## Background

■TThe rapid spread of infections due to the Omicron variant has been observed nationwide, and the number of deaths, not only the number of new positive cases, has reached a new record.

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

■A partial shortage of test kits and delays in processing occurred in various places, and deemed positives based on doctor’s diagnosis without a standard test became unavoidable.

■Requested isolation period was reduced for close contacts of asymptomatic infected people.

■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.

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

■Out of the 128 trials 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 activity restrictions will be lifted on March 6 and then activities will return to the level of November last year.

■We carried out seven scenarios for maximum tests per day in the range of 0.10-0.22% of the population by 0.02% interval.

■We ran 128 trials for each scenario and analyzed 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.

## Estimation Model

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

■Although the number of positive patients decreases at one point due to the test resource shortage, it increases or decreases gradually based on the capacity of tests.

## Definition of Days of Closure in the Estimation Model

■The case where the maximum number of tests per day is 0.10-0.22% of the population. Trends 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 (Trend 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 (Trend in Number of Actually Infected People)

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

■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 (Trend in Number of Patients with Severe Illness)

■The number of people with severe illness among patients captured 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 revised the parameters and expanded some models based on the recent situation and analyzed the impacts in the different scales of testing capacity.

■If the activity restriction 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 uncaptured infected people will increase, and the possibility of missing appropriate treatment for severe illness will increase.

Issues

■In simulations, the shortage of the resources necessary for testing and countermeasures occurs all at once overall.→ Modeling of the impact of the gradual 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.