Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic

Surveillance is critical to mounting an appropriate and effective response to pandemics. However, aggregated case report data suffers from reporting delays and can lead to misleading inferences. Different from aggregated case report data, line list data is a table contains individual features such as dates of symptom onset and reporting for each reported case and a good source for modeling delays. Current methods for modeling reporting delays are not particularly appropriate for line list data, which typically has missing symptom onset dates that are non-ignorable for modeling reporting delays. In this paper, we develop a Bayesian approach that dynamically integrates imputation and estimation for line list data. Specifically, this Bayesian approach can accurately estimate the epidemic curve and instantaneous reproduction numbers, even with most symptom onset dates missing. The Bayesian approach is also robust to deviations from model assumptions, such as changes in the reporting delay distribution or incorrect specification of the maximum reporting delay. We apply the Bayesian approach to COVID-19 line list data in Massachusetts and find the reproduction number estimates correspond more closely to the control measures than the estimates based on the reported curve.     

A metadata reporting framework (FRAMES) for synthesis of ecohydrological observations

Metadata describe the ancillary information needed for data preservation and independent interpretation, comparison across heterogeneous datasets, and quality assessment and quality control (QA/QC). Environmental observations are vastly diverse in type and structure, can be taken across a wide range of spatiotemporal scales in a variety of measurement settings and approaches, and saved in multiple formats. Thus, well-organized, consistent metadata are required to produce usable data products from diverse environmental observations collected across field sites. However, existing metadata reporting protocols do not support the complex data synthesis and model-data integration needs of interdisciplinary earth system research. We developed a metadata reporting framework (FRAMES) to enable management and synthesis of observational data that are essential in advancing a predictive understanding of earth systems. FRAMES utilizes best practices for data and metadata organization enabling consistent data reporting and compatibility with a variety of standardized data protocols. We used an iterative scientist-centered design process to develop FRAMES, resulting in a data reporting format that incorporates existing field practices to maximize data-entry efficiency. Thus, FRAMES has a modular organization that streamlines metadata reporting and can be expanded to incorporate additional data types. With FRAMES's multi-scale measurement position hierarchy, data can be reported at observed spatial resolutions and then easily aggregated and linked across measurement types to support model-data integration. FRAMES is in early use by both data originators (persons generating data) and consumers (persons using data and metadata). In this paper, we describe FRAMES, identify lessons learned, and discuss areas of future development.     

Determining the optimal load for jump squats: a review of methods and calculations

There has been an increasing volume of research focused on the load that elicits maximum power output during jump squats. Because of a lack of standardization for data collection and analysis protocols, results of much of this research are contradictory. The purpose of this paper is to examine why differing methods of data collection and analysis can lead to conflicting results for maximum power and associated optimal load. Six topics relevant to measurement and reporting of maximum power and optimal load are addressed: (a) data collection equipment, (b) inclusion or exclusion of body weight force in calculations of power, (c) free weight versus Smith machine jump squats, (d) reporting of average versus peak power, (e) reporting of load intensity, and (f) instructions given to athletes/ participants. Based on this information, a standardized protocol for data collection and reporting of jump squat power and optimal load is presented.     

Modelling under-reporting in epidemics

Under-reporting of infected cases is crucial for many diseases because of the bias it can introduce when making inference for the model parameters. The objective of this paper is to study the effect of under-reporting in epidemics by considering the stochastic Markovian SIR epidemic in which various reporting processes are incorporated. In particular, we first investigate the effect on the estimation process of ignoring under-reporting when it is present in an epidemic outbreak. We show that such an approach leads to under-estimation of the infection rate and the reproduction number. Secondly, by allowing for the fact that under-reporting is occurring, we develop suitable models for estimation of the epidemic parameters and explore how well the reporting rate and other model parameters can be estimated. We consider the case of a constant reporting probability and also more realistic assumptions which involve the reporting probability depending on time or the source of infection for each infected individual. Due to the incomplete nature of the data and reporting process, the Bayesian approach provides a natural modelling framework and we perform inference using data augmentation and reversible jump Markov chain Monte Carlo techniques.     

