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The Mode of Communication of Cholera
... The concept of evaluating water quality and classifying it based on cleanliness or pollution levels historically dates to 1848 in Germany [44]. In 1854, Snow made an innovative connection between poor water quality and the spread of cholera [45]. Much later, in 1965, Horton introduced the WQI to evaluate the quality of surface water [46]. ...
... The WQI rating scale includes five categories: Very good (91-100), good (71)(72)(73)(74)(75)(76)(77)(78)(79)(80)(81)(82)(83)(84)(85)(86)(87)(88)(89)(90), poor (51)(52)(53)(54)(55)(56)(57)(58)(59)(60)(61)(62)(63)(64)(65)(66)(67)(68)(69)(70), bad (31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50), and very bad (0-30). Temperature contributes to determining the coefficients m1 and m2, with m1 taking the value 0.5 when the temperature is above 34 °C and 1.0 when it's below 34 °C [46,82]. ...
... House index classifies river water quality into categories based on index values: high (71-100), reasonable(51- 70), polluted(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50), badly polluted [27,59,[99][100][101]. ...
... We will use two datasets to validate the spread model analyzed in this work. The first dataset is the cholera dataset compiled by John Snow in [13]. Dr. Snow mapped the deaths caused by cholera in the Soho District of London in 1854 to illustrate that the infection was being spread by contaminated water via a specific pump, the Broad Street pump, and not via the air, as was the belief at the time. ...
... By the assumption that there exist i, j ∈ [n] and l 1 , l 2 ∈ [T − 1] ∪ {0} such that (12) holds, Φ has column rank equal to two, with two unknowns. Therefore there exists a unique solution to (13) using the inverse or pseudoinverse. ...
... Assuming that the correct value for h and the A matrix are known, using (13) exactly recovers β and δ. If only two time-steps are used, the exact spread parameters can be recovered, consistent with Theorem 3. Using (13) with an incorrect h value to recover β and δ gives incorrect values for β and δ, but results in the right proportion between the two, consistent with Corollary 1. If the system is at the endemic state, the proportion between the spread parameters can be solved exactly using Corollary 2. ...
Models of spread processes over non-trivial networks are commonly motivated by modeling and analysis of biological networks, computer networks, and human contact networks. However, identification of such models has not yet been explored in detail, and the models have not been validated by real data. In this paper, we present several different spread models from the literature and explore their relationships to each other; for one of these processes, we present a sufficient condition for asymptotic stability of the healthy equilibrium, show that the condition is necessary and sufficient for uniqueness of the healthy equilibrium, and present necessary and sufficient conditions for learning the spread parameters. Finally, we employ two real datasets, one from John Snow's seminal work on cholera epidemics in London in the 1850's and the other one from the United States Department of Agriculture, to validate an approximation of a well-studied network-dependent susceptible-infected-susceptible (SIS) model.
... The study of John Snow of London's cholera outbreak of 1853-54 4 emphasizes the importance of using exogenous shocks that heterogeneously affect the units of a population. In 1852, the Lambeth Waterworks company-one of the major utility companies supplying water to several parts of the city-relocated their waterworks from Hungerford Market to fifteen miles upstream in the Thames, thereby "obtaining a supply of water quite free from the sewage of London" (Snow, 1855(Snow, (1965, page 68). Contrarily, Southwark & Vauxhall, a company competing with Lambeth Waterworks in several districts of London, left its intake pipe downstream in the Thames at Battersea. ...
... For example, Snow (1855Snow ( (1965) presented various sorts of evidence to establish the pre-treatment equivalence of houses that were exposed to pure and to contaminated water supplies-this is a good example of a thorough qualitative analysis of the 'as-if randomness' of a treatment. For instance, Snow explains in detail the 'as-if random' nature of the water supply that is unaffected by confounders including resident characteristics (e.g., wealth) or house characteristics (e.g., location). ...
... For instance, Snow explains in detail the 'as-if random' nature of the water supply that is unaffected by confounders including resident characteristics (e.g., wealth) or house characteristics (e.g., location). Additionally, Snow (1855Snow ( (1965) presented rich qualitative information on the context and the process of determining the watersupply source. For instance, he emphasized that absentee landlords decided which competing water companies would have served a particular address. ...
... Any agent visiting this place has a chance to become anomalous. This model simulates scenarios like the 1854 outbreak of cholera in London that was caused by a particular public water pump discovered by John Snow's spatial analysis of cholera cases [20]. For each of the aforementioned types of anomalous behaviors, and for each type of injection mechanism, we provide data resulting from a simulation of 1000 agents, each having four weeks of normal behavior (without any anomalies) and having at least four weeks of anomalous behavior (during which anomalies are injected as described above). ...
... Afterwards, agents adopt anomalous behavior for 7-14 days. In this model, agents do not infect each other and can only be infected through exposure to the infected location similar to the 1854 London Cholera outbreak [20]. ...
Human mobility anomaly detection based on location is essential in areas such as public health, safety, welfare, and urban planning. Developing models and approaches for location-based anomaly detection requires a comprehensive dataset. However, privacy concerns and the absence of ground truth hinder the availability of publicly available datasets. With this paper, we provide extensive simulated human mobility datasets featuring various anomaly types created using an existing Urban Patterns of Life Simulation. To create these datasets, we inject changes in the logic of individual agents to change their behavior. Specifically, we create four of anomalous agent behavior by (1) changing the agents' appetite (causing agents to have meals more frequently), (2) changing their group of interest (causing agents to interact with different agents from another group). (3) changing their social place selection (causing agents to visit different recreational places) and (4) changing their work schedule (causing agents to skip work), For each type of anomaly, we use three degrees of behavioral change to tune the difficulty of detecting the anomalous agents. To select agents to inject anomalous behavior into, we employ three methods: (1) Random selection using a centralized manipulation mechanism, (2) Spread based selection using an infectious disease model, and (3) through exposure of agents to a specific location. All datasets are split into normal and anomalous phases. The normal phase, which can be used for training models of normalcy, exhibits no anomalous behavior. The anomalous phase, which can be used for testing for anomalous detection algorithm, includes ground truth labels that indicate, for each five-minute simulation step, which agents are anomalous at that time. Datasets are generated using the maps (roads and buildings) for Atlanta and Berlin, having 1k agents in each simulation.
