Objectives-We developed methodology for and conducted a meta-analysis to examine how seasonal patterns of cryptosporidiosis, a primarily waterborne diarrheal illness, relate to precipitation and temperature fluctuations worldwide.Methods-Monthly cryptosporidiosis data were abstracted from 61 published epidemiological studies that cover various climate regions based on the Köppen Climate Classification. Outcome data was supplemented with monthly aggregated ambient temperature and precipitation for each study location. We applied a linear mixed-effects model to relate the monthly normalized cryptosporidiosis incidence with normalized location-specific temperature and precipitation data. We also conducted a sub-analysis of associations between the Normalized Difference Vegetation Index (NDVI), a remote sensing measure for the combined effect of temperature and precipitation on vegetation, and cryptosporidiosis in Sub-Saharan Africa.Results-Overall, and after adjusting for distance from the equator, an increase in temperature and precipitation predict an increase in cryptosporidiosis; the strength of relationship vary by climate subcategory. In moist tropical locations precipitation is a strong seasonal driver for cryptosporidiosis whereas temperature is in mid-latitude and temperate climates. When assessing lagged relationships temperature and precipitation remain strong predictors. In Sub-Saharan Africa, after adjusting for distance from the equator, low NDVI values are predictive of an increase in cryptosporidiosis in the following month.Discussion-In this study we propose novel methodology to assess relationships between disease outcomes and meteorological data on a global scale. Our findings demonstrate that while climatic conditions typically define a pathogen habitat area, meteorological factors affect timing and intensity of seasonal outbreaks. Therefore, meteorological forecasts can be utilized to develop focused prevention programs for waterborne cryptosporidiosis.

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.

BackgroundEpidemiologic studies are often confounded by the human and environmental interactions that are complex and dynamic spatio-temporal processes. Hence, it is difficult to discover nuances in the data and generate pertinent hypotheses. Dynamic mapping, a method to simultaneously visualize temporal and spatial information, was introduced to elucidate such complexities. A conceptual framework for dynamic mapping regarding principles and implementation methods was proposed.MethodsThe spatio-temporal dynamics of Salmonella infections for 2002 in the U.S. elderly were depicted via dynamic mapping. Hospitalization records were obtained from the Centers of Medicare and Medicaid Services. To visualize the spatial relationship, hospitalization rates were computed and superimposed onto maps of environmental exposure factors including livestock densities and ambient temperatures. To visualize the temporal relationship, the resultant maps were composed into a movie.ResultsThe dynamic maps revealed that the Salmonella infections peaked at specific spatio-temporal loci: more clusters were observed in the summer months and higher density of such clusters in the South. The peaks were reached when the average temperatures were greater than 83.4°F (28.6°C). Although the relationship of salmonellosis rates and occurrence of temperature anomalies was non-uniform, a strong synchronization was found between high broiler chicken sales and dense clusters of cases in the summer.ConclusionsDynamic mapping is a practical visual-analytic technique for public health practitioners and has an outstanding potential in providing insights into spatio-temporal processes such as revealing outbreak origins, percolation and travelling waves of the diseases, peak timing of seasonal outbreaks, and persistence of disease clusters.

In temperate regions, influenza typically arrives with the onset of colder weather. Seasonal waves travel over large spaces covering many climatic zones in a relatively short period of time. The precise mechanism for this striking seasonal pattern is still not well understood and the interplay of factors that influence the spread of infection and the emergence of new strains is largely unknown. The study of influenza seasonality has been fraught with problems. One of these is the ever shifting description of illness due to influenza and the use of both the historical definitions and new definitions based on actual isolation of the virus. The compilation of records describing influenza oscillations on a local and global scale is massive, but the value of these data is a function of the definitions used. In this review we argue that both observations of seasonality and deviation from the expected pattern stem from the nature of this disease. Heterogeneity in seasonal patterns may arrive from differences in behavior of specific strains, emergence of a novel strain or cross-protection from previously observed strains. Most likely the seasonal patterns emerge from interactions of individual factors behaving as coupled resonators. We emphasize that both seasonality and deviations from it may merely be a reflection of our inability to disentangle signal from noise, be it due to ambiguity in measurement and/or terminology. We conclude the review with suggestions for new promising and realistic directions with tangible consequences to model complex influenza dynamics in order to effectively control infection.

The results suggest strong disparities in healthcare practices in rural locations and vulnerable populations; infrastructure, proximity, and access to healthcare are significant predictors of influenza morbidity and mortality. These findings have important implications for influenza vaccination, testing, and treatment policies and practices targeting the growing fraction of patients with cognitive impairment.