Epidemiology

This paper combines considerations from ethics, medicine and public health policy to articulate and defend a systematic case for mask wearing mandates. The paper argues for two main claims of general interest in favour of these mandates. First, mask wearing mandates provide a more effective, just and fair way to tackle the ongoing COVID-19 pandemic than policy alternatives such as laissez-faire approaches, mask wearing recommendations and physical distancing measures. And second, the proffered objections against mask wearing mandates may justify some (...) exemptions for specific categories of individuals, but do not cast doubt on the justifiability of these mandates. Hence, unless some novel decisive objections are put forward against mask wearing mandates, governments should adopt such mandates. (shrink)

The Covid-19 global pandemic had a profound effect on scientific practice. During this time, officials crucially relied on the work done by modellers. This raises novel questions for the philosophy of science. Here, I investigate the possibility of ‘natural models’ in predicting the virus’ trajectory for epidemiological purposes. I argue that to the extent that these can be consideredscientific models, they support the possibility of a continuum from scientific models to natural models differing in artifactual commitment. In making my case, (...) I draw from work on model organisms and natural experiments as well as recent work in epidemiology. (shrink)

Should research projects involving epidemiological modelling be subject to ethical scrutiny and peer review prior to publication? Mathematical modelling had considerable impacts during the COVID-19 pandemic, leading to social distancing and lockdowns. Imperial College conducted research leading to the website publication of a paper, Report 9, on non-pharmaceutical interventions (NPIs) and COVID-19 mortality demand dated 16th March 2020, arguing for a Government policy of non-pharmaceutical interventions (e.g. lockdowns, social distancing, mask wearing, working from home, furlough, school closures, reduced family interaction (...) etc.) to counter COVID 19. Enquiries and Freedom of Information requests to the institution indicate that there was no formal ethical committee review of this specific research, nor was there any peer review prior to their online publication of Report 9. This paper considers the duties placed upon researchers, institutions and research funders under the UK ‘Concordat to Support Research Integrity’ (CSRI), across various bioethical domains, and whether ethical committee scrutiny should be required for this research. (shrink)

A large part of the contemporary phenomenology of medicine has been devoted to accounts of health and illness, arguing that they contribute to the improvement of health care. Less focus has been paid to the issue of prevention of disease and the associated difficulty of adhering to health-promoting behaviours, which is arguably of equal importance. This article offers a phenomenological account of this disease prevention, focusing on how we—as embodied beings—engage with health-promoting behaviours. It specifically considers how we engage with (...) oral hygiene regimens to prevent periodontitis and why we are not good at it. The article suggests that poor adherence to health-promoting behaviours can be explained with reference to the concept of the absent body, because prevention of disease is generally concerned with pre-symptomatic illness experience. The final section contains a discussion of some strategies for the improvement of disease prevention based on this viewpoint. (shrink)

Research in pharmacogenomics and precision medicine has recently introduced the concept of Polygenic Scores (PGSs), namely, indexes that aggregate the effects that many genetic variants are predicted to have on individual disease risk. The popularity of PGSs is increasing rapidly, but surprisingly little attention has been paid to the idealisations they make about phenotypic development. Indeed, PGSs rely on quantitative genetics models and methods, which involve considerable theoretical assumptions that have been questioned on various grounds. This comes with epistemological and (...) ethical concerns about the use of PGSs in clinical decision-making. In this paper, I investigate to what extent idealisations in genetics models can impact the data gathering and clinical interpretation of genomics findings, particularly the calculation and predictive accuracy of PGSs. Although idealisations are considered ineliminable components of scientific models, they may be legitimate or not depending on the epistemic aims of a model. I thus analyse how various idealisations have been introduced in classical models and progressively readapted throughout the history of genetic theorising. Notably, this process involved important changes in the epistemic purpose of such idealisations, which raises the question of whether they are legitimate in the context of contemporary genomics. (shrink)

Infectious diseases figure prominently as (counter)examples in debates on how to conceptualize “disease.” But crucial epidemiological distinctions are often not heeded in the debate, and pathological and clinical perspectives focusing on individual patients are favored at the expense of perspectives from epidemiology focusing on populations. In clarifying epidemiological concepts, this paper highlights the distinct contributions infectious disease epidemiology can make to the conception of “disease,” and the fact that this is at least tacitly recognized by medical personnel and philosophers. Crucially, (...) infectious disease epidemiology can help elucidate how carrying and transmitting infectious, communicable entities is a disease, even if the carriers themselves are not directly affected by symptoms detrimental to them. (shrink)

Most of the epidemiological models of the Covid-19 pandemic contain the reproduction number as a parameter. In this article we focus on some shortcomings regarding its role in driving health policies and political decisions. First, we summarize what R is and what it is used for. Second, we introduce a three-question matrix for the evaluation of any construct or parameter within a model. We then review the main literature about R to highlight some of its shortcomings and apply to them (...) our three-question matrix. Finally, we argue that these shortcomings are important for an epistemic and political evaluation of R. (shrink)

