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Bireylerin Kovid-19 Riskinin Uzay-zamansal Olarak Belirlenmesi

Yıl 2023, , 33 - 42, 31.01.2023
https://doi.org/10.17671/gazibtd.1135014

Öz

Mevcut çalışmalar örneğin şüpheli-bulaş-eksiltme modeli ve makine öğrenmesi modelleri her bir kişi ve alan için bulaş riskinin hesaplanmasına uygun değildir. Bu çalışmada mevcut yaklaşımların eksik yönlerinin giderilmesi için toplanan verilerin uzaysal ve zamansal tahminleme modeli olarak bir araya getirildiği bir dönüt işleme tasarımı önerilmektedir. Önerilen tasarım üç ana işleme aşaması içermektedir. Bunlar verinini üretilmesi, geri dönüş analizi ve gerçek zamanlı uzaysal ve zamansal değerlendirme süreçleridir. Verilerin üretilmesi aşamasında her bir bireyin Kovid-19 durumunun Markov olasılık işlemi kullanılarak üretildiği süreç yer alır. Bu aşamada hastalığın çoğalma parametreleri, semptonlu hastaların ve semptonsuz hastaların görülme sıklığı, toplam nüfus, hastalığı geçirmekte olan nüfus, ve hareket halinde olan nüfus sayıları kullanılarak her bir hasta için Kovid durumu ve hareket halinde olma durumu rastsal olarak güncellenir. Hareket verisi ise rastsal olarak belirlenen özel alanlar için oluşturulur. Bu veride kişilerin belirli bir alan içerisindeki etkileşimleri rastsal olarak hesaplanır. Geri dünüş analizi aşamasında toplanan istatistikler ve yerel olay verileri birleştirilerek doğrusal bir model yardımıyla her bir bireyin Kovid-19 riski tahmin edilir. Bu bağlamda yerel istatistilerin elde edilmesinde olasılıksal bir yakınsama yaklaşımı kullanılabilir. Değerlendirme aşamasında, geri dönüş analizinden elde edilen tüm etkileşimler kişilerin periodik olarak güncel Kovid-19 riskinin hesaplanmasında kullanılır. Daha sonra her bir kişinin üretilen verideki Kovid-19 bilgisi kullanılarak tamin başarısı o zaman aralağı için hesaplanır. Populasyon sayısı, yer/zaman ve hareketlilik oranınında bağımsız olarak her bir birey etkileşimi için hesaplanan Kappa önerilen tasarımın etkisinin önemli olduğunu göstermiştir.

Destekleyen Kurum

Bursa Teknik Üniversitesi

Proje Numarası

200COVİD04

Teşekkür

Bilgisayar Mühendisliği bölüm başkanlığına ve üniversitemizin BAP birimine maddi ve manevi desteklerinden ötürü teşekkür ederim.

