The global impact of the COVID-19 pandemic is widely recognized as a significant concern, with human flow playing a crucial role in its propagation. Consequently, recent research has focused on identifying and analyzing factors that can effectively regulate human flow. However, among the multiple factors that are expected to have an effect, few studies have investigated those that are particularly associated with human flow during the COVID-19 pandemic. In addition, few studies have investigated how regional characteristics and the number of vaccinations for these factors affect human flow. Furthermore, increasing the number of verified cases in countries and regions with insufficient reports is important to generalize conclusions. Therefore, in this study, a group-level analysis was conducted for Narashino City, Chiba Prefecture, Japan, using a human flow prediction model based on machine learning. High-importance groups were subdivided by regional characteristics and the number of vaccinations, and visual and correlation analyses were conducted at the factor level. The findings indicated that tree-based models, especially LightGBM, performed better in terms of prediction. In addition, the cumulative number of vaccinated individuals and the number of newly infected individuals are likely explanatory factors for changes in human flow. The analyses suggested a tendency to move with respect to the number of newly infected individuals in Japan or Tokyo, rather than the number of new infections in the area where they lived when vaccination had not started. With the implementation of vaccination, attention to the number of newly infected individuals in their residential areas may increase. However, after the spread of vaccination, the perception of infection risk may decrease. These findings can contribute to the proposal of new measures for efficiently controlling human flows and determining when to mitigate or reinforce specific measures.
Citation: Masaya Mori, Yuto Omae, Yohei Kakimoto, Makoto Sasaki, Jun Toyotani. Analyzing factors of daily travel distances in Japan during the COVID-19 pandemic[J]. Mathematical Biosciences and Engineering, 2024, 21(8): 6936-6974. doi: 10.3934/mbe.2024305
The global impact of the COVID-19 pandemic is widely recognized as a significant concern, with human flow playing a crucial role in its propagation. Consequently, recent research has focused on identifying and analyzing factors that can effectively regulate human flow. However, among the multiple factors that are expected to have an effect, few studies have investigated those that are particularly associated with human flow during the COVID-19 pandemic. In addition, few studies have investigated how regional characteristics and the number of vaccinations for these factors affect human flow. Furthermore, increasing the number of verified cases in countries and regions with insufficient reports is important to generalize conclusions. Therefore, in this study, a group-level analysis was conducted for Narashino City, Chiba Prefecture, Japan, using a human flow prediction model based on machine learning. High-importance groups were subdivided by regional characteristics and the number of vaccinations, and visual and correlation analyses were conducted at the factor level. The findings indicated that tree-based models, especially LightGBM, performed better in terms of prediction. In addition, the cumulative number of vaccinated individuals and the number of newly infected individuals are likely explanatory factors for changes in human flow. The analyses suggested a tendency to move with respect to the number of newly infected individuals in Japan or Tokyo, rather than the number of new infections in the area where they lived when vaccination had not started. With the implementation of vaccination, attention to the number of newly infected individuals in their residential areas may increase. However, after the spread of vaccination, the perception of infection risk may decrease. These findings can contribute to the proposal of new measures for efficiently controlling human flows and determining when to mitigate or reinforce specific measures.
