Mother-to-child transmission (MTCT) of infectious diseases remains a public health challenge in socially vulnerable regions with limited healthcare access. This study assessed the epidemiological situation, spatial distribution, and socioeconomic context of MTCT infections-Chagas disease (ChD), syphilis, HIV, and hepatitis B (HB)-in the Gran Chaco region (Argentina-Paraguay), 2018-2024. Epidemiological data from 2877 patients enrolled in an MTCT Plus programme were analysed, alongside socioeconomic variables and spatio-temporal cluster analysis using SaTScan software. Maternal seroprevalence of ChD was 4.1%, the highest among the infections evaluated. Syphilis prevalence was 0.8%, while no HIV or HBV infections were detected among screened pregnant women. Two statistically significant spatiotemporal clusters of maternal Trypanosoma cruzi seropositivity were identified: a household-level cluster in 2018 and a regional cluster during 2019-2021. The highest prevalence of maternal ChD seropositivity was observed in census tracts with greater socioeconomic vulnerability, although this spatial overlap was assessed descriptively. These findings highlight the effectiveness of integrated maternal-child health services in ensuring coverage, timely diagnosis, and treatment in vulnerable populations. The identified spatial patterns provide evidence to support targeted surveillance and coordinated binational public health strategies in border regions affected by persistent social inequalities.

Vector-borne diseases are expanding geographically, increasing the demand for reliable mosquito surveillance tools. Mechanistic models offer biologically grounded representations of mosquito population dynamics, yet their ability to generalise beyond their calibration conditions remains poorly investigated, mainly due to the scarcity of extensive longitudinal validation datasets. We evaluated two climate-driven models of Aedes aegypti population dynamics, one deterministic (Aguirre et al.) and one stochastic (DynamAedes), against weekly ovitrap data collected between 2015 and 2024 in ten Argentine localities spanning a broad climatic and latitudinal gradient. We assessed the model simulations in terms of spatio-temporal performance, peak detection, and seasonal timing (onset, end, and duration), using standardised egg-abundance time series (0-1 scale) for validation. Both models reproduced broad seasonal patterns, though performance varied by locality. The stochastic model exhibited local extinction events in the four southernmost localities, while the deterministic model predicted persistent near-zero abundance only in the driest locality. Weekly RMSE between observed and simulated data remained below 35% across all localities for both models. The Aguirre et al. model showed peak frequency closer to observations, whereas DynamAedes achieved higher detection sensitivity (43.5% versus 27.2%). Seasonal timing analyses revealed biases, including earlier onset and longer predicted seasons, particularly for DynamAedes. Overall, both models captured relevant features of Ae. aegypti population dynamics, but predictive performance was strongly context-dependent. These findings underscore the need for robust multi-site validation supported by long-term entomological data and stakeholder involvement in model co-development prior to operational use.

Orthohantaviruses represent a major zoonotic threat in Southern South America (SSA), where 13 native rodent species are recognized reservoir hosts of 15 different viral genotypes. The information on their geographic distribution remains scattered, limiting regional assessments of exposure risk. We compiled and curated occurrence records for the Orthohantavirus reservoir hosts in SSA and used ecological niche modeling to estimate their potential distributions. We integrated binary presence predictions to map reservoir and genotype richness as well as reservoir assemblages. The richness maps were combined with gridded human population data to estimate the number of people potentially exposed. Most Orthohantavirus reservoir species in SSA are distributed over eastern and northeastern Argentina, parts of Paraguay, Uruguay, and southern Brazil. Hotspots of reservoir richness were concentrated in the Humid Chaco, while the area of high genotype richness extends across the Chaco-Pampean plain. Exposure to a high diversity of pathogenic genotypes (≥7) was estimated to exceed 13 million people under the most precautionary scenario. This study provides the first integrated, region-wide assessment of Orthohantavirus reservoir distributions and richness in SSA, offering an updated ecological baseline to support surveillance, risk mapping, and prevention strategies.

