nasa-gesdisc/es-publications-researchareas huggingface dataset:
Modelling the Spatial Distribution of ASF-Positive wild boar carcasses in South Korea using 2019–2020 national surveillance data (2021)

In September 2019, African swine fever (ASF) was reported in South Korea for the first time. Since then, more than 651 ASF cases in wild boars and 14 farm outbreaks have been notified in the country. Despite the efforts to eradicate ASF among wild boar populations, the number of reported ASF-positive wild boar carcasses have increased recently. The purpose of this study was to characterize the spatial distribution of ASF-positive wild boar carcasses to identify the risk factors associated with the presence and number of ASF-positive wild boar carcasses in the affected areas. Because surveillance efforts have substantially increased in early 2020, we divided the study into two periods (2 October 2019 to 19 January 2020, and 19 January to 28 April 2020) based on the number of reported cases and aggregated the number of reported ASF-positive carcasses into a regular grid of hexagons of 3-km diameter. To account for imperfect detection of positive carcasses, we adjusted spatial zero-inflated Poisson regression models to the number of ASF-positive wild boar carcasses per hexagon. During the first study period, proximity to North Korea was identified as the major risk factor for the presence of African swine fever virus. In addition, there were more positive carcasses reported in affected hexagons with high habitat suitability for wild boars, low heat load index (HLI), and high human density. During the second study period, proximity to an ASF-positive carcass reported during the first period was the only significant risk factor for the presence of ASF-positive carcasses. Additionally, low HLI and elevation were associated with an increased number of ASF-positive carcasses reported in the affected hexagons. Although the proportion of ASF-affected hexagons increased from 0.06 (95% credible interval (CrI): 0.050.07) to 0.09 (95% CrI: 0.080.10), the probability of reporting at least one positive carcass in ASF-affected hexagons increased from 0.49 (95% CrI: 0.410.57) to 0.73 (95% CrI: 0.660.81) between the two study periods. These results can be used to further advance risk-based surveillance strategies in the Republic of Korea.

Research areas: Public Health
Assessment of effectiveness of DAMaN: a malaria intervention program initiated by Government of Odisha, India (2020)

India, a persistently significant contributor to the global malaria burden, rolled out several anti-malaria interventions at the national and state level to control and recently, to eliminate the disease. Odisha, the eastern Indian state with the highest malaria burden experienced substantial gains shown by various anti-malaria initiatives implemented under the National Vector-borne Disease Control Programme (NVBDCP). However, recalcitrant high-transmission pockets of malaria persist in hard-to-reach stretches of the state, characterised by limited access to routine malaria surveillance and the forested hilly topography favouring unbridled vector breeding. The prevalence of asymptomatic malaria in such pockets serves as perpetual malaria reservoir, thus hindering its elimination. Therefore, a project with the acronym DAMaN was initiated since 2017 by state NVBDCP, targeting locally identified high endemic pockets in 23 districts. DAMaN comprised biennial mass screening and treatment, provisioning of long-lasting insecticidal net (LLIN) and behavioural change communication. Subsequently, to inform policy, assessment of DAMaN was conceived that aims to estimate the coverage of the various components of the project; the prevalence of malaria, even at sub-patent level especially among pregnant/lactating women and children; and its impact on malaria incidence. A survey of DAMaN beneficiaries will measure coverage; and knowledge and practices related to LLIN; along with collection of blood specimens from a probability sample. A multi-stage stratified clustered sample of 2228 households (~33% having pregnant/lactating women) will be selected from 6 DAMaN districts. Routine DAMaN project data (20172018) and NVBDCP data (20132018) will be extracted. Rapid Diagnostic Test, Polymerase Chain Reaction and blood smear microscopy will be conducted to detect malarial parasitemia. In addition to measuring DAMaNs coverage and malarial prevalence in DAMaN pockets, its impact will be estimated using pre-post differences and Interrupted Time Series analysis using 2017 as the inflection point. The assessment may help to validate the unique strategies employed by DAMaN.

