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

Hayri Volkan Agun

Makale detayları

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

Yazarlar:

Hayri Volkan Agun

Yayıncı:

Gazi University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Bilişim Teknolojileri Dergisi Vol.16 Issue 1, pp.33-42

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001283474

pISSN:

1307-9697

Anahtar kelimeler:

Özet:

 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 individuals Covid-19 status and the individuals 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 individuals 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.

 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 individuals Covid-19 status and the individuals 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 individuals 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.

 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 individuals Covid-19 status and the individuals 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 individuals 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.

 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, yerzaman ve hareketlilik oranınında bağımsız olarak her bir birey etkileşimi için hesaplanan Kappa önerilen tasarımın etkisinin önemli olduğunu göstermiştir.

Makale detayları

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

Yazarlar:

Hayri Volkan Agun

Yayıncı:

Gazi University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Bilişim Teknolojileri Dergisi Vol.16 Issue 1, pp.33-42

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001283474

pISSN:

1307-9697

pandemic spread prediction

spatial-temporal analysis

stream processing

risk computation

pandemi yayılma tahmini

uzay-zamansal analiz

akış işleme

risk hesaplama

Sayı

Makale detayları

Yazarlar: Hayri Volkan Agun

Yayıncı: Gazi University

Ana Başlıklar: Bilgisayar Yazılımı

Dergi: Bilişim Teknolojileri Dergisi Vol.16 Issue 1, pp.33-42

Belge Tipi: Makale

Yayın Tarihi: 1/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001283474

pISSN: 1307-9697

Sayı

Yazarlar:

Hayri Volkan Agun

Yayıncı:

Gazi University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Bilişim Teknolojileri Dergisi Vol.16 Issue 1, pp.33-42

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001283474

pISSN:

1307-9697