Global Animal Disease Mapping Market is segmented by Application (Livestock_Poultry_Swine_Aquaculture_Veterinary Health), Type (GIS-Based Mapping_Epidemiological Software_Mobile Data Platforms_Satellite Monitoring_Cloud Analytics), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

Peridomestic exposure to Borrelia burgdorferi-infected Ixodes scapularis nymphs is considered the dominant means of infection with black-legged tick-borne pathogens in the eastern United States. Population level studies have detected a positive association between the density of infected nymphs and Lyme disease incidence. At a finer spatial scale within endemic communities, studies have focused on individual level risk behaviors, without accounting for differences in peridomestic nymphal density. This study simultaneously assessed the influence of peridomestic tick exposure risk and human behavior risk factors for Lyme disease infection on Block Island, Rhode Island. Tick exposure risk on Block Island properties was estimated using remotely sensed landscape metrics that strongly correlated with tick density at the individual property level. Behavioral risk factors and Lyme disease serology were assessed using a longitudinal serosurvey study. Significant factors associated with Lyme disease positive serology included one or more self-reported previous Lyme disease episodes, wearing protective clothing during outdoor activities, the average number of hours spent daily in tick habitat, the subject’s age and the density of shrub edges on the subject’s property. The best fit multivariate model included previous Lyme diagnoses and age. The strength of this association with previous Lyme disease suggests that the same sector of the population tends to be repeatedly infected. The second best multivariate model included a combination of environmental and behavioral factors, namely hours spent in vegetation, subject’s age, shrub edge density (increase risk) and wearing protective clothing (decrease risk). Our findings highlight the importance of concurrent evaluation of both environmental and behavioral factors to design interventions to reduce the risk of tick-borne infections.

Infectious disease experts have predicted a pandemic, saying it was not a question of if but when. Drawing on experiences with severe acute respiratory syndrome (SARS), avian influenza (H5N1), and novel influenza A (H1N1), the World Health Organization (WHO) and other health authorities, such as the Centers for Disease Control and Prevention (CDC), urged nations and local governments to prepare pandemic response plans. Many ministries of health and subnational departments of health around the world have activated those plans in response to coronavirus and are sharing data as required by the updated International Health Regulations.Esri's work with health organizations and government leaders has proven location intelligence from geographic information system (GIS) technology and data to be critical for the following:Assessing risk and evaluating threatsMonitoring and tracking outbreaksMaintaining situational awarenessEnsuring resource allocationNotifying agencies and communitiesThe current coronavirus disease pandemic presents an opportunity to build on the experience and readiness of Esri's existing global user community in health and human services. Through real-time maps, apps, and dashboards, GIS will also facilitate a seamless flow of relevant data as a component of the response from local to global levels. A compelling case exists for building on top of the public health GIS foundation that is already in place both in the United States and around the world.After reading this paper, leadership and senior staff should understand the following:The necessity to apply location intelligence to public health processes in coronavirus responseHow GIS can support immediate and long-term actionWhat resources Esri provides its customers

BackgroundThe WHO has established the disability-adjusted life year (DALY) as a metric for measuring the burden of human disease and injury globally. However, most DALY estimates have been calculated as national totals. We mapped spatial variation in the burden of human African trypanosomiasis (HAT) in Uganda for the years 2000–2009. This represents the first geographically delimited estimation of HAT disease burden at the sub-country scale.Methodology/Principal FindingsDisability-adjusted life-year (DALY) totals for HAT were estimated based on modelled age and mortality distributions, mapped using Geographic Information Systems (GIS) software, and summarised by parish and district. While the national total burden of HAT is low relative to other conditions, high-impact districts in Uganda had DALY rates comparable to the national burden rates for major infectious diseases. The calculated average national DALY rate for 2000–2009 was 486.3 DALYs/100 000 persons/year, whereas three districts afflicted by rhodesiense HAT in southeastern Uganda had burden rates above 5000 DALYs/100 000 persons/year, comparable to national GBD 2004 average burden rates for malaria and HIV/AIDS.Conclusions/SignificanceThese results provide updated and improved estimates of HAT burden across Uganda, taking into account sensitivity to under-reporting. Our results highlight the critical importance of spatial scale in disease burden analyses. National aggregations of disease burden have resulted in an implied bias against highly focal diseases for which geographically targeted interventions may be feasible and cost-effective. This has significant implications for the use of DALY estimates to prioritize disease interventions and inform cost-benefit analyses.

