We present different data analytic methodologies that have been applied in order to understand the evolution of the first wave of the Coronavirus disease 2019 in the Republic of Cyprus and the effect of different intervention measures that have been taken by the government. Change point detection has been used in order to estimate the number and locations of changes in the behaviour of the collected data. Count time series methods have been employed to provide short term projections and a number of various compartmental models have been fitted to the data providing with long term projections on the pandemic’s evolution and allowing for the estimation of the effective reproduction number.
All Keywords
【저자키워드】 Medical research, Diseases, Mathematics and computing, 【초록키워드】 coronavirus disease, Evolution, Coronavirus disease 2019, Intervention, Reproduction number, Cyprus, compartmental model, methodology, First wave, change, estimation, long term, short term, Government, collected data, measure, count, effective, applied, changes in, 【제목키워드】 pandemic, modeling,
【저자키워드】 Medical research, Diseases, Mathematics and computing, 【초록키워드】 coronavirus disease, Evolution, Coronavirus disease 2019, Intervention, Reproduction number, Cyprus, compartmental model, methodology, First wave, change, estimation, long term, short term, Government, collected data, measure, count, effective, applied, changes in, 【제목키워드】 pandemic, modeling,
우리는 키프로스 공화국에서 코로나바이러스 질병 2019의 첫 번째 물결의 진화와 정부가 취한 다양한 개입 조치의 영향을 이해하기 위해 적용된 다양한 데이터 분석 방법론을 제시합니다. 변화점 탐지는 수집된 데이터의 행동에서 변화의 수와 위치를 추정하기 위해 사용되었습니다. 카운트 시계열 방법은 단기 예측을 제공하기 위해 사용되었으며 전염병의 진화에 대한 장기 예측을 제공하고 유효 번식 수를 추정할 수 있도록 여러 다양한 구획 모델이 데이터에 맞춰졌습니다.