Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study

Age-stratified serosurvey data are often used to understand spatiotemporal trends in disease incidence and exposure through estimating the Force-of-Infection (FoI). Typically, median or mean FoI estimates are used as the response variable in predictive models, often overlooking the uncertainty in estimated FoI values when fitting models and evaluating their predictive ability. To assess how this uncertainty impact predictions, we compared three approaches with three levels of uncertainty integration. We propose a performance indicator to assess how predictions reflect initial uncertainty.In Colombia, 76 serosurveys (1980–2014) conducted at municipality level provided age-stratified Chagas disease prevalence data. The yearly FoI was estimated at the serosurvey level using a time-varying catalytic model. Environmental, demographic and entomological predictors were used to fit and predict the FoI at municipality level from 1980 to 2010 across Colombia.A stratified bootstrap method was used to fit the models without temporal autocorrelation at the serosurvey level. The predictive ability of each model was evaluated to select the best-fit models within urban, rural and (Amerindian) indigenous settings. Model averaging, with the 10 best-fit models identified, was used to generate predictions.Our analysis shows a risk of overconfidence in model predictions when median estimates of FoI alone are used to fit and evaluate models, failing to account for uncertainty in FoI estimates. Our proposed methodology fully propagates uncertainty in the estimated FoI onto the generated predictions, providing realistic assessments of both central tendency and current uncertainty surrounding exposure to Chagas disease.

Portable optically stimulated luminescence age map of a paleoseismic exposure

The quality and quantity of geochronologic data used to constrain the history of major earthquakes in a region exerts a first-order control on the accuracy of seismic hazard assessments that affect millions of people. However, evaluations of geochronological data are limited by uncertainties related to inherently complex depositional processes that may vary spatially and temporally. To improve confidence in models of earthquake timing, we use a high-density suite of radiocarbon and optically stimulated luminescence (OSL) ages with a grid of 342 portable OSL samples to explore spatiotemporal trends in geochronological data across an exemplary normal fault colluvial wedge exposure. The data reveal a two-dimensional age map of the paleoseismic exposure and demonstrate how vertical and horizontal trends in age relate to dominant sedimentary facies and soil characteristics at the site. Portable OSL data provide critical context for the interpretation of 14C and OSL ages, show that geochronologic age boundaries between pre- and post-earthquake deposits do not match stratigraphic contacts, and provide the basis for selecting alternate Bayesian models of earthquake timing. Our results demonstrate the potential to use emergent, portable OSL methods to dramatically improve paleoseismic constraints on earthquake timing.

Spatiotemporal Trends of Colorectal Cancer Mortality Due to Low Physical Activity and High Body Mass Index From 1990 to 2019: A Global, Regional and National Analysis

Background: Understanding the spatiotemporal trends of colorectal cancer (CRC) deaths caused by low physical activity (LPA) and high body mass index (BMI) is essential for the prevention and control of CRC. We assessed patterns of LPA and high BMI-induced CRC deaths from 1990 to 2019 at global, regional, and national levels.Methods: Data on CRC deaths due to LPA and high BMI was downloaded from the Global Burden of Disease 2019 Study. We calculated estimated annual percentage change (EAPC) to quantify spatiotemporal trends in the CRC age-standardized mortality rate (ASMR) due to LPA and high BMI.Results: In 2019, CRC deaths due to LPA and high BMI were estimated as 58.66 thousand and 85.88 thousand, and the corresponding ASMRs were 0.77/100,000 and 1.07/100,000, with EAPCs of−0.39 [95% confidence interval (CI):−0.49,−0.29] and 0.64[95% CI: 0.57, 0.71] from 1990 to 2019 respectively. Since 1990, the ASMR of CRC attributable to LPA and high BMI has been on the rise in many geographic regions, especially in low middle and middle sociodemographic index (SDI) regions. Thirteen countries had a significant downward trend in CRC ASMR attributed to LPA, with EAPCs < −1. And, only 4 countries had a significant downward trend in CRC ASMR attributable to high BMI, with EAPCs < −1. Countries with a higher baseline burden in 1990 and a higher SDI in 2019 had a faster decline in ASMR due to high BMI and LPA.Conclusions: The burden of CRC caused by LPA and high BMI is on the rise in many countries. Countries should adopt a series of measures to control the local prevalence of obesity and LPA in order to reduce disease burden, including CRC.

