Effective reproduction number to flows of people (1)
*The effective reproduction number is excerpted from Toyo Keizai Online
*People flow is the average value of 6 to 13 days before the corresponding day
Construction of a prediction model
１．Predict the number of newly infected people from the Japanese situation considering the impact of the variant (no definition of the vaccination rate)
２．Learned and corrected the data of other countries to consider the impact of vaccination in more detail
Model (1)：The impact of vaccination is not defined explicitly
・The construction of the networks is similar to the past one: https://www.mdpi.com/1660-4601/18/15/7799
・The input values include the weather information (minimum/maximum and average temperature), data on flows of people, weekdays or holidays, presence or non-presence of declaration of a state of emergency, and number of new infections to date as well as the newly added variation label (0: standard, 1: alpha, 2: delta)
・ Learning data is from August 1, 2020 to August 29, 2021 (excluded the initial one where the number of infected people is small from January to July 2020)
・The future values of weather data and the data of flows of people are assumed to be similar to those of last year
・The applicable cities of learning data are Tokyo, Osaka, and Aichi
・Predicted up to two months later (up to October 31, 2021)
Construction of a prediction model
https://doi.org/10.1038/d41586-021-02261-8
Model (2)
Separated the prediction model to make it possible to consider only the impact of pure vaccination
The input values of the network are the number of newly infected people and the effectiveness of vaccination
The applicable cities of learning data is Tel Aviv, London, NY, and Brussels
Conducted analysis on a combination of them for verification.
The cities of the test data are Tokyo, Osaka, and Aichi
＊The effectiveness of the vaccine of AstraZeneca is not time-dependent, so this approximation assumed that the effectiveness was the same as that of Pfizer.
Comparison of changes in flows of people between 2020 and 2021 (Tokyo)
Similar trend in July and August
The material this time: Used the data of flows of people of last year for the prediction of September
Data of the report on August 31 (only one foreign city)
Tokyo: Prediction of the number of newly infected people (weekly average) if weather conditions and flows of people are at the same level as last year Considering the impact of vaccines with new networks
UPDATE (Learned the data of four foreign cities)
Tokyo: Prediction of the number of newly infected people (weekly average) if weather conditions and flows of people are at the same level as last year Considering the impact of vaccines with new networks
What is the impact of the vaccination effect?
Model-1: Learned with the Japanese data
Potentially learned the impact of vaccination using the data of only the start period of vaccination (4/1 (start period of vaccination in Japan) to 8/31)
Model-2: Consider the vaccination effect incorporating overseas cases
(Tel Aviv, New York, Brussels, London: Vaccination completion rate of 50% or higher)
Data of the report on August 31 (only one foreign city)
Osaka: Prediction of the number of newly infected people (weekly average) if weather conditions and flows of people are at the same level as last year Considering the impact of vaccines with new networks
UPDATE (Learned the data of four foreign cities)
Osaka: Prediction of the number of newly infected people (weekly average) if weather conditions and flows of people are at the same level as last year Considering the impact of vaccines with new networks
Data of the report on August 31 (only one foreign city)
Aichi: Prediction of the number of newly infected people (weekly average) if weather conditions and flows of people are at the same level as last year Considering the impact of vaccines with new networks
UPDATE (Learned the data of four foreign cities)
Aichi: Prediction of the number of newly infected people (weekly average) if weather conditions and flows of people are at the same level as last year Considering the impact of vaccines with new networks
※Proportions of all generations in the total population. (Almost all of the disclosed proportions are proportions in the total population of ages above the target age range, so they are different from the proportions used here)
※Proportions of all generations in the total population. (Almost all of the disclosed proportions are proportions in the total population of ages above the target age range, so they are different from the proportions used here)
Prediction of the number of seriously ill patients in Tokyo
Model 1: Learned three cities, namely Tokyo, Osaka, and Aichi
Model 2: Considered the effect of vaccination using the data of Tel Aviv
End of September: The predicted value suddenly decreases
Possibility of the impact of the case of Osaka in the 4th wave
Model 1: Learned “Tokyo” only
Model 2: Considered the effect of vaccination using the data of Tel Aviv
Is it possible to simulate the shortage of hospital beds in and after mid-August?
