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COVID-19 Döneminde Koruyucu Sarf Malzemelerin Tüketiminin Tahmin Edilmesi

Year 2023, Volume: 37 Issue: 2, 120 - 136, 15.04.2023

Abstract

İnsanlık tarihinin başlangıcından bu yana yaşanan olağandışı dönemler kendine özgü düzenlerin oluş- masına neden olmuştur. Hastanelerde olağan dönemlerde çok önemli olmadığı öngörülen ve kolay yönetilebildiği varsayılan, maske, önlük ve dezenfektan gibi koruyucu sarf malzemelerinin kullanımı ve tedariği, 2019 koronavirüs hastalığı (COVID-19) pandemi dönemi ile birlikte kritik bir bakış açısı kazan- masına neden olmuştur. Bu çalışmada, öncelikle, bir hastane işleyişinde olağan durum sayılan 2019 yılı verileri dikkate alınarak bu koruyucu ve önleyici malzemelerin tedarik, stok ve tüketim süreçleri değer- lendirilmiştir. Çalışmanın ikinci kısmında ise pandemi döneminin gelişmesi esnasında oluşan veriler dikkate alınarak, bu koruyucu ve önleyici sarf malzemelerin tedarik ve kullanımlarında oluşan farklı- laşmalar modellenmiştir. Koruyucu sarf malzemelerinin kullanımlarının tahminini modellemek için doktor, hemşire, idari personel, hasta sayısı ve ameliyat sayısı bağımsız değişkenler olarak seçilmiştir. Bağımsız değişkenlerdeki değişimin koruyucu sarf malzemeler üzerindeki değişimlerini incelemek amacıyla çok değişkenli doğrusal regresyon analizi uygulanmıştır. N95 ve bağcıklı cerrahi maske ve lastikli maskenin tüketimi, COVID hasta sayısı ve sağlık çalışanı sayısı ile açıklanmıştır. El dezenfektan ve muayene eldiveni tüketimi doktor sayısı ve COVID hasta sayısı ile tahmin edilmiştir. Cerrahi eldiven tahmini, ameliyat sayısına bağlı olarak tahmin edilmiştir. Bu çalışmada, hastanelerde koruyucu sarf malzemelerinin tüketimlerinin tahmin edilmesine yardımcı olacak çok değişkenli modeller önerilmiştir.

References

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  • Binoj, J. S., Manikandan, N., Thejasree, P., Varaprasad, K. C., Prem Sai, N., & Manideep, M. (2021). Machinability studies on wire electrical dis- charge machining of Nickel alloys using multiple regression analysis. Materials Today: Proceedings. Proceedings, 39, 155–159. [CrossRef]
  • Catalina, T., Iordache, V., & Caracaleanu, B. (2013). Multiple regression model for fast prediction of the heating energy demand. Energy and Buildings, 57, 302–312. [CrossRef]
  • Čeh, M., Kilibarda, M., Lisec, A., & Bajat, B. (2018). Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments. ISPRS International Journal of Geo-Information, 7(5), 168. [CrossRef]
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  • Nahmias, S. (2008). Production and operation analysis. In (pp. 56-58) McGraw-Hill/Irwin Series Operation and Decision Science.
  • Olsen, A. A., McLaughlin, J. E., & Harpe, S. E. (2020). Using multiple linear regression in pharmacy education scholarship. Currents in Pharmacy Teaching and Learning, 12(10), 1258–1268. [CrossRef]
  • Rath, S., Tripathy, A., & Tripathy, A. R. (2020). Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model. Diabetes and Metabolic Syndrome, 14(5), 1467–1474. [CrossRef] Rhee, S. W. (2020). Management of used personal protective equipment and wastes related to COVID-19 in South Korea. Waste Management and Research, 38(8), 820–824. [CrossRef]
  • Roshan, R., Feroz, A. S., Rafique, Z., & Virani, N. (2020). Rigorous hand hygiene practices among health care workers reduce hospital-associated infections during the COVID-19 pandemic. Journal of Primary Care and Community Health, 11, 2150132720943331. [CrossRef]
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A Prediction for Medical Supplies Consumptions During Coronavirus Disease 2019