This compilation of layers provides a comprehensive view of CWD-related activities, facilitating informed decision-making and strategic planning for wildlife management and disease prevention efforts. To date, there has not yet been any cases of CWD detected in California and surveillance data will be updated regularly. This data is associated with the following layers: Chronic Wasting Disease Sampling Stations - 2025 - CDFW ds3154, Participating Meat Processors and Taxidermists - 2025 - CDFW ds3155, Chronic Wasting Disease Sampling Stations - 2023-2024 - CDFW ds3182, Participating Meat Processors and Taxidermists - 2023-2024 - CDFW ds3188, Approved Deer Carcass Disposal Sites - CDFW ds3156. These layers include CWD Testing Sites, Participating Meat Processors and Taxidermists (MPT), Carcass Disposal Sites, and comprehensive data on CWD surveillance spanning from 1999 to 2025. This compilation of layers provides a comprehensive view of CWD-related activities, facilitating informed decision-making and strategic planning for wildlife management and disease prevention efforts. For the latest information, please visit https://wildlife.ca.gov/CWD.

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 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

Percent positive (or average) responses over the total responses for each question for behaviors and age reported by B. burgdorferi seropositive and seronegative participants in serological surveys between 2005 and 2011.Use of any protective measure = use of either protective clothing, tick checking, repellent or avoiding brush.

Geographical tracking and mapping of COVID-19 (International Journal of Health Geographics).In 2019, a new virus causing severe acute respiratory syndrome emerged in Wuhan, Hubei Province, China. This paper offers pointers to, and describes, a range of practical online/mobile GIS and mapping dashboards and applications for tracking the coronavirus epidemic and associated events as they unfold around the world.Citation:Kamel Boulos, M.N., Geraghty, E.M. Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: how 21st century GIS technologies are supporting the global fight against outbreaks and epidemics. Int J Health Geogr 19, 8 (2020). https://doi.org/10.1186/s12942-020-00202-8_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...

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...

FHAAST provides support for both tactical and strategic forest health risk assessments. In addition, this program coordinates, in collaboration with the USDA Forest Service Forest Health Monitoring program (FHM), the development of a National Insect and Disease Risk Map (NIDRM) and database.FHAAST has completed the 2013 - 2027 National Insect and Disease Risk Map (2012 NIDRM); a nationwide strategic assessment and database of the potential hazard for tree mortality due to major forest insects and diseases. The goal of NIDRM is to summarize landscape-level patterns of potential insect and disease activity, consistent with the philosophy that science-based, transparent methods should be used to allocate pest-management resources across geographic regions and individual pest distributions. In other words: prioritize investment for areas where both hazard is significant and effective treatment can be efficiently implemented.NIDRM data can be used to:Identify the potential impacts of pests and pathogens to forest ecosystems throughout the US for the 2013 - 2027 timeframe.Generate forest pest and pathogen risk maps at a scale useful for resource planning and management purposes in many of our National Forests, National Parks, and other local units.Develop an effective strategic planning tool that can inform assessments of natural ecosystems and ensure resources for forest pest prevention, suppression, and restoration reaches the highest priority areas.Detect areas where hazardous fuels treatments coincide with lands at risk for forest pest activity, much of which is density driven. Efficiencies will be gained by prioritizing coincident areas.For a quick overview of the 2013 - 2027 assessment and to learn more information on the differences between the 2006 and 2012 NIDRMs download the executive summary (2 MB PDF).Explore forests vulnerable to attack from major insects and diseases by viewing the Interactive Story Map of the National Insect and Disease Risk MapThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

This dataset contains model-based county-level 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. Project 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 2022 county 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 census 2022 county boundary file in a GIS system to produce maps for 40 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

Table contains count and age-adjusted rate of heart disease deaths among county residents. Data are summarized at county, city, zip code and census tract level. Data are masked when the number of events is 1 to 10. Data are presented for zip codes (ZCTAs) fully within the county. Source: Santa Clara County Public Health Department, Vital Records Business Intelligence System, 2011-2020. Data as of 7/1/2021METADATA:notes (String): Lists table title, notes, sourcesgeolevel (String): Level of geographygeoname (String): Geography IDyear (String): Year of deathcount (Numeric): Number of heart disease deathsAArate (Numeric): Age-adjusted rate of heart disease deaths

