Forecasting respiratory infectious outbreaks using ED-based syndromic surveillance for febrile ED visits in a Metropolitan City

Kim, Tae Han; Hong, Ki Jeong; Shin, Sang Do; Park, Gwan Jin; Kim, Sung‐Wan; Hong, Nhayoung

doi:10.1016/j.ajem.2018.05.007

Cited by 20 publications

(22 citation statements)

References 17 publications

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“…First, we compared the groups on the odds of having at least one ED visit during age 12–17 years using all individuals from the larger parent study (CATS). Second, we compared the groups on the odds of high (vs low) ED utilization; similar to existing literature and due to observed data patterns, we defined ED utilization as “high” for individuals in the 95th percentile and above on number of ED visits [Cunningham, Mautner, Ku, Scott, & LaNoue, ; Kim et al, ] and “low” for individuals in the 94th percentile and under. Number of ED visits during adolescence was determined by summing the total number of visits occurring during age 12 through 17 years.…”

Section: Methodsmentioning

confidence: 99%

Emergency department utilization and monetary charges in adolescents with autism spectrum disorder, intellectual disability, and a population comparison group

et al. 2019

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Adolescents with autism spectrum disorder (ASD) and/or intellectual disability (ID) may utilize the emergency department (ED) more frequently than individuals in the general population. This study compared ED utilization and charges during adolescence among four groups of individuals: ASD‐only, ASD + ID, ID‐only, and a population comparison (PC) group. ED visits occurring during age 12–17 years were examined to identify non, low, and high utilizers. Logistic regression was used to compare groups on the odds of having at least one ED visit during adolescence. Generalized linear models were used to compare groups on number of ED visits and total charges, stratified by low and high ED utilization. Descriptive examination of presenting diagnoses was performed. Individuals with ID, with or without co‐occurring ASD, were significantly more likely to have at least one ED visit during adolescence. Among high ED utilizers, the ID‐only group had the most frequent ED visits but had significantly lower charges than the ASD‐only group. Individuals with ASD‐only and ASD + ID differed from the ID‐only and PC groups in presenting diagnoses. No differences between groups in number of ED visits or charges were observed among low utilizers. ID, with or without ASD, increased the odds of visiting the ED during adolescence. Adolescents with ID‐only had the most frequent ED visits, but individuals with ASD‐only had the highest ED charges and tended to be seen for psychiatric concerns. Further research is warranted to better characterize and meet the healthcare needs of individuals with ASD and/or ID during adolescence. Autism Res 2019, 12: 1129–1138. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. Lay Summary Frequent emergency department (ED) visits strain medical resources and are costlier than primary and urgent care. Our findings show that adolescents with intellectual disability (ID) may use the ED frequently for nonurgent conditions. Adolescents with autism spectrum disorder, without ID, use the ED less frequently but incur higher charges. Further research is needed to understand how to meet the unique needs of these populations in primary care to prevent overuse of the ED.

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Section: Methodsmentioning

confidence: 99%

Emergency department utilization and monetary charges in adolescents with autism spectrum disorder, intellectual disability, and a population comparison group

et al. 2019

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“…Han et al [ 10 ] used the ARIMA model to predict the monthly outpatient visits of a hospital in Sichuan. Some researchers also used the ARIMA model to forecast the incidence of infectious diseases [ 7 , 8 , 23 – 25 ]. Wang et al [ 7 ] used ARIMA model to predict the incidence of hepatitis B; Peng et al [ 24 ] established a seasonal ARIMA model based on historical data of in Jiangsu Province to control the development trend of the epidemic.…”

Section: Literaturementioning

confidence: 99%

Medical service demand forecasting using a hybrid model based on ARIMA and self-adaptive filtering method

Huang

et al. 2020

BMC Med Inform Decis Mak

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Background Accurate forecasting of medical service demand is beneficial for the reasonable healthcare resource planning and allocation. The daily outpatient volume is characterized by randomness, periodicity and trend, and the time series methods, like ARIMA are often used for short-term outpatient visits forecasting. Therefore, to further enlarge the prediction horizon and improve the prediction accuracy, a hybrid prediction model integrating ARIMA and self-adaptive filtering method is proposed. Methods The ARIMA model is first used to identify the features like cyclicity and trend of the time series data and to estimate the model parameters. The parameters are then adjusted by the steepest descent algorithm in the adaptive filtering method to reduce the prediction error. The hybrid model is validated and compared with traditional ARIMA by several test sets from the Time Series Data Library (TSDL), a weekly emergency department (ED) visit case from literature study, and the real cases of prenatal examinations and B-ultrasounds in a maternal and child health care center (MCHCC) in Ningbo. Results For TSDL cases the prediction accuracy of the hybrid prediction is improved by 80–99% compared with the ARIMA model. For the weekly ED visit case, the forecasting results of the hybrid model are better than those of both traditional ARIMA and ANN model, and similar to the ANN combined data decomposition model mentioned in the literature. For the actual data of MCHCC in Ningbo, the MAPE predicted by the ARIMA model in the two departments was 18.53 and 27.69%, respectively, and the hybrid models were 2.79 and 1.25%, respectively. Conclusions The hybrid prediction model outperforms the traditional ARIMA model in both accurate predicting result with smaller average relative error and the applicability for short-term and medium-term prediction.

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“…Secondly, the class R can be computed precisely. To better estimate the class R, several data sources can be integrated with SIR-based models, e.g., social media and call data records (CDR), which of course a high degree of uncertainty and complexity still remains [25][26][27][28][29][30][31][32]. Considering the above uncertainties involved in the advancement of SIR-based models the generalization ability are yet to be improved to achieve scalable model with high performance [33].…”

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confidence: 99%

COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach

et al. 2020

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Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptibleinfected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

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Forecasting respiratory infectious outbreaks using ED-based syndromic surveillance for febrile ED visits in a Metropolitan City

Abstract: A forecast model using syndromic surveillance based on the number of ED visits was feasible for early detection of ED respiratory infectious disease outbreak.

Cited by 20 publications

References 17 publications

Emergency department utilization and monetary charges in adolescents with autism spectrum disorder, intellectual disability, and a population comparison group

Emergency department utilization and monetary charges in adolescents with autism spectrum disorder, intellectual disability, and a population comparison group

Medical service demand forecasting using a hybrid model based on ARIMA and self-adaptive filtering method

COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach

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