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)

In a scenario characterized by unpredictable developments, such as the recent COVID-19 pandemic, epidemiological models have played a leading part, having been especially widely deployed for forecasting purposes. In this paper, two real-world examples of modeling are examined in support of the proposition that science can convey inconsistent as well as genuinely perspectival representations of the world. Reciprocally inconsistent outcomes are grounded on incompatible assumptions, whereas perspectival outcomes are grounded on compatible assumptions and illuminate different aspects of the (...) same object of interest. In both cases, models should be viewed as expressions of specific assumptions and unconstrained choices on the part of those designing them. The coexistence of a variety of models reflects a primary feature of science, namely its pluralism. It is herein proposed that recent over-exposure to science’s inner workings and disputes such as those pertaining to models, may have led the public to perceive pluralism as a flaw—or more specifically, as disunity or fragmentation, which in turn may have been interpreted as a sign of unreliability. In conclusion, given the inescapability of pluralism, suggestions are offered as to how to counteract distorted perceptions of science, and thereby enhance scientific literacy. (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)

Epidemiological models directly shape policy responses to public health crises. We argue that they also play a less obvious but important role in solving certain coordination problems and social dilemmas that arise during pandemics. This role is both ethically and epistemically valuable. However, it also gives rise to an underappreciated dilemma, as the features that make models good at solving coordination problems are often at odds with the features that make for a good scientific model. We examine (...) and develop this dilemma in the context of the COVID-19 pandemic, and suggest extensions to other domains. (shrink)

There are many tangled normative and technical questions involved in evaluating the quality of software used in epidemiological simulations. In this paper we answer some of these questions and offer practical guidance to practitioners, funders, scientific journals, and consumers of epidemiological research. The heart of our paper is a case study of the Imperial College London covid-19 simulator, set in the context of recent work in epistemology of simulation and philosophy of epidemiology.

The COVID-19 pandemic might be regarded as an example of a risky situation that demands proper action and decision-making in the absence of full information. It is noticeable, however, that scientists have divided into two camps concerning the best way of dealing with the very situation. Some of them have relied on mathematical models and typically proposed restrictive measures, while the others opted for the evidence-based approach and typically recommended more relaxed measures. I argue in this paper that practical (...) possibilities (or impossibilities) have been considerably neglected in those debates and current decisionmaking, especially by the proponents of mathematical epidemiology. (shrink)

We study the SIS and SIRI epidemic models discussing different approaches to compute the thresholds that determine the appearance of an epidemic disease. The stochastic SIS model is a well known mathematical model, studied in several contexts. Here, we present recursively derivations of the dynamic equations for all the moments and we derive the stationary states of the state variables using the moment closure method. We observe that the steady states give a good approximation of the quasi-stationary states of (...) the SIS model. We present the relation between the SIS stochastic model and the contact process introducing creation and annihilation operators. For the spatial stochastic epidemic reinfection model SIRI, where susceptibles S can become infected I, then recover and remain only partial immune against reinfection R, we present the phase transition lines using the mean field and the pair approximation for the moments. We use a scaling argument that allow us to determine analytically an explicit formula for the phase transition lines in pair approximation. (shrink)

The relation between theoretical models and metaphors has been studied since at least the 1950s. The relation between metaphors and mathematical modelling is less well researched. This article takes the media coverage of the foot and mouth modelling exercise in 2001 as an occasion to examine the metaphors of mathematical modelling that were proposed by the UK press during that time to make sense of this new scientific policy tool. One can detect a gradual change in metaphor use by (...) the newspapers from conceptualizing modellers as detectives and models as mapping tools to modellers as soldiers and heroes, to modellers as liars and models as tools to distort the truth. This seems to indicate a shift in reporting from seeing models as a legitimate and "objective" basis used by decision makers to pursue science-based policies towards seeing models as tools used to legitimize increasingly difficult political decisions. (shrink)

