# Spatial risk mapping of highly pathogenic avian influenza in Morocco using geographic information system and multi-criteria decision analysis: Implications for targeted surveillance and control

## TL;DR

This study maps high-risk areas for avian flu in Morocco to help authorities focus surveillance and control efforts.

## Contribution

The first comprehensive spatial risk maps for HPAI introduction and spread in Morocco using GIS and expert-based multi-criteria analysis.

## Key findings

- 25 provinces (33.3%) showed high-to-very high risk for HPAI introduction, especially in northern coastal and border regions.
- 41 provinces (41.3%) were at high-to-very high risk for HPAI spread, mainly in densely populated poultry and trade areas.
- The model proved stable, with minimal impact on risk classifications despite variations in expert-assigned weights.

## Abstract

Highly pathogenic avian influenza (HPAI) remains a global threat to poultry production, trade, and public health. While Morocco has not yet reported confirmed HPAI outbreaks, the endemic circulation of low-pathogenic avian influenza (LPAI) H9N2 since 2016, proximity to affected neighboring countries, and Morocco’s position along migratory bird flyways highlight the country’s vulnerability. This study aimed to identify high-risk areas for HPAI introduction and spread to inform risk-based surveillance and control policies.

We applied a spatial multi-criteria decision analysis integrated with geographic information systems at the provincial scale. Relevant risk factors were identified through a literature review and expert consultation, and categorized into the introduction (wetlands, live poultry imports, recreational bird imports, and poultry products) and spread (poultry density and type, live bird markets, transport networks, and human population density) domains. Weights were assigned to factors using the analytic hierarchy process based on responses from 73 poultry-sector experts. Data were normalized, integrated into composite risk maps, and validated against historical LPAI H9N2 outbreak data (2016). Sensitivity and uncertainty analyses were used to assess model robustness.

The final maps revealed that 25 provinces (33.3% of the national territory) exhibited high-to-very high risk of HPAI introduction, particularly along northern coastal provinces, border regions, and areas linked to recreational bird imports. For spread risk, 41 provinces (41.3%) were classified as high to very high, concentrated in the Casablanca–Settat, Rabat–Salé–Kenitra, Fès–Meknès, and Marrakech–Safi regions, which are characterized by dense poultry production, major trade hubs, and extensive transport networks. Sensitivity analyses confirmed the model’s stability, with variations in weight producing a minimal impact on risk classifications.

This study provides the first comprehensive spatial risk maps of HPAI introduction and spread in Morocco, highlighting priority provinces for early detection, targeted surveillance, and preventive biosecurity measures. Despite limitations arising from reliance on LPAI data and expert judgment, the approach offers a robust decision-support tool for veterinary authorities. The methodology is adaptable to regional applications and can be refined with real-time surveillance data, enhancing Morocco’s preparedness and resilience against future avian influenza incursions.