Background
■The rapid spread of infections due to the Omicron Variant has reached the entire nation.
■Priority Preventative Measures were implemented in Okinawa, Yamaguchi, and Hiroshima on January 9, followed by 13 prefectures including Tokyo on January 21.
■An explosion of infections and a rapid increase in the number of patients needing hospitalization could put a strain on medical care.
Number of hospitalized patients
Compared to the number of patients with symptoms thought to require hospitalization, the actual number of hospitalized patients is:
Greater at the start of the infection spread period.
Less around the peak.
The trend of the increase is linear, not exponential.
Does not change to follow the number of patients of a certain symptom level.
Does it change according to the availability of hospital beds?
Simulation
■Multi-agent model that moves in a two-dimensional space.
■The number of agents is one million. Look at the average and standard deviation of 128 trials.
■Of the 128 simulations up to January 20, select the 8 trials closest to the number of positive cases in Tokyo, then run 16 trials to continue each, for a total of 128 trials.
■Assuming that behavior change to some extent will occur due to the Priority Preventative Measures.
■Assuming that the 3rd vaccination is administered 7 months after the 2nd vaccination.
■The effects of pre-existing immunity and medicines are assumed to be 70%.
■The change in the speed of aggravation of symptoms due to the difference in toxicity is assumed to occur during or after middle severity. ← This reflects the fact that the Omicron strain is comparatively less likely to cause pneumonia.
■The speed of aggravation of symptoms associated with serious illness after middle severity will be simulated for when it is 4 to 20% of that of the Delta variant.
■Changes in the numbers of positive cases, middle severity patients and above, and seriously ill patients will be examined.
Simulation Result #1-1 (Change in number of positive cases）
■When the toxicity is 12% of the Delta variant and the behavioral restriction period is 17, 24, or 31 days.
■A rapid spread of infections is expected.
Simulation Result #1-2 (Change in number of positive cases）
■No difference in the peak is seen between behavioral restriction periods of 24 or 31 days, but there is a slight difference in how early the end comes.
■The number of positive cases peaks around February 19, at 1.00 ±0.02%. approx. 140,000
Simulation Result #2 (Change in number of quarantined patients）
■The total of known patients at each point in time. (The number of patients who have been identified as positive and have not yet recovered)
■The number of quarantined patients peaks around February 28, at 7.23±0.19 %. approx. 1.01 million
■May interfere with the maintenance of social functions.
Simulation Result #3 Change in number of middle severity patients）
■Assumed that the behavioral restriction period is 24 days and that the toxicity is 4 to 20% of the Delta variant.
■When the toxicity is 12%, the peak is around March 8, at 1.08±0.03 %. approx. 140,000
■May put a strain on medical resources for treatment such as hospitalization.
Simulation Result #4 Change in number of seriously ill patients）
■Assumed that the behavioral restriction period is 24 days and that the toxicity is 4 to 20% of the Delta variant.
■When the toxicity is 12%, the peak is around the end of March, at 0.0064±0.0006 %. approx. 893
Insights from the simulation
■Change in the number of patients needing hospitalization depending on virus toxicity was also analyzed.
■In any case, the spread of infections is expected to continue.
■Measures should need to be implemented to prevent a strain on the medical care, failure of social functions, and the like. Reviews of the standard for recuperation, distribution of medical resources, flexibility, and the like are also needed.
Subjects
■As the nature of the Omicron variant becomes clearer, the simulation should be updated from time to time taking into consideration such information.
■Resources for testing and recuperation are becoming strained, and there is a need to show by simulation the discrepancy between what it would look like and what it actually would be.
Supplemental information
■Details of the simulation → http://www.intlab.soka.ac.jp/~unemi/SimEpidemic1/info/simepidemic_sim_omicronB3.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 January 23, 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.
