When and why direct transmission models can be used for environmentally persistent pathogens.

Variants of the susceptible-infected-removed (SIR) model of Kermack & McKendrick (1927) enjoy wide application in epidemiology, offering simple yet powerful inferential and predictive tools in the study of diverse infectious diseases across human, animal and plant populations. Direct transmission models (DTM) are a subset of these that treat the processes of disease transmission as comprising a series of discrete instantaneous events. Infections transmitted indirectly by persistent environmental pathogens, however, are examples where a DTM description might fail and are perhaps better described by models that comprise explicit environmental transmission routes, so-called environmental transmission models (ETM). In this paper we discuss the stochastic susceptible-exposed-infected-removed (SEIR) DTM and susceptible-exposed-infected-removed-pathogen (SEIR-P) ETM and we show that the former is the timescale separation limit of the latter, with ETM host-disease dynamics increasingly resembling those of a DTM when the pathogen's characteristic timescale is shortened, relative to that of the host population. Using graphical posterior predictive checks (GPPC), we investigate the validity of the SEIR model when fitted to simulated SEIR-P host infection and removal times. Such analyses demonstrate how, in many cases, the SEIR model is robust to departure from direct transmission. Finally, we present a case study of white spot disease (WSD) in penaeid shrimp with rates of environmental transmission and pathogen decay (SEIR-P model parameters) estimated using published results of experiments. Using SEIR and SEIR-P simulations of a hypothetical WSD outbreak management scenario, we demonstrate how relative shortening of the pathogen timescale comes about in practice. With atttempts to remove diseased shrimp from the population every 24h, we see SEIR and SEIR-P model outputs closely conincide. However, when removals are 6-hourly, the two models' mean outputs diverge, with distinct predictions of outbreak size and duration.

Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves.

We construct a recursive Bayesian smoother, termed EpiFilter, for estimating the effective reproduction number, R, from the incidence of an infectious disease in real time and retrospectively. Our approach borrows from Kalman filtering theory, is quick and easy to compute, generalisable, deterministic and unlike many current methods, requires no change-point or window size assumptions. We model R as a flexible, hidden Markov state process and exactly solve forward-backward algorithms, to derive R estimates that incorporate all available incidence information. This unifies and extends two popular methods, EpiEstim, which considers past incidence, and the Wallinga-Teunis method, which looks forward in time. We find that this combination of maximising information and minimising assumptions significantly reduces the bias and variance of R estimates. Moreover, these properties make EpiFilter more statistically robust in periods of low incidence, where several existing methods can become destabilised. As a result, EpiFilter offers improved inference of time-varying transmission patterns that are advantageous for assessing the risk of upcoming waves of infection or the influence of interventions, in real time and at various spatial scales.

Climate change and child health: a scoping review and an expanded conceptual framework.

Climate change can have detrimental effects on child health and wellbeing. Despite the imperative for a fuller understanding of how climate change affects child health and wellbeing, a systematic approach and focus solely on children (aged <18 years) has been lacking. In this Scoping Review, we did a literature search on the impacts of climate change on child health from January, 2000, to June, 2019. The included studies explicitly linked an alteration of an exposure to a risk factor for child health to climate change or climate variability. In total, 2970 original articles, reviews, and other documents were identified, of which 371 were analysed. Employing an expanded framework, our analysis showed that the effects of climate change on child health act through direct and indirect pathways, with implications for determinants of child health as well as morbidity and mortality from a range of diseases. This understanding can be further enhanced by using a broader range of research methods, studying overlooked populations and geographical regions, investigating the costs and benefits of mitigation and adaptation for child health, and considering the position of climate change and child health within the UN Sustainable Development Goals. Present and future generations of children bear and will continue to bear an unacceptably high disease burden from climate change.

Connectivity, reproduction number, and mobility interact to determine communities' epidemiological superspreader potential in a metapopulation network.

