Population Density Research Articles

Modeling spatiotemporal variations in the density of unmarked populations using camera trap data: An application to free-roaming cats

Camera traps have revolutionized wildlife monitoring, providing reliable information on animal occupancy and abundance. In recent years, several models have been developed to estimate the absolute density of unmarked populations (i.e., those lacking individually recognizable markings), which was previously challenging. By refining these models, they may become efficient monitoring tools applicable to a wide range of species. In this study, we extended one such model, the Random Encounter and Staying Time (REST) model, within a state-space framework to explicitly account for spatiotemporal variations in animal density. We applied this model to free-roaming cats using data obtained from 209 camera traps deployed across a study area (836 km2) over a two-year period (June 2018–May 2020). The estimated cat density was generally <0.3 cats per km2, with higher densities observed near human settlements and occasional excursions into deeper forested areas (>500 m). Densities also exhibited seasonal variation, with higher values in winter and spring compared to summer. Our model estimates the “average” density of animal individuals available to camera traps over the temporal duration of interest. Although this metric differs from conventional population density measures and requires careful interpretation, it remains a valuable indicator of the ecological impact or functioning of animals, including free-roaming cats.

Assessing the influence of green space morphological spatial pattern on urban waterlogging: A case study of a highly-urbanized city.

The extensive expansion of impervious surfaces encroaches on green spaces and causes frequent urban waterlogging disasters. Previous studies have focused mainly on the influence of green space landscape pattern on waterlogging, with less attention given to green space morphological spatial pattern (MSPA). MSPA can be used to differentiate various types of land use morphologies from a microscopic perspective and reveal visualized spatial characteristics. Therefore, this study selected Shenzhen, a city with serious waterlogging problems, as the study area. The anthropogenic/natural environments and green space morphological spatial pattern were considered. Pearson correlation analysis and random forest regression were combined to investigate the influence of these drivers on the density of waterlogging hotspots and quantify the degree of importance for each driver. The results were supplemented with explanations using SHapley Additive exPlanations and Partial Dependence Plots. Pearson correlation analysis revealed that green space morphological spatial pattern, the proportion of green spaces, and the proportion of impervious surfaces were the dominant drivers. Additionally, the random forest regression showed that incorporating green space morphological spatial pattern and average tree height as potential drivers could strengthen the model's goodness-of-fit. While the proportion of impervious surfaces, the proportion of green spaces, and population density were important drivers, the green space morphological spatial pattern, specifically the "loop", "edge", and "core", was even more crucial and had an optimal design range. Therefore, green space morphological spatial pattern should be emphasized during the planning of "sponge cities" to maximize the ability of green spaces to mitigate waterlogging. In summary, our findings are expected to provide feasible suggestions for waterlogging control and green space planning.

Trichoscopic Analysis of Healthy Indian Males for Standardization of the Measurable Parameters: An Observational, Cross-sectional Study.

Trichoscopy is a simple, noninvasive tool to help in the diagnosis of various hair and scalp disorders. There is paucity of data on the normal trichoscopic parameters of hair density and diameter in the Indian population. The aim of this study was to establish the trichoscopic patterns of hair and scalp in healthy Indian males and to provide a framework for future reference. Quantitative trichoscopic analysis using a videodermoscope was done on healthy Indian males with no hair/scalp complaints. Images were taken from four areas: frontal, occipital, and both temporal areas, under 20x and 70x magnification. The following parameters were analyzed by the software: hair density and thickness; percentage of thin, medium, and thick hairs; percentage of anagen and telogen hairs; vellus hair density and hair count; follicular unit density; percentage of follicular hair units; single, double, triple, and four hairs per follicular unit; and distribution and pattern of scalp vasculature. A total of 120 healthy males were recruited in the study with a mean age of 24.6 years. The hair density was the highest in the frontal and occipital areas, while the hair thickness was similar in all the sites evaluated. The follicular unit density was the highest in the frontal and occipital areas with the temporal area having the highest number of single hair follicular units. The scalp vasculature varied with the area examined, the most common pattern being the pin-point type. It was a hospital-based study with a relatively small sample size. The software used in the study cannot detect gray hairs. Our study has attempted to fill the gaps in the medical literature by proposing comparable reference values for trichoscopy in healthy Indian males.

