Predicting COVID-19 new cases in California with Google Trends data and a machine learning approach

References

• Global cases by the center for systems science and engineering (CSSE) at Johns Hopkins University (JHU). Github.com; [accessed 2022 Jan 24]. https://github.com/CSSEGISandData/COVID-19.
   Google Scholar
• Karim SSA, Karim SSA. Omicron SARS-CoV-2 variant: a new chapter in the COVID-19 pandemic. The Lancet. 2021;398(10317):2126–28. doi:10.1016/s0140-6736(21)02758-6.
   PubMed Web of Science ®Google Scholar
• Ginsberg J, Mohebbi MH, Patel R, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature. 2009;457(7232):1012–14. doi:10.1038/nature07634.
   PubMed Web of Science ®Google Scholar
• Marinović AB, Swaan C, Van Steenbergen JE, Kretzschmar M. Quantifying reporting timeliness to improve outbreak control. Emerg Infect Dis. 2015;21(2):209–16. doi:10.3201/eid2102.130504.
   PubMed Web of Science ®Google Scholar
• Sahai SY, Gurukar S, KhudaBukhsh WR, Parthasarathy S, Rempała GA. A machine learning model for nowcasting epidemic incidence. Math Biosci Internet. 2022;343:108677. doi:10.1016/j.mbs.2021.108677.
   PubMed Web of Science ®Google Scholar
• Sherratt K, Abbott S, Meakin S, Hellewell J, Munday JD, Bosse N, Jit M, Funk S. Exploring surveillance data biases when estimating the reproduction number: with insights into subpopulation transmission of COVID-19 in England. Phil Trans R Soc B. 2021;376(1829). doi:10.1098/rstb.2020.0283.
   PubMed Web of Science ®Google Scholar
• Iuliano AD, Chang HY, Patel N, Threlkel R, Kniss K, Reich J, Steele M, Hall AJ, Fry AM, Reed C. Estimating under-recognized COVID-19 deaths, United States, march 2020-may 2021 using an excess mortality modelling approach. Lancet Reg Health Am. 2021;1:100019. doi:10.1016/j.lana.2021.100019.
   PubMedGoogle Scholar
• Tatar M, Habibdoust A, Wilson FA. Analysis of excess deaths during the COVID-19 pandemic in the state of Florida. Am J Public Health. 2021;111(4):704–7. doi:10.2105/ajph.2020.306130.
   PubMed Web of Science ®Google Scholar
• Böhning D, Rocchetti I, Maruotti A, Holling H. Estimating the undetected infections in the covid-19 outbreak by harnessing capture–recapture methods. Int J Infect Dis. 2020;97:197–201. doi:10.1016/j.ijid.2020.06.009.
   PubMed Web of Science ®Google Scholar
• CDC. Estimated COVID-19 burden Internet. Centers for Disease Control and Prevention. 2022 [accessed 2021 Dec 10]. https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/burden.html.
   Google Scholar
• Google search statistics. Internetlivestats.com; [accessed 2021 Dec 10]. https://www.internetlivestats.com/google-search-statistics/.
   Google Scholar
• Demographics of internet and home broadband usage in the U.S. Pew Research Center: Internet, Science & Tech. 2021 [accessed 2021 Dec 1]. https://www.pewresearch.org/internet/fact-sheet/internet-broadband/.
   Google Scholar
• The social life of health information. Pew Research Center. 2014 [accessed 2021 Dec 10]. https://www.pewresearch.org/fact-tank/2014/01/15/the-social-life-of-health-information/.
   Google Scholar
• Cook S, Conrad C, Fowlkes AL, Mohebbi MH, Cowling BJ. Assessing Google flu trends performance in the United States during the 2009 influenza virus a (H1N1) pandemic. PLoS one. 2011;6(8):e23610. doi:10.1371/journal.pone.0023610.
   PubMed Web of Science ®Google Scholar
• Yang S, Santillana M, Kou SC. Accurate estimation of influenza epidemics using Google search data via ARGO. Proc Natl Acad Sci. 2015;112(47):14473–78. doi:10.1073/pnas.1515373112.
