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Investigation of COVID-19 Using an Artificial Intelligence Based Approach

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Modeling, Control and Drug Development for COVID-19 Outbreak Prevention

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 366))

Abstract

The infectious disease COVID-19 is a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) which has become a pandemic. RT-PCR test is implemented in most of the countries to diagnose the disease. Prioritizing individuals for RT-PCR test is necessary in this pandemic situation. In this scenario, we have attempted to propose and analyze two models on the vulnerability of COVID-19 for a person using fuzzy inference system (FIS) based on whether or not the person has a travelling history. To formulate these two models we have used Mamdani type Fuzzy Inference System. In these two models, ‘Hygiene’, ‘Immunity’, ‘Quarantine’ and ‘Home-isolation’ are taken as the inputs and ‘Vulnerability to COVID-19’ is taken as the output variable. A thorough study of these two models using FIS is done which suggests in what percentage a person is vulnerable to COVID-19.

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References

  1. https://www.worldometers.info/coronavirus

  2. Ghinani, I., McPherson, T.D., Hunter, J.C., Kirking, H.L., Christiansen, D., Joshi, K., Layden, J.E., et al.: First known person-to-person transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the USA. Lancet (2020). https://doi.org/10.1016/s0140-6736(20)30607-3

  3. Lupia, T., Scabini, S., Pinna, S.M., Perri, G., Rosa, F.G., Corcione, S.: 2019 Novel coronavirus (2019-nCoV) outbreak: a new challenge. J. Glob. Antimicrobial Resistance (2020). https://doi.org/10.1016/j.jgar.2020.02.021

  4. Chang, F.-J., Lai, H.-C.: Adaptive neuro-fuzzy inference system for the prediction of monthly shoreline changes in northeastern Taiwan. Ocean Eng. 84, 145–156 (2014)

    Google Scholar 

  5. Mandal, M., Jana, S., Nandi, S.K., Khatua, A., Adak, S., Kar, T.K.: A model based study on the dynamics of COVID-19: prediction and control. Chaos Solit. Fract. https://doi.org/10.1016/j.chaos.109889 (2020)

  6. How is the COVID-19 Virus Detected using Real Time RT-PCR? IAEA. 27 March 2020. Retrieved 5 May 2020

    Google Scholar 

  7. Butt, C., Gill, J., Chun, D., Babu, B.A.: Deep learning system to screen coronavirus disease: 2019 pneumonia. Appl. Intell. https://doi.org/10.1007/s10489-020-01714-3 (2020)

  8. Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., Taao, Q., Sun, Z., Xia, L.: Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology (2019). https://doi.org/10.1148/radiol.2020200642 (2020)

  9. WHO|Novel Coronavirus-China. http://www.who.int (2020)

  10. Coronavirus Disease 2019(COVID-19)-Transmission. Centers for Disease Control and Prevention. http://www.cdc.gov. (2020)

  11. Hu, Z., Ge, Q., Jin, L., and Xiong, M. : Artificial intelligence forecasting of COVID-19 in China. arXiv:2002.07112 (2020)

  12. Ye, Y., Hou, S., Fan, Y., Qian, Y., Zhang, Y., Sun, S., Peng, Q., Laparo, K.: K-satellite: an AI-driven system and benchmark datasets for hierarchical community-level risk assessment to help combat COVID-19. arXiv:2003.12232 (2020)

  13. Whiting, C.C., Siebert, J., Newman, A.M., Du, H.W., Alizadeh, A.A., Goronzy, J., et al.: Large-scale and comprehensive immune profiling and functional analysis of normal human aging. PLoS ONE 10(7) (2015). https://doi.org/10.1371/journal.pone.0133627

  14. Smetana, J., Chlibek, R., Hanovcova, I., Sosovickova, R., Smetanova, L., Gal, P., et al.: Decreasing seroprevalence of measles antibodies after vaccination \(\pm \) possible gap in measles protection in adults in the Czech Republic. PLoS ONE 12(1), e0170257 (2017). https://doi.org/10.1371/journal.v

  15. Piccolominib, E.L., Zamaa, F.: Preliminary analysis of COVID-19 spread in Italy with an adaptive SEIRD model. arXiv:2003.09909v1 [q-bio.PE] (2020)

  16. Lin, Q., Zhao, S., Gao, D., Lou, Y., Yang, S., Musa, S.S., He, D.: A conceptual model for the outbreak of Coronavirus disease 2019 (COVID-19) in Wuhan, China with individual reaction and governmental action. Int. J. Infect. Diseases (2020). https://doi.org/10.1016/j.ijid.2020.02.058

  17. Alavi, N.: Quality determination of Mozafati dates using Mamdani fuzzy inference system. J. Saudi Soc. Agric. Sci 12, 137–142 (2013)

    Google Scholar 

  18. Afrinaldi, F., Zhang, H.C.: A fuzzy logic based aggregation method for life cycle impact assessment. J. Clean. Prod. 67, 159–172 (2014)

    Article  Google Scholar 

  19. Wua, J., Xionga, B., Anb, Q., Zhua, Q., Lianga, L.: Measuring the performance of thermal power firms in China via fuzzy enhanced Russell measure model with undesirable outputs. J. Clean. Prod. 102, 237–245 (2015)

