L’épidémiologie humaine

Si la France peut se targuer d'avoir été un berceau de l'épidémiologie, l'analyse objective des effectifs des chercheurs et des laboratoires montre qu'elle est actuellement sous-dimensionnée par rapport aux pays comparables. Pourtant, la demande en épidémiologie, aussi bien scientifique que sociale, grandit sans cesse. Commment faire face, qualitativement et quantitativement, a cette demande ? C'est pour répondre à cette question que l'Académie des sciences a suscité ce rapport sur l'épidémiologie humaine, dans lequel les conditions matérielles et institutionnelles de son développement sont examinées. Après une vue d'ensemble sur la définition, l'historique et l'état actuel de cette discipline, cet ouvrage s'attache à en décrire les méthodes. Il montre que l'épidémiologie moderne s'appuie depuis longtemps sur la statistique, mais aussi que, récemment, on assiste à une forte implication de nouveaux champs des mathématiques, notamment calcul des propabilités, analyse numérique, théorie des systèmes complexes, et modélisation en général, qui ouvrent de nouvelles possibilités d'applications. L'explosion actuelle des systèmes d'information touchant à la santé, construit dans d'autres buts que la recherche, et la possibilité de construire de nouveaux systèmes d'observation épidémiologique puissants sont analysés en tant que nouvelles opportunités pour la recherche. Le rapport décrit en quelques exemples comment l'épidémiologie moderne se développe en lien intime avec la biologie; il décrit aussi l'importance des sciences humaines et sociales, indispensables pour découvrir les facteurs de risques sociaux ou comportementaux. Le rapport examine également le rôle de l'épidémiologie en tant que science support de la décision médicale et de la Santé Publique. Enfin, il énonce les progrès néccessaires à accomplir dans l'enseignement, le besoin d'ouverture de l'épidémiologie aux étudiants, enseignants et chercheurs des disciplines non médicales et il suggère des recommendations organisationnelles

Transmission potential of Chikungunya fever in a two-wave epidemic in Reunion Island

International audienc

Estimating influenza latency and infectious period durations using viral excretion data.

Influenza infection natural history is often described as a progression through four successive stages: Susceptible–Exposed/Latent–Infectious–Removed (SEIR). The duration of each stage determines the average generation time, the time between infection of a case and infection of his/her infector. Recently, several authors have justified somewhat arbitrary choices in stage durations by how close the resulting generation time distribution was to viral excretion over time after infection. Taking this reasoning one step further, we propose that the viral excretion profile over time can be used directly to estimate the required parameters in an SEIR model. In our approach, the latency and infectious period distributions are estimated by minimizing the Kullback–Leibler divergence between the model-based generation time probability density function and the normalized average viral excretion profile. Following this approach, we estimated that the latency and infectious period last respectively 1.6 and 1.0 days on average using excretion profiles from experimental infections. Interestingly, we find that only 5% of cases are infectious for more than 2.9 days. We also discuss the consequences of these estimates for the evaluation of the efficacy of control measures such as isolation or treatment. We estimate that, under a best-case scenario where symptoms appear at the end of the latency period, index cases must be isolated or treated at most within 16 h after symptoms onset to avoid 50% of secondary cases. This study provides the first estimates of latency and infectious period for influenza based directly on viral excretion data. It provides additional evidence that isolation or treatment of cases would be effective only if adopted shortly after symptoms onset, and shows that four days of isolation may be enough to avoid most transmissions

Monitoring mortality as an indicator of influenza in Catalonia, Spain.

This study aimed to investigate the behaviour of two indicators of influenza activity in the area of Barcelona and to evaluate the usefulness of modelling them to improve the detection of influenza epidemics. DESIGN: Descriptive time series study using the number of deaths due to all causes registered by funeral services and reported cases of influenza-like illness. The study concentrated on five influenza seasons, from week 45 of 1988 to week 44 of 1993. The weekly number of deaths and cases of influenza-like illness registered were processed using identification of a time series ARIMA model. SETTING: Six large towns in the Barcelona province which have more than 60,000 inhabitants and funeral services in all of them. MAIN RESULTS: For mortality, the proposed model was an autoregressive one of order 2 (ARIMA (2,0,0)) and for morbidity it was one of order 3 (ARIMA (3,0,0)). Finally, the two time series were analysed together to facilitate the detection of possible implications between them. The joint study of the two series shows that the mortality series can be modelled separately from the reported morbidity series, but the morbidity series is influenced as much by the number of previous cases of influenza reported as by the previous mortality registered. CONCLUSIONS: The model based on general mortality is useful for detecting epidemic activity of influenza. However, because there is not an absolute gold standard that allows definition of the beginning of the epidemic, the final decision of when it is considered an epidemic and control measures recommended should be taken after evaluating all the indicators included in the influenza surveillance programme