The seasonality of diarrheal pathogens: A retrospective study of seven sites over three years

Abstract

Background: Pediatric diarrhea can be caused by a wide variety of pathogens, from bacteria to viruses to protozoa. Pathogen prevalence is often described as seasonal, peaking annually and associated with specific weather conditions. Although many studies have described the seasonality of diarrheal disease, these studies have occurred predominantly in temperate regions. In tropical and resource-constrained settings, where nearly all diarrhea-associated mortality occurs, the seasonality of many diarrheal pathogens has not been well characterized. As a retrospective study, we analyze the seasonal prevalence of diarrheal pathogens among children with moderate-to-severe diarrhea (MSD) over three years from the seven sites of the Global Enteric Multicenter Study (GEMS), a case-control study. Using data from this expansive study on diarrheal disease, we characterize the seasonality of different pathogens, their association with site-specific weather patterns, and consistency across study sites.

Methodology/principal findings: Using traditional methodologies from signal processing, we found that certain pathogens peaked at the same time every year, but not at all sites. We also found associations between pathogen prevalence and weather or "seasons," which are defined by applying modern machine-learning methodologies to site-specific weather data. In general, rotavirus was most prevalent during the drier "winter" months and out of phase with bacterial pathogens, which peaked during hotter and rainier times of year corresponding to "monsoon," "rainy," or "summer" seasons.

Conclusions/significance: Identifying the seasonally-dependent prevalence for diarrheal pathogens helps characterize the local epidemiology and inform the clinical diagnosis of symptomatic children. Our multi-site, multi-continent study indicates a complex epidemiology of pathogens that does not reveal an easy generalization that is consistent across all sites. Instead, our study indicates the necessity of local data to characterizing the epidemiology of diarrheal disease. Recognition of the local associations between weather conditions and pathogen prevalence suggests transmission pathways and could inform control strategies in these settings.

Fig 1. Analysis overview.

A. The number of eligible and enrolled children for the Bangladesh study site each fortnight (gold and blue bars, respectively). The number of rotavirus-confirmed samples from enrolled children are in purple. The estimated rotavirus prevalence among all eligible children which includes confirmed positive and estimated positive from the eligible children is illustrated by the black line. B. The power spectral density of the estimated rotavirus positive prevalence time series. Each colored line indicates the probability of a random time series signal with the same length and sampling being misinterpreted as a true signal. C. Seasons were identified using PCA to reduce the dimensionality of the weather variables and k-means clustering. The color of each month corresponds to the cluster and season. For Bangladesh, we call each season by the colloquial names “Summer,” “Monsoon,” and “Winter”. D. The estimated number of cases positive for a pathogen each fortnight (black line). Each background color corresponds to the data-driven season identified in C.

Fig 2. Annual peak dates of pathogens.

We used the phase information from the FFT of the number of eligible children estimated to be positive each fortnight to determine the date of the annual peak of each pathogen in each country (indicated as month–day in each square and color-coded by month of year). The significance (false-detection rate p_fd) of the annual cycle was obtained from the Lomb–Scargle periodogram of the most significant period between 11 and 13 months, indicated by **** for <0.1%, *** for <1%, ** for <5%, and * for <10%.

Fig 3. Estimated fortnightly cases positive for rotavirus, Shigella, and Cryptosporidium.

Gray bars plot the number of children with MSD visiting the study clinics each fortnight. The lines show the estimated number of these children positive for rotavirus, Shigella, and Cryptosporidium. The background shading indicates the timing of data-driven seasons at each site.

Fig 4. Significant differences in weather of fortnights with high vs low pathogen prevalence.

The weather covariates with significant differences (p<0.05 using the Wilcoxon rank–sum test) between fortnights with high vs low prevalence of pathogens are printed in the chart. Weather covariates that are higher during high-prevalence weeks are printed to the right of the vertical lines (“rainy” indicates higher rain in pathogen-associated fortnights, “hot” means higher temperatures, “RH humid” means higher relative humidity, and “SH humid” means higher specific humidity), and those that are lower during high-prevalence weeks are printed on the left (i.e., “not rainy”, “cold”, “RH dry”, and “SH dry”).

Fig 5. Relative prevalence of pathogens across seasons.

The Dunn test is used to compare the monthly cases between pairs of seasons. If there is a significant difference between seasons, their names are printed, with the season with the most cases per month on top and fewest on the bottom. Brackets indicate pairs of seasons with significant differences, and season names are color-coded (summer, winter, monsoon, hot/dry, cool/dry, etc) when there is a statistically significant difference between seasons (p<0.05 using a Bonferroni correction) and light gray otherwise. No country–pathogen combination had significant prevalence differences among all three seasons.

Fig 6. The top three pathogens by season.

For each country, the top three most frequently detected pathogens (out of the nine considered) among eligible children ages 0 to 23 months (left half of the plot) and 24 to 59 months old (right half) are shown. Bar heights are proportional to the percent of cases positive for a pathogen (which is also printed below each bar), and the pathogens are indicated by abbreviations and color as summarized in the legend. Pathogens considered are: rotavirus, adenovirus 40/41, norovirus GI, norovirus GII, Cryptosporidium, Shigella, ST-ETEC, Campylobacter, and V. cholerae. Note that the seasons are not plotted in a particular order and may be non-sequential.