Abstract
Infectious diseases are a leading cause of death globally. Decisions surrounding how to control an infectious disease outbreak currently rely on a subjective process involving surveillance and expert opinion. However, there are many situations where neither may be available. Modeling can fill gaps in the decision making process by using available data to provide quantitative estimates of outbreak trajectories. Effective reduction of the spread of infectious diseases can be achieved through collaboration between the modeling community and public health policy community. However, such collaboration is rare, resulting in a lack of models that meet the needs of the public health community. Here we show a Susceptible-Infectious-Recovered (SIR) model modified to include control measures that allows parameter ranges, rather than parameter point estimates, and includes a web user interface for broad adoption. We apply the model to three diseases, measles, norovirus and influenza, to show the feasibility of its use and describe a research agenda to further promote interactions between decision makers and the modeling community.
Highlights
Infectious diseases are a leading cause of death globally
The SIR model is a commonly used compartmental model used for infectious disease outbreaks
To expand the above model and introduce a scenario where a control measure is applied to an outbreak, we introduce two additional parameters: 1. λ or control measure effectiveness describes what fraction of individuals are removed from the susceptible population at each time point
Summary
Infectious diseases are a leading cause of death globally. Decisions surrounding how to control an infectious disease outbreak currently rely on a subjective process involving surveillance and expert opinion. Mandal et al provide a review of models used for malaria[10] and Bauch et al explore model use with respect to SARS and other emerging infectious diseases[6] Methods among these groups are often similar, but tend to focus on specific diseases and locations of interest. Agent-based models use a bottom-up approach where the agents (these are often people) interact with particular rules to simulate outbreaks[11] This allows simulations at high resolutions, but requires large amounts of data to parameterize the models, as well as substantial computational power. It is thought that they may reflect real world scenarios more accurately, but the lack of available epidemiological data necessitates assumptions that are difficult or impossible to test[11] These models further require computational resources inaccessible to an average health department.
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