   PubMed Web of Science ®Google Scholar
• Araz OM, Bentley D, Muelleman RL. Using Google flu Trends data in forecasting influenza-like–illness related ED visits in Omaha, Nebraska. Am J Emerg Med. 2014;32(9):1016–23. doi:10.1016/j.ajem.2014.05.052.
   PubMed Web of Science ®Google Scholar
• Lazer D, Kennedy R, King G, Vespignani A. The parable of Google flu: traps in big data analysis. Science. 2014;343(6176):1203–05. doi:10.1126/science.1248506.
   PubMed Web of Science ®Google Scholar
• Mavragani A, Gkillas K. COVID-19 predictability in the United States using Google trends time series. Sci Rep. 2020;10(1):1–2. doi:10.1038/s41598-020-77275-9.
   PubMed Web of Science ®Google Scholar
• Fantazzini D. Short-term forecasting of the COVID-19 pandemic using Google trends data: evidence from 158 countries. Applied Econometrics. 2020;59:33–54. doi:10.22394/1993-7601-2020-59-33-54.
   Google Scholar
• Effenberger M, Kronbichler A, Shin JI, Mayer G, Tilg H, Perco P. Association of the COVID-19 pandemic with internet search volumes: a Google TrendsTM analysis. Int J Infect Dis. 2020;95:192–7. doi:10.1016/j.ijid.2020.04.033.
   PubMed Web of Science ®Google Scholar
• Sun J, Gloor PA. Assessing the predictive power of online social media to analyze COVID-19 outbreaks in the 50 US states. Future Internet. 2021;13(7):184. doi:10.3390/fi13070184.
   Web of Science ®Google Scholar
• Ivakhnenko AG. The group method of data of handling; a rival of the method of stochastic approximation. Soviet Automatic Control. 1968;13(3):43–55.
   Google Scholar
• Yousaf M, Zahir S, Riaz M, Hussain SM, Shah K. Statistical analysis of forecasting COVID-19 for upcoming month in Pakistan. Chaos, Solitons Fractals. 2020;138:109926. doi:10.1016/j.chaos.2020.109926.
   PubMed Web of Science ®Google Scholar
• Doornik JA. Statistical short-term forecasting of the COVID-19 pandemic. J Clin Immunol Immunother. 2020;6(5):1–6. doi:10.24966/ciit-8844/1000046.
   Google Scholar
• Castle JL, Doornik JA, Hendry DF. The value of robust statistical forecasts in the COVID-19 pandemic. Natl Inst Econ Rev. 2021;256:19–43. doi:10.1017/nie.2021.9.
   Web of Science ®Google Scholar
• Alali Y, Harrou F, Ma J. A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models. Sci Rep. 2022;12(1):12. doi:10.1038/s41598-022-06218-3.
   PubMed Web of Science ®Google Scholar
• Kumar R, Khan F, Din S, Band SS, Mosavi A, Ibeke E. Recurrent neural network and reinforcement learning model for COVID-19 prediction. Front Public Health. 2021;9. doi:10.3389/fpubh.2021.744100.
   Web of Science ®Google Scholar
• Ayyoubzadeh SM, Ayyoubzadeh SM, Zahedi H, Ahmadi M, Kalhori SR. Predicting COVID-19 incidence through analysis of Google Trends data in Iran: data mining and deep learning pilot study. JMIR Public Health Surveill. 2020;6(2):e18828. doi:10.2196/18828.
   PubMedGoogle Scholar
• Prasanth S, Singh U, Kumar A, Tikkiwal VA, Chong PHJ. Forecasting spread of COVID-19 using google trends: a hybrid GWO-deep learning approach. Chaos, Solitons Fractals. 2021;142:110336. doi:10.1016/j.chaos.2020.110336.
   PubMed Web of Science ®Google Scholar
• Rovetta A, Castaldo L. Influence of mass media on Italian web users during the COVID-19 pandemic: infodemiological analysis. JMIRx Med. 2021;2:e32233. doi:10.2196/32233.
   PubMedGoogle Scholar
• Sato K, Mano T, Iwata A, Toda T. Need of care in interpreting Google trends-based COVID-19 infodemiological study results: potential risk of false-positivity. BMC Med Res Methodol. 2021 21;21(1). doi:10.1186/s12874-021-01338-2.
