Computational epidemiology is a multidisciplinary field that uses techniques from computer science, mathematics, geographic information science and public health to better understand issues central to epidemiology such as the spread of diseases or the effectiveness of a public health intervention.
In contrast with traditional epidemiology, computational epidemiology looks for patterns in unstructured sources of data, such as social media. It can be thought of as the hypothesis-generating antecedent to hypothesis-testing methods such as national surveys and randomized controlled trials. A mathematical model is developed which describes the observed behavior of the viruses, based on the available data. Then simulations of the model are performed to understand the possible outcomes given the model used. These simulations produce as results projections which can then be used to make predictions or verify the facts and then be used to plan interventions and meters towards the control of the disease's spread.
- Harvard Medical School Children's Hospital Boston Computational Epidemiology Group
- University of Iowa Computational Epidemiology Group (Compepi)
- The Network Dynamics and Simulation Science Laboratory at Virginia Tech
- The Center for Computational Epidemiology, Bioinformatics and Risk Analysis (CCEBRA) at Tuskegee University
- The Institute for Disease Modeling
- Laboratory for the Modeling of Biological and Socio-technical Systems
- Computational Epidemiology and Public Health Informatics Laboratory at University of Saskatchewan
- Sax Institute - Decision Analytics
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Source: Data courtesy of the U.S. National Library of Medicine. Since the data might have changed, please query MeSH on Computational epidemiology for any updates.