Quantifying the Spatiotemporal Variation of Evapotranspiration of Different Land Cover Types and the Contribution of Its Associated Factors in the Xiliao River Plain

Evapotranspiration (ET) is a vital constituent of the hydrologic cycle. Researching changes in ET is necessary for understanding variability in the hydrologic cycle. Although some studies have clarified the changes and influencing factors of ET on a regional or global scale, these variables are still unclear for different land cover types due to the range of possible water evaporation mechanisms and conditions. In this study, we first investigated spatiotemporal trends of ET in different land cover types in the Xiliao River Plain from 2000 to 2019. The correlation between meteorological, NDVI, groundwater depth, and topographic factors and ET was compared through spatial superposition analysis. We then applied the ridge regression model to calculate the contribution rate of each influencing factor to ET for different land cover types. The results revealed that ET in the Xiliao River Plain has shown a continuously increasing trend, most significantly in cropland (CRO). The correlation between ET and influencing factors differed considerably for different land cover types, even showing an opposite result between regions with and without vegetation. Only precipitation (PRCP) and NDVI had a positive impact on ET in all land cover types. In addition, we found that vegetation can deepen the limited depth of land absorbing groundwater, and the influence of topographic conditions may be mainly reflected in the water condition difference caused by surface runoff. The ridge regression model eliminates multicollinearity among influencing factors; R2 in all land cover types was over 0.6, indicating that it could be used to effectively quantify the contribution of various influencing factors to ET. According to the results of our model calculations, NDVI had the greatest impact on ET in grass (GRA), cropland (CRO), paddy (PAD), forest (FOR), and swamp (SWA), while PRCP was the main influencing factor in bare land (BAR) and sand (SAN). These findings imply that we should apply targeted measures for water resources management in different land cover types. This study emphasizes the importance of comprehensively considering differences among various hydrologic cycles according to land cover type in order to assess the contributions of influencing factors to ET.

Spatiotemporal Change Analysis of Soil Moisture Based on Downscaling Technology in Africa

Evaluating the long-term spatiotemporal variability in soil moisture (SM) over Africa is crucial for understanding how crop production is affected by drought or flooding. However, the lack of continuous and stable long-term series and high-resolution soil moisture records impedes such research. To overcome the inconsistency of different microwave sensors (Advanced Microwave Scanning Radiometer-EOS, AMSR-E; Soil Moisture and Ocean Salinity, SMOS; and Advanced Microwave Scanning Radiometer 2, AMSR2) in measuring soil moisture over time and depth, we built a time series reconstruction model to correct SM, and then used a Spatially Weighted Downscaling Model to downscale the SM data from three different sensors to a 1 km spatial resolution. The verification of the reconstructed data shows that the product has high accuracy, and can be used for application and analysis. The spatiotemporal trends of SM in Africa were examined for 2003–2017. The analysis indicated that soil moisture is declining in Africa as a whole, and it is notably higher in central Africa than in other subregions. The most significant decrease in SM was observed in the savanna zone (slope < −0.08 m3 m−3 and P < 0.001), followed by South Africa and Namibia (slope < −0.07 m3 m−3 and P < 0.01). Seasonally, the most significant downward trends in SM were observed during the spring, mainly over eastern and central Africa (slope < −0.07 m3 m−3, R < −0.58 and P < 0.001). The analysis of spatiotemporal changes in soil moisture can help improve the understanding of hydrological cycles, and provide benchmark information for drought management in Africa.