A Study on the Estimation of COVID-19 Daily Cases and Reproduction Number Using Adaptive Kalman Filter for USA, Brazil, Germany, India, Russia, Italy, Spain, United Kingdom, France, Turkey
Yıl: 2021 Cilt: 13 Sayı: 1 Sayfa Aralığı: 91 - 102 Metin Dili: İngilizce DOI: 10.5336/biostatic.2020-80186 İndeks Tarihi: 17-02-2022
A Study on the Estimation of COVID-19 Daily Cases and Reproduction Number Using Adaptive Kalman Filter for USA, Brazil, Germany, India, Russia, Italy, Spain, United Kingdom, France, Turkey
Öz: Objective: In the literature, non-linear mathematical growth models are often used to estimate the number of coronavirus disease-2019 (COVID-19) cases. Specific algorithms such as mathematical optimization technique need to be employed for parameter estimation. In this work, a novel method to estimate COVID-19 daily cases and reproduction number is proposed for COVID-19. Material and Methods: In this study, the daily number of COVID- 19 cases between January 01 and November 16, 2020 has been estimated online via AR(1) (autoregressive time-series model of order 1) and the adaptive Kalman filter (AKF). After calculating the estimate for daily cases, the reproduction number estimate was obtained. Results: It is quite a simple method to model the daily case number by time series with the time-varying parameter AR(1) stochastic process and estimated the time-varying parameter with online AKF. The method is online. Only the data points on the last day are sufficient. Conclusion: The COVID-19 data have been modeled in state space, and the AKF has been employed to estimate the number of daily cases. The estimation results were obtained for the number of daily cases using the AR(1) model. Since the estimation using the AR(1) stochastic process does not require any other modeling assumption, it is a simple approach to model the daily case number time series with the time-varying parameter AR(1) stochastic process and estimated the time-varying parameter with online AKF. We suggest that the simplest method for the reproduction number estimation will be obtained by modeling the daily case via an AR(1) model.
Anahtar Kelime: ABD, Brezilya, Almanya, Hindistan, Rusya, İtalya, İspanya, Birleşik Krallık, Fransa, Türkiye İçin Uyarlanabilir Kalman Filtresi Kullanarak COVID-19 Günlük Vakaları ve Üreme Sayısı Tahmini Üzerine Bir Çalışma
Öz: Amaç: Literatürde, doğrusal olmayan matematiksel büyüme modelleri, koronavirüs hastalığı-2019 [coronavirus disease-2019 (COVID-19)] vakalarının sayısını tahmin etmek için sıklıkla kullanılmaktadır. Parametre tahmini için matematiksel optimizasyon tekniği gibi özel algoritmaların kullanılması gerekir. Bu çalışmada, COVID-19 için günlük COVID-19 vakalarını ve çoğalma sayısını tahmin etmek için yeni bir yöntem önerilmiştir. Gereç ve Yöntemler: Bu çalışmada, 01 Ocak ve 16 Kasım 2020 tarihleri arasında günlük COVID-19 vakalarına dayalı olarak AR(1) (1 gecikmeli oto regresif zaman serisi modeli) ve uyarlanabilir Kalman filtresi (UKF) aracılığıyla günlük vaka tahmini çevrim içi olarak yapılmıştır. Günlük vakalar için tahmin, çoğalma sayısı tahmini elde edilmiştir. Bulgular: Günlük vaka sayısı zaman serilerini zamanla değişen AR(1) stokastik süreç ile modellemek ve çevrimiçi UKF ile zamanla değişen parametreyi tahmin etmek oldukça basit bir yöntemdir. Yöntem çevrim içidir. Yalnızca son gündeki veri noktaları yeterlidir. Sonuç: COVID-19 verileri durum uzayında modellenmiştir ve günlük vaka sayısını tahmin etmek için UKF kullanılmıştır. AR(1) modeli kullanılarak günlük vaka sayısı için elde edilen tahmin sonuçları. AR(1) stokastik sürecini kullanan tahmin, başka herhangi bir modelleme varsayımı gerektirmediğinden, basit bir yaklaşımdır. Günlük vaka sayısı zaman serilerini zamanla değişen AR(1) stokastik süreci ile modellemek oldukça basit bir yöntemdir ve zamanla değişen parametreyi çevrim içi UKF ile tahmin edilmiştir. Çoğalma sayısı tahmini için en basit yöntemin, günlük vakayı bir AR(1) modeli aracılığıyla modelleyerek elde edileceğini öneriyoruz.