... Using a map, Snow illustrated the relationship between cholera cases and a contaminated water supply. He discovered that the majority of those who died had lived near a specific water pump on Broad Street, with only a few cases living closer to another pump (Snow [29], cited by Smith [28]). This led him to believe that the Broad Street pump was the source of the outbreak and inspired him to create his diagram by mapping the areas where cholera cases had occurred (Figure 1). ...
... This led him to believe that the Broad Street pump was the source of the outbreak and inspired him to create his diagram by mapping the areas where cholera cases had occurred (Figure 1). [29] Mapping is a visual representation of geographical distribution, whereas disease mapping is an illustration of outbreak locations and a summary of the measurements or statistics applied to a specific group of infected individuals and their geographical linkage. The advantage of disease mapping is that it can visualise a spatial pattern that cannot be analysed in any other data presentation such as a table, graph, or chart [30]. ...
Tuberculosis (TB) transmission frequently occurs in a household or group within a population, resulting in a variety of spatial patterns. However, the apparent spatial clustering of TB may represent the ongoing transmission or co-location of associated risk factors, which can vary significantly based on the type of data available, the analysis methods used, and the dynamics of the underlying population. This study aims to review the spatial analyses used for monitoring the trends involving and associations between risk factors and TB cases by applying the concept of spatial epidemiology. The role of the Geographic Information System in spatial epidemiology is discussed. Previous studies involving spatial analysis of TB cases-which include kriging, spatial autocorrelation, kernel density estimation, hotspot analysis, and regression analysis-are reviewed. The type of analysis was chosen based on the purpose of each study, which could explain the role of the transmission to reactivation of the disease as a driver of TB spatial distribution. In diverse situations, a number of different spatial analysis techniques were used, with all the studies demonstrating significant heterogeneity in terms of the spatial distribution of TB. Future research is needed to determine the best methods to use in different situations and, where possible, consider unreported cases when using notification data. A combination of genotypic, molecular, and geospatial approaches to examine epidemiologically related cases could improve TB control and provide significant contributions to the current knowledge.
... Data visualisation plays an important role in statistical analysis and decision-making, allowing patterns and relationships to emerge that might otherwise remain hidden in raw data. One of the most famous early examples of discovery driven by visualisation comes from John Snow's 1854 cholera map (Snow, 1856), which is presented in Figure 1, and is widely recognised as a pioneering work in epidemiology and spatial statistics (Tufte and Robins, 1997). By plotting cholera cases on a street map of Soho, London, Snow succeeded in demonstrating a spatial pattern that linked cases to a contaminated water pump, providing critical evidence against the dominant miasma theory of disease transmission (Brody et al., 2000). ...
Data visualisation is a fundamental tool in statistical analysis, enabling the identification of patterns and relationships that might otherwise remain hidden in raw data. One of the most famous historical examples is John Snow's 1854 cholera map, which demonstrated the spatial clustering of cholera cases around a contaminated water pump in London. This study explores how Snow's visualisation can be effectively incorporated into statistics education as an interactive case study. Using R, we outline the steps involved in reproducing Snow's cholera map, demonstrating geospatial data manipulation, visualisation techniques, and spatial analysis. We discuss the pedagogical benefits of historical case studies in statistics courses, emphasising their role in fostering curiosity, critical thinking, and technical proficiency. Additionally, we explore how these methods can be extended beyond epidemiology to applications in public health, urban analytics and environmental science. By integrating historical datasets with modern computational tools, educators can create engaging, hands-on learning experiences that reinforce core statistical principles while illustrating the real-world impact of data analysis.
... Penggunaan SIG di bidang kesehatan bermula dari upaya untuk memahami dan memetakan pola penyebaran penyakit. Salah satu contoh paling awal adalah karya John Snow pada tahun 1854, yang menggunakan peta untuk mengidentifikasi sumber wabah kolera di London (Snow, 1854). Sejak saat itu, SIG telah mengalami perkembangan pesat dengan kemajuan teknologi komputer dan perangkat lunak. ...
... A common model in state policy evaluations is the classic two-way fixed effect difference-indifferences model (Dimick & Ryan, 2014;Wing et al., 2018). DID has a long history in policy evaluation, harkening back to John Snow's study of cholera in 1855 (Snow, 1855). The DID estimate essentially subtracts the observed pre-policy to post-policy change in the comparison group from the observed prepolicy to post-policy change in the policy group, hence the name "difference-in-differences." ...
This paper reviews and details methods for state policy evaluation to guide selection of a research approach based on evaluation setting and available data. We highlight key design considerations for an analysis, including treatment and control group selection, timing of policy adoption, expected effect heterogeneity, and data considerations. We then provide an overview of analytic approaches and differentiate between methods based on evaluation context, such as settings with no control units, a single treated unit, multiple treated units, or with multiple treatment cohorts. Methods discussed include interrupted time series models, difference-in-differences estimators, autoregressive models, and synthetic control methods, along with method extensions which address issues like staggered policy adoption and heterogenous treatment effects. We end with an illustrative example, applying the developed framework to evaluate the impacts of state-level naloxone standing order policies on overdose rates. Overall, we provide researchers with an approach for deciding on methods for state policy evaluations, which can be used to select study designs and inform methodological choices.
... To see how natural experiments may lead to causal discovery consider the famous example of Snow's discovery that water delivery systems were responsible for the great cholera epidemic in nineteenth century London (Snow, 1856). The bulk of Snow's efforts consisted in reasoning and measurement rather than intervention: for instance, when Snow plotted the affected households on a map of the city, what emerged were patterns of infection that precisely tracked differences in water-delivery systems. ...
The received view of scientific experimentation holds that science is characterized by experiment and experiment is characterized by active intervention on the system of interest. Although versions of this view are widely held, they have seldom been explicitly defended. The present essay reconstructs and defuses two arguments in defense of the received view: first, that intervention is necessary for uncovering causal structures, and second, that intervention conduces to better evidence. By examining a range of non-interventionist studies from across the sciences, I conclude that interventionist experiments are not, ceteris paribus, epistemically superior to non-interventionist studies and that the latter may thus be classified as experiment proper. My analysis explains why intervention remains valuable while at the same time elevating the status of some non-interventionist studies to that of experiment proper .
