Araştırma Makalesi
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Okul/İş Yeri Kapatılmasının COVID-19 Kaynaklı Vaka Sayılarına Etkisi

Yıl 2021, Sayı: 23, 62 - 69, 30.04.2021
https://doi.org/10.31590/ejosat.842793

Öz

Bulaşıcı hastalıklar eski zamanlardan beri insanlığa büyük zararlar vermişlerdir. Devam etmekte olan COVID-19 salgını şimdiye kadar milyonlarca insanın hasta olmasına ve ölmesine yol açmıştır ve yakın gelecekte de etkisini göstermeye devam edecektir. Etkili ilaç ve aşıların yokluğunda, bulaşıcı hastalıkları yavaşlatmanın ve durdurmanın yollarından biri de, kişiler arasındaki etkileşimlerin kısıtlanmasını ve hastalığın yayılmasını engelleyen okul/iş yeri kapatma yöntemidir. Bu çalışmada, toplam hasta ve vaka sayılarına etkilerini görmek için bir müdahale yöntemi olan okul/iş yeri kapatmayı göz önüne aldık. Okul ve iş yerlerinin salgının farklı zamanlarında, ayrı ayrı veya birlikte kapatılmasını içeren altı farklı senaryo, kişilerin okul, iş yeri ve evlerindeki insanlarla günlük etkileşim içinde olduğu SIR (Susceptible-Infectious-Recovery) Ağ (Network) modeli için test edilmiştir. Sistemdeki kişiler yaşlarına göre beş farklı gruba bölünmüş ve bir ev, iş yeri veya okula atanmışlardır. Kişiler günlük olarak, kendi ağlarındaki (ev, iş yeri veya okul) diğer kişilerle etkileşime girip, belirli bir olasılıkla hasta kişilerden enfekte olabilmektedirler. Hastalık bulaştırma olasılığı altı farklı ülkenin COVID-19 istatistiklerinden yararlanılarak hesaplanmıştır. Sonuçlara bakıldığından hangi senaryo olursa olsun, salgın başlangıcında uygulandığında, salgının zirve yaptığı zamanlarda uygulanmasına göre, hasta ve ölüm sayısını düşürmesi bakımından çok daha etkili olduğu görülmüştür. Salgın başladıktan iki hafta sonra uygulanan kapatma, toplam vakaları COVID-19 salgın şiddetinde (%3,2) %65 ve daha yüksek salgın şiddetinde (%10) %40 oranında azaltmıştır. Dahası 2 haftalık okul/işyeri kapatılmasının, 6 ve 9 gün arasında düzgün dağılım gösteren iyileşme zamanları baz alındığında salgını tamamen durduramadığı gözlenmiştir.