This essay charts a conceptual history of “planetary health,” which holds that population health and the continuity of human civilization depend on the integrity—the health—of the Earth’s life-support systems. It seeks to identify settler colonial and imperial genealogies of this distinctly ecological approach to human population health and flourishing, an assemblage of systems theory and planetary thinking as well as developments in environmental sciences and theories of sustainable development. Planetary health may be seen as a “third wave” of disease ecology, (...) an emergent resource for more complex, naturalistic, and scaled-up modeling in epidemiology—the latest installment in the search for multifactorial, relational, and integrated explanations of patterns of disease. The approach required a new planetary imaginary—emerging from a series of Earth-encompassing crises, such as global heating—which enlarged and systematized ecological configurations of human health and illness. In tracing and critically situating this conceptual history, the authors offer a partial perspective on what may yet become planetary health, drawing attention to some other conceptual routes that may be taken. (shrink)

In this paper, we analyse the relation between the use of environmental data in contemporary health sciences and related conceptualisations and operationalisations of the notion of environment. We consider three case studies that exemplify a different selection of environmental data and mode of data integration in data-intensive epidemiology. We argue that the diversification of data sources, their increase in scale and scope, and the application of novel analytic tools have brought about three significant conceptual shifts. First, we discuss the EXPOsOMICS (...) project, an attempt to integrate genomic and environmental data which suggests a reframing of the boundaries between external and internal environments. Second, we explore the MEDMI platform, whose efforts to combine health, environmental and climate data instantiate a reframing and expansion of environmental exposure. Third, we illustrate how extracting epidemiological insights from extensive social data collected by the CIDACS institute yields innovative attributions of causal power to environmental factors. Identifying these shifts highlights the benefits and opportunities of new environmental data, as well as the challenges that such tools bring to understanding and fostering health. It also emphasises the constraints that data selection and accessibility pose to scientific imagination, including how researchers frame key concepts in health-related research. (shrink)

ABSTRACT:What more can be said about COVID-19 and the social determinants of health? This article describes neglected perspectives that derive from the history of social epidemiology, a field that identifies the social etiology of disease and variations in disease incidence among people differentially located in the social structure. The "discovery" of social determinants of diseases like COVID-19 is nothing new for epidemiology: debate over how to analyze structural determinants versus individual-level risk factors persisted throughout the 19th and 20th centuries. By (...) the late 20th century, research had highlighted fundamental causes of health disparities, such as social conditions and structural racism; these are structural factors, embedded in the social fabric of life. Measurement of structured inequalities within systems faces complex and challenging problems, as research aims to better account for these lived realities at different levels of analysis and as multiple factors merge to influence outcomes (revealed in intersectionality theory). At each of these intersections, there are opportunities for bioethicists to consider their ethical implications. It is imperative to understand the social and ethical roots of our present conversations about health inequalities, in order to partner intelligently with researchers on the forefront of advocating for change. (shrink)

Epidemiology is a field torn between practices of surveillance and methods of analysis. Since the onset of COVID-19, epidemiological expertise has been mostly identified with the first, as dashboards of case and mortality rates took centre stage. However, since its establishment as an academic field in the early 20th century, epidemiology’s methods have always impacted on how diseases are classified, how knowledge is collected, and what kind of knowledge was considered worth keeping and analysing. Recent advances in digital epidemiology, this (...) article argues, are not just a quantitative expansion of epidemiology’s scope, but a qualitative extension of its analytical traditions. Digital epidemiology is enabled by deep and digital phenotyping, the large-scale re-purposing of any data scraped from the digital exhaust of human behaviour and social interaction. This technological innovation is in need of critical examination, as it poses a significant epistemic shift to the production of pathological knowledge. This article offers a critical revision of the key literature in this budding field to underline the extent to which digital epidemiology is envisioned to redefine the classification and understanding of disease from the ground up. Utilising analytical tools from science and technology studies, the article demonstrates the disruptive expectations built into this expansion of epidemiological surveillance. Given the sweeping claims and the radical visions articulated in the field, the article develops a tentative critique of what I call a fantasy of pathological omniscience; a vision of how data-driven engineering seeks to capture and resolve illness in the world, past, present and future. (shrink)

During the Covid-19 pandemic, public policy was not driven by findings from public health research, but by politicians’ desire to pursue their own interests. The media and politicians inflamed mass hysteria and then imposed ill-considered lockdowns to “solve” the problem. Lockdowns not only failed to protect those at risk from the virus, but also caused enormous collateral damage. Public choice theory helps explaining this decision-making. -/- .

Bioethics, Volume 36, Issue 6, Page 687-698, July 2022.