We investigate and analyze the dynamics of hepatitis B with various infection phases and multiple routes of transmission. We formulate the model and then fractionalize it using the concept of fractional calculus. For the purpose of fractionalizing, we use the Caputo–Fabrizio operator. Once we develop the model under consideration, existence and uniqueness analysis will be discussed. We use fixed point theory for the existence and uniqueness analysis. We also prove that the model under consideration possesses a bounded and positive solution. (...) We then find the basic reproductive number to perform the steady-state analysis and to show that the fractional-order epidemiological model is locally and globally asymptotically stable under certain conditions. For the local and global analysis, we use linearization, mean value theorem, and fractional Barbalat’s lemma, respectively. Finally, we perform some numerical findings to support the analytical work with the help of graphical representations. (shrink)

Toxic speech inflicts damage to mental and physical health. This process can be chronic or acute, temporary or permanent. Understanding how toxic speech inflicts these harms requires both an account of linguistic practices and, because language is inherently social, tools from epidemiology. This paper explores what we can learn from two epidemiological models: a common source model that emphasizes poisons, and a propagated transmission model that better fits contagions like viruses.

Nineteenth-century medical advances were entwined with a conceptual innovation: the idea that many cases of disease which were previously thought to have diverse causes could be explained by the action of a single kind of cause, for example a certain bacterial or parasitic infestation. The focus of modern epidemiology, however, is on chronic non-communicable diseases, which frequently do not seem to be attributable to any single causal factor. This paper is an effort to resolve the resulting tension. The paper criticises (...) the monocausal model of disease, so successful in the nineteenth century. It also argues that a multifactorial model of disease can only be satisfactory if it amounts to more than a mere rejection of the monocausal model. A third alternative, the contrastive model, is proposed and defended on the grounds that it links the notions of disease and of general explanation, while avoiding the philosophical naiveties and practical difficulties of the monocausal model. (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)

Computer models and simulations have become, since the 1960s, an essential instrument for scientific inquiry and political decision making in several fields, from climate to life and social sciences. Philosophical reflection has mainly focused on the ontological status of the computational modeling, on its epistemological validity and on the research practices it entails. But in computational sciences, the work on models and simulations are only two steps of a longer and richer process where operations on data are as (...) important as, and even more time and energy-consuming than modeling itself. Drawing on two study cases—computational embryology and computational epidemiology—this article contributes to filling the gap by focusing on the operations of producing and re-using data in computational sciences. The different phases of the scientific and artisanal work of modelers include data collection, aggregation, homogenization, assemblage, analysis and visualization. The article deconstructs the ideas that data are self-evident informational aggregates and that data-driven approaches are exempted from theoretical work. More importantly, the paper stresses the fact that data are constructed and theory laden not only in their fabrication, but also in their reusing. (shrink)

Policymakers increasingly draw on scientific methods, including simulation modeling, to justify their decisions. For these purposes, scientists and policymakers face an extensive choice of modeling strategies. Discussing the example of smallpox epidemiology, this paper distinguishes three types of strategies: Massive Simulation Models (MSMs), Abstract Simulation Models (ASMs) and Macro Equation Models (MEMs). By analyzing some of the main smallpox epidemic models proposed in the last 20 years, it discusses how to justify strategy choice with reference to (...) the core characteristics of these respective strategies. First, I argue that MEMs often suffice for policy purposes, and need to be replaced only if they are insufficiently robust. Such robustness results, however, only requires the use of ASMs, not MSMs. Second, I argue that although MSMs have larger potentials than ASMs in various dimensions, they are also more likely to fail—and that in many cases, this probability of failing outweighs their higher potential. In particular, these dimensions include the representation of the relevant target, the accurate measurement of the relevant parameters, the number of parameters included, the number of mechanisms modeled simultaneously, and the ways of dealing with structural uncertainty. While this in no way excludes the prospect that some MSMs provide good justifications for policy decisions, my arguments caution against a general preference for MSMs over ASMs for policy decision purposes in general and vaccination problems in particular. (shrink)

