nasa-gesdisc/es-publications-researchareas huggingface dataset:
Direct association between rainfall and non-typhoidal Salmonella bloodstream infections in hospital-admitted children in the Democratic Republic of Congo (2021)

Non-typhoidal Salmonella (NTS) ranks first among causes of bloodstream infection in children under five years old in the Democratic Republic of Congo and has a case fatality rate of 15%. Main host-associated risk factors are Plasmodium falciparum malaria, anemia and malnutrition. NTS transmission in sub-Saharan Africa is poorly understood. NTS bloodstream infections mostly occur during the rainy season, which may reflect seasonal variation in either environmental transmission or host susceptibility. We hypothesized that environment- and host-associated factors contribute independently to the seasonal variation in NTS bloodstream infections in children under five years old admitted to Kisantu referral hospital in 20132019. We used remotely sensed rainfall and temperature data as proxies for environmental factors and hospital data for host-associated factors. We used principal component analysis to disentangle the interrelated environment- and host-associated factors. With timeseries regression, we demonstrated a direct association between rainfall and NTS variation, independent of host-associated factors. While the latter explained 17.5% of NTS variation, rainfall explained an additional 9%. The direct association with rainfall points to environmental NTS transmission, which should be explored by environmental sampling studies. Environmental and climate change may increase NTS transmission directly or via host susceptibility, which highlights the importance of preventive public health interventions.

Research areas: Public Health
Digital discrimination: Political bias in Internet service provision across ethnic groups (2016)

The global expansion of the Internet is frequently associated with increased government transparency, political rights, and democracy. However, this assumption depends on marginalized groups getting access in the first place. Here we document a strong and persistent political bias in the allocation of Internet coverage across ethnic groups worldwide. Using estimates of Internet penetration obtained through network measurements, we show that politically excluded groups suffer from significantly lower Internet penetration rates compared with those in power, an effect that cannot be explained by economic or geographic factors. Our findings underline one of the central impediments to liberation technology, which is that governments still play a key role in the allocation of the Internet and can, intentionally or not, sabotage its liberating effects.

Research areas: Habitat Conversion/Fragmentation
Epidemiological model for the inhomogeneous spatial spreading of COVID-19 and other diseases (2021)

We suggest a novel mathematical framework for the in-homogeneous spatial spreading of an infectious disease in human population, with particular attention to COVID-19. Common epidemiological models, e.g., the well-known susceptible-exposed-infectious-recovered (SEIR) model, implicitly assume uniform (random) encounters between the infectious and susceptible sub-populations, resulting in homogeneous spatial distributions. However, in human population, especially under different levels of mobility restrictions, this assumption is likely to fail. Splitting the geographic region under study into areal nodes, and assuming infection kinetics within nodes and between nearest-neighbor nodes, we arrive into a continuous, reaction-diffusion, spatial model. To account for COVID-19, the model includes five different sub-populations, in which the infectious sub-population is split into pre-symptomatic and symptomatic. Our model accounts for the spreading evolution of infectious population domains from initial epicenters, leading to different regimes of sub-exponential (e.g., power-law) growth. Importantly, we also account for the variable geographic density of the population, that can strongly enhance or suppress infection spreading. For instance, we show how weakly infected regions surrounding a densely populated area can cause rapid migration of the infection towards the populated area. Predicted infection heat-maps show remarkable similarity to publicly available heat-maps, e.g., from South Carolina. We further demonstrate how localized lockdown/quarantine conditions can slow down the spreading of disease from epicenters. Application of our model in different countries can provide a useful predictive tool for the authorities, in particular, for planning strong lockdown measures in localized areassuch as those underway in a few countries.

Research areas: Public Health
Primate malarias as a model for cross-species parasite transmission (2022)

Parasites regularly switch into new host species, representing a disease burden and conservation risk to the hosts. The distribution of these parasites also gives insight into characteristics of ecological networks and genetic mechanisms of host-parasite interactions. Some parasites are shared across many species, whereas others tend to be restricted to hosts from a single species. Understanding the mechanisms producing this distribution of host specificity can enable more effective interventions and potentially identify genetic targets for vaccines or therapies. As ecological connections between human and local animal populations increase, the risk to human and wildlife health from novel parasites also increases. Which of these parasites will fizzle out and which have the potential to become widespread in humans? We consider the case of primate malarias, caused by
              Plasmodium
              parasites, to investigate the interacting ecological and evolutionary mechanisms that put human and nonhuman primates at risk for infection.
              Plasmodium
              host switching from nonhuman primates to humans led to ancient introductions of the most common malaria-causing agents in humans today, and new parasite switching is a growing threat, especially in Asia and South America. Based on a wild host-
              Plasmodium
              occurrence database, we highlight geographic areas of concern and potential areas to target further sampling. We also discuss methodological developments that will facilitate clinical and field-based interventions to improve human and wildlife health based on this eco-evolutionary perspective.

