Climate Factors Influencing Coccidioidomycosis Seasonality and Outbreaks

Although broad links between climatic factors and coccidioidomycosis have been established, the identification of simple and robust relationships linking climatic controls to seasonal timing and outbreaks of the disease has remained elusive. Using an adaptive data-oriented method for estimating date of exposure, in this article I analyze hypotheses linking climate and dust to fungal growth and dispersion, and evaluate their respective roles for Pima County, Arizona. Results confirm a strong bimodal disease seasonality that was suspected but not previously seen in reported data. Dispersion-related conditions are important predictors of coccidioidomycosis incidence during fall, winter, and the arid foresummer. However, precipitation during the normally arid foresummer 1.5–2 years before the season of exposure is the dominant predictor of the disease in all seasons, accounting for half of the overall variance. Cross-validated models combining antecedent and concurrent conditions explain 80% of the variance in coccidioidomycosis incidence

The role of age, ethnicity and environmental factors in modulating malaria risk in Rajasthali, Bangladesh

Background: Malaria is endemic in the Rajasthali region of the Chittagong Hill Tracts in Bangladesh and the Rajasthali region is the most endemic area of Bangladesh. Quantifying the role of environmental and socioeconomic factors in the local spatial patterns of malaria endemicity can contribute to successful malaria control and elimination. This study aimed to investigate the role of environmental factors on malaria risk in Rajasthali and to quantify the geographical clustering in malaria risk unaccounted by these factors

Environmental factors affecting ecological niche of Coccidioides species and spatial dynamics of valley fever in the United States.

Coccidioidomycosis is an understudied infectious disease acquired by inhaling fungal spores of Coccidioides species. While historically connected to the southwestern United States, the endemic region for this disease is not well defined. This study's objective was to estimate the impact of climate, soil, elevation and land cover on the Coccidioides species' ecological niche. This research used maximum entropy ecological niche modeling based on disease case data from 2015 to 2016. Results found mean temperature of the driest quarter, and barren, shrub, and cultivated land covers influential in characterizing the niche. In addition to hotspots in central California and Arizona, the Columbia Plateau ecoregion of Washington and Oregon showed more favorable conditions for fungus presence than surrounding areas. The identification of influential spatial drivers will assist in future modeling efforts, and the potential distribution map generated may aid public health officials in watching for potential hotspots, assessing vulnerability, and refining endemicity

Future Lyme disease risk in the southeastern United States based on projected land cover

Lyme disease is the most significant vector-borne disease in the United States, and its southward advance over several decades has been quantified. Previous research has examined the potential role of climate change on the disease’s expansion, but no studies have considered the role of future land cover upon its distribution. This research examines Lyme disease risk in the south-eastern U.S. based on projected land cover developed under four Intergovernmental Panel on Climate Change Scenarios: A1B, A2, B1, and B2. Land cover types and edge indices significantly associated with Lyme disease in Virginia were incorporated into a spatial Poisson regression model to quantify potential land cover suitability for Lyme disease in the south-eastern U.S. under each scenario. Our results indicate an intensification of potential land cover suitability for Lyme disease under the A scenarios and a decrease of potential land cover suitability under the B scenarios. The decrease under the B scenarios is a critical result, indicating that Lyme disease risk can be decreased by making different land cover choices. Additionally, health officials can focus efforts in projected high incidence areas

Coccidioidomycosis Incidence in Arizona Predicted by Seasonal Precipitation

The environmental mechanisms that determine the inter-annual and seasonal variability in incidence of coccidioidomycosis are unclear. In this study, we use Arizona coccidioidomycosis case data for 1995–2006 to generate a timeseries of monthly estimates of exposure rates in Maricopa County, AZ and Pima County, AZ. We reveal a seasonal autocorrelation structure for exposure rates in both Maricopa County and Pima County which indicates that exposure rates are strongly related from the fall to the spring. An abrupt end to this autocorrelation relationship occurs near the the onset of the summer precipitation season and increasing exposure rates related to the subsequent season. The identification of the autocorrelation structure enabled us to construct a “primary” exposure season that spans August-March and a “secondary” season that spans April–June which are then used in subsequent analyses. We show that October–December precipitation is positively associated with rates of exposure for the primary exposure season in both Maricopa County (R = 0.72, p = 0.012) and Pima County (R = 0.69, p = 0.019). In addition, exposure rates during the primary exposure seasons are negatively associated with concurrent precipitation in Maricopa (R = −0.79, p = 0.004) and Pima (R = −0.64, p = 0.019), possibly due to reduced spore dispersion. These associations enabled the generation of models to estimate exposure rates for the primary exposure season. The models explain 69% (p = 0.009) and 54% (p = 0.045) of the variance in the study period for Maricopa and Pima counties, respectively. We did not find any significant predictors for exposure rates during the secondary season. This study builds on previous studies examining the causes of temporal fluctuations in coccidioidomycosis, and corroborates the “grow and blow” hypothesis

