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In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic. This report summarizes the rich discussions that occurred during the workshop. The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data, social media, and wastewater monitoring. Significant advancements were noted in the development of predictive models, with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends. The role of open collaboration between various stakeholders in modelling was stressed, advocating for the continuation of such partnerships beyond the pandemic. A major gap identified was the absence of a common international framework for data sharing, which is crucial for global pandemic preparedness. Overall, the workshop underscored the need for robust, adaptable modelling frameworks and the integration of different data sources and collaboration across sectors, as key elements in enhancing future pandemic response and preparedness. [ABSTRACT FROM AUTHOR]

Researchers are often faced with evaluating the effect of a policy or program that was simultaneously initiated across an entire population of units at a single point in time, and its effects over the targeted population can manifest at any time period afterwards. In the presence of data measured over time, Bayesian time series models have been used to impute what would have happened after the policy was initiated, had the policy not taken place, in order to estimate causal effects. However, the considerations regarding the definition of the target estimands, the underlying assumptions, the plausibility of such assumptions, and the choice of an appropriate model have not been thoroughly investigated. In this paper, we establish useful estimands for the evaluation of large-scale policies. We discuss that imputation of missing potential outcomes relies on an assumption which, even though untestable, can be partially evaluated using observed data. We illustrate an approach to evaluate this key causal assumption and facilitate model elicitation based on data from the time interval before policy initiation and using classic statistical techniques. As an illustration, we study the Hospital Readmissions Reduction Program (HRRP), a US federal intervention aiming to improve health outcomes for patients with pneumonia, acute myocardial infraction, or congestive failure admitted to a hospital. We evaluate the effect of the HRRP on population mortality among the elderly across the US and in four geographic subregions, and at different time windows. We find that the HRRP increased mortality from pneumonia and acute myocardial infraction across at least one geographical region and time horizon, and is likely to have had a detrimental effect on public health. [ABSTRACT FROM AUTHOR]

BACKGROUND: Emissions from coal power plants have decreased over recent decades due to regulations and economics affecting costs of providing electricity generated by coal vis-à-vis its alternatives. These changes have improved regional air quality, but questions remain about whether benefits have accrued equitably across population groups. OBJECTIVES: We aimed to quantify nationwide long-term changes in exposure to particulate matter (PM) with an aerodynamic diameter ≤2.5 μm (PM2.5) associated with coal power plant SO2 emissions. We linked exposure reductions with three specific actions taken at individual power plants: scrubber installations, reduced operations, and retirements. We assessed how emissions changes in different locations have influenced exposure inequities, extending previous source-specific environmental justice analyses by accounting for location-specific differences in racial/ethnic population distributions. METHODS: We developed a data set of annual PM2.5 source impacts (“coal PM2.5”) associated with SO2 emissions at each of 1,237 U.S. coal-fired power plants across 1999–2020. We linked population-weighted exposure with information about each coal unit’s operational and emissions-control status. We calculate changes in both relative and absolute exposure differences across demographic groups. RESULTS: Nationwide population-weighted coal PM2.5 declined from 1.96 μg/m³ in 1999 to 0:06 μg/m³ in 2020. Between 2007 and 2010, most of the exposure reduction is attributable to SO2 scrubber installations, and after 2010 most of the decrease is attributable to retirements. Black populations in the South and North Central United States and Native American populations in the western United States were inequitably exposed early in the study period. Although inequities decreased with falling emissions, facilities in states across the North Central United States continue to inequitably expose Black populations, and Native populations are inequitably exposed to emissions from facilities in the West. DISCUSSION: We show that air quality controls, operational adjustments, and retirements since 1999 led to reduced exposure to coal power plant related PM2.5. Reduced exposure improved equity overall, but some populations continue to be inequitably exposed to PM2.5 associated with facilities in the North Central and western United States. [ABSTRACT FROM AUTHOR]

Since the beginning of the COVID-19 pandemic, many dashboards have emerged as useful tools to monitor its evolution, inform the public, and assist governments in decision-making. Here, we present a globally applicable method, integrated in a daily updated dashboard that provides an estimate of the trend in the evolution of the number of cases and deaths from reported data of more than 200 countries and territories, as well as 7-d forecasts. One of the significant difficulties in managing a quickly propagating epidemic is that the details of the dynamic needed to forecast its evolution are obscured by the delays in the identification of cases and deaths and by irregular reporting. Our forecasting methodology substantially relies on estimating the underlying trend in the observed time series using robust seasonal trend decomposition techniques. This allows us to obtain forecasts with simple yet effective extrapolation methods in linear or log scale. We present the results of an assessment of our forecastingmethodology and discuss its application to the production of global and regional risk maps. [ABSTRACT FROM AUTHOR]

