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Editor, Senior Moderator
PeerJ. 2018 Mar 8;6:e4440. doi: 10.7717/peerj.4440. eCollection 2018.
[h=1]Linking influenza epidemic onsets to covariates at different scales using a dynamical model.[/h] Roussel M[SUP]1,[/SUP][SUP]2[/SUP], Pontier D[SUP]1,[/SUP][SUP]2[/SUP], Cohen JM[SUP]3[/SUP], Lina B[SUP]4,[/SUP][SUP]5[/SUP], Fouchet D[SUP]1,[/SUP][SUP]2[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]Background:[/h] 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 regions (spatial scale) or years (annual scale) than others and (iii) earlier in some years than others within a region (spatiotemporal scale).
[h=4]Methods:[/h] 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 six epidemic years were provided by the Regional Influenza Surveillance Group (GROG) sentinel network.
[h=4]Results:[/h] 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.
[h=4]Discussion:[/h] Dynamical modeling 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.
[h=4]KEYWORDS:[/h] Climate; Mobility flows; Permutation tests; Population size; Proportion of children
PMID: 29568702 PMCID: PMC5845579 DOI: 10.7717/peerj.4440
Free PMC Article
[h=1]Linking influenza epidemic onsets to covariates at different scales using a dynamical model.[/h] Roussel M[SUP]1,[/SUP][SUP]2[/SUP], Pontier D[SUP]1,[/SUP][SUP]2[/SUP], Cohen JM[SUP]3[/SUP], Lina B[SUP]4,[/SUP][SUP]5[/SUP], Fouchet D[SUP]1,[/SUP][SUP]2[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]Background:[/h] 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 regions (spatial scale) or years (annual scale) than others and (iii) earlier in some years than others within a region (spatiotemporal scale).
[h=4]Methods:[/h] 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 six epidemic years were provided by the Regional Influenza Surveillance Group (GROG) sentinel network.
[h=4]Results:[/h] 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.
[h=4]Discussion:[/h] Dynamical modeling 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.
[h=4]KEYWORDS:[/h] Climate; Mobility flows; Permutation tests; Population size; Proportion of children
PMID: 29568702 PMCID: PMC5845579 DOI: 10.7717/peerj.4440
Free PMC Article