Effects of climatological parameters in modeling and forecasting seasonal influenza transmission in Abidjan, Cote d’Ivoire
| Autor: | Djibril Cherif, N.S. Dagnan, T. Williams, Daouda Coulibaly, Youssouf Traoré, P.D. Kouassi, K.D. Ekra, I. Tiembré, Herve A. Kadjo, Bertin Kouakou, Anderson K. N’gattia, N. Talla Nzussouo |
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| Jazyk: | angličtina |
| Předmět: |
Male
0301 basic medicine Multivariate statistics Multivariate analysis Rain 03 medical and health sciences Bayesian information criterion Influenza Human Statistics Humans Medicine Autoregressive integrated moving average business.industry lcsh:Public aspects of medicine Temperature Univariate Modeling Public Health Environmental and Occupational Health Bayes Theorem Humidity Regression analysis lcsh:RA1-1270 Climatological parameters Abidjan Models Theoretical Confidence interval Influenza 3. Good health Cote d'Ivoire 030104 developmental biology 13. Climate action Multivariate Analysis Regression Analysis Female Seasons Akaike information criterion business Forecasting Research Article |
| Zdroj: | BMC Public Health, Vol 16, Iss 1, Pp 1-7 (2016) BMC Public Health |
| ISSN: | 1471-2458 |
| DOI: | 10.1186/s12889-016-3503-1 |
| Popis: | Background In temperate regions, influenza epidemics occur in the winter and correlate with certain climatological parameters. In African tropical regions, the effects of climatological parameters on influenza epidemics are not well defined. This study aims to identify and model the effects of climatological parameters on seasonal influenza activity in Abidjan, Cote d’Ivoire. Methods We studied the effects of weekly rainfall, humidity, and temperature on laboratory-confirmed influenza cases in Abidjan from 2007 to 2010. We used the Box-Jenkins method with the autoregressive integrated moving average (ARIMA) process to create models using data from 2007–2010 and to assess the predictive value of best model on data from 2011 to 2012. Results The weekly number of influenza cases showed significant cross-correlation with certain prior weeks for both rainfall, and relative humidity. The best fitting multivariate model (ARIMAX (2,0,0) _RF) included the number of influenza cases during 1-week and 2-weeks prior, and the rainfall during the current week and 5-weeks prior. The performance of this model showed an increase of >3 % for Akaike Information Criterion (AIC) and 2.5 % for Bayesian Information Criterion (BIC) compared to the reference univariate ARIMA (2,0,0). The prediction of the weekly number of influenza cases during 2011–2012 with the best fitting multivariate model (ARIMAX (2,0,0) _RF), showed that the observed values were within the 95 % confidence interval of the predicted values during 97 of 104 weeks. Conclusion Including rainfall increases the performances of fitted and predicted models. The timing of influenza in Abidjan can be partially explained by rainfall influence, in a setting with little change in temperature throughout the year. These findings can help clinicians to anticipate influenza cases during the rainy season by implementing preventive measures. Electronic supplementary material The online version of this article (doi:10.1186/s12889-016-3503-1) contains supplementary material, which is available to authorized users. |
| Databáze: | OpenAIRE |
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