... Although current environmental problems are more complex, the germ theory is a historic example of environmental based disease prevention. John Snow controlled the cholera outbreak by identifying its source as the public water pump on Broad Street in 1854 (29), and Florence Nightingale reduced hospital mortality during the Crimean war from 42% to 2% through the introduction of sanitary measures (30). The current climate crisis, loss of biodiversity and pollution are more complex contemporary examples of how the environment affects health and how people affect their environment. ...
Summary
To provide care based on the health and life needs of people,
more care is needed for those with more needs, this is known as
proportionate universalism, a strategy to reduce health inequity.
By providing respectful, empowering and culturally competent
care, vulnerable and disempowered people can gain capabilities
and power. What is needed to implement this care model and
are lay persons and health care providers able to identify these
needs?
Participants identified the person, their community, the
(primary) healthcare providers, the health system, education
and food systems as the principal actors to advance people�centred care. All actors can contribute from their own knowledge
systems. In addition to medical, policy and contextual
knowledge, attention was asked for traditional knowledge,
challenging the monopoly of the dominant western model.
While lay persons in Belgium associated health with lifestyle,
media and access to healthcare, in Bolivia it was associated with
family and community support, access to healthy food and even
neighbourhood safety.
In the public health system in Bolivia, primary care providers and
a health committee composed of elected community
representatives share responsibility for the health of a
geographical defined population. They provide curative care,
health promotion and disease prevention in health facilities and
in the community. Data related to social determinants of health
are collected in family files.
The market project illustrates how a primary health care
structure based in a community and supported by community
leaders, local authorities and the health networkcan support the
design of a people-centred health plan. Processes necessary to
support this outcome were a collective health diagnosis,
continuous dialogue and bidirectional health education. This
community oriented primary care model led to the creation of a
community owned plan for a “healthful market”
... Modern epidemiology increasingly incorporates both spatial In the 1850s, John Snow used geographic information and linked a street water pump to the cholera outbreaks in Soho, London [12]. Following this, spatial analysis became an important part of epidemiology. ...
... Some prior research focuses on well-known historical visualizations and examines them from different perspectives. Koch [37] discusses the people's attitudes toward John Snow's cholera map [66] in 19th-century London. Shiode [64] utilizes historical records to quantitatively examine John Snow's waterborne transmission hypotheses. ...
Historical visualizations are a valuable resource for studying the history of visualization and inspecting the cultural context where they were created. When investigating historical visualizations, it is essential to consider contributions from different cultural frameworks to gain a comprehensive understanding. While there is extensive research on historical visualizations within the European cultural framework, this work shifts the focus to ancient China, a cultural context that remains underexplored by visualization researchers. To this aim, we propose a semi-automatic pipeline to collect, extract, and label historical Chinese visualizations. Through the pipeline, we curate ZuantuSet, a dataset with over 71K visualizations and 108K illustrations. We analyze distinctive design patterns of historical Chinese visualizations and their potential causes within the context of Chinese history and culture. We illustrate potential usage scenarios for this dataset, summarize the unique challenges and solutions associated with collecting historical Chinese visualizations, and outline future research directions.
... Under the canonical set-up with a treatment indicator and pre-and post-treatment time periods, the parallel trends assumption stipulates that the average outcome in the treated and untreated groups would have changed by the same amount post-treatment, under the scenario where neither group received the treatment [1]. DiD allows for the identification and estimation of causal effects in the absence of treatment randomization and has been used to estimate the effects of various treatments, exposures, and policies such as contaminated water on cholera incidence [2], minimum wage laws on unemployment [3], employment protection on productivity [4], and Medicare on mortality and medical spending [5], among many other applications across fields. ...
Differences-in-differences (DiD) is a causal inference method for observational longitudinal data that assumes parallel expected outcome trajectories between treatment groups under the (possible) counterfactual of receiving a specific treatment. In this paper DiD is extended to allow for (i) network dependency where outcomes, treatments, and covariates may exhibit between-unit latent correlation, and (ii) interference, where treatments can affect outcomes in neighboring units. In this setting, the causal estimand of interest is the average exposure effect among units with a specific exposure level, where the exposure is a function of treatments from potentially many units. Under a conditional parallel trends assumption and suitable network dependency conditions, a doubly robust estimator allowing for data-adaptive nuisance function estimation is proposed and shown to be consistent and asymptotically normal with variance reaching the semiparametric efficiency bound. The proposed methods are evaluated in simulations and applied to study the effects of adopting emission control technologies in coal power plants on county-level mortality due to cardiovascular disease.
... This is an example of the indirect effects that climate mediated disasters have on many infectious diseases. The 2022 cholera outbreak in Malawi after cyclone Freddy [104] demonstrates how historic outbreaks, such as the 1855 London cholera outbreak famously terminated by John Snow's epidemiological insights [105], are replicated when lessons learnt about water and sanitation cannot be effectively implemented. ...
Purpose of review
Life on earth, as we know it, is changing. The likelihood of more frequent pandemics and disease outbreaks is something that current global healthcare infrastructure is ill equipped to navigate. Human activity is forcing our planet into a new geologic epoch, the Anthropocene, which is typified by increased uncertainty resulting from human disruption of earth's life-giving ecosystems. Plagues and pandemics have always been unfortunate partners to periods of disruption, as they will be again if the frequency and severity of climate and conflict-mediated disasters increase in coming years. If we continue to exceed and degrade the planetary boundaries that protect human health, our children and their children will reap the consequences.
Recent findings
Scientists have defined nine ‘safe operating’ planetary boundaries for life in all its glorious diversity to thrive on planet earth. Recent evidence suggests that six of these nine boundaries have already been transgressed, but the potential implications for these transgressions upon child health is not well articulated. We highlight how contravention of these boundaries will impact infectious disease risk and humans’ ability to survive and thrive. We reflect specifically on how paediatricians are called upon to speak up for the most vulnerable members of our species, young children and as yet unborn future generations.
