A LASSO-Based Prediction Model for Child Influenza Epidemics: A Case Study of Shanghai, China

Child influenza is an acute infectious disease that places substantial burden on children and their families. Real-time accurate prediction of child influenza epidemics can aid scientific and timely decision-making that may reduce the harm done to children infected with influenza. Several models hav...
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Gespeichert in:

Autor*in:	Jin Zhu [verfasserIn] Yu Xu [verfasserIn] Guangjun Yu [verfasserIn] Jie Gao [verfasserIn] Yuan Liu [verfasserIn] Dayu Cheng [verfasserIn] Ci Song [verfasserIn] Jie Chen [verfasserIn] Tao Pei [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Übergeordnetes Werk:	In: Mathematical Problems in Engineering - Hindawi Limited, 2002, (2022)
Übergeordnetes Werk:	year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1155/2022/1775630

Katalog-ID:	DOAJ020760930

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spelling	10.1155/2022/1775630 doi (DE-627)DOAJ020760930 (DE-599)DOAJf7a2715ac1f5483096373ebb45fd6828 DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Jin Zhu verfasserin aut A LASSO-Based Prediction Model for Child Influenza Epidemics: A Case Study of Shanghai, China 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Child influenza is an acute infectious disease that places substantial burden on children and their families. Real-time accurate prediction of child influenza epidemics can aid scientific and timely decision-making that may reduce the harm done to children infected with influenza. Several models have been proposed to predict influenza epidemics. However, most existing studies focus on adult influenza prediction. This study demonstrates the feasibility of using the LASSO (least absolute shrinkage and selection operator) model to predict influenza-like illness (ILI) levels in children between 2017 and 2020 in Shanghai, China. The performance of the LASSO model was compared with that of other statistical influenza-prediction techniques, including autoregressive integrated moving average (ARIMA), random forest (RF), ordinary least squares (OLS), and long short-term memory (LSTM). The LASSO model was observed to exhibit superior performance compared to the other candidate models. Owing to the variable shrinkage and low-variance properties of LASSO, it eliminated unimportant features and avoided overfitting. The experimental results suggest that the LASSO model can provide useful guidance for short-term child influenza prevention and control for schools, hospitals, and governments. Engineering (General). Civil engineering (General) Mathematics Yu Xu verfasserin aut Guangjun Yu verfasserin aut Jie Gao verfasserin aut Yuan Liu verfasserin aut Dayu Cheng verfasserin aut Ci Song verfasserin aut Jie Chen verfasserin aut Tao Pei verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2022) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2022 https://doi.org/10.1155/2022/1775630 kostenfrei https://doaj.org/article/f7a2715ac1f5483096373ebb45fd6828 kostenfrei http://dx.doi.org/10.1155/2022/1775630 kostenfrei https://doaj.org/toc/1563-5147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2022
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allfieldsSound	10.1155/2022/1775630 doi (DE-627)DOAJ020760930 (DE-599)DOAJf7a2715ac1f5483096373ebb45fd6828 DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Jin Zhu verfasserin aut A LASSO-Based Prediction Model for Child Influenza Epidemics: A Case Study of Shanghai, China 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Child influenza is an acute infectious disease that places substantial burden on children and their families. Real-time accurate prediction of child influenza epidemics can aid scientific and timely decision-making that may reduce the harm done to children infected with influenza. Several models have been proposed to predict influenza epidemics. However, most existing studies focus on adult influenza prediction. This study demonstrates the feasibility of using the LASSO (least absolute shrinkage and selection operator) model to predict influenza-like illness (ILI) levels in children between 2017 and 2020 in Shanghai, China. The performance of the LASSO model was compared with that of other statistical influenza-prediction techniques, including autoregressive integrated moving average (ARIMA), random forest (RF), ordinary least squares (OLS), and long short-term memory (LSTM). The LASSO model was observed to exhibit superior performance compared to the other candidate models. Owing to the variable shrinkage and low-variance properties of LASSO, it eliminated unimportant features and avoided overfitting. The experimental results suggest that the LASSO model can provide useful guidance for short-term child influenza prevention and control for schools, hospitals, and governments. Engineering (General). Civil engineering (General) Mathematics Yu Xu verfasserin aut Guangjun Yu verfasserin aut Jie Gao verfasserin aut Yuan Liu verfasserin aut Dayu Cheng verfasserin aut Ci Song verfasserin aut Jie Chen verfasserin aut Tao Pei verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2022) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2022 https://doi.org/10.1155/2022/1775630 kostenfrei https://doaj.org/article/f7a2715ac1f5483096373ebb45fd6828 kostenfrei http://dx.doi.org/10.1155/2022/1775630 kostenfrei https://doaj.org/toc/1563-5147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2022