Research areas: Public Health
Spatio-temporal spread of COVID-19: Comparison of the inhomogeneous SEPIR model and data from South Carolina (2022)

During the COVID-19 pandemic authorities have been striving to obtain reliable predictions for the spreading dynamics of the disease. We recently developed a multi-sub-populations (multi-compartments: susceptible, exposed, pre-symptomatic, infectious, recovered) model, that accounts for the spatial in-homogeneous spreading of the infection and shown, for a variety of examples, how the epidemic curves are highly sensitive to location of epicenters, non-uniform population density, and local restrictions. In the present work we test our model against real-life data from South Carolina during the period May 22 to July 22 (2020). During this period, minimal restrictions have been employed, which allowed us to assume that the local basic reproduction number is constant in time. We account for the non-uniform population density in South Carolina using data from NASAs Socioeconomic Data and Applications Center (SEDAC), and predict the evolution of infection heat-maps during the studied period. Comparing the predicted heat-maps with those observed, we find high qualitative resemblance. Moreover, the Pearsons correlation coefficient is relatively high thus validating our model against real-world data. We conclude that the model accounts for the major effects controlling spatial in-homogeneous spreading of the disease. Inclusion of additional sub-populations (compartments), in the spirit of several recently developed models for COVID-19, can be easily performed within our mathematical framework.

Research areas: Public Health
Coccidioidomycosis (Valley Fever), Soil Moisture, and El Nino Southern Oscillation in California and Arizona (2022)

The soil-borne fungal disease coccidioidomycosis (Valley fever) is prevalent across the southwestern United States (US). Previous studies have suggested that the occurrence of this infection is associated with anomalously wet or dry soil moisture states described by the grow and blow hypothesis. The growth of coccidioidomycosis is favored by moist conditions both at the surface and in the root zone. A statistical analysis identified two areas in Arizona and central California, with a moderate-to-high number of coccidioidomycosis cases. A Wavelet Transform Coherence (WTC) analysis between El Nino Southern Oscillation (ENSO), coccidioidomycosis cases, surface soil moisture (SSM; 0 to 5 cm) from European Space Agency-Climate Change Initiative (ESA-CCI), and shallow root zone soil moisture (RZSM; 0 to 40 cm depth) from Soil MERGE (SMERGE) was executed for twenty-four CA and AZ counties. In AZ, only SSM was modulated by ENSO. When case values were adjusted for overreporting between 2009 to 2012, a moderate but significant connection between ENSO and cases was observed at a short periodicity (2.1 years). In central CA, SSM, RZSM, and cases all had a significant link to ENSO at longer periodicities (5-to-7 years). This study provides an example of how oceanic-atmospheric teleconnections can impact human health.

Research areas: Public Health
An ecological analysis of long-term exposure to PM2.5 and incidence of (2020)

Background. Ambient fine particulate matter (PM<SUB>2.5</SUB>) is associated with a wide range of acute and chronic health effects, including increased risk of respiratory infection. However, evidence specifically related to novel coronavirus disease (COVID-19) is limited. <P />Methods. COVID-19 case counts for 111 Canadian health regions were obtained from the COVID-19 Canada Open Data portal. Annual PM<SUB>2.5</SUB> data for 2000-2016 were estimated from a national exposure surface based on remote sensing, chemical transport modelling and ground observations, and minimum and maximum temperature data for 2000-2015 were based on a national interpolated surface derived from thin-plate smoothing splines. Population counts and sociodemographic data by health region were obtained from the 2016 census, and health data (self-rated health and prevalence of smoking, obesity, and selected chronic diseases) by health region, were obtained from the Canadian Community Health Survey. Data on total number of COVID-19 tests and changes in mobility comparing post-vs. pre-introduction of social distancing measures were available by province. Data were analyzed using negative binomial regression models. <P />Results. After controlling for province, temperature, demographic and health characteristics and days since peak incidence by health region, long-term PM<SUB>2.5</SUB> exposure exhibited a positive association with COVID-19 incidence (incidence rate ratio 1.07, 95% confidence interval 0.97-1.18 per μg/m<SUP>3</SUP>). This association was larger in magnitude and statistically significant in analyses excluding provinces that reported cases only for aggregated health regions, excluding health regions with less than median population density, and restricted to the most highly affected provinces (Quebec and Ontario). <P />Conclusions. We observed a positive association between COVID-19 incidence and long-term PM<SUB>2.5</SUB> exposure in Canadian health regions. The association was larger in magnitude and statistically significant in more highly affected health regions and those with potentially less exposure measurement error. While our results generate hypotheses for further testing, they should be interpreted with caution and require further examination using study designs less prone to bias.

