In Greater Tokyo, many people commute by train between the suburbs and downtown Tokyo for 1 to 2 h per day. The spread of influenza in the suburbs of Tokyo should be studied, including the role of commuters and the effect of government policies on the spread of disease. We analyzed the simulated spread of influenza in commuter towns along a suburban railroad, using the individual-based Monte Carlo method, and validated this analysis using surveillance data of the infection in the Tokyo suburbs. This simulation reflects the mechanism of the real spread of influenza in commuter towns. Three measures against the spread of influenza were analyzed: prohibition of traffic, school closure, and vaccination of school children. Prohibition of traffic was not effective after the introduction of influenza into the commuter towns, but, if implemented early, it was somewhat effective in delaying the epidemic. School closure delayed the epidemic and reduced the peak of the disease, but it was not as effective in decreasing the number of infected people. Vaccination of school children decreased the numbers not only of infected children but also of infected adults in the regional communities.
The first outbreak of pandemic H1N1 influenza in Japan was contained in the Kansai region in May 2009 by social distancing measures. Modelling methods are needed to estimate the validity of these measures before their implementation on a large scale. We estimated the transmission coefficient from outbreaks of pandemic H1N1 influenza among school children in Japan in summer 2009; using this transmission coefficient, we simulated the spread of pandemic H1N1 influenza in a virtual community called the virtual Chuo Line which models an area to the west of metropolitan Tokyo. Measures evaluated in our simulation included: isolation at home, school closure, post-exposure prophylaxis and mass vaccinations of school children. We showed that post-exposure prophylaxis combined with isolation at home and school closure significantly decreases the total number of cases in the community and can mitigate the spread of pandemic H1N1 influenza, even when there is a delay in the availability of vaccine.
Background
During the fourth COVID-19 wave in Japan, marked differences became apparent in the scale of the epidemic between metropolitan Tokyo in eastern Japan and Osaka prefecture in western Japan.
Methods
Public epidemic data were analyzed, with performance of mathematical simulations using simplified SEIR models.
Results
The increase in the number of infected persons per 100,000 population during the fourth wave of expansion was greater in Osaka than in Tokyo. The basic reproduction number in Osaka was greater than in Tokyo. Particularly, the number of infected people in their 20 s increased during the fourth wave: The generation-specific reproduction number for people in their 20 s was higher than for people of other generations. Both Tokyo and Osaka were found to have strong correlation between the increase in the number of infected people and the average number of people using the main downtown stations at night. Simulations showed vaccination of people in their 60 s and older reduced the number of infected people among the high-risk elderly population in the fourth wave. However, age-specific vaccination of people in their 20 s reduced the number of infected people more than vaccination of people in their 60 s and older.
Conclusions
Differences in the epidemic between Tokyo and Osaka are explainable by different behaviors of the most socially active generation. When vaccine supplies are adequate, priority should be assigned to high-risk older adults, but if vaccine supplies are scarce, simulation results suggest consideration of vaccinating specific groups among whom the epidemic is spreading rapidly.
The biological example of writing information on a small scale has inspired me to think of something that should be possible. Biology is not simply writing information ; it is doing something about it. A biological system can be exceedingly small. Many of the cells are very tiny, but they are very active ; they manufacture various substances ; they walk around ; they wiggle ; and they do all kinds of marvelous things-all on a very small scale. Also, they store information. Consider the possibility that we too can make a thing very small which does what we want-that we can manufacture an object that maneuvers at that level !
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