Kaynakça

  • Calvó-Armengol, Antoni, and Matthew O. Jackson (2007). “Networks in Labour Markets: Wage and Employment Dynamics and Inequality.” Journal of Economic Theory, 132(1): 27–46.
  • Chaney, T. (2014). “The Network Structure of International Trade.” American Economic Review, 104(11): 3600–3634.
  • Chao, D.L., Halloran, M.E., Obenchain, V.J. and Longini Jr, I.M., (2010). FluTE, a publicly available stochastic influenza epidemic simulation model. PLoS computational biology, 6(1), 1–8.
  • Craig, B. R., Phelan, T., Siedlarek, J. P., & Steinberg, J. (2020). Improving Epidemic Modelling with Networks. Economic Commentary, (2020-23).
  • Demirbilek, M. (2020). YAYsim: Salgın Modelleme ve Karar Destek Sistemi. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 7 (1), 104-112.
  • Dong, E., Du, H., & Gardner, L. (2020). An interactive web-based dashboard to track COVID-19 in real time. The Lancet infectious diseases, 20(5), 533-534.
  • Elliott, Matthew, Benjamin Golub, and Matthew O. Jackson (2014). “Financial Networks and Contagion.” American Economic Review, 104(10): 3115–3153.
  • Glasser, J., Taneri, D., Feng, Z., Chuang, J. H., Tll, P., Thompson, W., ...& Alexander, J. (2010). Evaluation of targeted inuenza vaccination strategies via population modeling. PloS one, 5(9), e12777.
  • Grefenstette, J.J., Brown, S.T., Rosenfeld, R., DePasse, J., Stone, N.T., Cooley, P.C., Wheaton, W.D., Fyshe, A., Galloway, D.D., Sriram, A. and Guclu, H., (2013). FRED (A Framework for Reconstructing Epidemic Dynamics): an open-source software system for modeling infectious diseases and control strategies using census-based populations. BMC public health, 13 (1), 940.
  • Hladish, T., Melamud, E., Barrera, L.A., Galvani, A. and Meyers, L.A., (2012). EpiFire: An open source C++ library and application for contact network epidemiology. BMC bioinformatics, 13(1), 76.
  • Kawai, S., Nanri, S., Ban, E., Inokuchi, M., Tanaka, T., Tokumura, M., ... & Sugaya, N. (2011). Inuenza vaccination of schoolchildren and inuenza outbreaks in a school. Clinical infectious diseases, 53(2), 130-136.
  • Keeling, M. J., & Eames, K. T. (2005). Networks and epidemic models. Journal of the Royal Society Interface, 2(4), 295-307.
  • Kermack, W. O. and McKendrick, A. G. (1927). A Contribution to the Mathematical Theory of Epidemics. Proc. R. Soc. A Math. Phys. Eng. Sci., 115(772), 700–721.
  • Kuylen, E., Stijven, S., Broeckhove, J. and Willem, L., (2017). Social Contact Patterns in an Individual-based Simulator for the Transmission of Infectious Diseases (Stride). In ICCS, January, 2438-2442.
  • Liu, S., Poccia, S., Candan, K.S., Chowell, G. and Sapino, M.L., (2016). epiDMS: data management and analytics for decision-making from epidemic spread simulation ensembles. The Journal of infectious diseases, 214, 427-432.
  • McConnell, J. (2002). Ready for the next influenza pandemic? The Lancet, 359(9312), 1133.
  • Medlock, J., & Galvani, A. P. (2009). Optimizing inuenza vaccine distribution. Science, 325(5948), 1705-1708.
  • Prieto, D. M., Das, T. K., Savachkin, A. A., Uribe, A., Izurieta, R., and Malavade, S. (2012). A systematic review to identify areas of enhancements of pandemic simulation models for operational use at provincial and local levels,” BMC Public Health, 12(1), 251.
  • The COVID-19 Pandemic: A Summary, https://thepathologist.com/subspecialties/the-covid-19-pandemic-a-summary, 1 December 2020.
  • Tsuzuki, S., Baguelin, M., Pebody, R., & van Leeuwen, E. (2019). Modelling the optimal target age group for seasonal inuenza vaccination in Japan. Vaccine.
  • Walters, C. E., Meslé, M. M. I. and Hall, I. M. (2018). Modelling the global spread of diseases: A review of current practice and capability. Epidemics, 25, 1–8.
  • Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of small-world networks. Nature, 393(6684), 440-442.
  • Webby, R. J., & Webster, R. G. (2003). Are we ready for pandemic influenza? Science, 302(5650), 1519-1522.
  • Worldometers.info, https://www.worldometers.info/coronavirus/, 29 November 2020.

The Effect of School/Workplace Closures on COVID-19 Related Incidents

Yıl 2021, Sayı: 23, 62 - 69, 30.04.2021
https://doi.org/10.31590/ejosat.842793

Öz

Contagious diseases have wreaked havoc on human communities since ancient times. Ongoing COVID-19 pandemic has caused millions of incidents and deaths so far and continues to effect all over the world in the near future. One of ways to stop and slow down a pandemic in absent from proper and effective drugs and vaccines is workplace/school closures limiting people interactions and spread of the disease. In this study, we consider workplace/school closures as an intervention strategy to observe the effect on overall incidents and deaths. Six scenarios, covering workplace and school closures together or separately and applications in different times during the pandemic, are tested for a SIR (Susceptible-Infectious-Recovery) network model where people can interact with others in their homes, schools, and workplaces daily. People in the model are divided into five age groups. Each individual is assigned to a home and school or workplace with a given probability regarding to his/her age. People contact with others in their networks (school, workplace, and home) every day and can be infected with a given probability if they interact with sick people. We calibrate sickness probability according to the attack rate derived from COVID-19 related data of six countries. Results show that applying any of intervention strategies as soon as the pandemic begins makes huge differences in terms of overall cases compared to applying them around the peak times. Overall cases decrease by 40% and 65% for the high attack rate (10%) and COVID-19 related attack rate (3.2%) when workplace/school closures are applied 2 weeks after the pandemic has started. Moreover, results imply that even closing schools and workplaces in two weeks does not stop the spread of diseases completely based on recovery times uniformly distributed between 6 and 9 days.

