Skip to main content

A Review on Predictive Systems and Data Models for COVID-19

Part of the Studies in Computational Intelligence book series (SCI,volume 923)

Abstract

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) or novel Coronavirus, responsible for the transmission of Coronavirus disease (COVID-19), represents the causative agent of a conceivably deadly sickness, and a global public health concern. In December 2019 in China (Wuhan), the spread of SARS-CoV-2 has taken the shape of a pandemic and affects the respiratory system and manifests as pneumonia in humans, influencing more than 216 nations so far. On January 12th, 2020, the World Health Organization (WHO) gave the name “2019-nCoV” for 2019 novel Coronavirus, and the infection further on February 11th, 2020, is authoritatively named as COVID-19. Instead of using various predictive systems and data models, the prevalence of COVID-19 is continuously increasing, affecting millions of individuals. This chapter focuses on predictive systems and data models utilized from the beginning of COVID-19 outbreak that helped in predicting the cases and deaths qualities of COVID-19 in the desire for giving a reference to future investigations and help in controlling the spread of further epidemics. And also suggest how these data models can help and enable policymakers to plan the regional and national healthcare systems required and design monitored active plans.

Keywords

  • Novel coronavirus
  • COVID-19
  • SARS-CoV2
  • Predictive systems
  • Data models

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-15-8534-0_7
  • Chapter length: 42 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   169.00
Price excludes VAT (USA)
  • ISBN: 978-981-15-8534-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   219.99
Price excludes VAT (USA)
Hardcover Book
USD   219.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., et al. (2020). Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. New England Journal of Medicine.

    Google Scholar 

  2. Qazi, S., Sheikh, K., Faheem, M., Khan, A. & Raza, K. (2020). A coadunation of biological and mathematical perspectives on the pandemic COVID-19: A review.

    Google Scholar 

  3. WHO. (2020). Coronavirus disease 2019 (COVID-19). Situation report 24. February 13, 2020. Geneva: World Health Organization.

    Google Scholar 

  4. https://www.who.int/emergencies/diseases/novel-coronavirus-2019. (Accessed on March 30, 2020).

  5. Camacho, A., Kucharski, A., Aki-Sawyerr, Y., White, M.A., Flasche, S., Baguelin, M., et al. (2015). Temporal changes in Ebola transmission in Sierra Leone and implications for control requirements: a real-time modelling study. PLoS currents7.

    Google Scholar 

  6. Funk, S., Ciglenecki, I., Tiffany, A., Gignoux, E., Camacho, A., Eggo, R. M., et al. (2017). The impact of control strategies and behavioural changes on the elimination of Ebola from Lofa County, Liberia. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1721), 20160302.

    CrossRef  Google Scholar 

  7. Riley, S., Fraser, C., Donnelly, C. A., Ghani, A. C., Abu-Raddad, L. J., Hedley, A. J., et al. (2003). Transmission dynamics of the etiological agent of SARS in Hong Kong: Impact of public health interventions. Science, 300(5627), 1961–1966.

    CrossRef  Google Scholar 

  8. Viboud, C., Sun, K., Gaffey, R., Ajelli, M., Fumanelli, L., Merler, S., et al. (2018). The RAPIDD ebola forecasting challenge: Synthesis and lessons learnt. Epidemics, 22, 13–21.

    CrossRef  Google Scholar 

  9. Cooper, B. S., Pitman, R. J., Edmunds, W. J., & Gay, N. J. (2006). Delaying the international spread of pandemic influenza. PLoS Med, 3(6), e212.

    CrossRef  Google Scholar 

  10. Kucharski, A. J., Camacho, A., Checchi, F., Waldman, R., Grais, R. F., Cabrol, J. C., et al. (2015). Evaluation of the benefits and risks of introducing Ebola community care centers. Sierra Leone. Emerging infectious diseases, 21(3), 393.

    CrossRef  Google Scholar 

  11. WHO Ebola Response Team. (2014). Ebola virus disease in West Africa—the first 9 months of the epidemic and forward projections. New England Journal of Medicine, 371(16), 1481–1495.

    CrossRef  Google Scholar 

  12. Kang, M., Song, T., Zhong, H., Hou, J., Wang, J., Li, J., et al. (2016). Contact tracing for imported case of middle east respiratory syndrome, China, 2015. Emerging Infectious Diseases, 22(9), 1644.

