CerMapp: A Cloud-Based Geospatial Prototype for National Wildlife Disease Surveillance

Abstract

CerMapp is a multi-platform and system application designed to address a critical gap in veterinary public health: the lack of a standardized, national-scale geodatabase for wildlife diseases. This gap has long hindered the effective application of GIS and remote sensing in spatial epidemiology. Currently deployed at the prototype level in Aosta Valley, NW Italy, the application’s core innovation is its ability to generate a structured, analysis-ready data repository, which serves as a foundational resource for One Health initiatives. Developed by the National Reference Center for Wildlife Diseases on the ESRI ArcGIS Survey123 platform v.3.24, CerMapp enables veterinarians, foresters, and wildlife professionals to easily collect and georeference field data, including species, health status, and photographic evidence using flexible methods such as Global Navigation Satellite System or manual map entry. Data collected via CerMapp are stored in a centralized geodatabase, facilitating risk analyses and detailed geospatial studies. This data can be integrated with remote sensing information processed on cloud platforms like Google Earth Engine or within traditional GIS software, contributing to a comprehensive and novel wildlife health registry. By promoting the rational and standardized collection of essential geospatial data, CerMapp data may support predictive disease modeling, risk assessment, and habitat suitability mapping for wildlife diseases, zoonoses, and vector-borne pathogens. Its scalable, user-friendly design ensures alignment with existing national systems like the Italian Animal Disease Information System (SIMAN), making advanced geospatial analysis accessible without requiring specialized digital skills from field operators or complex IT maintenance from institutions.

1. Introduction

Geographic Information System (GIS) data plays a crucial role in wildlife disease surveillance by linking health information with spatial and environmental context. However, the adoption of GIS and remote sensing technologies in veterinary and animal sciences still remains underutilized [1]. One of the major barriers in collecting more GIS data is due to the inadequate awareness and knowledge in geomatics [2,3,4], as well as effective legislation and measures designed to promote a thorough and well-coordinated digitalization of the public sector [4,5]. Without accurate location data, it becomes difficult to map animal movements, track disease outbreaks, and assess habitat suitability [6,7]. The integration of such types of data advances One Health services [8,9,10,11,12], particularly for predictive disease modeling, risk analysis and suitability habitat map [13,14,15].

In fact, by analyzing spatial patterns of disease outbreaks, environmental factors, and animal movements, researchers can develop sophisticated models to predict the spread of diseases, including wildlife diseases, zoonoses, and vector-borne diseases [16]. For example, GIS allows for the identification and mapping of areas at high risk of disease outbreaks, enabling proactive interventions and resource allocation [17,18,19]. Moreover, remote sensing data, such as satellite imagery and LiDAR, can be used to assess habitat quality, identify critical wildlife corridors, and evaluate the impact of human activities on animal populations [20,21,22,23]. This information is crucial for effective wildlife management and conservation planning [24,25].

Certainly, to fully realize the potential of GIS data and remote sensing in veterinary and animal sciences, it is essential to invest in building robust, georeferenced animal databases [26,27,28]. This can be achieved through collaborative efforts between researchers, wildlife managers, and veterinary professionals [26]. In addition, it is strategic to promote interdisciplinary research that integrates expertise in veterinary science, ecology, remote sensing, and GIS. At the same time, the development and implementation of training programs are fundamental to enhance the geomatics skills of veterinary and animal science professionals [27,29,30,31].

Following these challenges and embracing these innovative technologies, researchers can gain a deeper understanding of animal health, improve disease surveillance and control efforts, and ultimately contribute to a healthier planet for both humans and animals [32,33,34,35].

The One Health approach is a crucial framework for understanding and addressing the interconnection between human, animal, and environmental health [26,36,37]. One Health emphasizes the need for collaboration among diverse disciplines, including veterinary medicine, human medicine, ecology, environmental science, and social sciences [13,38,39,40,41]. It recognizes that the health of humans, animals, and the environment are inextricably linked, and that addressing health challenges requires a shared responsibility across sectors. One Health prioritizes prevention and early detection of health threats, rather than solely focusing on reactive responses to outbreaks. It aims to find sustainable solutions that protect both human and animal health while minimizing environmental impact [42].

Many infectious diseases that affect humans originate in animals (zoonotic diseases) [43,44,45]. Examples include COVID-19, influenza, Ebola, and rabies [44]. The One Health approach is critical for monitoring animal populations for disease outbreaks and can help identify potential threats to human health early on [46]. Understanding the animal–human–environment interface allows for the development of targeted interventions to prevent disease transmission [47,48]. There is still a lack of such types of studies in Europe [49,50,51,52] and Italy [53]. Nevertheless Earth Observation data and GIS applications are moving forward in Aosta Valley, NW Italy [54,55,56,57,58].

