Statistical analysis useful for analysis of epidemic epidemic data
Professor, Hokkaido University Graduate School of Medicine Hiroshi Nishiura
The epidemic of emerging and reemerging infectious diseases has been reported one after another due to the increased activation of international migration of humans and the increased opportunities for exposure to animals and nature.Starting with 2002 - 3 severe acute respiratory syndrome (SARS), various infectious epidemics have been reported, including highly pathogenic avian influenza, H1N1-2009 H1N1-2009, Middle Eastern Respiratory Syndrome, Ebola hemorrhagic fever, Jika fever .In addition, outbreaks such as rubella and measles that can be prevented by vaccination continue in developed countries / Japan.When these epidemics occur, it is first requested to understand the nature of infected natural history and the characteristics of propagation better before grasping the current situation by thinking about various trendy measures variously.Until now, in various foreign countries including Japan, methods of collecting observation data at the time of epidemic development have developed dramatically, and graduates of the National Institute of Infectious Diseases Research Institute (FETP) (Field Epidemiologist Training Program) are produced, and epidemiology The infrastructure started to be gradually adjusted and advanced modeling research became possible little by little.In this talk, we will explain how researches using mathematical models and statistical models have been utilized in infectious disease epidemiology and contributed to devising epidemic countermeasures, with examples of research experiences of previous lecturers I will introduce it briefly.In particular, in cases of jika fever and measles, JMP implements maximum likelihood estimation as part of code sharing with collaborators, so I would like to see that too.(1) Estimation of the risks and epidemic risks of people infected with imported infectious diseases, (2) Analysis of propagation patterns and countermeasures against epidemics, (3) Effects of new causality reasoning such as effect of isolation, etc. as a study example I would like to introduce experts who are interested in JMP and statistics.
