On COVID-19 outbreaks predictions: Issues on stability, parameter sensitivity, and precision

Stehlík, Milan; Kiseľák, Jozef; Dinamarca, M. Alejandro; Li, Y.; Ying, Yiqun

doi:10.1080/07362994.2020.1802291

Cited by 10 publications

(3 citation statements)

References 8 publications

(6 reference statements)

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“…On the contrary, studies of the microbiological underpinnings of the COVID-19 pandemic on human-tohuman differences have taken place. For instance, Stehlík et al (2020) have identified the exponential curve from a microbiological point of view as a reasonable model for the outbreak of COVID-19 epidemics. Furthermore, Buonsenso et al (2020) explored the microbiological and immunological aspects of SARS-CoV-2 infection in children, which emphasises the key distinctions from adult SARS-CoV-2 infection.…”

Section: Introductionmentioning

confidence: 99%

How Effective are Policy Interventions Against the COVID-19 Infection Rates?

Tang

Tan

2023

Etikonomi

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Studies on the COVID-19 pandemic are more likely to concentrate on the effects of the virus while ignoring its time-series characteristics, particularly its stationarity characteristics. Thus, this study attempts to investigate the effectiveness of policy interventions against COVID-19 by determining the permanent or transitory effects in 5 major regions and the ten most infected countries. Using the endogenous multiple breaks unit root tests introduced by Kapetanios (2005), the findings indicate that only the impacts of shocks to COVID-19 infection rates in France are likely to be permanent. However, the transitory effect is found in Brazil, Germany, Iran, Italy, Russia, Spain, Turkey, the United Kingdom, and the United States. The country where the shock has a permanent impact is suitable for policy interventions, including lockdowns, social isolation, and local isolation. While herd immunity, which protects the entire population against COVID-19, is better ideal for application in countries that experience shocks with a transitory effect.JEL Classification: C1, I15, I18How to Cite:Tang, C.F., & Tan, B.W. (2023). How Effective are Policy Interventions Against the COVID-19 Infection Rates? Etikonomi, 22(1), 1–14. https://doi.org/10.15408/etk.v22i1.28486.

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Section: Introductionmentioning

confidence: 99%

How Effective are Policy Interventions Against the COVID-19 Infection Rates?

Tang

Tan

2023

Etikonomi

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“… 2020 ; Stehlík et al. 2020 ). Where separate infection time series for a number of areas are available, this may assist forecasts through a borrowing strength mechanism (Haining et al.…”

Section: Introductionmentioning

confidence: 99%

A spatio-temporal autoregressive model for monitoring and predicting COVID infection rates

Congdon

2022

J Geogr Syst

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The COVID-19 epidemic has raised major issues with regard to modelling and forecasting outcomes such as cases, deaths and hospitalisations. In particular, the forecasting of area-specific counts of infectious disease poses problems when counts are changing rapidly and there are infection hotspots, as in epidemic situations. Such forecasts are of central importance for prioritizing interventions or making severity designations for different areas. In this paper, we consider different specifications of autoregressive dependence in incidence counts as these may considerably impact on adaptivity in epidemic situations. In particular, we introduce parameters to allow temporal adaptivity in autoregressive dependence. A case study considers COVID-19 data for 144 English local authorities during the UK epidemic second wave in late 2020 and early 2021, which demonstrate geographical clustering in new cases—linked to the then emergent alpha variant. The model allows for both spatial and time variation in autoregressive effects. We assess sensitivity in short-term predictions and fit to specification (spatial vs space-time autoregression, linear vs log-linear, and form of space decay), and show improved one-step ahead and in-sample prediction using space-time autoregression including temporal adaptivity.

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“…Some scholars have studied the development trend of the COVID-19 epidemic from the perspective of statistical modeling, such as machine learning [6][7][8], linear models [9][10][11], and exponential growth models [12,13]. Oliveira [18].…”

Section: Introductionmentioning

confidence: 99%

Research on Forecast of Global COVID-19 Development Trend

Cheng

Bai

et al. 2021

Preprint

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The rapid spread of Corona Virus Disease 2019 (COVID-2019) has seriously threatened people’s health and brought huge challenges to the medical systems of many countries. So it is necessary to predict the epidemic trend scientifically and accurately. In this paper, the development trend prediction of the global daily cumulative confirmed case, based on data from February 2, 2020 to August 14, 2021 using linear regression and nonlinear regression methods, which are auto regressive integrated moving average (ARIMA), wavelet neural network (WNN), support vector machine (SVM), recurrent neural network (RNN) and long short-term memory (LSTM). Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to evaluate the prediction accuracy of the five models. The experimental results showed that the LSTM used in this study is more accurate for predicting the development trend of global cumulative confirmed cases. And its three evaluation indexes were 0.0936%, 0.1123%, and 0.0962% respectively, which were the smallest compared with the other four models. The LSTM model was used to predict the global cumulative confirmed cases in the next five days. The prediction results showed that the global cumulative confirmed cases will rise steadily and exceed 205 million. Therefore, the health departments of various countries should take appropriate prevention and control measures in advance.

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On COVID-19 outbreaks predictions: Issues on stability, parameter sensitivity, and precision

Cited by 10 publications

References 8 publications

How Effective are Policy Interventions Against the COVID-19 Infection Rates?

How Effective are Policy Interventions Against the COVID-19 Infection Rates?

A spatio-temporal autoregressive model for monitoring and predicting COVID infection rates

Research on Forecast of Global COVID-19 Development Trend

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