The One Health approach integrates environmental considerations into health decision-making. In an increasingly interconnected world, health threats can spread rapidly [59,60,61,62]. Nevertheless, nowadays, the convergence of veterinary medicine [63], wildlife ecology, and geospatial technologies, collectively termed spatial epidemiology, is transforming our capacity to monitor, understand, and manage animal health. The foundation of this transformation lies in robust, geographically referenced data. However, a significant bottleneck has historically impeded progress: the lack of standardized, accessible, and spatially explicit data collection systems for animal populations, particularly in wildlife and extensive livestock farming. Geographic Information Systems (GISs) and remote sensing (RS) offer powerful analytical frameworks for modeling disease spread, assessing habitat suitability, and identifying environmental drivers of pathogen transmission [64,65,66]. Yet, their potential remains underutilized when divorced from high-quality, field-validated, ground-truth data. Traditional methods of data collection, relying on paper forms, disparate digital files, and delayed centralization, are prone to transcription errors, loss of information, and, most critically, a frequent absence of precise geographic coordinates [60,67,68,69,70]. This “spatial data gap” fundamentally limits the application of advanced analytical techniques, from simple risk mapping to complex machine learning models predicting outbreak trajectories [71,72,73]. In this context, the development of dedicated geomatic applications for mobile devices represents a paradigm shift. These tools are designed to operate the theoretical potential of GIS and RS by embedding geomatics principles directly into the field workflow of veterinarians, wildlife biologists, and conservation officers [74]. By leveraging the ubiquitous Global Navigation Satellite System (GNSS) capabilities of smartphones and tablets, these applications enable the real-time capture of accurate geographic coordinates alongside clinical, ecological, and observational data [75].

The core advantages of such applications are multifold:

• Standardization: They enforce data integrity through structured digital forms with mandatory fields, dropdown menus, and data validation rules, ensuring consistency across different users and over time.
• Immediate Georeferencing: Automatic capture of GPS coordinates eliminates manual entry errors and provides the essential spatial component for all subsequent analyses.
• Operational Efficiency: Offline functionality allows for reliable use in remote areas with limited connectivity, with data syncing to central cloud-based geodatabases once a connection is restored. The integration of multimedia, such as photographs, further enriches the dataset.
• Interoperability: Data collected in standardized formats can be seamlessly integrated into cloud computing platforms (e.g., Google Earth Engine) and desktop GIS software, creating a streamlined pipeline from field observation to advanced spatial analysis and decision-support.

The development of these applications is therefore not merely a technological upgrade but a fundamental enabler for a more proactive, predictive, and integrated approach to animal health [76,77,78]. By bridging the critical data gap between field observation and geospatial analysis, mobile geomatic apps are poised to become indispensable tools for advancing One Health surveillance, managing wildlife diseases, protecting biodiversity, and safeguarding livestock production systems in an era of global change [79,80,81,82]. To directly address this critical data gap, we developed CerMapp, an application tested in Aosta Valley Region (NW Italy). Its primary objective is to systematically create a centralized, geospatial database for wildlife health data, a resource that is currently missing. The application provides a standardized tool for field operators to collect and georeference data, employing flexible methods (from automatic GNSS to manual map-pointing) to ensure usability across diverse field conditions. The fundamental output, however, is the structured geodatabase itself, which is designed to be the foundational layer for rigorous spatial analysis and One Health surveillance, filling the void that has previously limited the use of GIS and remote sensing in this sector. This digital platform systematically collects and integrates field data on wildlife populations, health status, and mortality events. By enabling cross-referencing of wildlife data with public health databases and environmental parameters, CerMapp creates an operational framework for early detection of zoonotic threats, surveillance of disease patterns at the human–animal–environment interface, and identification of emerging health risks related to climate change and biodiversity loss. The system provides a practical tool for implementing the One Health approach through data-driven surveillance and coordinated interventions across sectors.

The aim of the present work is to develop an institutional scalable cross-platform application filling the gap in the wildlife health, with potentially open-source data for R&D and One Health management easily integrated with any other territorial, environmental and healthcare data [83,84,85,86,87,88]. This tool could be useful to anticipate the wishes of international entities such as the European Space Agency (ESA), Food and Agriculture Organization (FAO), World Health Organization (WHO), and the World Organization of Animal Health (WOAH) with the purpose of encouraging technological transfer in key sectors using Geomatics to reach a real One Health approach, as expressed in the last EO4Health User Forum https://eo4health.esa.int/ (last accessed on 27 October 2025). It could also represent a new technological support longing for One Health services [44,82,85,89,90,91,92,93,94,95].