Research areas: Public Health
Investigating barriers to tuberculosis evaluation in Uganda using geographic information systems (2015)

Reducing geographic barriers to tuberculosis (TB) care is a priority in high-burden countries where patients frequently initiate, but do not complete, the multi-day TB evaluation process. Using routine cross-sectional study from six primary-health clinics in rural Uganda from 2009 to 2012, we explored whether geographic barriers affect completion of TB evaluation among adults with unexplained chronic cough. We measured distance from home parish to health center and calculated individual travel time using a geographic information systems technique incorporating roads, land cover, and slope, and measured its association with completion of TB evaluation. In 264,511 patient encounters, 4,640 adults (1.8%) had sputum smear microscopy ordered; 2,783 (60%) completed TB evaluation. Median travel time was 68 minutes for patients with TB examination ordered compared with 60 minutes without (P < 0.010). Travel time differed between those who did and did not complete TB evaluation at only one of six clinics, whereas distance to care did not differ at any of them. Neither distance nor travel time predicted completion of TB evaluation in rural Uganda, although limited detail in road and village maps restricted full implementation of these mapping techniques. Better data are needed on geographic barriers to access clinics offering TB services to improve TB diagnosis.

Research areas: Public Health
Empirical Evidence for the Impact of Environmental Quality on Life Expectancy in African Countries (2021)

Background.
              Protecting the health of citizens is a central aim of sustainable development plans, due to the effect of health on social and economic development. However, studies show that environment-related diseases adversely affect the health status of a people, and this situation is worse for African countries. The Sustainable Development Goals (SDG) targets have included reducing environment-related deaths since 2015. However, there is a lack of empirical findings focused on the effects of environmental quality on life expectancy in Africa.
            
            
              Objectives.
              The present study examined the impact of environmental quality on life expectancy in 24 African countries.
            
            
              Methods.
              Time-series data ranging from 2000 to 2016 was used and the panel autoregressive distributed lag (ARDL)dynamic fixed effect (DFE) model was employed to analyze the data.
            
            
              Results.
              The results confirmed that, in the long run, improvements in environmental quality significantly increased life expectancy in the studied African countries during the study period. A unit increment in environmental performance index (EPI) and ecosystem vitality (EV) increased the life expectancy of Africans by 0.137 and 0.1417 years, respectively.
            
            
              Conclusions.
              To the best of the authors' knowledge, this is the first empirical (econometric) study using a broad measurement (indicator) of environmental quality to investigate its impact on life expectancy in African countries. The study recommends that the introduction of environmentally friendly economies (like renewable energy, land, water, and waste management), legal, socio-economic, demographic, and technological measures are essential to reduce environmental pollution and improve life expectancy in Africa.
            
            
              Competing Interests.
              The authors declare no competing financial interests.

Research areas: Public Health
Empirical predictive modeling approach to quantifying social vulnerability to natural hazards (2021)

Conventionally, natural hazard scholars quantify social vulnerability based on social indicators to manifest the extent to which locational communities are susceptible to adverse impacts of natural hazard events and are prone to limited or delayed recoveries. They usually overlook the different geographical distributions of social vulnerability at different hazard intensities and in distinct response and recovery phases, however. In addition, conventional approaches to quantifying social vulnerability usually establish the relationship between social indicators and social vulnerability with little evidence from empirical data science. In this article, we introduce a general framework of a predictive modeling approach to quantifying social vulnerability given intensity during a response or recovery phase. We establish the relationship between social indicators and social vulnerability with an empirical statistical method and historical data on hazard effects. The new metric of social vulnerability given an intensity measure can be coupled with hazard maps for risk analysis to predict adverse impacts or poor recoveries associated with future natural hazard events. An example based on data on casualties, house damages, and peak ground accelerations of the 2015 Gorkha earthquake in Nepal and pre-event social indicators at the district level shows that the proposed approach can be applied for vulnerability quantification and risk analysis in terms of specific hazard impacts.