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

Overview maps (PDF format) showing the results of our aerial surveys are available for the region (Arizona & New Mexico) and for specific areas. Note that as of 2012 we have stopped producing many of the map products shown below as we switched to a national system. For access to data and the ability to generate maps, please visit the Insect & Disease Survey Explorer hosted by our national office through the Forest Health portal. Resources in this dataset:Resource Title: Insect & Disease Survey Maps and Data. File Name: Web Page, url: https://www.fs.usda.gov/wps/portal/fsinternet/cs/detail/!ut/p/z1/04_Sj9CPykssy0xPLMnMz0vMAfIjo8zijQwgwNHCwN_DI8zPyBcqYKAfjlVBmA9cQRQx-g1wAEci9eNREIXf-HD9KKxWIPuAkBkFuaGhEQaZjgCVqf1Y/dz/d5/L2dBISEvZ0FBIS9nQSEh/?position=Not Yet Determined.Html&pname=Region 3- Insects &navtype=BROWSEBYSUBJECT&ss=1103&pnavid=140000000000000&navid=140110000000000&ttype=detail&cid=stelprdb5228467 Regional Maps and GIS data (AZ and NM)

Coral Disease Risk Mapping via Satellite Market Outlook

As per our latest research, the global coral disease risk mapping via satellite market size reached USD 312 million in 2024, with a robust compound annual growth rate (CAGR) of 15.6% observed over the past year. This rapid expansion is primarily driven by the escalating need for advanced monitoring technologies in marine conservation. Looking ahead, the market is projected to reach USD 1,045 million by 2033, underlining the growing importance of satellite-enabled solutions in addressing coral reef health and disease management on a global scale.

The primary growth driver for the coral disease risk mapping via satellite market is the increasing prevalence of coral diseases, which threaten the biodiversity and ecological integrity of marine environments worldwide. Coral reefs are under immense stress due to climate change, ocean acidification, pollution, and overfishing, all of which contribute to the proliferation of coral diseases. As a result, there is a heightened demand for accurate, real-time monitoring and predictive tools that leverage satellite-based technologies. These tools enable stakeholders to identify disease outbreaks promptly, assess their spatial extent, and implement timely intervention strategies, thereby mitigating ecological and economic losses. The integration of remote sensing, GIS mapping, and advanced data analytics has revolutionized coral disease surveillance, making it possible to monitor vast and often inaccessible reef systems with unprecedented precision and frequency.

Another significant factor fueling market growth is the increasing investment from governmental and non-governmental organizations in marine conservation initiatives. International bodies such as the United Nations, along with regional environmental agencies, are allocating substantial resources to protect and restore coral reefs. These investments are channeled into the development and deployment of cutting-edge satellite technologies, which form the backbone of modern coral disease risk mapping solutions. The collaboration between technology providers, research institutions, and conservation groups has led to the creation of comprehensive monitoring frameworks that combine satellite imagery, machine learning algorithms, and geospatial analytics. This multidisciplinary approach not only enhances the accuracy of disease risk assessments but also supports long-term conservation planning and policy formulation.

Technological advancements in satellite imaging and data analytics represent a crucial growth catalyst for the coral disease risk mapping via satellite market. Innovations in optical and radar satellite systems, coupled with the application of artificial intelligence and machine learning, have dramatically improved the resolution and interpretability of coral reef data. These technologies enable the detection of subtle changes in reef health, facilitating early warning systems for disease outbreaks. Furthermore, the integration of multispectral imaging and big data analytics allows for the synthesis of vast datasets, providing actionable insights for stakeholders across the research, government, and aquaculture sectors. The continuous evolution of satellite technology ensures that the market remains dynamic, with new capabilities emerging to address the complex challenges facing coral ecosystems.

From a regional perspective, the Asia Pacific region dominates the coral disease risk mapping via satellite market, accounting for over 38% of the global market share in 2024. This leadership is attributed to the region’s extensive coral reef systems, particularly in countries such as Australia, Indonesia, and the Philippines, which are highly susceptible to disease outbreaks. North America and Europe follow closely, driven by strong research infrastructure and proactive environmental policies. The Middle East & Africa and Latin America are also witnessing increased adoption of satellite-based coral monitoring solutions, supported by growing awareness and international collaboration in marine conservation efforts. Each region presents unique opportunities and challenges, shaped by the local ecological context, regulatory landscape, and technological readiness.