Atypical antipsychotics improve dendritic spine pathology in temporal lobe cortex neurons in a developmental rodent model of schizophrenia.

"Dendritic spine pathology" refers to alterations in density and morphology of dendritic spines, crucial in corticolimbic neurons in schizophrenia. These structural neuroplasticity changes contribute to the disease's neurobiological underpinnings, alongside alterations in other brain regions, such as temporal lobe cortices like the auditory cortex (Au1) and the entorhinal cortex (Ent), involved in sensory processing, memory, and learning. The neonatal ventral hippocampus lesion (NVHL) in rats exhibits behavioral abnormalities akin to schizophrenia symptoms and corticolimbic dendritic spine pathology, mitigated by atypical antipsychotic drugs (AADs) like risperidone (RISP) and olanzapine (OLZ). This study investigated NVHL-induced dendritic spine pathology in Au1 and Ent, evaluating RISP and OLZ effects. NVHL induced dendritic spine pathology mainly by reducing the dendritic spine density in Au1 and Ent neurons; both RISP and OLZ mitigated it, increasing dendritic spine density and mushroom spine population, the ones related with synaptic strengthening, while decreasing stubby spine population. These findings underscore the role of impaired neuroplasticity in the temporal lobe cortices in schizophrenia pathophysiology and highlight the relevance of the NVHL model for studying neuroplasticity mechanisms in the disease. They also contribute to the growing understanding of targeting structural and functional neuroplasticity for novel drugs in the pharmacotherapy of the disease.

Integrating AI and climate change scenarios for multi-risk assessment in the coastal municipalities of the Veneto region.

Global climate is experiencing exceptional warming, leading to a rise in extreme events worldwide. Coastal regions are particularly vulnerable to climate change (CC), due to dense populations, interconnected economies, and fragile ecosystems. These areas face escalating risks as CC intensifies the severity and frequency of extreme weather phenomena, like heavy precipitation, sea-level rise (SLR), storm surges. Integrated approaches are crucial to assess the combined impacts of atmospheric and marine hazards at the land-sea interface. Machine Learning (ML) offer innovative solutions to analyse multi-risk events, leveraging large and heterogeneous datasets and modelling complex, non-linear interactions. This study introduces a two-tier ML approach to estimate risks associated with extreme weather events for the Veneto coastal municipalities under current and future scenarios. The model, tested and validated with present-day data, showed satisfactory performance (error margin ∼20%). The model was applied to mid-term (until 2045) and long-term (until 2100) periods under different CC scenarios, represented by various Representative Concentration Pathways (RCP). Mid-term analysis reveals an increasing risk trend, driven by SLR under RCP8.5, underscoring the significance of considering non-linear interactions between multiple marine and atmospheric hazards. Long-term analysis highlights how future risks depend mainly on precipitation and SLR across the analysed CC scenarios (RCP2.6/4.5/8.5). Results indicate a gradual increase in the expected annual risk trend, with RCP8.5 scenario showing the most severe outcomes. By 2100, the risks under RCP8.5 are projected to be ten times higher than those observed during the historical period, highlighting the importance of developing effective strategies to address these challenges.

Neighborhoods Renewal Implications for the Post-Pandemic Era: A Study of COVID-19 Infections in Central Shanghai, China

Since the onset of the Coronavirus Disease 2019 (COVID-19), urban older neighborhoods have faced increased vulnerability, prompting research into neighborhood renewal and resilience. However, research on COVID-19 infection and influencing factors in China's older neighborhoods remains relatively scarce. This study analyzed COVID-19 infections in central Shanghai to identify neighborhood-level factors affecting transmission. Using principal component analysis (PCA) and person correlation coefficients (PCC) to process the data, we established multiple linear regression (MLR) and geographically weighted regression models (GWR). To explore nonlinear relationships, we incorporated the random forest method (RF). Results indicated that older neighborhoods had higher infection rates compared to newer ones. Socioeconomic and built environment factors significantly influenced infection rates. Specifically, higher population density, road network density, and the number of subway stations were positively correlated with increased infection rates. RF analysis revealed a complex, nonlinear relationship between the number of high-income residents and infection rates. This study integrates built environment, socioeconomic, and population characteristics factors using multiple modeling approaches to better understand their impact on infection rates. It also introduces research on mainland Chinese cities as case studies, offering valuable insights for updating older urban neighborhoods to enhance community resilience. However, the study did not fully consider the impact of policies at the time, and its findings are primarily applicable to older neighborhoods in cities similar to Shanghai. Future research should examine the effectiveness of various intervention policies, the long-term effects of neighborhood renewal on community resilience, and the applicability of these findings to other urban environments.