Research areas: Public Health
Detection of anomalies in the UV–vis reflectances from the Ozone Monitoring Instrument (2021)

Abstract. Various instrumental or geophysical artifacts, such as saturation, stray light or obstruction of light (either coming from the instrument or related to solar eclipses), negatively impact satellite measured ultraviolet and visible Earthshine radiance spectra and downstream retrievals of atmospheric and surface properties derived from these spectra. In addition, excessive noise such as from cosmic-ray impacts, prevalent within the South Atlantic Anomaly, can also degrade satellite radiance measurements. Saturation specifically pertains to observations of very bright surfaces such as sunglint over open water or thick clouds. When saturation occurs, additional photoelectric charge generated at the saturated pixel may overflow to pixels adjacent to a saturated area and be reflected as a distorted image in the final sensor output. When these effects cannot be corrected to an acceptable level for science-quality retrievals, flagging of the affected pixels is indicated. Here, we introduce a straightforward detection method that is based on the correlation, r, between the observed Earthshine radiance and solar irradiance spectra over a 10 nm spectral range; our decorrelation index (DI for brevity) is simply defined as a DI of 1r. DI increases with anomalous additive effects or excessive noise in either radiances, the most likely cause in data from the Ozone Monitoring Instrument (OMI), or irradiances. DI is relatively straightforward to use and interpret and can be computed for different wavelength intervals. We developed a set of DIs for two spectral channels of the OMI, a hyperspectral pushbroom imaging spectrometer. For each OMI spatial measurement, we define 14 wavelength-dependent DIs within the OMI visible channel (350498 nm) and six DIs in its ultraviolet 2 (UV2) channel (310370 nm). As defined, DIs reflect a continuous range of deviations of observed spectra from the reference irradiance spectrum that are complementary to the binary saturation possibility warning (SPW) flags currently provided for each individual spectral or spatial pixel in the OMI radiance data set. Smaller values of DI are also caused by a number of geophysical factors; this allows one to obtain interesting physical results on the global distribution of spectral variations.

Research areas: Validation
Predicting malaria cases using remotely sensed environmental variables in Nkomazi, South Africa (2019)

There has been a conspicuous increase in malaria cases since 2016/2017 over the three malaria-endemic provinces of South Africa. This increase has been linked to climatic and environmental factors. In the absence of adequate traditional environmental/climatic data covering ideal spatial and temporal extent for a reliable warning system, remotely sensed data are useful for the investigation of the relationship with, and the prediction of, malaria cases. Monthly environmental variables such as the normalised difference vegetation index (NDVI), the enhanced vegetation index (EVI), the normalised difference water index (NDWI), the land surface temperature for night (LSTN) and day (LSTD), and rainfall were derived and evaluated using seasonal autoregressive integrated moving average (SARIMA) models with different lag periods. Predictions were made for the last 56 months of the time series and were compared to the observed malaria cases from January 2013 to August 2017. All these factors were found to be statistically significant in predicting malaria transmission at a 2-months lag period except for LSTD which impact the number of malaria cases negatively. Rainfall showed the highest association at the two-month lag time (r=0.74; P<0.001), followed by EVI (r=0.69; P<0.001), NDVI (r=0.65; P<0.001), NDWI (r=0.63; P<0.001) and LSTN (r=0.60; P<0.001). SARIMA without environmental variables had an adjusted R2 of 0.41, while SARIMA with total monthly rainfall, EVI, NDVI, NDWI and LSTN were able to explain about 65% of the variation in malaria cases. The prediction indicated a general increase in malaria cases, predicting about 711 against 648 observed malaria cases. The development of a predictive early warning system is imperative for effective malaria control, prevention of outbreaks and its subsequent elimination in the region.

Research areas: Public Health
Predicting distribution of malaria vector larval habitats in Ethiopia by integrating distributed hydrologic modeling with remotely sensed data (2021)

Larval source management has gained renewed interest as a malaria control strategy in Africa but the widespread and transient nature of larval breeding sites poses a challenge to its implementation. To address this problem, we propose combining an integrated high resolution (50 m) distributed hydrological model and remotely sensed data to simulate potential malaria vector aquatic habitats. The novelty of our approach lies in its consideration of irrigation practices and its ability to resolve complex ponding processes that contribute to potential larval habitats. The simulation was performed for the year of 2018 using ParFlow-Common Land Model (CLM) in a sugarcane plantation in the Oromia region, Ethiopia to examine the effects of rainfall and irrigation. The model was calibrated using field observations of larval habitats to successfully predict ponding at all surveyed locations from the validation dataset. Results show that without irrigation, at least half of the area inside the farms had a 40% probability of potential larval habitat occurrence. With irrigation, the probability increased to 56%. Irrigation dampened the seasonality of the potential larval habitats such that the peak larval habitat occurrence window during the rainy season was extended into the dry season. Furthermore, the stability of the habitats was prolonged, with a significant shift from semi-permanent to permanent habitats. Our study provides a hydrological perspective on the impact of environmental modification on malaria vector ecology, which can potentially inform malaria control strategies through better water management.

