・This report is intended as an additional analysis following the June 16, 2021 report.
・"Verification of effects of infection prediction and countermeasures" (establishment of a model to replace SIR model), "demand forecasting and optimal allocation of necessary medical resources (beds, medical supplies, etc.)" required as "countermeasures against 2nd wave"
・https://www.covid19-ai.jp/ja-jp/presentation/2021rq3/article/article077/
・Points of additional analysis
・In this series of analyzes, simulations were performed to maximize the effect with limited vaccine resources, focusing on vaccine distribution based on conditional entropy. ( p.3 About this additional analysis)
・In the simulation of the infection situation that should be suppressed by vaccination, the effective reproduction number from last year was adopted until now, but we have decided to give this attention again given the fact that this year's effective reproduction is relatively moderate compared to that of last year. ( p.4 Assumption of effective reproduction number in additional analysis)
・At the same time, we considered the increase of 350,000 people during the Olympics, which was also taken into consideration in the analyses so far.
※Effective reproduction number: The number of people infected by one infected person
・Even if it is assumed that the effective reproduction number is close to the current number, it is desirable that the vaccination speed should be such that the population can be vaccinated at 0.6% or more per day after priority measures to prevent the spread of infectious disease are lifted.
・In addition, the following strategies can be taken according to the vaccination speed that can be achieved in order to effectively utilize the limited vaccine resources.
・If vaccination exceeds 0.2% / day of the population: Conditional entropy Hc maximization is effective
・If vaccination is 0.1% / day or less of the population: Average Rt maximization is effective (also Hc maximization can be used depending on the situation)
・In order to maximize the conditional entropy, it is desirable to allocate resources appropriately while monitoring the residence location of vaccinated persons.
About this additional analysis
・In this series of analyses including this additional analysis, we simulated a vaccination strategy focusing on maximizing conditional entropy based on information on the population of each region, and analyzed measures to maximize effectiveness even with limited vaccine resources. We analyzed the number of infected people by combining Pv: vaccination speed, α: travel activity, and Hc: conditional entropy.
Assumption of effective reproduction number in additional analysis
・Looking at the effective reproduction number in each prefecture, there was a time last year when the number reached 20, but this year it has remained at the upper limit of 2. Although it may be affected by mutated variants in the future, the current situation is stable.
・Effective reproduction will continue downhill if measures are taken such as state of emergency and priority measures to prevent the spread of infectious disease. On the other hand, the reaction when these were softened was not so remarkable, and it was estimated that the effective reproduction number increased by about +1. Additionally, there was a tendency to decrease back to the original value within the same time taken for the increase (approx. 2 weeks to 1 month). Focusing on this point, an additional analysis was performed on the state after the lifting.
Additional simulation results
・Vaccination: Pv and Hc: Conditional entropy value patterns were combined, and the state after lifting was simulated based on the above-mentioned effective reproduction number.
・Based on the simulation results, a large expansion will occur in the worst case with the lifting around 7/11, but as long as there is an immediate return to pv > 0.6 and Hc is maximized, the suppression effect is high. (pv >0.6: Vaccination speed of over 0.6% of the population per day.)※ If the speed exceeds 0.6% / day, activity can be returned even with the Δ variant.
・In addition, the following vaccination strategies based on conditional entropy Hc can be considered depending on the situation, especially when dealing with difficult situations such as the prevention of rapidly spreading variants and immediate lifting.
・If vaccination exceeds 0.2% / day of the population: Conditional entropy Hc maximization is effective
・If vaccination is 0.1% / day or less of the population: Average Rt maximization is effective (also Hc maximization can be used depending on the situation)