| [1] |
V. Saladino, D. Algeri, V. Auriemma, The psychological and social impact of Covid-19: new perspectives of well-being, Front. Psychol., (2020), 2550. https://doi.org/10.3389/fpsyg.2020.577684 doi: 10.3389/fpsyg.2020.577684
|
| [2] |
S. Shanbehzadeh, M. Tavahomi, N. Zanjari, I. Ebrahimi-Takamjani, S. Amiri-Arimi, Physical and mental health complications post-COVID-19: Scoping review, J. Psychosom. Res., 147 (2021), 110525. https://doi.org/10.1016/j.jpsychores.2021.110525 doi: 10.1016/j.jpsychores.2021.110525
|
| [3] |
I. Ali, O. M. Alharbi, COVID-19: Disease, management, treatment, and social impact, Sci. Total Environ., 728 (2020), 138861. https://doi.org/10.1016/j.scitotenv.2020.138861 doi: 10.1016/j.scitotenv.2020.138861
|
| [4] |
S. Pokhrel, R. Chhetri, A literature review on impact of COVID-19 pandemic on teaching and learning, Higher Educ. Future, 8 (2021), 133–141. https://doi.org/10.1177/2347631120983481 doi: 10.1177/2347631120983481
|
| [5] |
A. D. Kaye, C. N. Okeagu, A. D. Pham, R. A. Silva, J. J. Hurley, B. L. Arron, et al., Economic impact of COVID-19 pandemic on healthcare facilities and systems: International perspectives, Best Pract. Res. Clin. Anaesthesiol., 35 (2021), 293–306. https://doi.org/10.1016/j.bpa.2020.11.009 doi: 10.1016/j.bpa.2020.11.009
|
| [6] |
S. Naseer, S. Khalid, S. Parveen, K. Abbass, H. Song, M. V. Achim, COVID-19 outbreak: Impact on global economy, Front. Public Health, 10 (2023), 1009393. https://doi.org/10.3389/fpubh.2022.1009393 doi: 10.3389/fpubh.2022.1009393
|
| [7] |
M. Coccia, Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID, Sci. Total Environ., 729 (2020), 138474. https://doi.org/10.1016/j.scitotenv.2020.138474 doi: 10.1016/j.scitotenv.2020.138474
|
| [8] |
M. Coccia, How do low wind speeds and high levels of air pollution support the spread of COVID-19?, Atmos. Pollut. Res., 12 (2021), 437–445. https://doi.org/10.1016/j.apr.2020.10.002 doi: 10.1016/j.apr.2020.10.002
|
| [9] |
M. Coccia, The effects of atmospheric stability with low wind speed and of air pollution on the accelerated transmission dynamics of COVID-19, Int. J. Environ. Stud., 78 (2021), 1–27. https://doi.org/10.1080/00207233.2020.1802937 doi: 10.1080/00207233.2020.1802937
|
| [10] |
E. Bontempi, M. Coccia, International trade as critical parameter of COVID-19 spread that outclasses demographic, economic, environmental, and pollution factors, Environ. Res., 201 (2021), 111514. https://doi.org/10.1016/j.envres.2021.111514 doi: 10.1016/j.envres.2021.111514
|
| [11] |
E. Bontempi, M. Coccia, S. Vergalli, A. Zanoletti, Can commercial trade represent the main indicator of the COVID-19 diffusion due to human-to-human interactions? A comparative analysis between Italy, France, and Spain, Environ. Res., 201 (2021), 111529. https://doi.org/10.1016/j.envres.2021.111529 doi: 10.1016/j.envres.2021.111529
|
| [12] |
Y. Diao, S. Kodera, D. Anzai, J. Gomez-Tames, E. A. Rashed, A. Hirata, Influence of population density, temperature, and absolute humidity on spread and decay durations of COVID-19: A comparative study of scenarios in China, England, Germany, and Japan, One Health, 12 (2021), 100203. https://doi.org/10.1016/j.onehlt.2020.100203 doi: 10.1016/j.onehlt.2020.100203
|
| [13] |
C. Magazzino, M. Mele, M. Coccia, A machine learning algorithm to analyse the effects of vaccination on COVID-19 mortality, Epidemiol. Infect., 150 (2022), e168. https://doi.org/10.1017/S0950268822001418 doi: 10.1017/S0950268822001418
|
| [14] |
A. Nunez-Delgado, E. Bontempi, M. Coccia, M. Kumar, J. L. Domingo, SARS-CoV-2 and other pathogenic microorganisms in the environment, Environ. Res., 201 (2021), 111606. https://doi.org/10.1016/j.envres.2021.111606 doi: 10.1016/j.envres.2021.111606