Configuration of public transportation stations in Hong Kong based on population density prediction by machine learning

Configuration of public transportation stations in Hong Kong based on population density prediction by machine learning

Unveiling the seasonal transport and exposure risks of atmospheric microplastics in the southern area of the Yangtze River Delta, China.

This study investigates the prevalence and impacts of suspended atmospheric microplastics (SAMPs) in the coastal metropolitan city of Ningbo in the Yangtze River Delta Region, China. The sampling was conducted at both urban centre and urban-rural fringe areas, near the coast but distant from large urban populations. SAMP abundance ranged from 0.017 to 0.430 items m-³, with an average of 0.145±0.09 items m⁻³. The urban centre exhibited approximately 70% more SAMPs than the urban-rural fringe, highlighting the influence of population density and human activity on microplastic pollution. Fibres dominated SAMP composition at both sites, while urban samples featured a greater variety of microplastic forms, such as fragments, beads, and films. Rayon and Polyethylene terephthalate were the predominant polymers, which were found to be directly related to local industrial activities. SAMPs ranged in size from 20μm to 4984.4μm, with over 60% smaller than 1000μm. Seasonal variation followed a winter>autumn>spring>summer pattern. Correlation and principal component analyses identified atmospheric temperature, pressure, wind speed, and rainfall as key factors influencing SAMP abundance. Notably, backward trajectory analysis showed that oceanic air masses carried significantly fewer SAMPs compared to terrestrial air, diluting concentrations in coastal regions. Annually, an estimated 4.67×101³ microplastics are suspended over Ningbo. This is the first comprehensive study of SAMP pollution in this region, revealing interactions between local sources, environmental variations, air mass dynamics, and exposure. The findings underscore the need for targeted strategies to mitigate atmospheric microplastic pollution in coastal urban environments.

Regulatory characteristics of population density dynamics of forest insects and possible reasons for the observed narrow range of such characteristics

Regulatory characteristics of population density dynamics of forest insects and possible reasons for the observed narrow range of such characteristics

Revealing the process and mechanism of non-grain production of cropland in rapidly urbanized Deqing County of China.

Non-grain production of cropland (NGPCL) is a common consequence of rapid urbanization, but excessive NGPCL threatens food security and sustainable cropland use. However, the evolutionary processes and mechanisms of different NGPCL types remain largely unknown, compromising the scientific basis for NGPCL management. Thus, taking rapidly urbanized Deqing as the study area, this research constructed an NGPCL theoretical framework from the perspective of agricultural location, classified NGPCL types using Landsat images, random forest algorithm and Google Earth Engine, and revealed their spatiotemporal changes and different influencing factors through the Multinomial Logit Regression model, and provided targeted zoning and categorized policy suggestions. Our constructed theoretical framework modeled NGPCL by the complex interactions among geographical location, natural conditions, socioeconomic levels, and policy forces. From 2000 to 2022, NGPCL in Deqing County had a high degree and significantly increased from 68.65% to 89.19%. Pond farming and horticultural crops increased by 4046.46ha (38.0%) and 1307ha (27.1%), while tree planting slightly decreased by 54ha (-2.5%). However, these NGPCL expansions resulted in a drastic decrease of 5299ha (-65.5%) in grain crops plantation. Different NGPCL types showed varied spatial distribution characteristics and differentiated driving mechanism. Horticultural crops depended on geographical location and socioeconomic factors and were widely planted near towns and roads with high population density and economic strength. Pond farming mainly extended along the eastern flat rivers and relied on resource endowment. Tree planting was closely related to the natural condition and was concentrated in the sparsely populated mountainous areas. The NGPCL is the result of balancing various factors, and under conditions of extremely scarce cropland resources, the Chinese government needs to strike a reasonable threshold for non-grain production and propose targeted policies of zoning classification to prevent excessive NGPCL from threatening grain yield security.

