{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:42:58Z","timestamp":1787028178723,"version":"3.56.0"},"reference-count":38,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T00:00:00Z","timestamp":1750896000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Tokyo Tech World Research Hub Initiative (WRHI) Program of the Institute of Innovative Research, Tokyo Institute of Technology","award":["25K08167"],"award-info":[{"award-number":["25K08167"]}]},{"name":"JSPS KAKENHI","award":["25K08167"],"award-info":[{"award-number":["25K08167"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>This study investigates the feasibility of using GPS data and frequency of COVID-19-related blog words to forecast new infection trends through a linear regression analysis. By employing time series\u2019 trend decomposition and Spearman\u2019s rank correlation, we identify and select a set of significant variables from the GPS and blog data to construct two models: a fixed-period model and a sequential adaptive model that updates with each new wave of infections. Our findings reveal that the adaptive model more effectively captures long-term trends, achieving approximately 90% accuracy in forecasting infection rates seven days in advance. Despite challenges in forecasting exact values, this research demonstrates that combining GPS and blog data through a dynamic, wave-based learning model offers a promising direction for enhancing the forecasting accuracy of COVID-19 spread. This approach has significant implications for public health preparedness.<\/jats:p>","DOI":"10.3390\/e27070686","type":"journal-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T03:33:25Z","timestamp":1750995205000},"page":"686","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Forecast Model for COVID-19 Spread Trends Using Blog and GPS Data from Smartphones"],"prefix":"10.3390","volume":"27","author":[{"given":"Ryosuke","family":"Susuta","sequence":"first","affiliation":[{"name":"School of Computing, Institute of Science Tokyo, 4259 Nagatsuta-cho, Midori-ku, Yokohama 226-8502, Kanagawa, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenta","family":"Yamada","sequence":"additional","affiliation":[{"name":"Faculty of Global and Regional Studies, University of the Ryukyus, 1 Senbaru, Nishihara 903-0213, Okinawa, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hideki","family":"Takayasu","sequence":"additional","affiliation":[{"name":"School of Computing, Institute of Science Tokyo, 4259 Nagatsuta-cho, Midori-ku, Yokohama 226-8502, Kanagawa, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2467-614X","authenticated-orcid":false,"given":"Misako","family":"Takayasu","sequence":"additional","affiliation":[{"name":"School of Computing, Institute of Science Tokyo, 4259 Nagatsuta-cho, Midori-ku, Yokohama 226-8502, Kanagawa, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,26]]},"reference":[{"key":"ref_1","first-page":"700","article-title":"A contribution to the mathematical theory of epidemics","volume":"115","author":"Kermack","year":"1927","journal-title":"Proc. R. Soc. London Ser. A Contain. Pap. A Math. Phys. Character"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/0025-5564(95)92756-5","article-title":"Global stability for the SEIR model in epidemiology","volume":"125","author":"Li","year":"1995","journal-title":"Math. Biosci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3913","DOI":"10.1007\/s10489-020-01770-9","article-title":"A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19","volume":"50","author":"Mohamadou","year":"2020","journal-title":"Appl. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"23671","DOI":"10.1007\/s00521-020-05626-8","article-title":"A review on COVID-19 forecasting models","volume":"35","author":"Rahimi","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_5","first-page":"18807","article-title":"Interpretable sequence learning for COVID-19 forecasting","volume":"33","author":"Arik","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"106610","DOI":"10.1016\/j.asoc.2020.106610","article-title":"Forecasting of COVID19 per regions using ARIMA models and polynomial functions","volume":"96","author":"Fujita","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1007\/s40808-020-00890-y","article-title":"Spatial prediction of COVID-19 epidemic using ARIMA techniques in India","volume":"7","author":"Roy","year":"2021","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, Y., Yan, Z., Wang, D., Yang, M., Li, Z., Gong, X., Wu, D., Zhai, L., Zhang, W., and Wang, Y. (2022). Prediction and analysis of COVID-19 daily new cases and cumulative cases: Times series forecasting and machine learning models. BMC Infect. Dis., 22.","DOI":"10.1186\/s12879-022-07472-6"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1016\/j.procs.2021.01.036","article-title":"Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET","volume":"179","author":"Satrio","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1140\/epjds\/s13688-017-0124-6","article-title":"Are you getting sick? Predicting influenza-like symptoms using human mobility behaviors","volume":"6","author":"Barlacchi","year":"2017","journal-title":"EPJ Data Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Dong, G., Cai, L., Datta, D., Kumar, S., Barnes, L.E., and Boukhechba, M. (2021, January 8\u201310). Influenza-like symptom recognition using mobile sensing and graph neural networks. Proceedings of the Conference on Health, Inference, and Learning, Virtual.","DOI":"10.1145\/3450439.3451880"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ozaki, J., Shida, Y., Takayasu, H., and Takayasu, M. (2022). Direct modelling from GPS data reveals daily-activity-dependency of effective reproduction number in COVID-19 pandemic. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-22420-9"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, S., Wang, Y., Xue, J., Zhao, N., and Zhu, T. (2020). The Impact of COVID-19 Epidemic Declaration on Psychological Consequences: A Study on Active Weibo Users. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17062032"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"378","DOI":"10.4103\/1995-7645.279651","article-title":"Using Twitter and web news mining to predict COVID-19 outbreak","volume":"13","author":"Jahanbin","year":"2020","journal-title":"Asian Pac. J. Trop. