Statistical analysis based on quantile regression methods is more comprehensive, flexible, and less sensitive to outliers when compared to mean regression methods. When the link between different diseases are of interest, joint disease mapping is useful for inferring correlation between them. Most studies study this link through multiple correlated mean regressions. In this paper we propose a joint quantile regression framework for multiple diseases where different quantile levels can be considered. We are motivated by the theorized link between the presence of Malaria and the gene deficiency G6PD, where medical scientist have anecdotally discovered a possible link between high levels of G6PD and lower than expected levels of Malaria initially pointing towards the occurrence of G6PD inhibiting the occurrence of Malaria. This link cannot be investigated with mean regressions and thus the need for flexible joint quantile regression in a disease mapping framework arise. Our joint quantile disease mapping model can be used for linear and non-linear effects of covariates by stochastic splines, since we define it as a latent Gaussian model. We perform Bayesian inference of this model using the INLA framework embedded in the R software package INLA, resulting in a very efficient model even for large datasets. Finally, we illustrate the applicability of the model by analyzing the malaria and G6PD deficiency incidences, jointly, in 21 countries.

Sweat Sodium Loss Mapping Software Market Outlook

According to the latest research conducted in 2025, the global Sweat Sodium Loss Mapping Software market size has reached USD 312.6 million in 2024. The market is experiencing robust growth, with a Compound Annual Growth Rate (CAGR) of 13.2% projected during the forecast period. By 2033, the market is forecasted to reach USD 882.7 million, driven by the increasing adoption of precision health analytics and the rising focus on personalized hydration strategies across sports, healthcare, and military sectors. This growth trajectory is underpinned by advancements in wearable sensor technology and the integration of artificial intelligence in sweat analysis solutions, as per our latest research insights.

One of the primary growth factors propelling the Sweat Sodium Loss Mapping Software market is the escalating demand for real-time, data-driven insights into electrolyte loss during physical activities. Sports teams, elite athletes, and fitness enthusiasts are increasingly leveraging these software solutions to monitor and optimize hydration strategies, thereby enhancing performance and reducing the risk of heat-related illnesses. The integration of sweat analysis with wearable devices has made it possible to collect granular data on sodium loss, which can be mapped and analyzed to tailor individual hydration plans. This trend is further reinforced by the growing awareness of the adverse effects of improper hydration, such as muscle cramps, decreased endurance, and cognitive impairment, thereby fueling the adoption of sweat sodium loss mapping software across various end-user segments.

Another significant driver of market expansion is the growing emphasis on preventive healthcare and personalized medicine. Healthcare providers are increasingly utilizing sweat sodium loss mapping software to assess patient hydration status, particularly in populations vulnerable to electrolyte imbalances, such as the elderly, children, and individuals with chronic illnesses. This software enables clinicians to develop customized hydration and electrolyte replacement protocols, reducing hospital readmissions and improving patient outcomes. Additionally, research and development initiatives focusing on sweat biomarkers for early disease detection and monitoring are fostering innovation in this market, as institutions seek advanced analytical tools to interpret complex physiological data.

Technological advancements are also playing a pivotal role in shaping the Sweat Sodium Loss Mapping Software market. The integration of cloud-based analytics, artificial intelligence, and machine learning algorithms has significantly enhanced the accuracy and scalability of sodium loss mapping solutions. These technologies facilitate seamless data aggregation from multiple sources, real-time visualization, and predictive modeling, making the software indispensable for research institutes, sports organizations, and military applications. Furthermore, the emergence of interoperable platforms that can integrate with electronic health records (EHRs) and athlete management systems is expanding the utility and appeal of these solutions, positioning them as critical components of holistic health and performance monitoring ecosystems.

From a regional perspective, North America continues to dominate the Sweat Sodium Loss Mapping Software market, accounting for the largest revenue share in 2024. This leadership is attributed to the high adoption rate of advanced sports science technologies, a robust healthcare infrastructure, and significant investments in military health monitoring programs. Europe follows closely, driven by rising health consciousness, government initiatives promoting sports and wellness, and an active research community focused on human performance optimization. The Asia Pacific region is poised for the fastest growth, supported by increasing sports participation, expanding healthcare access, and a burgeoning market for wearable health technologies. Latin America and the Middle East & Africa are also witnessing gradual adoption, primarily through partnerships with global technology providers and growing awareness of the importance of hydration monitoring in extreme environmental conditions.