To clarify the proper role of values in science, focusing on controversial expert responses to Covid-19, this article examines the status of (in)convenient hypotheses. Polarizing cases like health experts downplaying mask efficacy to save resources for healthcare workers, or scientists dismissing “accidental lab leak” hypotheses in view of potential xenophobia, plausibly involve modifying evidential standards for (in)convenient claims. Societies could accept that scientists handle (in)convenient claims just like nonscientists, and give experts less political power. Or societies could hold scientists to (...) a higher bar, by expecting them not to modify evidential standards to avoid costs only incidentally tied to error. (shrink)

Quantitative assessments of when infectious disease outbreaks end are crucial, as resources targeted towards outbreak responses typically remain in place until outbreaks are declared over. Recent improvements and innovations in mathematical approaches for determining when outbreaks end provide public health authorities with more confidence when making end-of-outbreak declarations. Although quantitative analyses of outbreaks have a long history, more complex mathematical and statistical methodologies for analysing outbreak data were developed early in the 20th century and continue to be refined. Historically, such (...) methodologies focused primarily on factors affecting the early and middle phases of an outbreak, with less attention given to determining how and when outbreaks end. This review discusses mathematical modelling methods from the last 20 years that have been developed for determining the ends of infectious disease outbreaks, and considers factors that affect the accuracy of such determinations. When disease surveillance systems provide timely and representative data to inform models, the timings of end-of-outbreak declarations can be fine-tuned to allow outbreaks to be declared over quickly and with a low risk of being incorrect. Premature declarations that outbreaks are over can undermine earlier achievements in disease control and may result in a resurgence of cases, but unnecessary delays in declaring outbreaks over can cause significant economic and social harm. Appropriate declarations that balance the benefits of relaxing control measures against the risk of a surge in cases allow public health resources to be conserved (and economic and social pressures to be reduced) while limiting the potential for additional transmission. (shrink)

COVID-19 began in China in December 2019. As of January 2021, over a hundred million instances had been reported worldwide, leaving a deep socio-economic impact globally. Current investigation studies determined that artificial intelligence (AI) can play a key role in reducing the effect of the virus spread. The prediction of COVID-19 incidence in different countries and territories is important because it serves as a guide for governments, healthcare providers, and the general public in developing management strategies to battle the disease. (...) The prediction proved beneficial in the early months of 2020 to alert nations and territories in danger of an outbreak, allowing them to take preventative measures. Despite the fact that the COVID-19 outbreak has expanded to practically every location on the planet, the prediction has value in terms of monitoring the intensity of the spread and recovery, as well as determining the likelihood of a sequel or tertiary epidemic. COVID-19, like influenza, could become a seasonal or recurring epidemic in the future. COVID-19 activity must so be predicted and monitored now and in the future. (shrink)

Public funders of health research have been widely criticized on the grounds that their allocations of funding for disease-specific research do not reflect the relative burdens imposed by different diseases. For example, the US National Institutes of Health spends a much greater fraction of its budget on HIV/AIDS research and a much smaller fraction on migraine research than their relative contribution to the US burden of disease would suggest. Implicit in this criticism is a normative claim: Insofar as the scientific (...) opportunities are equal, each patient merits research into their condition proportional to the burden of disease for which that condition is responsible. This claim—the proportional view—is widely accepted but has never been fully specified or defended. In this paper, I explain what is required to specify the view, attempt to do so in the most charitable way, and then critically evaluate its normative underpinnings. I conclude that a severity-weighted proportional view is defensible. I close by drawing out five key lessons of my analysis for health research priority-setting. (shrink)

Here, an epidemiological model considering pro and anti-vaccination groups is proposed and analyzed. In this model, susceptible individuals can migrate between these two groups due to the influence of false and true news about safety and efficacy of vaccines. From this model, written as a set of three ordinary differential equations, analytical expressions for the disease-free steady state, the endemic steady state, and the basic reproduction number are derived. It is analytically shown that low vaccination rate and no influx to (...) the pro-vaccination group have similar impacts on the long-term amount of infected individuals. Numerical simulations are performed with parameter values of the COVID-19 pandemic to illustrate the analytical results. The possible relevance of this work is discussed from a public health perspective. (shrink)

Evidence-based medicine is a clinical decision making framework which makes claims about what physicians ought to do. Though heralded as the cutting edge of medical science evidence-based medicine is a value laden normative theory which implicitly depends on substantive views regarding what is morally good or right. In this paper, I provide an ethical analysis of evidence-based medicine. I consider its normative underpinnings in three ethical theories: utilitarianism, Kantian deontology, and virtue ethics. In the face of uncertainty, evidence-based medicine endorses (...) expected utility theory using the best available evidence in order to avoid doing more harm than good. In accordance with the Kantian respect for individuals as ends in themselves, evidence-based medicine calls for integrating the values and preferences of the patient. De-emphasizing intuition, clinical expertise, and pathophysiologic rationale emphasizes the need for the intellectual virtues of curiosity, critical thinking, and courage. Evidence-based medicine is a successful clinical practice that can be morally justified by all three major ethical theories. Although its focus on maximizing good health outcomes and integrating respect for individual patients has been emphasized, the role of the intellectual virtues in evidence- based medicine remains highly under-explored. (shrink)

