Although a sharp rise in cases was predicted (the graph on the left), the number of infections actually decreased from mid-April 2020 (the graph on the right). Dr. Kuniya used this data to work out the contact rate during the state of emergency.Towards creating a new model that also shows the impact on the economy From a System Informatics perspectiveAssociate Professor KUNIYA Toshikazu Graduate School of System Informatics07SPOTLIGHT I am currently conducting research into mathematical models to show infection prevalence. The majority of my research involves theoretically analyzing the mathematical characteristics of these models (such as SEIR models), but recently I have become interested in applying mathematical models to specic data on novel coronavirus infections. I began by using changes in the numbers of infected people in the period from January to February 2020 to predict future changes, and was able to correctly predict the dynamics of the epidemic up until a month and a half after this period (until mid-April 2020). Actually, based on my calculations, the prevalence of the infection in Japan would have peaked in summer with tens of thousands of new cases in a single day, as seen during that time in countries such as the USA and India. Fortunately, my prediction turned out to be wrong and the number of cases in Japan decreased from mid-April. I hypothesized that this was due to the state of emergency that was declared on April 7 2020, and investigated this using the model.Using mathematical models to predict the spread of the novel coronavirus First, I mathematically estimated the infectiousness of the novel coronavirus by taking into account aspects such as the changes in the number of cases, the percentage of asymptomatic people, and the average period during which an infected person can pass on the virus to others. Next, I added the data on the changes in the number of cases during the state of emergency and calculated using the mathematical model how much direct contact between people had decreased during this period compared to normal circumstances. The results revealed that the contact rate was 0.14 during the state of emergency, which means that contact between people decreased by 86%. I think that the reduced contact between people during the state of emergency had an impact on suppressing the spread of infection. However, we need to consider this fall in the infection rate in a slightly broader sense. Rather than saying that the number of people going out decreased by 80%, we need to take into account the eect of people keeping their distance from each other and wearing masks due to the state of emergency. Therefore, we must understand that there was a good reduction in the number of cases because all these eorts combined resulted in the contact rate falling by 80% percent.Next, I would like to work on a model that can show the changes in the number of infected people from the end of May onwards. With this model, I would like to show that there are cyclical wave-like trends in the number of infections, which reect the risks posed by people’s behaviour. For example, people are more careful when they know that infections are increasing but on the other hand, people are less vigilant when they hear that infections are decreasing. In addition, my next challenge is to create a mathematical model that can also consider the eect on the economy based on the big impact that the state of emergency and other factors have had.
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