Recently, a new coronavirus infection, or COVID 19, caused by the pathogen SARS-CoV 2, has been continuing to spread around the world rapidly. According to the World Health Organization (WHO), which declared this outbreak a pandemic, COVID 19 is a serious public health problem of international concern. Due to the lack of proven effective treatment and vaccination against COVID 19, precautions are considered by WHO to be strategic goals and a primary response to the pandemic. It is recommended that country guidelines adopt national health care programs aimed at assessing and reducing the risk of infection spread. Predictive analytics have begun to be actively used to compile population and personal forecasts of the progression of morbidity, mortality, assess the severity of the course of the disease, etc. This article provides an overview of available developments and publications on the use of predictive analytics in the management of COVID 19 pandemic.
References
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2. Hamed M.A. An overview on COVID 19: reality and expectation. Bull Natl Res Cent. 2020; 44(1): 86. doi : 10.1186/s42269 020 00341 9.
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14. Jewell N.P., Lewnard J.A., Jewell B.L. Predictive Mathematical Models of the COVID 19 Pandemic: Underlying Principles and Value of Projections. JAMA. 2020; 323(19): 1893–1894. doi : 10.1001/jama.2020.6585,
15. He S., Peng Y. & Sun K. SEIR modeling of the COVID 19 and its dynamics. Nonlinear Dyn. 2020 ; 101 : 1667–1680. doi : 10.1007/s11071-020-05743-y
16. Tolles J., Luong T. Modeling Epidemics With Compartmental Models. JAMA. 2020; 323(24): 2515–2516. doi:10.1001/jama.2020.8420,
17. Zhao Y.F., Shou M.H., Wang Z.X. Prediction of the Number of Patients Infected with COVID 19 Based on Rolling Grey Verhulst Models. Int. J. Environ. Res. Public Health 2020 ; 17 : 4582, doi : 10.3390/ijerph17124582
18. Why It’s So Freaking Hard To Make A Good COVID 19 Model. Available at: https://fivethirtyeight.com/features/why-its-so-freaking-hard-to-make-a-good-covid 19-model/
19. Google Cloud AI and Harvard Global Health Institute Collaborate on new COVID 19 forecasting model. Available at: https://cloud.google.com/blog/products/ai-machine-learning/google-cloud-is-releasing-the-covid 19-public-forecasts
20. Cleveland Clinic’s COVID 19 strategy driven by data modeling. Available at: https://searchbusinessanalytics.techtarget.com/feature/Cleveland-Clinics-COVID 19-strategy-driven-by-data-modeling
21. Harmon S.A., Sanford T.H., Xu S. et al. Artificial intelligence for the detection of COVID 19 pneumonia on chest CT using multinational datasets. Nat Commun. 2020 ; 11 : 4080. doi : 10.1038/ s41467 020 17971 2
22. Morozov S.P., Gombolevskij V.A., C h ernina V.Y u., Blohin I.A., Mokienko O.A., Vladzimirskij A.V., Belevskij A.S., Procenko D.N., Lysenko M.A., Zajrat'yanc O.V., Nikonov E.L. Prognozirovanie letal'nyh iskhodov pri covid 19 po dannym komp'yuternoj tomografii organov grudnoj kletki. Tuberkulez i bolezni legkih. 2020 ; 98 ( 6 ): 7–14. (In Russ).
23. Luchevaya diagnostika koronavirusnoj bolezni (COVID 19): organizaciya , metodologiya , interpretaciya rezul'tatov : preprint № CDT – 2020 – I. Versiya 2 ot 17.04.2020. sost. S.P. Morozov, D.N. Procenko , S.V. Smetanina i dr. Seriya « Luchshie praktiki luchevoj i instrumental'noj diagnostiki ». Vyp. 65. M.: GBUZ «NPKC DiT DZM», 2020. – 78 s. (In Russ).
24. Bayat V., Phelps S., Ryono R., Lee C., Parekh H., Mewton J., Sedghi F., Etminani P., Holodniy M. A SARS-CoV 2 Prediction Model from Standard Laboratory Tests. Clin Infect Dis. 2020 Aug 12: ciaa1175. doi : 10.1093/ cid /ciaa1175. Epub ahead of print.