    Article  Google Scholar 

  20. Saikia, D., Dutta, J.C.: Early diagnosis of dengue disease using fuzzy inference system (2016)

    Google Scholar 

  21. Abualigah, L.M., Khader, A.T., Al-Betar, M.A., Awadallah, M.A.: A krill herd algorithm for efficient text documents clustering. In IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE), pp. 67–72 (2016)

    Google Scholar 

  22. Abualigah, L.M., Khader, A.T., Al-Betar, M.A., Alomari, O.A.: Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering. Expert Syst. Appl. 84, 24–36 (2017)

    Article  Google Scholar 

  23. Putti, F.F., Filho, L.R.A.G., Gabriel, C.P.C., Neto, A.B., Bonini, C., dos S.B., Reis, A.R.: A fuzzy mathematical model to estimate the effects of global warming on the vitality of Laelia purpurata orchids. Math. Biosci. (2017). https://doi.org/10.1016/j.mbs.2017.03.005

  24. Khatua, A., Jana, S., Kar, T.K.: A fuzzy rule-based model to assess the effects of global warming, pollution and harvesting on the production of Hilsa fishes. Ecol. Inf. 57 (2020)

    Google Scholar 

  25. Adak, S., Jana, S.: A study on stegomyia indices in dengue control: a fuzzy approach. Soft Comput. (2020)

    Google Scholar 

  26. Adak, S., Jana, S.: A model to assess dengue using type 2 fuzzy inference system (2020). https://doi.org/10.1016/j.bspc.2020.102121

  27. Nabati, J., Nezami, A., Neamatollahi, E., Akbari, M.: GIS-based agro-ecological zoning for crop suitability using fuzzy inference system in semi-arid regions. Ecol. Indicators 117, (2020). https://doi.org/10.1016/j.ecolind.2020.106646

  28. Chowdhury, A., De, D.: FIS-RGSO: dynamic fuzzy inference system based reverse glowworm swarm optimization of energy and coverage in green mobile wireless sensor networks. Comput. Commun. 163, 12–34 (2020). https://doi.org/10.1016/j.comcom.2020.09.002

  29. Ebrahimi, M., Qaderi, F.: Determination of the most effective control methods of \({\text{SO}}_{2}\) pollution in Tehran based on adaptive neuro-fuzzy inference system. Chemosphere 263 (2021). https://doi.org/10.1016/j.chemosphere.2020.128002

  30. Raeihagh, H., Behbahaninia, A., Aleagha, M.M.: Risk assessment of sour gas inter-phase onshore pipeline using ANN and fuzzy inference system—Case study: The South Pars Gas field. J. Loss Prevent. Process Ind. 104238 (2020). https://doi.org/10.1016/j.jlp.2020.104238

  31. Cooper, E.L., Ma, M.J.: Understanding nutrition and immunity in disease management. J. Trad. Complementary Med. 1–6 (2016). https://doi.org/10.1016/j.jtcme.2016.12.002

  32. Claus, M., Dychus, N., Ebel, M., Damaschke, J., Maydych, V., Wolf, O.T., Kleinsorge, T., Watzl, C.: Measuring the immune system: a comprehensive approach for the analysis of immune functions in humans (2016). https://doi.org/10.1007/s00204-016-1809-5

  33. Jiang, X., Niu, Y., Li, X., Li, L., Cai, W., Chen, Y., Liao, B., Wang, E.: Is a 14-day quarantine period optimal for effectively controlling coronavirus disease 2019 (COVID-19) https://doi.org/10.1101/2020.03.15.20036533

  34. Lai, C.C., Shih, T.P., Ko, W.C., Tang, H.J., Hsueh, P.R.: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and corona virus disease-2019 (COVID-19): the epidemic and the challenges. Int. J. Antimicrobial Agents (2020). https://doi.org/10.1016/j.ijantimicag.2020.105924

    Article  Google Scholar 

  35. https://www.icmr.gov.in/

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Acknowledgements

The work of is financially supported by the Department of Science and Technology & Biotechnology, Govt. of West Bengal (vide memo no. 201 (Sanc.)/ST/P/S&T/16G-12/2018 dated 19-02-2019). Moreover, the authors are very much grateful to the anonymous reviewers and the editor Prof. Ahmad Taher Azar for their constructive comments and helpful suggestions to improve both the quality and presentation of the manuscript significantly.

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Authors and Affiliations

  1. Department of Mathematics, Ramsaday College, Amta, Howrah, 711401, India

    Sayani Adak & Soovoojeet Jana

  2. Indian Institute of Engineering Science and Technology, Shibpur, Howrah, 711103, West Bengal, India

    Sayani Adak & T. K. Kar

Authors
  1. Sayani Adak
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  2. Soovoojeet Jana
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  3. T. K. Kar
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Editors and Affiliations

  1. Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

    Ahmad Taher Azar

  2. Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

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Adak, S., Jana, S., Kar, T.K. (2022). Investigation of COVID-19 Using an Artificial Intelligence Based Approach. In: Azar, A.T., Hassanien, A.E. (eds) Modeling, Control and Drug Development for COVID-19 Outbreak Prevention. Studies in Systems, Decision and Control, vol 366. Springer, Cham. https://doi.org/10.1007/978-3-030-72834-2_13

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