Modeling and Forecasting COVID-19 Incidence Rates: A Time Series Analysis of Acute Respiratory Infections (ARI) in France Since Surveillance Initiation

Hanife Taylan Selamlar

Makale detayları

Modeling and Forecasting COVID-19 Incidence Rates: A Time Series Analysis of Acute Respiratory Infections (ARI) in France Since Surveillance Initiation

Yazarlar:

Hanife Taylan Selamlar

Yayıncı:

Balıkesir University

Ana Başlıklar:

Bulaşıcı Hastalıklar

Dergi:

Balıkesir Medical Journal Vol.7 Issue 3, pp.117-130

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001309857

pISSN:

2564-6664

Anahtar kelimeler:

Özet:

 Objective: This study aims to address the challenges of planning and managing the trajectory of the COVID-19 pandemic by evaluating the predictive abilities of three distinct forecasting models. The primary focus is on the ATA univariate forecasting method, ARIMA AutoRegressive Integrated Moving Average, and ETS Error-Trend-Seasonality models. These models are applied to a meticulously collected dataset comprising Acute Respiratory Infections ARI incidence rates in France, systematically collected since the initiation of surveillance. Methods: The purpose of the study was to conduct a comprehensive evaluation of forecasting models using the selected dataset to achieve its objective. The focus was on comparing the accuracy and performance of ATA univariate forecasting, ARIMA, and ETS models in predicting COVID-19 incidence rates. Additionally, the study incorporated a combination approach proven to be effective in enhancing forecasting performance. Results: According to the results obtained regarding forecast performance, the univariate models indicate that the ATA method exhibits the highest performance, while observations reveal that combinations of ATA and ARIMA methods enhance forecast accuracy. Conclusions: In summary, the most accurate approach for forecasting future Covid-19 incidence rates, specifically those derived from Acute Respiratory Infections ARI, has been a combination of the high-accuracy methods ATA and ARIMA. These findings enhance our understanding of the trajectory of the pandemic, providing a foundation for strategic planning and effective management.

 Objective: This study aims to address the challenges of planning and managing the trajectory of the COVID-19 pandemic by evaluating the predictive abilities of three distinct forecasting models. The primary focus is on the ATA univariate forecasting method, ARIMA AutoRegressive Integrated Moving Average, and ETS Error-Trend-Seasonality models. These models are applied to a meticulously collected dataset comprising Acute Respiratory Infections ARI incidence rates in France, systematically collected since the initiation of surveillance. Methods: The purpose of the study was to conduct a comprehensive evaluation of forecasting models using the selected dataset to achieve its objective. The focus was on comparing the accuracy and performance of ATA univariate forecasting, ARIMA, and ETS models in predicting COVID-19 incidence rates. Additionally, the study incorporated a combination approach proven to be effective in enhancing forecasting performance. Results: According to the results obtained regarding forecast performance, the univariate models indicate that the ATA method exhibits the highest performance, while observations reveal that combinations of ATA and ARIMA methods enhance forecast accuracy. Conclusions: In summary, the most accurate approach for forecasting future Covid-19 incidence rates, specifically those derived from Acute Respiratory Infections ARI, has been a combination of the high-accuracy methods ATA and ARIMA. These findings enhance our understanding of the trajectory of the pandemic, providing a foundation for strategic planning and effective management.

 Objective: This study aims to address the challenges of planning and managing the trajectory of the COVID-19 pandemic by evaluating the predictive abilities of three distinct forecasting models. The primary focus is on the ATA univariate forecasting method, ARIMA AutoRegressive Integrated Moving Average, and ETS Error-Trend-Seasonality models. These models are applied to a meticulously collected dataset comprising Acute Respiratory Infections ARI incidence rates in France, systematically collected since the initiation of surveillance. Methods: The purpose of the study was to conduct a comprehensive evaluation of forecasting models using the selected dataset to achieve its objective. The focus was on comparing the accuracy and performance of ATA univariate forecasting, ARIMA, and ETS models in predicting COVID-19 incidence rates. Additionally, the study incorporated a combination approach proven to be effective in enhancing forecasting performance. Results: According to the results obtained regarding forecast performance, the univariate models indicate that the ATA method exhibits the highest performance, while observations reveal that combinations of ATA and ARIMA methods enhance forecast accuracy. Conclusions: In summary, the most accurate approach for forecasting future Covid-19 incidence rates, specifically those derived from Acute Respiratory Infections ARI, has been a combination of the high-accuracy methods ATA and ARIMA. These findings enhance our understanding of the trajectory of the pandemic, providing a foundation for strategic planning and effective management.