2. Materials and Methods

2.1. Building CerMapp: A Tool for Wildlife Health Data Collection

CerMapp, is an application developed by CeRMAS (Italian Reference Center for Wildlife Diseases) using ArcGIS Survey123 platform (ESRI Inc., Redlands, CA, USA) [96]. It has been conceived not merely as a data collection tool, but as a system for building a standardized, national-scale geodatabase for wildlife health surveillance. While it enables flexible and pragmatic geolocation in the field, its core purpose is to generate a consistent, analysis-ready spatial dataset, thereby creating the essential missing infrastructure for wildlife disease monitoring and research. Based on a form-centric approach, it enables the creation and deployment of georeferenced surveys with offline functionality, making it particularly well-suited for remote environments with limited internet connectivity. It allows data collection based on smart, georeferenced forms, cross-platform and offline-ready. In this context, a smart form is a digital questionnaire that is dynamic, intelligent, and context-aware, far surpassing the capabilities of a simple paper form or a static digital survey. It is freely available at prototypal level in Aosta Valley (NW Italy) and is intended for use by veterinarians and the Forest Corp in this region in Italy (see Figure 1).

The app operates seamlessly on smartphones, tablets, and desktops across various operating systems and devices because of is based on ESRI technologies (see Figure 2). On mobile devices like smartphones and tablets, users simply need to download the ArcGIS Survey123 app and scan the QR code or input the CerMapp link https://arcg.is/1TDfrL0 (last accessed on 27 October 2025). Once accessed, the app undergoes dynamic upgrades and patches, with any bugs being fixed quickly through official updates. In fact, in case of errors by users’ messages are sent to ESRI and developers so that it can be fixed.

2.1.1. CerMapp Architecture

CerMapp is composed of three primary components:

• Survey123 Web Designer or Connect: used to design the survey form, define logic rules (e.g., required fields, conditional visibility), and integrate geospatial capabilities.
• Survey123 Mobile App: used by field operators to collect data. The app supports offline use, media attachments (e.g., photos), and geolocation services.
• ArcGIS Online Platform: a cloud-based system where all survey submissions are aggregated. Submitted data are accessible in real time via dashboards, feature layers, or downloadable formats (CSV, shapefiles, GeoJSON, etc.).

2.1.2. Form Design

The CerMapp application allows for the configuration of both mandatory and optional form fields through an intuitive interface (Figure 3). The app layout was optimized for mobile devices and tailored to support real-time data entry during field activities conducted by veterinary and environmental personnel. The data entry form of the preliminary release of CerMapp includes the following fields:

• Name: text input (mandatory) to record the operator’s first name.
• Surname: text input (mandatory) for the operator’s last name.
• Job/Role: text input (mandatory) to specify the professional title or role of the user.
• E-mail: text input (mandatory) with email validation to collect contact information.
• Affiliation: text input (mandatory) to specify the institution or organization (e.g., CFV, AUSL, IZS, RAVA).
• Date (dd-mm-yyyy): date selector (mandatory) allowing the user to define the date of the observation or intervention.
• Hour: optional time selector to specify the time of the event.
• Coordinates (WGS84): a geolocation field (mandatory), automatically retrieving the GNSS coordinates (in the WGS84 system) of the data entry point.
• Pictures: optional image field enabling users to upload photographic documentation of the event or observation.
• Other Info: an open text field for recording any additional observations or contextual information not captured by the previous fields.

Mandatory fields are clearly marked to ensure the collection of essential metadata for each survey entry. The app’s interface integrates a base map from ESRI and includes an embedded address search bar for improved spatial referencing.

Figure 3. CerMapp interface and fields. The asterisk indicates a required field.

Figure 3. CerMapp interface and fields. The asterisk indicates a required field.

The development of CerMapp followed a user-centered design process initiated through multidisciplinary stakeholder workshops. This approach translated specific scientific requirements for wildlife health surveillance into a structured data model, defining essential data fields through an iterative cycle of prototype testing and refinement with end-users to optimize both data quality and operational usability in the field.

The selection of data fields for the CerMapp form was driven by the requirement to collect essential metadata that aligns with the standardized reporting protocols of the National Animal Disease Information System (SIMAN) and fulfills the minimum criteria for rigorous geospatial analysis. Initial field selections were based on the operational needs and data requirements identified by the National Reference Center for Wildlife Diseases (CeRMAS). These preliminary fields were then refined through consultations with key stakeholder groups, including veterinarians from the Aosta Valley Regional Health Authority (AUSL) and officers from the Regional Forest Corp (CFV). Their feedback, gathered via informal workshops, was instrumental in ensuring the form’s practicality and relevance for field use, leading to the inclusion of fields for photographic documentation and manual geolocation to accommodate challenging field conditions.

2.1.3. User Access and Data Collection Procedure

Access to the survey is granted either by scanning a QR code (see Figure 4) or entering a provided URL into the app. Upon loading the form, the user can immediately begin entering data, even in the absence of an internet connection.

The offline functionality of CerMapp is crucial for ensuring reliable data collection in remote or mountainous areas with limited network coverage. All entered records are locally stored on the device and are automatically uploaded to the central database once an internet connection is restored.