Kaynakça

  • Y. Zeng, X. Guo, Q. Deng, S. Luo, H. Zhang, "Forecasting of COVID-19: spread with dynamic transmission rate", Journal of Safety Science and Resilience, 1(2), 91–96, 2020.
  • A. Singhal, P. Singh, B. Lall, S. Joshi, "Modeling and predic-tion of COVID-19 pandemic using Gaussian mixture mod-el", Chaos, Solitons and Fractals, 138, 2020.
  • L. Basnarkov, "SEAIR Epidemic spreading model of COVID-19", Chaos, Solitons and Fractals, 142, 110394, 2021.
  • A. Şenol, Y. Canbay, M. Kaya, “Trends in Outbreak Detec-tion in Early Stage by Using Machine Learning Approaches”, Bilişim Teknolojileri Dergisi, 14 (4), 355-366, 2021.
  • W. Getz, R. Salter, O. Muellerklein, H. Yoon, K. Tallam, “Modeling epidemics: A primer and Numerus Model Builder implementation”, Epidemics, 25, 9-19, 2018.
  • Adiga, A, Dubhashi, D, Lewis, B, Marathe, M, Venkatra-manan, S, Vullikanti, A. "Mathematical Models for COVID-19 Pandemic: A Comparative Analysis", Journal of the Indi-an Institute of Science, 100(4), 793–807, 2020.
  • O. Bjørnstad, K. Shea, M. Krzywinski, N. Altman, "Model-ing infectious epidemics", Nature methods, 17(5), 455–456, 2020.
  • S. Olaniyi, O. Obabiyi, K. Okosun, A. Oladipo, S. Adewale, "Mathematical modelling and optimal cost-effective control of COVID-19 transmission dynamics", European Physical Journal Plus, 135(11), 938, 2020.
  • A. Isdory, E. Mureithi, D. Sumpter, "The impact of human mobility on HIV transmission in Kenya", PLoS ONE, 10(11), 2015.
  • K. Al-Kindi, A. Alkharusi, D. Alshukaili, N. Al Nasiri, T. Al-Awadhi, Y. Charabi, A. El Kenawy, "Spatiotemporal As-sessment of COVID-19 Spread over Oman Using GIS Tech-niques", Earth Systems and Environment, 4(4), 797–811, 2020.
  • H. Unwin, S. Mishra, V. Bradley, A. Gandy, T. Mellan, et. al. "State-level tracking of COVID-19 in the United States". Nature Communications, 11(1), 1–9, 2020.
  • J. Sousa, J. Barata, "Tracking the Wings of Covid-19 by Modeling Adaptability with Open Mobility Data", Applied Artificial Intelligence, 35(1), 41–62, 2021.
  • F. Shahid, A. Zameer, M. Muneeb, "Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM", Chaos, Solitons and Fractals, 140, 110212, 2020.
  • C. Yeşilkanat, "Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm", Chaos, Solitons and Fractals, 140, 2020.
  • N. Punn, S. Sonbhadra, S. Agarwal, "COVID-19 epidemic analysis using machine learning and deep learning algo-rithms", medRxiv 2020.04.08.20057679, 2021.
  • V. Chimmula, L. Zhang, "Time series forecasting of COVID-19 transmission in Canada using LSTM networks", Chaos, Solitons and Fractals, 135, 2020.
  • A. Ramchandani, C. Fan, A. Mostafavi, "DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Sur-veillance of COVID-19 Using Heterogeneous Features and Their Interactions", IEEE Access, 8, 159915–159930, 2020.
  • Kafieh, R, Saeedizadeh, N, Arian, R, Amini, Z, Serej, N, Vaezi, A, Javanmard, S. "Isfahan and Covid-19: Deep spati-otemporal representation", Chaos, Solitons and Fractals, 141, 110339, 2020.
  • A. Rodriguez, N. Muralidhar, B. Adhikari, A. Tabassum, N. Ramakrishnan, B. Prakash, "Steering a historical disease forecasting model under a Pandemic: Case of Flu and COVID-19", 2020.
  • S. Chang, E. Pierson, P. Koh, J. Gerardin, B. Redbird, D. Grusky, J. Leskovec, "Mobility network models of COVID-19 explain inequities and inform reopening", Nature, 589 (7840), 82–87, 2021.
  • Y. Chen, Q. Li, H. Karimian, X. Chen, X. Li. "Spatio-temporal distribution characteristics and influencing factors of COVID-19 in China", Scientific Reports, 11(1):3717. PMID: 33580113, 2021.
  • D. Balcan, V. Colizza, B. Gonçalves, H. Hud, J. Ramasco, A. Vespignani, "Multiscale mobility networks and the spatial spreading of infectious diseases", Proceedings of the Na-tional Academy of Sciences of the United States of America, 106 (51), 21484–21489, 2009.
  • A. Gatrell, T. Bailey, P. Diggle, B. Rowlingson, "Spatial Point Pattern Analysis and Its Application in Geographical Epide-miology", Transactions of the Institute of British Geogra-phers, 21 (1), 256, 1996.
  • M. Kulldorff, R. Heffernan, J. Hartman, R. Assun\ccão, F. Mostashari, "A space-time permutation scan statistic for dis-ease outbreak detection", PLoS Medicine, 2 (3), 0216–0224, 2005.
  • T. Ng, T. Wen, "Spatially Adjusted Time-varying Reproduc-tive Numbers: Understanding the Geographical Expansion of Urban Dengue Outbreaks", Scientific Reports, 9 (1), 1–12, 2019.
  • F. Rihan, H. Alsakaji, C. Rajivganthi, "Stochastic SIRC epi-demic model with time-delay for COVID-19", Advances in Difference Equations, 2020(1), 502, 2019.
  • A. Arenas, W. Cota, J. Gómez-Gardeñes, S. Gómez, C. Granell, J. T. Matamalas, D. Soriano-Paños, and B. Steineg-ger, "Modeling the spatiotemporal epidemic spreading of COVID-19 and the impact of mobility and social distancing interventions." Physical Review X, 10(4), 041055, 2020.
  • M. Pinsky, and K. Samuel, An introduction to stochastic modeling, Elsevier Inc, 2010.
  • Internet: Census Block Group Data, SafeGraph Data. https://docs.safegraph.com/docs/open-census-data, 05.17.2022.
  • A. Viera, J. Garrett, “Understanding Interobserver Agre-ment :The Kappa Statistic”, Fam med, 37(5), 360–363, 2005.