Dissociation is the psychological aspect of stress response. It is an adaptive reaction to a stressor on a short-term basis but can be maladaptive if under- or overutilized and comprises the partitioning of consciousness, ranging from barely to rigidly partitioned. Current means of measuring dissociation focus primarily on DSM-IV-TR Dissociative Disorders and do not sufficiently account for adaptive sub-clinical forms or maladaptive under-dissociating. These measures of pathology are not useful for determining how well-adapted a population is to stress– i.e., how (...) well dissociation is generally put to use. The purpose of this paper is: (1) to analyze the general population and university student studies that have been conducted using current epidemiological methods of assessing dissociation in sub-clinical populations; and (2) to compare and contrast anthropological and epidemiological models of dissociation. An alternative method for assessing dissociation that synthesizes these two views is proposed. (shrink)

Over the last few decades, the Internet has brought about a myriad of benefits to almost every aspect of our daily lives. However, malware attacks have also widely proliferated, mainly aiming at legitimate network users, resulting in millions of dollars in damages if proper protection and response measures are not settled and enforced. In this context, the paper at hand proposes MalSEIRS, a novel dynamic model, to predict malware distribution in a network based on the SEIRS epidemiological model. As (...) a result, the time-dependent rates of infection, recovery, and loss of immunity enable us to capture the complex dynamism of malware spreading behavior, which is influenced by a variety of external circumstances. In addition, we describe both offensive and defensive techniques, based on the proposed MalSEIRS model, through extensive experimentation, as well as disclosing real-life malware campaigns that can be better understood by using the suggested model. (shrink)

In this paper I apply the epidemiological model of the spread of beliefs and how they become cultural representations to the field of fallacies. The model suggests that beliefs tend to replicate as a virus does in a potential epidemic, and those strains that are dominant in a given socio-cultural sphere become cultural representations. My ultimate aim is to denounce the fact that some presumptive argumentation schemes are widely applied as definitive arguments, but turn out to be instances of (...) common and traditional fallacies. Moreover, some such fallacies have managed to colonise the human mind and become cultural representations in society today. Adopting the approach I advocate here, we could say that the fallacy has become a belief, which has then managed to replicate like a virus, and finally the fallacy has become a cultural representation. One of the great harms that results from this process is that it is very difficult to open up effective lines of argument that expose the fallacious nature of these new and perverse cultural representations. (shrink)

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)

In the last decades, Systems Biology (including cancer research) has been driven by technology, statistical modelling and bioinformatics. In this paper we try to bring biological and philosophical thinking back. We thus aim at making diferent traditions of thought compatible: (a) causality in epidemiology and in philosophical theorizing—notably, the “sufcient-component-cause framework” and the “mark transmission” approach; (b) new acquisitions about disease pathogenesis, e.g. the “branched model” in cancer, and the role of biomarkers in this process; (c) the burgeoning of omics (...) research, with a large number of “signals” and of associations that need to be interpreted. In the paper we summarize frst the current views on carcinogenesis, and then explore the relevance of current philosophical interpretations of “cancer causes”. We try to ofer a unifying framework to incorporate biomarkers and omic data into causal models, referring to a position called “evidential pluralism”. According to this view, causal reasoning is based on both “evidence of diference-making” (e.g. associations) and on “evidence of underlying biological mechanisms”. We conceptualize the way scientists detect and trace signals in terms of information transmission, which is a generalization of the mark transmission theory developed by philosopher Wesley Salmon. Our approach is capable of helping us conceptualize how heterogeneous factors such as micro and macro-biological and psycho-social—are causally linked. This is important not only to understand cancer etiology, but also to design public health policies that target the right causal factors at the macro-level. (shrink)