If GPS signal acquisition fails or is unavailable (e.g., under dense canopy cover or indoors), users are given the option to manually indicate the observation location by either dropping a point on the map or drawing a polygon to delineate the area of interest. This ensures spatial data accuracy even in constrained conditions.

2.1.4. Cybersecurity and Access Control

To ensure that only authorized individuals can submit data, each user is required to input a unique User ID or email address upon accessing the survey. This ID will be verified against a pre-approved list of authorized users. The list of authorized users for the CerMapp application is controlled and managed by CeRMAS (Italian Reference Center for Wildlife Diseases). Access is restricted to the following qualified personnel:

• Veterinarians registered with the Professional Order and employed by local health authorities (ASL) or experimental zooprophylactic institutes (IZS).
• Other healthcare or research personnel affiliated with recognized institutions, subject to formal requests and verification of their professional status.
• Members of the Forest Corps are also granted access upon validation.

All applicants must undergo a verification process to confirm their institutional affiliation and professional credentials before being added to the user list.

For increased security, future versions of the survey will incorporate custom API solutions. These APIs will enable more robust user verification processes and could include integration with digital identity systems, such as SPID (Sistema Pubblico di Identità Digitale) in Italy, to authenticate users and ensure secure data submission. This integration will provide a higher level of confidence in user authenticity and data integrity. Notably, in this version, this kind of verification has not been implemented due to the limitations of ESRI’s architecture, which does not currently support such a system.

As an additional security measure, communication between the mobile app and the central database is encrypted via HTTPS, safeguarding data during transmission. Furthermore, administrators will have the ability to monitor submissions, track user activity, and ensure that data is properly handled according to established security protocols.

2.2. Data Collection and Management

Field data collected using CerMapp is stored in a hosted feature layer on the ArcGIS Online cloud platform. The collected data are synchronized either in real time or upon re-establishing connectivity. The hosted feature layer, accessible via a REST service, provides an interface for querying, downloading, and visualizing data. Users of the CerMapp application can view and access the data they have personally submitted. However, to access and view data submitted by all users, explicit authorization must be requested from and granted by CeRMAS. This ensures data privacy, security, and controlled sharing in line with institutional and regulatory requirements. This service supported integration with:

• Web-GIS applications for browser-based data visualization and interaction;
• Desktop GIS software (e.g., QGIS, SAGA GIS [97], ArcGIS Pro) for advanced geospatial analysis;
• Automated data pipelines (e.g., in Python vers 3.10 or R vers. 4.2.2) for data retrieval and processing.
• Retrieved data can be archived in a geodatabase for local storage, integration and analysis, and exported in standard formats (e.g., SHP, CSV) for versioning and sharing via platforms such as GitHub. Furthermore, the data can be made available for integration with remote sensing analyses using platforms like Google Earth Engine (GEE) [98,99] or Microsoft Planetary [59], enabling the combination of field observations with satellite and other Earth observation data for enhanced spatial analysis.

This workflow ensures seamless integration of field data collection, cloud-based storage, multi-platform access, and reproducible data sharing for advanced geospatial and remote sensing analyses (Figure 5).

2.3. Adoption of CerMapp Data for Geospatial Analysis in Google Earth Engine

To perform regional-scale spatial analysis using CerMapp collected data, submissions stored as hosted feature layers in ArcGIS Online are first exported via the ArcGIS API for Python. A possible workflow includes:

• connecting to ArcGIS Online via the Arcgis API for Python and querying the ArcGIS Survey123 item users with low ICT knowledge may find a simple user guide here (a) https://www.youtube.com/watch?v=-MB7IOKZrVk and (b) https://www.youtube.com/watch?v=YoyBCTZoPR0 (both accessed on 18 November 2025);
• exporting the survey’s hosted feature layer in CSV, shapefile, or file geodatabase format, along with any image attachments

Once downloaded, the exported point data (with WGS84 coordinates and attribute fields) are uploaded to GIS software or within cloud-computing-platform. In this case we have adopted Google Earth Engine (GEE) importing the collected data.

Within GEE, the CerMapp points are encapsulated as an ee.FeatureCollection, enabling integration with Earth observation datasets (e.g., Sentinel-2, Landsat, SRTM) accessible through the GEE Data Catalog.

2.4. CerMapp Prototypal Case. A Pilot Study: Anaplasma spp. Surveillance

To demonstrate CerMapp’s practical application, it has been presented a case study on Anaplasma spp. in wildlife. Prior to CerMapp, data on wildlife diseases in the Aosta Valley were collected using a mixture of paper forms and non-standardized digital reports (e.g., Excel spreadsheets). This process was prone to errors in data transcription, frequently lacked precise geographic coordinates, and caused significant delays between field observation and data centralization. For this study, field data including species, location, and other information were collected directly by veterinarians and personnel from the Forest Corp using the CerMapp application on their mobile devices during routine surveillance and control hunting activities. It is important to note that the samples for this case study were obtained through a combination of passive surveillance (e.g., found dead) and active sampling of hunted animals, reflecting the real-world operational context of wildlife monitoring. While the CerMapp application is designed to record the source of each sample, this specific metadata was not a differentiating factor for the purpose of this demonstrative analysis, which primarily aimed to showcase the geospatial data pipeline. In fact, the key advantage of CerMapp in this context was the immediate georeferencing of each sample (either via GPS or manual map selection) and the standardized recording of essential metadata, creating a clean, instantly available dataset for analysis. The resulting CerMapp dataset, in this case study testing comprising 100 observations from roe deer and wild boar. These data were then exported from the ArcGIS Online platform to explore its integration with environmental data.

