Background
■Infections spread rapidly in late July in Tokyo
■Claims heard at the time
■"Without a lockdown, infection will not be suppressed"
■"The human flow in Tokyo should be reduced by 50% (compared to the pre-declaration level), day and night" (50% compared to the pre-declaration level = an additional 20-30% from the first half of August)
■The analysis presented by the various research teams at the time was somewhat consistent with these claims
■" (Forthcoming) Examining the August 12 subcommittee's proposal for a 50% reduction in human flow"
■In Tokyo, infections declined rapidly in the second half of August, even though a variety of human flow data subsequently turned upward or stopped downward trend
■Here, we analyze the quantitative importance of several factors that may have contributed to the decline in infections since late August
■(October 25) Factors behind the infection decrease in Tokyo：https://covid19outputjapan.github.io/JP/files/FujiiNakata_SharpDecline_Slides_20211025.pdf
■Analysis results depend on the analysis method. We hope that you will not take our analysis as truth, but as one of many that will be coming out in the future
Most of the flow data increased or stopped decreasing from mid-August
Source: LocationMind xPop © LocationMind Inc.
Tokyo Metropolitan Institute of Medical Science （https://www.mhlw.go.jp/content/10900000/000847822.pdf ）
“LocationMind xPop” Data refers to people flows data collected by individual location data sent from mobile phone under users‘ consent, through applications* provided by NTT DOCOMO, INC. The data is processed collectively and statistically in order to conceal the private information. Original location data is GPS data (latitude, longitude) sent at a frequency of every 5 minutes at the shortest interval and does not include information that specifies individuals.
*Applications such as “docomo map navi" service（map navi・local guide）.
Important Points
■1. Widespread vaccination has been a major force in controlling infection continuously since late July
■2. However, vaccination alone is unlikely to explain the timing and rapidity of the decline in infections from late August
■3. We analyzed the quantitative importance of factors other than human flow and vaccination
■The following three can be quantitatively explained to some extent (note that these were not necessarily the most important)
■(For a variety of reasons) Basic reproduction numbers were lower than expected
■Risk-averse behavior due to tightness of medical capacity (not captured by human flow data)
■The existence of an approximately 120-day cycle (due to various reasons)
■The weather, the presence of non-PCR-positive infected individuals, and the higher-than-expected preventive effect of the vaccine on infection all seem to be quantitatively insignificant
Infection control effect of widespread vaccination continues from late July
■Vaccination has been a major force in controlling the spread of infection since late July and has been working continuously
■However, the changes in infection are not significantly affected by the presence or absence of vaccination
■A significant decrease in the contact rate parameter is necessary to explain the timing and rapidity of the decline in infections since late August
■Vaccination pace has been continuous, so that alone is unlikely to explain the timing and rapidity of the decline in infections since late August
Infection control effect of widespread vaccination continues from late July
■Vaccination has been a major force in controlling the spread of infection since late July and has been working continuously
■Widespread vaccination of the working-age population from late June is a major factor
Widespread vaccination alone is unlikely to explain the timing and rapidity of the decline in infections from late August
Mid-August outlook focused on human flow data
(Consideration of widespread vaccination)
Basic reproduction numbers may have been lower than expected
*It can also be interpreted as capturing the quantitative importance of factors that lower the threshold for population immunity acquisition (e.g., fragmented networks) that are not taken into account in standard epidemiological models.
**It can also be interpreted as capturing the quantitative importance of the presence of a factor that temporarily boosted the effective reproduction number from mid-July to early August, which was mistakenly taken as a signal of the infectivity of the Delta variant.
120-day cycle
*Factors that may contribute to cycle generation
Exogenous: Seasonality, new highly infectious mutant variants, chance, etc.
Endogenous: Government policies and people's actions
**The result that "120 cycles is of quantitative importance" suggests the importance of exploring why such cycles have been observed so far
People's risk aversion due to tightness of medical capacity
■Tweets from users who have actually performed actions such as karaoke and attending drinking parties
■Created by Toyoda Laboratory, Institute of Industrial Science, the University of Tokyo, using Twitter data provided by NTT Data
Scenario analysis taking into account the risk aversion behavior at the time
Summary
*Note that this hypothesis is unlikely to explain the continued decline of infections since October
Lessons
■Infection may decline rapidly without additional restriction on human flow
■Examples of additional restriction on human flow that were proposed, but did not materialize: Subcommittee on Novel Coronavirus Disease Control proposal made on August 12
■"By strengthening the intensive measures through August 26, we propose that human flow in Tokyo, day and night, be reduced to about 50% of the level in the first half of July, just before the start of the emergency measures." (Note: 50% of the first half of July = an additional 20-30% from the first half of August)
■An observation independent of "what are the factors for the rapid decline in infection"
■Lessons
■Going forward, we should be more cautious than ever about lockdowns and human flow policies
■Why? (1) Because the uncertainty of the effects of behavioral restriction policies has increased, and (2) Because these policies have significant costs (negative impact on society, economy, culture, and education)
■A classic of decision making in the face of uncertainty: W. Brainard (1967): Uncertainty and the Effectiveness of Policy
■This does not necessarily mean that they should be excluded from policy options altogether
■"Infection can be reduced without additional restriction on human flow" is not the same as "restriction on human flow is not always helpful in reducing infection"
■If it is thought that reducing human flow is effective in reducing infection, and if measures can be taken to mitigate the negative effects of this, they can be considered
■However, it is desirable to present convincing evidence in doing so
