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The correct interpretation of the result from a point-of-care device is crucial for an accurate and rapid diagnosis to guide subsequent treatment. Lateral flow tests (LFTs) use a well-established format that was designed to simplify the user experience. However, the LFT device architecture is inherently limited to detecting analytes that can be captured by molecular recognition. Microfluidic paper-based analytical devices (µPADs), like LFTs, have the potential to be used in diagnostic applications at the point of care. However, µPADs have not gained significant traction outside of academic laboratories, in part, because they have often demonstrated a lack of homogeneous shape or color in signal outputs, which consequently can lead to inaccurate interpretation of results by users. Here, we demonstrate a new class of µPADs that form colorimetric signals at the interfaces of converging liquid fronts (i.e., lines) to control where colorimetric signals are formed without relying on capture techniques. We demonstrate our approach by developing assays for three classes of analytes—an ion, an enzyme, and a small molecule—to measure using iron (III), acetylcholinesterase, and lactate, respectively. Additionally, we show these devices have the potential to support multiplexed assays by generating multiple lines in a common readout zone. These results highlight the ability of this new paper-based device architecture to aid the interpretation of assays that create soluble products by using flow to constrain those colorimetric products in a familiar, line-format output. 

Abstract Development of paper-based microfluidic devices that perform colorimetric measurements requires quantitative image analysis. Because the design geometries of paper-based microfluidic devices are not standardized, conventional methods for performing batch measurements of regularly spaced areas of signal intensity, such as those for well plates, cannot be used to quantify signal from most of these devices. To streamline the device development process, we have developed an open-source program called ColorScan that can automatically recognize and measure signal-containing zones from images of devices, regardless of output zone geometry or spatial arrangement. This program, which measures color intensity with the same accuracy as standard manual approaches, can rapidly process scanned device images, simultaneously measure identified output zones, and effectively manage measurement results to eliminate requirements for time-consuming and user-dependent image processing procedures. 

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1–4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.