Univariate logistic regression models of the association between human behaviors and landscape metrics and positive Lyme disease serology. Statistically significant results at p

Negative binomial regression univariate models of the association between lawn and shrub landscape metrics and the density of host-seeking Ixodes scapularis nymphs (statistically significant results at p

Coral Disease Risk Mapping via Satellite Market Outlook

According to our latest research, the global market size for Coral Disease Risk Mapping via Satellite reached USD 412.8 million in 2024, reflecting a robust expansion driven by technological advancements and increasing environmental concerns. The market is experiencing a healthy growth momentum, registering a CAGR of 12.7% during the forecast period. By 2033, the market is projected to attain a value of USD 1,203.5 million, primarily due to the growing adoption of satellite-based monitoring solutions and the urgent need for proactive coral reef conservation strategies worldwide. As per our latest research, the integration of advanced remote sensing technologies and data analytics is a major factor fueling this market’s growth trajectory.

The primary growth driver for the Coral Disease Risk Mapping via Satellite market is the escalating threat to coral reef ecosystems from climate change, pollution, and overfishing. Coral reefs are among the most biodiverse and valuable ecosystems on earth, yet they face unprecedented rates of disease outbreaks and degradation. The need for timely, accurate, and large-scale monitoring of coral health has never been greater. Satellite-based risk mapping offers a scalable and cost-effective solution, enabling real-time surveillance over vast and inaccessible marine areas. This capability is particularly crucial for early detection of disease outbreaks, which can significantly improve the effectiveness of intervention and restoration efforts, thus safeguarding biodiversity and supporting the livelihoods of millions dependent on healthy reefs.

Another significant factor contributing to market growth is the continuous evolution of satellite imaging technologies and the integration of artificial intelligence (AI) and machine learning (ML) algorithms. Satellite sensors now provide high-resolution, multi-spectral, and even hyperspectral data, which, when combined with sophisticated data analytics, can identify subtle changes in coral health and detect disease signatures with high precision. The synergistic use of GIS (Geographic Information Systems) and remote sensing has further enhanced the ability to map, model, and predict disease spread patterns. These technological advancements are not only increasing the accuracy and reliability of disease risk assessments but are also reducing the operational costs associated with traditional, labor-intensive field surveys.

Furthermore, the growing emphasis on sustainable marine resource management and international conservation commitments is propelling demand for coral disease risk mapping solutions. Governments, intergovernmental organizations, NGOs, and private stakeholders are increasingly investing in advanced monitoring infrastructure to meet regulatory requirements and global sustainability goals. The proliferation of public-private partnerships and funding for marine research initiatives has accelerated the deployment of satellite-based monitoring platforms. Additionally, heightened public awareness and advocacy for coral conservation are driving policy changes and fostering an environment conducive to the adoption of these technologies, thus contributing to the market’s robust expansion.

From a regional perspective, the Asia Pacific region is emerging as a dominant force in the Coral Disease Risk Mapping via Satellite market, accounting for the largest share in 2024. This dominance is attributed to the extensive coral reef systems in countries like Australia, Indonesia, and the Philippines, which are highly vulnerable to environmental threats. North America and Europe are also witnessing significant growth, supported by strong research infrastructure and active government involvement in marine conservation. Meanwhile, regions such as Latin America and the Middle East & Africa are gradually increasing their adoption of satellite-based monitoring, driven by international collaborations and capacity-building initiatives aimed at protecting their unique marine ecosystems.

Technology Analysis

The technology segment of the Coral Disease Risk Mapping via Satellite market encompasses remote sensing, GIS, machine learning, and data analytics—each playing a pivotal role in enhancing the accuracy and efficiency of disease risk assessment. Remote sensing forms the backbone of this segment, leveraging advanced satellite sensors capable of capturing high-resolution imagery across multiple spectral bands. This technology enable

Best fit (lowest QIC score) multivariate logistic regression model of the association between human behaviors and landscape metrics and positive Lyme disease serology.