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  1. Free, publicly-accessible full text available January 26, 2023
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  4. Abstract

    Clean water free of bacteria is a precious resource in areas where no centralized water facilities are available. Conventional chlorine disinfection is limited by chemical transportation, storage, and the production of carcinogenic by-products. Here, a smartphone-powered disinfection system is developed for point-of-use (POU) bacterial inactivation. The integrated system uses the smartphone battery as a power source, and a customized on-the-go (OTG) hardware connected to the phone to realize the desired electrical output. Through a downloadable mobile application, the electrical output, either constant current (20–1000 µA) or voltage (0.7–2.1 V), can be configured easily through a user-friendly graphical interface on the screen. The disinfection device, a coaxial-electrode copper ionization cell (CECIC), inactivates bacteria by low levels of electrochemically generated copper with low energy consumption. The strategy of constant current control is applied in this study to solve the problem of uncontrollable copper release by previous constant voltage control. With the current control, a high inactivation efficiency ofE. coli(~6 logs) is achieved with a low level of effluent Cu (~200 µg L−1) in the water samples within a range of salt concentration (0.2–1 mmol L−1). The smartphone-based power workstation provides a versatile and accurate electrical output with a simple graphical user interface. The disinfection device is robust,more »highly efficient, and does not require complex equipment. As smartphones are pervasive in modern life, the smartphone-powered CECIC system could provide an alternative decentralized water disinfection approach like rural areas and outdoor activities.

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  5. Chlorine disinfection inevitably generates carcinogenic by-products. Alternative non-chlorine-based techniques in centralized treatment plants cannot produce residual antimicrobial power in water disinfection systems. Here, we propose locally enhanced electric field treatment (LEEFT) for chemical-free water disinfection in pipes. A tubular LEEFT device with coaxial electrodes is rationally developed for easy adaption to current water distribution systems as a segment of the pipelines. The center electrode is modified with perpendicularly grown nanowires, so that the electric field strength near the tips of the nanowires is significantly enhanced for pathogen inactivation. We have demonstrated >6-log inactivation of bacteria with 1 V, a small voltage that can be generated in situ by flowing water.
  6. 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.
    Free, publicly-accessible full text available December 1, 2023
  7. Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting atmore »a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.« less
    Free, publicly-accessible full text available April 12, 2023