Abstract
BackgroundThe transmission of infectious disease amongst the human population is a complex process which requires advanced, often individual-based, models to capture the space-time details observed in reality.MethodsAn Individual Space-Time Activity-based Model (ISTAM) was applied to simulate the effectiveness of non-pharmaceutical control measures including: (1) refraining from social activities, (2) school closure and (3) household quarantine, for a hypothetical influenza outbreak in an urban area.ResultsAmongst the set of control measures tested, refraining from social activities with various compliance levels was relatively ineffective. Household quarantine was very effective, especially for the peak number of cases and total number of cases, with large differences between compliance levels. Household quarantine resulted in a decrease in the peak number of cases from more than 300 to around 158 for a 100% compliance level, a decrease of about 48.7%. The delay in the outbreak peak was about 3 to 17 days. The total number of cases decreased to a range of 3635-5403, that is, 63.7%-94.7% of the baseline value.When coupling control measures, household quarantine together with school closure was the most effective strategy. The resulting space-time distribution of infection in different classes of activity bundles (AB) suggests that the epidemic outbreak is strengthened amongst children and then spread to adults. By sensitivity analysis, this study demonstrated that earlier implementation of control measures leads to greater efficacy. Also, for infectious diseases with larger basic reproduction number, the effectiveness of non-pharmaceutical measures was shown to be limited.ConclusionsSimulated results showed that household quarantine was the most effective control measure, while school closure and household quarantine implemented together achieved the greatest benefit. Agent-based models should be applied in the future to evaluate the efficacy of control measures for a range of disease outbreaks in a range of settings given sufficient information about the given case and knowledge about the transmission processes at a fine scale.
Highlights
The transmission of infectious disease amongst the human population is a complex process which requires advanced, often individual-based, models to capture the space-time details observed in reality
We argue that agent-based models (ABM) provide a powerful means to increase our understanding of infectious disease transmission
This paper presents an analysis in which control measures are tested for a hypothetical influenza outbreak in Eemnes, a small city in the Netherlands, generated from the simulation of individuals’ movements around the city and consequent interactions using a published ABM: Individual Space-Time Activity-based Model (ISTAM) [39,40]
Summary
The transmission of infectious disease amongst the human population is a complex process which requires advanced, often individual-based, models to capture the space-time details observed in reality. Since the beginning of the new millennium, epidemics of severe acute respiratory syndrome (SARS), avian influenza (bird flu) and H1N1 influenza (swine flu) have emerged repeatedly amongst the human population raising public concerns, over future risk of disease, and underlying the need for increased understanding of transmission processes and the efficacy of several studies [10,11,12] have investigated the infection process directly by infecting healthy volunteers with the influenza virus in an experimental setting, for ethical reasons and because of the complexity involved, scientific experiments generally do not provide a feasible solution for the study of infectious disease transmission and the testing of control measures in a real world setting This is especially true across whole communities or cities. It is difficult to obtain all the data required for model building, validation and simulation, especially for bottom-up approaches that require knowledge at the micro level
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