Abstract
Background:
African swine fever (ASF) has disrupted the swine industry in the Cordillera Administrative Region of the Philippines.

Aim:
This study quantitatively simulated the economic impact of different ASF control approaches. The impact of the outbreak on regional livelihoods was also assessed qualitatively.

Methods:
A mixed-methods research design was used to combine the quantitative analysis and the results of the qualitative study. For the quantitative study, swine population, economic losses, and pork supply-demand gaps were projected using a Monte Carlo simulation from 2025 to 2030. A PAWN global sensitivity analysis with Latin hypercube sampling was conducted to determine which model parameters had the greatest influence on the output. Additionally, 10 stakeholders (one official from the Department of Agriculture, four backyard swine raisers, one butcher, one meat vendor, one eatery owner, one feed supplier, and one transporter/driver) from one highly affected locality were interviewed using a semi-structured questionnaire.

Results:
Under the baseline scenario, the total number of pigs in the region was projected to reach 183,782 (95% CI: 153,049–203,286) heads by 2030. The cumulative economic losses were projected to amount to 0.29 billion Philippine pesos (US$4.67 million), and the region was projected to face a pork shortage of 17,836 metric tons. The sensitivity analysis revealed that projected cumulative economic losses were most strongly influenced by biosecurity effectiveness. In terms of the 2030 pork supply-demand gap, the average weight of pigs was the most influential factor. The qualitative results also showed that the impact of ASF extends beyond economic losses. Respondents adapted by discovering new ways to earn a living and improving their biosecurity measures.

Conclusion:
The success of ASF control and recovery may depend on coordinated institutional support and household-level adaptation.

Key words: African swine fever (ASF); Cordillera Administrative Region; Sensitivity analysis; Stochastic modeling.