# Queueing Analysis of a Chagas Disease Control Campaign

## TL;DR

This paper develops a load-dependent queueing model to analyze and optimize door-to-door Chagas disease control campaigns, accounting for infestation dynamics and search processes, with application to a campaign in Arequipa, Peru.

## Contribution

It introduces a novel queueing model that captures infestation spread and search-dependent arrivals for disease control efforts, providing a tool for campaign optimization.

## Key findings

- Model estimates long-term removal rates of infested houses.
- Quantifies costs associated with different surveillance strategies.
- Demonstrates applicability to real-world Chagas control campaigns.

## Abstract

A critical component of preventing the spread of vector borne diseases such as Chagas disease are door-to-door campaigns by public health officials that implement insecticide application in order to eradicate the vector infestation of households. The success of such campaigns depends on adequate household participation during the active phase as well as on sufficient follow-up during the surveillance phase when newly infested houses or infested houses that had not participated in the active phase will receive treatment. Queueing models which are widely used in operations management give us a mathematical representation of the operational efforts needed to contain the spread of infestation. By modeling the queue as consisting of all infested houses in a given locality, we capture the dynamics of the insect population due to prevalence of infestation and to the additional growth of infestation by redispersion, i.e. by the spread of infestation to previously uninfested houses during the wait time for treatment. In contrast to traditional queueing models, houses waiting for treatment are not known but must be identified through a search process by public health workers. Thus, both the arrival rate of houses to the queue as well as the removal rate from the queue depend on the current level of infestation. We incorporate these dependencies through a load dependent queueing model which allows us to estimate the long run average rate of removing houses from the queue and therefore the cost associated with a given surveillance program. The model is motivated by and applied to an ongoing Chagas disease control campaign in Arequipa, Peru.