Recent approaches to the social epistemology of belief formation have appealed to an epidemiological model, on which the mechanisms explaining how we form beliefs from our society or community along the lines of infectious disease. More specifically, Alvin Goldman (2001) proposes an etiology of (social) belief along the lines of an epistemological epidemiology. On this “contagion model,” beliefs are construed as diseases that infect people via some socio-epistemic community. This paper reconsiders Goldman’s epidemiological approach in terms of epistemic (...) trust. By focusing on beliefs as diseases, Goldman misconstrues and underestimates the central role that epistemic trust plays in their formation (maintenance, revision, etc.). I suggest that we put trust, accordingly, as the center of an epidemiological model of social doxology—epistemic trust, rather than beliefs, is the disease with which one is infected. So, contra Goldman, beliefs themselves aren’t the disease—they are symptoms. Trust, on this approach, can be viewed as a pathology. This point connects Annette Baier’s (1994) work on moral trust—taking a cue from her “pathologies of trust.” The real pathology centered in social doxology is the epistemic trust manifested by those beliefs. Accordingly, I shall explore (and tentatively defend) an epidemiological model for such “pathological” epistemic trust inspired by Baier’s work on moral trust, one which can more adequately account for the infectious epistemic trust at work in social belief formation. (shrink)

This paper primarily argues that Epidemiology is Ecosystem Science. It will not only explore this notion in detail but will also relate it to the argument that Classical Chinese Medicine was/is Ecosystem Science. Ecosystem Science and Ecosystem Science share these characteristics: they do not subscribe to the monogenic conception of disease; they involve multi variables; the model of causality presupposed is multi-factorial as well as non-linear.

Corresponding Author, Health Policy & Ethics Fellow, Chronic Disease Prevention & Control Research Center, Department of Medicine, Baylor College of Medicine, 1709 Dryden, Suite 1025, Houston, TX 77030, USA. Tel.: 713.798.5482; Fax: 713 798 3990; Email: danielg{at}bcm.edu ' + u + '@' + d + ' '//--> . Abstract This article defends a broad model of public health, one that specifically addresses the social epidemiologic research suggesting that social conditions are primary determinants of health. The article proceeds by critiquing one (...) of the strongest arguments in favor of a narrow model, advanced by Mark Rothstein. The critique sets up the argument that a model of public health that does not address what actually causes health and disease is unlikely to improve public health. Assessing the substantial evidence regarding the social determinants of health, the article engages the policy paradox that precludes utopian prescriptions but demands more than mere expedience. CiteULike Connotea Del.icio.us What's this? (shrink)

What makes beliefs thrive? In this paper, we model the dissemination of bona fide science versus pseudoscience, making use of Dan Sperber's epidemiological model of representations. Drawing on cognitive research on the roots of irrational beliefs and the institutional arrangement of science, we explain the dissemination of beliefs in terms of their salience to human cognition and their ability to adapt to specific cultural ecologies. By contrasting the cultural development of science and pseudoscience along a number of dimensions, we (...) gain a better understanding of their underlying epistemic differences. Pseudoscience can achieve widespread acceptance by tapping into evolved cognitive mechanisms, thus sacrificing intellectual integrity for intuitive appeal. Science, by contrast, defies those deeply held intuitions precisely because it is institutionally arranged to track objective patterns in the world, and the world does not care much about our intuitions. In light of these differences, we discuss the degree of openness or resilience to conceptual change (evidence and reason), and the divergent ways in which science and pseudoscience can achieve cultural “success”. (shrink)

Cultural epidemiology is a theoretical framework that enables historical studies to be informed by cognitive science. It incorporates insights from evolutionary psychology (viz. cultural evolution is constrained by universal properties of the human cognitive apparatus that result from biological evolution) and from Darwinian models of cultural evolution (viz. population thinking: cultural phenomena are distributions of resembling items among a community and its habitat). Its research program includes the study of the multiple cognitive mechanisms that cause the distribution, on a (...) cultural scale, of representations and material cultural items. By a detailed analysis of the social cognitive causal chain that occurred in the past, one can find out – and specify – which are the factors of attraction that account for cultural stability as well as historical cultural change. After reviewing recent research and developments in cognitive history, I present the concept of cultural attractor and explain why cultural attractors are historically variable. In doing so, I emphasize the role of historically constituted cognitive mechanisms, which account for much of historical cultural developments. I argue that the framework of cultural epidemiology can better account for these important historical phenomena than either evolutionary psychology accounts of culture or dual inheritance theory. I conclude that describing and explaining the history of cultural attractors is a good research goal for historians. (shrink)

This chapter contains sections titled: Introduction Investing in Science: Child Health and U.S. Medicine in the Twentieth Century The Impact of Specific Medical Interventions The Changing Definition of ID The “Flynn Effect” and the Impact of Improved Public Health Conclusion References.