|
| [15] |
M. Coccia, COVID-19 pandemic over 2020 (with lockdowns) and 2021 (with vaccinations): similar effects for seasonality and environmental factors, Environ. Res., 208 (2022), 112711. https://doi.org/10.1016/j.envres.2022.112711 doi: 10.1016/j.envres.2022.112711
|
| [16] |
H. Kato, A. Takizawa, Human mobility and infection from Covid-19 in the Osaka metropolitan area, npj Urban Sustainability, 2 (2022), 20. https://doi.org/10.1038/s42949-022-00066-w doi: 10.1038/s42949-022-00066-w
|
| [17] |
Y. Nohara, T. Manabe, Impact of human mobility and networking on spread of COVID-19 at the time of the 1st and 2nd epidemic waves in Japan: An effective distance approach, PLoS One, 17 (2022), e0272996. https://doi.org/10.1371/journal.pone.0272996 doi: 10.1371/journal.pone.0272996
|
| [18] |
A. Lison, J. Persson, N. Banholzer, S. Feuerriegel, Estimating the effect of mobility on SARS-CoV-2 transmission during the first and second wave of the COVID-19 epidemic, Switzerland, March to December 2020, Eurosurveillance, 27 (2022), 2100374. https://doi.org/10.2807/1560-7917.ES.2022.27.10.2100374 doi: 10.2807/1560-7917.ES.2022.27.10.2100374
|
| [19] |
K. Hibiya, A. Shinzato, H. Iwata, T. Kinjo, M. Tateyama, K. Yamamoto, et al., Effect of voluntary human mobility restrictions on vector-borne diseases during the COVID-19 pandemic in Japan: A descriptive epidemiological study using a national database (2016 to 2021), PLoS One, 18 (2023), e0285107. https://doi.org/10.1371/journal.pone.0285107 doi: 10.1371/journal.pone.0285107
|
| [20] |
M. Zhang, S. Wang, T. Hu, X. Fu, X. Wang, Y. Hu, et al., Human mobility and COVID-19 transmission: a systematic review and future directions, Ann. GIS, 28 (2022), 501–514. https://doi.org/10.1080/19475683.2022.2041725 doi: 10.1080/19475683.2022.2041725
|
| [21] |
N. Askitas, K. Tatsiramos, B. Verheyden, Estimating worldwide effects of non-pharmaceutical interventions on COVID-19 incidence and population mobility patterns using a multiple-event study, Sci. Rep., 11 (2021), 1972. https://doi.org/10.1038/s41598-021-81442-x doi: 10.1038/s41598-021-81442-x
|
| [22] |
R. S. John, J. C. Miller, R. L. Muylaert, D. T. Hayman, High connectivity and human movement limits the impact of travel time on infectious disease transmission, J. R. Soc. Interface, 21 (2024), 20230425. https://doi.org/10.1098/rsif.2023.0425 doi: 10.1098/rsif.2023.0425
|
| [23] |
J. Li, C. Zhuang, W. Zou, A tale of lockdown policies on the transmission of COVID-19 within and between Chinese cities: A study based on heterogeneous treatment effect, Econ. Hum. Biol., 53 (2024), 101365. https://doi.org/10.1016/j.ehb.2024.101365 doi: 10.1016/j.ehb.2024.101365
|
| [24] |
M. G. Thompson, J. L. Burgess, A. L. Naleway, H. Tyner, S. K. Yoon, J. Meece, et al., Prevention and attenuation of Covid-19 with the BNT162b2 and mRNA-1273 vaccines, N. Engl. J. Med., 385 (2021), 320–329. https://doi.org/10.1056/NEJMoa2107058 doi: 10.1056/NEJMoa2107058
|
| [25] |
V. Hall, S. Foulkes, F. Insalata, P. Kirwan, A. Saei, A. Atti, et al., Protection against SARS-CoV-2 after Covid-19 vaccination and previous infection, N. Engl. J. Med., 386 (2022), 1207–1220. https://doi.org/10.1056/NEJMoa2118691 doi: 10.1056/NEJMoa2118691
|
| [26] |
S. J. Thomas, E. D. Moreira, N. Kitchin, J. Absalon, A. Gurtman, S. Lockhart, et al., Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine through 6 months, N. Engl. J. Med., 385 (2021), 1761–1773. https://doi.org/10.1056/NEJMoa2110345 doi: 10.1056/NEJMoa2110345
|
| [27] |
J. Sadoff, G. Gray, A. Vandebosch, V. C..c3..a1rdenas, G. Shukarev, B. Grinsztejn, et al., Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine through 6 months, N. Engl. J. Med., 384 (2021), 2187–2201. https://doi.org/10.1056/NEJMoa2101544 doi: 10.1056/NEJMoa2101544
|
