Evaluating Apple Inc Mobility Trend Data Related to the COVID-19 Outbreak in Japan: Statistical Analysis
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Abstract
In Japan, as a countermeasure against the COVID-19 outbreak, both the national and local governments issued voluntary restrictions against going out from residences at the end of March 2020 in preference to the lockdowns instituted in European and North American countries. The effect of such measures can be studied with mobility data, such as data which is generated by counting the number of requests made to Apple Maps for directions in select countries/regions, sub-regions, and cities.
Objective
We investigate the associations of mobility data provided by Apple Inc and an estimate an an effective reproduction number R(t).
Methods
We regressed R(t) on a polynomial function of daily Apple data, estimated using the whole period, and analyzed subperiods delimited by March 10, 2020.
Results
In the estimation results, R(t) was 1.72 when voluntary restrictions against going out ceased and mobility reverted to a normal level. However, the critical level of reducing R(t) to <1 was obtained at 89.3% of normal mobility.
Conclusions
We demonstrated that Apple mobility data are useful for short-term prediction of R(t). The results indicate that the number of trips should decrease by 10% until herd immunity is achieved and that higher voluntary restrictions against going out might not be necessary for avoiding a re-emergence of the outbreak.
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SciScore for 10.1101/2020.05.01.20087155: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:The present study has some limitations. First, although we examined the explanatory power for the COVID-19 outbreak nationwide, its applicability at the prefecture level must be verified. Secondly, one must be reminded that Apple and Docomo data indicate the proportion of users in an area who leave their residence. The data do not …
SciScore for 10.1101/2020.05.01.20087155: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:The present study has some limitations. First, although we examined the explanatory power for the COVID-19 outbreak nationwide, its applicability at the prefecture level must be verified. Secondly, one must be reminded that Apple and Docomo data indicate the proportion of users in an area who leave their residence. The data do not directly indicate a number of contacts or even a rate of contact. In other words, Apple and Docomo data reflect no intensity of the respective contacts. In fact, such measurement of contact intensity is extremely difficult. Methods to obtain such measurements stand as an objective for future research.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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