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

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

Lessons raised by the major 2010 dengue epidemics in the French West Indies

International audienceDengue fever has been endemo-epidemic in the whole Region of America. In 2010, Guadeloupe and Martinique experienced historical epidemics, with an estimated attack rate of 10% in two islands. When considering the temporal evolution of epidemiological indicators, an unusual increase in the number of dengue cases could be detected very early. Two main factors might have facilitated the settlement of a viral transmission despite the dry season: a low immunity of the population against the circulating serotype and particular climatic conditions, notably very high temperatures which could have improved both virus and vector efficiency. This unusual situation was considered as a warning sign, and indeed led to major outbreaks in both islands a few weeks later. This event underlines that follow-up of epidemiological indicators is necessary to detect the unusual situations as soon as possible. Furthermore, development of biological and modelling tools should be promoted, as well as integrated management strategies for dengue prevention and control

Increased resting heart rate with pollutants in a population based study

Background: Air pollution is associated with cardiovascular mortality. Changes in the autonomic nervous system may contribute to cardiac arrhythmias and cardiovascular mortality. This study investigated the relations between air pollutant concentrations of sulphur dioxide (SO(2)), ozone (O(3)), nitric dioxide (NO(2)), and resting heart rate (RHR) in a population based study. Methods: A sample of 863 middle aged men and women, living in Toulouse (MONICA centre) area, was randomly recruited. A cross sectional survey on cardiovascular risk factors was carried. RHR was measured twice in a sitting position after a five minute rest. Multivariate analyses with quintiles of RHR were performed using polytomous logistic regression. Models were adjusted for temperature, season, relative humidity, sex, physical activity, blood pressure, C reactive protein, and cardiovascular drugs. Results: For NO(2), the OR (odds ratio) (95% CI) associated with an increase of 5 µg/m(3) in the current day of medical examination was 1.14 (1.03 to 1.25) in quintile Q5 of RHR compared with Q1, p for trend = 0.003. For SO(2), OR was 1.16 (0.94 to 1.44) in Q5 compared with Q1, p for trend = 0.05, and for O(3), OR was 0.96 (0.91 to 1.01) in Q5 compared with Q1, p for trend = 0.11. No significant association was seen when the daily mean concentration of NO(2), SO(2), and O(3) was considered during the previous day as well as when day lag 2 or 3 was considered. The cumulative concentration (three consecutive days) of O(3) is negatively associated with RHR (p for trend = 0.02). Conclusion: Changes in pulse rate could reflect cardiac rhythm changes and may be part of the pathophysiological link between pollution and cardiovascular mortality

Rickettsia felis

China, using a modified virus discovery method based on cDNA-AFLP

Estimation de la mortalité attribuable aux particules (PM10) dans les 9 villes françaises participant au programme européen Apheis

La relation entre pollution atmosphérique et mortalité est maintenant admise avec un niveau de causalité qui permet de réaliser des évaluations d’impact sanitaire telle que celle présentée dans ce travail pour neuf villes françaises participant au programme Apheis et concernant les particules. Cette évaluation d’impact sanitaire est basée sur la méthodologie élaborée par l’OMS. Le nombre de décès évitables est compris entre 2,0 et 4,3, 4,0 et 8,9, et 15,0 et 31,5 pour 100 000 habitants respectivement pour les effets à très court terme, à court terme et à long terme. Deux scénarii de réduction des concentrations de particules, l’un abaissant les niveaux journaliers dépassant 20 µg/m3 jusqu’à cette valeur seuil, l’autre diminuant les niveaux journaliers de 5 µg/m3 systématiquement, sont proches pour les effets à très court terme et court terme. La stratégie de réduction à 20 µg/m3 se montre la plus favorable pour les effets à long terme. Cette stratégie confirme donc l’intérêt de la recommandation formulée au niveau national.The relationship between air pollution and mortality is now admissible with a sufficiently high level of causality proven. This link allows for health impact assessment to be carried out with a significant degree of accuracy, such as the case for the results which are presented here from the nine French cities involved in the Apheis programme. This health impact assessment is based on the methodology developed by the World Health Organization. The number of avoidable deaths is contained between categories ranging from 2.0 to 4.3, 4.0 to 8.9, and from 15.0 to 31.5 per 100,000 inhabitants according to very short term effects, short term effects and long term effects, respectively. There are two scenarios which can be envisioned for the reduction of fine particles levels which are capable of obtaining similar results for both very short term and short term effects. The first involves diminishing the daily concentrations which are above 20 µg/m3 until they reach this value, and the second entails systematically decreasing the daily levels by 5 µg/m3. The first strategy of reducing values to stabilize at 20 µg/m3 has been shown to be the one most favourable and promising for the long term effects. This strategy therefore confirms the reliability and strength of the recommendation formulated at the national level