Enhancing satellite-based emergency mapping: Identifying wildfires through geo-social media analysis

ABSTRACT When a disaster emerges, timely acquisition of information is crucial for a rapid situation assessment. Although automation in the standard satellite-based emergency mapping workflow has been advanced, delays still occur at crucial steps. In order to speed up the provision of satellite-based crisis products to emergency managers, this paper proposes a geo-social media-based approach that detects disaster events based on the spatio-temporal analysis of georeferenced, disaster-related Tweets. The proposed methodology is validated on the basis of two use cases: wildfires in Chile and British Columbia. The results show the general ability of Twitter to forecast events several days in advance, at least for the Chile use case. However, there are large spatial differences, as there is a correlation between population density and the reliability of Twitter data. Consequently, only few meaningful alerts could be generated for British Columbia, an area with very low population numbers.

The effect of particulate matter 2.5 on seasonal influenza transmission in 1,330 counties, China: A Bayesian spatial analysis based on Köppen Geiger climate zones classifications.

Previous research has linked seasonal influenza transmission with particulate matters (PM2.5). However, the effect of PM2.5 on seasonal influenza transmission varied by region. This study aims explore how PM2.5 influenced seasonal influenza transmission in the elderly across 1330 counties in two Köppen Geiger climate zones in China, incorporating the socio-economic factors to enhance climate-driven early warning systems (EWS) for influenza. Data included weekly 2015-2019 influenza cases in those aged >65 from China's national influenza surveillance system for 1330 counties in two Köppen Geiger climate zones: Temperate, Hot Summer with Dry Winter (Cwa) and No Dry Season (Cfa). PM2.5 data from 2015 to 2019 were sourced from Copernicus Atmosphere Monitoring Services. Additional data on floating population, population density and Gross Domestic Product (GDP) per capita were collected from pertinent departments. A Bayesian spatial autoregressive model assessed the association of PM2.5 and influenza transmission after adjustment of socio-economic factors. Our research results showed PM2.5 (per 1μg/m³ increase) was linked to increased influenza transmission in the Cwa zone during winter season (Relative Risk (RR)=1.023, 95% Credible Interval (CI):1.008-1.040) but not in the Cfa winter (RR=1.003, 95% CI: 0.992-1.015). Floating population significantly enhanced transmission in both zones (highest RR=1.362, 95% CI:1.181-1.583), while GDP per capita growth was associated with reduced transmission risk (highest RR=0.619, 95% CI: 0.445-0.861). The study identifies PM2.5 as a significant factor influencing influenza transmission in the elderly, with effects varying by climate zone, suggesting the need to incorporate PM2.5 and socio-economic factors into seasonal influenza EWS.

Increasing rat numbers in cities are linked to climate warming, urbanization, and human population.

Urban rats are commensal pests that thrive in cities by exploiting the resources accompanying large human populations. Identifying long-term trends in rat numbers and how they are shaped by environmental changes is critical for understanding their ecology, and projecting future vulnerabilities and mitigation needs. Here, we use public complaint and inspection data from 16 cities around the world to estimate trends in rat populations. Eleven of 16 cities (69%) had significant increasing trends in rat numbers, including Washington D.C., New York, and Amsterdam. Just three cities experienced declines. Cities experiencing greater temperature increases over time saw larger increases in rats. Cities with more dense human populations and more urbanization also saw larger increases in rats. Warming temperatures and more people living in cities may be expanding the seasonal activity periods and food availability for urban rats. Cities will have to integrate the biological impacts of these variables into future management strategies.