This chapter contains sections titled: Introduction Investing in Science: Child Health and U.S. Medicine in the Twentieth Century The Impact of Specific Medical Interventions The Changing Definition of ID The “Flynn Effect” and the Impact of Improved Public Health Conclusion References.

Agent-based models (ABMs) are one of the main sources of evidence for decisions regarding mitigation and suppression measures against the spread of SARS-CoV-2. These models have not been previously included in the hierarchy of evidence put forth by the evidence-based medicine movement, which prioritizes those research methods that deliver results less susceptible to the risk of confounding. We point out the need to assess the quality of evidence delivered by ABMs and ask the question of what is the (...) risk that assumptions entertained in ABMs do not include all the key factors and make model predictions susceptible to the problem of confounding. (shrink)

Epidemiological models of the transmission of SARS-CoV-2 played an important role in guiding the decisions of policy-makers during the pandemic. Such models provide output projections, in the form of time -series of infections, hospitalisations, and deaths, under various different parameter and scenario assumptions. In this paper I caution against handling these outputs uncritically: raw model-outputs should not be presented as direct projections in contexts where modelling results are required to support policy -decisions. I argue that model uncertainty (...) should be handled and communicated transparently. Drawing on methods used by climate scientists in the fifth IPCC report I suggest that this can be done by: attaching confidence judgements to projections based on model results; being transparent about how multi-model ensembles are supposed to deal with such uncertainty; and using expert judgement to ‘translate’ model-outputs into projections about the actual world. In a slogan: tell me what you think (and why), not (just) what your models say. I then diffuse the worry that this approach infects model-based policy advice with some undesirably subjective elements, and explore how my discussion fares if one thinks the role of a scientific advisor is to prompt action, rather than communicate information. (shrink)

The epidemiologist Bradford Hill famously argued that in epidemiology, specificity of association (roughly, the fact that an environmental or behavioral risk factor is associated with just one or at most a few medical outcomes) is strong evidence of causation. Prominent epidemiologists have dismissed Hill’s claim on the ground that it relies on a dubious `one-cause one effect’ model of disease causation. The paper examines this methodological controversy, and argues that specificity considerations do have a useful role to play in causal (...) inference in epidemiology. More precisely, I argue that specificity considerations help solve a pervasive inferential problem in contemporary epidemiology: the problem of determining whether an exposure-outcome correlation might be due to confounding by a social factor. This examination of specificity has interesting consequences for our understanding of the methodology of epidemiology. It highlights how the methodology of epidemiology relies on local tools designed to address specific inference problems peculiar to the discipline, and shows that observational causal inference in epidemiology can proceed with little prior knowledge of the causal structure of the phenomenon investigated. I also argue that specificity of association cannot (despite claims to the contrary) be entirely explained in terms of Woodward’s well-known concept of “one-to-one” causal specificity. This is because specificity as understood by epidemiologists depends on whether an exposure (or outcome) is associated with a `heterogeneous’ set of variables. This dimension of heterogeneity is not captured in Woodward’s notion, but is crucial for understanding the evidential import of specificity of association. (shrink)

The morbidity of tick‐borne encephalitis (TBE) varies yearly by as much as 10‐fold among the people of Western Siberia. This long‐term variation is dependent on many factors such as the density of the tick populations, the prevalence of TBE virus (TBEV) among sub‐adult ticks, the yearly virulence of the TBEV, and prophylactic measures. Here we highlight the role of small mammal hosts in the circulation of TBEV through the ecosystem. Refining classical models of non‐viremic horizontal transmission, we emphasize the (...) recently understood fact that the physiological and immunological status of the small mammal hosts affects the tick and virus‐host interactions. In addition to its theoretical interest, our approach may lead to some practical improvements in the precision of epidemiological forecasts and perhaps in forestalling the severity of outbreaks of TBE, or, at least, in forewarning medical authorities and the general public of impending TBE outbreaks. (shrink)