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"102993","DOI":"10.1016\/j.scs.2021.102993","article-title":"SNS big data analysis framework for COVID-19 outbreak prediction in smart healthy city","volume":"71","author":"Azzaoui","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"295","DOI":"10.12681\/psy_hps.39612","article-title":"Mental health information-seeking in Greece from the Global Financial Crisis to the COVID-19 pandemic: A multiple change-point Google Trends analysis","volume":"29","author":"Parpoula","year":"2024","journal-title":"Psychol. J. Hell. Psychol. Soc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s43856-025-00896-6","article-title":"Utilizing Google Trends data to enhance forecasts and monitor long COVID prevalence","volume":"5","author":"Chu","year":"2025","journal-title":"Commun. Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1177\/09622802221079347","article-title":"A distribution-free control charting technique based on change-point analysis for detection of epidemics","volume":"31","author":"Parpoula","year":"2022","journal-title":"Stat. Methods Med. Res."},{"key":"ref_19","unstructured":"Parpoula, C., Karagrigoriou, A., and Lambrou, A. (2017, January 15\u201317). Epidemic intelligence statistical modelling for biosurveillance. Proceedings of the Mathematical Aspects of Computer and Information Sciences: 7th International Conference, MACIS 2017, Vienna, Austria. Proceedings 7."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1038\/nature07634","article-title":"Detecting influenza epidemics using search engine query data","volume":"457","author":"Ginsberg","year":"2009","journal-title":"Nature"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Cook, S., Conrad, C., Fowlkes, A.L., and Mohebbi, M.H. (2011). Assessing Google flu trends performance in the United States during the 2009 influenza virus A (H1N1) pandemic. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0023610"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1126\/science.1248506","article-title":"The parable of Google Flu: Traps in big data analysis","volume":"343","author":"Lazer","year":"2014","journal-title":"Science"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yamada, K., Takayasu, H., and Takayasu, M. (2018). Estimation of Economic Indicator Announced by Government From Social Big Data. Entropy, 20.","DOI":"10.3390\/e20110852"},{"key":"ref_24","unstructured":"(2024, March 05). Information Available in English|Ministry of Health, Labour and Welfare|Government of Japan|\u539a\u751f\u52b4\u50cd\u7701. Available online: https:\/\/www.mhlw.go.jp\/stf\/english\/index.html."},{"key":"ref_25","unstructured":"(2023, November 30). GiNZA\u2014Japanese NLP Library|Universal Dependencies\u306b\u57fa\u3065\u304f\u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9\u65e5\u672c\u8a9eNLP\u30e9\u30a4\u30d6\u30e9\u30ea. Available online: https:\/\/megagonlabs.github.io\/ginza\/."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"012805","DOI":"10.1103\/PhysRevE.87.012805","article-title":"Empirical analysis of collective human behavior for extraordinary events in the blogosphere","volume":"87","author":"Sano","year":"2013","journal-title":"Phys. Rev. E"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1007\/s10994-014-5460-1","article-title":"Using causal discovery for feature selection in multivariate numerical time series","volume":"101","author":"Sun","year":"2015","journal-title":"Mach. Learn."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e19702","DOI":"10.2196\/19702","article-title":"Correlations of online search engine trends with coronavirus disease (COVID-19) incidence: Infodemiology study","volume":"6","author":"Higgins","year":"2020","journal-title":"JMIR Public Health Surveill."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e19421","DOI":"10.2196\/19421","article-title":"Using reports of symptoms and diagnoses on social media to predict COVID-19 case counts in mainland China: Observational infoveillance study","volume":"22","author":"Shen","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yamashita Rios de Sousa, A.M., Takayasu, H., and Takayasu, M. (2020). Segmentation of time series in up-and down-trends using the epsilon-tau procedure with application to USD\/JPY foreign exchange market data. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0239494"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chicco, D., and Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom., 21.","DOI":"10.1186\/s12864-019-6413-7"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Boughorbel, S., Jarray, F., and El-Anbari, M. (2017). Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0177678"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"72","DOI":"10.2307\/1412159","article-title":"The Proof and Measurement of Association between Two Things","volume":"15","author":"Spearman","year":"1904","journal-title":"Am. J. Psychol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Tang, N., Yuan, M., Chen, Z., Ma, J., Sun, R., Yang, Y., He, Q., Guo, X., Hu, S., and Zhou, J. (2023). Machine learning prediction model of tuberculosis incidence based on meteorological factors and air pollutants. Int. J. Environ. Res. Public Health, 20.","DOI":"10.3390\/ijerph20053910"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"012009","DOI":"10.1088\/1742-6596\/949\/1\/012009","article-title":"Multicollinearity and Regression Analysis","volume":"949","author":"Daoud","year":"2017","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_37","unstructured":"Gu, Q., Zhu, L., and Cai, Z. (2009, January 23\u201325). Evaluation measures of the classification performance of imbalanced data sets. Proceedings of the Computational Intelligence and Intelligent Systems: 4th International Symposium, ISICA 2009, Huangshi, China. Proceedings 4."},{"key":"ref_38","unstructured":"(2025, June 19). Available online: https:\/\/github.com\/Susutem\/A-forecast-model-for-COVID-19-spread-trends-using-blog-and-GPS-data-from-smartphones."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/7\/686\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:59:35Z","timestamp":1760032775000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/7\/686"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,26]]},"references-count":38,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["e27070686"],"URL":"https:\/\/doi.org\/10.3390\/e27070686","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,26]]}}}