Wastewater-based epidemiology(WBE)is emerging as an effective tool to provide early warnings of potential disease outbreaks within communities through detecting the presence of pathogens in wastewater before clinical ...Wastewater-based epidemiology(WBE)is emerging as an effective tool to provide early warnings of potential disease outbreaks within communities through detecting the presence of pathogens in wastewater before clinical cases are reported.Nevertheless,quantitative prediction of future clinical case is challenging as uncertainties of dynamic shedding and disease transmission patterns can lead to complex correlation between wastewater viral concentration and clinical cases.Such complexities,augmented by factors such as viral variant,public behavioral change,etc.,make it challenging to develop empirical models or data-driven models to provide accurate prediction of disease case for public health policy makings.To address this gap,this study developed an iterative data-driven framework utilizing Long-Short Time Memory(LSTM)neural networks for multi-timestep real-time predictions of future clinical cases based on WBE.The proposed LSTM model structure integrates both wastewater and historical clinical data as inputs.The prediction framework enables the update of LSTM model as more WBE dataset become available to enhance its adaptability to evolving pandemic stages.This framework was applied for real-time forecasting of COVID-19 clinical cases based on dataset of Ohio Wastewater Monitoring Project from July 2020 to October 2023.The developed iterative LSTM models were proven to achieve excellent performance in making clinical case predictions at different stages of COVID-19 pandemic.Early warning threshold of viral surge was defined by moving percentile method and results showed that the model achieved over 90%accuracy in future clinical case prediction and therefore demonstrated high reliability in pre-warning of potential disease outbreaks.This framework was also found to possess strong transferability across diverse geographic regions.The impacts of social policies and events on model predictions as well as the ramification of this model for future pandemics warning are discussed.展开更多
We present new parameterizations of the ChIMES physics informed machine-learned interatomic model for simulating carbon under conditions ranging from 300 K and 0 GPa to 10,000 K and 100 GPa,along with a new multi-fide...We present new parameterizations of the ChIMES physics informed machine-learned interatomic model for simulating carbon under conditions ranging from 300 K and 0 GPa to 10,000 K and 100 GPa,along with a new multi-fidelity active learning strategy.The resulting models show significant improvement in accuracy and temperature/pressure transferability relative to the original ChIMES carbon model developed in 2017 and can serve as a foundation for future transfer-learned ChIMES parameter sets.Applications to carbon melting point prediction,shockwave-driven conversion of graphite to diamond,and thermal conversion of nanodiamond to graphitic nanoonion are provided.Ultimately,we find the new models to be robust,accurate,and well-suited for modeling evolution in carbon systems under extreme conditions.展开更多
基金supported by the US National Science Foundation(No.1638320).
文摘Wastewater-based epidemiology(WBE)is emerging as an effective tool to provide early warnings of potential disease outbreaks within communities through detecting the presence of pathogens in wastewater before clinical cases are reported.Nevertheless,quantitative prediction of future clinical case is challenging as uncertainties of dynamic shedding and disease transmission patterns can lead to complex correlation between wastewater viral concentration and clinical cases.Such complexities,augmented by factors such as viral variant,public behavioral change,etc.,make it challenging to develop empirical models or data-driven models to provide accurate prediction of disease case for public health policy makings.To address this gap,this study developed an iterative data-driven framework utilizing Long-Short Time Memory(LSTM)neural networks for multi-timestep real-time predictions of future clinical cases based on WBE.The proposed LSTM model structure integrates both wastewater and historical clinical data as inputs.The prediction framework enables the update of LSTM model as more WBE dataset become available to enhance its adaptability to evolving pandemic stages.This framework was applied for real-time forecasting of COVID-19 clinical cases based on dataset of Ohio Wastewater Monitoring Project from July 2020 to October 2023.The developed iterative LSTM models were proven to achieve excellent performance in making clinical case predictions at different stages of COVID-19 pandemic.Early warning threshold of viral surge was defined by moving percentile method and results showed that the model achieved over 90%accuracy in future clinical case prediction and therefore demonstrated high reliability in pre-warning of potential disease outbreaks.This framework was also found to possess strong transferability across diverse geographic regions.The impacts of social policies and events on model predictions as well as the ramification of this model for future pandemics warning are discussed.
基金supported by LLNL LDRD 23-SI-006.LLNL-JRNL-861515.
文摘We present new parameterizations of the ChIMES physics informed machine-learned interatomic model for simulating carbon under conditions ranging from 300 K and 0 GPa to 10,000 K and 100 GPa,along with a new multi-fidelity active learning strategy.The resulting models show significant improvement in accuracy and temperature/pressure transferability relative to the original ChIMES carbon model developed in 2017 and can serve as a foundation for future transfer-learned ChIMES parameter sets.Applications to carbon melting point prediction,shockwave-driven conversion of graphite to diamond,and thermal conversion of nanodiamond to graphitic nanoonion are provided.Ultimately,we find the new models to be robust,accurate,and well-suited for modeling evolution in carbon systems under extreme conditions.
