion (BIC) and goodness-of-fit criteria. In all, we found that wind speed is not a limiting factor for measles transmission in Kano. Our findings highlight the need to pay more attention to the weather/climate variations and increase the immunity of susceptible population for possible measles reduction.