Analyzing Strategies for Containing Avian Influenza

• Main Authors: Lee, Eva K., Liu, YiFan, El-Tahawy, Abdelgawad, Fasina, Folorunso O.
• Format: Text
• Language: English
• Published: American Medical Informatics Association 2023
• Subjects: Articles; Avian flu
• Online Access: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10148328/; http://www.ncbi.nlm.nih.gov/pubmed/37128393

In this paper, we couple a general-purpose infectious disease theory with a computational modeling framework to analyze strategies for avian influenza containment. We focus on virus transmission among domestic poultry populations to optimize and evaluate the effectiveness of three containment strategies and their combinations: reducing the contact rate among domestic birds, reducing the population of infected birds, and reducing the transportation of infected birds. We illustrate their usage during a two-wave avian flu outbreak in Nigeria. Our findings show that reducing contacts by 20% via cluster isolation early in the first wave can achieve containment rapidly. It also helps avert the second wave. Slaughtering infected birds is not as effective, requiring scheduled killings of over 80% of the poultry while failing to avert the second wave. This practice also risks damaging the local economy and potential secondary infections from the carcasses of infected birds. Reducing transportation between northern and southern Nigeria does not offer good containment since the disease spread began in both regions simultaneously. Reducing transportation has an impact when applied to neighboring regions and cities, or when the initial incidence of the disease is localized. Combination strategies prove to be the most practical and cost-effective to implement. The use of 3D-effectiveness visualized plots allows policymakers to evaluate multiple combination strategies and choose the one that optimizes containment while also adhering to budget constraints, resource availability, and management preference. The generalized mathematical theory and modeling framework is highly flexible and can be applied to other diseases, including those with multiple hosts, multiple species involvement, and across a broad array of heterogeneous regions.