Summary
Post COVID-19 initiatives to improve pandemic preparedness and response are certainly warranted, but pandemic prevention should include committed efforts not to exceed safe planetary boundaries. Willingly exceeding these boundaries has deep moral consequences that are poorly articulated by current ethical frameworks. Paediatricians are best placed to develop and champion the neglected ‘third dimension’ of medical ethics, recognizing the moral imperative to protect the long-term best interests of children and future generations.
... The canonical origin story of field epidemiology describes John Snow as the archetypical figure who in 1854, used a dot map to identify and remove the Broad Street pump as the source of a cholera outbreak in London, England (5). In reality, as described in Jim Downs' Maladies of Empire, field epidemiology more likely took root in less acceptable circumstances, in closed population studies among non-consenting participants primarily for the economic benefit of Western colonising nations including plantations, slave trading ships, and war camps (6). ...
For too long, the theory and practice of infectious disease outbreak response has been the domain of a small number of experienced responders. The COVID-19 pandemic brought global attention to the requirements of effective outbreak response, and the need for preparation across the key pillars. Decisionmakers, early career practitioners and those in the field now have access to a comprehensive text that brings together evidence based and practical insights from the best in the business. Dale Fisher, Professor of Medicine at the National University of Singapore, was chair of WHO’s Global Outbreak Alert and Response Network prior to and throughout most of the pandemic. In this massive collaborative effort, he marshals nearly 100 top public health leaders and experts from the front lines to present 37 chapters on pandemic preparedness and response, drawing heavily on experiences from COVID-19, as well as from Ebola, MERS, SARS-1, influenza and other outbreaks of modern times. The contributors include experts from health ministries and Centres for Disease Control and national public health institutions around the world, from international organizations like the WHO, MSF, IFRC and UNICEF and from research institutions and various NGOs from dozens of countries, adding to the diversity and richness of the descriptions. The book can be used as a reference or as a textbook, where each chapter describes the features of outbreak preparedness, including field epidemiology, risk communications, managing health services in a pandemic, vaccine management, leadership, contact tracing and laboratory management and testing amongst others.
... Anaesthetician and epidemiologist John Snow plotted incidences of the disease on a map of the Soho district. 1,2 This exercise led him to the hypothesis that cholera might be a water-borne illness and, furthermore, that the Soho outbreak might have come from a single water pump. This was complicated, however, by the existence of several cholera cases that were closer to other community water pumps. ...
Contact tracing is an essential tool in slowing and containing outbreaks of contagious diseases. Current contact tracing methods range from interviews with public health personnel to Bluetooth pings from smartphones. While all methods offer various benefits, it is difficult for different methods to integrate with one another. Additionally, for contact tracing mobile applications, data privacy is a concern to many as GPS data from users is saved to either a central server or the user's device. The current paper describes a method called spatial cross-recurrence quantification analysis (SpaRQ) that can combine and analyze contact tracing data, regardless of how it has been obtained, and generate a risk profile for the user without storing GPS data. Furthermore, the plots from SpaRQ can be used to investigate the nature of the infectious agent, such as how long it can remain viable in air or on surfaces after an infected person has passed, the chance of infection based on exposure time, and what type of exposure is maximally infective.
... Space-time epidemiology (Knox & Bartlett, 1964) is based on the concept that various characteristics of the pathogenic agents and the environment interact in order to alter the probability of disease occurrence and form temporal or spatial patterns (Snow, 1855;Ward & Carpenter, 2000). Epidemiology aims to identify these patterns and factors, to assess the relevant uncertainty sources, and to describe disease in the population. ...
Understanding the spread of any disease is a highly complex and interdisciplinary exercise as biological, social, geographic, economic, and medical factors may shape the way a disease moves through a population and options for its eventual control or eradication. Disease spread poses a serious threat in animal and plant health and has implications for ecosystem functioning and species extinctions as well as implications in society through food security and potential disease spread in humans. Space-time epidemiology is based on the concept that various characteristics of the pathogenic agents and the environment interact in order to alter the probability of disease occurrence and form temporal or spatial patterns. Epidemiology aims to identify these patterns and factors, to assess the relevant uncertainty sources, and to describe disease in the population. Thus disease spread at the population level differs from the approach traditionally taken by veterinary practitioners that are principally concerned with the health status of the individual. Patterns of disease occurrence provide insights into which factors may be affecting the health of the population, through investigating which individuals are affected, where are these individuals located and when did they become infected. With the rapid development of smart sensors, social networks, as well as digital maps and remotely-sensed imagery spatio-temporal data are more ubiquitous and richer than ever before. The availability of such large datasets (Big data) poses great challenges in data analysis. In addition, increased availability of computing power facilitates the use of computationally-intensive methods for the analysis of such data. Thus new methods as well as case studies are needed to understand veterinary and ecological epidemiology. A special issue aimed to address this topic.
... In the 1800s, John Snow had to go door to door during an epidemic of cholera to uncover its mechanisms of spread. 1 He recorded where people were getting their drinking water from in order to pinpoint the source of the outbreak. Here we scale up this approach using machine learning to detect potential sources of foodborne illness in real time. ...
Machine learning has become an increasingly powerful tool for solving complex problems, and its application in public health has been underutilized. The objective of this study is to test the efficacy of a machine-learned model of foodborne illness detection in a real-world setting. To this end, we built FINDER, a machine-learned model for real-time detection of foodborne illness using anonymous and aggregated web search and location data. We computed the fraction of people who visited a particular restaurant and later searched for terms indicative of food poisoning to identify potentially unsafe restaurants. We used this information to focus restaurant inspections in two cities and demonstrated that FINDER improves the accuracy of health inspections; restaurants identified by FINDER are 3.1 times as likely to be deemed unsafe during the inspection as restaurants identified by existing methods. Additionally, FINDER enables us to ascertain previously intractable epidemiological information, for example, in 38% of cases the restaurant potentially causing food poisoning was not the last one visited, which may explain the lower precision of complaint-based inspections. We found that FINDER is able to reliably identify restaurants that have an active lapse in food safety, allowing for implementation of corrective actions that would prevent the potential spread of foodborne illness.