Disease epidemic outbreaks on human metapopulation networks are often driven by a small number of superspreader nodes, which are primarily responsible for spreading the disease throughout the network. Superspreader nodes typically are characterized either by their locations within the network, by their degree of connectivity and centrality, or by their habitat suitability for the disease, described by their reproduction number (R). Here we introduce a model that considers simultaneously the effects of network properties and R on superspreaders, as opposed to previous research which considered each factor separately. This type of model is applicable to diseases for which habitat suitability varies by climate or land cover, and for direct transmitted diseases for which population density and mitigation practices influences R. We present analytical models that quantify the superspreader capacity of a population node by two measures: probability-dependent superspreader capacity, the expected number of neighboring nodes to which the node in consideration will randomly spread the disease per epidemic generation, and time-dependent superspreader capacity, the rate at which the node spreads the disease to each of its neighbors. We validate our analytical models with a Monte Carlo analysis of repeated stochastic Susceptible-Infected-Recovered (SIR) simulations on randomly generated human population networks, and we use a random forest statistical model to relate superspreader risk to connectivity, R, centrality, clustering, and diffusion. We demonstrate that either degree of connectivity or R above a certain threshold are sufficient conditions for a node to have a moderate superspreader risk factor, but both are necessary for a node to have a high-risk factor. The statistical model presented in this article can be used to predict the location of superspreader events in future epidemics, and to predict the effectiveness of mitigation strategies that seek to reduce the value of R, alter host movements, or both.

Impact of environmental changes on infectious diseases: Key findings from an international conference in Trieste, Italy in May 2017.

Elsevier's 2nd conference on "Impact of Environmental Changes on Infectious Diseases" (IECID), convened in May 2017 in Trieste, Italy, brought together some 120 researchers from more than 20 countries. They presented the latest findings and discussed the impact of current and predicted future environmental changes on infectious disease dynamics in humans, livestock and wildlife in different parts of the world. Particular emphasis was placed on food-, vector- and water-borne diseases within the general theme of infectious diseases of poverty and emerging and re-emerging diseases. The potential impact of mobility, travel, population growth, trade and globalization on infectious disease dynamics against the background of a changing climate, land use, air quality and urbanization on individual, population, ecosystem and planetary health were addressed. Speakers at the conference were encouraged to put forth their talks into stand-alone manuscripts, which resulted in a unique collection of 13 articles, now brought together into a thematic issue of Acta Tropica. In this umbrella piece, we synthesize key findings from the published articles and highlight potential actions that might be taken forward to prevent and mitigate the impact of environmental change on infectious diseases. The work presented is salient in the current era of the Sustainable Development Goals.

Wealth distribution under the spread of infectious diseases.

We develop a mathematical framework to study the economic impact of infectious diseases by integrating epidemiological dynamics with a kinetic model of wealth exchange. The multiagent description leads to the study of the evolution over time of a system of kinetic equations for the wealth densities of susceptible, infectious, and recovered individuals, whose proportions are driven by a classical compartmental model in epidemiology. Explicit calculations show that the spread of the disease seriously affects the distribution of wealth, which, unlike the situation in the absence of epidemics, can converge toward a stationary state with a bimodal form. Furthermore, simulations confirm the ability of the model to describe different phenomenon characteristics of economic trends in situations compromised by the rapid spread of an epidemic, such as the unequal impact on the various wealth classes and the risk of a shrinking middle class.

[From "culprits" to "victims:" the expansion and professionalization of the healthcare system in the province of Mendoza in the late nineteenth and early twentieth century]. / De "culpables" a "víctimas": expansión y profesionalización del sistema de salud en la provincia Mendoza a fines del siglo XIX y principios del XX.

From the late 19th century to the beginning of the 20th, the province of Mendoza presented problematic sanitary conditions due to rapid demographic and urban growth, the scarcity of public services, and the poor state of the old colonial city (destroyed by the 1861 earthquake), which facilitated the spread of various infectious diseases. The objective of this article is to inquire into the ways in which the healthcare system in the province of Mendoza both expanded and became increasingly professionalized from the late 19th to early 20th century. We explore how these factors, along with the predominant social representations of disease that permeated the discourses of governing elites, influenced public policy aimed at combating the diseases of the time. To that end, we consulted a wide range of written documents and photographic material that allowed us to analyze changes in discourse as well as public policy.

Entre fines del siglo XIX y comienzos del XX, la provincia de Mendoza presentaba un estado sanitario marcado por el crecimiento demográfico y urbanístico, la escasez de los servicios públicos y la destrucción de la antigua ciudad colonial como consecuencia del terremoto de 1861, lo que propiciaba un ambiente favorable para el desarrollo de diversas enfermedades infectocontagiosas. El objetivo de este artículo es indagar cómo se fue profesionalizando y expandiendo el sistema de salud en la provincia de Mendoza a fines del siglo XIX e inicios del XX, y cómo esos factores, junto con las representaciones sobre la enfermedad que predominaban en el discurso de la elite gobernante, incidieron en las políticas públicas para combatir las dolencias de la época. Para ello se consultaron diversos documentos escritos y fotográficos que permitieron analizar las modificaciones del discurso y las políticas públicas implementadas.

An exact and implementable computation of the final outbreak size distribution under Erlang distributed infectious period.

