Time series analysis of COVID-19 infection curve: A change-point perspective

Jiang, Feiyu; Zhao, Zifeng; Shao, Xiaofeng

doi:10.1016/j.jeconom.2020.07.039

Cited by 60 publications

(49 citation statements)

References 27 publications

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“…Their research found that the growth rate of the pandemic is indeed correlated with the time points at which public interventions (policies) were decided. Jiang et al 16 performed a change-point analysis based on COVID-19 health outcomes (cases, deaths) using a piecewise linear trend model. They analyzed the trajectory of cumulative COVID-19 cases and deaths across 30 countries.…”

Section: Covid-19 Infections and The Effect Of Policies Over Time: A Change-point Perspectivementioning

confidence: 99%

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Lag time between state-level policy interventions and change points in COVID-19 outcomes in the United States

Dey¹,

Lee²,

Chakraborty³

et al. 2021

Patterns

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Lag time between state-level policy interventions and change points in COVID-19 outcomes in the United StatesHighlights d Time series models can feature non-stationarity and correlation in COVID-19 outcomes d Data-driven algorithms detect change points in COVID-19 outcomes due to policy changes d Five patterns with geographical similarities are found in the COVID-19 trajectory d The COVID-19 trajectory changes in about 10-14 days after policy implementation

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Section: Covid-19 Infections and The Effect Of Policies Over Time: A Change-point Perspectivementioning

confidence: 99%

“…Jiang et al. 16 performed a change-point analysis based on COVID-19 health outcomes (cases, deaths) using a piecewise linear trend model. They analyzed the trajectory of cumulative COVID-19 cases and deaths across 30 countries.…”

Section: Introductionmentioning

confidence: 99%

Lag time between state-level policy interventions and change points in COVID-19 outcomes in the United States

Dey¹,

Lee²,

Chakraborty³

et al. 2021

Patterns

View full text Add to dashboard Cite

show abstract

“…As the number of cases increases and more data becomes available, various researches [1] , [2] , [3] , [4] , [5] , [6] , [7] develop a range of mathematical models or employ machine learning algorithms to forecast the transmission of SARS-CoV-2. Previous studies have also employed LSTM [8] , [9] , [10] , [11] , [12] or XGBoost [13] , [14] , [15] , [16] , [17] , [18] , [19] models to forecast the spread of COVID-19 and identify the most influential COVID-19 indicators.…”

Section: Introductionmentioning

confidence: 99%

Time series prediction of COVID-19 transmission in America using LSTM and XGBoost algorithms

Luo

Zhang

et al. 2021

Results in Physics

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In this paper, we establish daily confirmed infected cases prediction models for the time series data of America by applying both the long short-term memory (LSTM) and extreme gradient boosting (XGBoost) algorithms, and employ four performance parameters as MAE, MSE, RMSE, and MAPE to evaluate the effect of model fitting. LSTM is applied to reliably estimate accuracy due to the long-term attribute and diversity of COVID-19 epidemic data. Using XGBoost model, we conduct a sensitivity analysis to determine the robustness of predictive model to parameter features. Our results reveal that achieving a reduction in the contact rate between susceptible and infected individuals by isolated the uninfected individuals, can effectively reduce the number of daily confirmed cases. By combining the restrictive social distancing and contact tracing, the elimination of ongoing COVID-19 pandemic is possible. Our predictions are based on real time series data with reasonable assumptions, whereas the accurate course of epidemic heavily depends on how and when quarantine, isolation and precautionary measures are enforced.

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“…A number of authors have published papers on models for the occurrence of Coronavirus pandemic. In [10], the trajectory of the cumulative confirmed cases and deaths of COVID-19 (in log scale) via a piecewise linear trend model is given a model. Peipei and et al [11] integrated the most updated COVID-19 epidemiological data before June 16, 2020, into the Logistic model to fit the cap of epidemic trend.…”

Section: Introductionmentioning

confidence: 99%

Forecasting COVID-19 disease cases using the SARIMA-NNAR hybrid model

Demir

Kirişçi²

2021

Preprint

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Background: COVID 19 is a new disease that is associated with high morbidity that has spread around the world. Credible estimating is crucial for control and prevention. Nowadays, hybrid models have become popular, and these models have been widely implemented. Better estimation accuracy may be attained using time-series models. Thus, our aim is to forecast the number of COVID 19 cases with time-series models. Objective: Using time series models to predict deaths due to COVID 19. Design: SARIMA, NNAR, and SARIMA-NNAR hybrid time series models were used using the COVID 19 information of the Republic of Turkey Health Ministry. Participants: We analyzed data on COVID 19 in Turkey from March 11, 2020, to February 22, 2021. Main Measures: Daily numbers of COVID-19 confirmed cases and deaths. Materials and methods: We fitted a seasonal autoregressive integrated moving average (SARIMA) neural network nonlinear autoregressive (NNAR) hybrid model with COVID 19 monthly cases from March 11, 2020, to February 22, 2021, in Turkey. Additionally, a SARIMA model, an NNAR model, and a SARIMA NNAR hybrid model were established for comparison and estimation. Results The RMSE, MAE, and MAPE values of the NNAR model were obtained the lowest in the training set and the validation set. Thus, the NNAR model demonstrates excellent performance whether in fitting or forecasting compared with other models. Conclusions The NNAR model that fits this study is the most suitable for estimating the number of deaths due to COVID 19. Hence, it will facilitate the prevention and control of COVID 19.

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Time series analysis of COVID-19 infection curve: A change-point perspective

Cited by 60 publications

References 27 publications

Lag time between state-level policy interventions and change points in COVID-19 outcomes in the United States

Lag time between state-level policy interventions and change points in COVID-19 outcomes in the United States

Time series prediction of COVID-19 transmission in America using LSTM and XGBoost algorithms

Forecasting COVID-19 disease cases using the SARIMA-NNAR hybrid model

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