Use of soil moisture active passive satellite data and WorldClim 2.0 data to predict the potential distribution of visceral leishmaniasis and its vector Lutzomyia longipalpis in Sao Paulo and Bahia states, Brazil.

Visceral leishmaniasis (VL) is a neglected tropical disease transmitted by Lutzomyia longipalpis, a sand fly widely distributed in Brazil. Despite efforts to strengthen national control programs reduction in incidence and geographical distribution of VL in Brazil has not yet been successful; VL is in fact expanding its range in newly urbanized areas. Ecological niche models (ENM) for use in surveillance and response systems may enable more effective operational VL control by mapping risk areas and elucidation of eco-epidemiologic risk factors. ENMs for VL and Lu. longipalpis were generated using monthly WorldClim 2.0 data (30-year climate normal, 1-km spatial resolution) and monthly soil moisture active passive (SMAP) satellite L4 soil moisture data. SMAP L4 Global 3-hourly 9-km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V004 were obtained for the first image of day 1 and day 15 (0:00-3:00 hour) of each month. ENM were developed using MaxEnt software to generate risk maps based on an algorithm for maximum entropy. The jack-knife procedure was used to identify the contribution of each variable to model performance. The three most meaningful components were used to generate ENM distribution maps by ArcGIS 10.6. Similar patterns of VL and vector distribution were observed using SMAP as compared to WorldClim 2.0 models based on temperature and precipitation data or water budget. Results indicate that direct Earth-observing satellite measurement of soil moisture by SMAP can be used in lieu of models calculated from classical temperature and precipitation climate station data to assess VL risk.

Geohealth: biology based mapping of vector borne disease in the Americas using NASA satellite data / Geosaúde: mapeamento da biologia das doenças vetoriais nas Américas utilizando dados de satélites da NASA

Implementation of a geospatial surveillance and response system data resource for vector borne disease in the Americas (GeoHealth) will be tested using NASA satellite data, geographic information systems and ecological niche modeling to characterize the environmental suitability and potential for spread of endemic and epizootic vector borne diseases. The initial focus is on developing prototype geospatial models for visceral leishmaniasis, an expanding endemic disease in Latin America, and geospatial models for dengue and other Aedes aegypti borne arboviruses (zika, chikungunya), emerging arboviruses with potential for epizootic spread from Latin America and the Caribbean and establishment in North America. Geospatial surveillance and response system open resource data bases and models will be made available, with training courses, to other investigators interested in mapping and modeling other vector borne diseases in the western hemisphere and contributing brokered data to an expanding GeoHealth data resource as part of the NASA AmeriGEOSS initiative.(AU)

A implementação de uma fonte de dados de vigilância e um sistema de resposta geoespacial para doenças transmitidas por vetores nas Américas (GeoHealth) será testada utilizando dados provenientes de satélites da NASA, sistemas de informações geográficas e modelagem do nicho ecológico, para caracterizar a suceptibilidade ambiental e o potencial de dispersão de doenças endêmicas e epizooticas transmitidas por vetores vetores. O foco inicial será o desenvolvimento de protótipos de modelos geoespaciais para a leishmaniose visceral, uma doença endêmica e em expansão na América Latina, e modelos geoespaciais para dengue e outros transmitidos pelo Aedes aegypti (zika, chikungunya), arbovírus emergentes com potencial para disseminação epizoótica pela América Latina e Caribe e estabelecimento na América do Norte. Sistemas de vigilância e resposta geoespacial e modelos de recursos em bases de dados abertas serão diponibilizados, com cursos de treinamento, para outros pesquisadores interessados em mapear e modelar outras doenças transmitidas por vetores no hemisfério ocidental e contribuir intermediando dados para uma fonte de dados GeoHealth em expansão, como parte da Iniciativa AmeriGEOSS, da NASA. (AU)

Geohealth: biology based mapping of vector borne disease in the Americas using NASA satellite data / Geosaúde: mapeamento da biologia das doenças vetoriais nas Américas utilizando dados de satélites da NASA

Implementation of a geospatial surveillance and response system data resource for vector borne disease in the Americas (GeoHealth) will be tested using NASA satellite data, geographic information systems and ecological niche modeling to characterize the environmental suitability and potential for spread of endemic and epizootic vector borne diseases. The initial focus is on developing prototype geospatial models for visceral leishmaniasis, an expanding endemic disease in Latin America, and geospatial models for dengue and other Aedes aegypti borne arboviruses (zika, chikungunya), emerging arboviruses with potential for epizootic spread from Latin America and the Caribbean and establishment in North America. Geospatial surveillance and response system open resource data bases and models will be made available, with training courses, to other investigators interested in mapping and modeling other vector borne diseases in the western hemisphere and contributing brokered data to an expanding GeoHealth data resource as part of the NASA AmeriGEOSS initiative.