Year 2023, Volume: 37 Issue: 2, 120 - 136, 15.04.2023

Abstract

Extraordinary periods experienced since the beginning of human history have caused the formation of specific patterns. The current coronavirus disease 2019 pandemic we are experiencing has pro- vided critical viewpoint on the use and supply of preventive consumable materials like masks, gowns, and disinfectant. These are used as hygienic items to protect against infectious diseases and are assumed not to be very significant and easily managed in hospitals during normal periods. This study first assessed the supply, stock, and consumption processes for these protective and preventive items considering data from 2019, considered a normal period in hospital operation. In the second part of the study, the differences in supply and use of these items were modeled based on data dur- ing the development of the pandemic. To estimate the use of consumption of the protective equip- ment, number of doctors, healthcare workers, administrative personnel, patients, and surgeries were chosen as independent variables. Multivariate linear regression analysis was applied to examine the changes in the independent variables on protective consumables. It has been observed that dif- ferent variables are effective in estimating the consumption of each protective consumable. N95 mask, tie band surgical mask, and medical face mask consumptions were explained by the number of coronavirus disease patients and healthcare workers. Hand disinfectant and examination glove consumption were predicted with the number of doctor and coronavirus disease patients. Surgical glove prediction was estimated by using the number of surgeries. In this study, multivariate regres- sion models are proposed to help predict the consumption of protective consumables in hospitals.