Create a basic Story Map: Disease investigations (Learn ArcGIS PDF Lesson). This lesson will show you how to prepare a story map explaining John Snow’s famous investigation of the 1854 cholera outbreak in London._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

This dataset contains model-based place (incorporated and census designated places) level estimates for the PLACES 2022 release in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2020 or 2019 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 estimates. The 2022 release uses 2020 BRFSS data for 25 measures and 2019 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening) that the survey collects data on every other year. These data can be joined with the 2019 Census TIGER/Line place boundary file in a GIS system to produce maps for 29 measures at the place level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

This dataset contains model-based county-level estimates for the PLACES 2022 release in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. Project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2020 or 2019 data, Census Bureau 2020 or 2019 county population estimates, and American Community Survey (ACS) 2016–2020 or 2015–2019 estimates. The 2022 release uses 2020 BRFSS data for 25 measures and 2019 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening) that the survey collects data on every other year. These data can be joined with the census 2020 county boundary file in a GIS system to produce maps for 29 measures at the county level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

This dataset contains model-based place (incorporated and census designated places) estimates in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia —at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2020 population estimates, and American Community Survey (ACS) 2018–2022 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. These data can be joined with the 2020 Census place boundary file in a GIS system to produce maps for 40 measures at the place level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

Drone-Assisted Vineyard Disease Heatmap Market Outlook

According to our latest research, the global drone-assisted vineyard disease heatmap market size reached USD 412.6 million in 2024, demonstrating robust momentum driven by the increasing adoption of precision agriculture techniques. The market is projected to grow at a CAGR of 15.8% during the forecast period, with the value expected to reach USD 1,340.2 million by 2033. This significant expansion is primarily attributed to the rising need for efficient disease detection and management in vineyards, as well as the integration of advanced drone technologies and data analytics platforms.

One of the key growth factors propelling the drone-assisted vineyard disease heatmap market is the escalating incidence of vineyard diseases and pests, which directly impact crop quality and yield. Traditional disease monitoring methods are often labor-intensive, time-consuming, and lack the precision required to identify early-stage infestations. The advent of drone technology, equipped with multispectral and thermal imaging sensors, has revolutionized disease detection by enabling real-time, high-resolution mapping of affected areas. This capability not only enhances the accuracy of disease identification but also facilitates timely intervention, ultimately reducing crop losses and improving vineyard profitability. The growing awareness among vineyard owners about the economic benefits of early disease detection and the increasing regulatory pressure to minimize chemical interventions are further accelerating market adoption.

Another major driver is the technological advancements in drone hardware and analytical software, which have significantly improved the efficiency and reliability of vineyard disease heatmaps. Modern drones are now capable of covering large vineyard expanses in a single flight, capturing granular data that can be processed using AI-powered software to generate actionable insights. The integration of machine learning algorithms enables the identification of subtle disease patterns and stress indicators that are often missed by the naked eye. Additionally, the development of cloud-based platforms for data storage and analysis has made it easier for vineyard managers to access and interpret disease heatmaps from remote locations. These innovations are not only reducing operational costs but also empowering vineyards of all sizes to adopt precision agriculture practices.

The increasing focus on sustainability and environmental stewardship in viticulture is also contributing to the growth of the drone-assisted vineyard disease heatmap market. By providing targeted disease management solutions, drone-generated heatmaps help minimize the use of pesticides and fungicides, thereby reducing the environmental footprint of vineyard operations. This aligns with the global shift towards sustainable agriculture and the adoption of best practices to meet consumer demand for eco-friendly wine products. Moreover, government initiatives and funding for the adoption of smart farming technologies are creating a conducive environment for the expansion of the market, particularly in developed economies where regulatory frameworks support innovation in agriculture.

From a regional perspective, North America and Europe are leading the market, driven by the presence of large-scale vineyards, high technological adoption rates, and supportive regulatory policies. The Asia Pacific region is emerging as a high-growth market, fueled by expanding vineyard acreage in countries such as China and Australia and increasing investments in smart agriculture solutions. Latin America and the Middle East & Africa are also witnessing gradual adoption, supported by rising awareness and government initiatives aimed at modernizing the agricultural sector. Overall, the global outlook for the drone-assisted vineyard disease heatmap market remains highly positive, with strong growth prospects across all major regions.

