Tool for tracking all-cause mortality and estimating excess mortality to support the COVID-19 pandemic response.

ProblemQuantifying mortality from coronavirus disease (COVID-19) is difficult, especially in countries with limited resources. Comparing mortality data between countries is also challenging, owing to differences in methods for reporting mortality.ContextTracking all-cause mortality (ACM) and comparing it with expected ACM from pre-pandemic data can provide an estimate of the overall burden of mortality related to the COVID-19 pandemic and support public health decision-making. This study validated an ACM calculator to estimate excess mortality during the COVID-19 pandemic.ActionThe ACM calculator was developed as a tool for computing expected ACM and excess mortality at national and subnational levels. It was developed using R statistical software, was based on a previously described model that used non-parametric negative binomial regression and was piloted in several countries. Goodness-of-fit was validated by forecasting 2019 mortality from 2015-2018 data.OutcomeThree key lessons were identified from piloting the tool: using the calculator to compare reported provisional ACM with expected ACM can avoid potential false conclusions from comparing with historical averages alone; using disaggregated data at the subnational level can detect excess mortality by avoiding dilution of total numbers at the national level; and interpretation of results should consider system-related performance indicators.DiscussionTimely tracking of ACM to estimate excess mortality is important for the response to COVID-19. The calculator can provide countries with a way to analyse and visualize ACM and excess mortality at national and subnational levels

High-sensitivity computational miniaturized terahertz spectrometer using a plasmonic filter array and a modified multilayer residual CNN

Spectrometer miniaturization is desired for handheld and portable applications, yet nearly no miniaturized spectrometer is reported operating within terahertz (THz) waveband. Computational strategy, which can acquire incident spectral information through encoding and decoding it using optical devices and reconstruction algorithms, respectively, is widely employed in spectrometer miniaturization as artificial intelligence emerges. We demonstrate a computational miniaturized THz spectrometer, where a plasmonic filter array tailors the spectral response of a blocked-impurity-band detector. Besides, an adaptive deep-learning algorithm is proposed for spectral reconstructions with curbing the negative impact from the optical property of the filter array. Our spectrometer achieves modest spectral resolution (2.3 cm−1) compared with visible and infrared miniaturized spectrometers, outstanding sensitivity (e.g., signal-to-noise ratio, 6.4E6: 1) superior to common benchtop THz spectrometers. The combination of THz optical devices and reconstruction algorithms provides a route toward THz spectrometer miniaturization, and further extends the applicable sphere of the THz spectroscopy technique

WHO Global Situational Alert System: a mixed methods multistage approach to identify country-level COVID-19 alerts

Background Globally, since 1 January 2020 and as of 24 January 2023, there have been over 664 million cases of COVID-19 and over 6.7 million deaths reported to WHO. WHO developed an evidence-based alert system, assessing public health risk on a weekly basis in 237 countries, territories and areas from May 2021 to June 2022. This aimed to facilitate the early identification of situations where healthcare capacity may become overstretched.Methods The process involved a three-stage mixed methods approach. In the first stage, future deaths were predicted from the time series of reported cases and deaths to produce an initial alert level. In the second stage, this alert level was adjusted by incorporating a range of contextual indicators and accounting for the quality of information available using a Bayes classifier. In the third stage, countries with an alert level of ‘High’ or above were added to an operational watchlist and assistance was deployed as needed.Results Since June 2021, the system has supported the release of more than US$27 million from WHO emergency funding, over 450 000 rapid antigen diagnostic testing kits and over 6000 oxygen concentrators. Retrospective evaluation indicated that the first two stages were needed to maximise sensitivity, where 44% (IQR 29%–67%) of weekly watchlist alerts would not have been identified using only reported cases and deaths. The alerts were timely and valid in most cases; however, this could only be assessed on a non-representative sample of countries with hospitalisation data available.Conclusions The system provided a standardised approach to monitor the pandemic at the country level by incorporating all available data on epidemiological analytics and contextual assessments. While this system was developed for COVID-19, a similar system could be used for future outbreaks and emergencies, with necessary adjustments to parameters and indicators