Infectious Disease Underreporting Is Predicted by Country-Level Preparedness, Politics, and Pathogen Severity.

Underreporting of infectious diseases is a pervasive challenge in public health that has emerged as a central issue in characterizing the dynamics of the COVID-19 pandemic. Infectious diseases are underreported for a range of reasons, including mild or asymptomatic infections, weak public health infrastructure, and government censorship. In this study, we investigated factors associated with cross-country and cross-pathogen variation in reporting. We performed a literature search to collect estimates of empirical reporting rates, calculated as the number of cases reported divided by the estimated number of true cases. This literature search yielded a dataset of reporting rates for 32 pathogens, representing 52 countries. We combined epidemiological and social science theory to identify factors specific to pathogens, country health systems, and politics that could influence empirical reporting rates. We performed generalized linear regression to test the relationship between the pathogen- and country-specific factors that we hypothesized could influence reporting rates, and the reporting rate estimates that we collected in our literature search. Pathogen- and country-specific factors were predictive of reporting rates. Deadlier pathogens and sexually transmitted diseases were more likely to be reported. Country epidemic preparedness was positively associated with reporting completeness, while countries with high levels of media bias in favor of incumbent governments were less likely to report infectious disease cases. Underreporting is a complex phenomenon that is driven by factors specific to pathogens, country health systems, and politics. In this study, we identified specific and measurable components of these broader factors that influence pathogen- and country-specific reporting rates and used model selection techniques to build a model that can guide efforts to diagnose, characterize, and reduce underreporting. Furthermore, this model can characterize uncertainty and correct for bias in reported infectious disease statistics, particularly when outbreak-specific empirical estimates of underreporting are unavailable. More precise estimates can inform control policies and improve the accuracy of infectious disease models.

Challenges in reported COVID-19 data: best practices and recommendations for future epidemics.

The proliferation of composite data sources tracking the COVID-19 pandemic emphasises the need for such databases during large-scale infectious disease events as well as the potential pitfalls due to the challenges of combining disparate data sources. Multiple organisations have attempted to standardise the compilation of disparate data from multiple sources during the COVID-19 pandemic. However, each composite data source can use a different approach to compile data and address data issues with varying results.We discuss some best practices for researchers endeavouring to create such compilations while discussing three key categories of challenges: (1) data dissemination, which includes discrepant estimates and varying data structures due to multiple agencies and reporting sources generating public health statistics on the same event; (2) data elements, such as date formats and location names, lack standardisation, and differing spatial and temporal resolutions often create challenges when combining sources; and (3) epidemiological factors, including missing data, reporting lags, retrospective data corrections and changes to case definitions that cannot easily be addressed by the data compiler but must be kept in mind when reviewing the data.Efforts to reform the global health data ecosystem should bear such challenges in mind. Standards and best practices should be developed and incorporated to yield more robust, transparent and interoperable data. Since no standards exist yet, we have highlighted key challenges in creating a comprehensive spatiotemporal view of outbreaks from multiple, often discrepant, reporting sources and provided guidelines to address them. In general, we caution against an over-reliance on fully automated systems for integrating surveillance data and strongly advise that epidemiological experts remain engaged in the process of data assessment, integration, validation and interpretation to identify, diagnose and resolve data challenges.

Bespoke mirror fabrication for quantum simulation with light in open-access microcavities.

In this work, we use focused ion beam (FIB) milling to generate custom mirror shapes for quantum simulation in optical microcavities. In the paraxial limit, light in multimode optical microcavities follows an equation of motion which is equivalent to Schrödinger's equation, with the surface topography of the mirrors playing the role of the potential energy landscape. FIB milling allows us to engineer a wide variety of trapping potentials for microcavity light, through exquisite control over the mirror topography, including 2D box, 1D waveguide, and Mexican hat potentials. The 2D box potentials are sufficiently flat over tens of microns, that the optical modes of the cavity, found by solving Schrödinger's equation on the measured cavity topography, are standing-wave modes of the box, rather than localised to deviations. The predicted scattering loss due to surface roughness measured using atomic force microscopy is found to be 177 parts per million, which corresponds to a cavity finesse of 2.2 × 104 once other losses have been taken into account. Spectra from dye-filled microcavities formed using these features show thermalised light in flat 2D potentials close to dye resonance, and spectrally-resolved cavity modes at the predicted frequencies for elliptical potentials. These results also represent a first step towards realising superfluid light and quantum simulation in arbitrary-shaped optical microcavities using FIB milling.