Component Analysis

The component segment of the drone-assisted vineyard disease heatmap market is categorized into hardware, software, and services, each playing a vital role in the overall ecosystem. The hardware sub-segment, which includes drones, cameras, sensors, and related equipment, accounts for the largest market share in 2024. The demand for advanced drones equipped with multispectral, hyperspectral, and thermal imaging sensors is surging as vineyard managers seek high

The global arborist software market was valued at USD 350.79 Million in 2022 and is projected to reach USD 881.04 Million by 2030, registering a CAGR of 12.2% for the forecast period 2023-2030. Factors Affecting Arborist Software Market Growth

Growing awareness of tree care coupled with benefits of arborist software

With increased awareness of environmental conservation and the importance of urban green spaces, there's a rising demand for professional tree care services. Growing environmental education coupled with technology adoption in tree management helps to drive the arborist software demand. Arborist software helps urban planners, municipalities, and property owners effectively manage and care for trees in cities and suburbs. Arborist software streamlines various tasks like tree inventory management, maintenance scheduling, and communication with clients. This leads to improved efficiency and productivity for arborists.

The Restraining Factor of Arborist Software:

Data Security, privacy concerns;

Data security and privacy concerns are indeed significant factors that can impact the adoption of arborist software. Arborist software often stores information about clients' properties, contact details, and potentially even financial information. Many arborist software solutions use location data to map and manage trees. This location data could be misused if it falls into the wrong hands.

Market Opportunity:

Rising need to improve tree inventory practices;

The rising need to improve tree inventory practices is driven by several factors, including urbanization, environmental awareness, and advancements in technology. As cities grow and expand, urban planners need accurate tree inventory data to ensure that trees are integrated into urban design. Tree inventory helps prevent conflicts between infrastructure development and tree preservation. Arborists software helps to create and maintain digital inventories of trees, including information about species, location, size, health, and maintenance history. In addition, features like Geographic Information Systems (GIS), remote sensing, and mobile data collection technologies have made it easier to create, update, and manage tree inventories.

The COVID-19 impact on Arborist Software Market

The COVID-19 pandemic had various impacts on industries and markets, including the arborist software market. During lockdowns and restrictions, some tree care activities might have been deprioritized due to the sudden focus on healthcare sector. However, the pandemic accelerated digital transformation across industries. Arborists who were previously reliant on manual processes might have recognized the benefits of adopting software for tasks like inventory management, reporting, and client communication. Introduction of Arborist Software

An arborist is a professional who specializes in the cultivation, management, and study of trees, shrubs, and other woody plants. Arborists are trained in tree care practices, including planting, pruning, disease and pest management, and overall tree health maintenance. Arborist software are tools used to assist arborists in their work. These software solutions can provide various functionalities to help arborists manage and maintain trees effectively. Arborists can use software to create and maintain digital inventories of trees, including information about species, location, size, health, and maintenance history. Some common features of arborist software include tree inventory management, health assessment, risk assessment, mapping and GIS integration etc.

The global 3D cardiac mapping systems market is experiencing robust growth, projected to reach a value of $538.60 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 11.40% from 2025 to 2033. This expansion is fueled by several key factors. Technological advancements in electroanatomical mapping, basket catheter mapping, and real-time positional management are significantly improving the accuracy and efficiency of cardiac procedures, leading to better patient outcomes and reduced procedural times. The increasing prevalence of cardiac arrhythmias and congenital heart defects, coupled with a growing aging population globally, is driving demand for advanced diagnostic and therapeutic tools like 3D cardiac mapping systems. Furthermore, the rising adoption of minimally invasive procedures and the increasing preference for image-guided interventions are contributing to market growth. Hospitals and clinics are the primary end-users, followed by diagnostic centers, reflecting the pivotal role of these systems in sophisticated cardiac care. The market's regional distribution shows significant participation from North America and Europe, driven by advanced healthcare infrastructure and high adoption rates of innovative medical technologies. Asia Pacific, however, is expected to experience substantial growth during the forecast period, owing to increasing healthcare expenditure and rising awareness about advanced cardiac care. Key players such as Olympus Corporation, Medtronic, and GE Healthcare are continuously investing in research and development, enhancing product portfolios, and strategically expanding their global reach, further driving market competitiveness and innovation. The competitive landscape is characterized by a mix of established players and emerging companies vying for market share. Strategic partnerships, mergers and acquisitions, and the introduction of innovative products with enhanced functionalities are defining the market dynamics. Future growth will likely be influenced by factors such as regulatory approvals for new technologies, reimbursement policies, and the increasing integration of 3D cardiac mapping systems with other advanced medical devices. The market is expected to witness further segmentation and specialization, driven by specific clinical applications and patient needs. The focus on data analytics and artificial intelligence integration within these systems is likely to further propel market growth in the coming years, leading to more personalized and effective treatment strategies for various cardiac conditions. Recent developments include: May 2024: Biosense Webster Inc., a cardiac arrhythmia treatment devices provider and part of Johnson & Johnson MedTech, launched the CARTO 3 System Version 8, the latest version of the company’s leading three-dimension (3D) heart mapping system used in cardiac ablation procedures., March 2023: Medtronic received a CE (Conformité Européenne) Mark for the Affer Mapping and Ablation System, including the Sphere-9 Catheter and the Affera Prism-1 Mapping Software.. Key drivers for this market are: Rising Prevalence of Cardiovascular Diseases Coupled with the Rising Geriatric Population, Increasing Number of Technological Advancements. Potential restraints include: Rising Prevalence of Cardiovascular Diseases Coupled with the Rising Geriatric Population, Increasing Number of Technological Advancements. Notable trends are: The Electroanatomical Mapping Segment is Expected to Hold Significant Market Share During the Forecast Period.