25. Yan, L., Zhang, H., Goncalves, J. et al. An interpretable mortality prediction model for COVID 19 patients. Nat Mach Intell. 2020 ; 2 : 283–288. doi : 10.1038/s42256 020 0180 7
26. Liang W., Yao J., Chen A. et al. Early triage of critically ill COVID 19 patients using deep learning. Nat Commun. 2020 ; 11 : 3543. doi : 10.1038/s41467 020 17280 8
27. Schalekamp S., Huisman M., van Dijk R.A., Boomsma M.F., Freire Jorge P.J., de Boer W.S., Herder G.J.M., Bonarius M., Groot O.A., Jong E., Schreuder A., Schaefer-Prokop C.M. Model-based Prediction of Critical Illness in Hospitalized Patients with COVID 19. Radiology. 2020 Aug 13: 202723. doi : 10.1148/radiol.2020202723. Epub ahead of print.
28. Jehi L., Ji X., Milinovich A., Erzurum S., Merlino A., Gordon S., Young J.B., Kattan M.W. Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID 19. PLoS One. 2020; 15(8): e0237419. doi : 10.1371/journal.pone.0237419.
29. FDA greenlights ICU AI for predicting fatal COVID 19 complications. Available at: https://www.fiercebiotech.com/medtech/fda-greenlights-icu-ai-for-predicting-fatal-covid 19-complications
30. Bartkowiak B., Snyder A.M., Benjamin A., Schneider A., Twu N.M., Churpek M.M., Roggin K.K., Edelson D.P. Validating the Electronic Cardiac Arrest Risk Triage ( eCART ) Score for Risk Stratification of Surgical Inpatients in the Postoperative Setting: Retrospective Cohort Study. Ann Surg. 2019; 269(6): 1059–1063. doi : 10.1097/SLA.0000000000002665.
31. Covid 19: NHS datasets ‘not sophisticated enough’ to flag high risk patients. Available at: https://www.digitalhealth.net/2020/03/covid 19-nhs-datasets-not-sophisticated-enough-to-flag-high-risk-patients/
32. Israel is using AI to flag high-risk covid 19 patients. Available at: https://www.technologyreview.com/2020/04/24/1000543/israel-ai-prediction-medical-testing-data-high-risk-covid 19-patients/
33. Israel Aerospace razrabotala AI-model' dlya prognozirovaniya progressirovaniya bolezni u pacientov s COVID 19. (In Russ). Available at: https://evercare.ru/news/israel-aerospace-razrabotala-ai-model-dlya-prognozirovaniya-progressirovaniya-bolezni-u
34. How AI can determine which coronavirus patients require hospitalization. Available at: https://thenextweb.com/neural/2020/04/02/ai-can-help-manage-hospital-resources-during-the-coronavirus-crisis-syndication/
2. Hamed M.A. An overview on COVID 19: reality and expectation. Bull Natl Res Cent. 2020; 44(1): 86. doi : 10.1186/s42269 020 00341 9.
3. Khalifa M. Health Analytics Types, Functions and Levels: A Review of Literature. Stud Health Technol Inform. 2018; 251: 137–140.
