Seasonality of Rotavirus in South Asia: A Meta-Analysis Approach Assessing Associations with Temperature, Precipitation, and Vegetation Index

Abstract

BackgroundRotavirus infection causes a significant proportion of diarrhea in infants and young children worldwide leading to dehydration, hospitalization, and in some cases death. Rotavirus infection represents a significant burden of disease in developing countries, such as those in South Asia.MethodsWe conducted a meta-analysis to examine how patterns of rotavirus infection relate to temperature and precipitation in South Asia. Monthly rotavirus data were abstracted from 39 published epidemiological studies and related to monthly aggregated ambient temperature and cumulative precipitation for each study location using linear mixed-effects models. We also considered associations with vegetation index, gathered from remote sensing data. Finally, we assessed whether the relationship varied in tropical climates and humid mid-latitude climates.ResultsOverall, as well as in tropical and humid mid-latitude climates, low temperature and precipitation levels are significant predictors of an increased rate of rotaviral diarrhea. A 1°C decrease in monthly ambient temperature and a decrease of 10 mm in precipitation are associated with 1.3% and 0.3% increase above the annual level in rotavirus infections, respectively. When assessing lagged relationships, temperature and precipitation in the previous month remained significant predictors and the association with temperature was stronger in the tropical climate. The same association was seen for vegetation index; a seasonal decline of 0.1 units results in a 3.8% increase in rate of rotavirus.ConclusionsIn South Asia the highest rate of rotavirus was seen in the colder, drier months. Meteorological characteristics can be used to better focus and target public health prevention programs.

On-plot drinking water supplies and health: A systematic review

“…Many older and less rigorous studies did not use multivariate regression and did not adjust for individual factors such as age, socioeconomic status, or sanitation access. Additionally, several studies did not report the time and duration of the study, which are important factors for studies on diarrheal diseases since they are subject to seasonal effects (Jagai et al, 2012) and seasonality is known to affect source water quality and choice of source (Kostyla et al, 2015).…”

Rotavirus seasonality in urban sewage from Argentina: Effect of meteorological variables on the viral load and the genetic diversity

“…Both vaccines are available in Argentina, and their incorporation in the National Immunization Program is under consideration for the next years. Thereby, the rotavirus circulation pattern is driven by the natural history of wild type Seasonal fluctuations in the rotavirus infection are well documented (Cook et al, 1990;Levy et al, 2009;Jagai et al, 2012). Rotavirus exhibits distinct seasonality, being considered a winter disease in some parts of the world.…”

“…Observational studies of human rotavirus disease have suggested that lower temperature, lower relative humidity and lower levels of rainfall are associated with an increased risk of rotavirus disease (Jagai et al, 2012;D'Souza et al, 2008). Recently, the influence of birth rates and transmission routes has also been suggested to be involved in the seasonality of rotavirus incidence (Atchison et al, 2009;Pitzer et al, 2011).…”

Quantification of Rotavirus Diarrheal Risk Due to Hydroclimatic Extremes Over South Asia: Prospects of Satellite‐Based Observations in Detecting Outbreaks

“…In the process to eliminate the influence of population, we standardized and scaled the disease cases for each of the selected cities and combined the disease cases into a single series of the same time frame (January 2013 to June 2015) to conduct spatial analysis. The standardization method was adopted from Jagai et al (2012), where we considered our scaled values as z-scores of rotavirus risk. As a result of removing the effect of population, the analysis thus represents the severity of disease cycle rather than actual cases of diseases.…”

“…Moors et al (2013) integrated several climatic effects to explain the pattern of diarrheal disease outbreaks over India; however, a deterministic quantification of the diseases based on the climatic effects was absent. Jagai et al (2012) has conducted a meta-analysis of rotavirus over South Asia but did not consider the climate extremes. Accurate identification of climatic events is also important for disease modeling.…”