nasa-gesdisc/es-publications-researchareas huggingface dataset:
Climate predicts geographic and temporal variation in mosquito-borne disease dynamics on two continents (2021)

Climate drives population dynamics through multiple mechanisms, which can lead to seemingly context-dependent effects of climate on natural populations. For climate-sensitive diseases, such as dengue, chikungunya, and Zika, climate appears to have opposing effects in different contexts. Here we show that a model, parameterized with laboratory measured climate-driven mosquito physiology, captures three key epidemic characteristics across ecologically and culturally distinct settings in Ecuador and Kenya: the number, timing, and duration of outbreaks. The model generates a range of disease dynamics consistent with observed Aedes aegypti abundances and laboratory-confirmed arboviral incidence with variable accuracy (2885% for vectors, 4488% for incidence). The model predicted vector dynamics better in sites with a smaller proportion of young children in the population, lower mean temperature, and homes with piped water and made of cement. Models with limited calibration that robustly capture climate-virus relationships can help guide intervention efforts and climate change disease projections.

Research areas: Public Health
Effect of Geospatial Weather Features on COVID-19 Spread in Maharashtra State Using Machine Learning (2022)

COVID-19 has become the most pestilential disease that has severely spread all over the world with several variants where the second wave of COVID-19 (2.0) has devastated India and worldwide most. This research is focused to assess the association among COVID-19 confirmed cases, weather features, mainly temperature (T), relative humidity (RH), and precipitation (P). The most affected populated state Maharashtra is considered as the research area for which we have collected newly confirmed COVID-19 cases and weather data that were considered for the first variant (COVID-19 1.0) and second variant (COVID-19 2.0) from March, 2020 to June, 2021. In this work, regression techniques have been applied to investigate daily new confirmed COVID-19 cases where the decision tree predicts the most accurate results. Outcomes demonstrate that temperature and relative humidity played an important role in this forecasting scheme as relative humidity is negative, and the temperature has positive correlation with COVID-19 affected cases. This application could be used for displaying and anticipating confirmed cases every day.

Research areas: Public Health
Spatio-Temporal Dynamics of Plasmodium falciparum and Plasmodium vivax (2021)

Aims: This study examines the dynamics of malaria as influenced by meteorological factors in French Guiana from 2005 to 2019. It explores spatial hotspots of malaria transmission and aims to determine the factors associated with variation of hotspots with time. Methods: Data for individual malaria cases came from the surveillance system of the Delocalized Centers for Prevention and Care (CDPS) (n = 17) from 20052019. Meteorological data was acquired from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) database. The BoxJenkins autoregressive integrated moving average (ARIMA) model tested stationarity of the time series, and the impact of meteorological indices (issued from principal component analysisPCA) on malaria incidence was determined with a general additive model. Hotspot characterization was performed using spatial scan statistics. Results: The current sample includes 7050 eligible Plasmodium vivax (n = 4111) and Plasmodium falciparum (n = 2939) cases from health centers across French Guiana. The first and second PCA-derived meteorological components (maximum/minimum temperature/minimum humidity and maximum humidity, respectively) were significantly negatively correlated with total malaria incidence with a lag of one week and 10 days, respectively. Overall malaria incidence decreased across the time series until 2017 when incidence began to trend upwards. Hotspot characterization revealed a few health centers that exhibited spatial stability across the entire time series: Saint Georges de lOyapock and Antecume Pata for P. falciparum, and Saint Georges de lOyapock, Antecume Pata, Regina and Camopi for P. vivax. Conclusions: This study highlighted changing malaria incidence in French Guiana and the influences of meteorological factors on transmission. Many health centers showed spatial stability in transmission, albeit not temporal. Knowledge of the areas of high transmission as well as how and why transmission has changed over time can inform strategies to reduce the transmission of malaria in French Guiana. Hotspots should be further investigated to understand other influences on local transmission, which will help to facilitate elimination.

Research areas: Public Health
Landscape dynamics of a vectorborne disease in the western
            US
            : How vectorhabitat relationships inform disease hotspots (2022)