This dataset contains model-based county-level estimates for the PLACES 2021 release in GIS-friendly format. PLACES is the expansion of the original 500 Cities Project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code Tabulation Area (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. Project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2019 or 2018 data, Census Bureau 2019 or 2018 county population estimates, and American Community Survey (ACS) 2015–2019 or 2014–2018 estimates. The 2021 release uses 2019 BRFSS data for 22 measures and 2018 BRFSS data for 7 measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours a night). Seven measures are based on the 2018 BRFSS data because the relevant questions are only asked every other year in the BRFSS. These data can be joined with the census 2015 county boundary file in a GIS system to produce maps for 29 measures at the county level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=024cf3f6f59e49fe8c70e0e5410fe3cf

BackgroundClimate change is widely recognised to threaten human health, wellbeing and livelihoods, including through its effects on the emergence, spread and burdens of climate–and water-sensitive infectious diseases. However, the scale and mechanisms of the impacts are uncertain and it is unclear whether existing forecasting capacities will foster successful local-level adaptation planning, particularly in climate vulnerable regions in developing countries. The purpose of this scoping review was to characterise and map priority climate- and water-sensitive diseases, map existing forecasting and surveillance systems in climate and health sectors and scope out the needs and potential to develop integrated climate-driven early warning forecasting systems for long-term adaptation planning and interventions in the south Asia region.MethodsWe searched Web of Science Core Collection, Scopus and PubMed using title, abstract and keywords only for papers focussing on climate-and water-sensitive diseases and explicit mention of either forecasting or surveillance systems in south Asia. We conducted further internet search of relevant national climate adaptation plans and health policies affecting disease management. We identified 187 studies reporting on climate-sensitive diseases and information systems in the south Asia context published between 1992 and 2024.ResultsWe found very few robust, evidenced-based forecasting systems for climate- and water- sensitive infectious diseases, which suggests limited operationalisation of decision-support tools that could inform actions to reduce disease burdens in the region. Many of the information systems platforms identified focussed on climate-sensitive vector-borne disease systems, with limited tools for water-sensitive diseases. This reveals an opportunity to develop tools for these neglected disease groups. Of the 34 operational platforms identified across the focal countries, only 13 (representing 38.2%) are freely available online and all were developed and implemented by the human health sector. Tools are needed for other south Asian countries (Afghanistan, Sri Lanka, Bhutan) where the risks of infectious diseases are predicted to increase substantially due to climate change, drought and shifts in human demography and use of ecosystems.ConclusionAltogether, the findings highlight clear opportunities to invest in the co-development and implementation of contextually relevant climate-driven early warning tools and research priorities for disease control and adaptation planning.

Cotton root rot is a century-old cotton disease that now can be effectively controlled with Topguard Terra fungicide. Because this disease tends to occur in the same general areas within fields in recurring years, site-specific application of the fungicide only to infested areas can be as effective as and considerably more economical than uniform application. The overall objective of this research was to demonstrate how site-specific fungicide application could be implemented based on historical remote sensing imagery and using variable-rate technology. Procedures were developed for creating binary prescription maps from historical airborne and high-resolution satellite imagery. Two different variable-rate liquid control systems were adapted to two existing cotton planters, respectively, for site-specific fungicide application at planting. One system was used for site-specific application on multiple fields in 2015 and 2016 near Edroy, Texas, and the other system was used on multiple fields in both years near San Angelo, Texas. Airborne multispectral imagery taken during the two growing seasons was used to monitor the performance of the site-specific treatments. Results based on prescription maps derived from historical airborne and satellite imagery of two fields in 2015 and one field in 2016 are reported in this article. Two years of field experiments showed that the prescription maps and the variable-rate systems performed well and that site-specific fungicide treatments effectively controlled cotton root rot. Reduction in fungicide use was 41%, 43%, and 63% for the three fields, respectively. The methodologies and results of this research will provide cotton growers, crop consultants, and agricultural dealers with practical guidelines for implementing site-specific fungicide application using historical imagery and variable-rate technology for effective management of cotton root rot. Resources in this dataset:

Resource Title: A ground picture of cotton root rot File Name: IMG_0124.JPG Resource Description: A cotton root rot-infested area in a cotton field near Edroy, TX.

Resource Title: An aerial image of a cotton field File Name: Color-infrared image of a field.jpg Resource Description: Aerial color-infrared (CIR) image of a cotton field infested with cotton root rot.

Resource Title: As-applied fungicide application data File Name: Jim Ermis-Farm 1-Field 11 Fungicide Application.csv Resource Description: As-applied fungicide application rates for variable rate application of Topguard to a cotton field infested with cotton rot

Chronic wasting disease (CWD) is a transmissible spongiform encephalopathy that was first detected in captive cervids in Colorado, United States (US) in 1967, but has since spread into free-ranging white-tailed deer (Odocoileus virginianus) populations across the US and Canada. In some areas, the disease is considered endemic in wild deer populations, and governmental wildlife agencies have employed epidemiological models to understand long-term environmental risk. However, continued rapid spread of CWD into new regions of the continent has underscored the need for extension of these models into broader tools applicable for wide use by wildlife agencies. Additionally, efforts to semi-automate models will facilitate access of technical scientific methods to broader audiences. We introduce software (Habitat Risk) designed to link a previously published epidemiological model with spatially referenced environmental and disease testing data enabling agency personnel to make up-to-date, localized, data-driven predictions regarding the odds of CWD detection in surrounding areas after an outbreak is discovered. Habitat Risk requires pre-processing publicly available environmental datasets and standardization of disease testing (surveillance) data, after which an autonomous computational workflow terminates in a user interface that displays an interactive map of disease risk. We demonstrated the use of the Habitat Risk software with surveillance data of white-tailed deer from Tennessee, US. This data release includes the data inputs necessary to run the 1st script of the Habitat Risk Software which includes a .csv and preprocessed spatial layers described in Mitchell et al. 2021 but provided here for ease of running the example and surveillance disease testing data (included here). Data included in the .csv are not true locations of samples, rather modified data to be used solely for purposes of running through the R-code Mitchell, C., Walter, W. D., Hollingshead, N., & Schuler, K. 2021. Processing of Geospatial Data for the Habitat Risk Software [Software]. Cornell University Library eCommons Repository. https://doi.org/10.7298/2tt1-yy48

This is the Zenodo archive for the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada" (Mucaki EJ, Shirley BC and Rogan PK. F1000Research 2021, 10:1312, DOI: 10.12688/f1000research.75891.1). This study aimed to produce community-level geo-spatial mapping of patterns and clusters of symptoms, and of confirmed COVID-19 cases, in near real-time in order to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. This archive will contain data and image files from this study, which were too numerous to be included in the manuscript for this study. It also provides all program files pertaining to the Geostatistical Epidemiology Toolbox (Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript and other software developed (cluster, outlier, streak identification and pairing)..

We also provide a guide which provides a general description of the contents of the four sections in this archive (Documentation_for_Sections_of_Zenodo_Archive.docx). If you have any intent to utilize the data provided in Section 3, we greatly advise you to review this document as it describes the output of all geostatistical analyses performed in this study in detail.

Data Files:

Section 1. "Section_1.Tables_S1_S7.Figures_S1_S11.zip"

This section contains all additional tables and figures described in the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada". Additional tables S1 to S7 are presented in an Excel document. These 7 tables provide summary statistics of various geostatistical tests described in the study (“Section 1 – Tables S1-S4”) and lists all identified single and paired high-case cluster streaks (“Section 1 – Tables S5-S7”). This section also contains 11 additional figures referred to in the manuscript (“Section 1 – Figures S1-S11”) both individually and within a Word document which describes them.