2.5. Testing the Data Pipeline

Specifically, to test the power of the potentiality of this app the above-mentioned pipeline was followed by samples of Anaplasma spp. [100] data collected within CerMapp onto different species of wildlife (roe deer, wild boars). Land Surface Temperature (LST) anomalies, increasingly frequent under climate change scenarios, have been shown to influence the occurrence and dynamics of various wildlife diseases. Shifts in LST can affect host behavior, vector activity, pathogen development, and environmental persistence, altering the epidemiological landscape of both vector-borne and environmentally transmitted infections. Several wildlife diseases, including parasitic, bacterial, and fungal infections, have been linked to LST variability, particularly in ecosystems where temperature strongly governs ecological interactions. Among these, Anaplasma spp. tick-borne intracellular bacteria affecting a wide range of wild ungulates and carnivores deserve special attention due to their strong dependence on vector dynamics. Temperature anomalies, especially in spring and summer, can affect tick phenology, abundance, and host-seeking behavior, potentially expanding the window and geography of transmission [2,101]. The persistence of Anaplasma spp. in wildlife populations is therefore closely tied to environmental factors such as LST, which can shape both the spatial distribution of vector species and the risk of exposure at the wildlife-livestock interface.

In this simple demonstration, LST data were integrated into the spatial analysis as a potential predictor of Anaplasma spp. transmission risk, supporting a One Health approach to understanding and managing vector-borne bacterial infections in wildlife. Specifically, we demonstrate how field data collected through the CerMapp system can be cross-referenced and imported into GIS software or cloud-based platforms, enabling high-resolution analyses such as the spatial zoning of areas with increased exposure risk. The app facilitates analysis enabling the collection of data and interaction with others. In the study case by coupling regions with Anaplasma spp. occurrence and areas of significant thermal anomaly. A K-means clustering analysis was performed on the LST anomaly layer generated for the reference year 2022, using the GEE script provided in Appendix A. This integrative approach allows for a scalable and transferable workflow to identify thermal-ecological niches potentially favoring Anaplasma spp. transmission linking Earth Observation data with CerMapp data. In this case, the workflow begins with the acquisition and processing of Landsat 5 TM, 7 ETM+, 8 OLI/TIRS, and 9 OLI-2/TIRS-2 satellites collections images spanning 1984 to 2022, specifically focusing on July–August periods to maintain seasonal consistency. These datasets are processed using the Sofia Ermida Landsat SMW-LST algorithm [102] to ensure accurate LST retrieval across different sensor generations. The input data undergoes rigorous quality control, including cloud masking using QA_PIXEL band information and filtering for scenes with less than 15% cloud cover. The analysis concentrates on the Aosta Valley region [87]. This streamlined workflow highlights how data collected via CerMapp can be rapidly leveraged for exploratory, cloud-based geospatial analysis.

3. Results

CerMapp end-user front end has been reported in Figure 2 as it can be displayed within a smartphone. The CerMapp QR code generated alternatively to the URL has been reported in Figure 4.

The users filled the given fields as explained in the Section 2. A sample of the collected data has been reported below to give an idea of the structure of the GeoDB created.

CerMapp allows for analytics on data collection and also provides access to the GeoDB where the data is stored.

This geospatial demonstration utilized data from the CerMapp application to analyze the distribution of Anaplasma spp. in wildlife, specifically focusing on roe deer and wild boar in the area of study during 2022. Data from 100 animals (95 roe deer, 5 wild boar), including both positive and negative infection results, were compared with Land Surface Temperature (LST) anomalies. Diagnostic results are associated with the geospatial record through a structured, post-collection data integration process performed by CeRMAS (Center for Research and Monitoring of Anthropogenic Systems, or analogous central authority). The procedure is as follows:

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Data Collection & Submission: The user initially creates and submits a georeferenced record via the mobile application. This record contains essential metadata, including geographic coordinates, timestamp, and user-defined field observations, but it is inherently devoid of diagnostic laboratory data at this stage.

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Laboratory Analysis: The physical sample linked to the geospatial record is transported to a designated laboratory for analysis. Following standardized protocols, the laboratory conducts the required diagnostic assays.