An Ecosystem Health Index for a large and variable river basin: Methodology,challenges and continuous improvement in Queensland’s Fitzroy Basin

Report cards are an increasingly popular method for summarising and communicating relative environmental performance and ecosystem health, including in aquatic environments. They are usually underpinned by an Ecosystem Health Index (EHI) that combines various individual indicators to produce an overall ecosystem health “score”. As a result of public water quality concerns, an integrated means of monitoring and reporting on aquatic ecosystem health was needed for the Fitzroy Basin in central Queensland, Australia. The Fitzroy Partnership for River Health was formed to address this need, and developed an EHI and report card for the Basin using existing monitoring data collected from various third parties including regulated companies operations and government. At 142,000 square kilometres, the Fitzroy Basin is the largest catchment draining to the World Heritage Listed Great Barrier Reef. The Fitzroy Basin provides an example of how to deliver an effective aquatic ecosystem health reporting system in a large and complex river basin. We describe the methodology used to develop an adaptive EHI for the Fitzroy Basin that addresses variability, complexity and scale issues associated with reporting across large areas. As well, we report how to manage the design and reporting stages given limitations in data collection and scientific understanding.     

Assessing the reporting of Dengue,Chikungunya and Zika to the National Surveillance System in Colombia from 2014–2017: A Capture-recapture analysis accounting for misclassification of arboviral diagnostics

BackgroundChikungunya, dengue, and Zika are three different arboviruses which have similar symptoms and are a major public health issue in Colombia. Despite the mandatory reporting of these arboviruses to the National Surveillance System in Colombia (SIVIGILA), it has been reported that the system captures less than 10% of diagnosed cases in some cities.Methodology/Principal findingsTo assess the scope and degree of arboviruses reporting in Colombia between 2014–2017, we conducted an observational study of surveillance data using the capture-recapture approach in three Colombian cities. Using healthcare facility registries (capture data) and surveillance-notified cases (recapture data), we estimated the degree of reporting by clinical diagnosis. We fit robust Poisson regressions to identify predictors of reporting and estimated the predicted probability of reporting by disease and year. To account for the potential misclassification of the clinical diagnosis, we used the simulation extrapolation for misclassification (MC-SIMEX) method. A total of 266,549 registries were examined. Overall arboviruses’ reporting ranged from 5.3% to 14.7% and varied in magnitude according to age and year of diagnosis. Dengue was the most notified disease (21–70%) followed by Zika (6–45%). The highest reporting rate was seen in 2016, an epidemic year. The MC-SIMEX corrected rates indicated underestimation of the reporting due to the potential misclassification bias.ConclusionsThese findings reflect challenges on arboviruses’ reporting, and therefore, potential challenges on the estimation of arboviral burden in Colombia and other endemic settings with similar surveillance systems.     

How much are stranding records affected by variation in reporting rates? A case study of small delphinids in the Bay of Biscay

Marine vertebrate strandings offer an opportunistic sampling scheme that can provide abundant data over long periods. Because the stranding process involves biological, physical and sociological parameters, confounding complicates the interpretation of results. The statistical analysis of these data relies on generalized linear or additive models in order to infer long-term trends, but does not easily account for drift or variation in reporting rates. Here, we capitalized on county-level (administrative) variation following the passing of a law for compulsory reporting of stranded marine mammals in France to investigate how variation in reporting rates may affect the observed trend in stranded small delphinids in the Bay of Biscay. Using a time-series spanning more than 30 years across eight administrative counties, we built variance partitioning models for the analysis of count data. We discussed the choice of an appropriate likelihood to conclude the Negative Binomial useful and interpretable in the context of small delphinid strandings. We expanded the model with a recent methodology to detect structural breaks in the time series, focusing on overdispersion. We performed statistical robustness checks with respect to variations in reporting rates and discuss their causal interpretation in the context of observational data. Stranding frequencies increased on average 7-fold over 30 years. We conclude that reporting rates to the French stranding network have been stable since the early 1990s, and the average 3-fold increase in stranded small delphinids observed in the Bay of Biscay since 1990 is due to other factors, including bycatch. Codes and data are available to replicate the analysis to other national stranding networks.     