    CrossRef  Google Scholar 

  13. Public Health England. (2019). MERS-CoV close contact algorithm. Public health investigation and management of close contacts of Middle East Respiratory Coronavirus (MERS-CoV) cases (v17 29 January 2019). https://assets.publishing.service.gov.uk/ government/uploads/system/uploads/attachment_data/file/776218/ MERS-CoV_Close_contacts_algorithm.pdf (Accessed Feb 6 2020).

  14. Khan, F. N., Qazi, S., Tanveer, K., & Raza, K. (2017). A review on the antagonist Ebola: A prophylactic approach. Biomedicine & Pharmacotherapy, 96, 1513–1526.

    CrossRef  Google Scholar 

  15. Definition “Artificial Intelligence.” Available from https://www.merriam-webster.com/dictionary/artificial%20intelligence.

  16. Turing, A. M. (2009). Computing machinery and intelligence. In R. Epstein, G. Roberts, & G. Beber (Eds.), Parsing the turing test, 23–65.

    Google Scholar 

  17. Yokota, H., Goto, M., Bamba, C., Kiba, M., & Yamada, K. (2017). Reading efficiency can be improved by minor modification of assigned duties; a pilot study on a small team of general radiologists. Japanese Journal of Radiology, 35(5), 262–268.

    CrossRef  Google Scholar 

  18. Kreuzhuber, K. (2020). How AI, big data and machine learning can be used against the Corona virus. ARS Electronica Blog, 19.

    Google Scholar 

  19. Bogoch, I. I., Watts, A., Thomas-Bachli, A., Huber, C., Kraemer, M. U., & Khan, K. (2020). Pneumonia of unknown aetiology in Wuhan, China: Potential for international spread via commercial air travel. Journal of Travel Medicine, 27(2), p.taaa008.

    Google Scholar 

  20. Hollister, M. (2020, March). AI can help with the COVID-19 crisis-but the right human input is key. In World economic forum (Vol. 30).

    Google Scholar 

  21. Naudé, W. (2020). Artificial intelligence against COVID-19: An early review.

    Google Scholar 

  22. Akhtar, M., Kraemer, M. U., & Gardner, L. M. (2019). A dynamic neural network model for predicting risk of Zika in real time. BMC Medicine, 17(1), 171.

    CrossRef  Google Scholar 

  23. Hao, K. (2020, March 13). This is how the CDC is trying to forecast Coronaviruss spread. MIT Technology Review, p. 2020.

    Google Scholar 

  24. Rowan, I. (2020). What happens to AI when the world stops (COVID-19) (p. 31). Medium: Towards Data Science.

    Google Scholar 

  25. Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google Flu: traps in big data analysis. Science, 343(6176), 1203–1205.

    CrossRef  Google Scholar 

  26. Ortutay, B., & Klepper, D. (2020). Virus outbreak means (mis) information overload: How to cope. (Vol. 22). AP News.

    Google Scholar 

  27. The Coronavirus is forcing tech giants to make a risky bet on AI. https://www.theverge.com/interface/2020/3/18/21183549/coronavirus-content-moderators-facebook-google-twitter.

  28. Song, P. X., Wang, L., Zhou, Y., He, J., Zhu, B., Wang, F., et al. (2020). An epidemiological forecast model and software assessing interventions on COVID-19 epidemic in China. MedRxiv.

    Google Scholar 

  29. Maier, B. F., & Brockmann, D. (2020). Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China. Science, 368(6492), 742–746.

    CrossRef  Google Scholar 

  30. Sarkar, T. (2020). Analyze NY times Covid-19 dataset (p. 30). Medium: Towards Data Science.

    Google Scholar 

  31. Bullock, J., Pham, K. H., Lam, C. S. N., & Luengo-Oroz, M. (2020). Mapping the landscape of artificial intelligence applications against COVID-19. arXiv preprint arXiv:2003.11336.

  32. Rosebrock, A. (2020). Detecting COVID-19 in X-ray images with Keras, TensorFlow, and deep learning.  https://www.pyimagesearch.com/2020/03/16/detecting-covid-19-in-x-rayimages-with-keras-tensorflow-and-deep-learning.