Research areas: Public Health
A Persistent Hotspot of Schistosoma mansoni Infection in a Five-Year Randomized Trial of Praziquantel Preventative Chemotherapy Strategies (2017)

Persistent hotspots have been described after mass drug administration (MDA) for the control of schistosomiasis, but they have not been studied during the course of a multiyear MDA program.In data from a 5-year study of school-based and village-wide preventive chemotherapy strategies for Schistosoma mansoni, spatial scan statistics were used to find infection hotspots in 3 populations: 5- to 8-year-olds, 9- to 12-year-olds, and adults. Negative binomial regression was used to analyze changes from baseline, and receiver operating characteristic analyses were used to predict which villages would reach prevalence and intensity endpoints.We identified a persistent hotspot, not associated with study arm, where S. mansoni infection prevalence and intensity did not decrease as much as in villages outside the hotspot. Significant differences from baseline were realized after 1 year of MDA: we did not identify factors that moderated this relationship. Villages meeting specified endpoints at year 5 were predicted from prior year data with moderately high sensitivity and specificity.The MDA strategies were less effective at reducing prevalence and intensity in the hotspot compared with other villages. Villages that reached year 5 endpoints could be detected earlier, which may provide the opportunity to amend intervention strategies.

Research areas: Public Health
Compilation and spatio-temporal analysis of publicly available total solar and UV irradiance data in the contiguous United States (2019)

Skin cancer is the most common type of cancer in the United States, the majority of which is caused by overexposure to ultraviolet (UV) irradiance, which is one component of sunlight. National Environmental Public Health Tracking Program at CDC has collaborated with partners to develop and disseminate county-level daily UV irradiance (20052015) and total solar irradiance (19912012) data for the contiguous United States. UV irradiance dataset was derived from the Ozone Monitoring Instrument (OMI), and solar irradiance was extracted from National Solar Radiation Data Base (NSRDB) and SolarAnywhere data. Firstly, we produced daily population-weighted UV and solar irradiance datasets at the county level. Then the spatial distributions and long-term trends of UV irradiance, solar irradiance and the ratio of UV irradiance to solar irradiance were analyzed. The national average values across all years are 4300 Wh/m2, 2700 J/m2 and 130 mW/m2 for global horizontal irradiance (GHI), erythemally weighted daily dose of UV irradiance (EDD) and erythemally weighted UV irradiance at local solar noon time (EDR), respectively. Solar, UV irradiances and the ratio of UV to solar irradiance all increased toward the South and in some areas with high altitude, suggesting that using solar irradiance as indicator of UV irradiance in studies covering large geographic regions may bias the true pattern of UV exposure. National annual average daily solar and UV irradiances increased significantly over the years by about 0.3% and 0.5% per year, respectively. Both datasets are available to the public through CDC's Tracking network. The UV irradiance dataset is currently the only publicly-available, spatially-resolved, and long-term UV irradiance dataset covering the contiguous United States. These datasets help us understand the spatial distributions and temporal trends of solar and UV irradiances, and allow for improved characterization of UV and sunlight exposure in future studies.