Prior studies have examined the relationship between organizational ethical climates and an array of work-related attitudes and behaviors. However, current research is hampered by the lack of knowledge on how ethical climates relate to organizational outcomes. To advance research on this topic, we developed three unobtrusive measures of organizational ethical climates based on words used in annual letters to shareholders of Fortune 500 companies and examine how organizational ethical climates relate to corporate reputation and a readability index. Results reveal that our measures of organizational ethical climates align with well-known reputation measures as well as the overall readability of letters to shareholders. Our theory and evidence open a novel path for both researchers and practitioners to benefit from an initial validation of measures of organizational ethical climates in studying corporate reputation. [ABSTRACT FROM AUTHOR]

Summary: Coal burning power plants are a frequent target of regulatory programmes because of their emission of chemicals that are known precursors to the formation of ambient particulate air pollution. Health impact assessments of emissions from coal power plants typically rely on assumed causal relationships between emissions, ambient pollution and health, many of which have never been empirically verified. We offer a novel statistical evaluation of some of these presumed causal relationships, integrating the formality of causal inference methods with repurposed tools from atmospheric science to accommodate the central challenge of long‐range pollution transport of emissions from power plants to exposed populations. The statistical approach follows recent work on Bayesian methods for deploying principal stratification and causal mediation analysis in tandem to characterize the extent to which decreased sulphur dioxide emissions from 410 power plants across the USA impact mortality and hospitalization outcomes across Medicare beneficiaries residing across 12370 locations in a manner that is mediated through reductions of ambient fine particulate pollution. The result is the first epidemiological investigation integrating causal inference methodology with direct measurements of coal emissions, pollution transport, ambient pollution and human health in a single analysis, indicating the potential for data science at the intersection of statistics, epidemiology and atmospheric science. [ABSTRACT FROM AUTHOR]

This study seeks to examine the mechanisms by which a corporation's use of philanthropy affects its reputation for corporate social performance (CSP), which the authors conceive of as consisting of two dimensions: CSP awareness and CSP perception. Using signal detection theory (SDT), the authors model signal amplitude (the amount contributed), dispersion (number of areas supported), and consistency (presence of a corporate foundation) on CSP awareness and perception. Overall, this study finds that characteristics of firms' portfolio of philanthropic activities are a greater predictor of CSP awareness than of CSP perception. Awareness increases with signal amplitude, dispersion, and consistency. CSP perception is driven by awareness and corporate reputation. The authors' contention that corporate philanthropy is a complex variable is upheld, as we find that CSP signal characteristics influence CSP awareness and perception independently and asymmetrically. The authors conclude by proposing avenues for future research. [ABSTRACT FROM AUTHOR]

This article introduces the special issue on "Corporate Reputation: Being Good and Looking Good." Three of the five included articles help to reinforce a conclusion that "being good" and "looking good" are not dichotomous, mutually exclusive conditions. Rather, the two dimensions are linked in some kind of causal relationship for which continuing conceptual and empirical research is desirable. A fourth article concerns the reputational effects of the stock-option backdating scandal. The fifth article offers a critique of conventional approaches to defining and measuring reputation. [ABSTRACT FROM AUTHOR]

This study examines events at the conclusion of the 2006 stock option backdating scandal: the departures of C-level executives from firms implicated in backdating. The authors ask whether removing executives brings closure to the scandal, or if executive turnover creates greater uncertainty. Using a sample of 236 executive departures, the authors find that although overall market reaction to executive departures is negative, those departures involving a firm's CEO or CFO ameliorate the market reaction. The authors also find that market reaction worsens when the CEO "resigns," versus being terminated, and when the firm involved has been previously identified as socially responsible. Results suggest that firms, by shaping who and how executives depart, may amplify or dampen the damage of existing scandalizing events. [ABSTRACT FROM AUTHOR]

The most popular measure of corporate reputation is the ranking of the most admired companies. But what exactly do we admire in people and firms of good reputation? This article is about the ethical dimension of corporate reputation. It integrates the trait approach in personality psychology and philosophical ethics to the study of reputation and related concepts as a way to account for the discontinuities between reputation at the individual and corporate levels under conditions of uncertainty. Through an examination of the distinction between perceptions and grounding of reputation, the article also sheds light on the explanatory power of reputation concepts and critically appraises the possibility of character and organizational virtue. [ABSTRACT FROM AUTHOR]

