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After slowing down the spread of the novel coronavirus COVID-19, many countries have started to relax their severe confinement measures in the face of critical damage to socioeconomic structures. At this point, it is desirable to monitor the degree to which political measures or social affairs have exerted influence on the spread of disease; however, tracing back individual transmission of infections whose incubation periods are long and highly variable seems to be difficult. Nevertheless, it may be possible to estimate the changes that may have occurred in the past, if we can suitably fit a proper model to daily event-occurrences. We have devised a state-space method for fitting the Hawkes process to a given dataset of daily confirmed cases. This method detects changes occurring in the spread of the contagion in each country. Furthermore, this method can assess the impact of social events in terms of the temporally varying reproduction number representing the average number of cases directly caused by a single infected case. This information might serve as a reference for the behavioral guidelines that should be adopted according to the varying risk of infection.
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