Estimating Infection-Related Human Mobility Networks Based on Time Series Data of COVID-19 Infection in Japan
<i>Background:</i> Comprehensive and evidence-based countermeasures against emerging infectious diseases have become increasingly important in recent years. COVID-19 and many other infectious diseases are spread by human movement and contact, but complex transportation networks in the 21...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/18/9236 |
| _version_ | 1827663705126469632 |
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| author | Tetsuya Yamada Shoi Shi |
| author_facet | Tetsuya Yamada Shoi Shi |
| author_sort | Tetsuya Yamada |
| collection | DOAJ |
| description | <i>Background:</i> Comprehensive and evidence-based countermeasures against emerging infectious diseases have become increasingly important in recent years. COVID-19 and many other infectious diseases are spread by human movement and contact, but complex transportation networks in the 21st century make it difficult to predict disease spread in rapidly changing situations. It is especially challenging to estimate the network of infection transmission in countries where traffic and human movement data infrastructure is not yet developed. <i>Methods:</i> In this study, we devised a method utilizing an ordinary and partial differential equations-based mathematical model and a modified mathematical optimization method to estimate the network of transmission of COVID-19 from the time series data of its infection and applied it to determine its spread across areas in Japan. Furthermore, utilizing the estimated human mobility network, we predicted the spread of infection using the Tokyo Olympics as a model. <i>Findings:</i> We incorporated the effects of soft lockdowns, such as the declaration of a state of emergency, and changes in the infection network due to government-sponsored travel promotion, and revealed that the estimated effective distance captured human mobility changing dynamically in the different stages of the pandemic. The model predicted that the Tokyo Olympic and Paralympic Games would increase the number of infected cases in the host prefectures by up to 80%. <i>Interpretation:</i> The models used in this study are available online, and our data-driven infection network models are scalable, whether it be at the level of a city, town, country, or continent, and applicable anywhere in the world, as long as the time-series data of infections per region is available. These estimations of effective distance and the depiction of infectious disease networks based on actual infection data are expected to be useful in devising data-driven countermeasures against emerging infectious diseases worldwide. |
| first_indexed | 2024-03-10T00:49:23Z |
| format | Article |
| id | doaj.art-d68ae35e2f3940a59b5c30cf80737052 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T00:49:23Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d68ae35e2f3940a59b5c30cf807370522023-11-23T14:55:13ZengMDPI AGApplied Sciences2076-34172022-09-011218923610.3390/app12189236Estimating Infection-Related Human Mobility Networks Based on Time Series Data of COVID-19 Infection in JapanTetsuya Yamada0Shoi Shi1Faculty of Medicine, The University of Tokyo, Tokyo 113-0033, JapanInternational Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba 305-8575, Japan<i>Background:</i> Comprehensive and evidence-based countermeasures against emerging infectious diseases have become increasingly important in recent years. COVID-19 and many other infectious diseases are spread by human movement and contact, but complex transportation networks in the 21st century make it difficult to predict disease spread in rapidly changing situations. It is especially challenging to estimate the network of infection transmission in countries where traffic and human movement data infrastructure is not yet developed. <i>Methods:</i> In this study, we devised a method utilizing an ordinary and partial differential equations-based mathematical model and a modified mathematical optimization method to estimate the network of transmission of COVID-19 from the time series data of its infection and applied it to determine its spread across areas in Japan. Furthermore, utilizing the estimated human mobility network, we predicted the spread of infection using the Tokyo Olympics as a model. <i>Findings:</i> We incorporated the effects of soft lockdowns, such as the declaration of a state of emergency, and changes in the infection network due to government-sponsored travel promotion, and revealed that the estimated effective distance captured human mobility changing dynamically in the different stages of the pandemic. The model predicted that the Tokyo Olympic and Paralympic Games would increase the number of infected cases in the host prefectures by up to 80%. <i>Interpretation:</i> The models used in this study are available online, and our data-driven infection network models are scalable, whether it be at the level of a city, town, country, or continent, and applicable anywhere in the world, as long as the time-series data of infections per region is available. These estimations of effective distance and the depiction of infectious disease networks based on actual infection data are expected to be useful in devising data-driven countermeasures against emerging infectious diseases worldwide.https://www.mdpi.com/2076-3417/12/18/9236COVID-19Japanstate of emergencySEIR modeldiffusion equationMCMC |
| spellingShingle | Tetsuya Yamada Shoi Shi Estimating Infection-Related Human Mobility Networks Based on Time Series Data of COVID-19 Infection in Japan Applied Sciences COVID-19 Japan state of emergency SEIR model diffusion equation MCMC |
| title | Estimating Infection-Related Human Mobility Networks Based on Time Series Data of COVID-19 Infection in Japan |
| title_full | Estimating Infection-Related Human Mobility Networks Based on Time Series Data of COVID-19 Infection in Japan |
| title_fullStr | Estimating Infection-Related Human Mobility Networks Based on Time Series Data of COVID-19 Infection in Japan |
| title_full_unstemmed | Estimating Infection-Related Human Mobility Networks Based on Time Series Data of COVID-19 Infection in Japan |
| title_short | Estimating Infection-Related Human Mobility Networks Based on Time Series Data of COVID-19 Infection in Japan |
| title_sort | estimating infection related human mobility networks based on time series data of covid 19 infection in japan |
| topic | COVID-19 Japan state of emergency SEIR model diffusion equation MCMC |
| url | https://www.mdpi.com/2076-3417/12/18/9236 |
| work_keys_str_mv | AT tetsuyayamada estimatinginfectionrelatedhumanmobilitynetworksbasedontimeseriesdataofcovid19infectioninjapan AT shoishi estimatinginfectionrelatedhumanmobilitynetworksbasedontimeseriesdataofcovid19infectioninjapan |