The increasing availability of global meteorological data has enabled it to be used as input for multiple environmental and climate-driven models. However, the accuracy of these datasets can vary depending on the climatic variable, region, and spatial scale considered. This study evaluated the performance of two global datasets, NCEP GDAS/FNL and GPM_3IMERGDL, in Argentina between 2015 and 2024. These datasets were selected based on their use in a population dynamics model of Aedes aegypti. Five climatic variables were analysed: minimum, mean and maximum temperature; precipitation; and relative humidity. Modelled data were compared with observations from 18 weather stations across 12 Argentinian climatic zones. Temporal analysis included calculating daily bias and evaluating seasonal differences using linear mixed models. Spatially, performance metrics (correlation, RMSE and mean bias) and their relationship with geographical variables (latitude, longitude and altitude) were assessed using multiple regression. Significant inter-seasonal biases were detected in all three temperatures and in relative humidity. Thermal variables showed strong correlation ($\mathrm{R}>0.8$) and low error (RMSE between 4.3 and $6.0{ }^{\circ} \mathrm{C}$). Precipitation and relative humidity exhibited a weaker fit ($\mathbf{R}$ of 0.38 and 0.68, respectively) and greater spatial variability. Regression analysis revealed that altitude was the main driver of spatial variation in temperature error, whereas latitude and longitude influenced precipitation error. These findings highlight the need to validate global datasets in relation to the geographical and temporal context of application. Future work could explore how different meteorological inputs influence the predictions of climate-sensitive models.

Asthma remains a major public health concern in Latin America, where underreporting and unequal access to healthcare services challenge accurate assessments of its prevalence and mortality. In this study, we explored the use of remote sensing data and machine learning techniques to predict asthma mortality in Argentina at departmental level from 2001 to 2022. We used the Random Forest (RF) algorithm to model the Normalized Asthma Mortality Rate (NAMR). A walk-forward validation approach with expanding windows was applied to train, test, and tune a RF model in a two-stage approach—classification followed by regression—using predictor variables derived from satellite-based observations such as burned areas, and Particulate Matter with 2.5 micrometers in diameter or less ($\text{P M}_{2.5}$), along with Population Density (PD), and lagged and feature engineered variables. Exploratory spatial analyses revealed a weak but statistically significant spatial autocorrelation in NAMR and stronger spatial autocorrelation in PD. The walk-forward validation approach showed that RF classification model was able to correctly identify most departments with asthma mortality (mean accuracy $0.775 \pm 0.012$), although it tended to miss a proportion of actual cases (mean recall 0.611 $\pm 0.047$). The RF regression model demonstrated a moderate ability to explain variations in NAMR across departments (mean $\text{R}^{\text{2}} \text{0. 4 4 0} \pm \text{0. 1 3 8}$). SHAP (SHapley Additive Explanations) analysis highlighted PD, past mortality rates, and the PD×PM2.5 interaction as the main factors contributing to the 2022 NAMR predictions. Our findings underscore the potential of integrating remotely sensed environmental data and machine learning for identifying asthma mortality risk patterns in data-sparse settings. Future research should incorporate additional spatial and environmental predictors and explore spatially explicit and deep learning models to enhance predictive accuracy.

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.

Aim Biodiversity monitoring at global scales has been identified as one of the priorities to halt biodiversity loss. In this context, Latin America and the Caribbean (LAC), home to 60% of the global biodiversity, play an important role in the development of an integrative biodiversity monitoring platform. In this review, we explore to what extent LAC has advanced in the adoption of remote sensing for biodiversity monitoring and what are the gaps and opportunities to integrate local monitoring into global efforts to halt biodiversity loss. Location Latin America and the Caribbean. Time period 1995 to 2022. Taxa studied Terrestrial organisms. Methods We reviewed the application of remote sensing for biodiversity monitoring in LAC aiming to identify gaps and opportunities across countries, ecosystem types and research networks. Results Our analysis illustrates how the use of remote sensing in LAC is disproportionately low in relation to the biodiversity it supports. Main conclusions Build upon this analysis, we present, discuss and offer perspectives regarding four gaps identified in the application of remote sensing for biodiversity monitoring in Latin America and the Caribbean, namely (1) alignment between remote sensing data resolution and ecosystem structure; (2) investment in research, institutions and capacity building within researchers and stakeholders; (3) decolonized practices that promote access to publishing outlets and pluralistic participation among countries that facilitate exchange of experiences and capacity building; and (4) development of networks within and across regions to advance in ground surveys, ensure access and to foster the use of remote sensing data.

