Digital GIS Workflow Links Farmer Knowledge Surveys to Veterinary Surveillance in Kazakhstan

Researchers in Kazakhstan have built and field-tested a digital workflow that plugs georeferenced knowledge, attitude, and practice (KAP) surveys directly into veterinary surveillance, offering a proof-of-concept for how behavioral data can be mapped alongside disease information at the animal–human–environment interface.

The study, published as a Brief Research Report in Frontiers in Veterinary Science, was led by Ainur Nurpeisova of the Kazakh Scientific Research Veterinary Institute in Almaty, with colleagues from the institute and from Tecton Analytics in Astana. The team reports its work in the journal’s Veterinary Epidemiology and Economics section.

At the core of the framework is ArcGIS Survey123, a mobile data-collection platform. The researchers developed disease-specific questionnaire modules for three livestock conditions: anthrax, blackleg, and mastitis. Each module was implemented as a proof-of-concept field application, allowing surveyors to gather standardized KAP responses from livestock owners while the system automatically records the geographic location of each interview.

According to the authors, the workflow unifies several components in a single digital pipeline: mobile data collection, automated geolocation, standardized KAP indicators, quality-control procedures, livestock-density context, and GIS-enabled spatial visualization. By combining these elements, the system is designed to let veterinary teams interpret survey responses in their spatial and epidemiological context rather than as isolated questionnaire results.

The framework is explicitly positioned as a methodological demonstration rather than an epidemiological finding. The authors state that the study does not provide a nationally representative assessment of Kazakh livestock owners’ knowledge and practices. Instead, its contribution is to show the feasibility of combining georeferenced behavioral data with disease-specific veterinary surveillance layers so that interpretation and decision-making can be spatially informed.

That distinction matters for how the results should be read. The reported outcome is the successful construction and implementation of the workflow itself, not measured levels of farmer knowledge or attitude changes. Any statement about what livestock owners in Kazakhstan know or do would require the kind of nationally representative survey this proof-of-concept does not claim to deliver.

The authors outline several practical uses the framework could support once deployed more broadly. Because KAP responses can be linked with epidemiological and geographic datasets, the system is intended to inform targeted risk communication, farmer education, vaccination planning, and veterinary outreach. In each case, the geographic layer allows interventions to be directed to specific locations rather than applied uniformly.

The study also frames the work within One Health surveillance, which integrates epidemiological, behavioral, and geographic information to support evidence-based decision-making across the animal–human–environment interface. The authors suggest the workflow has potential for future One Health applications through integration with human-health, environmental, and climatic data, extending the same georeferenced survey structure beyond livestock disease alone.

As a proof-of-concept, the workflow provides a practical methodological foundation for veterinary surveillance in Kazakhstan and a template that other surveillance programs could adapt. The article was received on 17 July 2026 and accepted on 16 October 2026, with correspondence directed to Raikhan Nissanova and Nurkuisa Mirzabekovich Rametov. The authors note that all claims in the article are solely their own and do not necessarily represent those of their affiliated organizations or the publisher.

Subject of Research: A GIS-enabled digital workflow for integrating georeferenced KAP surveys into veterinary surveillance in Kazakhstan

Article Title: A GIS-enabled workflow for georeferenced veterinary KAP surveys: a proof-of-concept study in Kazakhstan

Article References: Nurpeisova, A., Abay, Z., Nissanova, R., Bekbergen, A., Suchshikh, V., Yussupov, M., Begassyl, K., Abutalip, A., Kassenov, M., & Rametov, N. (2026). A GIS-enabled workflow for georeferenced veterinary KAP surveys: a proof-of-concept study in Kazakhstan. Frontiers in Veterinary Science, 13, Article 1940797. https://doi.org/10.3389/fvets.2026.1940797

Image Credits: AI Generated

DOI: 10.3389/fvets.2026.1940797

Keywords: GIS, KAP surveys, veterinary surveillance, Kazakhstan, ArcGIS Survey123, One Health, anthrax, blackleg, mastitis, livestock, spatial analysis, proof-of-concept