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callnumber-first	T - Technology
author	Jin Zhu
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title	A LASSO-Based Prediction Model for Child Influenza Epidemics: A Case Study of Shanghai, China
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title_full	A LASSO-Based Prediction Model for Child Influenza Epidemics: A Case Study of Shanghai, China
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title_sort	lasso-based prediction model for child influenza epidemics: a case study of shanghai, china
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title_auth	A LASSO-Based Prediction Model for Child Influenza Epidemics: A Case Study of Shanghai, China
abstract	Child influenza is an acute infectious disease that places substantial burden on children and their families. Real-time accurate prediction of child influenza epidemics can aid scientific and timely decision-making that may reduce the harm done to children infected with influenza. Several models have been proposed to predict influenza epidemics. However, most existing studies focus on adult influenza prediction. This study demonstrates the feasibility of using the LASSO (least absolute shrinkage and selection operator) model to predict influenza-like illness (ILI) levels in children between 2017 and 2020 in Shanghai, China. The performance of the LASSO model was compared with that of other statistical influenza-prediction techniques, including autoregressive integrated moving average (ARIMA), random forest (RF), ordinary least squares (OLS), and long short-term memory (LSTM). The LASSO model was observed to exhibit superior performance compared to the other candidate models. Owing to the variable shrinkage and low-variance properties of LASSO, it eliminated unimportant features and avoided overfitting. The experimental results suggest that the LASSO model can provide useful guidance for short-term child influenza prevention and control for schools, hospitals, and governments.
abstractGer	Child influenza is an acute infectious disease that places substantial burden on children and their families. Real-time accurate prediction of child influenza epidemics can aid scientific and timely decision-making that may reduce the harm done to children infected with influenza. Several models have been proposed to predict influenza epidemics. However, most existing studies focus on adult influenza prediction. This study demonstrates the feasibility of using the LASSO (least absolute shrinkage and selection operator) model to predict influenza-like illness (ILI) levels in children between 2017 and 2020 in Shanghai, China. The performance of the LASSO model was compared with that of other statistical influenza-prediction techniques, including autoregressive integrated moving average (ARIMA), random forest (RF), ordinary least squares (OLS), and long short-term memory (LSTM). The LASSO model was observed to exhibit superior performance compared to the other candidate models. Owing to the variable shrinkage and low-variance properties of LASSO, it eliminated unimportant features and avoided overfitting. The experimental results suggest that the LASSO model can provide useful guidance for short-term child influenza prevention and control for schools, hospitals, and governments.
abstract_unstemmed	Child influenza is an acute infectious disease that places substantial burden on children and their families. Real-time accurate prediction of child influenza epidemics can aid scientific and timely decision-making that may reduce the harm done to children infected with influenza. Several models have been proposed to predict influenza epidemics. However, most existing studies focus on adult influenza prediction. This study demonstrates the feasibility of using the LASSO (least absolute shrinkage and selection operator) model to predict influenza-like illness (ILI) levels in children between 2017 and 2020 in Shanghai, China. The performance of the LASSO model was compared with that of other statistical influenza-prediction techniques, including autoregressive integrated moving average (ARIMA), random forest (RF), ordinary least squares (OLS), and long short-term memory (LSTM). The LASSO model was observed to exhibit superior performance compared to the other candidate models. Owing to the variable shrinkage and low-variance properties of LASSO, it eliminated unimportant features and avoided overfitting. The experimental results suggest that the LASSO model can provide useful guidance for short-term child influenza prevention and control for schools, hospitals, and governments.
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title_short	A LASSO-Based Prediction Model for Child Influenza Epidemics: A Case Study of Shanghai, China
url	https://doi.org/10.1155/2022/1775630 https://doaj.org/article/f7a2715ac1f5483096373ebb45fd6828 http://dx.doi.org/10.1155/2022/1775630 https://doaj.org/toc/1563-5147
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doi_str	10.1155/2022/1775630
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up_date	2024-07-03T16:49:24.254Z
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A LASSO-Based Prediction Model for Child Influenza Epidemics: A Case Study of Shanghai, China

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