The global spread of Aedes aegypti and the associated public health risk have stimulated the development of several mathematical models to predict population dynamics in response to biological or environmental changes in real, future, or simulated scenarios. The aim of this study is to identify published articles on differential equation-based population dynamics models of Aedes aegypti, highlight their differences and commonalities, and examine their application in surveillance and control programs. Following the PRISMA guidelines, a systematic review was conducted in seven electronic databases (Scopus, PUBMED, IEEE Xplore, Science Direct, DOAJ, Scielo, and Google Scholar), with the last update on 8 February 2023. The initial search yielded 513 studies, of which 31 were finally selected. The articles analyzed showed great variability in the equations, processes, and variables included, with temperature being the most common environmental factor. Only a few models incorporated spatial heterogeneity or validation methods. Our findings suggest that improving the generation of temporal and spatially explicit forecasts through interdisciplinary collaboration, the use of new technologies, and validation with field data is essential for these models to effectively support public health efforts. Differential equation-based population dynamics models offer valuable insights and could greatly benefit mosquito surveillance programs if standardized and tailored to relevant scales.

The evolutionary dynamics of the ecoregions of southern South America and the species that inhabit them have been poorly studied, and few biogeographic hypotheses have been proposed and tested. Quaternary climatic oscillations are among the most important processes that have led to the current distribution of genetic variation in different regions of the world. In this work, we studied the evolutionary history and distribution of the Córdoba vesper mouse (Calomys venustus), a characteristic rodent of the region of which little is known about its natural history. Since the population dynamics of this species are influenced by climatic factors, this rodent is a suitable model to study the effects of Quaternary climatic oscillations in central Argentina. The mitochondrial cytochrome b gene was sequenced to analyze the phylogeography of C. venustus, and ecological niche modeling tools were used to map its potential distributions. The results of these approaches were combined to provide additional spatially explicit information about this species' past. Our results suggest that the Espinal was the area of origin of this species, which expanded demographically and spatially during the last glacial period. A close relationship was found between the Espinal and the Mountain Chaco. These results are consistent with previous studies and emphasize the role of the Espinal in the biogeographic history of southern South America as an area of origin of several species.

Ovitraps are a widely used method for mosquito detection and monitoring, especially Aedes mosquitoes. Eggs present in ovitraps must be routinely counted to generate up-to-date information on potential spread of mosquito-borne diseases. This task is tedious, time consuming and prone to errors if done manually by eye counting. In this contribution, we introduce the Ovitrap Monitor, an online open source and user-friendly integrated application that semi-automatically counts mosquito eggs from low-medium resolution mobile phone pictures. A high correlation was found between counts performed manually by a technician and those obtained with the app using an extensive dataset of more than 750 ovitrap pictures. The application features an intuitive user interface and time-series plots and maps to facilitate data flow and speed up evidence-based decision-making within health organisations battling mosquito-borne diseases. Besides being open source, the Ovitrap Monitor is also backed by test data to guarantee its implementation through benchmarking and enforce research in the public health field.

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.

In this work, we examined the relationship of Oligoryzomys longicaudatus' (the Andes virus [ANDV] host, commonly known as the colilargo) occupancy and the cover of dominant woody and herbaceous plant species recorded in censuses along traplines for mice. We found that O. longicaudatus occupancy probability increased with high percentages of Rosa rubiginosa, Plantago lanceolata, Rumex acetosella, and Holcus lanatus, while it decreased with an increased cover of Mulinum spinosum and Ochetophila trinervis. The four positively related species are exotic plants. R. rubiginosa, the most conspicuous one, is capable of invading all types of habitats and forms dense shrublands in the ecotone between forest and steppe. These results are partly consistent with diet studies indicating that sweet briar fruits are the main item consumed by colilargos. The relationship of the ANDV main host with such an invasive plant poses a likely increased ANDV transmission risk to local communities making use of sweet briar's fruits. We discuss further implications of this problem in relation to hantavirus epidemiology in southern Argentina.

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.