... This shift in cholera control strategies represents an extreme form of the pharmaceuticalization of global health. For over 150 years, water and sanitation have been recognized as the fundamental solution to cholera (Snow 1855). However, cholera vaccines have experienced an irresistible rise, becoming the cornerstone of cholera control in the twenty-first century. ...
Cholera vaccines have existed since the nineteenth century but were largely considered an ineffective control strategy for much of their history. However, in 2012, cholera vaccination campaigns were piloted in Haiti and Guinea using a preexisting vaccine formula. These initial efforts quickly expanded to dozens of countries. A global stockpile of millions of doses was established, positioning cholera vaccines as a cornerstone to the Global Task Force on Cholera Control’s Roadmap to ending cholera by 2030. What factors contributed to this remarkable turnaround? This piece explores the epistemic, moral, and industrial reconfigurations that sustained the crafting of a global vaccine success story and its ramifications within a shifting global health landscape, including the potential displacement of water and sanitation interventions. The research is based on my participation in cholera vaccine introductions as a medical NGO worker and on symmetric ethnographic fieldwork conducted in African settings targeted for reactive cholera vaccination and in global North centers influencing global cholera vaccine policy.
... If night soil is not appropriately managed, a city immediately becomes polluted, and citizens are forced to live under severe environmental conditions. As clearly indicated by Snow (1855), the inappropriate management of night soil can cause epidemics such as cholera. Thus, night soil management has traditionally been a very important social issue for a city's survival. ...
To manage night soil, establishing a modern sewerage system is necessary, but its completion takes a long time. If night soil is not appropriately managed, a city can immediately become paralyzed. How do governments manage night soil prior to the completion of a sewerage system in modern society? This paper describes the contemporary policy history of night soil management for the twenty-three wards of Tokyo, which is a unique and informative case. The Tokyo Metropolitan Government (TMG) attempted to maintain the traditional system of managing night soil, which was delivered to farmland as a source of agricultural fertilizer until the 1960s. Because the demand for artificial chemical fertilizers has dramatically increased across Japan since the 1960s, the TMG was forced to depend on dumping night soil into the sea to keep Tokyo clean. In 1967, the leftist and liberalist Ryokichi Minobe was elected governor on a promise to promote welfare and environmental policy. However, he saw no choice but to dispose of night soil into the sea until the completion of a modern sewerage system. The transition in the management of night soil, from delivering it to farmland to disposing of it in the sea, was a considerable change, but the decision was made within the scope of routine administrative work. This paper presents a detailed study of the management of a social nuisance in the field of policy history.
... A good review of this model is found in Ref. [19]. In 1855, another notable model was studied by John Snow (1813-1858) about the cholera cases in London [20]. A few years later, in 1873, William Budd (1811-1880) analyzed the typhoid spread [21]. ...
... Chronological representation of major milestones of wastewater-based epidemiology[17][18][19][20][21][22][23][24][25][26][27]. ...
During the recent COVID-19 pandemic, wastewater-based epidemiological (WBE) surveillance played a crucial role in evaluating infection rates, analyzing variants, and identifying hot spots in a community. This expanded the possibilities for using wastewater to monitor the prevalence of infectious diseases. The full potential of WBE remains hindered by several factors, such as a lack of information on the survival of pathogens in sewage, heterogenicity of wastewater matrices, inconsistent sampling practices, lack of standard test methods, and variable sensitivity of analytical techniques. In this study, we review the aforementioned challenges, cost implications, process automation, and prospects of WBE for full-fledged wastewater-based community health screening. A comprehensive literature survey was conducted using relevant keywords, and peer reviewed articles pertinent to our research focus were selected for this review with the aim of serving as a reference for research related to wastewater monitoring for early epidemic detection.
... This common trend assumption does, however, not impose that the levels of visibility or sentiment are the same in different news outlets and, therefore, allows for variants of the above-described selection bias, which are constant over time (see Lechner 2011). 13 Under the common trend assumption, we may apply a difference-in-differences (DiD) approach, a widely used causal inference method (see Ashenfelter 1978;Card and Krueger 1994;Snow 1855). It is based on (i) computing the average difference of media coverage in the treated outlet after versus before the alleged political interference, (ii) computing the same average difference over time for the control outlets, and (iii) subtracting the before-after difference of the control outlets from the before afterdifference of the treated outlet. ...
Mounting concern surrounds the influence of political actors on journalism, especially as media outlets face increasing financial pressures. These circumstances can give rise to instances of media capture, a mutually corrupting relationship between political actors and media organizations. However, empirical evidence substantiating such mechanisms and their consequences remains limited, particularly in the context of Western democracies. This chapter investigates a recent case in which a former Austrian chancellor allegedly colluded with a tabloid newspaper to receive better news coverage in exchange for increased ad placements by government institutions. We employ automated content analysis to investigate political news articles from seventeen prominent Austrian news outlets spanning 2012–2021 ( n = 222,659). Adopting a difference-in-differences approach, we find a substantial increase in media visibility of the former Austrian chancellor within the news outlet that is alleged to have received bribes, as well as a decrease in favorability for challenger candidates. Although this study does not aim to prove or disprove the involvement of specific political actors or media organizations in unethical or illegal activities, it introduces an innovative method for detecting unusual patterns in media reporting. Findings are discussed in the context of current threats to media independence and underscore the crucial need to protect journalistic integrity and ensure unbiased information for the public.
... For almost two centuries, civil registration data has inspired epidemiologists, social scientists, and statisticians (see, for example, Durkheim, 1897;Quetelet & Smits, 1832;Snow, 1855). The introduction of family reconstitution by Henry and Fleury (1956) ushered in a new era of research, as historians have been collecting person observations from archival records to reconstruct life courses and family relations ever since (Fauve-Chamoux et al., 2016). ...
Reconstruction of historical persons and family ties is the bread and butter of many researchers and genealogists. With the increasing digital availability of historical person records, the scope and depth of person reconstructions speaks to the imagination of researchers and genealogists. Yet, the lack of standardisation in the description of historical person data has hurt the interoperability and sustainability of both small and large databases. Persons in Context, or PiCo, presents a data model for historical person records within the Resource Description Framework (RDF). RDF or Linked Data is specifically designed for clear, unambiguous information exchange between multiple parties over the internet. We show how reuse of existing ontologies and concentric description are the building blocks of a flexible, straightforward, and stringent data model that emphasises provenance.
