title: Predicting malaria occurrence in southwest and north central Nigeria using meteorological parameters.
abstract: Malaria is a major public health problem especially in the tropics with the potential to significantly increase in response to changing weather and climate. This study explored the impact of weather and climate and its variability on the occurrence and transmission of malaria in Akure, the tropical rain forest area of southwest and Kaduna, in the savanna area of Nigeria. We investigate this supposition by looking at the relationship between rainfall, relative humidity, minimum and maximum temperature, and malaria at the two stations. This study uses monthly data of 7 years (2001-2007) for both meteorological data and record of reported cases of malaria infection. Autoregressive integrated moving average (ARIMA) models were used to evaluate the relationship between weather factors and malaria incidence. Of all the models tested, the ARIMA (1, 0, 1) model fits the malaria incidence data best for Akure and Kaduna according to normalized Bayesian information criterion (BIC) and goodness-of-fit criteria. Humidity and rainfall have almost the same trend of association in all the stations while maximum temperature share the same negative association at southwestern stations and positive in the northern station. Rainfall and humidity have a positive association with malaria incidence at lag of 1 month. In all, we found that minimum temperature is not a limiting factor for malaria transmission in Akure but otherwise in the other stations.
authors.affiliation: Department of Meteorology, Federal University of Technology, Akure, Ondo State, Nigeria. desmondbobola@yahoo.com
authors.forename: A
authors.initials: A
authors.lastname: Akinbobola
authors.forename: J Bayo
authors.initials: JB
authors.lastname: Omotosho
mesh_terms: D002980:Climate; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D019353:Endemic Diseases / Q000941:ethics*; D006801:Humans; D015994:Incidence; D008288:Malaria / Q000453:epidemiology*; D015233:Models, Statistical; D009549:Nigeria / Q000453:epidemiology; D016016:Proportional Hazards Models; D018570:Risk Assessment / Q000379:methods; D014887:Weather