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Temperature time series with high spatial and temporal resolutions are important for several applications. The new MODIS Land Surface Temperature (LST) collection 6 provides numerous improvements compared to collection 5. However, being remotely sensed data in the thermal range, LST shows gaps in cloud-covered areas. We present a novel method to fully reconstruct MODIS daily LST products for central Europe at 1 km resolution and globally, at 3 arc-min. We combined temporal and spatial interpolation, using emissivity and elevation as covariates for the spatial interpolation. The reconstructed MODIS LST for central Europe was calibrated to air temperature data through linear models that yielded R2 values around 0.8 and RMSE of 0.5 K. This new method proves to scale well for both local and global reconstruction. We show examples for the identification of extreme events to demonstrate the ability of these new LST products to capture and represent spatial and temporal details. A time series of global monthly average, minimum and maximum LST data and long-term averages is freely available for download.

Orthohantaviruses, family Hantaviridae, are zoonotic agents that pose a significant public health threat, particularly in South America, where they cause severe respiratory illnesses in humans. Despite their importance, knowledge gaps remain regarding the distributions of both the viruses and their rodent hosts in Southern South America, a region characterized by a great complexity of viral genotypes and reservoirs. This review provides an updated overview of orthohantavirus hosts and their associated viral genotypes in Argentina, Chile, Paraguay, and Uruguay. Through an extensive literature review, we identified 14 rodent species that serve as reservoir hosts for 15 distinct orthohantavirus genotypes. These rodent hosts inhabit a variety of ecosystems, from forests and arid zones to grasslands and wetlands, and even modified or anthropized habitats, demonstrating a wide geographic and ecological range. Our findings highlight the diversity of orthohantaviruses in this region, reflecting their complex evolutionary histories. Maintaining an up-to-date knowledge base on this topic is essential for effective decision-making in public health.

Strategies for the prevention of arboviral diseases transmitted by 'Aedes aegypti' have traditionally focused on vector control. This remains the same to this day, despite a lack of documented evidence on its efficacy due to a lack of coverage and sustainability. The continuous growth of urban areas and generally unplanned urbanization, which favor the presence of 'Ae. aegypti', demand resources, both material and human, as well as logistics to effectively lower the population's risk of infection. These considerations have motivated the development of tools to identify areas with a recurrent concentration of arboviral cases during an outbreak to be able to prioritize preventive actions and optimize available resources. This study explores the existence of spatial patterns of dengue incidence in the locality of Tartagal, in northeastern Argentina, during the outbreaks that occurred between 2010 and 2020. Approximately half (50.8%) of the cases recorded during this period were concentrated in 35.9% of the urban area. Additionally, an important overlap was found between hotspot areas of dengue and chikungunya (Kendall's W = 0.92; 'p'-value < 0.001) during the 2016 outbreak. Moreover, 65.9% of the cases recorded in 2022 were geolocalized within the hotspot areas detected between 2010 and 2020. These results can be used to generate a risk map to implement timely preventive control strategies that prioritize these areas to reduce their vulnerability while optimizing the available resources and increasing the scope of action.

New approaches to the study of cardiometabolic disease (CMD) distribution include analysis of built environment (BE), with spatial tools as suitable instruments. We aimed to characterize the spatial dissemination of CMD and the associated risk factors considering the BE for people attending the Non-Invasive Cardiology Service of Hospital Nacional de Clinicas in Córdoba City, Argentina during the period 2015-2020. We carried out an observational, descriptive, cross-sectional study performing non-probabilistic convenience sampling. The final sample included 345 people of both sexes older than 35 years. The CMD data were collected from medical records and validated techniques and BE information was extracted from Landsat-8 satellite products. A geographic information system (GIS) was constructed to assess the distribution of CMD and its risk factors in the area. Out of the people sampled, 41% showed the full metabolic syndrome and 22.6% only type-2 diabetes mellitus (DM2), a cluster of which was evidenced in north-western Córdoba. The risk of DM2 showed an association with high values of the normalized difference vegetation index (NDVI) (OR= 0.81; 95% CI: - 0.30 to 1.66; p=0.05) and low normalized difference built index (NDBI) values that reduced the probability of occurrence of DM2 (OR= -1.39; 95% CI: -2.62 to -0.17; p=0.03). Considering that the results were found to be linked to the environmental indexes, the study of BE should include investigation of physical space as a fundamental part of the context in which people develop medically within society. The novel collection of satellite-generated information on BE proved efficient.

