Abstract

BackgroundIn order to be prepared for an infectious disease outbreak it is important to know what interventions will or will not have an impact on reducing the outbreak. While some interventions might have a greater effect in mitigating an outbreak, others might only have a minor effect but all interventions will have a cost in implementation. Estimating the effectiveness of an intervention can be done using computational modelling. In particular, comparing the results of model runs with an intervention in place to control runs where no interventions were used can help to determine what interventions will have the greatest effect on an outbreak.MethodsTo test the effects of a school closure policy on the spread of an infectious disease (in this case measles) we run simulations closing schools based on either the proximity of the town to the initial outbreak or the centrality of the town within the network of towns in the simulation. To do this we use a hybrid model that combines an agent-based model with an equation-based model. In our analysis, we use three measures to compare the effects of different intervention strategies: the total number of model runs leading to an outbreak, the total number of infected agents, and the geographic spread of outbreaks.ResultsOur results show that closing down the schools in the town where an outbreak begins and the town with the highest in degree centrality provides the largest reduction in percent of runs leading to an outbreak as well as a reduction in the geographic spread of the outbreak compared to only closing down the town where the outbreak begins. Although closing down schools in the town with the closest proximity to the town where the outbreak begins also provides a reduction in the chance of an outbreak, we do not find the reduction to be as large as when the schools in the high in degree centrality town are closed.ConclusionsThus we believe that focusing on high in degree centrality towns during an outbreak is important in reducing the overall size of an outbreak.

Highlights

  • In order to be prepared for an infectious disease outbreak it is important to know what interventions will or will not have an impact on reducing the outbreak

  • Characteristics include the total population of the town, the number of primary schools, the number of secondary schools, the number of students who live in the town, the total number of students who attend school in the town including those commuting in and the number of agents in the town who are not immune and susceptible to measles are included and the town where the outbreaks in our experiments described begin is highlighted in blue

  • Following the analysis methods used in experiment one, in this second experiment we use the same three measures to compare the interventions: the total number of runs that lead to an outbreak out of the 300 runs, the total number of infected agents, and the geographic spread of the outbreak

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Summary

Introduction

In order to be prepared for an infectious disease outbreak it is important to know what interventions will or will not have an impact on reducing the outbreak. Comparing the results of model runs with an intervention in place to control runs where no interventions were used can help to determine what interventions will have the greatest effect on an outbreak. An intervention that only has a minor impact on an outbreak but uses a considerable amount of resources might not be the best strategy. One main reason for this is there is no control scenario to compare what would have happened if the intervention was not implemented. It is difficult to determine what was a result of the intervention and what would have happened if the intervention was not implemented. We use the model to look at intervention policies in particular school closure policies, we run a number of different scenarios to determine which policy is the best in reducing outbreaks.

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