## Background

■The rapid spread of infections due to the Omicron variant has spread nationwide, and the number of deaths, not just the number of new positive cases, has reached a new maximum.

■The quasi state of emergency applied in Tokyo and other areas was extended to March 6.

■A partial lack of test kits and delays in processing occurred in various places, and deemed positives based on examination by a doctor began to appear.

■Isolation periods for close contacts of asymptomatic infected people were reduced.

■In Tokyo, etc., the weekly average number of positive patients started to decrease from around February 10.

■On the other hand, tightness in the supply of tests and hospital beds continued and the rate of positive results is tending to increase.

■Third vaccinations accelerated to 1 million a day nationwide.

■It is necessary to consider a scenario assuming uncaptured infected people.

## Simulation

■We use a multi-agent model that moves in two-dimensional space. The number of agents is one million people.

■We expanded the settings for places of gathering so that we could adjust the small world nature of infection networks that connect people.

■Out of the 128 simulations conducted to January 24, we selected eight trials from those close to the trend in the number of positive patients in Tokyo, followed by 16 trials each, for a total of 128 trials.

■In addition, from the simulations up to January 30, we took eight trials and conducted a total of 128 trials in the same way.

■It was assumed third vaccinations were carried out in 0.71% of the population per day from February 1.

■It was assumed that action restrictions will be lifted on March 6 and action will return to the level of November last year.

■We carried out seven simulations in total in 0.02% increments for maximum tests per day in the range of 0.10-0.22% of the population.

■We ran trials 128 times for each scenario and observed their means and standard deviations.

■The number of positive patients, the number of patients in isolation, the number of people actually infected and the number of patients with severe illness were examined.

## Simulation Result #1-1 (Change in Number of Positive Cases)

■The case where the maximum number of tests per day is 0.10-0.22% of the population. Changes since July 4.

■Although the number of positive patients decreases at one point due to the tightness of testing, it increases or decreases gradually based on the number of tests.

## Simulation Result #1-2 (Change in Number of Positive Cases)

■The case where the maximum number of tests per day is 0.10-0.22% of the population. Changes since December 1.

■At less than 0.12%, the number of positive results decreases. At 0.14% or more, the number of positive results increases temporarily.

## Simulation Result #2 (Change in Number of Patients in Isolation)

■Total number of patients grasped at each time point (number of patients who have not recovered after a positive result). The number of patients is influenced by the number of tests.

■It may be necessary to model a shorter isolation period, etc., for the discrepancy from the actual number.

## Simulation Result #3 (Change in Number of Actually Infected People)

■The actual number of infected people regardless of whether or not they have symptoms or have had tests at each time point.

■The peak period is around March 5 regardless of testing capacity. About 14.51 ±0.25% or about 2.03 million people.

## Simulation Result #4 (Change in Number of Patients with Severe Illness)

■The number of people with severe illness among patients grasped as infected. The number of patients with severe illness in this sense is smaller with fewer tests.

■Under the scenario of the lifting of restrictions on March 6, the number of people with severe illness may increase again.

## Insights from the simulation

■We reviewed the parameters and expanded some models based on the recent situation and analyzed cases of the impacts of differences in the scale of testing capacity.

■If the action declaration is lifted on March 6, the number of people with severe illness may increase even if the number of people infected decreases.

■If the number of tests is insufficient, the number of infected people not grasped will increase, and the possibility of missing appropriate treatment for severe illness will increase.

Issues

■In simulations, the scarcity of the resources necessary for testing and countermeasures occurs all at once overall.→Modeling of the gradual impact of the dispersion of inventories is required.

■The number of tests set in the simulation is smaller than the actual number of tests, because the pseudo-symptomatic patients are not included in the test subjects.It is necessary to estimate the number of test recipients with pseudo symptoms and analyze based on the actual number of tests.

Supplemental information

■Details of the simulations→ http://www.intlab.soka.ac.jp/~unemi/SimEpidemic1/info/simepidemic_sim_omicronC1.html

■Details of the simulation model → http://www.intlab.soka.ac.jp/~unemi/SimEpidemic1/info/simepidemic-model191.html

■So that we could conform to the transition of the weekly mean number of positive cases by February 13, we mainly adjusted the transition of the parameter with the frequency of gatherings and the rate of infectivity, and established a subsequent scenario for its continuation.