 Amaç: Bu çalışma, COVID-19 pandemisinin trajedisini planlama ve yönetme zorluklarına karşı üç farklı tahmin modelinin öngörü yeteneklerini değerlendirerek ele almayı amaçlamaktadır. Temel odak noktası, ATA tek değişkenli tahmin yöntemi, ARIMA OtoRegresif Entegre Hareketli Ortalama ve ETS Hata-Eğilim-Mevsimlilik modelleridir. Bu modeller, Fransada gözetimin başlangıcından bu yana titizlikle toplanan Akut Solunum Enfeksiyonları ARI insidans oranlarını içeren bir veri setine uygulanmıştır. Yöntem: Çalışmanın amacına ulaşmak için seçilen veri setini kullanarak tahmin modellerinin kapsamlı bir değerlendirmesini yapmak amaçlanmıştır. Odak noktası, COVID-19 insidans oranlarını tahmin etmede ATA tek değişkenli tahmin, ARIMA ve ETS modellerinin doğruluğunu ve performansını karşılaştırmaktır. Ayrıca, çalışma, tahmin performansını artırmada etkili olduğu kanıtlanmış bir kombinasyon yaklaşımını da içermiştir. Bulgular: Tahmin performansına ilişkin elde edilen sonuçlara göre, tek değişkenli modeller, ATA yönteminin en yüksek performansı sergilediğini gösterirken gözlemler, ATA ve ARIMA yöntemlerinin kombinasyonlarının tahmin doğruluğunu artırdığını göstermektedir. Sonuç: Özetle, gelecekteki Covid-19 insidans oranlarını, özellikle Akut Solunum Enfeksiyonları ASE kaynaklı olanları tahmin etmede en doğru yaklaşım, ATA ve ARIMA gibi yüksek doğrulukta yöntemlerin kombinasyonu olmuştur. Bu bulgular, pandeminin seyrine dair anlayışımızı artırarak stratejik planlama ve etkili yönetim için bir temel sağlamaktadır.

Makale detayları

Modeling and Forecasting COVID-19 Incidence Rates: A Time Series Analysis of Acute Respiratory Infections (ARI) in France Since Surveillance Initiation

Yazarlar:

Hanife Taylan Selamlar

Yayıncı:

Balıkesir University

Ana Başlıklar:

Bulaşıcı Hastalıklar

Dergi:

Balıkesir Medical Journal Vol.7 Issue 3, pp.117-130

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001309857

pISSN:

2564-6664

Forecasting

Covid 19

ATAForecasting

ETS

ARIMA

Acute Respiratory Infections (ARI)Öngörü

Covid19

ATAÖngörü

ETS

ARIMA

Akut Solunum Enfeksiyonları (ASE)

Sayı

Makale detayları

Yazarlar: Hanife Taylan Selamlar

Yayıncı: Balıkesir University

Ana Başlıklar: Bulaşıcı Hastalıklar

Dergi: Balıkesir Medical Journal Vol.7 Issue 3, pp.117-130

Belge Tipi: Makale

Yayın Tarihi: 1/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001309857

pISSN: 2564-6664

Sayı

Yazarlar:

Hanife Taylan Selamlar

Yayıncı:

Balıkesir University

Ana Başlıklar:

Bulaşıcı Hastalıklar

Dergi:

Balıkesir Medical Journal Vol.7 Issue 3, pp.117-130

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001309857

pISSN:

2564-6664