Vesicular stomatitis (VS) is a vector‑borne viral disease that causes lesions in livestock, premises, county and state quarantines, and important economic losses. We investigated vector–habitat characteristics for vectors associated with VS in regions of recurrent disease within the western United States (US) that consistently lead to the environment where vector, host, and pathogen populations intersect to enable pathogen transmission. We analyzed the habitats of previously identified insect vectors, including black flies (BFs) (Simulium vittatum complex), biting midges (BMs) (Culicoides variipennis complex, which includes Culicoides sonorensis), and sand flies (SFs) (Lutzomyia shannoni) in six regions of interest (ROIs) containing hotspots of VS ranging from Texas (TX) to Wyoming. This analysis broadened the understanding of (1) how regions of reoccurring VS differ from the broader western US, (2) how geographically separated regions and hotspots are similar across time, and (3) how vector–environment habitat a priori knowledge relates to observed hotspots. Analysis of watershed factors (livestock densities, land‑cover proportions, stream and lake densities, and irrigation methods) indicated a complex system separating areas with high, recurring VS from the broader western US. Although no single characteristic separated the six ROIs from other areas, we found two distinct emerging groups (northern ROI and TX). Hotspots, estimated from monthly VS concentrations, evolved northward throughout the year and most hotspots were closer to flowing water and agricultural land than the broader ROI. All ROIs contained environmental conditions suitable for multiple vectors at some point in the year, but BFs had the highest suitability scores, whereas BM scores were lower and varied annually with higher suitability in summer. SFs had the lowest suitability score in all ROIs, consistent with their low likelihood of being vectors. BM habitat patches were often orders of magnitude smaller than BF patches, and hotspot patches reinforce the likelihood that BF may be the most critical vector in northern ROI, whereas both BM and BF have similar likelihood in southern ROI. Given limited existing vector data, this analysis provides an alternate pathway for using habitat information to associate likely vectors responsible for transmission. Results could support early warning and mitigation efforts to reduce the incidence of VS.

Research areas: Public Health
Warming temperatures could expose more than 1.3 billion new people to Zika virus risk by 2050 (2021)

AbstractIn the aftermath of the 2015 pandemic of Zika virus (ZIKV), concerns over links between climate change and emerging arboviruses have become more pressing. Given the potential that much of the world might remain at risk from the virus, we used a previously established temperaturedependent transmission model for ZIKV to project climate change impacts on transmission suitability risk by midcentury (a generation into the future). Based on these model predictions, in the worstcase scenario, over 1.3 billion new people could face suitable transmission temperatures for ZIKV by 2050. The next generation will face substantially increased ZIKV transmission temperature suitability in North America and Europe, where naive populations might be particularly vulnerable. Mitigating climate change even to moderate emissions scenarios could significantly reduce global expansion of climates suitable for ZIKV transmission, potentially protecting around 200 million people. Given these suitability risk projections, we suggest an increased priority on research establishing the immune history of vulnerable populations, modeling when and where the next ZIKV outbreak might occur, evaluating the efficacy of conventional and novel intervention measures, and increasing surveillance efforts to prevent further expansion of ZIKV.

Research areas: Public Health
Global patterns of human and livestock respiration (2018)

Carbon emissions from human and animals has been neglected by previous studies in estimating the carbon cycle of ecosystem. This study first estimates the spatial-temporal patterns of carbon emissions density from human and livestock respiration among countries around the world from 19602014. Then we simulate the soil heterotrophic respiration (Rh) to analyze the contribution of human and livestock respiration to total heterotrophic respiration of global ecosystem. Our results show that the respiration of human and livestock respectively contribute more than 1% of the total carbon output from heterotrophic respiration in most countries and affect more than 5% in almost half of the countries. Moreover, the effect of livestock respiration is slightly greater than that of human beings. Therefore, the estimation of heterotrophic respiration should not only consider Rh in these countries, human and livestock respiration are equally important in the research on regional carbon budget.

Research areas: Greenhouse Gases
Spatial global assessment of the pest Bagrada hilaris (Burmeister) (2019)

AbstractBackgroundThe insect Bagrada hilaris (Burmeister) an important pest worldwide, mainly due to the serious economic losses incurred and the large number of zones invaded. However, current and future spatial distributions of this pest, and the total area of cropland potentially affected have not been estimated. Here, we aim to: (1) estimate the potential geographic distribution of B. hilaris; (2) quantify the total area of cropland potentially affected worldwide, and in two recently colonized zones (California and Chile); and (3) estimate future changes in distribution under different climate change scenarios.ResultsWe found that B. hilaris shows high environmental suitability in Mediterranean and arid regions, potentially affecting 1 108 184.1 km2 of cropland worldwide. The most affected continents were Asia and America, with 309 659.8 and 294 638.6 km2 of cropland at risk. More than 50% of cropland areas are at risk in seven countries. In California and central Chile, 43.7% and 50% of susceptible crops are at a high level of risk, respectively. Climate change scenarios predict an increase in the potential distribution of B. hilaris worldwide; America being the most affected continent.ConclusionsOur results provide a spatially explicit baseline from which to focus efforts on the prevention, management and control of this pest worldwide. 2018 Society of Chemical Industry