Getting the Data Flowing: Lessons Learned from Existing Reporting Systems in the Forestry Sector in Indonesia for REDD+ MRV

In the context of REDD+, Measurement, Reporting and Verification (MRV) is one way to manage forest change information. A national carbon and non-carbon database will be used in REDD+ to negotiate compensation schemes with the international community. Much of this data will be collected at the local level, thus a reporting system that can integrate these locally collected data into the national database is crucial. In this paper we compare and draw lessons from three existing local to national reporting systems that include the participation of local communities: 1) the government extension services, 2) the government owned forestry company, and 3) a private logging company in Indonesia, and provide recommendations for REDD+ reporting systems. The results suggest that the main desired conditions for effective data flow are: benefits to motivate local participation, based on contributions to reporting activities; simple data format and reporting procedures to allow local participation in the reporting process, and to support data aggregation at the national level; a facilitator to mediate data aggregation at the village level to ensure data consistency, completeness and accuracy; and a transparent and clear data flow. Under these conditions, continuous, accountable and consistent data flow from the local level will reach the national level where it can be fully utilized.     

Statistical Reporting Errors and Collaboration on Statistical Analyses in Psychological Science

Statistical analysis is error prone. A best practice for researchers using statistics would therefore be to share data among co-authors, allowing double-checking of executed tasks just as co-pilots do in aviation. To document the extent to which this ‘co-piloting’ currently occurs in psychology, we surveyed the authors of 697 articles published in six top psychology journals and asked them whether they had collaborated on four aspects of analyzing data and reporting results, and whether the described data had been shared between the authors. We acquired responses for 49.6% of the articles and found that co-piloting on statistical analysis and reporting results is quite uncommon among psychologists, while data sharing among co-authors seems reasonably but not completely standard. We then used an automated procedure to study the prevalence of statistical reporting errors in the articles in our sample and examined the relationship between reporting errors and co-piloting. Overall, 63% of the articles contained at least one p-value that was inconsistent with the reported test statistic and the accompanying degrees of freedom, and 20% of the articles contained at least one p-value that was inconsistent to such a degree that it may have affected decisions about statistical significance. Overall, the probability that a given p-value was inconsistent was over 10%. Co-piloting was not found to be associated with reporting errors.     

Addressing delayed case reporting in infectious disease forecast modeling

Infectious disease forecasting is of great interest to the public health community and policymakers, since forecasts can provide insight into disease dynamics in the near future and inform interventions. Due to delays in case reporting, however, forecasting models may often underestimate the current and future disease burden.In this paper, we propose a general framework for addressing reporting delay in disease forecasting efforts with the goal of improving forecasts. We propose strategies for leveraging either historical data on case reporting or external internet-based data to estimate the amount of reporting error. We then describe several approaches for adapting general forecasting pipelines to account for under- or over-reporting of cases. We apply these methods to address reporting delay in data on dengue fever cases in Puerto Rico from 1990 to 2009 and to reports of influenza-like illness (ILI) in the United States between 2010 and 2019. Through a simulation study, we compare method performance and evaluate robustness to assumption violations. Our results show that forecasting accuracy and prediction coverage almost always increase when correction methods are implemented to address reporting delay. Some of these methods required knowledge about the reporting error or high quality external data, which may not always be available. Provided alternatives include excluding recently-reported data and performing sensitivity analysis. This work provides intuition and guidance for handling delay in disease case reporting and may serve as a useful resource to inform practical infectious disease forecasting efforts.     

Data Management Tools to Measure the Impact of Core Facilities

The Biomolecular Research Center at Boise State University is a research core facility that supports the study of biomolecules with an emphasis on protein structure and function, molecular interactions, and imaging. The mission of the core is to facilitate access to instrumentation that might otherwise be unavailable because of the cost, training for new users, and scientific staff with specialized skills to support early-stage investigators, as well as more established senior investigators. Data collection and management of users and their research output is essential to understand the impact of the center on the research environment and research productivity. However, challenges are often encountered when trying to fully quantify the impact of a core facility on the institution, as well as on the career success of individual investigators. This challenge can be exacerbated under the conditions of unprecedented growth in biomedical research and shared core facility use that has been experienced at Boise State University, an institution of emerging research excellence. Responding to these challenges required new approaches to information management, reporting, assessment, and evaluation. Our specific data management, evaluation, and assessment challenges included 1) collection and management of annual reporting information from investigators, staff, and students in a streamlined manner that did not lead to reporting fatigue; 2) application of software for analyzing synergy among programs’ management strategy and investigator success; and 3) consolidation of core facility management, billing, and reporting capabilities into 1 cohesive system. The data management tools adopted had a beneficial effect by saving time, reducing administrative burden, and streamlining reporting. Practices implemented for data management have facilitated effective evaluation and future program planning. The substantial burden of assessment requirements necessitates early consideration of a strategy for data management to allow assessment of impact.     