Research areas: Public Health
Mapping child growth failure across low-and middle-income countries (2020)

Childhood malnutrition is associated with high morbidity and mortality globally1. Undernourished children are more likely to experience cognitive, physical, and metabolic developmental impairments that can lead to later cardiovascular disease, reduced intellectual ability and school attainment, and reduced economic productivity in adulthood2. Child growth failure (CGF), expressed as stunting, wasting, and underweight in children under five years of age (059 months), is a specific subset of undernutrition characterized by insufficient height or weight against age-specific growth reference standards35. The prevalence of stunting, wasting, or underweight in children under five is the proportion of children with a height-for-age, weight-for-height, or weight-for-age z-score, respectively, that is more than two standard deviations below the World Health Organizations median growth reference standards for a healthy population6. Subnational estimates of CGF report substantial heterogeneity within countries, but are available primarily at the first administrative level (for example, states or provinces)7; the uneven geographical distribution of CGF has motivated further calls for assessments that can match the local scale of many public health programmes8. Building from our previous work mapping CGF in Africa9, here we provide the first, to our knowledge, mapped high-spatial-resolution estimates of CGF indicators from 2000 to 2017 across 105 low- and middle-income countries (LMICs), where 99% of affected children live1, aggregated to policy-relevant first and second (for example, districts or counties) administrative-level units and national levels. Despite remarkable declines over the study period, many LMICs remain far from the ambitious World Health Organization Global Nutrition Targets to reduce stunting by 40% and wasting to less than 5% by 2025. Large disparities in prevalence and progress exist across and within countries; our maps identify high-prevalence areas even within nations otherwise succeeding in reducing overall CGF prevalence. By highlighting where the highest-need populations reside, these geospatial estimates can support policy-makers in planning interventions that are adapted locally and in efficiently directing resources towards reducing CGF and its health implications.

Research areas: Public Health
The effect of livestock density on Trypanosoma brucei gambiense and T. b. rhodesiense: A causal inference-based approach (2022)

Domestic and wild animals are important reservoirs of the rhodesiense form of human African trypanosomiasis (rHAT), however quantification of this effect offers utility for deploying non-medical control activities, and anticipating their success when wildlife are excluded. Further, the uncertain role of animal reservoirsparticularly pigsthreatens elimination of transmission (EOT) targets set for the gambiense form (gHAT). Using a new time series of high-resolution cattle and pig density maps, HAT surveillance data collated by the WHO Atlas of HAT, and methods drawn from causal inference and spatial epidemiology, we conducted a retrospective ecological cohort study in Uganda, Malawi, Democratic Republic of the Congo (DRC) and South Sudan to estimate the effect of cattle and pig density on HAT risk. For rHAT, we found a positive effect for cattle (RR 1.61, 95% CI 0.90, 2.99) and pigs (RR 2.07, 95% CI 1.15, 2.75) in Uganda, and a negative effect for cattle (RR 0.88, 95% CI 0.71, 1.10) and pigs (RR 0.42, 95% CI 0.23, 0.67) in Malawi. For gHAT we found a negative effect for cattle in Uganda (RR 0.88, 95% CI 0.50, 1.77) and South Sudan (RR 0.63, 95% CI 0.54, 0.77) but a positive effect in DRC (1.17, 95% CI 1.04, 1.32). For pigs, we found a positive gHAT effect in both Uganda (RR 2.02, 95% CI 0.87, 3.94) and DRC (RR 1.23, 95% CI 1.10, 1.37), and a negative association in South Sudan (RR 0.66, 95% CI 0.50, 0.98). These effects did not reach significance for the cattle-rHAT effect in Uganda or Malawi, or the cattle-gHAT and pig-gHAT effects in Uganda. While ecological bias may drive the findings in South Sudan, estimated E-values and simulation studies suggest unmeasured confounding and underreporting are unlikely to explain our findings in Malawi, Uganda, and DRC. Our results suggest cattle and pigs may be important reservoirs of rHAT in Uganda but not Malawi, and that pigsand possibly cattlemay be gHAT reservoirs.

Research areas: Public Health
Estimating error covariance and correlation region in UV irradiance data fusion by combining TOMS-OMI and UVMRP ground observations (2018)

Surface ultraviolet (UV) observations can be obtained from satellite or ground observations. This paper uses one data fusion technique (similar to Kalman filter) to combine the advantages from both sources of observations, aiming at achieving a better estimate of surface UV. In this paper, new mathematical methods and algorithms were developed to estimate the error covariance and correlation region, which are the most important components in this data fusion technique. This technique was applied to the satellite data from the Total Ozone Mapping Spectrometer (TOMS)-Ozone Monitoring Instrument (OMI) combined with ground measurements from UV-B Monitoring and Research Program (UVMRP) within the region of continental U.S. from 2005 to 2015. Numerical experiments showed that the technique is effective, and TOMS-OMI data were improved by combining UVMRP data. In addition, the innovative ensemble-based method is generic and can be applied to other fields for data fusion/assimilation.