A large body of literature looks at how firms develop and maintain their reputation. Little is known, however, about factors leading to a damaged corporate reputation. In this article, the authors compare two sets of predictors of reputational damage following a reported breach of norms: the characteristics of the breach and the characteristics of the actor reporting the breach. Theoretically, the authors argue that the latter is likely to prevail over the former. The authors test this proposition in the highly normative context of corporate social responsibility (CSR). Building on a global data set of over 8,600 CSR-related norm breaches, directed against 451 firms on the Fortune ranking reputation over the 2006-2009 period, the authors find empirical support for the idea that reputation damage is not really driven by the severity and novelty of the allegation, but by the type of source reporting the issue as well as the credibility of this source. Hence, these results lend some support to a socially constructed view of reputational damage, in which being portrayed by prominent actors as deviating from the norm is more important than the actual deviation from the goal of the norm itself. [ABSTRACT FROM AUTHOR]

Grounded in stakeholder theory and a resource-based view of the firm, this longitudinal research demonstrates the evolution of corporate social responsibility (CSR) and firm reputation over time. Drawing on a 5-year sample of 285 major U.S. firms obtained from the KLD database and Fortune's Most Admired Companies, we find that the proposed dynamic relationship predicts evolving stakeholder expectations to incite organizations to improve their social performance to earn reputational benefits. Contrary to the often labeled stickiness of reputation, we find a "Red Queen" effect that supports reputation as a dynamic construct where the change in CSR does predict a change in corporate reputation. Similarly, we find that the change in reputation over time varies by industry, being most pronounced for manufacturing. From a practical perspective, this relationship across time may incite managers to create sustainable competitive advantage by continuously investing in doing good to reap the benefits of looking good and looking even better with time. [ABSTRACT FROM AUTHOR]

Propensity score matching is a common tool for adjusting for observed confounding in observational studies, but is known to have limitations in the presence of unmeasured confounding. In many settings, researchers are confronted with spatially-indexed data where the relative locations of the observational units may serve as a useful proxy for unmeasured confounding that varies according to a spatial pattern. We develop a new method, termed distance adjusted propensity score matching (DAPSm) that incorporates information on units' spatial proximity into a propensity score matching procedure. We show that DAPSm can adjust for both observed and some forms of unobserved confounding and evaluate its performance relative to several other reasonable alternatives for incorporating spatial information into propensity score adjustment. The method is motivated by and applied to a comparative effectiveness investigation of power plant emission reduction technologies designed to reduce population exposure to ambient ozone pollution. Ultimately, DAPSm provides a framework for augmenting a "standard" propensity score analysis with information on spatial proximity and provides a transparent and principled way to assess the relative trade-offs of prioritizing observed confounding adjustment versus spatial proximity adjustment. [ABSTRACT FROM AUTHOR]

Importance: The US Environmental Protection Agency is required to reexamine its National Ambient Air Quality Standards (NAAQS) every 5 years, but evidence of mortality risk is lacking at air pollution levels below the current daily NAAQS in unmonitored areas and for sensitive subgroups.Objective: To estimate the association between short-term exposures to ambient fine particulate matter (PM2.5) and ozone, and at levels below the current daily NAAQS, and mortality in the continental United States.Design, Setting, and Participants: Case-crossover design and conditional logistic regression to estimate the association between short-term exposures to PM2.5 and ozone (mean of daily exposure on the same day of death and 1 day prior) and mortality in 2-pollutant models. The study included the entire Medicare population from January 1, 2000, to December 31, 2012, residing in 39 182 zip codes.Exposures: Daily PM2.5 and ozone levels in a 1-km × 1-km grid were estimated using published and validated air pollution prediction models based on land use, chemical transport modeling, and satellite remote sensing data. From these gridded exposures, daily exposures were calculated for every zip code in the United States. Warm-season ozone was defined as ozone levels for the months April to September of each year.Main Outcomes and Measures: All-cause mortality in the entire Medicare population from 2000 to 2012.Results: During the study period, there were 22 433 862 million case days and 76 143 209 control days. Of all case and control days, 93.6% had PM2.5 levels below 25 μg/m3, during which 95.2% of deaths occurred (21 353 817 of 22 433 862), and 91.1% of days had ozone levels below 60 parts per billion, during which 93.4% of deaths occurred (20 955 387 of 22 433 862). The baseline daily mortality rates were 137.33 and 129.44 (per 1 million persons at risk per day) for the entire year and for the warm season, respectively. Each short-term increase of 10 μg/m3 in PM2.5 (adjusted by ozone) and 10 parts per billion (10-9) in warm-season ozone (adjusted by PM2.5) were statistically significantly associated with a relative increase of 1.05% (95% CI, 0.95%-1.15%) and 0.51% (95% CI, 0.41%-0.61%) in daily mortality rate, respectively. Absolute risk differences in daily mortality rate were 1.42 (95% CI, 1.29-1.56) and 0.66 (95% CI, 0.53-0.78) per 1 million persons at risk per day. There was no evidence of a threshold in the exposure-response relationship.Conclusions and Relevance: In the US Medicare population from 2000 to 2012, short-term exposures to PM2.5 and warm-season ozone were significantly associated with increased risk of mortality. This risk occurred at levels below current national air quality standards, suggesting that these standards may need to be reevaluated. [ABSTRACT FROM AUTHOR]

