Understanding disease control: influence of epidemiological and economic factors

TL;DR

This paper models disease spread on a network and compares control strategies based on cost-effectiveness, revealing how epidemiological factors influence optimal intervention size and timing for emerging diseases.

Contribution

It introduces a local spread model that evaluates control strategies considering epidemiological and economic factors, highlighting the sensitivity of control size to costs and detection delays.

Findings

Optimal control neighborhood size depends on epidemiological factors.

Prevention effort scales with infection neighborhood size.

Detection and control delays significantly impact strategy effectiveness.

Abstract

We present a local spread model of disease transmission on a regular network and compare different control options ranging from treating the whole population to local control in a well-defined neighborhood of an infectious individual. Comparison is based on a total cost of epidemic, including cost of palliative treatment of ill individuals and preventive cost aimed at vaccination or culling of susceptible individuals. Disease is characterized by pre- symptomatic phase which makes detection and control difficult. Three general strategies emerge, global preventive treatment, local treatment within a neighborhood of certain size and only palliative treatment with no prevention. The choice between the strategies depends on relative costs of palliative and preventive treatment. The details of the local strategy and in particular the size of the optimal treatment neighborhood weakly depends on disease infectivity but strongly depends on other epidemiological factors. The required extend of prevention is proportional to the size of the infection neighborhood, but this relationship depends on time till detection and time till treatment in a non-nonlinear (power) law. In addition, we show that the optimal size of control neighborhood is highly sensitive to the relative cost, particularly for inefficient detection and control application. These results have important consequences for design of prevention strategies aiming at emerging diseases for which parameters are not known in advance.