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Data Integration by CeRMAS: Upon completion, the laboratory transmits the certified analytical results to CeRMAS. CeRMAS then performs a critical data fusion operation. Using a unique identifier (e.g., a sample barcode or record ID that links the sample to the original geospatial entry), the diagnostic results are programmatically merged with the existing geospatial record in the central database.

In summary, the user does not manually input diagnostic findings. The CeRMAS system automatically implements and attaches these results to the corresponding georeferenced entry in a subsequent phase. This ensures data integrity, eliminates manual transcription errors, and maintains a clear audit trail from field collection to final analytical result within the consolidated database.

A map was generated to visualize the distribution of Anaplasma spp. positive and negative cases across the study area (Figure 6). Of the 100 samples analyzed, 70% tested positive for Anaplasma spp. Most of these positive cases were geographically concentrated in regions characterized by higher elevations (around 600–1000 m) and lower temperature anomalies, suggesting a potential correlation between these environmental variables and pathogen presence.

A separate map displaying LST anomalies for the summer of 2022 was created (Figure 7). Significant thermal anomalies were observed in almost the whole region, particularly in areas with known wildlife habitat concentrations. These anomalies represented both absolute and percentage changes in temperature compared to the long-term baseline (1984–2022). Higher-than-average temperatures were noted in the central parts and near mountain peaks of the Aosta Valley, while cooler regions were located on the glaciers.

A K-means clustering analysis was performed to identify potential spatial correlations between LST anomalies and Anaplasma spp. occurrence. The analysis, which included 100 points, generated different clusters based on LST anomaly patterns (Figure 8). The results indicated that regions with high thermal anomalies (hotter regions) were associated with a higher density of Anaplasma spp. positive cases, whereas areas with lower thermal anomalies showed fewer positive cases. These findings suggest that elevated temperatures could play a role in influencing the occurrence of Anaplasma spp. in wildlife, particularly in relation to tick phenology and vector dynamics. The code adopted after performing zonal statistics on each point considering the LST anomalies (in Figure 7) can be found in the Appendix A within the Github URL. Notably the results obtained have not been further explored because the main aim of the work is just to show a potential fast pipeline that data collected by CerMapp may offer combining Earth Observation Data from cloud computing. The final cluster obtained has been obtained in Figure 8 and the statistics reported in

Table 1. Cluster mean LST values from CerMapp Anaplasma spp. data.

ID Cluster	Mean LST Anomalies (°C)
1	3.78
2	6.07
3	1.92

below.

The majority of positive cases of Anaplasma spp. are found in Cluster 1 and 2, where LST anomalies are higher compared to the other clusters (see Table 1).

4. Discussion

It is important to note that this analysis is demonstrative in nature, showcasing the versatility of the CerMapp platform and its ability to integrate its data within GIS or cloud-based platforms for spatial analysis. The results are not intended to draw definitive conclusions about the relationship between Anaplasma spp. prevalence and thermal anomalies in wildlife populations but rather to illustrate the potential of the platform for conducting integrated geospatial analyses. This integrative approach allows for the coupling of field-collected data with Earth observation datasets, enabling the identification of potential ecological niches that could influence disease dynamics. The workflow presented here is scalable and transferable, making it an effective tool for studying the interplay between environmental factors and wildlife health in a variety of contexts.

By integrating CerMapp data with remote sensing products (such as land covers, spectral time-series, GeoAI features extraction, etc.), the workflow can be adapted to other regions and environmental conditions, providing valuable insights into the ecological and epidemiological dynamics of vector-borne diseases in wildlife. Further validation and studies are required to establish more definitive links between LST anomalies and Anaplasma spp. transmission risk.

A critical aspect that should be emphasized is the potential cost-efficiency for public administration. Many regional administrations already invest in GIS tools, including those provided by ESRI. In this context, the cost of deploying CerMapp can be considered reasonable and strategic, especially given the capacity to create a portfolio of applications supporting multiple domains of public administration. A single investment may thus serve to enhance the digitalization of various sectors, including environmental surveillance, public health, civil protection, and veterinary services.

Although similar applications could be developed using open-source platforms such as QField and other Free and Open-Source Software (FOSS) alternatives, it is essential to weigh this choice against the institutional IT capabilities and the hidden costs of maintenance and support. Many public agencies lack in-house informatics staff or do not possess the necessary expertise to keep and update custom-built open-source tools. In contrast, ESRI-based platforms offer robust infrastructure, automatic updates, and bug fixes directly provided by the vendor—greatly reducing the operational burden for non-technical users. In this case study, the analysis successfully identified distinct spatial patterns of potential environmental correlation. While further investigation is required, this case study effectively demonstrates CerMapp’s utility in facilitating advanced geo-epidemiological analyses for wildlife health surveillance, rather than providing an exhaustive study of the pathogen itself.