Mapping Disaster Hazards and Risks – A Case Study in Kuala Lumpur City (KLC)

Kuala Lumpur, the capital city of Malaysia, is vulnerable to various natural and man-made disasters. Kuala Lumpur City (KLC) encounters distinctive difficulties in managing urban disasters, mainly because of its dense population, critical infrastructure, and economic importance. Mapping the hazards and risks of disasters in KLC is crucial for enhancing its resilience against disasters and ensuring the effectiveness of emergency response, humanitarian aid, and disaster relief efforts. The objective of this study is to develop a comprehensive master plan for the management of disasters in KLC. The plan will serve as the initial phase in the civil-military coordinating response architecture for humanitarian aid and disaster relief (HADR). The approach entails creating a hazard map delineating regions susceptible to flash fl oods and landslides, frequently occurring in KLC. In addition, a vulnerability map was generated to evaluate the region’s proneness to disasters and identify safe areas within KLC. Additionally, road vulnerability assessments were performed to determine the most direct route for HADR activities. Furthermore, a customised disaster risk map was created exclusively for the KLC. This work contributes to the growing body of literature on urban disaster management, offering valuable insights for other metropolitan areas facing similar challenges.

A statistical shape and density model can accurately predict bone morphology and regional femoral bone mineral density variation in children.

Finite element analysis (FEA) is a widely used tool to predict bone biomechanics in orthopaedics for prevention, treatment, and implant design. Subject-specific FEA models are more accurate than generic adult-scaled models, especially for a paediatric population, due to significant differences in bone geometry and bone mineral density. However, creating these models can be time-consuming, costly and requires medical imaging. To address these limitations, population-based models have been successful in characterizing bone shape and density variation in adults. However, children are not small adults and need their own population-based model to generate accurate and accessible musculoskeletal geometry and bone mineral density in a paediatric population. Therefore, this study aimed to create a biomechanical research tool to predict the personalized shape and density of the paediatric femur using a statistical shape and density model for a population of children aged from 4 to 18 years old. Femur morphology and bone mineral density were extracted from 330 CT scans of children. Variations in shape and density were captured using Principal Component Analysis (PCA). Principal components were correlated to demographic and linear bone measurements to create a predictive statistical shape-density model, which was used to predict femoral shape and density. A leave-one-out analysis showed that the shape-density model can predict the femur geometry with a root mean square error (RMSE) of 1.78 ± 0.46 mm and the bone mineral density with a normalized RMSE ranging from 8.9 % to 13.5 % across various femoral regions. These results underscore the model's potential to reflect real-world physiological variations in the paediatric femur. This statistical shape and density model has the potential for clinical application in rapidly generating personalized computational models using partial or no medical imaging data.

Social affiliation among sub-adult male koalas in a high-density population

Koalas are generally considered to be a solitary species. However, during observations of free-ranging koalas in a high-density population, sub-adult males (3.5–5.5 years) were seen engaging in affiliative behaviours, including grooming, genital sniffing, and vocalising. These interactions were exclusive to males of this age group and are likely a result of the unusually high population density. In such populations, where home range overlap is common, these behaviours may help reduce stress, establish dominance hierarchies, and reduce conflict through familiarisation.

Re-emergence of Aedes aegypti (Linnaeus) in Egypt: Predicting distribution shifts under climate changes.

Aedes aegypti, the primary vector of several medically significant arboviruses-including dengue fever, yellow fever, chikungunya and Zika-was successfully eradicated from Egypt in 1963. However, since 2011, there have been increasing reports of its re-emergence, alongside dengue outbreaks in southern Egyptian governorates, raising significant public health concerns. This study aimed to model the current and future distribution of Ae. aegypti in Egypt. Local occurrence data were integrated with bioclimatic, anthropogenic and biological environmental variables to identify key factors influencing the distribution of Ae. aegypti. Maximum entropy (MaxEnt) modelling demonstrated strong predictive performance (area under the receiver operating characteristic curve [AUC] mean = 0.975; true skill statistic [TSS] mean = 0.789). The key determinants of habitat suitability were identified as human population density, annual precipitation and the normalised difference vegetation index (NDVI). Current predictions indicate that suitable habitats for Ae. aegypti are concentrated in the Nile Valley, Nile Delta, Fayoum Basin, Red Sea coast and South Sinai. Projections under future climate change scenarios suggest an expansion of suitable habitats, particularly in the Nile Delta region. By 2050, the model predicts a 61%-68% increase in suitable habitat area, with a further 64%-69% increase by 2070, depending on the future climate scenarios. These findings are crucial for informing vector control and disease prevention strategies, particularly considering Egypt's status as one of the world's leading tourist destinations.