  33. Maghdid, H. S., Ghafoor, K. Z., Sadiq, A. S., Curran, K., & Rabie, K. (2020). A novel ai-enabled framework to diagnose coronavirus covid 19 using smartphone embedded sensors: Design study. arXiv preprint arXiv:2003.07434.

  34. Rawat, W., & Wang, Z. (2017). Deep convolutional neural networks for image classification: A comprehensive review. Neural Computation, 29(9), 2352–2449.

    MathSciNet  MATH  CrossRef  Google Scholar 

  35. Chen, E., Lerman, K., & Ferrara, E. (2020). Covid-19: The first public coronavirus twitter dataset. arXiv preprint arXiv:2003.07372.

  36. Ross, C., & Robbins, R. (2020). Debate flares over using AI to detect Covid-19 in lung scans. Stat, 30.

    Google Scholar 

  37. Yan, L., Zhang, H. T., Xiao, Y., Wang, M., Sun, C., Liang, J., et al. (2020). Prediction of criticality in patients with severe Covid-19 infection using three clinical features: A machine learning-based prognostic model with clinical data in Wuhan. MedRxiv.

    Google Scholar 

  38. Jiang, X., Coffee, M., Bari, A., Wang, J., Jiang, X., Huang, J., et al. (2020). Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity. CMC: Computers. Materials and Continua, 63, 537–551.

    CrossRef  Google Scholar 

  39. Coldeway, D. (2019, October 3). Molecule. one uses machine learning to make synthesizing new drugs a snap. TechCrunch.

    Google Scholar 

  40. Fleming, N. (2018). Computer-calculated compounds. Nature, 557(7707), S55–S57.

    CrossRef  Google Scholar 

  41. Segler, M. H., Preuss, M., & Waller, M. P. (2018). Planning chemical syntheses with deep neural networks and symbolic AI. Nature, 555(7698), 604–610.

    CrossRef  Google Scholar 

  42. Smith, S., 6. things we learned about artificial intelligence in drug discovery from 330 scientists. BenchSci Blog, 19.

    Google Scholar 

  43. Beck, B. R., Shin, B., Choi, Y., Park, S., & Kang, K. (2020). Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Computational and Structural Biotechnology Journal.

    Google Scholar 

  44. Stebbing, J., Phelan, A., Griffin, I., Tucker, C., Oechsle, O., Smith, D., et al. (2020). COVID-19: combining antiviral and anti-inflammatory treatments. The Lancet Infectious Diseases, 20(4), 400–402.

    CrossRef  Google Scholar 

  45. Regalado, A. (2020). A Coronavirus vaccine will take at least 18 months if it works at all.

    Google Scholar 

  46. Vanderslott, S., Pollard, A., & Thomas, T. (2020). Coronavirus vaccine: here are the steps it will need to go through during development. The Conversation, 30.

    Google Scholar 

  47. Rivas, A. (2020). Drones and artificial intelligence to enforce social isolation during COVID-19 outbreak (p. 26). Medium: Towards Data Science.

    Google Scholar 

  48. Chun, A. (2020). In a time of coronavirus (p. 18). South China Morning Post: Chinas investment in AI is paying off in a big way.

    Google Scholar 

  49. Dickson, B. (2020). Why AI might be the most effective weapon we have to fight COVID-19. (Vol. 21). The Next Web.

    Google Scholar 

  50. Carroll, J. (2020, March 19). Coronavirus outbreak: can machine vision and imaging play a part. Vision systems design.

    Google Scholar 

  51. Maslan, C. (2020, March 30). Social distancing detection for COVID-19. Medium.

    Google Scholar 

  52. Petropoulos, G. (2020 March 23). Artificial intelligence in the fight against COVID-19. Bruegel.

    Google Scholar 

  53. Harari, Y. N. (2020). The world after coronavirus. Financial Times20.

    Google Scholar 

  54. Mahalle, P. N., Sable, N. P., Mahalle, N. P., & Shinde, G. R. (2020). Predictive analytics of COVID-19 using information, communication and technologies.

    Google Scholar 

  55. Chen, M., Hao, Y., Hwang, K., Wang, L., & Wang, L. (2017). Disease prediction by machine learning over big data from healthcare communities. Ieee Access, 5, 8869–8879.

