Linking influenza epidemic onsets to covariates at different scales using a dynamical model

Abstract

BackgroundEvaluating 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).MethodsHere, 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.ResultsOur 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.DiscussionDynamical 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.

A Downscaling Approach to Compare COVID-19 Count Data from Databases Aggregated at Different Spatial Scales

“…We include (5): January land surface temperatures ( LST_January ) from the US National Oceanic and Atmospheric Administration for meteorological (NOAA) Geostationary Operational Environmental Satellites (GOES) (United States National Oceanic and Atmospheric Administration, 2020). Temperatures in January can affect the life cycle of COVID‐19 and their propensity to spread (Roussel et al., 2018), but can also affect human behaviour. The effects of cold temperatures on human transmission of COVID‐19 are not clear.…”

“…To make predictions at the pixel level (about 5 km resolution), we gather covariate data from raster data (covariates aggregated into regular grids) which cover China. Climate and meteorological factors can affect the life cycle of respiratory diseases and their propensity to spread (Roussel et al., 2018). However, the extent of the role of climate drivers on the spread of COVID‐19 compared to those associated with human behaviour is not well understood (Baker et al., 2020).…”

A downscaling approach to compare COVID-19 count data from databases aggregated at different spatial scales

“…To make predictions at pixel-level (about 5 km resolution), we gather covariate data from various sources, mainly raster data (covariates aggregated into regular grids) which cover China. Climate and meteorological factors can affect the life cycle of respiratory diseases and their propensity to spread [31] . However the extent of the role of climate drivers on the spread of COVID-19 compared to those associated with human behaviour is not well understood [32] .…”

“…We include (5): January land surface temperatures ( LST_January ) from the US National Oceanic and Atmospheric Administration for meteorological (NOAA) Geostationary Operational Environmental Satellites (GOES) [37] ). Temperatures in January can affect the life cycle of COVID-19 and their propensity to spread [31] , but can also affect human behaviour. The effects of cold temperatures on human transmission of COVID-19 are not clear.…”