Aedes aegypti, the mosquito species transmitting dengue, zika, chikungunya and yellow fever viruses, is fully adapted to thrive in urban areas. The temporal activity of this mosquito, however, varies within urban areas which might imply different transmission risk. In this work, we hypothesize that temporal differences in mosquito activity patterns are determined by local environmental conditions. Hence, we explore the existence of groups of temporal patterns in weekly time series of Ae. aegypti ovitraps records (2017–2019) by means of time series clustering. Next, with the aim of predicting risk in places with no mosquito field data, we use machine learning classification tools to assess the association of temporal patterns with environmental variables derived from satellite imagery and predict temporal patterns over the city area to finally test the relationship with dengue incidence. We found three groups of temporal patterns that showed association with land cover diversity, variability in vegetation and humidity and, heterogeneity measured by texture indices estimated over buffer areas surrounding ovitraps. Dengue incidence on a neighborhood basis showed a weak but positive association with the percentage of pixels belonging to only one of the temporal patterns detected. The understanding of the spatial distribution of temporal patterns and their environmental determinants might then become highly relevant to guide the allocation of prevention and potential interventions. Further investigation is still needed though to incorporate other determinants not considered here.

Invasive Aedes mosquitoes are major vectors of arboviral diseases such as dengue, Zika, and chikungunya, posing an increasing threat to global public health. Their recent geographic expansion calls for predictive models to simulate population dynamics and transmission risk. Temperature is a key driver in these models, influencing traits that affect vector competence. Numerous datasets on temperature-dependent traits exist for Aedes aegypti and Aedes albopictus , though they are scattered, inconsistent, and difficult to synthesise. For emerging species like Aedes japonicus and Aedes koreicus , such datasets are scarce. To address these gaps, we developed AedesTraits , an open-access, machine-readable dataset aligned with VecTraits standards. It compiles and systematises experimental data on temperature-dependent traits across these four Aedes species, covering life-history, morphological, physiological, and behavioural traits. Our synthesis highlights existing knowledge gaps and identifies under-studied species and traits. By promoting data systematisation and accessibility, AedesTraits supports Aedes –borne disease modelling and fosters international collaboration in the development of forecasting tools for arbovirus outbreaks.

We use a Species Distribution Modeling (SDM) approach along with Geographic Information Systems (GIS) techniques to examine the potential distribution of hantavirus pulmonary syndrome (HPS) caused by Andes virus (ANDV) in southern Argentina and, more precisely, define and estimate the area with the highest infection probability for humans, through the combination with the distribution map for the competent rodent host (Oligoryzomys longicaudatus). Sites with confirmed cases of HPS in the period 1995–2009 were mostly concentrated in a narrow strip (~90 km × 900 km) along the Andes range from northern Neuquén to central Chubut province. This area is characterized by high mean annual precipitation (~1,000 mm on average), but dry summers (less than 100 mm), very low percentages of bare soil (~10% on average) and low temperatures in the coldest month (minimum average temperature −1.5 °C), as compared to the HPS-free areas, features that coincide with sub-Antarctic forests and shrublands (especially those dominated by the invasive plant Rosa rubiginosa), where rodent host abundances and ANDV prevalences are known to be the highest. Through the combination of predictive distribution maps of the reservoir host and disease cases, we found that the area with the highest probability for HPS to occur overlaps only 28% with the most suitable habitat for O. longicaudatus. With this approach, we made a step forward in the understanding of the risk factors that need to be considered in the forecasting and mapping of risk at the regional/national scale. We propose the implementation and use of thematic maps, such as the one built here, as a basic tool allowing public health authorities to focus surveillance efforts and normally scarce resources for prevention and control actions in vast areas like southern Argentina.

In this work we assessed the environmental factors associated with the spatial distribution of a cutaneous leishmaniasis (CL) outbreak during 2015-2016 in north-eastern Argentina to understand its typical or atypical eco-epidemiological pattern. We combined locations of human CL cases with relevant predictors derived from analysis of remote sensing imagery in the framework of ecological niche modelling and trained MaxEnt models with cross-validation for predictors estimated at different buffer areas relevant to CL vectors (50 and 250 m radii). To account for the timing of biological phenomena, we considered environmental changes occurring in two periods, 2014-2015 and 2015-2016. The remote sensing analysis identified land cover changes in the surroundings of CL cases, mostly related to new urbanization and flooding. The distance to such changes was the most important variable in most models. The weighted average map denoted higher suitability for CL in the outskirts of the city of Corrientes and in areas close to environmental changes. Our results point to a scenario consistent with a typical CL outbreak, i.e. changes in land use or land cover are the main triggering factor and most affected people live or work in border habitats.