Social sciences research in neglected tropical diseases 2: A bibliographic analysis

The official published version of the article can be found at the link below.Background There are strong arguments for social science and interdisciplinary research in the neglected tropical diseases. These diseases represent a rich and dynamic interplay between vector, host, and pathogen which occurs within social, physical and biological contexts. The overwhelming sense, however, is that neglected tropical diseases research is a biomedical endeavour largely excluding the social sciences. The purpose of this review is to provide a baseline for discussing the quantum and nature of the science that is being conducted, and the extent to which the social sciences are a part of that. Methods A bibliographic analysis was conducted of neglected tropical diseases related research papers published over the past 10 years in biomedical and social sciences. The analysis had textual and bibliometric facets, and focussed on chikungunya, dengue, visceral leishmaniasis, and onchocerciasis. Results There is substantial variation in the number of publications associated with each disease. The proportion of the research that is social science based appears remarkably consistent (<4%). A textual analysis, however, reveals a degree of misclassification by the abstracting service where a surprising proportion of the "social sciences" research was pure clinical research. Much of the social sciences research also tends to be "hand maiden" research focused on the implementation of biomedical solutions. Conclusion There is little evidence that scientists pay any attention to the complex social, cultural, biological, and environmental dynamic involved in human pathogenesis. There is little investigator driven social science and a poor presence of interdisciplinary science. The research needs more sophisticated funders and priority setters who are not beguiled by uncritical biomedical promises

AFLP analysis reveals high genetic diversity but low population structure in Coccidioides posadasiiisolates from Mexico and Argentina

BACKGROUND: Coccidioides immitis and C. posadasii cause coccidioidomycosis, a disease that is endemic to North and South America, but for Central America, the incidence of coccidioidomycosis has not been clearly established. Several studies suggest genetic variability in these fungi; however, little definitive information has been discovered about the variability of Coccidioides fungi in Mexico (MX) and Argentina (AR). Thus, the goals for this work were to study 32 Coccidioides spp. isolates from MX and AR, identify the species of these Coccidioides spp. isolates, analyse their phenotypic variability, examine their genetic variability and investigate the Coccidioides reproductive system and its level of genetic differentiation. METHODS: Coccidioides spp. isolates from MX and AR were taxonomically identified by phylogenetic inference analysis using partial sequences of the Ag2/PRA gene and their phenotypic characteristics analysed. The genetic variability, reproductive system and level of differentiation were estimated using AFLP markers. The level of genetic variability was assessed measuring the percentage of polymorphic loci, number of effective allele, expected heterocygosity and Index of Association (I(A)). The degree of genetic differentiation was determined by AMOVA. Genetic similarities among isolates were estimated using Jaccard index. The UPGMA was used to contsruct the corresponding dendrogram. Finally, a network of haplotypes was built to evaluate the genealogical relationships among AFLP haplotypes. RESULTS: All isolates of Coccidioides spp. from MX and AR were identified as C. posadasii. No phenotypic variability was observed among the C. posadasii isolates from MX and AR. Analyses of genetic diversity and population structure were conducted using AFLP markers. Different estimators of genetic variability indicated that the C. posadasii isolates from MX and AR had high genetic variability. Furthermore, AMOVA, dendrogram and haplotype network showed a small genetic differentiation among the C. posadasii populations analysed from MX and AR. Additionally, the I(A) calculated for the isolates suggested that the species has a recombinant reproductive system. CONCLUSIONS: No phenotypic variability was observed among the C. posadasii isolates from MX and AR. The high genetic variability observed in the isolates from MX and AR and the small genetic differentiation observed among the C. posadasii isolates analysed, suggest that this species could be distributed as a single genetic population in Latin America

Internet of Things for Sustainable Human Health

The sustainable health IoT has the strong potential to bring tremendous improvements in human health and well-being through sensing, and monitoring of health impacts across the whole spectrum of climate change. The sustainable health IoT enables development of a systems approach in the area of human health and ecosystem. It allows integration of broader health sub-areas in a bigger archetype for improving sustainability in health in the realm of social, economic, and environmental sectors. This integration provides a powerful health IoT framework for sustainable health and community goals in the wake of changing climate. In this chapter, a detailed description of climate-related health impacts on human health is provided. The sensing, communications, and monitoring technologies are discussed. The impact of key environmental and human health factors on the development of new IoT technologies also analyzed