Abstract
A novel coronavirus disease has emerged (later named COVID-19) and caused the world to enter a new reality, with many direct and indirect factors influencing it. Some are human-controllable (e.g. interventional policies, mobility and the vaccine); some are not (e.g. the weather). We have sought to test how a change in these human-controllable factors might influence two measures: the number of daily cases against economic impact. If applied at the right level and with up-to-date data to measure, policymakers would be able to make targeted interventions and measure their cost. This study aims to provide a predictive analytics framework to model, predict and simulate COVID-19 propagation and the socio-economic impact of interventions intended to reduce the spread of the disease such as policy and/or vaccine. It allows policymakers, government representatives and business leaders to make better-informed decisions about the potential effect of various interventions with forward-looking views via scenario planning. We have leveraged a recently launched open-source COVID-19 big data platform and used published research to find potentially relevant variables (features) and leveraged in-depth data quality checks and analytics for feature selection and predictions. An advanced machine learning pipeline has been developed armed with a self-evolving model, deployed on a modern machine learning architecture. It has high accuracy for trend prediction (back-tested with r-squared) and is augmented with interpretability for deeper insights.
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References
Gorbalenya, A.E., Baker, S.C., Baric, R.S., de Groot, R.J., Drosten, C., Gulyaeva, A.A.: The species Severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat. Microbiol. 5(4), 536–544 (2020). https://doi.org/10.1038/s41564-020-0695-z.Medline:32123347
Liu, P., Beeler, P.: Chakrabarty RK. COVID-19 progression timeline and effectiveness of response-to-spread interventions across the United States. medRxiv (2020)
Hossain, M., et al.: The effects of border control and quarantine measures on global spread of COVID-19. In: Alvin and Zhu, Xiaolin and Jia,Pengfei and Wen, Tzai-Hung and Pfeiffer, Dirk and Yuan, Hsiang-Yu, The effects of border control and quarantine measures on global spread of COVID-19 (2020)
Rocha Filho, T.M., et al.: Expected impact of COVID-19 outbreak in a major metropolitan area in Brazil. medRxiv (2020)
Giordano, G., Blanchini, F., Bruno, R., Colaneri, P., Di Filippo, A., Di Matteo, A., Colaneri, M.: Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy. Nat. Med. 26(6), 855–860 (2020). https://doi.org/10.1038/s41591-020-0883-7
Shi, P., et al.: The impact of temperature and absolute humidity on the coronavirus disease 2019 (COVID-19) outbreak-evidence from China. MedRxiv (2020)
Bhattacharjee, S.: Statistical investigation of relationship between spread of coronavirus disease (COVID-19) and environmental factors based on study of four mostly affected places of China and five mostly affected places of Italy (2020). arXiv preprint arXiv:2003.11277.2020.
C3 AI COVID-19 Data Lake overview. https://c3.ai/customers/COVID-19-data-lake/, Accessed 23 Nov 2020
C3 AI COVID-19 Data Lake API Documentation. https://c3.ai/COVID-19-api-documentation/, Accessed 23 Nov 2020
IBM Weather Company Data Packages. https://www.ibm.com/products/weather-company-data-packages/, Accessed 23 Nov 2020
Corona Data Scraper COVID-19 Coronavirus Case Data. https://coronadatascraper.com/#home/, Accessed 23 Nov 2020 accessed 2020–11–23.
University of Oxford Coronavirus Government Response Tracker. https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker/, Accessed 23 Nov 2020
Couture, V., Dingel, J.I., Green, A.E., Handbury, J., Williams, K.R.: Measuring movement and social contact with smartphone data: a real-time application to COVID-19. Working Paper 27560, National Bureau of Economic Research (2020)
Dong, E., Du, H., Gardner, L.: An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect. Dis. S1473(20), 30120–30121 (2020). https://doi.org/10.1016/S1473-3099(20)30120-1. PMID: 32087114
Economic Tracker. https://tracktherecovery.org/, Accessed 23 Nov 2020
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Altmann, A., Tolo ̧si, L., Sander, O., Lengauer, T.: Permutation importance: a corrected feature importance measure. Bioinformatics 26(10), 1340–1347 (2010)
Friedman, J.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)
Ribeiro, M.T., Singh, S., Guestrin, C.: Model-agnostic interpretability of machine learning (2016). arXiv:1606.05386
Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Proceedings of the 31st Neural Information Processing Systems (NIPS-17) (2017). arXiv:1705.07874v2
Nizolenko, L.P., Bachinsky, A.G., Bazhan, S.I.: Evaluation of influenza vaccination efficacy: a universal epidemic model. Biomed. Res. Int. 2016, 1–8 (2016). https://doi.org/10.1155/2016/5952890. PMID: 27668256
Rt COVID-19. https://rt.live/, Accessed 23 Nov 2020
Codebook for the Oxford COVID-19 Government Response Tracker. https://github.com/OxCGRT/COVID-policy-tracker/blob/master/documentation/codebook.md, Accessed 23 Nov 2020
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Wu, H., Banerjee, R., Venkatachalam, I., Chougale, P. (2022). Impact of Interventional Policies Including Vaccine on COVID-19 Propagation and Socio-economic Factors: Predictive Model Enabling Simulations Using Machine Learning and Big Data. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 296. Springer, Cham. https://doi.org/10.1007/978-3-030-82199-9_60
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DOI: https://doi.org/10.1007/978-3-030-82199-9_60
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