Forecast of the trend in incidence of acute hemorrhagic conjunctivitis in China from 2011–2019 using the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Exponential Smoothing (ETS) models

Liu, Huan; Li, Chenxi; Shao, Yi; Zhang, Xin; Zhai, Zhao; Wang, Xing; Qi, Xiaoying; Wang, Jiahui; Hao, Yanhua; Wu, Qunhong; Jiao, Mingli

doi:10.1016/j.jiph.2019.12.008

Cited by 70 publications

(49 citation statements)

References 20 publications

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“…AHC has no specific preventive measures. Public health authorities should strengthen surveillance of the epidemic among males, students, farmers and other high-risk groups, as well as high-risk areas in the northeast, central and western regions, to improve the availability and equality of medical and health resources in all regions 29 . In the seasons with high incidence of AHC, schools and other key public places should be mainly monitored.…”

Section: Discussionmentioning

confidence: 99%

Epidemiological Characteristics and Spatiotemporal Analysis of Acute Hemorrhagic Conjunctivitis from 2004 to 2018 in Chongqing, China

Jing

Zhao²,

et al. 2020

Sci Rep

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Chongqing is one of the five provinces in China that has the highest incidence of acute hemorrhagic conjunctivitis (AHC). Data of AHC cases from 2004 to 2018 were obtained from National Notifiable Diseases Reporting Information System (NNDRIS). Descriptive statistical methods were used to analyze the epidemiological characteristics; incidence maps were used to reflect incidence trends in each district; spatial autocorrelation was used to identify hotspot regions and spatiotemporal patterns of AHC outbreaks; spatiotemporal scan were conducted to identify AHC clusters. A total of 30,686 cases were reported with an annual incidence of 7.04 per 100,000. The incidence rates were high in 2007 and 2014, and large epidemics were observed in 2010 with the seasonal peak in September. Individuals aged 10-19 years, males, students and farmers were the prime high-risk groups. Except for 2012 and 2013, the spatial distribution of AHC did not exhibit significant global spatial autocorrelation. Local indicators of spatial association showed that the high-risk regions are Chengkou and Wuxi. The spatiotemporal scan indicated that all clusters occurred in September 2010, and the high-incidence clusters were mainly distributed in the northeast of Chongqing. The results could assist public health agencies to consider effective preventive measures based on epidemiological factors and spatiotemporal clusters in different regions. Acute hemorrhagic conjunctivitis (AHC) is a highly infectious viral disease caused by enterovirus 70 (EV70), coxsackievirus A24 variant (CA24v), or adenoviruses 1,2. It is characterized by rapid onset of symptoms, quick dissemination and short incubation period 3. The main manifestations of AHC are bilateral eye pain, eyelid swelling, conjunctival congestion, keratitis, foreign body sensation and increased ocular secretions 4. Generally, the majority of patients exhibit obvious symptoms but good prognosis. A small number of patients will rapidly develop systemic symptoms such as fever, fatigue and limb pain. A few patients develop serious complications and fatal infections 5. Since it was first reported in Ghana in 1969 6 , AHC has seen several periodic outbreaks around the world 7 , mainly in Asia, Africa and Latin America 4. The first outbreak of AHC in China was reported in Hong Kong in 1971 8. At present, it has spread to all provinces and cities, including some remote areas 9. From 2005 to 2012, the reported incidence of AHC in China was 4.12 per 100,000, and Guangxi, Guangdong, Chongqing, Sichuan and Hubei were ranked as the top 5 incidence provinces 10. Currently, AHC is still an important public health problem in China 11 , which is worth exploring and studying. Most of the previous studies on AHC focused on the epidemiological and etiological characteristics 12. However, the research on the spatial and temporal characteristics of AHC was still scarce. The geographical information system (GIS) has been widely used to analyze the spatiotemporal characteristics of diseases, helping to monitor and prevent...

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Section: Discussionmentioning

confidence: 99%

Epidemiological Characteristics and Spatiotemporal Analysis of Acute Hemorrhagic Conjunctivitis from 2004 to 2018 in Chongqing, China

Jing

Zhao²,

et al. 2020

Sci Rep

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“…In particular, such an approach proved to be accurate in the forecasting of the percentage of visits for influenza-like illness in urban and rural areas of Shenyang (China). More recently, [27] used the SARIMA method -in conjunction with models belonging to the class exponential smoothingto predict the trend of acute hemorrhagic conjunctivitis disease and used the obtained outcomes to provide evidence for the government to formulate policies regarding its prevention in mainland China.…”

Section: Introductionmentioning

confidence: 99%

Predictive Capacity of COVID-19 Test Positivity Rate

Fenga

Gáspari

2021

Preprint

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COVID-19 infections can spread silently, due to the simultaneous presence of significant numbers of both critical and asymptomatic to mild cases. While for the former reliable data are available (in the form of number of hospitalization and/or beds in intensive care units), this is not the case of the latter. Hence, analytical tools designed to generate reliable forecast and future scenarios, should be implemented to help decision makers planning ahead (e.g. medical structures and equipment). Previous work of one of the authors shows that an alternative formulation of the Test Positivity Rate (TPR), i.e. the proportion of the number of persons tested positive in a given day, exhibits a strong correlation with the number of patients admitted in hospital and intensive care units. In this paper, we investigate the lagged correlation structure between the newly defined TPR and the hospitalized people time series, exploiting a rigorous statistical model, the Seasonal Auto Regressive Moving Average (SARIM A). The rigorous analytical framework chosen, i.e. the stochastic processes theory, allowed for a reliable forecasting about 12 days ahead, of those quantities. The proposed approach would also allow decision makers to forecast the number of beds in hospitals and intensive care units needed 12 days ahead. The obtained results show that a standardized TPR index is a valuable metric to monitor the growth of the COVID-19 epidemic. The index can be computed on daily basis and it is probably one of the best forecasting tools available today for predicting hospital and intensive care units overload, being an optimal compromise between simplicity of calculation and accuracy.