P-1282. Saving Lives and Costs: Respiratory Syncytial Virus Wastewater Surveillance to Guide all infant prophylaxis in Ontario, Canada

Abstract Background Wastewater-based surveillance (WBS) of respiratory syncytial virus (RSV) detected seasonal activity up to one month earlier than clinical surveillance (CS) during the 2022 RSV season in two Ontario cities, Canada. The associated cost-consequence analysis comparing the use of WBS vs CS to guide the start of provincial immunoprophylaxis programs showed WBS-guided prophylaxis realized savings of CAD $2.1-3.5m in the first year and $13.7-16.6m over 1-3 years due to early RSV detection. Further investigation is needed to explore the reproducibility and scalability of WBS for RSV in a provincial surveillance initiative. Geospatial distribution of the 12 sentinel sampling locations across Ontario, Canada Methods In collaboration with the Ontario Ministry of Health (MOH), 12 locations in the province were selected as sentinel WBS sites based on population density, socio-economic strata, and their significance as primary pediatric centres where palivizumab is administered (Figure 1). City-wide, 24-hour composite wastewater samples were collected and screened for RSV by RT-qPCR three to seven times a week. We monitored RSV-WBS data and pediatric RSV-related hospitalizations (RSVH) between September 1, 2023, to March 15, 2024. Cumulative fraction of locations for the onset of the 2023 RSV season in Ontario, Canada Date of onset of regional RSV activity as identify by wastewater surveillance at each of the 12 sentinel locations during the 2023 RSV season, represented as a cumulative fraction. Results During the study period, RSV was detected in 76% (1092/1440) of all wastewater samples collected, with detection at all locations. WBS effectively tracked the geographical variation in viral activity across the province. There was a 73-day variation in the RSV season onset across locations, with northern sites lagging southern locations by 62 ± 11 days (Figure 2). WBS detected regional RSV activity up to 15 days prior to the provincial start date of October 30, 2023, as determined by CS. Considering the estimated WBS cost of $0.50 per eligible infant compared to an average of $5,000 per RSVH, and an estimated fewer RSVHs per year, provincial WBS-guided prophylaxis could significantly reduce healthcare costs and the burden of RSV associated illness. Conclusion RSV WBS could be a highly cost-effective strategy that identifies the early onset of the RSV season, thereby optimizing immunoprophylaxis timing. Additionally, it is scalable to communities of varying sizes and socioeconomic strata and reflects geospatial variation in regional RSV activity supporting its use in a provincial-wide WBS initiative. Disclosures Bosco Paes, MD, Professor Emeritus, AstraZeneca: advisor/lecturer outside the scope of this study|Sanofi: advisor/lecturer outside the scope of this study John Fullarton, n/a, AstraZeneca: Employer has received payment from AstraZeneca for work on various projects outside the scope of this study|Sanofi: Employer has received payment from Sanofi for work on various projects outside the scope of this study Barry Rodgers-Gray, n/a, AstraZeneca: Employer has received payment from AstraZeneca for work on various projects outside the scope of this study|Sanofi: Employer has received payment from Sanofi for work on various projects outside the scope of this study

P-1433. Characterization and Georeferencing of Tuberculosis Cases in Pediatric Patients at a Reference Hospital in México in 13 years