Surveillance is critical to efficiently control and prevent mosquito-borne diseases such as Dengue. Surveillance relies on sampling the target region for arthropod vectors over time. However, in most cases the sampling framework is ad hoc and relies only on expert opinion. We sought to improve the efficiency of mosquito surveillance in Córdoba (Argentina) by designing a spatial sampling scheme within complex urban areas that would optimize ovitrap collections. We classified a very high resolution (VHR) satellite image following an object based (OBIA) approach and estimated several landscape metrics over which we applied a k-means clustering. The objective was to identify an optimal distribution for the ovitrap network characterizing the urban coverage of the city at three types of territorial units: neighbourhoods, census tracts and Thiessen polygons around health care facilities. We distributed 150 ovitraps throughout the city based on the identified environmental groups and compared results with the current strategy used by the Ministry of Health. Stratified ovitrap distributions for census tracts or Thiessen polygons performed best compared to the current strategy in terms of environmental variability covered, i.e., relevant environmental groups are either subsampled or oversampled in the current distribution. Because of the general availability of these environmental data sets and algorithms, the approach could be applied in most urban areas where vector borne disease control is challenging.

Aedes aegypti is the main vector of dengue, chikungunya and zika viruses, which together have resulted in the highest rate of disease and mortality among emerging and/or re-emerging viruses in the Americas. Vector surveillance is a key tool for prevention and control of these diseases. In this context, a proper distribution of sensors within a city will provide timely and precise information to guide public health actions. Under the assumption that environmental variability will determine different probabilities of mosquito presence and activity, our objective was to characterise the urban coverage of Córdoba city at neighbourhood and census tracts levels in order to determine an optimal distribution for the ovitrap network based on the environmental variability. To this aim we first classified very high resolution (VHR) satellite imagery following an object based (GEOBIA) approach. Then, we estimated several landscape metrics for neighbourhood and census tracts polygons and performed a k-mean clustering to determine groups of environmentally similar polygons over the city. After different tests, we defined four environmental clusters for the census tracts and three for the neighbourhoods. Finally, we distributed 150 ovitraps over the city based on the environmental groups defined and compared this distribution with the one used by the Health Ministry, a random one and, a systematic one. It was observed that the arbitrary distribution is the least environmentally representative of the city both for neighbourhoods and census tracts. Instead, the ovitrap distribution stratified by clusters at census tracts level was the best option as it properly covers the environmental variability detected over the city.

Spatial disease modeling remains an important public health tool. For cholera, the presence of zero counts is common. The Poisson model is inadequate to (1) capture over-dispersion, and (2) distinguish between excess zeros arising from non-susceptible and susceptible populations. In this study, we develop zero-inflated (ZI) mixture spatially varying coefficient (SVC) models to (1) distinguish between the sources of the excess zeros and (2) uncover the spatially varying effects of precipitation and temperature (LST) on cholera. We demonstrate the potential of the models using cholera data from Ghana. A striking observation is that the Poisson model outperformed the ZI mixture models in terms of fit. The ZI Negative Binomial (ZINB) outperformed the ZI Poisson (ZIP) model. Subject to our objectives, we make inferences using the ZINB model. The proportion of zeros estimated with the ZINB model is 0.41 and exceeded what would have been estimated using a Poisson model which is 0.35. We observed the spatial trends of the effects of precipitation and LST to have both increasing and decreasing gradients; an observation implying that the use of only the global coefficients would lead to wrong inferences. We conclude that (1) the use of ZI mixture models has epidemiological significance. Therefore, its choice over the Poisson model should be based on an epidemiological concept rather than model fit and, (2) the extension of ZI mixture models to accommodate spatially varying coefficients uncovered remarkable varying effects of the covariates. These findings have significant implications for public health monitoring of cholera.

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.

This dataset consists of 300 ovitrap sticks pictures that contain at least one Aedes aegypty egg. The pictures were taken with an iPhone 7 mobile phone and were used to assess the performance of a mosquito egg counter algorithm and application (https://ovitrap-monitor.netlify.app/). The ovitraps are part of a weekly surveillance program carried out in the city of Córdoba (Argentina) by the Health Ministry authorities. This dataset is a smaple obtained between December 2021 and March 2022. We also include a text file with the code of the ovitraps and the number of eggs counted by a technician under magnifying glasses (i.e., observed counts).

We updated the distribution of Phlebotominae (Diptera: Psychodidae) species and human cases of leishmaniasis in the Province of Corrientes, Argentina. Evandromyia correalimai (Martins, Coutinho & Lutz, 1970) is a new record for the province, reported in the urban area of Santo Tomé. We include the currently known distribution map of Phlebotominae sandfly species in Corrientes, and the localities where they were recorded, as well as a map of vector species and reported human cases of leishmaniases.