To an increasing extent ethical controversies affect and sometimes obstruct public health work and epidemiological research. In order to improve communication between the concerned parties a model for identification and analysis of ethical conflicts in individual-based research has been worked out in co-operation between epidemiologists and moral philosophers. The model has two dimensions. One dimension specifies relevant ethical principles (as beneficence, non-maleficence, autonomy and justice). The other dimension specifies the groups of persons involved in the conflict under consideration (for (...) example: the study-population, individuals who may benefit from the results, the researchers and their personnel, the community at large). The model has been applied to the problem of legitimacy of case-register research and to problems in psychiatric health services research as well as epidemiological research. (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)

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 challenges posed by chronic illness have pointed out to epidemiologists the multifactorial complex nature of disease causality. This notion has been referred to as a web of causality. This web extends theoretically beyond risk markers. It includes determinants of emergence/non-emergence of disease. This web of determinants is a form of complex system. Due to its complexity, the determinants within such system are not linked to each others in a linear, predictable manner only. Predictability is possible only on a short-term (...) basis, and unpredictability sets in over the long run. Understanding such a system of determinants calls for articulation and testing of complex models which synthesize our knowledge of multiple determinants at many scales, both biological and otherwise. Given the complexity of this web and existing knowledge about the nonlinearity of such systems, the following question is posed: Can the challenge of studying causality be adequately addressed if emphasis continues to be placed on using tools and methods that are geared towards looking at such system from a linear paradigm? Or is it time to add to the epidemiologic research agenda the notion of nonlinearity and its relevant form of analytical approaches that are being tested in other disciplines? Furthermore, the question posed here applies as well to the study of determinants of health. Addressing determinants of heath adds further complexity to our task. (shrink)

The article takes the renewed popularity and interest in epidemiological modelling for Covid-19 as a point of departure to ask how modelling has historically shaped epidemiological reasoning. The focus lies on a particular model, developed in the late 1920s through a collaboration of the former field-epidemiologists and medical officer, Wade Hampton Frost, and the biostatistician and population ecologist Lowell Reed. Other than former approaches to epidemic theory in mathematical formula, the Reed-Frost epidemic theory was materialised in a simple (...) mechanical analogue: a box with coloured marbles and a wooden trough. The article reconstructs how the introduction of this mechanical model has reshaped epidemiological reasoning by shifting the field from purely descriptive to analytical practices. It was not incidental that the history of this model coincided with the foundation of epidemiology as an academic discipline, as it valorised and institutionalised new theoretical contributions to the field. Through its versatility, the model shifted the field’s focus from mono-causal explanations informed by bacteriology, eugenics or sanitary perspectives towards the systematic consideration of epidemics as a set of interdependent and dynamic variables. (shrink)

This article traces the history of the Susceptible, Infected, Recovered (SIR) model in early 20th-century epidemiology (1904–27). The aim is to test the hypothesis that the active stance taken by Ross, Hudson, McKendrick, and Kermack represents a turn in the history of modern epidemiology, shifting the classical method of statistical epidemiology from a data-based model to a theory-based model. The article shows that epidemiological modeling is based on a mathematical simplification of epidemics at the time of the microbiological complexification (...) of epidemiology. What were the implications of the mathematical theory of epidemics for the development of modern epidemiology? How can epidemic dynamics be expressed mathematically without compromising the biological representation? This is exactly what is at stake here: from the work of Ross to the standardization of the SIR model by McKendrick and Kermack, modeling has been confronted with the problem of relating its equations to the facts. (shrink)

Genetic epidemiology is a rapidly advancing field due to the recent availability of large amounts of omics data. In recent years, it has become possible to obtain omics information at the single‐cell level, so genetic epidemiological models need to be updated to integrate with single‐cell expression data. In this perspective paper, we propose a cell population‐based framework for genetic epidemiology in the single‐cell era. In this framework, genetic diversity influences phenotypic diversity through the diversity of cell population profiles, (...) which are defined as high‐dimensional probability distributions of the state spaces of biomolecules of each omics layer. We discuss how biomolecular experimental measurement data can capture the different properties of this distribution. In particular, single‐cell data constitute a sample from this population distribution where only some coordinate values are observable. From a data analysis standpoint, we introduce methodology for feature extraction from cell population profiles. Finally, we discuss how this framework can be applied not only to genetic epidemiology but also to systems biology. (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)