    CrossRef  Google Scholar 

  56. Shinde, G. R., Kalamkar, A. B., Mahalle, P. N., Dey, N., Chaki, J., & Hassanien, A. E. (2020). Forecasting models for coronavirus disease (COVID-19): A survey of the state-of-the-art. SN Computer Science, 1(4), 1–15.

    CrossRef  Google Scholar 

  57. Zaman, G., Jung, I. H., Torres, D. F., & Zeb, A. (2017). Mathematical modeling and control of infectious diseases.

    Google Scholar 

  58. Choisy, M., Guégan, J. F. & Rohani, P. (2007). Mathematical modeling of infectious diseases dynamics. In Encyclopedia of infectious diseases: Modern methodologies, (Vol. 379).

    Google Scholar 

  59. Siettos, C. I., & Russo, L. (2013). Mathematical modeling of infectious disease dynamics. Virulence, 4(4), 295–306.

    CrossRef  Google Scholar 

  60. Hellewell, J., Abbott, S., Gimma, A., Bosse, N. I., Jarvis, C. I., Russell, T. W., et al. (2020). Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. The Lancet Global Health.

    Google Scholar 

  61. Kucharski, A. J., Russell, T. W., Diamond, C., Liu, Y., Edmunds, J., Funk, S., et al. (2020). Early dynamics of transmission and control of COVID-19: a mathematical modelling study. The Lancet Infectious Diseases.

    Google Scholar 

  62. Vespignani, A., Tian, H., Dye, C., Lloyd-Smith, J. O., Eggo, R. M., Shrestha, M., et al. (2020). Modelling COVID-19. Nature Reviews Physics, 1–3.

    Google Scholar 

  63. Arti, M. K., & Bhatnagar, K. Modeling and Predictions for COVID 19 Spread in India. (Vol. 10). ResearchGate.

    Google Scholar 

  64. Marchant, R., Samia, N. I., Rosen, O., Tanner, M. A., Cripps, S. (2020). Learning as we go: An examination of the statistical accuracy of COVID19 daily death count predictions. arXiv preprint arXiv:2004.04734.

  65. Perc, M., Gorišek Miksić, N., Slavinec, M., & Stožer, A. (2020). Forecasting Covid-19. Frontiers in Physics, 8, 127.

    CrossRef  Google Scholar 

  66. Petropoulos, F., & Makridakis, S. (2020). Forecasting the novel coronavirus COVID-19. PLoS ONE, 15(3), e0231236.

    CrossRef  Google Scholar 

  67. Billah, B., King, M. L., Snyder, R. D., & Koehler, A. B. (2006). Exponential smoothing model selection for forecasting. International Journal of Forecasting, 22(2), 239–247.

    CrossRef  Google Scholar 

  68. Prem, K., Liu, Y., Russell, T. W., Kucharski, A. J., Eggo, R. M., Davies, N., et al. (2020). The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: A modelling study. The Lancet Public Health.

    Google Scholar 

  69. Anastassopoulou, C., Russo, L., Tsakris, A., & Siettos, C. (2020). Data-based analysis, modelling and forecasting of the COVID-19 outbreak. PLoS ONE, 15(3), e0230405.

    CrossRef  Google Scholar 

  70. Lin, Q., Zhao, S., Gao, D., Lou, Y., Yang, S., Musa, S. S., et al. (2020). A conceptual model for the outbreak of Coronavirus disease 2019 (COVID-19) in Wuhan, China with individual reaction and governmental action. International Journal of Infectious Diseases.

    Google Scholar 

  71. He, D., Dushoff, J., Day, T., Ma, J., & Earn, D. J. (2013). Inferring the causes of the three waves of the 1918 influenza pandemic in England and Wales. Proceedings of the Royal Society B: Biological Sciences, 280(1766), 20131345.

    CrossRef  Google Scholar 

  72. Tang, B., Wang, X., Li, Q., Bragazzi, N. L., Tang, S., Xiao, Y., et al. (2020). Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions. Journal of Clinical Medicine, 9(2), 462.

    CrossRef  Google Scholar 

  73. Tang, B., Bragazzi, N. L., Li, Q., Tang, S., Xiao, Y., & Wu, J. (2020). An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov). Infectious Disease Modelling, 5, 248–255.

