Sample Size Calculations for Variant Surveillance in the Presence of Biological and Systematic Biases

As demonstrated during the SARS-CoV-2 pandemic, detecting and tracking the emergence and spread of pathogen variants is an important component of monitoring infectious disease outbreaks. Pathogen genome sequencing has emerged as the primary tool for variant characterization, so it is important to consider the number of sequences needed when designing surveillance programs or studies, both to ensure accurate conclusions and to optimize use of limited resources. However, current approaches to calculating sample size for variant monitoring often do not account for the biological and logistical processes that can bias which infections are detected and which samples are ultimately selected for sequencing. In this manuscript, we introduce a framework that models the full process from infection detection to variant characterization and demonstrate how to use this framework to calculate appropriate sample sizes for sequencing-based surveillance studies. We consider both cross-sectional and continuous sampling, and we have implemented our method in a publicly available tool that allows users to estimate necessary sample sizes given a specific aim (e.g., variant detection or measuring variant prevalence) and sampling method. Our framework is designed to be easy to use, while also flexible enough to be adapted to other pathogens and surveillance scenarios.

Objections

This chapter addresses theoretical and empirical objections that critics have presented against the epistemic argument for democracy presented in the previous chapter (the argument from collective wisdom). The objections this chapter addresses include those based on the average voter’s alleged incompetence and systematic biases, as well as those that challenge the relevance of deductive arguments for democracy. The metrics by which political scientists and economists claim to measure voters’ incompetence are elitist and the argument “garbage in, garbage out” on which people like Brennan rely to criticize democracy fail to take into account the fact that collective intelligence is not a linear function of individual competence but an emergent property that crucially depends on group properties, including cognitive diversity, and thus not captured by Brennan’s purely individualistic framework. Inferring from individual input to collective outcomes is thus neither empirical nor demonstrative. Systematic biases would be, and often are, a problem for democracy but not more than for oligarchies of knowers. In a free and diverse public sphere the public and its democratic representatives have more opportunities to debias themselves, at least over time, than small groups of homogenously thinking elites.

The EU self-surplus puzzle: an indication of VAT fraud?

The world runs a trade surplus with itself: the reported values of exports exceed the reported values of imports. This is logically impossible but a well-known empirical fact. Less well-known is the fact that, in recent years, the EU has a trade surplus with itself that amounts to more than 80% of the global surplus. In this paper, we show that this EU self-surplus is worth a striking 307 billion Euro in 2018, equaling 1.9% of the Union’s GDP, which persists both in goods and services trade accounts. We further examine discrepancies in goods and services trade accounts at the country and country pair level. These are strongest between neighboring countries and exist for members of the Euro Area as well as non-members. Around the 2004 Eastern Enlargement, the EU self-surplus quadrupled. Our estimations suggest that Cyprus, Ireland, Luxembourg, and Sweden are EU Members with the most inaccurate statistical regimes. We observe systematic biases which unlikely root in random measurement error. By contrast, we suspect that a large fraction of the EU’s self-surplus puzzle seems related to fraud in value added tax (VAT). VAT exemptions for exporters provide strong incentives for the over-declaration of true export values. The resulting loss in tax income could amount to as much as 64 billion Euro per year.

The First CHIME/FRB Fast Radio Burst Catalog

We present a catalog of 536 fast radio bursts (FRBs) detected by the Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst (CHIME/FRB) Project between 400 and 800 MHz from 2018 July 25 to 2019 July 1, including 62 bursts from 18 previously reported repeating sources. The catalog represents the first large sample, including bursts from repeaters and nonrepeaters, observed in a single survey with uniform selection effects. This facilitates comparative and absolute studies of the FRB population. We show that repeaters and apparent nonrepeaters have sky locations and dispersion measures (DMs) that are consistent with being drawn from the same distribution. However, bursts from repeating sources differ from apparent nonrepeaters in intrinsic temporal width and spectral bandwidth. Through injection of simulated events into our detection pipeline, we perform an absolute calibration of selection effects to account for systematic biases. We find evidence for a population of FRBs—composing a large fraction of the overall population—with a scattering time at 600 MHz in excess of 10 ms, of which only a small fraction are observed by CHIME/FRB. We infer a power-law index for the cumulative fluence distribution of α = − 1.40 ± 0.11 ( stat. ) − 0.09 + 0.06 ( sys. ) , consistent with the −3/2 expectation for a nonevolving population in Euclidean space. We find that α is steeper for high-DM events and shallower for low-DM events, which is what would be expected when DM is correlated with distance. We infer a sky rate of [ 820 ± 60 ( stat. ) − 200 + 220 ( sys. ) ] / sky / day above a fluence of 5 Jy ms at 600 MHz, with a scattering time at 600 MHz under 10 ms and DM above 100 pc cm−3.

The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data

Cloud droplet number concentration (Nd) is of central importance to observation-based estimates of aerosol indirect effects, being used to quantify both the cloud sensitivity to aerosol and the base state of the cloud. However, the derivation of Nd from satellite data depends on a number of assumptions about the cloud and the accuracy of the retrievals of the cloud properties from which it is derived, making it prone to systematic biases. A number of sampling strategies have been proposed to address these biases by selecting the most accurate Nd retrievals in the satellite data. This work compares the impact of these strategies on the accuracy of the satellite retrieved Nd, using a selection of insitu measurements. In stratocumulus regions, the MODIS Nd retrieval is able to achieve a high precision (r2 of 0.5–0.8). This is lower in other cloud regimes, but can be increased by appropriate sampling choices. Although the Nd sampling can have significant effects on the Nd climatology, it produces only a 20 % variation in the implied radiative forcing from aerosol-cloud interactions, with the choice of aerosol proxy driving the overall uncertainty. The results are summarised into recommendations for using MODIS Nd products and appropriate sampling.

Projected Future Changes in The Equatorial Wave Spectrum in CMIP6

The simulation of the Madden-Julian Oscillation (MJO) and convectively coupled equatorial waves (CCEWs) is considered in 13 state-of-the-art models from phase 6 of the Coupled Model Intercomparison Project (CMIP6). We use frequency-wavenumber power spectra of the models and observations for Outgoing Longwave Radiation (OLR) and zonal velocity at 250 hPa (U250), and consider the historical and end-of-century projections for the SSP245 and SSP585 scenarios. The models simulate a spectrum quantitatively resembling that observed, though systematic biases exist. MJO and Kelvin waves (KW) are mostly underestimated, while equatorial Rossby waves (ER) are overestimated. The models project a moderate future increase in power for the MJO, a robust increase for Kelvin waves (KW) and weaker power values for most other wavenumber-frequency combinations, including higher wavenumber ER. In addition to strengthening, KW also shift toward higher phase speeds (or equivalent depths). Models with a more realistic MJO in their control climate tend to simulate a stronger intensification, and models with a more realistic KW in their control climate tend to simulate a weaker intensification.