| [28] |
Y. Z. Huang, C. C. Kuan, Vaccination to reduce severe COVID-19 and mortality in COVID-19 patients: a systematic review and meta-analysis, Eur. Rev. Med. Pharmacol. Sci., 26 (2022). https://doi.org/10.26355/eurrev_202203_28248 doi: 10.26355/eurrev_202203_28248
|
| [29] |
B. Trogen, A. Caplan, Risk compensation and COVID-19 vaccines, Ann. Intern. Med., 174 (2021), 858–859. https://doi.org/10.7326/M20-8251 doi: 10.7326/M20-8251
|
| [30] |
K. P. Iyengar, P. Ish, R. Botchu, V. K. Jain, R. Vaishya, Influence of the Peltzman effect on the recurrent COVID-19 waves in Europe, Postgrad. Med. J., 98 (2022), e110–e111. https://doi.org/10.1136/postgradmedj-2021-140234 doi: 10.1136/postgradmedj-2021-140234
|
| [31] |
J. Guo, C. Deng, F. Gu, Vaccinations, mobility and COVID-19 transmission, Int. J. Environ. Res. Public Health, 19 (2021), 97. https://doi.org/10.3390/ijerph19010097 doi: 10.3390/ijerph19010097
|
| [32] |
L. L. Liang, H. M. Le, C. Y. Wu, C. Y. Sher, A. McGuire, Human mobility increased with vaccine coverage and attenuated the protection of COVID-19 vaccination: A longitudinal study of 107 countries, J. Glob. Health, 13 (2023). https://doi.org/10.7189/jogh.13.06009 doi: 10.7189/jogh.13.06009
|
| [33] |
M. Coccia, Improving preparedness for next pandemics: Max level of COVID-19 vaccinations without social impositions to design effective health policy and avoid flawed democracies, Environ. Res., 213 (2022), 113566. https://doi.org/10.1016/j.envres.2022.113566 doi: 10.1016/j.envres.2022.113566
|
| [34] |
H. Barbosa, S. Hazarie, B. Dickinson, A. Bassolas, A. Frank, H. Kautz, et al., Uncovering the socioeconomic facets of human mobility, Sci. Rep., 11 (2021), 8616. https://doi.org/10.1038/s41598-021-87407-4 doi: 10.1038/s41598-021-87407-4
|
| [35] | C. Kang, S. Gao, X. Lin, Y. Xiao, Y. Yuan, Y. Liu, et al., Analyzing and geo-visualizing individual human mobility patterns using mobile call records, in Proceedings of the 18th International Conference on Geoinformatics, (2010), 1–7. https://doi.org/10.1109/GEOINFORMATICS.2010.5567857 |
| [36] |
A. Paez, F. A. Lopez, T. Menezes, R. Cavalcanti, M. G. d. R. Pitta, A spatio‐temporal analysis of the environmental correlates of COVID‐19 incidence in Spain, Geogr. Anal., 53 (2021), 397–421. https://doi.org/10.1111/gean.12241 doi: 10.1111/gean.12241
|
| [37] |
T. Chowdhury, H. Chowdhury, E. Bontempi, M. Coccia, H. Masrur, S. M. Sait, et al., Are mega-events super spreaders of infectious diseases similar to COVID-19? A look into Tokyo 2020 Olympics and Paralympics to improve preparedness of next international events, Environ. Sci. Pollut. Res., 30 (2023), 10099–10109. https://doi.org/10.1007/s11356-022-22660-2 doi: 10.1007/s11356-022-22660-2
|
| [38] |
M. Murakami, K. Fujii, W. Naito, M. Kamo, M. Kitajima, T. Yasutaka, et al., COVID-19 infection risk assessment and management at the Tokyo 2020 Olympic and Paralympic Games: A scoping review, J. Infect. Public Health, 17 (2024), 18–26. https://doi.org/10.1016/j.jiph.2023.03.025 doi: 10.1016/j.jiph.2023.03.025
|
| [39] |
S. Hu, C. Xiong, M. Yang, H. Younes, W. Luo, L. Zhang, A big-data driven approach to analyzing and modeling human mobility trend under non-pharmaceutical interventions during COVID-19 pandemic, Transp. Res. Part C: Emerg. Technol., 124 (2021), 102955. https://doi.org/10.1016/j.trc.2020.102955 doi: 10.1016/j.trc.2020.102955
|
| [40] |
D. Nakamoto, S. Nojiri, C. Taguchi, Y. Kawakami, S. Miyazawa, M. Kuroki, et al., The impact of declaring the state of emergency on human mobility during COVID-19 pandemic in Japan, Clin. Epidemiol. Glob. Health, 17 (2022), 101149. https://doi.org/10.1016/j.cegh.2022.101149 doi: 10.1016/j.cegh.2022.101149
|
| [41] |
M. Chakraborty, M. S. Mahmud, T. J. Gates, S. Sinha, Analysis and prediction of human mobility in the United States during the early stages of the COVID-19 pandemic using regularized linear models, Transp. Res. Rec., 2677 (2023), 380–395. https://doi.org/10.1177/03611981211067794 doi: 10.1177/03611981211067794
