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. 2020 Oct 13;7(1):346.
doi: 10.1038/s41597-020-00677-x.

An analecta of visualizations for foodborne illness trends and seasonality

Ryan B Simpson  1 , Bingjie Zhou  1 , Tania M Alarcon Falconi  1 , Elena N Naumova  2
Affiliations

An analecta of visualizations for foodborne illness trends and seasonality

Ryan B Simpson et al. Sci Data. .

Abstract

Disease surveillance systems worldwide face increasing pressure to maintain and distribute data in usable formats supplemented with effective visualizations to enable actionable policy and programming responses. Annual reports and interactive portals provide access to surveillance data and visualizations depicting temporal trends and seasonal patterns of diseases. Analyses and visuals are typically limited to reporting the annual time series and the month with the highest number of cases per year. Yet, detecting potential disease outbreaks and supporting public health interventions requires detailed spatiotemporal comparisons to characterize spatiotemporal patterns of illness across diseases and locations. The Centers for Disease Control and Prevention's (CDC) FoodNet Fast provides population-based foodborne-disease surveillance records and visualizations for select counties across the US. We offer suggestions on how current FoodNet Fast data organization and visual analytics can be improved to facilitate data interpretation, decision-making, and communication of features related to trend and seasonality. The resulting compilation, or analecta, of 436 visualizations of records and codes are openly available online.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
A multi-panel plot: a rotated histogram of monthly rate frequency (left panel) sharing the vertical monthly rate-axis with the time series of monthly rates (right panel) for salmonellosis in the US from 1996–2017. The red line indicates the median rates while the blue line is a NBHR model fit with seasonal oscillators and three (linear, quadratic, and cubic) trend terms.
Fig. 2
Fig. 2
A multi-panel plot for visualizing seasonal signatures of salmonellosis monthly rates in the US from 1996–2017. This includes overlaid annual time series plots of monthly rates, a box plot of average monthly rates for the 22-year period, and overlaid annual radar plots of monthly rates. Background colours indicate the four seasons defined by solar solstices and equinoxes: winter (blue), spring (green), summer (yellow), and autumn (orange).
Fig. 3
Fig. 3
A multi-panel plot for improved visualization of the annual seasonal signatures of monthly rates of salmonellosis in the US from 1996–2017. The top panel provides a box plot of monthly rates for each month of year. The bottom heatmap shows the distribution of monthly rates for each year where darker hues indicate greater rates. The right panel provides a rotated bar graph of yearly rates.
Fig. 4
Fig. 4
A multi-panel plot for visualizing the annual peak timing and amplitude of salmonellosis in the US from 1996–2017. The top-left panel shows the peak timing of salmonellosis by year; the bottom-right panel shows the annual amplitude by year. The bottom-left panel shows their combination: a scatterplot between peak timing and amplitude. Marker colour intensity indicates more historic vs. more recent data, horizontal and vertical whiskers provide measures of uncertainty, and red lines indicate median peak timing and amplitude across 22 years.
Fig. 5
Fig. 5
A multi-panel plot for comparing seasonal signatures and yearly rates of salmonellosis in the US from 1996–2017 disaggregated by the ten FoodNet-surveyed states. The top panel provides a box plot of monthly rates for each month of year for the US. The calendar heatmap uses shared horizontal axes to show the distribution of monthly rates for each year and each state. Darker hues indicate higher rates while empty cells with blue borders indicate years when FoodNet surveillance was not conducted. The right panel provides a rotated bar graph of yearly rates. Given sizable differences in rates across states we applied a high-order calibration colour scheme.
Fig. 6
Fig. 6
A multi-panel plot for visualizing the annual peak timing and amplitude of salmonellosis in ten FoodNet-reporting states and the US from 1996–2017. The top-left panel shows the average peak timing of salmonellosis per location while the bottom-right panel shows the average amplitude per location. The bottom-left panel shows a combined scatterplot between peak timing and amplitude estimates.
Fig. 7
Fig. 7
A multi-panel plot for comparing seasonal signatures and yearly rates of nine FoodNet-reported infections in the US from 1996–2017. The top panel provides a scatterplot of average peak timing and amplitude estimates per pathogen across the 22-year period. The bottom heatmap uses shared horizontal axes to show the distribution of monthly rates for each year and each disease (campylobacteriosis (Camp), listeriosis (List), salmonellosis (Salm), shigellosis (Shig), infections caused by Shiga toxin-producing Escherichia coli O157 and non-O157 (Ecol), vibriosis (Vibr), infections caused by Yersinia enterocolitica (Yers), cryptosporidiosis (Cryp) and cyclosporiasis (Cycl)). Darker hues indicate greater rates while empty cells with blue borders indicate years when FoodNet surveillance was not conducted. The right panel provides a rotated bar graph of yearly rates.
Fig. 8
Fig. 8
A multi-panel plot for visualizing the average peak timing and amplitude of nine FoodNet-reported pathogens in the US from 1996–2017. The top-left panel shows the average peak timing of each disease while the bottom-right panel shows the average amplitude per disease (campylobacteriosis (Camp), listeriosis (List), salmonellosis (Salm), shigellosis (Shig), infections caused by Shiga toxin-producing Escherichia coli O157 and non-O157 (Ecol), vibriosis (Vibr), infections caused by Yersinia enterocolitica (Yers), cryptosporidiosis (Cryp) and cyclosporiasis (Cycl)). The bottom-left panel shows a combined scatterplot between peak timing and amplitude estimates.
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