Despite its phenomenal success since its inception in the early nineteen-nineties, the evidence-based medicine movement has not succeeded in shaking off an epistemological critique derived from the experiential or tacit dimensions of clinical reasoning about particular individuals. This critique claims that the evidence-based medicine model does not take account of tacit knowing as developed by the philosopher Michael Polanyi. However, the epistemology of evidence-based medicine is premised on the elimination of the tacit dimension from clinical judgment. This is demonstrated through (...) analyzing the dichotomy between clinical and statistical intuition in evidence-based medicine’s epistemology of clinical reasoning. I argue that clinical epidemiology presents a more nuanced epistemological model for the application of statistical epidemiology to the clinical context. Polanyi’s theory of tacit knowing is compatible with the model of clinical reasoning associated with clinical epidemiology, but not evidence-based medicine. (shrink)

This aricle discusses the similarity between the propagation of pathogens (viruses and worms) on computer networks and the proliferation of pathogens in cellular organisms (organisms with genetic material contained within a membrane-encased nucleus). It introduces several biological mechanisms which are used in these organisms to protect against such pathogens and presents security models for networked computers inspired by several biological paradigms, including genomics (RNA interference), proteomics (pathway mapping), and physiology (immune system). In addition, the study of epidemiological (...) class='Hi'>models for disease control can inspire methods for controlling the spread of pathogens across multiple nodes of a network. It also presents results based on the authors’ research in immune system modeling. (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)

We argue for a foundational epistemic claim and a hypothesis about the production and uses of mathematical epidemiological models, exploring the consequences for our political and socio-economic lives. First, in order to make the best use of scientific models, we need to understand why models are not truly representational of our world, but are already pitched towards various uses. Second, we need to understand the implicit power relations in numbers and models in public policy, and, (...) thus, the implications for good governance if numbers and models are used as the exclusive drivers of decision making. (shrink)

Social research, from economics to demography and epidemiology, makes extensive use of statistical models in order to establish causal relations. The question arises as to what guarantees the causal interpretation of such models. In this paper we focus on econometrics and advance the view that causal models are ‘augmented’ statistical models that incorporate important causal information which contributes to their causal interpretation. The primary objective of this paper is to argue that causal claims are established on (...) the basis of a plurality of evidence. We discuss the consequences of ‘evidential pluralism’ in the context of econometric modelling. (shrink)

Philosophy of epidemiology has recently emerged as a distinct branch of philosophy. The field will surely benefit from pluralism, reflected in the broad range of topics and perspectives in this special issue. Here, I argue that a healthy pluralistic field of philosophy of epidemiology has social responsibilities that require the field as a whole to engage actively with research on social determinants of health and health disparities. Practicing epidemiologists and the broader community of public health scientists have gradually acknowledged that (...) much of their attention ought to be paid to these, i.e. inequitable between-population health variations that largely reflect the world’s inequitable distribution of resources and social conditions. The paper illustrates how and why health disparities and social determinants of health, through no ill will, become de facto secondary concerns in Alex Broadbent’s field-defining Philosophy of Epidemiology, showing the ease with which these topics can be incidentally sidelined. As means for philosophy of epidemiology to meet its social responsibility obligations, I suggest philosophy of epidemiology expand its attention to two particular lines research. First, the paper discusses Geoffrey Rose’s concept of “causes of incidence”—causes of disparities between populations—and argues for the importance of engaging with these causes. Second, the paper argues for the value of engaging with Bruce Link and Jo Phelan’s “fundamental causes” model of how flexible social resources serve as buffers from harms, generating a plethora of shifting and locally-contingent health effects. (shrink)

Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence, business, epidemiology, social science and economics.