Section 2. "Section_2.Localized_Hotspot_Lists.zip"

All localized hotspots (identified through kriging analysis) were catalogued for each municipality evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex). These files indicate the FSA in which the hotspot was identified, the date in which it was identified (utilizing 3-day case data at the postal code level), the amount of cases which occurred within the FSA within these 3 dates, the range of cases interpolated by kriging analysis (between 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-50, >50), and whether or not the FSA was deemed a hotspot by Gi* relative to the rest of Ontario on any of the three dates evaluated. Please see Section 4 for map images of these localized hotspots.

Section 3. "Section_3.All-Data_Files.Kriging_GiStar_Local_and_GlobalMorans.2020_2021"

Section 3 – All output files from the geostatistical tests performed in this study are provided in this section. This includes the output from Ontario-wide FSA-level Gi* and Cluster and Outlier analyses, and PC-level Cluster and Outlier, Spatial Autocorrelation, and kriging analysis of 6 municipal regions. It also includes kriging analysis of 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan). This section also provides data files from our analyses of stratified case data (by age, gender, and at-risk condition). All coordinates presented in these data files are given in “PCS_Lambert_Conformal_Conic” format. Case values between 1-5 were masked (appear as “NA”).

Section 4. "Section_4.All_Map_Images_of_Geostat_Analyses.zip"

Sets of image files which map the results of our geostatistical analyses onto a map of Ontario or within the municipalities evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex) are provided. This includes: Kriging analysis (PC-level), Local Moran's I cluster and outlier analysis (FSA and PC-level), normal and space-time Gi* analysis, and all images for all analyses performed on stratified data (by age, gender and at-risk condition). Kriging contour maps are also included for 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan).

Software:

This Zenodo archive also provides all program files pertaining to the Geostatistical Epidemiology Toolbox (Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript. This geostatistical toolbox was developed by CytoGnomix Inc., London ON, Canada and is distributed freely under the terms of the GNU General Public License v3.0. It can be easily modified to accommodate other Canadian provinces and, with some additional effort, other countries.

This distribution of the Geostatistical Epidemiology Toolbox does not include postal code (PC) boundary files (which are required for some of the tools included in the toolbox). The PC boundary shapefiles used to test the toolbox were obtained from DMTI (https://www.dmtispatial.com/canmap/) through the Scholar's Geoportal at the University of Western Ontario (http://geo2.scholarsportal.info/). The distribution of these files (through sharing, sale, donation, transfer, or exchange) is strictly prohibited. However, any equivalent PC boundary shape file should suffice, provided it contains polygon boundaries representing postal code regions (see guide for more details).

Software File 1. "Software.GeostatisticalEpidemiologyToolbox.zip"

The Geostatistical Epidemiology Toolbox is a set of custom Python-based geoprocessing tools which function as any built-in tool in the ArcGIS system. This toolbox implements data preprocessing, geostatistical analysis and post-processing software developed to evaluate the distribution and progression of COVID-19 cases in Canada. The purpose of developing this toolbox is to allow external users without programming knowledge to utilize the software scripts which generated our analyses and was intended to be used to evaluate Canadian datasets. While the toolbox was developed for evaluating the distribution of COVID-19, it could be utilized for other purposes.

The toolbox was developed to evaluate statistically significant distributions of COVID-19 case data at Canadian Forward Sortation Area (FSA) and Postal Code-level in the province of Ontario utilizing geostatistical tools available through the ArcGIS system. These tools include: 1) Standard Gi* analysis (finds areas where cases are significantly spatially clustered), 2) spacetime based Gi* analysis (finds areas where cases are both spatially and temporally clustered), 3) cluster and outlier analysis (determines if high case regions are an regional outlier or part of a case cluster), 4) spatial autocorrelation (determines the cases in a region are clustered overall) and, 5) Empirical Bayesian Kriging analysis (creates contour maps which define the interpolation of COVID-19 cases in measured and unmeasured areas). Post-processing tools are included that import these all of the preceding results into the ArcGIS system and automatically generate PNG images.

This archive also includes a guide ("UserManual_GeostatisticalEpidemiologyToolbox_CytoGnomix.pdf") which describes in detail how to set up the toolbox, how to format input case data, and how to use each tool (describing both the relevant input parameters and the structure of the resultant output files).