Kaynakça

  • Calvó-Armengol, Antoni, and Matthew O. Jackson (2007). “Networks in Labour Markets: Wage and Employment Dynamics and Inequality.” Journal of Economic Theory, 132(1): 27–46.
  • Chaney, T. (2014). “The Network Structure of International Trade.” American Economic Review, 104(11): 3600–3634.
  • Chao, D.L., Halloran, M.E., Obenchain, V.J. and Longini Jr, I.M., (2010). FluTE, a publicly available stochastic influenza epidemic simulation model. PLoS computational biology, 6(1), 1–8.
  • Craig, B. R., Phelan, T., Siedlarek, J. P., & Steinberg, J. (2020). Improving Epidemic Modelling with Networks. Economic Commentary, (2020-23).
  • Demirbilek, M. (2020). YAYsim: Salgın Modelleme ve Karar Destek Sistemi. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 7 (1), 104-112.
  • Dong, E., Du, H., & Gardner, L. (2020). An interactive web-based dashboard to track COVID-19 in real time. The Lancet infectious diseases, 20(5), 533-534.
  • Elliott, Matthew, Benjamin Golub, and Matthew O. Jackson (2014). “Financial Networks and Contagion.” American Economic Review, 104(10): 3115–3153.
  • Glasser, J., Taneri, D., Feng, Z., Chuang, J. H., Tll, P., Thompson, W., ...& Alexander, J. (2010). Evaluation of targeted inuenza vaccination strategies via population modeling. PloS one, 5(9), e12777.
  • Grefenstette, J.J., Brown, S.T., Rosenfeld, R., DePasse, J., Stone, N.T., Cooley, P.C., Wheaton, W.D., Fyshe, A., Galloway, D.D., Sriram, A. and Guclu, H., (2013). FRED (A Framework for Reconstructing Epidemic Dynamics): an open-source software system for modeling infectious diseases and control strategies using census-based populations. BMC public health, 13 (1), 940.
  • Hladish, T., Melamud, E., Barrera, L.A., Galvani, A. and Meyers, L.A., (2012). EpiFire: An open source C++ library and application for contact network epidemiology. BMC bioinformatics, 13(1), 76.
  • Kawai, S., Nanri, S., Ban, E., Inokuchi, M., Tanaka, T., Tokumura, M., ... & Sugaya, N. (2011). Inuenza vaccination of schoolchildren and inuenza outbreaks in a school. Clinical infectious diseases, 53(2), 130-136.
  • Keeling, M. J., & Eames, K. T. (2005). Networks and epidemic models. Journal of the Royal Society Interface, 2(4), 295-307.
  • Kermack, W. O. and McKendrick, A. G. (1927). A Contribution to the Mathematical Theory of Epidemics. Proc. R. Soc. A Math. Phys. Eng. Sci., 115(772), 700–721.
  • Kuylen, E., Stijven, S., Broeckhove, J. and Willem, L., (2017). Social Contact Patterns in an Individual-based Simulator for the Transmission of Infectious Diseases (Stride). In ICCS, January, 2438-2442.
  • Liu, S., Poccia, S., Candan, K.S., Chowell, G. and Sapino, M.L., (2016). epiDMS: data management and analytics for decision-making from epidemic spread simulation ensembles. The Journal of infectious diseases, 214, 427-432.
  • McConnell, J. (2002). Ready for the next influenza pandemic? The Lancet, 359(9312), 1133.
  • Medlock, J., & Galvani, A. P. (2009). Optimizing inuenza vaccine distribution. Science, 325(5948), 1705-1708.
  • Prieto, D. M., Das, T. K., Savachkin, A. A., Uribe, A., Izurieta, R., and Malavade, S. (2012). A systematic review to identify areas of enhancements of pandemic simulation models for operational use at provincial and local levels,” BMC Public Health, 12(1), 251.
  • The COVID-19 Pandemic: A Summary, https://thepathologist.com/subspecialties/the-covid-19-pandemic-a-summary, 1 December 2020.
  • Tsuzuki, S., Baguelin, M., Pebody, R., & van Leeuwen, E. (2019). Modelling the optimal target age group for seasonal inuenza vaccination in Japan. Vaccine.
  • Walters, C. E., Meslé, M. M. I. and Hall, I. M. (2018). Modelling the global spread of diseases: A review of current practice and capability. Epidemics, 25, 1–8.
  • Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of small-world networks. Nature, 393(6684), 440-442.
  • Webby, R. J., & Webster, R. G. (2003). Are we ready for pandemic influenza? Science, 302(5650), 1519-1522.
  • Worldometers.info, https://www.worldometers.info/coronavirus/, 29 November 2020.
Toplam 24 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Mustafa Demirbilek 0000-0002-1520-2882

Yayımlanma Tarihi 30 Nisan 2021
Yayımlandığı Sayı Yıl 2021 Sayı: 23

Kaynak Göster

APA Demirbilek, M. (2021). The Effect of School/Workplace Closures on COVID-19 Related Incidents. Avrupa Bilim Ve Teknoloji Dergisi(23), 62-69. https://doi.org/10.31590/ejosat.842793