Web portal to an image database for high-resolution three-dimensional reconstruction

The exponential increase of image data in high-resolution reconstructions by electron cryomicroscopy (cryoEM) has posed a need for efficient data management solutions in addition to powerful data processing procedures. Although relational databases and web portals are commonly used to manage sequences and structures in biological research, their application in cryoEM has been limited due to the complexity in accomplishing the dual tasks of interacting with proprietary software and simultaneously providing data access to users without database knowledge. Here, we report our results in developing web portal to SQL image databases used by the Image Management and Icosahedral Reconstruction System (IMIRS) to manage cryoEM images for subnanometer-resolution reconstructions. Fundamental issues related to the design and deployment of web portals to image databases are described. A web browser-based user interface was designed to accomplish data reporting and other database-related services, including user authentication, data entry, graph-based data mining, and various query and reporting tasks with interactive image manipulation capabilities. With an integrated web portal, IMIRS represents the first cryoEM application that incorporates both web-based data reporting tools and a complete set of data processing modules. Our examples should thus provide general guidelines applicable to other cryoEM technology development efforts.     

Global Reporting Initiative's environmental reporting: A study of oil and gas companies

In this paper, we ask if the use of a voluntary standard assessment system for environmental reporting could help mitigate the damage caused by oil and gas companies to developing nations. What level of data reporting by these companies is needed to allow the assessment of environmental practices? To answer these questions, we evaluate the environmental practices of eight oil and gas companies against the Sustainability Reporting Guidelines issued in 2006 by the Global Reporting Initiative (GRI). Content analysis of their environmental reports 2009 indicates that they made reasonable efforts to disclose their environmental performance in accordance with the GRI Sustainability Reporting Guidelines. These guidelines appear to provide a robust and readily available tool for reporting comprehensive progress concerning all aspects of environmental activities. The voluntary adoption of the guidelines by a vast majority of the oil and gas companies increases transparency, credibility and comparability in sustainability reporting. In addition, five of the eight oil and gas companies disclosed that they had obtained third-party assurance of their sustainability reports.     

What Affects Authors’ and Editors’ Use of Reporting Guidelines? Findings from an Online Survey and Qualitative Interviews

Objectives

To identify and understand, through data from multiple sources, some of the factors that affect authors’ and editors’ decisions to use reporting guidelines in the publication of health research.

Design

Mixed methods study comprising an online survey and semi-structured interviews with a sample of authors (online survey: n = 56; response rate = 32%; semi-structured interviews: n = 5) and journal editors (online survey: n = 43; response rate = 27%; semi-structured interviews: n = 6) involved in publishing health and medical research. Participants were recruited from an earlier study examining the effectiveness of the TREND reporting guideline.

Results

Four types of factors interacted to affect authors’ and editors’ likelihood of reporting guideline use; individual (e.g. having multiple reasons for use of reporting guidelines); the professional culture in which people work; environmental (e.g. policies of journals); and, practical (e.g. having time to use reporting guidelines). Having multiple reasons for using reporting guidelines was a particularly salient factor in facilitating reporting guidelines use for both groups of participants.

Conclusions

Improving the completeness and consistency of reporting of research studies is critical to the integrity and synthesis of health research. The use of reporting guidelines offers one potentially efficient and effective means for achieving this, but decisions to use (or not use) reporting guidelines take many factors into account. These findings could be used to inform future studies that might, for example, test the factors that we have identified within a wider theoretical framework for understanding changes in professional practices. The use of reporting guidelines by senior professionals appears to shape the expectations of what constitutes best practice and can be assimilated into the culture of a field or discipline. Without evidence of effectiveness of reporting guidelines, and sustained, multifaceted efforts to improve reporting, little progress seems likely to be made.     

A modelling strategy for recovery data from birds ringed as nestlings.

In this paper we propose a strategy for analysing recovery data from birds ringed as nestlings. The approach advocated starts with a global model, involving calendar year dependence of both reporting and first-year survival rates, and age-dependence of survival rates for older birds. Likelihood ratio tests are then used to choose between a range of submodels. The strategy is illustrated through application to three data sets, on mallards, herring gulls, and blue-winged teal. The effect of age-dependence operating also on reporting rates is examined through matched simulations, since a model with age-dependent reporting rates cannot be fitted directly. This reveals an underestimation of the first-year survival rates, when the probability of recovery for first-year birds is greater than that for older birds. It is argued that this bias may not be serious and indeed may be allowed for in practice. For mallards and teal, comparisons are drawn with the results from other models that additionally analyse recoveries of birds ringed as adults; the same general conclusions are reached.     

