Computational epidemiology

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Pythagoras0 (토론 | 기여)님의 2020년 12월 26일 (토) 05:29 판 (→‎메타데이터: 새 문단)
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  1. We found that standard computational epidemiology models that do not incorporate demographic and socioeconomic trends quickly diverged from past mortality and population size estimates, while our approach remained consistent with observed data over decadal time courses.[1]
  2. Incorporating demographic and socioeconomic trends in computational epidemiology is feasible through the “open source” approach, and could critically alter population health projections and model-based evaluations of health policy interventions in unintuitive ways.[1]
  3. Eric Lofgren and other researchers in his lab at WSU are using computational epidemiology — a combination of computer simulations and complex mathematics — to model the spread of diseases, like COVID-19,...[2]
  4. This Expeditions project will enable novel implementations of global infectious disease computational epidemiology by advancing computational foundations, engineering principles, theoretical understanding, and novel technologies.[3]
  5. With funding from the Centers for Disease Control and Prevention, the University of Iowa Computational Epidemiology Research Group is helping hospital patients, workers, and visitors stay safe from COVID-19 and other diseases.[4]
  6. We provide an overview of the state of the art in computational epidemiology, which is a multi-disciplinary research area, that overlaps different areas in computer science, including data mining, machine learning, high performance computing and theoretical computer science, as well as mathematics, economics and statistics.[5]
  7. The Computational Epidemiology lab aims to tackle this problem by using computer-based simulation models and by taking advantage of the proliferation of smart phones and other mobile technology to collect data as it pertains to public health.[6]
  8. In contrast with traditional epidemiology, computational epidemiology looks for patterns in unstructured sources of data, such as social media.[7]
  9. This is a graduate-level Computer Science (CS) course on computational epidemiology, which is the study and development of computational techniques and tools for modeling, simulating, predicting, forecasting, surveilling, mitigating, and visualizing the spread of disease.[8]

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