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“…24 Of the above 30 possible methods, we determined which one more closely simulates the target series by comparing the goodness of fit measures across models, including the Akaike Information Criterion (AIC), the Schwarz Information Criterion (BIC), the Hannan-Quinn Criterion (HQ), the average mean square error (AMSE), and the Log-likelihood (LL). 22,25 The specification with lower values of the AIC, BIC, HQ, and AMSE, as well as a higher LL value, was recommended as the preferred.…”

Section: Ets Modelmentioning

confidence: 99%

“…Over the last decades, though the popular exponential smoothing (ES) approaches under a linear assumption have extensively been adopted for different predictive purposes, 21 paralleling the methodological developments, researchers have relaxed the linear assumption of the ES approaches by embedding them in a modern nonlinear model framework, called Error-Trend-Seasonal (ETS) framework (which signifies the three traits including error, trend, and seasonality of a time series). [22][23][24][25][26] Such a framework may be more suitable for handling such data with different traits because they take into consideration the possible additive or multiplicative combinations of the secular trend, seasonal pattern, and random disturbances of the data with 30 candidate models. 19,22,26,27 Recently, researchers have shown an increased interest in the domain of forecasting (such as the forecast for the electricity consumption, 28 electricity generation, 29 bond yields, 29 the number of tourists, 29 acute hemorrhagic conjunctivitis incidence, 25 and tuberculosis incidence) 22 with the ETS framework due to its so many advantages.…”

Section: Introductionmentioning

confidence: 99%

“…[22][23][24][25][26] Such a framework may be more suitable for handling such data with different traits because they take into consideration the possible additive or multiplicative combinations of the secular trend, seasonal pattern, and random disturbances of the data with 30 candidate models. 19,22,26,27 Recently, researchers have shown an increased interest in the domain of forecasting (such as the forecast for the electricity consumption, 28 electricity generation, 29 bond yields, 29 the number of tourists, 29 acute hemorrhagic conjunctivitis incidence, 25 and tuberculosis incidence) 22 with the ETS framework due to its so many advantages. The ETS models are greatly helpful in capturing the dynamic dependence structure of a target series, considering the rapid fluctuation, changing trends, cyclic variation, and random fluctuation in the given series.…”

Section: Introductionmentioning

confidence: 99%

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<p>Estimating the Prevalence and Mortality of Coronavirus Disease 2019 (COVID-19) in the USA, the UK, Russia, and India</p>

Wang¹,

Xu²,

Yao³

et al. 2020

IDR

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The aim of this study is to apply the advanced error-trend-seasonal (ETS) framework to forecast the prevalence and mortality series of COVID-19 in the USA, the UK, Russia, and India, and the predictive performance of the ETS framework was compared with the most frequently used autoregressive integrated moving average (ARIMA) model. Materials and Methods: The prevalence and mortality data of COVID-19 in the USA, the UK, Russia, and India between 20 February 2020 and 15 May 2020 were extracted from the WHO website. Then, the data subsamples between 20 February 2020 and 3 May 2020 were treated as the training horizon, and the others were used as the testing horizon to construct the ARIMA models and the ETS models. Results: Based on the model evaluation criteria, the ARIMA (0,2,1) and ETS (M,MD,N), sparse coefficient ARIMA (0,2,(1,6)) and ETS (A,AD,M), ARIMA (1,1,1) and ETS (A,MD, A), together with ARIMA (2,2,1) and ETS (A,M,A) specifications were identified as the preferred ARIMA and ETS models for the prevalence data in the USA, the UK, Russia, and India, respectively; the ARIMA (0,2,1) and ETS (M,A,M), ARIMA (0,2,1) and ETS (M,A, N), ARIMA (0,2,1) and ETS (A,A,N), coupled with ARIMA (0,2,2) and ETS (M,M,N) specifications were selected as the optimal ARIMA and ETS models for the mortality data in these four countries, respectively. Among these best-fitting models, the ETS models produced smaller forecasting error rates than the ARIMA models in all the datasets. Conclusion: The ETS framework can be used to nowcast and forecast the long-term temporal trends of the COVID-19 prevalence and mortality in the USA, the UK, Russia, and India, and which provides a notable performance improvement over the most frequently used ARIMA model. Our findings can aid governments as a reference to prepare for and respond to the COVID-19 pandemic both in restricting the transmission of the disease and in lowering the disease-related deaths in the upcoming days.

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Forecast of the trend in incidence of acute hemorrhagic conjunctivitis in China from 2011–2019 using the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Exponential Smoothing (ETS) models

Cited by 70 publications

References 20 publications

Epidemiological Characteristics and Spatiotemporal Analysis of Acute Hemorrhagic Conjunctivitis from 2004 to 2018 in Chongqing, China

Epidemiological Characteristics and Spatiotemporal Analysis of Acute Hemorrhagic Conjunctivitis from 2004 to 2018 in Chongqing, China

Predictive Capacity of COVID-19 Test Positivity Rate

<p>Estimating the Prevalence and Mortality of Coronavirus Disease 2019 (COVID-19) in the USA, the UK, Russia, and India</p>

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