Dynamic resource allocation for controlling pathogen spread on a large metapopulation network.

Area of Science:

• Epidemiology
• Network Science
• Resource Allocation

Background:

• Controlling infectious disease spread in large populations, particularly livestock, is complex.
• Animal trade networks facilitate rapid disease propagation, necessitating effective control strategies.
• Optimal dynamic resource allocation is crucial but challenging for social planners.

Purpose of the Study:

• To develop and evaluate novel scoring methods for optimal resource allocation in livestock disease control.
• To compare the effectiveness of greedy algorithms against heuristic approaches using vaccination and treatment.
• To assess the impact of herd health information versus network topology on disease eradication.

Main Methods:

• Utilized an epidemiological-demographic model based on animal demographics and trade data.
• Adapted a greedy approach within a metapopulation framework to derive new allocation scores.
• Conducted intensive simulations to compare greedy scores with topology-based and other heuristic methods.

Main Results:

• Greedy scores, particularly those using herd health status, significantly reduced disease prevalence.
• Topology-based scores showed some efficacy in limiting spread but were less effective for eradication.
• Greedy approaches were highly effective, though not always the absolute best performing, in simulations.

Conclusions:

• Integrating herd health information is critical for eradicating livestock diseases, surpassing network topology alone.
• Developed greedy scores offer a powerful tool for dynamic resource allocation in disease control.
• The approach is adaptable to various epidemiological models and control measures in metapopulation settings.