4. Healthcare Analytics Market Size By Product (Descriptive Analytics, Predictive Analytics, Prescriptive Analytics), By Application (Operations Management, Financial Management, Population Health Management, Clinical Management) By End-Use (Hospitals, Clinics, Others), Industry Analysis Report, Regional Outlook, Application Potential, Price Trends, Competitive Market Share & Forecast, 2019–2025. Available at: https://www.gminsights.com/industry-analysis/healthcare-analytics-market
5. Global Healthcare Analytics Markets, 2019–2024 by Type, Solution, Deployment Model, Application, End-user, and Geography. ResearchAndMarkets.com. Available at: https://www.researchandmarkets.com/reports/4858254/ healthcare-analytics-market-forecasts-from 2019
6. Healthcare Analytics Market Size, Share & Industry Analysis, By Product (Descriptive, Predictive, and Prescriptive), By Application (Financial Analytics, Population Health Analytics, Clinical Analytics, and Operations and Administrative Analytics), By End User (Payers, Providers, and Others) and Regional Forecast, 2019– 2026. Available at: https://www.fortunebusinessinsights.com/healthcare-analytics-market 102641
7. Healthcare Analytics Market by Type (Predictive, Prescriptive), Component (Hardware, Software, and Services), Delivery Mode (Cloud), Application (Clinical, RCM, Claims, Fraud, Risk, PHM), End user (Payer, Provider) and Geography- Global Forecast to 2027. Available at: https://www.meticulousresearch.com/product/healthcare-analytics-market/
8. Coronavirus ‘could infect 60% of global population if unchecked’. Available at: https://www.theguardian.com/world/2020/feb/11/coronavirus-expert-warns-infection-could-reach 60-of-worlds-population
9. Harvard Professor Sounds Alarm on ‘Likely’ Coronavirus Pandemic: 40% to 70% of World Could Be Infected This Year. Available at: https://www.mediaite.com/news/harvard-professor-sounds-alarm-on-likely-coronavirus-pandemic 40-to 70-of-world-could-be-infected-this-year/
10. Coronavirus: Up to 70% of Germany could become infected – Merkel. Available at: https://www.bbc.com/news/ world-us-canada 51835856
11. The Cognitive Bias That Makes Us Panic About Coronavirus. Available at: https://www.bloomberg.com/opinion/articles/2020–02–28/coronavirus-panic-caused-by-probability-neglect
12. Koronavirus : opasnaya illyuziya smertnosti. (In Russ). Available at: https://habr.com/ru/post/494896/
13. Putin o koronaviruse : «Nam nuzhen professional'nyj prognoz ». (In Russ). Available at: https://vostokmedia.com/news/society/13–04–2020/putin-o-koronaviruse-nam-nuzhen-professionalnyy-prognoz
14. Jewell N.P., Lewnard J.A., Jewell B.L. Predictive Mathematical Models of the COVID 19 Pandemic: Underlying Principles and Value of Projections. JAMA. 2020; 323(19): 1893–1894. doi : 10.1001/jama.2020.6585,
15. He S., Peng Y. & Sun K. SEIR modeling of the COVID 19 and its dynamics. Nonlinear Dyn. 2020 ; 101 : 1667–1680. doi : 10.1007/s11071-020-05743-y
16. Tolles J., Luong T. Modeling Epidemics With Compartmental Models. JAMA. 2020; 323(24): 2515–2516. doi:10.1001/jama.2020.8420,
17. Zhao Y.F., Shou M.H., Wang Z.X. Prediction of the Number of Patients Infected with COVID 19 Based on Rolling Grey Verhulst Models. Int. J. Environ. Res. Public Health 2020 ; 17 : 4582, doi : 10.3390/ijerph17124582
18. Why It’s So Freaking Hard To Make A Good COVID 19 Model. Available at: https://fivethirtyeight.com/features/why-its-so-freaking-hard-to-make-a-good-covid 19-model/
19. Google Cloud AI and Harvard Global Health Institute Collaborate on new COVID 19 forecasting model. Available at: https://cloud.google.com/blog/products/ai-machine-learning/google-cloud-is-releasing-the-covid 19-public-forecasts
20. Cleveland Clinic’s COVID 19 strategy driven by data modeling. Available at: https://searchbusinessanalytics.techtarget.com/feature/Cleveland-Clinics-COVID 19-strategy-driven-by-data-modeling
21. Harmon S.A., Sanford T.H., Xu S. et al. Artificial intelligence for the detection of COVID 19 pneumonia on chest CT using multinational datasets. Nat Commun. 2020 ; 11 : 4080. doi : 10.1038/ s41467 020 17971 2
22. Morozov S.P., Gombolevskij V.A., C h ernina V.Y u., Blohin I.A., Mokienko O.A., Vladzimirskij A.V., Belevskij A.S., Procenko D.N., Lysenko M.A., Zajrat'yanc O.V., Nikonov E.L. Prognozirovanie letal'nyh iskhodov pri covid 19 po dannym komp'yuternoj tomografii organov grudnoj kletki. Tuberkulez i bolezni legkih. 2020 ; 98 ( 6 ): 7–14. (In Russ).