Background: Whether hospitals with the highest risk-standardized readmission rates (RSRRs) subsequently experienced the greatest improvement after passage of the Medicare Hospital Readmissions Reduction Program (HRRP) is unknown.Objective: To evaluate whether passage of the HRRP was followed by acceleration in improvement in 30-day RSRRs after hospitalizations for acute myocardial infarction (AMI), congestive heart failure (CHF), or pneumonia and whether the lowest-performing hospitals had faster acceleration in improvement after passage of the law than hospitals that were already performing well.Design: Pre-post analysis stratified by hospital performance groups.Setting: U.S. acute care hospitals.Patients: 15 170 008 Medicare patients discharged alive from 2000 to 2013.Intervention: Passage of the HRRP.Measurements: 30-day readmission rates after hospitalization for AMI, CHF, or pneumonia for hospitals in the highest-performance (0% penalty), average-performance (>0% and <0.50% penalty), low-performance (≥0.50% and <0.99% penalty), and lowest-performance (≥0.99% penalty) groups.Results: Of 2868 hospitals serving 1 109 530 Medicare discharges annually, 30.1% were highest performers, 44.0% were average performers, 16.8% were low performers, and 9.0% were lowest performers. After controlling for prelaw trends, an additional 67.6 (95% CI, 66.6 to 68.4), 74.8 (CI, 74.0 to 75.4), 85.4 (CI, 84.0 to 86.8), and 95.1 (CI, 92.6 to 97.5) readmissions per 10 000 discharges were found to have been averted per year in the highest-, average-, low-, and lowest-performance groups, respectively, after passage of the law.Limitation: Inability to distinguish between improvement caused by the magnitude of the penalty or by different levels of health improvement in different patient populations.Conclusion: After passage of the HRRP, 30-day RSRRs for myocardial infarction, heart failure, and pneumonia decreased more rapidly than before the law's passage. Improvement was most marked for hospitals with the lowest prelaw performance.Primary Funding Source: National Institutes of Health. [ABSTRACT FROM AUTHOR]

In this paper, we derive the asymptotic statistical properties of a class of generalized discrepancies introduced by Cui and Freeden (SIAM J. Sci. Comput., 1997) to test equidistribution on the sphere. We show that they have highly desirable properties and encompass several statistics already proposed in the literature. In particular, it turns out that the limiting distribution is an (infinite) weighted sum of chi-squared random variables. Issues concerning the approximation of this distribution are considered in detail and explicit bounds for the approximation error are given. The statistics are then applied to assess the equidistribution of Hammersley low discrepancy sequences on the sphere and the uniformity of a dataset concerning magnetic orientations. [ABSTRACT FROM AUTHOR]

We consider scenario approximation of problems given by the optimization of a function over a constraint that is too difficult to be handled but can be efficiently approximated by a finite collection of constraints corresponding to alternative scenarios. The covered programs include min-max games, and semi-infinite, robust and chance-constrained programming problems. We prove convergence of the solutions of the approximated programs to the given ones, using mainly epigraphical convergence, a kind of variational convergence that has demonstrated to be a valuable tool in optimization problems. [ABSTRACT FROM AUTHOR]

The analytic hierarchy process (AHP) is a decision-making procedure widely used in management for establishing priorities in multicriteria decision problems. Underlying the AHP is the theory of ratio-scale measures developed in psychophysics since the middle of the last century. It is, however, well known that classical ratio-scaling approaches have several problems. We reconsider the AHP in the light of the modern theory of measurement based on the so-called separable representations recently axiomatized in mathematical psychology. We provide various theoretical and empirical results on the extent to which the AHP can be considered a reliable decision-making procedure in terms of the modern theory of subjective measurement. [ABSTRACT FROM AUTHOR]

The article evaluates the general-purpose scripting programming language Python.