|
| [42] |
M. U. G. Kraemer, C. H. Yang, B. Gutierrez, C. H. Wu, B. Klein, D. M. Pigott, et al., The effect of human mobility and control measures on the COVID-19 epidemic in China, Science, 368 (2020), 493–497. https://doi.org/10.1126/science.abb4218 doi: 10.1126/science.abb4218
|
| [43] |
H. Fang, L. Wang, Y. Yang, Human mobility restrictions and the spread of the novel coronavirus (2019-nCoV) in China, J. Public Econ., 191 (2020), 104272. https://doi.org/10.1016/j.jpubeco.2020.104272 doi: 10.1016/j.jpubeco.2020.104272
|
| [44] |
J. Y. L. Chan, S. M. H. Leow, K. T. Bea, W. K. Cheng, S. W. Phoong, Z. W. Hong, et al., Mitigating the multicollinearity problem and its machine learning approach: a review, Mathematics, 10 (2022), 1283. https://doi.org/10.3390/math10081283 doi: 10.3390/math10081283
|
| [45] |
C. Strobl, A. L. Boulesteix, T. Kneib, T. Augustin, A. Zeileis, Conditional variable importance for random forests, BMC Bioinf., 9 (2008), 1–11. https://doi.org/10.1186/1471-2105-9-307 doi: 10.1186/1471-2105-9-307
|
| [46] | Prime Minister's Office of Japan, Prime minister's office of Japan. Available from: https://japan.kantei.go.jp. |
| [47] | Ministry of Health, Labour and Welfare of Japan, Welcome to ministry of health, labour and welfare. Available from: https://www.mhlw.go.jp/english. |
| [48] | Tokyo Metropolitan Government, Home-tokyo metropolitan government. Available from: https://www.metro.tokyo.lg.jp/english/index.html. |
| [49] | Japan Meteorological Agency, Japan meteorological agency. Available from: https://www.jma.go.jp/jma/indexe.html. |
| [50] | Portal Site of Official Statistics of Japan, Portal site of official statistics of Japan. Available from: https://www.e-stat.go.jp/en. |
| [51] |
T. Watanabe, T. Yabu, Japan's voluntary lockdown, PLoS One, 16 (2021), e0252468. https://doi.org/10.1371/journal.pone.0252468 doi: 10.1371/journal.pone.0252468
|
| [52] |
Q. He, Z. Zhang, Y. Xie, The impact of COVID-19 on Americans' attitudes toward China: Does local incidence rate matter?, Soc. Psychol. Q., 85 (2022), 84–107. https://doi.org/10.1177/01902725211072773 doi: 10.1177/01902725211072773
|
| [53] | X. Chen, X. Di, How the covid-19 pandemic influences human mobility? similarity analysis leveraging social media data, in Proceedings of the IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), IEEE, (2022), 2955–2960. https://doi.org/10.1109/ITSC55140.2022.9922060 |
| [54] |
J. Yuan, M. Li, G. Lv, Z. K. Lu, Monitoring transmissibility and mortality of COVID-19 in Europe, Int. J. Infect. Dis., 95 (2020), 311–315. https://doi.org/10.1016/j.ijid.2020.03.050 doi: 10.1016/j.ijid.2020.03.050
|
| [55] |
H. Ito, N. Kawazoe, Examining transportation mode changes during COVID-19 in Toyama, Japan, Reg. Stud. Reg. Sci., 10 (2023), 253–272. https://doi.org/10.1080/21681376.2023.2180425 doi: 10.1080/21681376.2023.2180425
|
| [56] |
Y. Hara, H. Yamaguchi, Japanese travel behavior trends and change under COVID-19 state-of-emergency declaration: Nationwide observation by mobile phone location data, Transp. Res. Interdiscip. Perspect., 9 (2021), 100288. https://doi.org/10.1016/j.trip.2020.100288 doi: 10.1016/j.trip.2020.100288
|
| [57] |
F. Benita, Human mobility behavior in COVID-19: A systematic literature review and bibliometric analysis, Sustainable Cities Soc., 70 (2021), 102916. https://doi.org/10.1016/j.scs.2021.102916 doi: 10.1016/j.scs.2021.102916
|
| [58] |
E. Thuillier, L. Moalic, S. Lamrous, A. Caminada, Clustering weekly patterns of human mobility through mobile phone data, IEEE Trans. Mobile Comput., 17 (2017), 817–830. https://doi.org/10.1109/TMC.2017.2742953 doi: 10.1109/TMC.2017.2742953
|
| [59] | T. Mahajan, G. Singh, G. Bruns, An experimental assessment of treatments for cyclical data, in Proceedings of the 2021 Computer Science Conference for CSU Undergraduates, Virtual, 6 (2021), 22. |