    CrossRef  Google Scholar 

  74. Thompson, R. N. (2020). Novel coronavirus outbreak in Wuhan, China, 2020: Intense surveillance is vital for preventing sustained transmission in new locations. Journal of Clinical Medicine, 9(2), 498.

    CrossRef  Google Scholar 

  75. DeFelice, N. B., Little, E., Campbell, S. R., & Shaman, J. (2017). Ensemble forecast of human West Nile virus cases and mosquito infection rates. Nature Communications, 8(1), 1–6.

    CrossRef  Google Scholar 

  76. Luo, J. (2020). When Will COVID-19 End? data-driven prediction. Singapore University of Technology and Design. (http://www.sutd.edu.sg).

  77. Wynants, L., Van Calster, B., Bonten, M. M., Collins, G. S., Debray, T. P., De Vos, M., et al. (2020.) Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. Bmj369.

    Google Scholar 

  78. Xie, J., Hungerford, D., Chen, H., Abrams, S. T., Li, S., Wang, G., et al. (2020). Development and external validation of a prognostic multivariable model on admission for hospitalized patients with COVID-19.

    Google Scholar 

  79. Qi, X., Jiang, Z., Yu, Q., Shao, C., Zhang, H., Yue, H., et al. (2020). Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: A multicenter study. medRxiv.

    Google Scholar 

  80. Yuan, M., Yin, W., Tao, Z., Tan, W., & Hu, Y. (2020). Association of radiologic findings with mortality of patients infected with 2019 novel coronavirus in Wuhan. China. PloS one, 15(3), e0230548.

    CrossRef  Google Scholar 

  81. Feng, C., Huang, Z., Wang, L., Chen, X., Zhai, Y., Zhu, F., et al. (2020). A novel triage tool of artificial intelligence assisted diagnosis aid system for suspected COVID-19 pneumonia in fever clinics.

    Google Scholar 

  82. Song, C. Y., Xu, J., He, J. Q., Lu, Y.Q. (2020). COVID-19 early warning score: a multi-parameter screening tool to identify highly suspected patients. MedRxiv.

    Google Scholar 

  83. Meng, Z., Wang, M., Song, H., Guo, S., Zhou, Y., Li, W., et al. (2020). Development and utilization of an intelligent application for aiding COVID-19 diagnosis. medRxiv.

    Google Scholar 

  84. Lopez-Rincon, A., Tonda, A., Mendoza-Maldonado, L., Claassen, E., Garssen, J., Kraneveld, A. D. (2020). Accurate identification of sars-cov-2 from viral genome sequences using deep learning. bioRxiv.

    Google Scholar 

  85. Moons, K. G., Wolff, R. F., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., et al. (2019). PROBAST: A tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Annals of Internal Medicine, 170(1), W1–W33.

    CrossRef  Google Scholar 

  86. Zheng, C., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., et al. (2020). Deep learning-based detection for COVID-19 from chest CT using weak label. medRxiv.

    Google Scholar 

  87. Riley, R. D., Ensor, J., Snell, K. I., Harrell, F. E., Martin, G. P., Reitsma, J. B., et al. (2020). Calculating the sample size required for developing a clinical prediction model. Bmj, 368.

    Google Scholar 

  88. Enfield, K., Miller, R., Rice, T., Thompson, B. T., & Truwit, J. (2011). Limited utility of SOFA and APACHE II prediction models for ICU triage in pandemic influenza. Chest, 140(4), 913A.

    CrossRef  Google Scholar 

  89. Van Calster, B., & Vickers, A. J. (2015). Calibration of risk prediction models: impact on decision-analytic performance. Medical Decision Making, 35(2), 162–169.

    CrossRef  Google Scholar 

  90. Peak, C. M., Childs, L. M., Grad, Y. H., & Buckee, C. O. (2017). Comparing nonpharmaceutical interventions for containing emerging epidemics. Proceedings of the National Academy of Sciences, 114(15), 4023–4028.

    CrossRef  Google Scholar 

  91. Abbott, S., Hellewell, J., Munday, J., Funk, S., & CMMID nCoV working group. (2020). The transmissibility of novel Coronavirus in the early stages of the 2019–20 outbreak in Wuhan: Exploring initial point-source exposure sizes and durations using scenario analysis. Wellcome open research5.