Background The rapid and often uncontrolled rural–urban migration in Sub-Saharan Africa is transforming urban landscapes expected to provide shelter for more than 50% of Africa’s population by 2030. Consequently, the burden of malaria is increasingly affecting the urban population, while socio-economic inequalities within the urban settings are intensified. Few studies, relying mostly on moderate to high resolution datasets and standard predictive variables such as building and vegetation density, have tackled the topic of modeling intra-urban malaria at the city extent. In this research, we investigate the contribution of very-high-resolution satellite-derived land-use, land-cover and population information for modeling the spatial distribution of urban malaria prevalence across large spatial extents. As case studies, we apply our methods to two Sub-Saharan African cities, Kampala and Dar es Salaam. Methods Openly accessible land-cover, land-use, population and OpenStreetMap data were employed to spatially model Plasmodium falciparum parasite rate standardized to the age group 2–10 years (PfPR 2–10 ) in the two cities through the use of a Random Forest (RF) regressor. The RF models integrated physical and socio-economic information to predict PfPR 2–10 across the urban landscape. Intra-urban population distribution maps were used to adjust the estimates according to the underlying population. Results The results suggest that the spatial distribution of PfPR 2–10 in both cities is diverse and highly variable across the urban fabric. Dense informal settlements exhibit a positive relationship with PfPR 2–10 and hotspots of malaria prevalence were found near suitable vector breeding sites such as wetlands, marshes and riparian vegetation. In both cities, there is a clear separation of higher risk in informal settlements and lower risk in the more affluent neighborhoods. Additionally, areas associated with urban agriculture exhibit higher malaria prevalence values. Conclusions The outcome of this research highlights that populations living in informal settlements show higher malaria prevalence compared to those in planned residential neighborhoods. This is due to (i) increased human exposure to vectors, (ii) increased vector density and (iii) a reduced capacity to cope with malaria burden. Since informal settlements are rapidly expanding every year and often house large parts of the urban population, this emphasizes the need for systematic and consistent malaria surveys in such areas. Finally, this study demonstrates the importance of remote sensing as an epidemiological tool for mapping urban malaria variations at large spatial extents, and for promoting evidence-based policy making and control efforts.

We examined population density of Oligoryzomys longicaudatus (colilargo) and prevalence of Andes virus (ANDV) antibody at regional and landscape spatial scales in northwestern Chubut Province (Argentina) and contrasted it with climatic variables recorded by meteorologic stations near the study area. Mice were trapped in late summer–early fall (March–April) for 3 years (2007–2009). The composition of the rodent assemblage and species representation in the community varied among years, regions (forest, ecotone, and steppe), and landscape units (Nothofagus and Austrocedrus forests, sweet briar shrublands, and without sweet briar shrublands). Colilargos occurred in all regions and landscape units within the study area, from dense forest to open habitats such as steppe. The species dominated the rodent assemblages of ecotone and forest at a regional scale and the assemblages in sweet briar shrublands and Austrocedrus forests at a landscape scale. Abundance of colilargos also varied among periods, regions, and landscape units. Antibodies to ANDV were found in all regions but not in every landscape unit. Thus there is a potential for human hantavirus pulmonary syndrome (HPS) cases to occur not only in forests and shrublands, but also in steppe. At a landscape scale, Nothofagus forests appeared to pose a higher risk than Austrocedrus in wet years, because colilargo abundance and ANDV antibody prevalence were significantly greater. Within ecotone, sweet briar shrublands posed greater risk than habitats without sweet briar. Sweet briar shrublands were the landscape unit with the highest colilargo abundances during the driest periods. Sweet briar shrublands may play an important role in HPS dynamics, and should be considered when designing prevention policies. Nuestro objetivo fue examinar la densidad poblacional de Oligoryzomys longicaudatus (colilargo) y la prevalencia de virus Andes (ANDV) a escala regional y de paisaje en el noroeste de la provincia de Chubut, Argentina. Los muestreos se llevaron a cabo durante el verano tardío–otoño temprano (Marzo–Abril) por un período de 3 años (2007 a 2009). La composición de la comunidad de roedores y la representación de cada especie en la misma varió entre años, regiones (bosque, ecotono y estepa) y unidades de paisaje (bosques de Nothofagus y de Austrocedrus, matorrales de rosa mosqueta y matorrales sin rosa mosqueta). O. longicaudatus fue encontrado en todas las regiones y unidades de paisaje del área de estudio. Esta especie dominó los ensambles de roedores de bosques y ecotono a escala regional y los ensambles de los matorrales de rosa mosqueta y bosques de Austrocedrus a escala de paisaje. La abundancia de colilargos varió entre períodos, regiones y unidades de paisaje. Se detectaron anticuerpos contra ANDV en todas las regiones, pero no en todas las unidades de paisaje. Por lo tanto, el riesgo de enfermedad en humanos existe no sólo en bosque y ecotono, sino también en estepa. Además, a escala de paisaje, los bosques de Nothofagus parecieron implicar un mayor riesgo que los de Austrocedrus en años húmedos, ya que las abundancias de colilargos y la prevalencia de ANDV fueron significativamente mayores en los primeros. Dentro del ecotono, los matorrales de rosa mosqueta significaron un mayor riesgo que los matorrales sin rosa mosqueta. Los primeros constituyeron la unidad de paisaje con las mayores abundancias de colilargos durante los períodos secos. Estos matorrales podrían tener un papel importante en la dinámica del Síndrome Pulmonar por Hantavirus y debieran ser considerados a la hora de diseñar medidas de prevención.