Research areas: Agriculture
Impact of Short-Term Exposure to Extreme Temperatures on Mortality: A (2022)

In light of climate change, health risks are expected to be exacerbated by more frequent high temperatures and reduced by less frequent cold extremes. To assess the impact of different climate change scenarios, it is necessary to describe the current effects of temperature on health. A time-stratified case-crossover design fitted with conditional quasi-Poisson regressions and distributed lag non-linear models was applied to estimate specific temperature-mortality associations in nine urban agglomerations in Belgium, and a random-effect meta-analysis was conducted to pool the estimates. Based on 307,859 all-cause natural deaths, the mortality risk associated to low temperature was 1.32 (95% CI: 1.211.44) and 1.21 (95% CI: 1.081.36) for high temperature relative to the minimum mortality temperature (23.1 C). Both cold and heat were associated with an increased risk of cardiovascular and respiratory mortality. We observed differences in risk by age category, and women were more vulnerable to heat than men. People living in the most built-up municipalities were at higher risk for heat. Air pollutants did not have a confounding effect. Evidence from this study helps to identify specific populations at risk and is important for current and future public health interventions and prevention strategies.

Research areas: Public Health
Modelling the persistence and control of Rift Valley fever virus in a spatially heterogeneous landscape (2021)

The persistence mechanisms of Rift Valley fever (RVF), a zoonotic arboviral haemorrhagic fever, at both local and broader geographical scales have yet to be fully understood and rigorously quantified. We developed a mathematical metapopulation model describing RVF virus transmission in livestock across the four islands of the Comoros archipelago, accounting for island-specific environments and inter-island animal movements. By fitting our model in a Bayesian framework to 20042015 surveillance data, we estimated the importance of environmental drivers and animal movements on disease persistence, and tested the impact of different control scenarios on reducing disease burden throughout the archipelago. Here we report that (i) the archipelago network was able to sustain viral transmission in the absence of explicit disease introduction events after early 2007, (ii) repeated outbreaks during 20042020 may have gone under-detected by local surveillance, and (iii) co-ordinated within-island control measures are more effective than between-island animal movement restrictions.

Research areas: Public Health
Burden of climate change on malaria mortality (2018)

In 2016, an estimated 445,000 deaths and 216 million cases of malaria occurred worldwide, while 70% of the deaths occurred in children under five years old. Changes in climatic exposures such as temperature and precipitation make malaria one of the most climate sensitive outcomes. Using a global malaria mortality dataset for 105 countries between 1980 and 2010, we find a non-linear relationship between temperature and malaria mortality and estimate that the global optimal temperature threshold beyond which all-age malaria mortality increases is 20.8 C, while in the case of child mortality; a significantly lower optimum temperature of 19.3 is estimated. Our results also suggest that this optimal temperature is 28.4 C and 26.3 C in Africa and Asia, respectively  the continents where malaria is most prevalent. Furthermore, we estimate that child mortality (ages 04) is likely to increase by up to 20% in some areas due to climate change by the end of the 21st century.

Research areas: Public Health
Fluctuations in anthropogenic nighttime lights from satellite imagery for five cities in Niger and Nigeria (2018)

Dynamic measures of human populations are critical for global health management but are often overlooked, largely because they are difficult to quantify. Measuring human population dynamics can be prohibitively expensive in under-resourced communities. Satellite imagery can provide measurements of human populations, past and present, to complement public health analyses and interventions. We used anthropogenic illumination from publicly accessible, serial satellite nighttime images as a quantifiable proxy for seasonal population variation in five urban areas in Niger and Nigeria. We identified population fluxes as the mechanistic driver of regional seasonal measles outbreaks. Our data showed 1) urban illumination fluctuated seasonally, 2) corresponding population fluctuations were sufficient to drive seasonal measles outbreaks, and 3) overlooking these fluctuations during vaccination activities resulted in below-target coverage levels, incapable of halting transmission of the virus. We designed immunization solutions capable of achieving above-target coverage of both resident and mobile populations. Here, we provide detailed data on brightness from 20002005 for 5 cities in Niger and Nigeria and detailed methodology for application to other populations.

Research areas: Public Health
Data-driven and interpretable machine-learning modeling to explore the (2021)

Abstract
            
              Background
              
                Improving the knowledge and understanding of the environmental determinants of malaria vector abundance at fine spatiotemporal scales is essential to design locally tailored vector control intervention. This work is aimed at exploring the environmental tenets of human-biting activity in the main malaria vectors (
                Anopheles gambiae s.s.
                ,
                Anopheles coluzzii
                and
                Anopheles funestus
                ) in the health district of Diebougou, rural Burkina Faso.
              
