Background and Overview
■The explosive spread of the Delta variant has made it difficult for some public health centers and medical institutions to respond to the situation.
■The number of new positive cases started to decline in Tokyo after the peak on August 19.
■It is necessary to consider extending the state of emergency or transition to priority measures for prevention of the spread of the disease.
■If the transition is delayed, the degree of the rebound will be smaller.
■Due to the attenuation of the immunity effect of vaccination over time, high levels of infection may persist after autumn this year.
Simulation
■The number of agents is one million. From the 64 trials from December 22 last year to August 14 this year, four trials are selected that are close to the actual data. As a continuation of these, we ran subsequent simulations to see the average and standard deviation.
■Three patterns are compared of the transition from the state of emergency to priority measures for prevention of the spread of the disease on September 13, 20, and 27.
■#1 Change in number of new positive cases (page 5)
■#2 Change in number of infected people (page 6)
■#3 Change in number of seriously ill patients (page 7) - The criteria for severity of illness in the simulator are adjusted based on the changes in Tokyo.
■#4 Change in number of inapparent infected people (asymptomatic and untested) (page 8)
■#5 Sensitivity of the transition to the parameters assumed in the scenarios (page 9)
Simulation Scenarios
■The scenario was set to conform to the change in the number of new positive cases in Tokyo up to September 5.
■It was assumed that the probability of infection had decreased due to decrease in the frequency of risky gatherings and individual infection control measures taken since around the Obon holidays.
■It was assumed that the temporarily overwhelmed healthcare system resulted in decreasing of contact tracing capture rates to about half compared with normal situation and the delay of test results was doubled.
■★Three dates for easing behavioral restrictions: September 13, 20, and 27.
■☆Three minimum values for gatherings frequency: 0.3%, 0.4%, and 0.5%.
■We conducted 64 simulations for each of the nine combinations of the dates for easing behavioral restrictions (★) and the gatherings frequency(☆) to see the average and standard deviation.
Supplemental information: Details of each parameter in the simulation
Probability of Infection
In the simulation, when an uninfected individual and an infectious individual approach within a certain distance, infection occurs with a set probability. Assuming that infection control measures are taken from around the Obon holidays, the probability of infection rate drops to 55% from the previous level of around 85%. It returns to the 85% as the state of emergency is lifted.
Contact Tracing
We define contact as when individuals approach each other at a distance that is infectious in the simulation, and record the history of contact with others for each individual. However, a decision to make a record or not depends on the probability determined by contact tracing. As soon as an individual is found to be infected, those in contact with the individual are also tested, so if the contact tracing rate is higher, it may contribute to the control of infection. In the simulation, we assumed that the rate will gradually raise from August 16 from 20% which is the original rate and only 10% will be recorded on September 1 which is half of the original rate. It is assumed that the situation will gradually be relieved from September 6.
Delay in Test Results
Due to the overwhelmed healthcare system, confirming test results likely takes more time. This may result in the spread of infection because asymptomatic infected people are not quarantined for an extended period. In the simulation, we assumed that test which is usually confirmed results in one day gradually takes more time to confirm from August 16, and requires two days on August 30 reaching a peak. It is assumed that the situation will gradually be relieved from September 6.
http://www.intlab.soka.ac.jp/~unemi/SimEpidemic1/info/simepidemic_sim_cont.html
http://www.intlab.soka.ac.jp/~unemi/SimEpidemic1/info/simepidemic-model186.html
Simulation Result #1 Change in number of new positive cases
■If the transition to the priority measures for prevention of the spread of the disease is delayed, the number of the positive cases will decline, but a high level of infection persists after a resurgence.
Simulation Result #2 Change in number of infected people
■If the transition to the priority measures for prevention of the spread of the disease is delayed, the number of infected people will decline, but a high level of infection persists after a resurgence.
Simulation Result #3 Change in number of seriously ill patients (Tokyo standard)
■If the transition to the priority measures for prevention of the spread of the disease is delayed, the number of seriously ill patients will decline, but a high level of infection persists after a resurgence.
The discrepancy between the actual data and the simulation results after June-end is most likely due to the fact that the increase of the rate of serious illness with the Delta variant, which has been more often found from the time onward, was not properly simulated.
Simulation Result #4 Change in number of inapparent infected people
■If the transition to the priority measures for prevention of the spread of the disease is delayed, the number of inapparent infected people will decline, but a high level of infection persists after a resurgence.
Sensitivity of the changes in the parameters assumed in the scenario
■For the purpose of confirming the validity of the scenario, a simulation was conducted to examine the sensitivity of the values set in the scenario.
■The probability of infection greatly affects to the sensitivity, while the contact tracing slightly affects to it.
■Since taking action to reduce the probability of infection is important, it is suggested that wearing a mask, washing hands, and disinfection are effective.
Sensitivity of the changes in the parameters assumed in the scenario
■In both cases, the resurgence starts about a week after easing behavioral restrictions, but if easing is delayed, a resurgence becomes smaller.
■The analysis of sensitivity suggests that the measures are effective to reduce the probability of infection such as wearing a mask, washing hands, and disinfection.
■Since attenuation of the immunity effect over time may result in persistently high numbers of infected people, it would be necessary to consider the third dose in order to revive economic and cultural activities.
For more information ->
http://www.intlab.soka.ac.jp/~unemi/SimEpidemic1/info/simepidemic_sim_emg2109A.html
Supplemental information: Assumptions in the simulation and how to interpret the graph