A Spatio-Temporal Approach For Determining Individual's Covid-19 Risks

Yıl 2023, , 33 - 42, 31.01.2023
https://doi.org/10.17671/gazibtd.1135014

Öz

Current state of art approaches such as the susceptible-infected-removed model and machine learning models are not optimized for modeling the risks of individuals and modeling the effects of local restrictions. To improve the drawback of these approaches, the feedback processing framework is proposed where previously accumulated global statistics and the model estimates generated from the spatial-temporal data are combined to improve the performance of the local prediction. The proposed framework is evaluated in three processing stages: generation of the simulation dataset, feedback analysis, and evaluation for the spatial-temporal and real-time pandemic analysis. In the data generation stage, the corresponding state of the illness for each person is modeled by a Markov stochastic process. In this stage, the parameters such as the reproduction rate, symptomatic rate, asymptomatic rate, population count, infected count, and the average mobility rate are used to update the individual's Covid-19 status and the individual's movements. The movement data of each person is generated randomly for several places of interest. In the feedback analysis stage, both the aggregated statistics and the local event data are combined in a linear model to infer a score for the Covid-19 probability of the person. In this respect, a stochastic model can be used to approximate the local statistics. In the evaluation stage, the result of the feedback analysis for all the interactions is used to classify the state of the individuals periodically. Later the accuracy of the evaluation for each person is obtained by comparing the individual's prediction with the real data generated in the same time interval. The Kappa scores independent from different populations, locations, and mobility rates obtained for every interaction indicate a significant difference from the random statistics.