Software File 2: “Software.Additional_Programs_for_Cluster_Outlier_Streak_Idendification_and_Pairing.zip"

In the manuscript associated with this archive, Perl scripts were utilized to evaluate postal code-level Cluster and Outlier analysis to identify significantly, highly clustered postal codes over consecutive periods (i.e., high-case cluster “streaks”). The identified streaks are then paired to those in close proximity, based on the neighbors of each postal code from PC centroid data ("paired streaks"). Multinomial logistic regression models were then derived in the R programming language to measure the correlation between the number of cases reported in each paired streak, the interval of time separating each streak, and the physical distance between the two postal codes. Here, we provide the 3 Perl scripts and the R markdown file which perform these tasks:

“Ontario_City_Closest_Postal_Code_Identification.pl”

Using an input file with postal code coordinates (by centroid), this program identifies the nearest neighbors to all postal codes for a given municipal region (the name of this region is entered on the command line). Postal code centroids were calculated in ArcGIS using the “Calculate Geometry” function against DMTI postal code boundary files (not provided). Input from other sources could be used, however, as long as the input includes a list of coordinates with a unique label associated with a particular municipality.

The output of this program (for the same municipal region being evaluated) is required for the following two Perl

This accelerator contains information of ICD 9 and ICD 10 codes as these both codes are different in the fundamental structure and concepts. This accelerator help to understand the differences between them for a smooth transition from ICD-9 to ICD-10.

This dataset contains model-based census tract level estimates for the PLACES 2022 release in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2020 or 2019 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 estimates. The 2022 release uses 2020 BRFSS data for 25 measures and 2019 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening) that the survey collects data on every other year. These data can be joined with the census tract 2015 boundary file in a GIS system to produce maps for 29 measures at the census tract level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

Colon Map 3-D Reconstruction Software Market Outlook

According to our latest research, the global Colon Map 3-D Reconstruction Software market size reached USD 412.8 million in 2024 and is projected to grow at a CAGR of 12.3% from 2025 to 2033, reaching a forecasted value of USD 1,169.2 million by 2033. The significant growth in the market is primarily driven by the rising adoption of advanced imaging technologies in healthcare, increased prevalence of colorectal diseases, and growing investments in healthcare IT infrastructure. The need for accurate diagnosis, improved surgical planning, and enhanced research capabilities are fueling the demand for colon map 3-D reconstruction software across the globe.

One of the principal growth factors for the Colon Map 3-D Reconstruction Software market is the increasing incidence of colorectal cancer and other gastrointestinal disorders worldwide. Early and precise diagnosis is critical in improving patient outcomes, and 3-D reconstruction software provides clinicians with detailed visualization of the colon, enabling better assessment and intervention strategies. The rising awareness among healthcare professionals regarding the benefits of 3-D reconstruction, such as higher accuracy and reduced procedure times, is further propelling the adoption of these solutions. Additionally, advancements in imaging modalities, such as CT and MRI, have significantly enhanced the quality and utility of 3-D reconstructions, making them indispensable tools in modern medical practice.

Another major driver for market expansion is the growing integration of artificial intelligence and machine learning algorithms within colon map 3-D reconstruction software. These technologies facilitate automated segmentation, improved image quality, and faster processing times, thereby increasing the efficiency and effectiveness of diagnostic and surgical planning workflows. The healthcare industry’s ongoing shift toward digitization and the implementation of electronic health records have also created a conducive environment for the adoption of sophisticated imaging software. Furthermore, collaborations between software vendors, medical device manufacturers, and healthcare providers are fostering innovation and accelerating the introduction of new features and functionalities in colon mapping solutions.

The market is also benefiting from the increasing focus on minimally invasive procedures and personalized medicine. 3-D reconstruction software plays a crucial role in preoperative planning, allowing surgeons to simulate and optimize interventions tailored to individual patient anatomy. This not only enhances surgical precision but also minimizes risks and improves recovery outcomes. The expanding application scope of colon map 3-D reconstruction software in research, particularly in the study of gastrointestinal diseases and the development of new therapies, is opening new avenues for market growth. As healthcare systems worldwide prioritize patient safety and cost-effectiveness, the demand for advanced imaging and planning tools is expected to remain robust.

Regionally, North America currently dominates the Colon Map 3-D Reconstruction Software market due to its well-established healthcare infrastructure, high adoption rates of advanced medical technologies, and strong presence of leading market players. However, the Asia Pacific region is anticipated to witness the fastest growth during the forecast period, driven by rising healthcare investments, increasing awareness about colorectal diseases, and government initiatives to modernize healthcare delivery. Europe also holds a significant market share, supported by robust research activities and favorable reimbursement policies. The Middle East & Africa and Latin America, while smaller in market size, are emerging as promising regions due to improving healthcare access and growing demand for diagnostic imaging solutions.