... A good review of this model is found in Ref. [19]. In 1855, another notable model was studied by John Snow (1813-1858) about the cholera cases in London [20]. A few years later, in 1873, William Budd (1811-1880) analyzed the typhoid spread [21]. ...
One way to study the spread of disease is through mathematical models. The most successful models compartmentalize the host population according to their infectious stage, e.g., susceptible (S), infected (I), exposed (E), and recovered (R). The composition of these compartments leads to the SI, SIS, SIR, and SEIR models. In this Chapter, we present and compare three formulations of SI, SIS, SIR, and SEIR models in the framework of standard (integer operators), fractional (Caputo sense), and fractal derivatives (Hausdorff sense). As an application of the SI model, we study the evolution of AIDS cases in Bangladesh from 2001 to 2021. For this case, our simulations suggest that fractal formulation describes the data well. For the SIS model, we consider syphilis data from Brazil from 2006 to 2017. In this case, the three frameworks describe the data with good accuracy. We used data from Influenza A to adjust the SIR model in previous approaches and observed that the fractional formulation was better. The last application considers the COVID-19 data from India in the range 2020-04-10 to 2020-12-31 to adjust the parameters of the SEIR model. The standard formulation fits the data better than the other approaches. As a common result, all models exhibit steady solutions in the different formulations. The time to reach a steady solution is correlated to the considered approach. The standard and fractal formulations reach the steady state earlier when compared with the fractional formulation.
... The genesis of GIS can be traced back to before the computer era, illustrated by Dr. John Snow's seminal 1854 cholera map of London, which pinpointed the outbreak's source to a contaminated water well (Snow, 1854). ...
In the dynamic field of architecture and urban planning research, the multifaceted role played by Geographic Information Systems (GIS) as indispensable methodologies can not be underestimated. The primary objective of this study is to unravel the significant applications and methodologies of GIS, aiming to furnish the academic and professional communities in both architecture and urban planning with a comprehensive understanding of the subject. The research looks into definitions, diverse applications of GIS, and methodologies employed in architectural and urban planning research. As the paper progresses, special attention is directed toward the evaluation of GIS's impact on spatial analyses and decision-making processes within the sphere of urban planning. A critical lens is applied to discern how GIS not only facilitates data-driven decision-making but also plays a pivotal role in fostering sustainability and resilience in the ever-evolving urban landscape. The research culminates in a comparative analysis that sheds light on the distinct roles and methodologies of GIS in both architecture and urban planning. By identifying commonalities and distinctions, the study provides a holistic perspective, enabling a simple understanding of GIS applications and their implications in diverse research domains. In conclusion, this research paper not only contributes to the existing body of knowledge but also offers actionable recommendations for enhancing the integration of GIS methodologies in both architectural and urban planning research.
... Moreover, Snow's interdisciplinary approach, involving collaboration with local authorities and community engagement, highlighted the importance of collective action in addressing public health challenges. His work demonstrated that effective public health interventions require the combined efforts of medical professionals, policymakers, and the public [1,8,13,18]. ...
John Snow (15 March 1813-16 June 1858) stands as a seminal figure in the fields of epidemiology and anesthesia. His groundbreaking work in tracing the source of cholera outbreaks and advancing the practice of anesthesia has left an indelible mark on modern medicine. Born in York, England, Snow's early passion for science and dedication to medical practice led him to become a pioneering force in his field. His meticulous methods, including the innovative use of spatial analysis and statistical mapping, challenged prevailing theories and laid the groundwork for modern public health initiatives. Snow's contributions to anesthesia, particularly his work with ether and chloroform, revolutionized surgical practices, significantly improving patient care and safety. This article delves into Snow's life, achievements, and the innovative processes he employed, underscoring his enduring impact on human health. By examining his legacy, we aim to enhance our understanding of medical history and inspire both present and future healthcare professionals, honoring the legacy of this medical hero.
This master's thesis conducted a spatial analysis of COVID-19 infections and deaths in the municipalities of Presidente Prudente and Botucatu - SP. Given that the pandemic is now over, but that we are, and will continue to be living with the virus for many years to come, continued studies on the impacts of the disease on people's lives is crucial. The choice of these municipalities is justified by the absence of intra-municipal studies and analyses of intermediard municipalities in the state of São Paulo, which are considered to be directly responsible for the dynamics of the spatial diffusion of COVID-19, acting as dispersal hubs for the virus in the countryside of the state. Using data compiled at the Epidemiological Surveillance and Social Assistance centers of these two municipalities, this study performs a series of spatial and geostatistical analyses, as well as using descriptive statistics to identify patterns common to the two municipalities, especially in areas of greater social vulnerability. In addition to identifying areas with higher concentrations of COVID-19 cases and deaths, the Vulnerability Propensity Index (IPV) is developed to identify areas of greater socioeconomic vulnerability. Thus, the work contributes to spatial analysis methodologies through Geographic Information Systems (GIS), not only for understanding COVID-19, but also by composing a list of methodological guidelines that can be applied to other severe respiratory syndromes. The results obtained at the end of the master's thesis comprise a synthesis map of the vulnerabilities of COVID-19 in the cities studied. Thus, it allows for advances in the spatial analysis of infectious diseases and for other waves of COVID-19, as well as being able to collaborate with urban health planning, highlighting priority areas for surveillance in future outbreaks of the disease and for continuous education actions.
In this article, we highlight the contributions of passive experiments that address important exercise‐related questions in integrative physiology and medicine. Passive experiments differ from active experiments in that passive experiments involve limited or no active intervention to generate observations and test hypotheses. Experiments of nature and natural experiments are two types of passive experiments. Experiments of nature include research participants with rare genetic or acquired conditions that facilitate exploration of specific physiological mechanisms. In this way, experiments of nature are parallel to classical “knockout” animal models among human research participants. Natural experiments are gleaned from data sets that allow population‐based questions to be addressed. An advantage of both types of passive experiments is that more extreme and/or prolonged exposures to physiological and behavioral stimuli are possible in humans. In this article, we discuss a number of key passive experiments that have generated foundational medical knowledge or mechanistic physiological insights related to exercise. Both natural experiments and experiments of nature will be essential to generate and test hypotheses about the limits of human adaptability to stressors like exercise. © 2023 American Physiological Society. Compr Physiol 13:4879‐4907, 2023.