The ethical question is whether university mask mandates should be relaxed. I argue that the use of face masks by healthy individuals has uncertain benefits, which potential harms may outweigh, and should therefore be voluntary. Systematic reviews by the World Health Organization (WHO) and Cochrane Acute Respiratory Infections concluded that the use of face masks by healthy individuals in the community lacks effectiveness in reducing viral transmission based on moderate-quality evidence. The only two randomized controlled trials of face masks published (...) during the pandemic found little to no benefit. Without high-quality evidence, it is difficult to justify a requirement rather than a recommendation. Notwithstanding, one might argue that the precautionary principle justifies mask mandates. If the precautionary principle can justify implementing mask mandates due to the risk of forgoing possible benefit, then it might also be able to justify not implementing mask mandates due to the risk of potential harm caused by the intervention. It is commonly thought that there is little to lose from the use of face masks, but this is not necessarily true. It is possible that masks have done more harm than good. (shrink)

Lockdowns, or modern quarantines, involve the use of novel restrictive non-pharmaceutical interventions (NPIs) to suppress the transmission of COVID-19. In this paper, I aim to critically analyze the emerging history and philosophy of lockdowns, with an emphasis on the communication of health evidence and risk for informing policy decisions. I draw a distinction between evidence-based and modeling-based decision-making. I argue that using the normative framework of evidence-based medicine would have recommended against the use of lockdowns. I first review the World (...) Health Organization’s evidence-based pandemic preparedness plans for respiratory viruses. I then provide a very brief history of COVID-19 modeling, which was cited as justification for the use of lockdowns in the U.K., the U.S., and much of the world. I focus on the so-called Imperial College model designed by Neil Ferguson et al. as well as the so-called Oxford model designed by José Lourenço et al. I analyze the evidence-based pandemic response known as ‘mitigation’, and I compare it with Ferguson et al.’s experimental strategy known as ‘suppression’. I summarize the strengths and weaknesses of these strategies based on their diametric aims and each model’s parametric assumptions. Based on my critical analysis of the suppression strategy, I attempt to expose what has been called the ‘logic of lockdowns’, which Sunetra Gupta of the Oxford model group has suggested is flawed. Finally, I consider Trisha Greenhalgh’s objection to evidence-based policy based on the precautionary principle, and I attempt to offer a response. I conclude with a brief narrative review of the emerging randomized evidence on restrictive NPIs, which seems to support my claim that mitigation was the strategy that would have been recommended by evidence-based medicine. If this is true, then COVID-19 modeling may serve as an important reminder of the enduring lesson of evidence-based medicine: that one should always ‘Trust the Evidence!’ for better health policy. (shrink)

For children, the collateral damage of the COVID-19 pandemic response has been considerable. In this paper, we use the framework of evidence-based medicine to argue that child abuse is another negative side effect of COVID-19 lockdowns. While it was certain that school closures would have profound social and economic costs, it remains uncertain whether they have any effect on COVID-19 transmission. There is emerging evidence that lockdowns significantly worsened child abuse on a global scale. Low-income and middle-income countries are particularly (...) vulnerable to increases in child abuse. The best available external evidence from systematic research during the pandemic demonstrates an increase in the risk of child maltreatment, an increase in child maltreatment hospitalisations and a concerning decrease in official child maltreatment referrals. The paradoxical phenomenon of increased hospitalisations and decreased reports is unlikely to be explained by a genuine decrease in child abuse. We conclude that lockdowns have an unacceptably high risk of negative side effects for children, as evidenced by child abuse, the true extent of which appears to be masked by lockdown-related disruptions to schools and other surveillance systems. The desire for a sense of security may be a tempting bias towards emphasising the resilience of children, but it is ethically problematic to push children towards abuse in the name of public health. It is our view that the collateral damage of prolonged school closures for society’s most vulnerable members is a powerful ethical consideration against any pandemic response which involves their use. (shrink)

During the COVID-19 epidemic, the name of the 19th-century English physician John Snow (1813-1858) has cropped up to a surprising extent, notably in connection with the severe cholera epidemic of 1854 in the district of Golden Square, London. It is repeatedly stated that Snow brought this epidemic of waterborne disease to an end by removing the handle of the Broad Street pump. It is also widely known that this story is a myth. Nonetheless, the Broad Street pump story as told (...) by Snow's close friend Benjamin Ward Richardson remains embedded, partly, it is argued, because of its appeal to areas of the cultural consciousness. In America, Snow and his work on the epidemiology of cholera, including the Broad Street pump story, achieved a serious status which has endured, in one form or another, to the present day. In contrast to Britain, the heroic age of public health in America coincided with the optimism of the bacteriological revolution and higher hopes for medical science. However, this rapidly changing environment exacerbated differences of opinion as to what the small and emergent specialty of epidemiology should look like, what its project was, and where it should be based. Different versions of Snow's persona came to represent basic and often conflicting conceptions of epidemiology and the status (or lack of it) of its practitioners. For many, consciously or otherwise, the removal of the Broad Street pump handle remained an individualistic triumph, a single human intervention which resembled modern medicine in providing a “cure.”. (shrink)