Quality control for plant metabolomics: reporting MSI-compliant studies

The Metabolomics Standards Initiative (MSI) has recently released documents describing minimum parameters for reporting metabolomics experiments, in order to validate metabolomic studies and to facilitate data exchange. The reporting parameters encompassed by MSI include the biological study design, sample preparation, data acquisition, data processing, data analysis and interpretation relative to the biological hypotheses being evaluated. Herein we exemplify how such metadata can be reported by using a small case study – the metabolite profiling by GC-TOF mass spectrometry of Arabidopsis thaliana leaves from a knockout allele of the gene At1g08510 in the Wassilewskija ecotype. Pitfalls in quality control are highlighted that can invalidate results even if MSI reporting standards are fulfilled, including reliable compound identification and integration of unknown metabolites. Standardized data processing methods are proposed for consistent data storage and dissemination via databases.     

The Functional Genomics Experiment model (FuGE): an extensible framework for standards in functional genomics

The Functional Genomics Experiment data model (FuGE) has been developed to facilitate convergence of data standards for high-throughput, comprehensive analyses in biology. FuGE models the components of an experimental activity that are common across different technologies, including protocols, samples and data. FuGE provides a foundation for describing entire laboratory workflows and for the development of new data formats. The Microarray Gene Expression Data society and the Proteomics Standards Initiative have committed to using FuGE as the basis for defining their respective standards, and other standards groups, including the Metabolomics Standards Initiative, are evaluating FuGE in their development efforts. Adoption of FuGE by multiple standards bodies will enable uniform reporting of common parts of functional genomics workflows, simplify data-integration efforts and ease the burden on researchers seeking to fulfill multiple minimum reporting requirements. Such advances are important for transparent data management and mining in functional genomics and systems biology.     

Effect of Publicly Reporting Performance Data of Medicine Use on Injection Use: A Quasi-Experimental Study

Background

Inappropriate use of prescribing pharmaceuticals, particularly injections, not only affects the quality of medical care, but also leads to an increase in medical expenses. Publicly reporting performance data of medical care is becoming a common health policy tool adopted to supervise medical quality. To our knowledge, few studies about public reporting applied to medicine use have been reported. This study intended to introduce public reporting in the field of medicine use, and evaluate the effect of publicly reporting performance data of medicine use on the use of injections.

Methods

The research sites were 20 primary healthcare institutions in Q City, Hubei. By matching, the institutions were divided into the intervention group and control group. A quasi-experimental design was applied in this study. In the intervention group, the performance data of medicine use were publicly reported. The injection prescribing rates of the two groups before and after intervention were measured and compared. Difference-in-difference method and logistic regression were employed to estimate the effect of public reporting on injection use.

Results

Public reporting led to a reduction of approximately 4% in the injection prescribing rate four months after intervention (OR = 0.96; 95%CI: 0.94, 0.97). The intervention effect was inconsistent in each month after intervention, and it was most positive in the second month after intervention (OR = 0.90; 95%CI: 0.89, 0.92).

Conclusions

In general, publicly reporting performance data of medicine use may have positive effects on injection use to some extent. Further research is needed to investigate the mechanism by which public reporting influences injection use. Comprehensive measures are also necessary to promote the rational use of injections.     

eCatch: Enabling collaborative fisheries management with technology

Modernizing data systems to inform collaborative management is critical to adaptively managing fisheries in an era of climate change. In 2006, The Nature Conservancy of California purchased 13 federal groundfish permits in California with the objective of managing the fishing and reporting activities in a manner that protected sensitive habitats and species. At that time, collecting data for this fishery was done with paper logbooks. This made queries and visualization that could inform management decisions towards our objective impossible in a timely manner. To solve this problem, we built successive software prototypes that leveraged location-aware mobile devices, cloud-based computing, and visualization and query of geographic data over the web. The resulting software, eCatch, enabled avoidance of sensitive species and habitats and quantitative reporting on performance metrics related to those activities. What started as a technology solution to a problem of timely scientific monitoring revealed collateral benefits of collaboration with the fishing industry and markets that support sustainable activities.