A new DoE-MTOPSIS based prediction model suggestion to capture potential SARS-CoV-2 reactivated patients

Yıl 2021, Cilt: 34 Sayı: 4, 1051 - 1062, 01.12.2021

Yusuf Tansel İç

https://doi.org/10.35378/gujs.757464

Öz

Difficulties to use convenient data during the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) pandemic outbreak and complexities of the problem attitude crucial challenges in infectious disease modelling studies. Motivated by the on-going reach to predict a potential reactivated SARS-CoV-2 (COVID-19), we suggest a prediction model that beyond the clinical characteristics based evaluation approaches. In particular, we developed a possibly available and more efficient prediction model to predict a potential reactivated SARS-CoV-2 (COVID-19) patient. Our paper aims to explore the applicability of a modified Technique for Order Preference by Similarity to Ideal Solutions (MTOPSIS) integrated Design of Experiment (DoE) method to predict a potential reactivated COVID-19 patient in real-time clinical or laboratory applications. The presented novel model may be of interest to the readers studying similar research areas. We illustrate MTOPSIS integrated DoE method by applying it to the COVID-19 pandemic real clinical cases from Wuhan/China-based data. Despite the small sample size, our study provides an encouraging preliminary model framework. Finally, a step by step algorithm is suggested in the study for future research perspectives.

Anahtar Kelimeler

SARS-CoV-2, COVID-19, Laboratory medicine, Design of experiment, TOPSIS meta-model

Kaynakça

• [1] Ye, G., Pan, Z., Pan, Y., Deng, Q., Chen, L., Li, J., Li, Y., Wang, X., “Clinical characteristics of severe acute respiratory syndrome coronavirus 2 reactivation” Journal of Infection, 80(5): e14-e17, (2020).
• [2] Zhu, Y., Chen, Y.Q., “On a statistical transmission model in analysis of the early phase of COVID-19 outbreak”, Stat Biosci, In Press, (2020). https://doi.org/10.1007/s12561-020-09277-0.
• [3] Cheng, Z.J., Shan, J., “2019 Novel coronavirus: where we are and what we know”, Infection, 48: 155–163, (2020).
• [4] Salzberger, B., Glück, T., Ehrenstein, B., “Successful containment of COVID-19: the WHO-report on the COVID-19 outbreak in China”, Infection, 48: 151–153, (2020).
• [5] Baraboutis, I.G., Gargalianos, P., Aggelonidou, E. Adraktas, A., “Initial real-life experience from a designated COVID-19 centre in Athens, Greece: A proposed therapeutic algorithm. SN Compr. Clin. Med., 2(6): 1-5, (2020).
• [6] Alizargar, J., “Risk of reactivation or reinfection of novel coronavirus (COVID-19)”, J Formos Med Assoc., 119(6): 1123, (2020).
• [7] Smith, J., “South Korea reports more recovered coronavirus patients testing positive again”, Available from:https://www.reuters.com/article/us-health-coronavirus-southkorea/south-korea-reports-more-recovered-coronavirus-patients-testing-positive-again-idUSKCN21V0JQ.6. Access date: 03/12/2020.
• [8] Kayat S., “Doctor’s Note: Can the coronavirus reactivate?”, Available from : https://www. aljazeera. com/indepth/features/doctor-note-coronavirus-reactivate-200412062905537.html., Access date: 04 / 12/2020
• [9] Prytherch, D.R., Smith, G.B., Schmidt, P.E., Featherstone, P.I., “ViEWS—Towards a national early warning score for detecting adult inpatient deterioration”, Resuscitation, 81: 932–7, (2010).
• [10] Meylan, S., Akrour, R., Regina, J., Bart, P.A., Dami, F., Calandra, T., “An early warning score to predict ICU admission in COVID-19 positive patients”, Journal of Infection, 81(5): 816-846, (2020).
• [11] Overton, C.E., Stage, H. B., Ahmad, S., Curran-Sebastian, J., Dark, P., Das, R., Fearon, E., Felton, T., Fyles, M., Gent, N., Hall, I., House, T., Lewkowicz, H., Pang, X., Pellis, L., Sawko, R., Ustianowski, A., Vekaria, B., Webb, L., “Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example”, Infectious Disease Modelling, 5: 409-441,(2020).
• [12] Ogundokun, R. O., Lukman, A.F., Kibria, G.B., Awotunde, J.B., Aladeitan, B.B., “Predictive modelling of COVID-19 confirmed cases in Nigeria”, Infectious Disease Modelling, 5: 543-548, (2020).
• [13] Dawoud, I., “Modelling Palestinian COVID-19 cumulative confirmed cases: A comparative study”, Infectious Disease Modelling, 5: 748-754, (2020).
• [14] Wang, Y., “Predict new cases of the coronavirus 19; in Michigan, USA or other countries using Crow-AMSAA method”, Infectious Disease Modelling, 5: 459-477, (2020).
• [15] Ic, Y.T., “An experimental design approach using TOPSIS method for the selection of computer- integrated manufacturing technologies”, Robot. Comput. Integr. Manuf., 28: 245– 256, (2012).
• [16] Ic, Y.T., “A TOPSIS based design of experiment approach to assess company ranking”, Appl. Math. Comput., 227: 630–647, (2014).
• [17] Ic, Y.T., “Development of a new multi-criteria optimization method for engineering design problems”, Res. Eng. Des., 27(4): 413–436, (2016).
• [18] Ic, Y.T., Yurdakul, M., “Understanding the effect of assignment of importance scores of evaluation criteria randomly in the application of DOE-TOPSIS in decision making”. In: Advances in Intelligent Systems and Computing, Springer, 411-424, (2018).
• [19] Chakraborty, S., Chatterjee, P., Das, P.P., “A DoE–TOPSIS method-based meta-model for parametric optimization of non-traditional machining processes”, Journal of Modelling in Management, 14(2):430-455, (2019).
• [20] Bilbao-Terol, A., Arenas-Parra, M., Alvarez-Otero, S., Cañal-Fernández, V., “Integrating corporate social responsibility and financial performance”, Management Decision, 57 (2): 324-348, (2019).
• [21] Ic, Y.T., Şimşek, E., “Operating window perspective integrated TOPSIS approach for hybrid electrical automobile selection”, SN Applied Sciences, 1(11): 1314, (2019).
• [22] Yang, W., Cao, Q., Qin, L.E., Wang, X., Cheng, Z., Pan, A., Dai, J., Sun, Q., Zhao, F., Qu, Z., Yan, F., “Clinical characteristics and imaging manifestations of the 2019 novel coronavirus disease (COVID-19):A multi-centre study in Wenzhou city, Zhejiang, China”, Journal of Infection, 80(4): 388–393, (2020).
• [23] Pellis, L., Cauchemez, S., Ferguson, N.M., Fraser, C., “Systematic selection between age and household structure for models aimed at emerging epidemic predictions”, Nature Communications, 11(1), 1-11, (2020).
• [24] Valdano, E., Poletto, C., Boelle, P.Y., Colizza, V., “Reorganization of nurse scheduling reduces the risk of healthcare associated infections”, medRxiv, 19007724, (2019).
• [25] Lau, M.S., Dalziel, B.D., Funk, S., McClelland, A., Tiffany, A., Riley, S., Metcalf, C. J. E., Grenfell, B.T., “Spatial and temporal dynamics of super spreading events in the 2014–2015 West Africa Ebola epidemic”, Proceedings of the National Academy of Sciences, 114(9), 2337-2342, (2017).