The utility of LASSO-based models for real time forecasts of endemic infectious diseases: A cross country comparison

Chen, Yirong; Chu, Collins Wenhan; Chen, Mark; Cook, Alex R.

doi:10.1016/j.jbi.2018.02.014

Cited by 32 publications

(30 citation statements)

References 63 publications

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“…For instance, in computational biology the LASSO has been used for analyzing gene expression data from mRNA and microRNA data [34,35] to address basic molecular biological questions. For studying diseases it has been used for investigating infection diseases [36], various cancer types [37,38], diabetes [39], and cardiovascular diseases [40]. In the computational social sciences the LASSO has been used to study data from social media [41], the stock market [42], economy [43], and political science [44].…”

Section: Applications In the Literaturementioning

confidence: 99%

High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection

Emmert‐Streib

Dehmer

2019

MAKE

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Regression models are a form of supervised learning methods that are important for machine learning, statistics, and general data science. Despite the fact that classical ordinary least squares (OLS) regression models have been known for a long time, in recent years there are many new developments that extend this model significantly. Above all, the least absolute shrinkage and selection operator (LASSO) model gained considerable interest. In this paper, we review general regression models with a focus on the LASSO and extensions thereof, including the adaptive LASSO, elastic net, and group LASSO. We discuss the regularization terms responsible for inducing coefficient shrinkage and variable selection leading to improved performance metrics of these regression models. This makes these modern, computational regression models valuable tools for analyzing high-dimensional problems.

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Section: Applications In the Literaturementioning

confidence: 99%

High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection

Emmert‐Streib

Dehmer

2019

MAKE

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“…[31] adopted nowcasting in one of the sub-model but not at the ensemble model level. LASSO has been used in dengue nowcasting but none of them utilized internet searches as exogenous regressor [13], [20]. The limited dengue forecasting model which incorporated GTs on the other hand, was not built for nowcasting and did not take climatory conditions into consideration [24].…”

Section: Discussionmentioning

confidence: 99%

“…Such attempts since 2011 include Autoregressive Integrated Moving Average (ARIMA), Knorr-Held two-component (K-H), Poisson multivariate regression, step-down linear regression, generalized boosted regression, and negative binomial regression [13]. Recent researches such as Chen, Chu, Chen and Cook [20] looked at LASSO as nowcasting model for its feature selection benefit along with the capability of incorporating autoregressive terms as predictors just as in the classical time series models. The final LASSO model is geared for real life implementation as it was calibrated with multiple training and validation stages [13].…”

Section: Nowcasting With Lassomentioning

confidence: 99%

“…The Singapore Ministry of Health and the Environmental Public Health Operations Department of the NEA were able to redirect hospital sources and dengue campaign accordingly. [20] evaluated LASSO algorithm in nowcasting infectious diseases in Japan (chickenpox and Hand, foot, and mouth disease (HFMD)), Singapore (dengue and HFMD), Taiwan (dengue) and Thailand (chickenpox, dengue and malaria). Wavelet analyses was first performed to study the periodicity (amplitude, time and frequency) of each disease in each locality, out of which lags of disease incidence and climatic variables up to 26 weeks were determined as autogressive predictors.…”

Section: Nowcasting With Lassomentioning

confidence: 99%

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Review on Nowcasting using Least Absolute Shrinkage Selector Operator (LASSO) to Predict Dengue Occurrence in San Juan and Iquitos as Part of Disease Surveillance System

Tang¹,

Subramanian

2019

PEN

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Dengue which was first detected mainly in South East Asia during 1940s is now a serious public health concern across the subtropical and temperate regions of Americas, Europe and China due to the change in global climate and international travel. Hence, 3.9 billion people in 128 countries are exposed to the danger of potentially fatal dengue infection. This is a review paper of various dengue forecasting methodology to identify suitable models for predicting the disease occurrence in San Juan, Puerto Rico and Iquitos, Peru. Least Absolute Shrinkage Selector Operator (LASSO) model using climatic variables and Google Trends search terms as predictors was proposed to forecast dengue cases four weeks in advance. LASSO's flexibility in incorporating a variety of predictors and its ease of interpretation present LASSO as a compelling case against the general predictive models. Public health regulators could make use of such nowcasting model to facilitate the timing of vector control and public health campaigns along with the medical resource allocation to cope with potential dengue outbreaks.

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“…In previous studies, the prediction methods for the occurrence, spread, and change in infectious diseases mainly included regression prediction models [ 1 , 2 ], Markov chain models [ 3 ], Bayesian networks [ 4 – 6 ], and other machine learning methods [ 7 – 9 ]. Most of the previous studies have been based on research related to influenza, with a few studies conducted on human immunodeficiency virus (HIV) infection [ 10 , 11 ] and SARS [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction and analysis of Corona Virus Disease 2019

et al. 2020

PLoS ONE

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The outbreak of Corona Virus Disease 2019 (COVID-19) in Wuhan has significantly impacted the economy and society globally. Countries are in a strict state of prevention and control of this pandemic. In this study, the development trend analysis of the cumulative confirmed cases, cumulative deaths, and cumulative cured cases was conducted based on data from Wuhan, Hubei Province, China from January 23, 2020 to April 6, 2020 using an Elman neural network, long short-term memory (LSTM), and support vector machine (SVM). A SVM with fuzzy granulation was used to predict the growth range of confirmed new cases, new deaths, and new cured cases. The experimental results showed that the Elman neural network and SVM used in this study can predict the development trend of cumulative confirmed cases, deaths, and cured cases, whereas LSTM is more suitable for the prediction of the cumulative confirmed cases. The SVM with fuzzy granulation can successfully predict the growth range of confirmed new cases and new cured cases, although the average predicted values are slightly large. Currently, the United States is the epicenter of the COVID-19 pandemic. We also used data modeling from the United States to further verify the validity of the proposed models.

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The utility of LASSO-based models for real time forecasts of endemic infectious diseases: A cross country comparison

Cited by 32 publications

References 63 publications

High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection

High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection

Review on Nowcasting using Least Absolute Shrinkage Selector Operator (LASSO) to Predict Dengue Occurrence in San Juan and Iquitos as Part of Disease Surveillance System

Prediction and analysis of Corona Virus Disease 2019

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