In the last year of the Great War, Italy was also hit by the Spanish flu. The Civic Hospitals faced a deadly disaster with insufficient resources. All the heavy workload fell on the female nursing staff, who were the only ones able ensure the continuity of the hospital services. This study aimed to explore the impact of the influenza on the health of the nurses at the Maggiore Hospital in Milan during the second and third epidemic waves. Historical research was (...) conducted between February and May 2020. Primary sources were retrieved from the historical archives of the Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico and the daily newspaper Corriere della Sera. In the autumn of 1918, the Maggiore Hospital in Milan changed its organization to hospitalise patients affected by the influenza pandemic. Although the hospital managers wanted to protect their healthcare staff from the risks of contagion by means of prophylaxis rules, 388 lay nurses and 80 religious sister nurses were affected by this insidious disease. The second and third waves of the pandemic claimed 25 victims of duty. Remembered for their altruism and spirit of abnegation, the hospital community honoured their sacrifice, and the citizens expressed their gratitude. (shrink)

This article examines how chikungunya virus disease is epidemiologically and politically invisible in Brazil, unlike other diseases related to the Aedes Aegypti mosquito, such as Zika, dengue, and yellow fever. It demonstrates the intricacy of identifying the presence of chikungunya, as its effects are generally materialised in pain, which is difficult to measure and quantify, and thus is invisible to medical and state bureaucracy. As with other chronic diseases, chikungunya transforms identities and social relations among those affected. By analysing the (...) situation in Natal, in Northeast Brazil, and considering epidemics as social, economic, and political narratives as well as biomedical phenomena, the article asks how chikungunya might end when it has not even officially started. (shrink)

Like COVID-19, new infectious disease outbreaks emerge almost annually, and studies predict that this trend will continue due to a variety of factors, including an aging population, ease of travel, and globalization of the economy. In response to episodic public health crises, governments and organizations develop, implement, and enforce policies, procedures, protocols, and programs. The epidemiological triad is both a model of disease causation and fundamentally used to design and deploy such control measures. Here we adapt this model to the (...) workplace setting and use the epidemiological triad to characterize the related ethical challenges in implementing the control measures employers face as a guide for a workplace intervention framework. Through this approach, our aim is to show how an integrated ethical framework, grounded in epidemiological principles, has important implications for how we categorize, understand, and resolve the difficult decisions that emerge in the workplace under pandemic conditions. (shrink)

Pre-existing medical conditions and co-infections are common to all human populations, although the natures of the pre-existing conditions and the types of co-infections vary. For these reasons, among others, the arrival of a highly infectious pathogenic agent may differentially affect the disease burden in different sub-populations, as a function of varying combinations of endemic disease, chronic disease, genetic or epigenetic vulnerabilities, compromised immunological status, and socially determined risk exposure. The disease burden may also vary considerably by age cohort and socio-economic (...) status. The social consequences of infection, the medical sequelae, and the paths of recovery from infection may also vary, according to these variables. As a result, different sub-populations may have different experiences of a disease process, including when and how an epidemic comes to an end. This essay suggests that historians' engagement with path-breaking biological and medical anthropological research will allow new approaches to understanding disease processes that will enrich the study of epidemics and their endings. It is organized in three sections. The first discusses some general approaches to and assumptions about epidemics that are used by many historians. The second introduces new perspectives on the study of historical epidemics opened up by the medical anthropological concept of syndemic disease interactions and by an engagement with biological scientific perspectives. It briefly discusses two rural epidemics-one of anemia caused by the syndemic interaction of hookworm and malaria co-infections in early 20th-century British Malaya, and the other of rebound infections caused by the loss of acquired immunity to falciparum malaria in mid-20th-century Liberia-that illustrate, respectively, the utility of the concept of syndemic disease interactions and the centrality of immunological status in understanding epidemic outbreaks in these discrete populations. The third section addresses some of the research considerations involved in moving beyond the single pathogen model of epidemic disease. (shrink)

How could the initial, drastic decisions to implement “lockdowns” to control the spread of COVID-19 infections be justifiable, when they were made on the basis of such uncertain evidence? We defend the imposition of lockdowns in some countries by first, and focusing on the UK, looking at the evidence that undergirded the decision, second, arguing that this provided us with sufficient grounds to restrict liberty given the circumstances, and third, defending the use of poorly-empirically-constrained epidemiological models as tools that can (...) legitimately guide public policy. (shrink)