| [60] |
T. Horanont, S. Phithakkitnukoon, T. W. Leong, Y. Sekimoto, R. Shibasaki, Weather effects on the patterns of people's everyday activities: a study using GPS traces of mobile phone users, PLoS One, 8 (2013), e81153. https://doi.org/10.1371/journal.pone.0081153 doi: 10.1371/journal.pone.0081153
|
| [61] |
M. Yuan, Y. Lin, Model selection and estimation in regression with grouped variables, J. R. Stat. Soc. Ser. B: Stat. Methodol., 68 (2006), 49–67. https://doi.org/10.1111/j.1467-9868.2005.00532.x doi: 10.1111/j.1467-9868.2005.00532.x
|
| [62] | J. Friedman, T. Hastie, R. Tibshirani, A note on the group lasso and a sparse group lasso, preprint, arXiv: 1001.0736. https://doi.org/10.48550/arXiv.1001.0736 |
| [63] |
N. Simon, J. Friedman, T. Hastie, R. Tibshirani, A sparse-group lasso, J. Comput. Graph. Stat., 22 (2013), 231–245. https://doi.org/10.1080/10618600.2012.681250 doi: 10.1080/10618600.2012.681250
|
| [64] |
L. Breiman, Random forests, Mach. Learn., 45 (2001), 5–32. https://doi.org/10.1023/A:1010933404324 doi: 10.1023/A:1010933404324
|
| [65] |
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, et al., Scikit-learn: Machine learning in Python, J. Mach. Learn. Res., 12 (2011), 2825–2830. https://doi.org/10.48550/arXiv.1201.0490 doi: 10.48550/arXiv.1201.0490
|
| [66] | G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, et al., Lightgbm: A highly efficient gradient boosting decision tree, Adv. Neural Inf. Process. Syst., 30 (2017). |
| [67] | T. Akiba, S. Sano, T. Yanase, T. Ohta, M. Koyama, Optuna: A next-generation hyperparameter optimization framework, in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, (2019), 2623–2631. https://doi.org/10.1145/3292500.3330701 |
| [68] |
B. Gregorutti, B. Michel, P. Saint-Pierre, Grouped variable importance with random forests and application to multiple functional data analysis, Comput. Stat. Data Anal., 90 (2015), 15–35. https://doi.org/10.1016/j.csda.2015.04.002 doi: 10.1016/j.csda.2015.04.002
|
| [69] | T. Hastie, R. Tibshirani, J. H. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2 (2009). https://doi.org/10.1007/978-0-387-21606-5 |
| [70] |
S. Luo, T. Chen, Two derivative algorithms of gradient boosting decision tree for silicon content in blast furnace system prediction, IEEE Access, 8 (2020), 196112–196122. https://doi.org/10.1109/ACCESS.2020.3034566 doi: 10.1109/ACCESS.2020.3034566
|
| [71] |
X. Zeng, Length of stay prediction model of indoor patients based on light gradient boosting machine, Comput. Intell. Neurosci., 2022. https://doi.org/10.1155/2022/9517029 doi: 10.1155/2022/9517029
|
| [72] |
Y. Hu, Z. Sun, Y. Han, W. Li, L. Pei, Evaluate pavement skid resistance performance based on Bayesian-LightGBM using 3D surface macrotexture data, Materials, 15 (2022), 5275. https://doi.org/10.3390/ma15155275 doi: 10.3390/ma15155275
|
| [73] |
S. Okamoto, State of emergency and human mobility during the COVID-19 pandemic in Japan, J. Transp. Health, 26 (2022), 101405. https://doi.org/10.1016/j.jth.2022.101405 doi: 10.1016/j.jth.2022.101405
|
| [74] | S. Fukui, Long-term changes in human mobility responses to COVID-19-related information in Japan, medRxiv, (2022), 2022–08. https://doi.org/10.1101/2022.08.15.22278703 |
| [75] |
K. Henk, F. Rosing, F. Wolff, S. B. Frenzel, R. van Dick, V. A. Erkens, et al., An examination and extension of the Peltzman effect during the Covid-19 pandemic, Curr. Res. Ecol. Soc. Psychol., 4 (2023), 100091. https://doi.org/10.1016/j.cresp.2023.100091 doi: 10.1016/j.cresp.2023.100091
|
| [76] |
S. Peltzman, The effects of automobile safety regulation, J. Polit. Econ., 83 (1975), 677–725. https://doi.org/10.1086/260352 doi: 10.1086/260352
|
| [77] |
