Background. Evaluating the factors favoring the onset of influenza epidemics is a critical public health issue for surveillance, prevention and control. While past outbreaks provide important insights for understanding epidemic onsets, their statistical analysis is challenging since the impact of a factor can be viewed at different scales. Indeed, the same factor can explain why epidemics are more likely to begin i) during particular weeks of the year (global scale); ii) earlier in particular

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... gions (spatial scale) or years (annual scale) than others and iii) earlier in some years than others within a region (spatiotemporal scale). Methods. Here, we present a statistical approach based on dynamical modeling of infectious diseases to study epidemic onsets. We propose a method to disentangle the role of covariates at different scales and use a permutation procedure to assess their significance. Epidemic data gathered from 18 French regions over 6 epidemic years were provided by the Regional Influenza Surveillance Group (GROG) sentinel network. Results. Our results failed to highlight a significant impact of mobility flows on epidemic onset dates. Absolute humidity had a significant impact, but only at the spatial scale. No link between demographic covariates and influenza epidemic onset dates could be established. Discussion. Dynamical modelling presents an interesting basis to analyze spatiotemporal variations in the outcome of epidemic onsets and how they are related to various types of covariates. The use of these models is quite complex however, due to their mathematical complexity. Furthermore, because they attempt to integrate migration processes of the virus, such models have to be much more explicit than pure statistical approaches. We discuss the relation of this approach to survival analysis, which present significant differences but may constitute an interesting alternative for non-methodologists. PeerJ reviewing PDF | Manuscript to be reviewed 24 ABSTRACT 25 Background. Evaluating the factors favoring the onset of influenza epidemics is a critical public 26 health issue for disease surveillance, prevention and control. While past outbreaks provide 27 important insights for understanding epidemic onsets, their statistical analysis is challenging 28 because the impact of a factor can be viewed at different scales. Indeed, the same factor can 29 explain why epidemics are more likely to begin i) during particular weeks of the year (global 30 scale); ii) earlier in particular regions (spatial scale) or years (annual scale) than others and iii) 31 earlier in some years than others within a region (spatiotemporal scale). 32 Methods. Here, we present a statistical approach based on dynamical modeling of infectious 33 diseases to study epidemic onsets. We propose a method to disentangle the role of covariates at 34 different scales and use a permutation procedure to assess their significance. Epidemic data 35 gathered from 18 French regions over 6 epidemic years were provided by the Regional Influenza 36 Surveillance Group (GROG) sentinel network. 37 Results. Our results failed to highlight a significant impact of mobility flows on epidemic onset 38 dates. Absolute humidity had a significant impact, but only at the spatial scale. No link between 39 demographic covariates and influenza epidemic onset dates could be established. 40 Discussion. 41 Dynamical modelling presents an interesting basis to analyze spatiotemporal variations in the 42 outcome of epidemic onsets and how they are related to various types of covariates. The use of 43 these models is quite complex however, due to their mathematical complexity. Furthermore, 44 because they attempt to integrate migration processes of the virus, such models have to be much 45 more explicit than pure statistical approaches. We discuss the relationship of this approach to PeerJ reviewing PDF |