Anahtar Kelime: Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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| APA | ÖZBEK L, Demirtas H (2021). A Study on the Estimation of COVID-19 Daily Cases and Reproduction Number Using Adaptive Kalman Filter for USA, Brazil, Germany, India, Russia, Italy, Spain, United Kingdom, France, Turkey. , 91 - 102. 10.5336/biostatic.2020-80186 |
| Chicago | ÖZBEK Levent,Demirtas Hakan A Study on the Estimation of COVID-19 Daily Cases and Reproduction Number Using Adaptive Kalman Filter for USA, Brazil, Germany, India, Russia, Italy, Spain, United Kingdom, France, Turkey. (2021): 91 - 102. 10.5336/biostatic.2020-80186 |
| MLA | ÖZBEK Levent,Demirtas Hakan A Study on the Estimation of COVID-19 Daily Cases and Reproduction Number Using Adaptive Kalman Filter for USA, Brazil, Germany, India, Russia, Italy, Spain, United Kingdom, France, Turkey. , 2021, ss.91 - 102. 10.5336/biostatic.2020-80186 |
| AMA | ÖZBEK L,Demirtas H A Study on the Estimation of COVID-19 Daily Cases and Reproduction Number Using Adaptive Kalman Filter for USA, Brazil, Germany, India, Russia, Italy, Spain, United Kingdom, France, Turkey. . 2021; 91 - 102. 10.5336/biostatic.2020-80186 |
| Vancouver | ÖZBEK L,Demirtas H A Study on the Estimation of COVID-19 Daily Cases and Reproduction Number Using Adaptive Kalman Filter for USA, Brazil, Germany, India, Russia, Italy, Spain, United Kingdom, France, Turkey. . 2021; 91 - 102. 10.5336/biostatic.2020-80186 |
| IEEE | ÖZBEK L,Demirtas H "A Study on the Estimation of COVID-19 Daily Cases and Reproduction Number Using Adaptive Kalman Filter for USA, Brazil, Germany, India, Russia, Italy, Spain, United Kingdom, France, Turkey." , ss.91 - 102, 2021. 10.5336/biostatic.2020-80186 |
| ISNAD | ÖZBEK, Levent - Demirtas, Hakan. "A Study on the Estimation of COVID-19 Daily Cases and Reproduction Number Using Adaptive Kalman Filter for USA, Brazil, Germany, India, Russia, Italy, Spain, United Kingdom, France, Turkey". (2021), 91-102. https://doi.org/10.5336/biostatic.2020-80186 |
| APA | ÖZBEK L, Demirtas H (2021). A Study on the Estimation of COVID-19 Daily Cases and Reproduction Number Using Adaptive Kalman Filter for USA, Brazil, Germany, India, Russia, Italy, Spain, United Kingdom, France, Turkey. Türkiye Klinikleri Biyoistatistik Dergisi, 13(1), 91 - 102. 10.5336/biostatic.2020-80186 |
| Chicago | ÖZBEK Levent,Demirtas Hakan A Study on the Estimation of COVID-19 Daily Cases and Reproduction Number Using Adaptive Kalman Filter for USA, Brazil, Germany, India, Russia, Italy, Spain, United Kingdom, France, Turkey. Türkiye Klinikleri Biyoistatistik Dergisi 13, no.1 (2021): 91 - 102. 10.5336/biostatic.2020-80186 |
| MLA | ÖZBEK Levent,Demirtas Hakan A Study on the Estimation of COVID-19 Daily Cases and Reproduction Number Using Adaptive Kalman Filter for USA, Brazil, Germany, India, Russia, Italy, Spain, United Kingdom, France, Turkey. Türkiye Klinikleri Biyoistatistik Dergisi, vol.13, no.1, 2021, ss.91 - 102. 10.5336/biostatic.2020-80186 |
| AMA | ÖZBEK L,Demirtas H A Study on the Estimation of COVID-19 Daily Cases and Reproduction Number Using Adaptive Kalman Filter for USA, Brazil, Germany, India, Russia, Italy, Spain, United Kingdom, France, Turkey. Türkiye Klinikleri Biyoistatistik Dergisi. 2021; 13(1): 91 - 102. 10.5336/biostatic.2020-80186 |
| Vancouver | ÖZBEK L,Demirtas H A Study on the Estimation of COVID-19 Daily Cases and Reproduction Number Using Adaptive Kalman Filter for USA, Brazil, Germany, India, Russia, Italy, Spain, United Kingdom, France, Turkey. Türkiye Klinikleri Biyoistatistik Dergisi. 2021; 13(1): 91 - 102. 10.5336/biostatic.2020-80186 |
| IEEE | ÖZBEK L,Demirtas H "A Study on the Estimation of COVID-19 Daily Cases and Reproduction Number Using Adaptive Kalman Filter for USA, Brazil, Germany, India, Russia, Italy, Spain, United Kingdom, France, Turkey." Türkiye Klinikleri Biyoistatistik Dergisi, 13, ss.91 - 102, 2021. 10.5336/biostatic.2020-80186 |
| ISNAD | ÖZBEK, Levent - Demirtas, Hakan. "A Study on the Estimation of COVID-19 Daily Cases and Reproduction Number Using Adaptive Kalman Filter for USA, Brazil, Germany, India, Russia, Italy, Spain, United Kingdom, France, Turkey". Türkiye Klinikleri Biyoistatistik Dergisi 13/1 (2021), 91-102. https://doi.org/10.5336/biostatic.2020-80186 |