Difference-in-Differences (DiD) is arguably the most popular quasi-experimental research design. Its canonical form, with two groups and two periods, is well-understood. However, empirical practices can be ad hoc when researchers go beyond that simple case. This article provides an organizing framework for discussing different types of DiD designs and their associated DiD estimators. It discusses covariates, weights, handling multiple periods, and staggered treatments. The organizational framework, however, applies to other extensions of DiD methods as well.
This study presents a wastewater-based mathematical model for assessing the transmission dynamics of the SARS-CoV-2 pandemic in Miami-Dade County, Florida. The model, which takes the form of a deterministic system of nonlinear differential equations, monitors the temporal dynamics of the disease, as well as changes in viral RNA concentration in the county’s wastewater system (which consists of three sewage treatment plants). The model was calibrated using the wastewater data during the third wave of the SARS-CoV-2 pandemic in Miami-Dade (specifically, the time period from July 3, 2021 to October 9, 2021). The calibrated model was used to predict SARS-CoV-2 case and hospitalization trends in the county during the aforementioned time period, showing a strong correlation between the observed (detected) weekly case data and the corresponding weekly data predicted by the calibrated model. The model’s prediction of the week when maximum number of SARS-CoV-2 cases will be recorded in the county during the simulation period precisely matches the time when the maximum observed/reported cases were recorded (which was August 14, 2021). Furthermore, the model’s projection of the maximum number of cases for the week of August 14, 2021 is about 15 times higher than the maximum observed weekly case count for the county on that day (i.e., the maximum case count estimated by the model was 15 times higher than the actual/observed count for confirmed cases). This result is consistent with the result of numerous SARS-CoV-2 modeling studies (including other wastewater-based modeling, as well as statistical models) in the literature. Furthermore, the model accurately predicts a one-week lag between the peak in weekly COVID-19 case and hospitalization data during the time period of the study in Miami-Dade, with the model-predicted hospitalizations peaking on August 21, 2021. Detailed time-varying global sensitivity analysis was carried out to determine the parameters (wastewater-based, epidemiological and biological) that have the most influence on the chosen response function—the cumulative viral load in the wastewater. This analysis revealed that the transmission rate of infectious individuals, shedding rate of infectious individuals, recovery rate of infectious individuals, average fecal load per person per unit time and the proportion of shed viral RNA that is not lost in sewage before measurement at the wastewater treatment plant were most influential to the response function during the entire time period of the study. This study shows, conclusively, that wastewater surveillance data can be a very powerful indicator for measuring (i.e., providing early-warning signal and current burden) and predicting the future trajectory and burden (e.g., number of cases and hospitalizations) of emerging and re-emerging infectious diseases, such as SARS-CoV-2, in a community.
Infectious diseases have long been acknowledged as significant public health menaces by both the general public and health authorities, emphatically underscoring the crucial necessity for highly efficacious prevention and control strategies. Within the realm of statistical physics and complex systems, optimal control theory emerges as a fundamental and indispensable framework for formulating these preventive measures. Simultaneously, networked reaction–diffusion systems have emerged as essential tools for comprehensively understanding the complex dynamics of infectious disease transmission. These systems integrate diverse and essential aspects of human spatial behavior, including habitat distribution, small-world network properties, and large-scale movement patterns, key elements in the in-depth study of complex systems. Consequently, there is a rapidly burgeoning interest in exploring the optimal control problems associated with these reaction–diffusion equations. However, study on the complex dynamics and optimal control of network infectious disease models remains limited, especially in the context of higher-order networks that introduce additional layers of complexity. This article reviews recent advances in the dynamics and optimal control of networked reaction–diffusion systems, underscoring their vital and irreplaceable role in disease prevention and control. We deep dive into the dynamics within both regular and complex networks, investigating how network structure and diffusion parameters influence disease transmission. Furthermore, we comprehensively expound upon several optimal control strategies, including sparse and local optimal control, and propose a comprehensive approach that integrates both reaction and diffusion terms. Finally, we outline future research directions, emphasizing the great potential of integrated strategies to effectively tackle spatial disease transmission, thereby providing a solid theoretical foundation and practical guidance for related fields within the expansive domain of statistical physics and complex systems.
In the last two centuries, there has been a general improvement in the health of people worldwide that has been attributed largely to changes in nutrition, hygiene, and public health. At the beginning of the 19th century, the burden of morbidity and mortality from infectious diseases such as malaria, cholera, measles, tuberculosis, and diarrheal disease, and nutritional deficiency diseases such as pellagra, rickets, and vitamin A deficiency, were relatively high in Europe, North America, and much of the rest of world. By the end of the 20th century, these diseases were largely eradicated from industrialized countries, but many of these diseases and their associated morbidity and mortality continue to be major problems in developing countries today. Mortality rates from infectious diseases have generally been declining in industrialized countries over the last 200 years, and improved nutrition and resistance to disease as well as better hygiene and sanitation have been cited as the main factors for a reduction in infectious disease mortality rather than technological advances in medicine (1–4).
Untreated sewage discharges leading to environmental contamination are increasingly common in communities across the globe. The cause of these discharges ranges from sewer lines in disrepair, blockages, and in the era of more extreme wet weather events, the infiltration of stormwater into the system during heavy downpours. Regardless of the driver of these events, the aftermath results in raw sewage spilling into local waterways, city streets, and commercial and residential structures. Historical research in public health has thoroughly documented the connection between exposure to untreated sewage and waterborne disease. Recent research has detected antibiotic-resistant bacteria at wastewater treatment facilities at a time when deaths by antibiotic-resistant infections are on the rise. However, no research has explored the exposure pathways of antibiotic-resistant bacteria during sanitary sewer overflows and household-level sewage backups. In this commentary, we aim to introduce this new frontier of environmental health risks and disasters. To do this, we describe the history of modern sanitation and sewer infrastructure with a particular focus on wastewater infrastructure in the U.S. We also explore emerging risks and current methods for identifying antibiotic-resistant bacteria in the environment. We end with future directions for interdisciplinary scholarship at the nexus of urban planning, engineering, and public health by introducing the Water Emergency Team (WET) Project. WET is a community-based multi-method effort to identify environmental risks in the aftermath of household backups through (1) residential surveys, (2) indoor visual inspections, (3) environmental sampling, and (4) laboratory processing and reporting. Our hope is that by introducing this comprehensive approach to environmental risks analysis, other scholars will join us in this effort and ultimately towards addressing this grand challenge of our time.
