Estimating importation cases using mobility data
Background Mobility restrictions were commonly used to limit interaction with other people and stop the spatial spread of the COVID-19 infection. A statistical model was developed to estimate the number of imported cases in each Japanese prefecture using epidemiological data and inter-prefectural mobility.
Methods The inter-prefectural mobility rate based on mobile phone data and prevalence estimates in the origin prefectures was used to predict the number of imported cases crossing prefectural borders. Using surveillance data of cases with a history of inter-prefectural travel, the simplistic model was quantified. The impact of the mobility rate and prevalence at the origin on imported cases was then explored with simulations.
Results Compared with the observed number of imported cases, the overall pattern was captured over time. Although Hokkaido and Okinawa are northernmost and southernmost prefectures, respectively, they were sensitive to differing prevalence rates in Tokyo and Osaka and the mobility rate. Other prefectures were also sensitive to mobility change, assuming that an increment in the mobility rate was seen in all prefectures.
Conclusions Our findings indicate the need to account for the weight of an inter-prefectural mobility network when implementing human mobility-related countermeasures. If the mobility rate were maintained lower than the observed rate, the number of imported cases could have been maintained at lower levels than the observed, potentially preventing the unnecessary spatial spread of COVID-19 in late 2020.