Statistical analysis useful for infectious disease epidemic data analysis
Hiroshi Nishiura, Professor, Graduate School of Medicine, Hokkaido University
Due to the increased international movement of humans and increased exposure to animals and the natural world, epidemics of emerging and re-emerging infectious diseases have been reported one after another. Starting with Severe Acute Respiratory Syndrome (SARS) in 2002-3, various infectious disease epidemics have been reported, including highly pathogenic avian influenza, novel influenza H1N1-2009, Middle East Respiratory Syndrome, Ebola hemorrhagic fever, and Zika fever. In addition, outbreaks of rubella and measles, which are preventable by vaccination, continue in developed countries and Japan. When such epidemics occur, it is first necessary to better understand the natural history of infection and the characteristics of transmission and to grasp the current situation accurately before considering various epidemic countermeasures. Up until now, methods of collecting observation data at the time of epidemic outbreaks have developed dramatically in Japan and other countries, and epidemiological infrastructure is gradually being established, such as graduating students from the National Institute of Infectious Diseases' FETP (Field Epidemiologist Training Program), and advanced modeling research is gradually becoming possible. In this presentation, I will briefly summarize how research using mathematical and statistical models has been utilized in infectious disease epidemiology and contributed to devising epidemic countermeasures, while also sharing examples from my research experience. In particular, in the cases of Zika fever and measles, I implemented maximum likelihood estimation in JMP as part of code sharing with my collaborators, so I would like you to take a look at that as well. I would like to introduce to experts who are interested in JMP and statistics, using research examples such as (1) estimating the risk of imported infectious disease carriers traveling to Japan and epidemic risks, (2) analysis of transmission patterns and epidemic countermeasures, and (3) new causal inference efforts such as the effects of quarantine.