    Google Scholar 

  92. Imai, N. (2020, January 24) Report 3: Transmissibility of 2019-nCoV. Imperial College London.

    Google Scholar 

  93. Kucharski, A. J., Eggo, R. M., Watson, C. H., Camacho, A., Funk, S., & Edmunds, W. J. (2016). Effectiveness of ring vaccination as control strategy for Ebola virus disease. Emerging Infectious Diseases, 22(1), 105.

    CrossRef  Google Scholar 

  94. Riou, J., & Althaus, C. L. (2020). Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020. Eurosurveillance, 25(4), 2000058.

    CrossRef  Google Scholar 

  95. Woelfel, R., Corman, V. M., Guggemos, W., Seilmaier, M., Zange, S., Mueller, M. A., et al. (2020). Clinical presentation and virological assessment of hospitalized cases of coronavirus disease 2019 in a travel-associated transmission cluster. MedRxiv.

    Google Scholar 

  96. Andrijevic, M., Cuaresma, J. C., Muttarak, R., & Schleussner, C. F. (2020). Governance in socioeconomic pathways and its role for future adaptive capacity. Nature Sustainability, 3(1), 35–41.

    CrossRef  Google Scholar 

  97. Pirouz, B., Arcuri, N., Pirouz, B., Palermo, S. A., Turco, M., & Maiolo, M. (2020). Development of an assessment method for evaluation of sustainable factories. Sustainability, 12(5), 1841.

    CrossRef  Google Scholar 

  98. Pirouz, B., Arcuri, N., Maiolo, M., Talarico, V.C., Piro, P. (2020, January). A new multi-objective dynamic model to close the gaps in sustainable development of industrial sector. In IOP Conference Series: Earth and Environmental Science (Vol. 410, No. 1, p. 012074). IOP Publishing.

    Google Scholar 

  99. Darwish, A., Rahhal, Y., & Jafar, A. (2020). A comparative study on predicting influenza outbreaks using different feature spaces: application of influenza-like illness data from early warning alert and response system in Syria. BMC Research Notes, 13(1), 1–8.

    CrossRef  Google Scholar 

  100. Zhao, S., Musa, S. S., Lin, Q., Ran, J., Yang, G., Wang, W., et al. (2020). Estimating the unreported number of novel coronavirus (2019-nCoV) cases in China in the first half of January 2020: A data-driven modelling analysis of the early outbreak. Journal of Clinical Medicine, 9(2), 388.

    CrossRef  Google Scholar 

  101. Nishiura, H., Kobayashi, T., Yang, Y., Hayashi, K., Miyama, T., Kinoshita, R., et al. (2020). The rate of underascertainment of novel coronavirus (2019-nCoV) infection: Estimation using Japanese passengers data on evacuation flights.

    Google Scholar 

  102. Jung, S. M., Akhmetzhanov, A. R., Hayashi, K., Linton, N. M., Yang, Y., Yuan, B., et al. (2020). Real-time estimation of the risk of death from novel coronavirus (COVID-19) infection: inference using exported cases. Journal of Clinical Medicine, 9(2), 523.

    CrossRef  Google Scholar 

  103. Ture, M., & Kurt, I. (2006). Comparison of four different time series methods to forecast hepatitis A virus infection. Expert Systems with Applications, 31(1), 41–46.

    CrossRef  Google Scholar 

  104. Shaman, J., & Karspeck, A. (2012). Forecasting seasonal outbreaks of influenza. Proceedings of the National Academy of Sciences, 109(50), 20425–20430.

    CrossRef  Google Scholar 

  105. Shaman, J., Karspeck, A., Yang, W., Tamerius, J., & Lipsitch, M. (2013). Real-time influenza forecasts during the 2012–2013 season. Nature Communications, 4(1), 1–10.

    CrossRef  Google Scholar 

  106. Shaman, J., Yang, W., & Kandula, S. (2014). Inference and forecast of the current west African Ebola outbreak in Guinea (p. 6). PLoS currents: Sierra Leone and Liberia.

    Google Scholar 

  107. Massad, E., Burattini, M. N., Lopez, L. F., & Coutinho, F. A. (2005). Forecasting versus projection models in epidemiology: the case of the SARS epidemics. Medical Hypotheses, 65(1), 17–22.