One of the key strengths of CerMapp lies in its offline data collection capabilities, including manual geolocation when GPS signals are unavailable. This feature proves essential for data collection in remote or mountainous areas, such as those frequently encountered in wildlife studies. While the use of commercial platforms implies a degree of dependency on licensing policies, the ability to export raw data ensures flexibility and interoperability, allowing for the construction of independent databases and integration into custom workflows.

Data storage options are adaptable and can include both local infrastructure and cloud-based solutions offered by ESRI, with potential for integration with high-performance cloud computing platforms. Methodologically, CerMapp fosters a geomatics-based data collection approach, marking a valuable transfer of technological capability to the veterinary public health sector. Indeed, discussions about AI, machine learning, Earth observation, and predictive epidemiology become irrelevant without a foundational system for standardized, structured, and widespread data collection, particularly concerning wildlife, which serves as a critical reservoir for zoonotic diseases [103,104,105,106].

Nonetheless, one of the primary challenges remains the standardization and institutional scaling of such platforms. Ensuring the reliability and traceability of data requires more rigorous user validation protocols, ideally implemented through nationally accredited entities such as the Ministry of Health or national consortia like CINECA, rather than relying solely on regional ICT in-house providers.

Looking ahead, several practical challenges must be navigated for the successful national scaling of CerMapp. Firstly, data sharing agreements and policy barriers between different regional and national institutions (e.g., health authorities, environmental agencies, and research bodies) represent a significant hurdle. Establishing clear data governance frameworks that define ownership, access rights, and usage protocols is essential to foster trust and collaboration. Secondly, the long-term operational sustainability of the platform requires careful consideration. While initial development costs might be contained, securing stable funding for ongoing cloud services, software licenses, and dedicated human support for user management and technical maintenance is critical to avoid project obsolescence. Thirdly, maintaining the engagement and participation of field data collectors (e.g., veterinarians, foresters) is crucial for data continuity. This can be achieved by demonstrating the tangible value of the data they collect for instance, by providing them with access to analytical results, dashboards, or reports that directly inform their own work thereby creating a feedback loop that reinforces participation. Finally, a key challenge lies in balancing rigorous data quality control with data privacy, especially concerning the precise location information of wildlife findings. While granular coordinates are essential for high-resolution spatial analysis, they could potentially be misused if sensitive species locations are revealed. Strategies such as data anonymization, aggregation for public-facing outputs, and tiered access controls must be implemented to mitigate privacy risks while preserving the scientific utility of the dataset for authorized research.

An additional strength of the CerMapp ecosystem is its data sharing and integration potential, enabling both local and full-cloud analyses. This was demonstrated by the exploratory case linking Anaplasma spp. prevalence to LST anomalies using Google Earth Engine (GEE). Although illustrative, this example reflects the broader vision of CerMapp as a tool for supporting veterinary approaches through geospatial intelligence and data-driven insight [107,108,109,110]. The Anaplasma case study, while preliminary, serves to illustrate the end-to-end workflow enabled by CerMapp. Unlike previous methods, the app provided a standardized and immediate means of creating a georeferenced wildlife health dataset. This directly facilitated the subsequent integration with remote sensing data in GEE, a process that would have been far more cumbersome, if not impossible, with the fragmented and often non-geospatial data collected previously. Thus, the primary contribution of CerMapp in this example was not the analytical result itself, but the creation of a high-quality, analysis-ready foundational dataset.

The CerMapp application presents a range of advantages that make it a valuable tool for data collection and analysis in the context of integrated geospatial health monitoring. One of its most significant strengths is its ability to operate offline, enabling data collection even in areas with limited or no internet connectivity. This includes the option for manual geolocation, which is particularly important in remote or mountainous regions where GPS coverage may be inconsistent or absent [111,112,113,114,115].

Moreover, being a no-code, cloud-based tool, ArcGIS Survey123 offers additional value by eliminating the need for advanced development skills or complex infrastructure management. This enables institutions to deploy robust data collection solutions without incurring the costs of building in-house applications or outsourcing development to third-party companies. The accessibility of no-code and low-code platforms such as ArcGIS Survey123 aligns with broader trends that highlight their potential to democratize software development and accelerate the delivery of functional solutions [5].

While ArcGIS Survey123 requires a license, this is rarely a limiting factor as many public institutions already have ArcGIS platform licenses for activities such as cartography, environmental monitoring, and planning. In Italy, public institutions often access ArcGIS through national or regional agreements, and programs like Esri Italia’s Small Local Government ELA further lower licensing barriers for small municipalities (Esri Italia, 2025).

Furthermore, CerMapp supports a wide range of applications across multiple sectors of public administration. This versatility translates into cost-efficiency, as a single investment in the platform can serve the needs of various departments, including environmental monitoring, veterinary public health, civil protection, and territorial planning. Another important feature is the ability to export raw data from the application. This functionality ensures that users are not locked into a proprietary system, offering flexibility in terms of database construction, data migration, and integration with external analytical workflows. It is worth noting that access to data for export is granted only upon formal authorization and in compliance with European General Data Protection Regulation (GDPR) and internal security policies. Any use or publication of the data must involve CeRMAS and the development team to ensure proper handling, anonymization, and respect for intellectual property rights. The goal is to promote research and data sharing responsibly, safeguarding privacy and data integrity.

