The COVID-19 pandemic has sparked unprecedented public health and social measures (PHSM) by national and local governments, including border restrictions, school closures, mandatory facemask use and stay at home orders. Quantifying the effectiveness of these interventions in reducing disease transmission is key to rational policy making in response to the current and future pandemics. In order to estimate the effectiveness of these interventions, detailed descriptions of their timelines, scale and scope are needed. The Health Intervention Tracking for COVID-19 (HIT-COVID) is a curated and standardized global database that catalogues the implementation and relaxation of COVID-19 related PHSM. With a team of over 200 volunteer contributors, we assembled policy timelines for a range of key PHSM aimed at reducing COVID-19 risk for the national and first administrative levels (e.g. provinces and states) globally, including details such as the degree of implementation and targeted populations. We continue to maintain and adapt this database to the changing COVID-19 landscape so it can serve as a resource for researchers and policymakers alike.

To find efficient methods for classifying mine seismic events, two features extraction approaches were proposed. Features of source parameters including the seismic moment, the seismic energy, the energy ratio of S-to P-wave, the static stress drop, time of occurrence, and the number of triggers were selected, counted, and analyzed in approach I. Waveform characteristics consisting of two slope values and the coordinates of the first peak and the maximum peak were extracted as the discriminating parameters in approach II. The discriminating performance of the two approaches was compared and discussed by applying the Bayes discriminant analysis to the characteristic parameters extracted. Classification results show that 83.5% of the original grouped cases are correctly classified by approach I, and 97.1% of original grouped cases are correctly classified by approach II. The advantages and limitations pertaining to each classifier were discussed by plotting the event magnitude versus sample number. Comparative analysis shows that the proposed method of approach II not only has a low misjudgment rate but also displays relative constancy when the testing samples fluctuate with seismic magnitude and energy.

Glyphosate, which has been widely reported to be a toxic pollutant, is often present at trace amounts in the environment. In this study, a novel copper-aluminum metal hydroxide doped graphene nanoprobe (labeled as CuAl–LDH/Gr NC) was first developed to construct a non-enzymatic electrochemical sensor for detection trace glyphosate. The characterization results showed that the synthesized CuAl–LDH had a high-crystallinity flowered structure, abundant metallic bands and an intercalated functional group. After mixed with Gr, the nanocomposites provided a larger surface area and better conductivity. The as-prepared CuAl–LDH/Gr NC dramatically improved the enrichment capability for glyphosate to realize the stripping voltammetry detection. The logarithmic linear detection range of the sensor was found to be 2.96 × 10−9–1.18 × 10−6 mol L−1 with the detection limit of 1 × 10−9 mol L−1 with excellent repeatability, good stability and anti-interference ability. Further, the sensor achieved satisfactory recovery rates in spiked surface water, ranging from 97.64% to 108.08%, demonstrating great accuracy and practicality.

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