    CrossRef  Google Scholar 

  108. Ong, J. B. S., Mark, I., Chen, C., Cook, A. R., Lee, H. C., Lee, V. J., et al. (2010). Real-time epidemic monitoring and forecasting of H1N1-2009 using influenza-like illness from general practice and family doctor clinics in Singapore. PLoS ONE, 5(4), e10036.

    CrossRef  Google Scholar 

  109. Pirouz, B., Shaffiee Haghshenas, S., Shaffiee Haghshenas, S., & Piro, P. (2020). Investigating a serious challenge in the sustainable development process: analysis of confirmed cases of COVID-19 (new type of coronavirus) through a binary classification using artificial intelligence and regression analysis. Sustainability, 12(6), 2427.

    CrossRef  Google Scholar 

  110. Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems Man and Cybernetics, 23(3), 665–685.

    CrossRef  Google Scholar 

  111. Wei, L. Y. (2016). A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting. Applied Soft Computing, 42, 368–376.

    CrossRef  Google Scholar 

  112. Cheng, C. H., Wei, L. Y., Liu, J. W., & Chen, T. L. (2013). OWA-based ANFIS model for TAIEX forecasting. Economic Modelling, 30, 442–448.

    CrossRef  Google Scholar 

  113. Pousinho, H. M. I., Mendes, V. M. F., & Catalão, J. P. D. S. (2012). Short-term electricity prices forecasting in a competitive market by a hybrid PSO–ANFIS approach. International Journal of Electrical Power & Energy Systems, 39(1), 29–35.

    CrossRef  Google Scholar 

  114. Svalina, I., Galzina, V., Lujić, R., & ŠImunović, G. (2013). An adaptive network-based fuzzy inference system (ANFIS) for the forecasting: The case of close price indices. Expert Systems with Applications, 40(15), 6055–6063.

    CrossRef  Google Scholar 

  115. Ekici, B. B., & Aksoy, U. T. (2011). Prediction of building energy needs in early stage of design by using ANFIS. Expert Systems with Applications, 38(5), 5352–5358.

    CrossRef  Google Scholar 

  116. Cheng, C. H., & Wei, L. Y. (2010). One step-ahead ANFIS time series model for forecasting electricity loads. Optimization and Engineering, 11(2), 303–317.

    MATH  CrossRef  Google Scholar 

  117. Kumar, D. T., Soleimani, H., & Kannan, G. (2014). Forecasting return products in an integrated forward/reverse supply chain utilizing an ANFIS. International Journal of Applied Mathematics and Computer Science, 24(3), 669–682.

    MathSciNet  MATH  CrossRef  Google Scholar 

  118. Ho, Y. C., & Tsai, C. T. (2011). Comparing ANFIS and SEM in linear and nonlinear forecasting of new product development performance. Expert Systems with Applications, 38(6), 6498–6507.

    CrossRef  Google Scholar 

  119. Al-Qaness, M. A., Ewees, A. A., Fan, H., & Abd El Aziz, M. (2020). Optimization method for forecasting confirmed cases of COVID-19 in China. Journal of Clinical Medicine, 9(3), 674.

    CrossRef  Google Scholar 

  120. Abd Elaziz, M., Ewees, A. A., & Alameer, Z. (2019). Improving adaptive neuro-fuzzy inference system based on a modified salp swarm algorithm using genetic algorithm to forecast crude oil price. Natural Resources Research, 1–16.

    Google Scholar 

  121. Telles, C. R. (2020). COVID-19, an overview about the epidemic virus behavior.

    Google Scholar 

  122. Catalão, J. P. D. S., Pousinho, H. M. I., & Mendes, V. M. F. (2010). Hybrid wavelet-PSO-ANFIS approach for short-term electricity prices forecasting. IEEE Transactions on Power Systems, 26(1), 137–144.

    CrossRef  Google Scholar 

  123. Bagheri, A., Peyhani, H. M., & Akbari, M. (2014). Financial forecasting using ANFIS networks with quantum-behaved particle swarm optimization. Expert Systems with Applications, 41(14), 6235–6250.

    CrossRef  Google Scholar 

  124. Ewees, A.A., Abd El Aziz, M., & Elhoseny, M. (2017, July). Social-spider optimization algorithm for improving ANFIS to predict biochar yield. In 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1–6). IEEE.