With the limited initial availability of COVID-19 vaccines in the first months of 2021, decision-makers had to determine the order in which different groups were prioritized. Our aim was to find out what normative approaches to the allocation of scarce preventive resources were embedded in the national COVID-19 vaccination schedules. We systematically reviewed and compared prioritization regulations in 27 members of the European Union, the United Kingdom, and Israel. We differentiated between two types of priority categories: groups that have increased (...) infection fatality rate (IFR) compared to the average for the general population and groups chosen because their members experience increased risk of being infected (ROI). Our findings show a clear trend: all researched schedules prioritized criteria referring to IFR (being over 65 years old and coexisting health conditions) over the ROI criteria (eg occupation and housing conditions). This is surprising since, in the context of treatment, it is common and justifiable to adopt different allocation principles (eg introducing a saving more life-year approach or prioritizing younger patients). We discuss how utilitarian, prioritarian, and egalitarian principles can be applied to interpret normative differences between the allocation of curative and preventive interventions. (shrink)

The COVID-19 pandemic has made it especially visible that mortality data are a key component of epidemiological models, being a single indicator that provides information about various health aspects, such as disease prevalence and effectiveness of interventions, and thus enabling predictions on many fronts. In this paper we illustrate the interrelation between facts and values in death statistics, by analyzing the rules for death certification issued by the World Health Organization. We show how the notion of the underlying cause of (...) death can change in view of public health goals. This brings us to a general point about how non-epistemic factors, such as values and goals, are reflected in the choice of different measures in epidemiological models. We finally argue that this analysis is not only relevant from a theoretical point of view but also has important practical consequences. (shrink)

In the ongoing pandemic, death statistics influence people’s feelings and government policy. But when does COVID-19 qualify as the cause of death? As philosophers of medicine interested in conceptual clarification, we address the question by analyzing the World Health Organization’s rules for the certification of death. We show that for COVID-19, WHO rules take into account both facts and values.

BackgroundThe coronavirus disease (COVID-19) treatment must be based on scientific methods such as clinical trials. Trials involving human subjects and those requiring a risk-benefit analysis may occasionally face challenges owing to the time limitations in the pandemic. MethodologyThis study analyses the WHO's International Clinical Trials Registry Platform and clinicaltrials.gov, where most COVID-19 clinical trials are registered, according to ethical criteria including study design, conflicts of interest, enrollment of healthcare workers, study locations, site-, design-, and participant-related issues. The discussion is based (...) on three aspects: the quality of the information to be produced, the relevance to significant health problems, and the creation or evaluation of interventions, policies, or practices that promote individual or public health. ResultsThere were significant differences between the two platforms regarding the investigational medicinal product (IMP), the comparator, ethics committee/institutional review board approval, plan to share individual participant data, study phase, site, IMP, and design-related issues. Conflict of interest, sponsor information, and management of vulnerable groups were the main areas wherein both platforms lacked sufficient information. ConclusionWith this analysis, we aimed to define a minimum set of ethical criteria for clinical trial platforms to obtain standardization between these two platforms. (shrink)

BackgroundThe coronavirus disease (COVID-19) treatment must be based on scientific methods such as clinical trials. Trials involving human subjects and those requiring a risk-benefit analysis may occasionally face challenges owing to the time limitations in the pandemic. MethodologyThis study analyses the WHO's International Clinical Trials Registry Platform and clinicaltrials.gov, where most COVID-19 clinical trials are registered, according to ethical criteria including study design, conflicts of interest, enrollment of healthcare workers, study locations, site-, design-, and participant-related issues. The discussion is based (...) on three aspects: the quality of the information to be produced, the relevance to significant health problems, and the creation or evaluation of interventions, policies, or practices that promote individual or public health. ResultsThere were significant differences between the two platforms regarding the investigational medicinal product (IMP), the comparator, ethics committee/institutional review board approval, plan to share individual participant data, study phase, site, IMP, and design-related issues. Conflict of interest, sponsor information, and management of vulnerable groups were the main areas wherein both platforms lacked sufficient information. ConclusionWith this analysis, we aimed to define a minimum set of ethical criteria for clinical trial platforms to obtain standardization between these two platforms. (shrink)

Defining and identifying causal intervention effects for transmissible infectious disease outcomes is challenging because a treatment – such as a vaccine – given to one individual may affect the infection outcomes of others. Epidemiologists have proposed causal estimands to quantify effects of interventions under contagion using a two-person partnership model. These simple conceptual models have helped researchers develop causal estimands relevant to clinical evaluation of vaccine effects. However, many of these partnership models are formulated under structural assumptions that preclude realistic (...) infectious disease transmission dynamics, limiting their conceptual usefulness in defining and identifying causal treatment effects in empirical intervention trials. In this paper, we propose causal intervention effects in two-person partnerships under arbitrary infectious disease transmission dynamics, and give nonparametric identification results showing how effects can be estimated in empirical trials using time-to-infection or binary outcome data. The key insight is that contagion is a causal phenomenon that induces conditional independencies on infection outcomes that can be exploited for the identification of clinically meaningful causal estimands. These new estimands are compared to existing quantities, and results are illustrated using a realistic simulation of an HIV vaccine trial. (shrink)