23. Luchevaya diagnostika koronavirusnoj bolezni (COVID 19): organizaciya , metodologiya , interpretaciya rezul'tatov : preprint № CDT – 2020 – I. Versiya 2 ot 17.04.2020. sost. S.P. Morozov, D.N. Procenko , S.V. Smetanina i dr. Seriya « Luchshie praktiki luchevoj i instrumental'noj diagnostiki ». Vyp. 65. M.: GBUZ «NPKC DiT DZM», 2020. – 78 s. (In Russ).
24. Bayat V., Phelps S., Ryono R., Lee C., Parekh H., Mewton J., Sedghi F., Etminani P., Holodniy M. A SARS-CoV 2 Prediction Model from Standard Laboratory Tests. Clin Infect Dis. 2020 Aug 12: ciaa1175. doi : 10.1093/ cid /ciaa1175. Epub ahead of print.
25. Yan, L., Zhang, H., Goncalves, J. et al. An interpretable mortality prediction model for COVID 19 patients. Nat Mach Intell. 2020 ; 2 : 283–288. doi : 10.1038/s42256 020 0180 7
26. Liang W., Yao J., Chen A. et al. Early triage of critically ill COVID 19 patients using deep learning. Nat Commun. 2020 ; 11 : 3543. doi : 10.1038/s41467 020 17280 8
27. Schalekamp S., Huisman M., van Dijk R.A., Boomsma M.F., Freire Jorge P.J., de Boer W.S., Herder G.J.M., Bonarius M., Groot O.A., Jong E., Schreuder A., Schaefer-Prokop C.M. Model-based Prediction of Critical Illness in Hospitalized Patients with COVID 19. Radiology. 2020 Aug 13: 202723. doi : 10.1148/radiol.2020202723. Epub ahead of print.
28. Jehi L., Ji X., Milinovich A., Erzurum S., Merlino A., Gordon S., Young J.B., Kattan M.W. Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID 19. PLoS One. 2020; 15(8): e0237419. doi : 10.1371/journal.pone.0237419.
29. FDA greenlights ICU AI for predicting fatal COVID 19 complications. Available at: https://www.fiercebiotech.com/medtech/fda-greenlights-icu-ai-for-predicting-fatal-covid 19-complications
30. Bartkowiak B., Snyder A.M., Benjamin A., Schneider A., Twu N.M., Churpek M.M., Roggin K.K., Edelson D.P. Validating the Electronic Cardiac Arrest Risk Triage ( eCART ) Score for Risk Stratification of Surgical Inpatients in the Postoperative Setting: Retrospective Cohort Study. Ann Surg. 2019; 269(6): 1059–1063. doi : 10.1097/SLA.0000000000002665.
31. Covid 19: NHS datasets ‘not sophisticated enough’ to flag high risk patients. Available at: https://www.digitalhealth.net/2020/03/covid 19-nhs-datasets-not-sophisticated-enough-to-flag-high-risk-patients/
32. Israel is using AI to flag high-risk covid 19 patients. Available at: https://www.technologyreview.com/2020/04/24/1000543/israel-ai-prediction-medical-testing-data-high-risk-covid 19-patients/
33. Israel Aerospace razrabotala AI-model' dlya prognozirovaniya progressirovaniya bolezni u pacientov s COVID 19. (In Russ). Available at: https://evercare.ru/news/israel-aerospace-razrabotala-ai-model-dlya-prognozirovaniya-progressirovaniya-bolezni-u
34. How AI can determine which coronavirus patients require hospitalization. Available at: https://thenextweb.com/neural/2020/04/02/ai-can-help-manage-hospital-resources-during-the-coronavirus-crisis-syndication/
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