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      Time series analysis of dengue incidence in Guadeloupe, French West Indies: Forecasting models using climate variables as predictors

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          Abstract

          Background

          During the last decades, dengue viruses have spread throughout the Americas region, with an increase in the number of severe forms of dengue. The surveillance system in Guadeloupe (French West Indies) is currently operational for the detection of early outbreaks of dengue. The goal of the study was to improve this surveillance system by assessing a modelling tool to predict the occurrence of dengue epidemics few months ahead and thus to help an efficient dengue control.

          Methods

          The Box-Jenkins approach allowed us to fit a Seasonal Autoregressive Integrated Moving Average (SARIMA) model of dengue incidence from 2000 to 2006 using clinical suspected cases. Then, this model was used for calculating dengue incidence for the year 2007 compared with observed data, using three different approaches: 1 year-ahead, 3 months-ahead and 1 month-ahead. Finally, we assessed the impact of meteorological variables (rainfall, temperature and relative humidity) on the prediction of dengue incidence and outbreaks, incorporating them in the model fitting the best.

          Results

          The 3 months-ahead approach was the most appropriate for an effective and operational public health response, and the most accurate (Root Mean Square Error, RMSE = 0.85). Relative humidity at lag-7 weeks, minimum temperature at lag-5 weeks and average temperature at lag-11 weeks were variables the most positively correlated to dengue incidence in Guadeloupe, meanwhile rainfall was not. The predictive power of SARIMA models was enhanced by the inclusion of climatic variables as external regressors to forecast the year 2007. Temperature significantly affected the model for better dengue incidence forecasting (p-value = 0.03 for minimum temperature lag-5, p-value = 0.02 for average temperature lag-11) but not humidity. Minimum temperature at lag-5 weeks was the best climatic variable for predicting dengue outbreaks (RMSE = 0.72).

          Conclusion

          Temperature improves dengue outbreaks forecasts better than humidity and rainfall. SARIMA models using climatic data as independent variables could be easily incorporated into an early (3 months-ahead) and reliably monitoring system of dengue outbreaks. This approach which is practicable for a surveillance system has public health implications in helping the prediction of dengue epidemic and therefore the timely appropriate and efficient implementation of prevention activities.

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          Most cited references 46

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          Dengue and dengue hemorrhagic fever.

          Dengue fever, a very old disease, has reemerged in the past 20 years with an expanded geographic distribution of both the viruses and the mosquito vectors, increased epidemic activity, the development of hyperendemicity (the cocirculation of multiple serotypes), and the emergence of dengue hemorrhagic fever in new geographic regions. In 1998 this mosquito-borne disease is the most important tropical infectious disease after malaria, with an estimated 100 million cases of dengue fever, 500,000 cases of dengue hemorrhagic fever, and 25,000 deaths annually. The reasons for this resurgence and emergence of dengue hemorrhagic fever in the waning years of the 20th century are complex and not fully understood, but demographic, societal, and public health infrastructure changes in the past 30 years have contributed greatly. This paper reviews the changing epidemiology of dengue and dengue hemorrhagic fever by geographic region, the natural history and transmission cycles, clinical diagnosis of both dengue fever and dengue hemorrhagic fever, serologic and virologic laboratory diagnoses, pathogenesis, surveillance, prevention, and control. A major challenge for public health officials in all tropical areas of the world is to develop and implement sustainable prevention and control programs that will reverse the trend of emergent dengue hemorrhagic fever.
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            On a measure of lack of fit in time series models

             G M Ljung,  G. BOX (1978)
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              Potential effect of population and climate changes on global distribution of dengue fever: an empirical model.

              Existing theoretical models of the potential effects of climate change on vector-borne diseases do not account for social factors such as population increase, or interactions between climate variables. Our aim was to investigate the potential effects of global climate change on human health, and in particular, on the transmission of vector-borne diseases. We modelled the reported global distribution of dengue fever on the basis of vapour pressure, which is a measure of humidity. We assessed changes in the geographical limits of dengue fever transmission, and in the number of people at risk of dengue by incorporating future climate change and human population projections into our model. We showed that the current geographical limits of dengue fever transmission can be modelled with 89% accuracy on the basis of long-term average vapour pressure. In 1990, almost 30% of the world population, 1.5 billion people, lived in regions where the estimated risk of dengue transmission was greater than 50%. With population and climate change projections for 2085, we estimate that about 5-6 billion people (50-60% of the projected global population) would be at risk of dengue transmission, compared with 3.5 billion people, or 35% of the population, if climate change did not happen. We conclude that climate change is likely to increase the area of land with a climate suitable for dengue fever transmission, and that if no other contributing factors were to change, a large proportion of the human population would then be put at risk.
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                Author and article information

                Journal
                BMC Infect Dis
                BMC Infectious Diseases
                BioMed Central
                1471-2334
                2011
                9 June 2011
                : 11
                : 166
                1471-2334-11-166
                10.1186/1471-2334-11-166
                3128053
                21658238
                Copyright ©2011 Gharbi et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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                Research Article

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