R. O. Nanda, A. A. Nursetyo, A. L. Ramadona, M. A. Imron, A. Fuad, A. Setyawan, et al., Community mobility and COVID-19 dynamics in Jakarta, Indonesia, Int. J. Environ. Res. Public Health, 19 (2022), 6671. https://doi.org/10.3390/ijerph19116671 doi: 10.3390/ijerph19116671
|
| [78] |
M. Coccia, COVID-19 vaccination is not a sufficient public policy to face crisis management of next pandemic threats, Public Organ. Rev., 23 (2023), 1353–1367. https://doi.org/10.1007/s11115-022-00661-6 doi: 10.1007/s11115-022-00661-6
|
| [79] |
M. Shibamoto, S. Hayaki, Y. Ogisu, COVID-19 infection spread and human mobility, J. Jap. Int. Econ., 64 (2022), 101195. https://doi.org/10.1016/j.jjie.2022.101195 doi: 10.1016/j.jjie.2022.101195
|
| [80] |
H. Kwon, C. Koylu, Revealing associations between spatial time series trends of COVID-19 incidence and human mobility: an analysis of bidirectionality and spatiotemporal heterogeneity, Int. J. Health Geogr., 22 (2023), 33. https://doi.org/10.1186/s12942-023-00357-0 doi: 10.1186/s12942-023-00357-0
|
| [81] |
A. Serisier, S. Beale, Y. Boukari, S. Hoskins, V. Nguyen, T. Byrne, et al., A case-crossover study of the effect of vaccination on SARS-CoV-2 transmission relevant behaviours during a period of national lockdown in England and Wales, Vaccine, 41 (2023), 511–518. https://doi.org/10.1016/j.vaccine.2022.11.073 doi: 10.1016/j.vaccine.2022.11.073
|
| [82] |
K. L. Andrejko, J. R. Head, J. A. Lewnard, J. V. Remais, Longitudinal social contacts among school-aged children during the COVID-19 pandemic: the Bay Area Contacts among Kids (BACK) study, BMC Infect. Dis., 22 (2022), 242. https://doi.org/10.1186/s12879-022-07218-4 doi: 10.1186/s12879-022-07218-4
|
| [83] | Ministry of Health, Labour and Welfare of Japan, COVID-19 Vaccine Q & A. Available from: https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/vaccine_qa_archive.html#effect_4. |
| [84] | Nippon Television Network Corporation, Corporate data. Available from: https://www.ntv.co.jp/english/an/cd.html. |
| [85] | TV Asahi Corporation, Corporate profile. Available from: https://company.tv-asahi.co.jp/e/profile/index.html. |
| [86] | TBS HOLDINGS, INC., Corporate profile. Available from: https://www.tbsholdings.co.jp/en/about/corporate/companyprofile.html. |
| [87] | TV TOKYO Corporation, Corporate profile. Available from: https://www.tv-tokyo.co.jp/kaisha/company/profile.html. |
| [88] | Fuji Television Network, Inc., Corporate profile. Available from: https://www.fujitv.com/about/corporate_profile. |
| [89] |
M. Coccia, Effects of strict containment policies on COVID-19 pandemic crisis: lessons to cope with next pandemic impacts, Environ. Sci. Pollut. Res., 30 (2023), 2020–2028. https://doi.org/10.1007/s11356-022-22024-w doi: 10.1007/s11356-022-22024-w
|
| [90] |
S. Lee, E. Ko, K. Jang, S. Kim, Understanding individual-level travel behavior changes due to COVID-19: Trip frequency, trip regularity, and trip distance, Cities, 135 (2023), 104223. https://doi.org/10.1016/j.cities.2023.104223 doi: 10.1016/j.cities.2023.104223
|
| [91] |
I. Benati, M. Coccia, Global analysis of timely COVID-19 vaccinations: improving governance to reinforce response policies for pandemic crises, Int. J. Health Gov., 27 (2022), 240–253. https://doi.org/10.1108/IJHG-07-2021-0072 doi: 10.1108/IJHG-07-2021-0072
|
| [92] |
M. Coccia, Optimal levels of vaccination to reduce COVID-19 infected individuals and deaths: A global analysis, Environ. Res., 204 (2022), 112314. https://doi.org/10.1016/j.envres.2021.112314 doi: 10.1016/j.envres.2021.112314
|
| [93] |
M. Coccia, I. Benati, Negative effects of high public debt on health systems facing pandemic crisis: Lessons from COVID-19 in Europe to prepare for future emergencies, AIMS Public Health, 11 (2024), 477–498. https://doi.org/10.3934/publichealth.2024024 doi: 10.3934/publichealth.2024024
|
| [94] |
M. Coccia, Preparedness of countries to face COVID-19 pandemic crisis: strategic positioning and factors supporting effective strategies of prevention of pandemic threats, Environ. Res., 203 (2022), 111678. https://doi.org/10.1016/j.envres.2021.111678 doi: 10.1016/j.envres.2021.111678