Proje Numarası

200COVİD04

Kaynakça

  • Y. Zeng, X. Guo, Q. Deng, S. Luo, H. Zhang, "Forecasting of COVID-19: spread with dynamic transmission rate", Journal of Safety Science and Resilience, 1(2), 91–96, 2020.
  • A. Singhal, P. Singh, B. Lall, S. Joshi, "Modeling and predic-tion of COVID-19 pandemic using Gaussian mixture mod-el", Chaos, Solitons and Fractals, 138, 2020.
  • L. Basnarkov, "SEAIR Epidemic spreading model of COVID-19", Chaos, Solitons and Fractals, 142, 110394, 2021.
  • A. Şenol, Y. Canbay, M. Kaya, “Trends in Outbreak Detec-tion in Early Stage by Using Machine Learning Approaches”, Bilişim Teknolojileri Dergisi, 14 (4), 355-366, 2021.
  • W. Getz, R. Salter, O. Muellerklein, H. Yoon, K. Tallam, “Modeling epidemics: A primer and Numerus Model Builder implementation”, Epidemics, 25, 9-19, 2018.
  • Adiga, A, Dubhashi, D, Lewis, B, Marathe, M, Venkatra-manan, S, Vullikanti, A. "Mathematical Models for COVID-19 Pandemic: A Comparative Analysis", Journal of the Indi-an Institute of Science, 100(4), 793–807, 2020.
  • O. Bjørnstad, K. Shea, M. Krzywinski, N. Altman, "Model-ing infectious epidemics", Nature methods, 17(5), 455–456, 2020.
  • S. Olaniyi, O. Obabiyi, K. Okosun, A. Oladipo, S. Adewale, "Mathematical modelling and optimal cost-effective control of COVID-19 transmission dynamics", European Physical Journal Plus, 135(11), 938, 2020.
  • A. Isdory, E. Mureithi, D. Sumpter, "The impact of human mobility on HIV transmission in Kenya", PLoS ONE, 10(11), 2015.
  • K. Al-Kindi, A. Alkharusi, D. Alshukaili, N. Al Nasiri, T. Al-Awadhi, Y. Charabi, A. El Kenawy, "Spatiotemporal As-sessment of COVID-19 Spread over Oman Using GIS Tech-niques", Earth Systems and Environment, 4(4), 797–811, 2020.
  • H. Unwin, S. Mishra, V. Bradley, A. Gandy, T. Mellan, et. al. "State-level tracking of COVID-19 in the United States". Nature Communications, 11(1), 1–9, 2020.
  • J. Sousa, J. Barata, "Tracking the Wings of Covid-19 by Modeling Adaptability with Open Mobility Data", Applied Artificial Intelligence, 35(1), 41–62, 2021.
  • F. Shahid, A. Zameer, M. Muneeb, "Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM", Chaos, Solitons and Fractals, 140, 110212, 2020.
  • C. Yeşilkanat, "Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm", Chaos, Solitons and Fractals, 140, 2020.
  • N. Punn, S. Sonbhadra, S. Agarwal, "COVID-19 epidemic analysis using machine learning and deep learning algo-rithms", medRxiv 2020.04.08.20057679, 2021.
  • V. Chimmula, L. Zhang, "Time series forecasting of COVID-19 transmission in Canada using LSTM networks", Chaos, Solitons and Fractals, 135, 2020.
  • A. Ramchandani, C. Fan, A. Mostafavi, "DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Sur-veillance of COVID-19 Using Heterogeneous Features and Their Interactions", IEEE Access, 8, 159915–159930, 2020.
  • Kafieh, R, Saeedizadeh, N, Arian, R, Amini, Z, Serej, N, Vaezi, A, Javanmard, S. "Isfahan and Covid-19: Deep spati-otemporal representation", Chaos, Solitons and Fractals, 141, 110339, 2020.
  • A. Rodriguez, N. Muralidhar, B. Adhikari, A. Tabassum, N. Ramakrishnan, B. Prakash, "Steering a historical disease forecasting model under a Pandemic: Case of Flu and COVID-19", 2020.
  • S. Chang, E. Pierson, P. Koh, J. Gerardin, B. Redbird, D. Grusky, J. Leskovec, "Mobility network models of COVID-19 explain inequities and inform reopening", Nature, 589 (7840), 82–87, 2021.
  • Y. Chen, Q. Li, H. Karimian, X. Chen, X. Li. "Spatio-temporal distribution characteristics and influencing factors of COVID-19 in China", Scientific Reports, 11(1):3717. PMID: 33580113, 2021.
  • D. Balcan, V. Colizza, B. Gonçalves, H. Hud, J. Ramasco, A. Vespignani, "Multiscale mobility networks and the spatial spreading of infectious diseases", Proceedings of the Na-tional Academy of Sciences of the United States of America, 106 (51), 21484–21489, 2009.
  • A. Gatrell, T. Bailey, P. Diggle, B. Rowlingson, "Spatial Point Pattern Analysis and Its Application in Geographical Epide-miology", Transactions of the Institute of British Geogra-phers, 21 (1), 256, 1996.
  • M. Kulldorff, R. Heffernan, J. Hartman, R. Assun\ccão, F. Mostashari, "A space-time permutation scan statistic for dis-ease outbreak detection", PLoS Medicine, 2 (3), 0216–0224, 2005.
  • T. Ng, T. Wen, "Spatially Adjusted Time-varying Reproduc-tive Numbers: Understanding the Geographical Expansion of Urban Dengue Outbreaks", Scientific Reports, 9 (1), 1–12, 2019.
  • F. Rihan, H. Alsakaji, C. Rajivganthi, "Stochastic SIRC epi-demic model with time-delay for COVID-19", Advances in Difference Equations, 2020(1), 502, 2019.
  • A. Arenas, W. Cota, J. Gómez-Gardeñes, S. Gómez, C. Granell, J. T. Matamalas, D. Soriano-Paños, and B. Steineg-ger, "Modeling the spatiotemporal epidemic spreading of COVID-19 and the impact of mobility and social distancing interventions." Physical Review X, 10(4), 041055, 2020.
  • M. Pinsky, and K. Samuel, An introduction to stochastic modeling, Elsevier Inc, 2010.
  • Internet: Census Block Group Data, SafeGraph Data. https://docs.safegraph.com/docs/open-census-data, 05.17.2022.
  • A. Viera, J. Garrett, “Understanding Interobserver Agre-ment :The Kappa Statistic”, Fam med, 37(5), 360–363, 2005.
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Yazılımı
Bölüm Makaleler
Yazarlar

Hayri Volkan Agun 0000-0002-4253-8920

Proje Numarası 200COVİD04
Yayımlanma Tarihi 31 Ocak 2023
Gönderilme Tarihi 23 Haziran 2022
Yayımlandığı Sayı Yıl 2023

Kaynak Göster

APA Agun, H. V. (2023). A Spatio-Temporal Approach For Determining Individual’s Covid-19 Risks. Bilişim Teknolojileri Dergisi, 16(1), 33-42. https://doi.org/10.17671/gazibtd.1135014