Epidemiology and ARIMA model of positive-rate of influenza viruses among children in Wuhan, China: A nine-year retrospective study

He, Zhirui; Tao, Hongbing

doi:10.1016/j.ijid.2018.07.003

Cited by 119 publications

(101 citation statements)

References 34 publications

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“…The ACF graph determines whether previous values in the series are related to the following values. The PACF graph finds out the degree of correlation between a variable and a lag of the said variable that is not explained by correlation at all loworder lags (He and Tao, 2018). Estimated autocorrelations for the time series of Italy, Spain, and France are shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…A time series is simply expressed as a set of data points ordered in time (Fanoodi et al, 2019). Time series analysis aims to reveal reliable and meaningful statistics and use this knowledge to predict future values of the series (Liu et al, 2011;Elevli et al, 2016;He and Tao, 2018;Benvenuto et al, 2020). The ARIMA model was introduced by Box and Jenkins in the 1970s (Box et al, 2015).…”

Section: Arima Modelsmentioning

confidence: 99%

“…The ARIMA model deals with non-stationary time series. The differenced stationary time series can be modelled as an ARMA model to perform the ARIMA model (He and Tao, 2018).…”

Section: Arima Modelsmentioning

confidence: 99%

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Estimation of COVID-19 prevalence in Italy, Spain, and France

Ceylan

2020

Science of The Total Environment

525

453

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Europe has become the epicentre of the virus and hit the continent harder than China.• The apparent mortality rate of COVID-19 is approximately 13% in Italy, 11% in Spain, and 15% in France. • Time series models are significant in predicting the prevalence of the COVID-19 pandemic. • ARIMA (0,2,1), ARIMA (1,2,0), and ARIMA (0,2,1) were chosen as the best models for Italy, Spain, and France, respectively. were collected from the World Health Organization website. Several ARIMA models were formulated with different ARIMA parameters. ARIMA (0,2,1), ARIMA (1,2,0), and ARIMA (0,2,1) models with the lowest MAPE values (4.7520, 5.8486, and 5.6335) were selected as the best models for Italy, Spain, and France, respectively. This study shows that ARIMA models are suitable for predicting the prevalence of COVID-19 in the future. The results of the analysis can shed light on understanding the trends of the outbreak and give an idea of the epidemiological stage of these regions. Besides, the prediction of COVID-19 prevalence trends of Italy, Spain, and France can help take precautions and policy formulation for this epidemic in other countries.

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Section: Resultsmentioning

confidence: 99%

Section: Arima Modelsmentioning

confidence: 99%

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Estimation of COVID-19 prevalence in Italy, Spain, and France

Ceylan

2020

Science of The Total Environment

525

453

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“…All tests allow to accept the null hypothesis of normality, homoskedasticity, and autocorrelation of the residuals (Table 3). 11 The basic estimated equation is the following:…”

Section: Data Descriptionmentioning

confidence: 99%

“…9 To carry out the econometric analysis I used Gretl (version 2020a), and R (version 3.6.3) 10 I use two different approaches because, as stated by Gujarati and Porter (2009), there is no a recognized uniformly powerful test for detecting unit root. 11 The only exceptions are Emilia Romagna and Lombardy, that are affected by non-normality and autocorrelation, respectively. If the normality is not a necessary condition for forecasting, the violation of the independence assumption may generate some problems, by suggesting greater prudence when interpreting the results.…”

Section: Data Descriptionmentioning

confidence: 99%

An ARIMA Model to Forecast the Spread and the Final Size of COVID-2019 Epidemic in Italy

Perone

2020

Preprint

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Coronavirus disease (COVID-2019) is a severe ongoing novel pandemic that is spreading quickly across the world. Italy, that is widely considered one of the main epicenters of the pandemic, has registered the highest COVID-2019 death rates and death toll in the world, to the present day. In this article I estimate an autoregressive integrated moving average (ARIMA) model to forecast the epidemic trend over the period after April 4, 2020, by using the Italian epidemiological data at national and regional level. The data refer to the number of daily confirmed cases officially registered by the Italian Ministry of Health (www.salute.gov.it) for the period February 20 to April 4, 2020. The main advantage of this model is that it is easy to manage and fit. Moreover, it may give a first understanding of the basic trends, by suggesting the hypothetic epidemic's inflection point and final size. Highlights:❖ ARIMA models allow in an easy way to investigate COVID-2019 trends, which are nowadays of huge economic and social impact.❖ These data may be used by the health authority to continuously monitor the epidemic and to better allocate the available resources.❖ The results suggest that the epidemic spread inflection point, in term of cumulative cases, will be reached at the end of May.❖ Further useful and more precise forecasting may be provided by updating these data or applying the model to other regions and countries.

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Nowcasting influenza‐like illness (ILI) via a deep learning approach using google search data: An empirical study on Taiwan ILI

Zhao

Chen³

et al. 2021

Int J of Intelligent Sys

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Influenza outbreaks have brought increasing challenges to public health systems globally. The effective and efficient tracking of influenza can help authorities make informed and proactive decisions. In this study, we focus on nowcasting influenza epidemics at regional level. To alleviate the information lag between the release of the Centers for Disease Control's influenza‐like illness (ILI) reports and real‐time influenza activity, we incorporate Google search data and holiday effects to facilitate the ILI nowcasting. We develop a deep learning framework by extending the spatiotemporal residual network (ST‐ResNet) to nowcast ILI rates in irregular‐shaped region. We investigate the effect of temporal and spatial dependencies among irregular‐shaped regions as well as external influence on ILI nowcasting. Various forecasting models, including time series models, penalized regression models, and other deep learning models, are employed for evaluating the performance of the proposed framework. Moreover, based on city‐level ILI data in Taiwan, we conduct extensive experiments for methods validation. The results show that the extended ST‐ResNet can effectively capture the complex spatiotemporal dependencies of ILI activity and the effects of external variables. Additionally, the strategy of incorporating Google search data and holiday effects improves the prediction. The findings may provide insights into the utility of various statistical and deep learning methods for effective ILI tracking at regional level and facilitate informed decision making for public health authorities.

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Epidemiology and ARIMA model of positive-rate of influenza viruses among children in Wuhan, China: A nine-year retrospective study

Abstract: Additional policies must be formulated to prevent and control influenza. The wide use of influenza vaccines, especially for influenza B, especially for influenza B(Yamagata) and B(Victoria), can potentially reduce the effects of influenza on children of China.

Cited by 119 publications

References 34 publications

Estimation of COVID-19 prevalence in Italy, Spain, and France

Estimation of COVID-19 prevalence in Italy, Spain, and France

An ARIMA Model to Forecast the Spread and the Final Size of COVID-2019 Epidemic in Italy

Nowcasting influenza‐like illness (ILI) via a deep learning approach using google search data: An empirical study on Taiwan ILI

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