Impact of Climate Factors in Modeling and Predicting the Transmission of Meningitis in Africa: The Case of Burkina Faso
Author(s): Haoua Tall, Issaka Yameogo, Ryan Novak, Lionel L Ouedraogo, Ouedraogo Y Ousmane, Tene Alima Essoh, Jennifer C Moisi, Bazongo Baguinebie, Souleymane Sakande
Background: Meningitis is a major cause of morbidity in the world. Previous studies showed that weather factors influence the occurrence of meningitis. A multiple linear regression model was developed to forecast meningitis cases in Burkina Faso using weather factors. However, the multivariate linear regression model based on times series data may produce fallacious results given the autocorrelation of errors.
Aims: The aim of the study is to develop a model to quantify the effect of weather factors on meningitis cases, and then predict the expected weekly incidences of meningitis for each district.
Data and Methods: The weekly cases of meningitis come from the Ministry of Health and covers the period 2005-2017. Weather data were collected daily in 10 meteorological stations from 2005 to 2017 and were provided by the national meteorological Agency of Burkina Faso. An ARIMAX and a multivariate linear regression models were estimated separately for each district.
Results: The multivariate linear model is inappropriate to model the number of meningitis cases due to autocorrelation of errors. With the ARIMAX Model, relative humidity is significantly associated with a decrease of meningitis cases in all of the 10 districts while temperature is significantly associated with an increase of meningitis cases in 6 of 10 districts. The effects of wind speed and rainfall are not significant at the 5% level in all 10 districts. Prediction errors reveal that ARIMAX model has better predictive power than multiple linear model.
Conclusion: The ARIMAX model is more appropriate than the multivariate linear model to analyze the dynamics of meningitis cases. Weather factors such as temperature and relative humidity have a signi