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To restrict or not to restrict? Use of artificial neural network to evaluate the effectiveness of mitigation policies: A case study of Turkey

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  • Çaparoğlu, Ömer Faruk
  • Ok, Yeşim
  • Tutam, Mahmut

Abstract

Outbreaks, epidemics or pandemics have increased over the last years, increasing the morbidity and mortality over large geographical areas, as well as causing financial crises and irreversible social changes. Coping with emerging infectious diseases such as Covid-19, different mitigation policies are developed by countries. However, the benefit of each mitigation policy is still not well-explored due to the considerable difference between implementations of policies in each country. The question is which policies play a significant role in controlling Covid-19 transmission. Developing two models used in Artificial Neural Network, this study investigates the impact of mitigation policies or strategies (a combination of policies) by considering different vaccination and mutation scenarios. The former model requires the prediction of reproduction number based on the number of cases reported in previous days; whereas, the latter model is constructed based on the number of people impacted by a mitigation policy or strategy. Although the first model yields more accurate results, it requires the use of historical data; hence, the passage of time during a critical period of fighting against Covid-19. The benefit of the second model is that it can be implemented more quickly by determining a coefficient for each policy or strategy based on the restricted population and/or limited mobility. Testing different scenarios through a real-world example from Turkey, we find mitigation policies or strategies play a significant role in controlling Covid-19; as well as vaccination and mutation scenarios. Our results suggest continuous and predetermined mitigation policies or strategies should be implemented to control the spread of infectious diseases in addition to a successful vaccination program.

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  • Çaparoğlu, Ömer Faruk & Ok, Yeşim & Tutam, Mahmut, 2021. "To restrict or not to restrict? Use of artificial neural network to evaluate the effectiveness of mitigation policies: A case study of Turkey," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
  • Handle: RePEc:eee:chsofr:v:151:y:2021:i:c:s0960077921006007
    DOI: 10.1016/j.chaos.2021.111246
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    1. Jun, Wang & Yuyan, Luo & Lingyu, Tang & Peng, Ge, 2018. "Modeling a combined forecast algorithm based on sequence patterns and near characteristics: An application for tourism demand forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 108(C), pages 136-147.
    2. T Déirdre Hollingsworth & Don Klinkenberg & Hans Heesterbeek & Roy M Anderson, 2011. "Mitigation Strategies for Pandemic Influenza A: Balancing Conflicting Policy Objectives," PLOS Computational Biology, Public Library of Science, vol. 7(2), pages 1-11, February.
    3. Okuonghae, D. & Omame, A., 2020. "Analysis of a mathematical model for COVID-19 population dynamics in Lagos, Nigeria," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    4. Cleo Anastassopoulou & Lucia Russo & Athanasios Tsakris & Constantinos Siettos, 2020. "Data-based analysis, modelling and forecasting of the COVID-19 outbreak," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-21, March.
    5. Kassa, Semu M. & Njagarah, John B.H. & Terefe, Yibeltal A., 2020. "Analysis of the mitigation strategies for COVID-19: From mathematical modelling perspective," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    6. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    7. Mishra, Bimal Kumar & Keshri, Ajit Kumar & Rao, Yerra Shankar & Mishra, Binay Kumar & Mahato, Buddhadeo & Ayesha, Syeda & Rukhaiyyar, Bansidhar Prasad & Saini, Dinesh Kumar & Singh, Aditya Kumar, 2020. "COVID-19 created chaos across the globe: Three novel quarantine epidemic models," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    8. Wieczorek, Michał & Siłka, Jakub & Woźniak, Marcin, 2020. "Neural network powered COVID-19 spread forecasting model," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    9. Atangana, Abdon, 2020. "Modelling the spread of COVID-19 with new fractal-fractional operators: Can the lockdown save mankind before vaccination?," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    10. Sun, Tingzhe & Wang, Yan, 2020. "Modeling COVID-19 epidemic in Heilongjiang province, China," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    11. Behnood, Ali & Mohammadi Golafshani, Emadaldin & Hosseini, Seyedeh Mohaddeseh, 2020. "Determinants of the infection rate of the COVID-19 in the U.S. using ANFIS and virus optimization algorithm (VOA)," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    12. Castillo, Oscar & Melin, Patricia, 2020. "Forecasting of COVID-19 time series for countries in the world based on a hybrid approach combining the fractal dimension and fuzzy logic," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    13. Babaei, A. & Ahmadi, M. & Jafari, H. & Liya, A., 2021. "A mathematical model to examine the effect of quarantine on the spread of coronavirus," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    14. Chimmula, Vinay Kumar Reddy & Zhang, Lei, 2020. "Time series forecasting of COVID-19 transmission in Canada using LSTM networks," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    15. Koopmans, Ruud, 2020. "A virus that knows no borders? Exposure to and restrictions of international travel and the global diffusion of COVID-19," Discussion Papers, Research Unit: Migration, Integration, Transnationalization SP VI 2020-103, WZB Berlin Social Science Center.
    16. Yousefpour, Amin & Jahanshahi, Hadi & Bekiros, Stelios, 2020. "Optimal policies for control of the novel coronavirus disease (COVID-19) outbreak," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    17. Nida Shahid & Tim Rappon & Whitney Berta, 2019. "Applications of artificial neural networks in health care organizational decision-making: A scoping review," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-22, February.
    18. Ribeiro, Matheus Henrique Dal Molin & da Silva, Ramon Gomes & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2020. "Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    Full references (including those not matched with items on IDEAS)

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