            
            
              Methods
              
                Anopheles
                human-biting activity was monitored in 27 villages during 15 months (in 20172018), and environmental variables (meteorological and landscape) were extracted from high-resolution satellite imagery. A two-step data-driven modeling study was then carried out. Correlation coefficients between the biting rates of each vector species and the environmental variables taken at various temporal lags and spatial distances from the biting events were first calculated. Then, multivariate machine-learning models were generated and interpreted to (i) pinpoint primary and secondary environmental drivers of variation in the biting rates of each species and (ii) identify complex associations between the environmental conditions and the biting rates.
              
            
            
              Results
              Meteorological and landscape variables were often significantly correlated with the vectors biting rates. Many nonlinear associations and thresholds were unveiled by the multivariate models, for both meteorological and landscape variables. From these results, several aspects of the bio-ecology of the main malaria vectors were identified or hypothesized for the Diebougou area, including breeding site typologies, development and survival rates in relation to weather, flight ranges from breeding sites and dispersal related to landscape openness.
            
            
              Conclusions
              Using high-resolution data in an interpretable machine-learning modeling framework proved to be an efficient way to enhance the knowledge of the complex links between the environment and the malaria vectors at a local scale. More broadly, the emerging field of interpretable machine learning has significant potential to help improve our understanding of the complex processes leading to malaria transmission, and to aid in developing operational tools to support the fight against the disease (e.g. vector control intervention plans, seasonal maps of predicted biting rates, early warning systems).
            
            
              Graphical abstract

Research areas: Public Health
Divergent geography of Salmonella wangata and Salmonella typhimurium epidemiology in New South Wales, Australia (2019)

Salmonella enterica serovar Wangata is an important cause of salmonellosis in the state of New South Wales, Australia. Standard surveillance has not identified a common food source and cases have been attributed to an unknown environmental or wildlife reservoir. Investigation of the spatial distribution of cases may provide valuable insights into local risk factors for infection and the potential role of the environment and wildlife. Using conditional autoregressive analysis, we explored the association between laboratory-confirmed cases of S. Wangata reported to the New South Wales Department of Health and human socio-demographic, climate, land cover and wildlife features. For comparison, a model was also fitted to investigate the association of cases of Salmonella enterica serovar Typhimurium, an established foodborne serotype, with the same features. To determine if cases of S. Wangata were associated with potential wildlife reservoir species, additional variables were included in the S. Wangata model that indicated areas of high suitability for each species. We found that cases of S. Wangata were associated with warmer temperatures, proximity to wetlands and amphibian species richness. In contrast, cases of S. Typhimurium were associated with human demographic features (proportion of the population comprising children <5 years old), climate (mean annual precipitation and mean annual temperature) and land cover (proportion comprising urban and evergreen broadleaf forest). These findings support the hypothesis that S. Wangata is likely to be associated with an environmental source. Whilst we expected S. Typhimurium to be associated with the human socio-demographic feature, the significance of the land cover features was surprising and might suggest the epidemiology of S. Typhimurium in Australia is more complex than currently understood.

Research areas: Public Health
An ecological analysis of PM2. 5 concentrations and lung cancer mortality rates in China (2015)

Objective To explore the association between Particulate Matter (PM)2.5 (particles with an aerodynamic diameter less than 2.5 m) and lung cancer mortality rates and to estimate the potential risk of lung cancer mortality related to exposure to high PM2.5 concentrations.
Design Geographically weighted regression was performed to evaluate the relation between PM2.5 concentrations and lung cancer mortality for males, females and for both sexes combined, in 2008, based on newly available long-term data. Lung cancer fatalities from long-term exposure to PM2.5 were calculated according to studies by Pope III et al and the WHO air quality guidelines (AQGs).
Setting 31 provinces in China.
Results PM2.5 was associated with the lung cancer mortality of males, females and both sexes combined, in China, although there were exceptions in several regions, for males and females. The number of lung cancer fatalities calculated by the WHO AQGs ranged from 531 036 to 532 004, whereas the number calculated by the American Cancer Society (ACS) reached 614 860 after long-term (approximately 34 years) exposure to PM2.5 concentrations since 2008.
Conclusions There is a positive correlation between PM2.5 and lung cancer mortality rate, and the relationship between them varies across the entire country of China. The number of lung cancer fatalities estimated by ACS was closer to the actual data than those of the WHO AQGs. Therefore, the ACS estimate of increased risk of lung cancer mortality from long-term exposure to PM2.5 might be more applicable for evaluating lung cancer fatalities in China than the WHO estimate.

Research areas: Public Health