Operating within the ESRI ecosystem also offers key benefits, such as robust customer support, regular bug fixes, and automatic software updates, all of which reduce the burden on institutional IT teams and ensure long-term sustainability. CerMapp also plays a crucial role in facilitating the transfer of geomatics methodologies to traditionally less digitized sectors, such as veterinary medicine and public health. This transfer is essential for building institutional capacity in spatial epidemiology. The platform’s compatibility with cloud computing and advanced spatial analysis environments further enhances its analytical potential. Finally, by promoting data collection according to standardized geomatic principles, CerMapp lays the groundwork for high-quality datasets that can be used in machine learning applications and GeoAI frameworks, where data consistency and reliability are foundational requirements.

Despite these strengths, CerMapp is not without limitations. Its dependence on commercial licensing models and the associated policy decisions made by ESRI may raise concerns about long-term accessibility and scalability, particularly for institutions with limited financial resources. Adoption may also be constrained in organizations with low levels of digital literacy or insufficient IT infrastructure to support advanced geospatial technologies. While open-source alternatives, such as QField, may offer lower initial costs, they typically require greater technical support and maintenance efforts, which may not be feasible for all public agencies. Additionally, the current identity verification mechanisms employed within the system—often relying on credentials issued by regional ICT providers may lack the level of rigor and institutional standardization needed for deployment at a national scale, especially when handling sensitive or health-related data. To maximize its impact, CerMapp should aim to move beyond its initial implementation in the Aosta Valley and be extended to the national level. This would involve strengthening interoperability across regions, linking CerMapp to georeferenced databases and genetic data, thereby enabling the creation of integrated biosanitary geographic information systems (GIS) which means geographical data coupled with genetics data of wildlife diseases and foot traffic and vehicles data to assess risk analysis. Such systems would support more complex spatial and temporal analyses of zoonotic and vector-borne diseases [116,117,118].

In parallel, this would open new opportunities to align with growing national and European and worldwide investments in Earth observation missions, and to provide ready-to-use epidemiological services based on satellite data for One Health strategies; most importantly, by promoting standardized data collection practices rooted in geomatics, CerMapp creates the necessary infrastructure for the effective application of machine learning, statistical modeling, and GeoAI. Without such foundational tools, investments in advanced data science techniques risk being inefficient or underutilized. Structured, scalable, and validated data collection frameworks like CerMapp are thus indispensable for the next generation of geospatial health intelligence and should be prioritized in the ongoing digital transformation of public health surveillance systems (see detailed information in the Supplementary Materials).

Limitations of the Study

Despite its promising capabilities and successful prototype deployment, the CerMapp system and this study have several limitations that should be acknowledged.

Firstly, the current prototype is operational only in the Aosta Valley region, which limits the generalizability of our findings and the demonstrated workflow to a national scale. The ecological and operational conditions in this alpine region may not be fully representative of other Italian landscapes.

Secondly, the platform’s dependence on the commercial ESRI ArcGIS ecosystem, while offering advantages in support and stability, creates a dependency on licensing models and vendor policy decisions. This could pose a challenge for long-term accessibility and scalability, particularly for institutions with limited financial resources or a strong preference for open-source solutions.

Thirdly, the current user authentication mechanism, which relies on credentials managed by regional ICT providers or a simple user list, lacks the rigor of a nationally accredited digital identity system (e.g., SPID). This represents a potential constraint for ensuring data security and traceability at a national level, especially when handling sensitive health-related information.

Furthermore, the illustrative case study on Anaplasma spp., while effectively demonstrating the data pipeline, is based on a relatively small sample size (n = 100) and is primarily intended as a proof-of-concept. The observed correlations with LST anomalies are preliminary and require more extensive data collection and rigorous statistical modeling to draw definitive epidemiological conclusions.

Finally, the long-term sustainability of the platform depends on securing stable funding for cloud services, software licenses, and dedicated technical support. Widespread adoption may also be constrained in organizations with low levels of digital literacy or insufficient IT infrastructure to support such geospatial technologies.

5. Conclusions

CerMapp’s primary objective is to align with the vision of international organizations such as the ESA, FAO, WHO, and WOAH. These organizations advocate for technological advancements in key sectors to enhance disease prevention and implement a true One Health approach.

Currently, a significant barrier to achieving this goal is the lack of robust data collection and the absence of comprehensive geo-bio-databases. By facilitating the collection of high-quality geospatial data on animal health, CerMapp aims to address this critical gap. This data will be invaluable for supporting evidence-based decision-making, improving disease surveillance and prediction, and ultimately contributing to a more effective and integrated approach to human, animal, and environmental health.