   Web of Science ®Google Scholar
• Satpathy P, Kumar S, Prasad P. Suitability of Google TrendsTM for Digital Surveillance during ongoing COVID-19 epidemic: a case study from India. Disaster Med Public Health Prep. 2021;17. doi:10.1017/dmp.2021.249.
   PubMed Web of Science ®Google Scholar
• WHO. Report of the WHO-China joint mission on coronavirus disease 2019 (COVID-19). WHO; 2020 [accessed 2021 Dec 2]. https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf.
   Google Scholar
• Hollimon N. Symptoms of coronavirus. WebMD. 2020. https://www.webmd.com/covid/covid-19-symptoms#1.
   Google Scholar
• Mavragani A, Ochoa G. Google Trends in infodemiology and infoveillance: methodology framework. JMIR Public Health Surveill. 2019;5(2):e13439. doi: 10.2196/13439.
   PubMedGoogle Scholar
• COVID-19 time-series metrics by county and state - California health and human services open data portal. Chhs.ca.gov. [accessed 2021 Dec 15]. https://data.chhs.ca.gov/dataset/covid-19-time-series-metrics-by-county-and-state.
   Google Scholar
• Google.com. [accessed 2021 Dec 17]. https://www.google.com/trends.
   Google Scholar
• Rovetta A. Reliability of google trends: analysis of the limits and potential of web infoveillance during COVID-19 pandemic and for future research. Front Res Metr Anal Internet. 2021;6. doi:10.3389/frma.2021.670226.
   PubMedGoogle Scholar
• Seifaddini M, Habibdoust A. Improving bitcoin price prediction power by time-scale decomposition and GMDH-type neural network: a comparison of different periods and features. J Math Model. 2023 Mar 1;11(1):1–7.
   Google Scholar
• Hong X, Zhao Y, Kausar N, Mohammadzadeh A, Pamucar D, Al Din Ide N. A new decision-making GMDH neural network: effective for limited and Fuzzy Data. Comput Intell Neurosci. 2022;2022:1–8. doi:10.1155/2022/2133712.
   Web of Science ®Google Scholar
• Mehrankia A, Meybodi MR, Mirzaie K. Prediction of heart attacks using biological signals based on recurrent GMDH neural network. Neural Process Lett. 2022;54(2):987–1008. doi: 10.1007/s11063-021-10667-8.
   Web of Science ®Google Scholar
• Zhang Z, Yin J, Liu C. A modular real-time tidal prediction model based on grey-GMDH neural network. Appl Artif Intell. 2018;32:165–85. doi:10.1080/08839514.2018.1451220.
   Web of Science ®Google Scholar
• Amanifard N, Nariman-Zadeh N, Borji M, Khalkhali A, Habibdoust A. Modelling and pareto optimization of heat transfer and flow coefficients in microchannels using GMDH type neural networks and genetic algorithms. Energy Convers Manage. 2008;49(2):311–25. doi:10.1016/j.enconman.2007.06.002.
   Web of Science ®Google Scholar
• Li Z, Liu F, Yang W, Peng S, Zhou J. A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans Neural Netw Learn Syst. 2021;33(12): 6999–7019 . doi:10.1109/tnnls.2021.3084827.
   Web of Science ®Google Scholar
• Yamashita R, Nishio M, DO GR, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights Imaging. 2018;9(4):611–29. doi:10.1007/s13244-018-0639-9.
   PubMed Web of Science ®Google Scholar
• Graves A. 2012. Long short-term memory. In: Supervised sequence labelling with recurrent neural networks. Studies in computational intelligenceVol. 385. Berlin, Heidelberg: Springer; doi:10.1007/978-3-642-24797-2_4. [46] Grubbs FE. Sample criteria for testing outlying observations. Annals of Mathematical Statistics. 1950;21:27–58. https://doi.org/10.1214/aoms/1177729885
   Google Scholar
• Mariano RS, Preve DPA. Statistical tests for multiple forecast comparison. J Econom. 2012;169:123–30. doi:10.1016/j.jeconom.2012.01.014.
   Web of Science ®Google Scholar
• Burzyńska J, Bartosiewicz A, Rękas M. The social life of COVID-19: early insights from social media monitoring data collected in Poland. Health Informatics J. 2020;26:3056–65. doi:10.1177/1460458220962652.
   PubMed Web of Science ®Google Scholar