Research areas: Public Health
Development and validation of improved PM2. 5 models for public health applications using remotely sensed aerosol and meteorological data (2019)

In this study, Moderate Resolution Imaging Spectrometer (MODIS) satellite measurements of aerosol optical depth (AOD) from different retrieval algorithms have been correlated with ground measurements of fine particulate matter less than 2.5 m (PM2.5). Several MODIS AOD products from different satellites (Aqua vs. Terra), retrieval algorithms (Dark Target vs. Deep Blue), collections (5.1 vs. 6), and spatial resolutions (10 km vs. 3 km) for cities in the Western, Midwestern, and Southeastern USA have been evaluated. We developed and validated PM2.5 prediction models using remotely sensed AOD data. These models were further improved by incorporating meteorological variables (temperature, relative humidity, precipitation, wind gust, and wind direction) from the North American Land Data Assimilation System Phase 2 (NLDAS-2). Adding these meteorological data significantly improved the simulation quality of all the PM2.5 models, especially in the Western USA. Temperature, relative humidity, and wind gust were significant meteorological variables throughout the year in the Western USA. Wind speed was the most significant meteorological variable for the cold season while for the warm season, temperature was the most prominent one in the Midwestern and Southeastern USA. Using this satellite-derived PM2.5 data can improve the spatial coverage, especially in areas where PM2.5 ground monitors are lacking, and studying the connections between PM2.5 and public health concerns including respiratory and cardiovascular diseases in the USA can be further advanced.

Research areas: Air Quality
Modeling the Environmental Suitability for Aedes (Stegomyia) aegypti and Aedes (Stegomyia) albopictus (Diptera: Culicidae) in the Contiguous United States (2017)

Abstract
            The mosquitoes Aedes (Stegomyia) aegypti (L.)(Diptera:Culicidae) and Ae. (Stegomyia) albopictus (Skuse) (Diptera:Culicidae) transmit dengue, chikungunya, and Zika viruses and represent a growing public health threat in parts of the United States where they are established. To complement existing mosquito presence records based on discontinuous, non-systematic surveillance efforts, we developed county-scale environmental suitability maps for both species using maximum entropy modeling to fit climatic variables to county presence records from 19602016 in the contiguous United States. The predictive models for Ae. aegypti and Ae. albopictus had an overall accuracy of 0.84 and 0.85, respectively. Cumulative growing degree days (GDDs) during the winter months, an indicator of overall warmth, was the most important predictive variable for both species and was positively associated with environmental suitability. The number (percentage) of counties classified as environmentally suitable, based on models with 90 or 99% sensitivity, ranged from 1,443 (46%) to 2,209 (71%) for Ae. aegypti and from 1,726 (55%) to 2,329 (75%) for Ae. albopictus. Increasing model sensitivity results in more counties classified as suitable, at least for summer survival, from which there are no mosquito records. We anticipate that Ae. aegypti and Ae. albopictus will be found more commonly in counties classified as suitable based on the lower 90% sensitivity threshold compared with the higher 99% threshold. Counties predicted suitable with 90% sensitivity should therefore be a top priority for expanded mosquito surveillance efforts while still keeping in mind that Ae. aegypti and Ae. albopictus may be introduced, via accidental transport of eggs or immatures, and potentially proliferate during the warmest part of the year anywhere within the geographic areas delineated by the 99% sensitivity model.

Research areas: Public Health
The dark cloud with a silver lining: Assessing the impact of the SARS COVID-19 pandemic on the global environment (2020)

The Severe Acute Respiratory Syndrome-Coronavirus Disease 2019 (COVID-19) pandemic caused by a novel coronavirus known as SARS-CoV-2 has caused tremendous suffering and huge economic losses. We hypothesized that extreme measures of partial-to-total shutdown might have influenced the quality of the global environment because of decreased emissions of atmospheric pollutants. We tested this hypothesis using satellite imagery, climatic datasets (temperature, and absolute humidity), and COVID-19 cases available in the public domain. While the majority of the cases were recorded from Western countries, where mortality rates were strongly positively correlated with age, the number of cases in tropical regions was relatively lower than European and North American regions, possibly attributed to faster human-to-human transmission. There was a substantial reduction in the level of nitrogen dioxide (NO2: 0.00002 mol m2), a low reduction in CO (<0.03 mol m2), and a low-to-moderate reduction in Aerosol Optical Depth (AOD: ~0.10.2) in the major hotspots of COVID-19 outbreak during FebruaryMarch 2020, which may be attributed to the mass lockdowns. Our study projects an increasing coverage of high COVID-19 hazard at absolute humidity levels ranging from 4 to 9 g m3 across a large part of the globe during AprilJuly 2020 due to a high prospective meteorological suitability for COVID-19 spread. Our findings suggest that there is ample scope for restoring the global environment from the ill-effects of anthropogenic activities through temporary shutdown measures.

Research areas: Air Quality