    Google Scholar 

  125. Al-Qaness, M. A., Abd Elaziz, M., & Ewees, A. A. (2018). Oil consumption forecasting using optimized adaptive neuro-fuzzy inference system based on sine cosine algorithm. IEEE Access, 6, 68394–68402.

    CrossRef  Google Scholar 

  126. Al-qaness, M. A., Abd Elaziz, M., Ewees, A. A., & Cui, X. (2019). A modified adaptive neuro-fuzzy inference system using multi-verse optimizer algorithm for oil consumption forecasting. Electronics, 8(10), 1071.

    CrossRef  Google Scholar 

  127. Abd El Aziz, M., Hemdan, A. M., Ewees, A. A., Elhoseny, M., Shehab, A., Hassanien, A. E. et al. (2017, June). Prediction of biochar yield using adaptive neuro-fuzzy inference system with particle swarm optimization. In 2017 IEEE PES PowerAfrica (pp. 115–120). IEEE.

    Google Scholar 

  128. Ardabili, S. F., Mosavi, A., Ghamisi, P., Ferdinand, F., Varkonyi-Koczy, A. R., Reuter, U., et al. (2020). Covid-19 outbreak prediction with machine learning. Available at SSRN 3580188.

    Google Scholar 

  129. Chen, Y. C., Lu, P. E. & Chang, C. S. (2020) A Time-dependent SIR model for COVID-19. arXiv 2020. arXiv preprint arXiv:2003.00122.

  130. Volpert, V., Banerjee, M., & Petrovskii, S. (2020). On a quarantine model of coronavirus infection and data analysis. Mathematical Modelling of Natural Phenomena, 15, 24.

    MathSciNet  CrossRef  Google Scholar 

  131. Liu, Z., Magal, P., Seydi, O., & Webb, G. (2020). Predicting the cumulative number of cases for the COVID-19 epidemic in China from early data. arXiv preprint arXiv:2002.12298.

  132. Zhang, F., Zhang, J., Cao, M., & Hui, C. (2020). A simple ecological model captures the transmission pattern of the coronavirus COVID-19 outbreak in China. medRxiv.

    Google Scholar 

  133. Nah, K., Otsuki, S., Chowell, G., & Nishiura, H. (2016). Predicting the international spread of middle east respiratory syndrome (MERS). BMC Infectious Diseases, 16(1), 1–9.

    CrossRef  Google Scholar 

  134. Russo, L., Anastassopoulou, C., Tsakris, A., Bifulco, G.N., Campana, E.F., Toraldo, G., et al. (2020). In Tracing DAY-ZERO and forecasting the fade out of the COVID-19 outbreak in lombardy, Italy: A compartmental modelling and numerical optimization approach. medRxiv.

    Google Scholar 

  135. Bannister-Tyrrell, M., Meyer, A., Faverjon, C., & Cameron, A. (2020). Preliminary evidence that higher temperatures are associated with lower incidence of COVID-19, for cases reported globally up to 29th February 2020. medRxiv.

    Google Scholar 

  136. Giordano, G., Blanchini, F., Bruno, R., Colaneri, P., Di Filippo, A., Di Matteo, A. et al. (2020). A SIDARTHE model of COVID-19 epidemic in Italy. arXiv preprint arXiv:2003.09861.

  137. Weber, A., Ianelli, F., & Goncalves, S. (2020). Trend analysis of the COVID-19 pandemic in China and the rest of the world. arXiv preprint arXiv:2003.09032.

  138. Park, S. W., Bolker, B. M., Champredon, D., Earn, D. J., Li, M., Weitz, J. S., et al. (2020). Reconciling early-outbreak estimates of the basic reproductive number and its uncertainty: Framework and applications to the novel coronavirus (SARS-CoV-2) outbreak. MedRxiv.

    Google Scholar 

  139. Luo, J. (2020). Predictive Monitoring of COVID-19. SUTD Data-Driven Innovation Lab.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fatima Nazish Khan .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Khan, F.N., Khanam, A.A., Ramlal, A., Ahmad, S. (2021). A Review on Predictive Systems and Data Models for COVID-19. In: Raza, K. (eds) Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis. Studies in Computational Intelligence, vol 923. Springer, Singapore. https://doi.org/10.1007/978-981-15-8534-0_7

Download citation