There is extensive evidence that network structure (e.g., air transport, rivers, or roads) may significantly enhance the spread of epidemics into the surrounding geographical area. A new compartmental modeling framework is proposed which couples well-mixed (ODE in time) population centers at the vertices, 1D travel routes on the graph’s edges, and a 2D continuum containing the rest of the population to simulate how an infection spreads through a population. The edge equations are coupled to the vertex ODEs through junction conditions, while the domain equations are coupled to the edges through boundary conditions. A numerical method based on spatial finite differences for the edges and finite elements in the 2D domain is described to approximate the model, and numerical verification of the method is provided. The model is illustrated on two simple and one complex example geometries, and a parameter study example is performed. The observed solutions exhibit exponential decay after a certain time has passed, and the cumulative infected population over the vertices, edges, and domain tends to a constant in time but varying in space, i.e., a steady state solution.
The life course approach scrutinizes factors that shape the development of diseases over time. Tooth loss, which is influenced by social, behavioral and biological factors, can occur at various stages of life and tends to become more prevalent in later years. This systematic review examined the influence of socioeconomic, psychosocial, biological and behavioral adversities in life on the likelihood of tooth loss. Searches were conducted in the Embase, PubMed, Web of Science, Ovid, PsycINFO, Scopus and LILACS databases. Reference management was performed using EndNote online. The risk of bias was appraised using the Newcastle–Ottawa Scale (NOS). The electronic searches yielded 1366 records, 17 of which (13 cohort and four cross-sectional studies) met the inclusion criteria. According to the NOS, all studies had a low risk of bias. Two studies found a link between a lower education and higher incidence of tooth loss and socioeconomic status exerted a significant influence in 47% of the studies. Disadvantaged socioeconomic trajectories and health-related factors, such as smoking, general health perception and oral health behaviors, increased the likelihood of tooth loss. Factors such as dental visits, a history of toothache and exposure to fluoridated water influenced the likelihood of tooth loss. Individuals who experienced adversities in socioeconomic, behavioral and biological aspects throughout their life course were more prone to tooth loss.
This study examines the impact of decentralisation on corruption levels in water utilities across central India, challenging the widely held assumption that decentralisation reduces corruption. Using experience-based measures and a natural experiment approach, the research compares corruption levels between centralised and decentralised water supply agencies Madhya Pradesh and Chhattisgarh. The study employs a longitudinal survey conducted from over five years, focusing on bribery in various aspects of water service provision. Results indicate that decentralised agencies exhibit significantly higher levels of corruption compared to centralised ones, particularly in the initial stages of decentralisation. While a gradual reduction in corruption is observed over time, the difference between decentralised and centralised utilities remains significant in the medium term. These findings contradict the simplistic view of decentralisation as a cure-all for governance issues and highlight the need for a more nuanced understanding of its effects on public service provision. The study contributes to the ongoing debate on decentralisation policies and their implications for corruption in developing countries, offering valuable insights for managers
Within the realm of the Majapahit Empire, Indonesia (ca. 730 to 550 BCE), terracotta ring wells were used as latrines. Such use discontinued after the demise of the empire and no traces are found of them elsewhere in the Indonesian Archipelago. This mystery of their abandonment at Majapahit has never previously been explored but a viable solution is presented here based on historical precedents, hydrogeological reasoning and climate change.
To cope with risks of increasing climate changes and curb carbon emission, various policies have been implemented to facilitate energy transition in China. However, it remains unclear that whether the cash flow of energy enterprises is affected by energy transition policy and whether they invest more on R&D activities to transition. To answer these questions, we utilize a difference-in-differences method to detect the impact of the Peaking-Carbon-Dioxide-Emissions policy on the cash flow uncertainty of the energy enterprises and examine the interaction between it and R&D expenditures by using the sample of listed energy enterprises in China during 2008–2021. We find that the energy transition policy has a positive effect on the cash flow uncertainty of the energy enterprises, and the higher cash flow uncertainty after the policy further decreases the R&D expenditure of the energy enterprises. We also find that this negative role of the cash flow uncertainty is partially conducted by the reduction proportion of the long-term loan. In addition, the over-valued enterprises have stronger incentives to squeeze the expenses of R&D activities. Last, we capture the heterogeneity that the energy enterprises with less political connections and in more developed areas prefer prudent strategy management to maintain their investments in R&D activities.
The highest priority during the response to an outbreak or epidemic is to limit morbidity and mortality by preventing new cases and improving outcomes in those who are affected. Epidemiologic research is an essential component of such a response. Well-designed and rapidly executed observational epidemiologic studies, often done in conjunction with laboratory and environmental investigations and informed by relevant behavioral and social sciences knowledge, can help guide the development and implementation of interventions that ameliorate the current outbreak, as well as contribute to more effective and earlier responses to future outbreaks. While generic, “off the shelf” protocols for studies of some recurring, outbreak-prone infectious diseases (e.g., cholera and influenza) may help expedite such studies, they are no substitute for well-trained, experienced epidemiologists who are available for rapid deployment wherever and whenever they are needed to plan and carry out such studies. Experience with recent epidemics and pandemics caused by a wide range of infectious disease agents (e.g., Ebola, Zika, severe acute respiratory syndrome [SARS or SARS-1], influenza, and coronavirus disease 2019 [COVID-19]) illustrates the central contribution of observational epidemiologic studies to enhanced understanding of and improved response to both new and old infectious disease threats. Following the COVID-19 pandemic, there will be considerable investment, at least in the short term, in accelerating outbreak response research, including its epidemiologic dimensions. Assuring that such research is thoughtfully designed and carefully implemented is a high priority.
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