|
| [95] |
M. Coccia, Pandemic prevention: lessons from COVID-19, Encyclopedia, 1 (2021), 36. https://doi.org/10.3390/encyclopedia1020036 doi: 10.3390/encyclopedia1020036
|
| [96] |
M. Coccia, Sources, diffusion and prediction in COVID-19 pandemic: lessons learned to face next health emergency, AIMS Public Health, 10 (2023), 145. https://doi.org/10.3934/publichealth.2023012 doi: 10.3934/publichealth.2023012
|
| [97] |
I. Benati, M. Coccia, Effective contact tracing system minimizes COVID-19 related infections and deaths: policy lessons to reduce the impact of future pandemic diseases, J. Public Admin. Govern., 12 (2022). https://doi.org/10.5296/jpag.v12i2.19834 doi: 10.5296/jpag.v12i2.19834
|
| [98] |
S. Khalilpourazari, H. H. Doulabi, A. Ö. Çiftçioğlu, G. W. Weber, Gradient-based grey wolf optimizer with Gaussian walk: Application in modelling and prediction of the COVID-19 pandemic, Expert Syst. Appl., 177 (2021), 114920. https://doi.org/10.1016/j.eswa.2021.114920 doi: 10.1016/j.eswa.2021.114920
|
| [99] | E. Savku, N. Azevedo, G. Weber, Optimal control of stochastic hybrid models in the framework of regime switches, in Modeling, Dynamics, Optimization and Bioeconomics Ⅱ: DGS Ⅲ, Porto, Portugal, February 2014, and Bioeconomy Ⅶ, Berkeley, USA, March 2014-Selected Contributions 3, Springer, (2014), 371–387. https://doi.org/10.1007/978-3-319-55236-1_18 |
| [100] |
A. Özmen, E. Kropat, G. W. Weber, Robust optimization in spline regression models for multi-model regulatory networks under polyhedral uncertainty, Optimization, 66 (2017), 2135–2155. https://doi.org/10.1080/02331934.2016.1209672 doi: 10.1080/02331934.2016.1209672
|
| [101] |
E. Kropat, A. Özmen, G. W. Weber, S. Meyer-Nieberg, O. Defterli, Fuzzy prediction strategies for gene-environment networks–Fuzzy regression analysis for two-modal regulatory systems, RAIRO-Oper. Res., 50 (2016), 413–435. https://doi.org/10.1051/ro/2015044 doi: 10.1051/ro/2015044
|
| [102] |
E. Kropat, G. W. Weber, Fuzzy target-environment networks and fuzzy-regression approaches, Numer. Algebra Control Optim., 8 (2018), 135–155. https://doi.org/10.3934/naco.2018008 doi: 10.3934/naco.2018008
|
| [103] |
E. Özceylan, T. Paksoy, Fuzzy multi-objective linear programming approach for optimising a closed-loop supply chain network, Int. J. Prod. Res., 51 (2013), 2443–2461. https://doi.org/10.1080/00207543.2012.740579 doi: 10.1080/00207543.2012.740579
|
| [104] |
P. Haimerl, T. Hartl, Modeling COVID-19 infection rates by regime-switching unobserved components models, Econometrics, 11 (2023), 10. https://doi.org/10.3390/econometrics11020010 doi: 10.3390/econometrics11020010
|
| [105] |
R. Ananda, L. Harsyiah, M. Alfian, Classification of perceptions of the Covid-19 vaccine using multivariate adaptive regression spline, J. Varian, 6 (2023), 137–148. https://doi.org/10.30812/varian.v6i2.2639 doi: 10.30812/varian.v6i2.2639
|
| [106] |
F. Chen, H. Nakanishi, Y. Sekizawa, S. Ochi, M. So, Investigating the causal effects of COVID-19 vaccination on the adoption of protective behaviors in Japan: Insights from a fuzzy regression discontinuity design, PLoS One, 19 (2024), e0305043. https://doi.org/10.1371/journal.pone.0305043 doi: 10.1371/journal.pone.0305043
|
| [107] |
A. Szmelter-Jarosz, J. Ghahremani-Nahr, H. Nozari, A neutrosophic fuzzy optimisation model for optimal sustainable closed-loop supply chain network during COVID-19, J. Risk Financ. Manage., 14 (2021), 519. https://doi.org/10.3390/jrfm14110519 doi: 10.3390/jrfm14110519
|
Figures(8) / Tables(6)
Masaya Mori, Yuto Omae, Yohei Kakimoto, Makoto Sasaki, Jun Toyotani. Analyzing factors of daily travel distances in Japan during the COVID-19 pandemic[J]. Mathematical Biosciences and Engineering, 2024, 21(8): 6936-6974. doi: 10.3934/mbe.2024305
DownLoad: