Influenza is a respiratory virus that causes significant morbidity and mortality throughout the world every year. Seasonal epidemics of influenza occur in the late fall and winter in the United States annually, but there are variations in its timing from year to year. Further, although the timing of epidemic waves in the United States are similar, there is variation between different populations. It is not well understood why these differences exist. Understanding the spatial and temporal variation in the timing of influenza is important because it shapes our understanding of preventive actions that can be taken to limit the spread of the virus.
Past studies that have examined the timing of influenza have been limited by the fact that they have used influenza-like illness (ILI) as an indicator of influenza. ILI has traditionally been the conventional indicator of influenza because the illness does not present unique symptoms. As such, spatial and temporal variation in the relative timing of influenza A, influenza B, and ILI have not been investigated extensively. Additionally, there has been concern raised about implications of the imprecise nature of ILI in treating patients that it is believed may have influenza.
This study addressed this gap and concern by utilizing influenza-specific data from clinics and hospitals throughout the United States to evaluate spatial variation in the timing of influenza across the United States in the 2016-17 influenza season. Results from influenza rapid tests were aggregated by urban area as a means of evaluating associations between epidemic timing and independent variables in different locations. The timing of influenza A and B epidemics was tested for spatial autocorrelation and incorporated in regression models to identify potential relationships between epidemic timing and several variables (including dew point, temperature, population, population density, and cumulative seasonal vaccination rates). Forward stepwise regression was then conducted to identify a set of variables that may be best suited to explain the timing of these milestones, and spatial lag regression was conducted to account for spatial autocorrelation in these variables.
This analysis indicated that higher average dew point and temperature and greater population and population density were both associated with earlier epidemic beginnings and later epidemic endings, while higher cumulative seasonal vaccination rates were associated with earlier epidemic endings for influenza A and B. Forward stepwise regression yielded models that generally differed for each epidemic milestone and type of influenza, indicating that different sets of variables might be best suited to explain different milestones of epidemics. Spatial lag regression improved model fit for the forward stepwise models for which there was residual spatial autocorrelation.
This is one of the first studies to evaluate the timing of different points within an epidemic. The techniques I used to study timing are well-suited for the study of future epidemics of infectious diseases, including influenza, that seek to identify and clarify potential associations between independent variables and epidemic timing.