ABSTRACT

This research introduced PORCI-ASF, a novel Geographic Information System (GIS)-integrated decision support system for the proactive surveillance, risk assessment, and control of African Swine Fever (ASF) within the swine industry. Employing a multi-faceted approach, PORCI-ASF leverages Multi-Criteria Decision Analysis (MCDA), specifically integrating the Analytic Hierarchy Process (AHP) and the Weighted Sum Model (WSM) to quantitatively assess ASF risk. This risk assessment is grounded in a comprehensive understanding of contributing factors derived from a thematic analysis of farmer interviews. Identified key risk factors encompass biosecurity vulnerabilities (inadequate biosecurity implementation, high-risk backyard farming practices, and the inherent vulnerability of smallholder farms), surveillance system inefficiencies (delays and inconsistencies in reporting, reliance on manual and slow reporting methods, and limitations in monitoring capacity), and socio-economic and behavioral constraints (influences of farmer attitudes and behaviors, along with the impact of market instability and resource limitations). The resultant risk map, dynamically visualized using the Mapbox platform, facilitates targeted interventions and resource allocation. Demonstrating robust adherence to ISO/IEC 25010 quality standards, PORCI-ASF is poised for wide-scale implementation. Future developments will incorporate modules for vaccination records, clearance procedures, pig movement tracking, and border control data, thereby enhancing predictive capabilities and refining control strategies. This data-driven approach positions PORCI-ASF as a significant advancement in ASF management, warranting further research to evaluate its long-term efficacy and scalability across diverse swine production contexts.

Keywords:African swine fever, multi-criteria decision analysis, analytical hierarchy process, surveillance, gis-system.