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  1. Abstract Objectives

    Respiratory syncytial virus (RSV) is a significant cause of pediatric hospitalizations. This article aims to utilize multisource data and leverage the tensor methods to uncover distinct RSV geographic clusters and develop an accurate RSV prediction model for future seasons.

    Materials and Methods

    This study utilizes 5-year RSV data from sources, including medical claims, CDC surveillance data, and Google search trends. We conduct spatiotemporal tensor analysis and prediction for pediatric RSV in the United States by designing (i) a nonnegative tensor factorization model for pediatric RSV diseases and location clustering; (ii) and a recurrent neural network tensor regression model for county-level trend prediction using the disease and location features.

    Results

    We identify a clustering hierarchy of pediatric diseases: Three common geographic clusters of RSV outbreaks were identified from independent sources, showing an annual RSV trend shifting across different US regions, from the South and Southeast regions to the Central and Northeast regions and then to the West and Northwest regions, while precipitation and temperature were found as correlative factors with the coefficient of determination R2≈0.5, respectively. Our regression model accurately predicted the 2022-2023 RSV season at the county level, achieving R2≈0.3 mean absolute error MAE < 0.4 and a Pearson correlation greater than 0.75, which significantly outperforms the baselines with P-values <.05.

    Conclusion

    Our proposed framework provides a thorough analysis of RSV disease in the United States, which enables healthcare providers to better prepare for potential outbreaks, anticipate increased demand for services and supplies, and save more lives with timely interventions.

     
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  2. Free, publicly-accessible full text available August 4, 2024
  3. Abstract

    In this work, we aim to accurately predict the number of hospitalizations during the COVID-19 pandemic by developing a spatiotemporal prediction model. We propose HOIST, an Ising dynamics-based deep learning model for spatiotemporal COVID-19 hospitalization prediction. By drawing the analogy between locations and lattice sites in statistical mechanics, we use the Ising dynamics to guide the model to extract and utilize spatial relationships across locations and model the complex influence of granular information from real-world clinical evidence. By leveraging rich linked databases, including insurance claims, census information, and hospital resource usage data across the U.S., we evaluate the HOIST model on the large-scale spatiotemporal COVID-19 hospitalization prediction task for 2299 counties in the U.S. In the 4-week hospitalization prediction task, HOIST achieves 368.7 mean absolute error, 0.6$${R}^{2}$$R2and 0.89 concordance correlation coefficient score on average. Our detailed number needed to treat (NNT) and cost analysis suggest that future COVID-19 vaccination efforts may be most impactful in rural areas. This model may serve as a resource for future county and state-level vaccination efforts.

     
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  4. We demonstrate InGaAs/InP balanced photodiodes onSi3N4waveguides with record-high 3-dB bandwidth of 30 GHz, 0.72 A/W responsivity, and high common mode rejection ratio (CMRR) of 26 dB at 30 GHz.

     
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  5. We report InGaAs/InP based p-i-n photodiodes with an external quantum efficiency (EQE) above 98% from 1510 nm to 1575 nm. For surface normal photodiodes with a diameter of 80 µm, the measured 3-dB bandwidth is 3 GHz. The saturation current is 30.5 mA, with an RF output power of 9.3 dBm at a bias of −17 V at 3 GHz.

     
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  6. Lithium niobate on insulator (LNOI) has become an intriguing platform for integrated photonics for applications in communications, microwave photonics, and computing. Whereas, integrated devices including modulators, resonators, and lasers with high performance have been recently realized on the LNOI platform, high-speed photodetectors, an essential building block in photonic integrated circuits, have not been demonstrated on LNOI yet. Here, we demonstrate for the first time, heterogeneously integrated modified uni-traveling carrier photodiodes on LNOI with a record-high bandwidth of 80 GHz and a responsivity of 0.6 A/W at a 1550-nm wavelength. The photodiodes are based on an n-down InGaAs/InP epitaxial layer structure that was optimized for high carrier transit time-limited bandwidth. Photodiode integration was achieved using a scalable wafer die bonding approach that is fully compatible with the LNOI platform.

     
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  7. Abstract Objective

    We aim to develop a hybrid model for earlier and more accurate predictions for the number of infected cases in pandemics by (1) using patients’ claims data from different counties and states that capture local disease status and medical resource utilization; (2) utilizing demographic similarity and geographical proximity between locations; and (3) integrating pandemic transmission dynamics into a deep learning model.

    Materials and Methods

    We proposed a spatio-temporal attention network (STAN) for pandemic prediction. It uses a graph attention network to capture spatio-temporal trends of disease dynamics and to predict the number of cases for a fixed number of days into the future. We also designed a dynamics-based loss term for enhancing long-term predictions. STAN was tested using both real-world patient claims data and COVID-19 statistics over time across US counties.

    Results

    STAN outperforms traditional epidemiological models such as susceptible-infectious-recovered (SIR), susceptible-exposed-infectious-recovered (SEIR), and deep learning models on both long-term and short-term predictions, achieving up to 87% reduction in mean squared error compared to the best baseline prediction model.

    Conclusions

    By combining information from real-world claims data and disease case counts data, STAN can better predict disease status and medical resource utilization.

     
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  8. Abstract Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages. 
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