Abstract Background The 2022 "TB Global Report" documents a rise of Tuberculosis (TB) cases worldwide and emphasizes the importance of surveillance strategies. Geographic Information Systems (GIS) characterize geo-temporal trends of communicable diseases and assist in decision-making, including resource allocation. The use of GIS in pediatric TB is limited in Mexico, where the incidence of TB among children is high. Mexico’s public health system lacks outpatient clinics (OC) for diagnosis, treatment and prevention of TB. Identifying strategic areas to implement these facilities may reduce the burden of TB. Geographic distribution of pediatric patients with TB (n=145) diagnosed at the Pediatric Reference Hospital (HRMI) from 2010-2023. Interpretation of NNI and z-score revealed clusttering pattern for pulmonary, lymph node ad meningeal pedaitric TB Methods Subjects &amp;lt; 18 years with TB diagnosed at the Pediatric reference hospital (HRMI) in NL Mexico from 2010-2023 were included. Regional marginalization indices were obtained from official local authorities. Distance analysis techniques, nearest neighbor index (NNI) and hierarchical clustering were applied using QGis®. Kernel density estimation was calculated, and results were visualized with heat maps. Map 1. Distribution and Heat Map pf Pediatric TB Cases Diagnosed at the Pediatric Reference Hospital (HRMI) of NL, Mex. Heat Map was projected by Kernel Density Estimation of 31736.8 meters (SD ± 25279.2 meters), showing hot spots at the Metropolitan Area of Nuevo Leon. Results Clinical and geographic data of 145 subjects were included. Three areas showed the highest case concentration. NNI from the total sample showed a dispersion pattern; however, a clustering pattern was observed for pulmonary (p&amp;lt; 0.05), meningeal and lymph node TB (Table 1). Before diagnosis, most patients had at least one visit to a public OC where TB was not suspected. Average distance to the nearest public OC was 0.7 km and to HRMI 21.3 km. Case-to-hospital distance analysis showed that 65% of cases lived &amp;gt;10 km from HRMI. The majority of cases occurred in areas with high (23.5%) and medium (44%) marginalization indices. Map 2. Heat Maps of Pedaitric TB Subtypes with Clustter Distribution by Kernel Density Estimation (KDE) A. Pulmonary Pediatric TB: KDE 6434,8 mts B. Meningeal Pediatric TB: KDE 12,828 mts C. Lymph Node Pediatric TB: KDE 9,756 mts Conclusion We identify pediatric TB hotspots in NL, Mexico. The clustering patterns identified for pulmonary, lymph node, and meningeal TB indicate a non-random distribution, possibly influenced by marginalization, population density, and healthcare access. Long case-to-hospital distances and failure of general OCs to suspect pediatric TB highlight the importance of implementing specialized TB OCs in strategic high burden areas. Increasing the visibility of pediatric TB and implementing public health strategies according to community needs is crucial. Our full report will be shared with local health authorities in order to propose the creation of TB OCs. A. Map of distance Case-To-Hospital, category was determine by 4 gropus: Very Low (0 - 2.9 km), Low (3 - 9.9 km), High ( 10 - 29.9 km) and Very High ( &amp;gt; 30 km) B. Map of distance Case-To-Nearest OC, it demostrates that every case, was less tan 3km from an OC C. Map of relation of each case location and the Marginalization Index obtained by national authorities, showing vast majority in medium and high index areas Disclosures All Authors: No reported disclosures

Long-term impacts of salinity and temperature changes on Brachionus calyciflorus populations: understanding the role of intraspecific variability.

Worldwide, many coastal freshwater ecosystems suffer from seawater intrusion. In addition to this stressor, it is likely that the biota inhabiting these ecosystems will also need to deal with climate change-related temperature fluctuations. The resilience of populations to long-term exposure to these stressors will depend on their genetic diversity, a key for their adaptation to changing environments. Accordingly, this study aimed to understand the long-term effects of salinity and temperature on the population density dynamics of the rotifer Brachionus calyciflorus by considering intra-specific variability. Six clonal lineages of B. calyciflorus, exhibiting differential lethal sensitivity (LC50,24h) to salinity, were exposed for at least 34days, to a control and to artificial seawater (at a conductivity corresponding to the LC70,24h for the most tolerant clonal lineage = 9.89 mS/cm), under three temperatures: 17, 20 (standard) and 23°C. Long-term exposure to artificial seawater affected population densities, leading to the extirpation of some salinity-tolerant clonal lineages earlier than that of salinity-sensitive lineages. This inversion in short- and long-term sensitivity may suggest a higher susceptibility of populations when exposed to long periods of increased salinity. The negative effects caused by artificial seawater were enhanced at 17°C and 23°C, with an even earlier occurrence of extirpation of some clonal lineages, namely, two clonal lineages considered tolerant to artificial seawater. The results suggest the potential synergistic effects of the two abiotic stressors when combined. Overall, a lack of association between the clonal lineages' short- and long-term sensitivity to salinity or their sensitivity to salinity under different temperature scenarios was observed. These results suggest an increased risk to the resilience of B. calyciflorus populations exposed to climate change-related scenarios of increased salinity and temperature fluctuations owing to an enhanced reduction in their genetic variability.