Advanced Application Technologies to Boost Big Data Utilization for Multiple-Field Scientific Discovery and Social Problem Solving

Detecting premonitory signs and real-time forecasting of pandemic using big biological data
Detecting premonitory signs and real-time forecasting of pandemic using big biological data

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HOME > RESEARCH PROJECTS > Real-time analysis of the Middle East Respiratory Syndrome (MERS)

Real-time analysis of the Middle East Respiratory Syndrome (MERS)

Real-time analysis of infectious diseases is to analyze emerging infectious disease while it is expanding, especially at the early stage of the epidemic, to accurately restore the research achievement to the broader public. In the urgent epidemic of Ebola and MERS, Nishiura’s lab analyzed big data from day to night, and the response has been reflected to governmental institutions and peer reviewed journals.

From May to July 2015, MERS has been identified in multiple hospitals of the Republic of Korea. In the midst of the epidemic, one of the most important research questions was to identify the case fatality ratio. To identify the risk factor of case fatality ratio and the real-time risk of death, a model applying the delay of onset to death was needed to be considered, which in statistical sciences, it is considered as the estimate model corresponding to censoring data. During the epidemic of MERS, we estimated the mortality rate by age and the presence of underlying diseases. Our result showed that elderly with underlying comorbidities has an especially high risk of death compared to other groups. Additionally, in this research we were able to estimate the distribution of the time from onset to death.

  • JST
  • CREST
  • ISM
  • KYOTO UNIVERSITY
  • HOKKAIDO UNIVERCITY Research Center for Zoonosis Control
  • HOKKAIDO UNIVERCITY Graduate School ob Medicine School ob Medicine
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