A implementação de uma fonte de dados de vigilância e um sistema de resposta geoespacial para doenças transmitidas por vetores nas Américas (GeoHealth) será testada utilizando dados provenientes de satélites da NASA, sistemas de informações geográficas e modelagem do nicho ecológico, para caracterizar a suceptibilidade ambiental e o potencial de dispersão de doenças endêmicas e epizooticas transmitidas por vetores vetores. O foco inicial será o desenvolvimento de protótipos de modelos geoespaciais para a leishmaniose visceral, uma doença endêmica e em expansão na América Latina, e modelos geoespaciais para dengue e outros transmitidos pelo Aedes aegypti (zika, chikungunya), arbovírus emergentes com potencial para disseminação epizoótica pela América Latina e Caribe e estabelecimento na América do Norte. Sistemas de vigilância e resposta geoespacial e modelos de recursos em bases de dados abertas serão diponibilizados, com cursos de treinamento, para outros pesquisadores interessados em mapear e modelar outras doenças transmitidas por vetores no hemisfério ocidental e contribuir intermediando dados para uma fonte de dados GeoHealth em expansão, como parte da Iniciativa AmeriGEOSS, da NASA.

Modelling the ecological niche of hookworm in Brazil based on climate.

The distribution of hookworm in schistosomiasis-endemic areas in Brazil was mapped based on climate suitability. Known biological requirements of hookworm were fitted to data in a monthly long-term normal climate grid (18 x 18 km) using geographical information systems. Hookworm risk models were produced using the growing degree day (GDD) water budget (WB) concept. A moisture-adjusted model (MA-GDD) was developed based on accumulation of monthly temperatures above a base temperature of 15 °C (below which there is no lifecycle progression of Necator americanus) conditional on concurrent monthly values (rain/potential, evapotranspiration) of over 0.4. A second model, designated the gradient index, was calculated based on the monthly accumulation of the product of GDD and monthly WB values (GDD x WB). Both parameters had a significant positive correlation to hookworm prevalence. In the northeastern part of Brazil (the Caatinga), low hookworm prevalence was due to low soil moisture content, while the low prevalence in southern Brazil was related to low mean monthly temperatures. Both environmental temperature and soil moisture content were found to be important parameters for predicting the prevalence of N. americanus.

Environmental and socio-economic risk modelling for Chagas disease in Bolivia.

Accurately defining disease distributions and calculating disease risk is an important step in the control and prevention of diseases. Geographical information systems (GIS) and remote sensing technologies, with maximum entropy (Maxent) ecological niche modelling computer software, were used to create predictive risk maps for Chagas disease in Bolivia. Prevalence rates were calculated from 2007 to 2009 household infection survey data for Bolivia, while environmental data were compiled from the Worldclim database and MODIS satellite imagery. Socio-economic data were obtained from the Bolivian National Institute of Statistics. Disease models identified altitudes at 500-3,500 m above the mean sea level (MSL), low annual precipitation (45-250 mm), and higher diurnal range of temperature (10-19 °C; peak 16 °C) as compatible with the biological requirements of the insect vectors. Socio-economic analyses demonstrated the importance of improved housing materials and water source. Home adobe wall materials and having to fetch drinking water from rivers or wells without pump were found to be highly related to distribution of the disease by the receiver operator characteristic (ROC) area under the curve (AUC) (0.69 AUC, 0.67 AUC and 0.62 AUC, respectively), while areas with hardwood floors demonstrated a direct negative relationship (-0.71 AUC). This study demonstrates that Maxent modelling can be used in disease prevalence and incidence studies to provide governmental agencies with an easily learned, understandable method to define areas as either high, moderate or low risk for the disease. This information may be used in resource planning, targeting and implementation. However, access to high-resolution, sub-municipality socio-economic data (e.g. census tracts) would facilitate elucidation of the relative influence of poverty-related factors on regional disease dynamics.

Mapping and predicting malaria transmission in the People's Republic of China, using integrated biology-driven and statistical models.

The purpose of this study was to deepen our understanding of Plasmodium vivax malaria transmission patterns in the People's Republic of China (P.R. China). An integrated modeling approach was employed, combining biological and statistical models. A Delphi approach was used to determine environmental factors that govern malaria transmission. Key factors identified (i.e. temperature, rainfall and relative humidity) were utilized for subsequent mapping and modeling purposes. Yearly growing degree days, annual rainfall and effective yearly relative humidity were extracted from a 15-year time series (1981-1995) of daily environmental data readily available for 676 locations in P.R. China. A suite of eight multinomial regression models, ranging from the null model to a fully saturated one were constructed. Two different information criteria were used for model ranking, namely the corrected Akaike's information criterion and the Bayesian information criterion. Mapping was based on model output data, facilitated by using ArcGIS software. Temperature was found to be the most important environmental factor, followed by rainfall and relative humidity in the Delphi evaluation. However, relative humidity was found to be more important than rainfall and temperature in the ranking list according to the three single environmental factor regression models. We conclude that the distribution of the mosquito vector is mainly related to relative humidity, which thus determines the extent of malaria transmission. However, in regions with relative humidity >60%, temperature is the major driver of malaria transmission intensity. By integrating biology-driven models with statistical regression models, reliable risk maps indicating the distribution of transmission and the intensity can be produced. In a next step, we propose to integrate social and health systems factors into our modeling approach, which should provide a platform for rigorous surveillance and monitoring progress towards P. vivax malaria elimination in P.R. China.