References

  • Araiza-Aguilar, J. A., Rojas-Valencia, M. N., & Aguilar-Vera, R. A. (2020). Forecast generation model of municipal solid waste using multiple linear regression. Global Journal of Environmental Science and Management, 6(1), 1–14. [CrossRef]
  • Aranda, A., Ferreira, G., Mainar-Toledo, M. D., Scarpellini, S., & Llera Sas- tresa, E. (2012). Multiple regression models to predict the annual energy consumption in the Spanish banking sector. Energy and Buildings, 49, 380–387. [CrossRef]
  • Bianco, V., Manca, O., & Nardini, S. (2009). Electricity consumption forecasting in Italy using linear regression models. Energy, 34(9), 1413–1421. [CrossRef]
  • Binoj, J. S., Manikandan, N., Thejasree, P., Varaprasad, K. C., Prem Sai, N., & Manideep, M. (2021). Machinability studies on wire electrical dis- charge machining of Nickel alloys using multiple regression analysis. Materials Today: Proceedings. Proceedings, 39, 155–159. [CrossRef]
  • Catalina, T., Iordache, V., & Caracaleanu, B. (2013). Multiple regression model for fast prediction of the heating energy demand. Energy and Buildings, 57, 302–312. [CrossRef]
  • Čeh, M., Kilibarda, M., Lisec, A., & Bajat, B. (2018). Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments. ISPRS International Journal of Geo-Information, 7(5), 168. [CrossRef]
  • Chahal, H., Jyoti, J., & Wirtz, J. (2018). Understanding the role of business analytics: Some applications. In Understanding the role of business analytics. Springer. [CrossRef]
  • Chapman, S. N. (2006). The fundamentals of production planning and control (pp. 18). Pearson Education, Inc.
  • Chaurasia, V., & Pal, S. (2020). COVID-19 pandemic: Arima and regression model-based worldwide death cases predictions. SN Computer Science, 1(5), 288. [CrossRef]
  • Cihan, P. (2022). The machine learning approach for predicting the number of intensive care, intubated patients and death: The COVID-19 pandemic in Turkey. Sigma Journal of Engineering and Natural Sciences – Sigma Mühendislik ve Fen Bilimleri Dergisi, 1. [CrossRef]
  • Cohen, J., & Rodgers, Y. V. M. (2020) Contributing factors to personal protective equipment shortages during the COVID-19 pandemic. Preventive Medicine, 141(September), 106263. [CrossRef]
  • Crivellari, A., Beinat, E., Caetano, S., Seydoux, A., & Cardoso, T. (2022). Multi-target CNN-LSTM regressor for predicting urban distribution of short-term food delivery demand. Journal of Business Research, 144, 844–853. [CrossRef]
  • Demirkol Akyol, Ş. (2022). Covid-19 pandemisi sürecinde hijyen ürünleri üreten bir firmada tedarik zinciri ağı tasarımı. European Journal of Science and Technology, 35, 387–394. [CrossRef]
  • Ekingen, E., & Demir, B. (2021). Covid-19 salgın dönemi̇nde bir kamu hastanesinde kişisel koruyucu ekipman kullanımındaki deği̇şi̇mlerin incelenmesi̇. İnönü Üniversitesi Sağlık Hizmetleri Meslek Yüksek Okulu Dergisi, 9(2), 642–654. [CrossRef]
  • Flynn, B. B., Huo, B., & Zhao, X. (2010). The impact of supply chain integration on performance: A contingency and configuration approach. Journal of Operations Management, 28(1), 58–71. [CrossRef]
  • Furman, E., Cressman, A., Shin, S., Kuznetsov, A., Razak, F., Verma, A., & Diamant, A. (2021). Prediction of personal protective equipment use in hospitals during COVID-19. Health Care Management Science, 24(2), 439–453. [CrossRef]
  • Galwey, N. W. (2014). Introduction to mixed modelling: Beyond regression and analysis of variance (2nd ed). [CrossRef]
  • Ghinea, C., Drăgoi, E. N., Comăniţă, E. D., Gavrilescu, M., Câmpean, T., Curteanu, S., & Gavrilescu, M. (2016). Forecasting municipal solid waste generation using prognostic tools and regression analysis. Journal of Environmental Management, 182, 80–93. [CrossRef]
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. In Vectors. [CrossRef]
  • Johnson, M. (2020). Wuhan 2019 novel coronavirus - 2019-nCoV. Materials and Methods, 10, 1–5. [CrossRef]
  • Mena, C., Karatzas, A., & Hansen, C. (2022). International trade resilience and the Covid-19 pandemic. Journal of Business Research, 138, 77–91. [CrossRef]
  • Minitab. (2021). Minitab Statistical Software Release 18 for Windows. Minitab 21 Release. https://www.minitab.com/en-us/
  • Montgomery, D. C., & Runger, G. C. (2003). Applied statistics and probability for engineers. In Journal of the Royal Statistical Society, Series A (Vol. 158). John Wiley & Sons, Inc. [CrossRef]
  • Nahmias, S. (2008). Production and operation analysis. In (pp. 56-58) McGraw-Hill/Irwin Series Operation and Decision Science.
  • Olsen, A. A., McLaughlin, J. E., & Harpe, S. E. (2020). Using multiple linear regression in pharmacy education scholarship. Currents in Pharmacy Teaching and Learning, 12(10), 1258–1268. [CrossRef]
  • Rath, S., Tripathy, A., & Tripathy, A. R. (2020). Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model. Diabetes and Metabolic Syndrome, 14(5), 1467–1474. [CrossRef] Rhee, S. W. (2020). Management of used personal protective equipment and wastes related to COVID-19 in South Korea. Waste Management and Research, 38(8), 820–824. [CrossRef]
  • Roshan, R., Feroz, A. S., Rafique, Z., & Virani, N. (2020). Rigorous hand hygiene practices among health care workers reduce hospital-associated infections during the COVID-19 pandemic. Journal of Primary Care and Community Health, 11, 2150132720943331. [CrossRef]
  • Uyanık, G. K., & Güler, N. (2013). A study on multiple linear regression analysis. Procedia – Social and Behavioral Sciences, 106, 234–240. [CrossRef]
  • van der Laan, E., van Dalen, J., Rohrmoser, M., & Simpson, R. (2016). Demand forecasting and order planning for humanitarian logistics: An empirical assessment. Journal of Operations Management, 45(1), 114–122. [CrossRef]
  • World Health Organization (WHO). (2020). Emergency Global Supply Chain System (COVID-19) catalogue. https://www.who.int/ publications/i/item/emergency-global-supply-chain-system- (covid-19)-catalogue
  • World Health Organization (WHO). (2020). Rational use of personal pro- tective equipment for coronavirus disease 2019 (COVID-19) and considerations during severe shortages. Who, 1–7. https://apps.who.int/ iris/handle/10665/331695
There are 31 citations in total.

Details

Primary Language English
Subjects Economics
Journal Section Research Articles
Authors

İlkay Saraçoğlu 0000-0003-3338-4912

Ramazan Yaman This is me 0000-0002-4115-1417

Çağrı Serdar Elgörmüş This is me 0000-0001-9412-6568

Early Pub Date April 12, 2023
Publication Date April 15, 2023
Published in Issue Year 2023 Volume: 37 Issue: 2

Cite

APA Saraçoğlu, İ., Yaman, R., & Elgörmüş, Ç. S. (2023). A Prediction for Medical Supplies Consumptions During Coronavirus Disease 2019. Trends in Business and Economics, 37(2), 120-136.

Content of this journal is licensed under a Creative Commons Attribution 4.0 International License

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