This book presents a comprehensive theory of the ethics and political philosophy of public health surveillance based on reciprocal obligations among surveillers, those under surveillance, and others potentially affected by surveillance practices. Public health surveillance aims to identify emerging health trends, population health trends, treatment efficacy, and methods of health promotion--all apparently laudatory goals. Nonetheless, as with anti-terrorism surveillance, public health surveillance raises complex questions about privacy, political liberty, and justice both of and in data use. Individuals and groups can (...) be chilled in their personal lives, stigmatized or threatened, and used for the benefit of others when health information is wrongfully collected or used. Transparency and openness about data use, public involvement in decisions, and just distribution of the benefits of surveillance are core elements in the justification of surveillance practices. Understanding health surveillance practices, the concerns it raises, and how to respond to them is critical not only to ethical and trustworthy but also to publicly acceptable and ultimately sustainable surveillance practices. The book is of interest to scholars and practitioners of the ethics and politics of public health, bioethics, privacy and data technology, and health policy. These issues are ever more pressing in pandemic times, where misinformation can travel quickly and suspicions about disease spread, treatment efficacy, and vaccine safety can have devastating public health effects. (shrink)

Epidemiological models have played a central role in the COVID-19 pandemic, particularly when urgent decisions were required and available evidence was sparse. They have been used to predict the evolution of the disease and to inform policy-making. In this paper, we address two kinds of epidemiological models widely used in the pandemic, namely, compartmental models and agent-based models. After describing their essentials—some real examples are invoked—we discuss their main strengths and weaknesses. Then, on the basis of this analysis, we make (...) a comparison between their respective merits concerning three different goals: prediction, explanation, and intervention. We argue that there are general considerations which could favour any of those sorts of models for obtaining the aforementioned goals. We conclude, however, that preference for particular models must be grounded case-by-case since additional contextual factors, as the peculiarities of the target population and the aims and expectations of policy-makers, cannot be overlooked. (shrink)

Although every emerging infectious disease occurs in a unique context, the behaviour of previous pandemics offers an insight into the medium- and long-term outcomes of the current threat. Where an informative historical analogue exists, epidemiologists and policymakers should consider how the insights of the past can inform current forecasts and responses.

The novel coronavirus disease is regarded as one of the most imminent disease outbreaks which threaten public health on various levels worldwide. Because of the unpredictable outbreak nature and the virus’s pandemic intensity, people are experiencing depression, anxiety, and other strain reactions. The response to prevent and control the new coronavirus pneumonia has reached a crucial point. Therefore, it is essential—for safety and prevention purposes—to promptly predict and forecast the virus outbreak in the course of this troublesome time to have (...) control over its mortality. Recently, deep learning models are playing essential roles in handling time-series data in different applications. This paper presents a comparative study of two deep learning methods to forecast the confirmed cases and death cases of COVID-19. Long short-term memory and gated recurrent unit have been applied on time-series data in three countries: Egypt, Saudi Arabia, and Kuwait, from 1/5/2020 to 6/12/2020. The results show that LSTM has achieved the best performance in confirmed cases in the three countries, and GRU has achieved the best performance in death cases in Egypt and Kuwait. (shrink)

A nonlinear differential equation model is proposed to study the dynamics of HIV/AIDS and its effects on workforce productivity. The disease-free equilibrium point of the model is shown to be locally asymptotically stable when the associated basic reproduction number $$\mathcal{{R}}_{0}$$ is less than unity. The model is also shown to exhibit multiple endemic states for some parameter values when $$\mathcal{{R}}_{0} 1$$. Global asymptotic stability of the disease-free equilibrium is guaranteed only when the fractions of the Susceptible subclass populations are within (...) some bounds. Optimal control analysis of the model revealed that the most cost effective strategy that should be adopted in the fight against HIV/AIDS spread within the workforce is one that seeks to prevent infections and the treatment of infected individuals. (shrink)

Birth cohort studies can be used not only to generate population-level quantitative data, but also to recompose persons. The crux is how we understand data and persons. Recomposition entails scavenging for various (including unrecognised) data. It foregrounds the perspective and subjectivity of survey participants, but without forgetting the partiality and incompleteness of the accounts that it may generate. Although interested in the singularity of individuals, it attends to the historical and relational embeddedness of personhood. It examines the multiple and complex (...) temporalities that suffuse people’s lives, hence departing from linear notions of the life course. It implies involvement, as well as reflexivity, on the part of researchers. It embraces the heterogeneity and transformations over time of scientific archives and the interpretive possibilities, as well as incompleteness, of birth cohort studies data. Interested in the unfolding of lives over time, it also shines light on meaningful biographical moments. (shrink)

