Shogo Isoda
Yuki Matsuda
ABSTRACT
In this paper, we aim to reveal the relationship between the number of people infected with COVID-19 in each ward in Tokyo and the changes in human mobility behavior using demographic information (population density, number of restaurants, etc.) and mobility data collected from GPS data of residents in Tokyo's 23 wards. The results confirmed that changes in human mobility behavior extracted from mobility data in each ward were an important feature related to the number of people infected by COVID-19 on the previous day's difference. These results suggest that the transition of the number of infected people in COVID-19 is largely due to human mobility behavior.
- Samuel Engle, John Stromme, and Anson Zhou. 2020. Staying at home: mobility effects of covid-19. Available at SSRN (2020).Google Scholar
- Nuria Oliver, Bruno Lepri, Harald Sterly, Renaud Lambiotte, Sébastien Deletaille, Marco De Nadai, Emmanuel Letouzé, Albert Ali Salah, Richard Benjamins, Ciro Cattuto, et al. 2020. Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle.Google Scholar
- Tokyo. 2020. Anti-Coronavirus infection website. Retrieved Sept 15, 2020 from https://stopcovid19.metro.tokyo.lg.jp/Google Scholar
- Amy Wesolowski, Nathan Eagle, Andrew J Tatem, David L Smith, Abdisalan M Noor, Robert W Snow, and Caroline O Buckee. 2012. Quantifying the impact of human mobility on malaria. Science 338, 6104 (2012), 267--270.Google Scholar
- Takahiro Yabe, Kota Tsubouchi, Naoya Fujiwara, Takayuki Wada, Yoshihide Sekimoto, and Satish V Ukkusuri. 2020. Non-Compulsory Measures Sufficiently Reduced Human Mobility in Japan during the COVID-19 Epidemic. arXiv preprint arXiv:2005.09423 (2020).Google Scholar
Index Terms
How much does human mobility behavior affect the COVID-19 infection spread?: poster abstract
Recommendations
Data-driven mobility models for COVID-19 simulation
ARIC '20: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent CitiesAgent-based models (ABM) play a prominent role in guiding critical decision-making and supporting the development of effective policies for better urban resilience and response to the COVID-19 pandemic. However, many ABMs lack realistic representations ...
Characterizing the spread of COVID-19 from human mobility patterns and SocioDemographic indicators
ARIC '20: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent CitiesMobility is an indicator of human movement through space and time. With the increasing availability of geolocated data (from GPS, accelerometers, etc.), it is now possible to examine individual as well as group human mobility patterns. Human mobility is ...
Predicting the spread of COVID-19 in China with human mobility data
SIGSPATIAL '21: Proceedings of the 29th International Conference on Advances in Geographic Information SystemsThe coronavirus disease 2019 (COVID-19) break-out in late December 2019 has spread rapidly worldwide. Existing studies have shown that there is a significant correlation between large-scale human movements and the spread of the epidemic. However, there ...
Comments