Component Analysis

The Component segment of the Colon Map 3-D Reconstruction Softw

Supplementary File 1 (SuppFile1.docx) Supporting Tables S1-S12 and Figures S1-S3

Supplementary File 2 (SuppFile2.xlsx) Supporting Excel Sheets DLG1-DLG19: Nine ordering results from the software JoinMap, OneMap, and Lep-MAP with the sum of adjacent recombination fractions (SARF) under the end line, presented in each excel sheet corresponding to the linkage group of the female parent Populus deltoides in order.

Supplementary File 3 (SuppFile3.xlsx) Supporting Excel Sheets SLG1-SLG19: Nine ordering results from the software JoinMap, OneMap, and Lep-MAP with the sum of adjacent recombination fractions (SARF) under the end line, presented in each excel sheet corresponding to the linkage group of the male parent Populus simonii in order.

Supplementary File 4 (SuppFile4.xlsx) Supporting Excel Sheets DLG1-DLG19: Detailed information about the genetic map of the female parent Populus deltoides. Each sheet, corresponding to a linkage group in order, presents SNP interval distances and cumulative distances, SNP linkage phases, and SNP flanking sequences and their mapped positions on the reference genome of Populus trichocarpa.

Supplementary File 5 (SuppFile5.xlsx) Supporting Excel Sheets SLG1-SLG19: Detailed information about the genetic map of the male parent Populus simonii. Each sheet, corresponding to a linkage group in order, presents SNP interval distances and cumulative distances, SNP linkage phases, and SNP flanking sequences and their mapped positions on the reference genome of Populus trichocarpa.

Supplementary File 6 (SuppFile6.txt) Annotations from the blast hits, GO terms, Enzyme codes, and KEGG maps for the candidate genes of the identified QTLs for the tree height.

Supplementary File 7 (SuppFile7.txt) Annotations from the blast hits, GO terms, Enzyme codes, and KEGG maps for the candidate genes of the identified QTLs for the diameter at breast height.

Supplementary File 8 (SuppFile8.zip) The chromosome-level sequences of Populus simonii in FASTA format assembled with 689 contigs.

Supplementary File 9 (SuppFile9.pdf) The 19 chromosomes anchored with the draft genome assembly of Populus simonii and the two parental linkage maps. Each page in order presents the connections between the physical positions on the assembled chromosome and the linkage map positions on the left panel and the scatter plots of the physical position against the genetic position on the two linkage maps on the right panel. Boxes of alternating shades represent the contigs within the assembled chromosome. The r-value on each plot is the Pearson correlation coefficient between the SNP positions on the physical and linkage maps.

The global brain visual analysis software market is experiencing robust growth, driven by the increasing adoption of advanced neuroimaging techniques and the rising prevalence of neurological disorders requiring precise diagnosis and treatment. The market, estimated at $2 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $6 billion by 2033. This growth is fueled by several key factors. The increasing availability of high-resolution neuroimaging data, coupled with advancements in artificial intelligence (AI) and machine learning (ML) algorithms, is enhancing the accuracy and speed of brain visual analysis. Furthermore, the growing demand for personalized medicine and the need for objective biomarkers in clinical trials are driving the adoption of these sophisticated software solutions. The cloud-based segment holds significant market share, owing to its scalability, cost-effectiveness, and accessibility. Major applications include medical diagnosis (e.g., Alzheimer's disease, epilepsy), scientific research (e.g., understanding brain function, cognitive neuroscience), and other specialized applications like sports performance analysis. North America currently dominates the market, followed by Europe and Asia Pacific, with emerging economies in Asia exhibiting significant growth potential. However, challenges such as high software costs, data security concerns, and the need for skilled professionals to interpret the analysis results are hindering broader market penetration. The competitive landscape is characterized by a mix of established players and emerging technology companies. Key players like Bionic, Physio-Tech, Cleveland FES, Brain Products, Symbiotic Devices, Brain Vision, and Mathworks are continuously innovating to enhance their product offerings and expand their market reach. The strategic collaborations between software developers and healthcare providers are further accelerating market growth. The future of brain visual analysis software hinges on integrating advanced analytics capabilities, developing user-friendly interfaces, and addressing data privacy and security concerns. The focus on developing AI-powered solutions that can automatically detect and classify neurological abnormalities will significantly impact the future trajectory of the market, making it even more critical for healthcare and research.