Were governments justified in imposing lockdowns to contain the spread of the COVID-19 pandemic? We argue that a convincing answer to this question is to date wanting, by critically analyzing the factual basis of a recent paper, “How Government Leaders Violated Their Epistemic Duties During the SARS-CoV-2 Crisis” (Winsberg et al. 2020). In their paper, Winsberg et al. argue that government leaders did not, at the beginning of the pandemic, meet the epistemic requirements necessitated to impose lockdowns. We focus on (...) Winsberg et al.’s contentions that knowledge about COVID-19 resultant projections were inadequate; that epidemiologists were biased in their estimates of relevant figures; that there was insufficient evidence supporting the efficacy of lockdowns; and that lockdowns cause more harm than good. We argue that none of these claims are sufficiently supported by evidence, thus impairing their case against lockdowns, and leaving open the question of whether lockdowns were justified. (shrink)

We were slightly concerned, upon having read Eric Winsberg, Jason Brennan and Chris Surprenant’s reply to our paper “Were Lockdowns Justified? A Return to the Facts and Evidence”, that they may have fundamentally misunderstood the nature of our argument, so we issue the following clarification, along with a comment on our motivations for writing such a piece, for the interested reader.

Models not only represent but may also influence their targets in important ways. While models’ abilities to influence outcomes has been studied in the context of economic models, often under the label ‘performativity’, we argue that this phenomenon also pertains to epidemiological models, such as those used for forecasting the trajectory of the Covid-19 pandemic. After identifying three ways in which a model by the Covid-19 Response Team at Imperial College London may have influenced scientific advice, policy, and individual responses, (...) we consider the implications of epidemiological models’ performative capacities. We argue, first, that performativity may impair models’ ability to successfully predict the course of an epidemic; but second, that it may provide an additional sense in which these models can be successful, namely by changing the course of an epidemic. (shrink)

Nyansa nye sika na w'akyekyere asie. One critical factor that has contributed to the spread of the virus COVID-19 and resulting illnesses and deaths is both the conceptual and the ethical confusion between the prioritization of individual rights over social duties. The adherence to the belief in the priority of rights over duties has motivated some individuals to refrain from social distancing and, as a result, has placed themselves and other individuals at serious risk to health and life. My argument (...) is that the ethical enjoinder of social duty possesses priority over the ethical value of individual rights especially in times of global crisis. I demonstrate this point by arguing that the concept of individual right is derivative from the concept of social duty and through the argument that the concept of social duty is more efficacious in addressing global threats to human life than is the concept of individual rights. What is needed is an in-depth revision of the moral ordering of rights and duties and a vision of the human being as inherently other directed with duties toward others. I shall examine two specific ethical systems, that of Confucian and African, Akan moral philosophy that exemplify such a revision of the moral order. (shrink)

How is scientific data used to represent phenomena and as evidence for claims about phenomena? In this paper, I propose that a specific type of claims – evidential claims – is involved in data practices to define and restrict the representational and evidential content of a dataset. I present an account of data practices in the epidemiology of the exposome based on the notion of evidential claims, which helps unpack the approaches, assumptions and warrants that connect different stages of research. (...) I identify three different strategies to generate different types of evidential claims in this case. The macro strategy, which individuates the dataset that serves as the initial evidential space for research. The micro strategy, which is used to generate evidential claims about the microscopic and individual component of target phenomena. The association strategy, that uses evidence from the other strategies to identify a dataset as representation of the different levels and relations of exposure and disease. Differentiating between these strategies sheds light on the multi-faceted landscape of biomedical research on environment and health; and the roles of data and evidence in the process of inquiry. (shrink)

The Global Burden of Disease Study is one of the largest-scale research collaborations in global health, producing critical data for researchers, policy-makers, and health workers about more than 350 diseases, injuries, and risk factors. Such an undertaking is, of course, extremely complex from an empirical perspective. But it also raises complex ethical and philosophical questions. In this volume, a group of leading philosophers, economists, epidemiologists, and policy scholars identify and discuss these philosophical questions. Better appreciating the philosophical dimensions of a (...) study like the GBD can make possible a more sophisticated interpretation of its results, and it can improve epidemiological studies in the future, so that they are better suited to produce results that help us to improve global health. (shrink)

In recent decades, social and behavioral scientists have begun to explore how and why human beings thrive or flourish and to consider whether traits indicative of thriving or flourishing may themselves influence physical well-being. This stands in contrast to the historical tendency in these fields to focus on pathology: mental illness, psychological dysfunction, deviant behavior, social problems, and so on. In epidemiology, too, the influence of pathology is seen in a tacit emphasis on risk factors for disease outcomes and for (...) rates of morbidity and mortality, as opposed to, say, protective factors promotive of good health. Methodologically, there is nothing substantial that would prohibit looking at the world... (shrink)