Research areas: Validation
Predicting risks from downthedrain chemicals in a developing country: Mexico and linear alkylbenzene sulfonate as a case study (2018)

AbstractIt is recognized that the amount of natural dilution available can make a significant difference in the exposure and risk assessment of chemicals that emanate from wastewater treatment plants (WWTPs). However, data availability is a common limiting factor in exposure assessments for emerging markets. In the present study, we used a novel approach to derive dilution factors for the receiving waters within 5 km of wastewater discharge points in Mexico by combining locally measured river volumes, ecoregion categorization, data on WWTP capacity, and global river network models. Distributions of wastewater effluent into receiving stream dilution factors were developed for the entire country and organized by ecoregion type to explore spatial differences. The distribution of dilution factors in Mexico ranged from >1000 in tropical and temperate ecoregions to 1 in desert ecoregions. To demonstrate its utility, dilution factors were used to develop a probabilistic model to explore the potential ecological risks of the highvolume surfactant linear alkylbenzene sulfonate (LAS), commonly used in downthedrain cleaning products. The predicted LAS river exposure values were below the predicted noeffect concentration in all regions. The methodology developed for Mexico can be used to derive refined exposure assessments in other countries with emerging markets throughout the world, resulting in more realistic risk assessments. Environ Toxicol Chem 2018;37:24752486. 2018 SETAC

Research areas: Water Quality
Suitability of the MODIS-NDVI Time-Series for a Posteriori Evaluation of the Citrus Tristeza Virus Epidemic (2020)

The technological advances of remote sensing (RS) have allowed its use in a number of fields of application including plant disease depiction. In this study, an RS approach based on an 18-year (i.e., 20012018) time-series analysis of Normalized Difference Vegetation Index (NDVI) data, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and processed with TIMESAT free software, was applied in Sicily (insular Italy). The RS approach was carried out in four orchards infected by Citrus tristeza virus (CTV) at different temporal stages and characterized by heterogeneous conditions (e.g., elevation, location, plant age). The temporal analysis allowed the identification of specific metrics of the NDVI time-series at the selected sites during the study period. The most reliable parameter which was able to identify the temporal evolution of CTV syndrome and the impact of operational management practices was the Base value (i.e., average NDVI during the growing seasons, which reached R2 values up to 0.88), showing good relationships with Peak value, Small integrated value and Amplitude, with R2 values of 0.63, 0.70 and 0.75, respectively. The approach herein developed is valid to be transferred to regional agencies involved in and/or in charge of the management of plant diseases, especially if it is integrated with ground-based early detection methods or high-resolution RS approaches, in the case of quarantine plant pathogens requiring control measures at large-scale level.

Research areas: Public Health
Suitability of gridded climate datasets for use in environmental epidemiology (2018)

Epidemiologic analyses of the health effects of meteorological exposures typically rely on observations from the nearest weather station to assess exposure for geographically diverse populations. Gridded climate datasets (GCD) provide spatially resolved weather data that may offer improved exposure estimates, but have not been systematically validated for use in epidemiologic evaluations. As a validation, we linearly regressed daily weather estimates from two GCDs, PRISM and Daymet, to observations from a sample of weather stations across the conterminous United States and compared spatially resolved, population-weighted county average temperatures and heat indices from PRISM to single-pixel PRISM values at the weather stations to identify differences. We found that both Daymet and PRISM accurately estimate ambient temperature and mean heat index at sampled weather stations, but PRISM outperforms Daymet for assessments of humidity and maximum daily heat index. Moreover, spatially-resolved exposure estimates differ from point-based assessments, but with substantial inter-county heterogeneity. We conclude that GCDs offer a potentially useful approach to exposure assessment of meteorological variables that may, in some locations, reduce exposure measurement error, as well as permit assessment of populations distributed far from weather stations.

Research areas: Validation