This paper deals with a stochastic SIR (Susceptible-Infected-Recovered) model with Erlang(k,µ) distributed infectious period commonly referred as SIkR model. We show that using the total number of remaining Erlang stages as the state variable, we do not need to keep track of the stages of individual infections, and can employ a first step analysis to efficiently obtain quantities of interest. We study the distribution of the total number of recovered individuals and the distribution of the maximum number of individuals who are simultaneously infected until the end of the disease. In the literature, final outbreak size is calculated only for a small population size exactly and derivations of approximate analytic solutions from asymptotic results are suggested for larger population sizes. We numerically demonstrate that our methods are implementable on large size problem instances.

A note on species richness and the variance of epidemic severity.

The commonly observed negative correlation between the number of species in an ecological community and disease risk, typically referred to as "the dilution effect", has received a substantial amount of attention over the past decade. Attempts to test this relationship experimentally have revealed that, in addition to the mean disease risk decreasing with species number, so too does the variance of disease risk. This is referred to as the "variance reduction effect", and has received relatively little attention in the disease-diversity literature. Here, we set out to clarify and quantify some of these relationships in an idealized model of a randomly assembled multi-species community undergoing an epidemic. We specifically investigate the variance of the community disease reproductive ratio, a multi-species extension of the basic reproductive ratio [Formula: see text], for a family of random-parameter community SIR models, and show how the variance of community [Formula: see text] varies depending on whether transmission is density or frequency-dependent. We finally outline areas of further research on how changes in variance affect transmission dynamics in other systems.

Treating head and neck tumors during the SARS-CoV-2 epidemic, 2019 to 2020: Sichuan Cancer Hospital.

Since December 2019, a number of patients with novel coronavirus pneumonia (NCP) have been identified in Wuhan, Hubei Province, China. NCP has rapidly spread to other provinces and cities in China and other countries in the world. Due to the rapid increase in reported cases in China and around the world, on January 30, 2020, the World Health Organization (WHO) Emergency Committee announced that NCP is a Public Health Emergency of International Concern (PHEIC). However, there are relatively few suggestions and measures for tumor patients, especially patients with head and neck tumors. This article summarizes the prevention and control of disease in our medical institution to provide a reference for front-line head and neck surgeons.

Realigning the conventional routes of transmission: an improved model for occupational exposure assessment and infection prevention.

Current recommendations for standard and transmission-based precautions in place for patients who are suspected or known to be infected or colonized with infectious agents are best suited to prevent the transfer of micro-organisms to other patients - that is, to prevent the acquisition of a healthcare-associated infection, rather than to protect the healthcare worker from self-contamination resulting in a potential occupationally acquired infection. This article reviews current recommended infection prevention and control practices and offers a framework for better protection and controls from an occupational health point of view. We offer a model with two exposure routes - contact and aerosol - resulting from work activities and environments, shifting the focus away from particular pathogenic micro-organisms' typical methods for spreading to patients or to other non-workers in hospital and community settings.

Success of Big Infectious Disease Reimbursement Policy in China.

Big infectious diseases do harm to the whole society, and it is highly crucial to control them on time. The major purpose of this article is to theoretically demonstrate that the Chinese government's intervention in large-scale infectious diseases is successful and efficient. Two potential strategies were considered: strategy 1 was infectious disease without government intervention, and strategy 2 was infectious disease with government intervention. By evolution model, this article illustrates the efficiency of big infectious disease reimbursement policy in China. Without government reimbursement, this article finds that high expenditures accelerate the disease infection. The number of infected persons decreases under big infectious disease reimbursement policy in China. The higher the treatment costs, the more important the government intervention. Big infectious disease reimbursement policy in China can serve as an efficient example to cope with big infectious diseases.

De "culpables" a "víctimas": expansión y profesionalización del sistema de salud en la provincia Mendoza a fines del siglo XIX y principios del XX / From "culprits" to "victims": the expansion and professionalization of the healthcare system in the province of Mendoza in the late nineteenth and early twentieth century

RESUMEN Entre fines del siglo XIX y comienzos del XX, la provincia de Mendoza presentaba un estado sanitario marcado por el crecimiento demográfico y urbanístico, la escasez de los servicios públicos y la destrucción de la antigua ciudad colonial como consecuencia del terremoto de 1861, lo que propiciaba un ambiente favorable para el desarrollo de diversas enfermedades infectocontagiosas. El objetivo de este artículo es indagar cómo se fue profesionalizando y expandiendo el sistema de salud en la provincia de Mendoza a fines del siglo XIX e inicios del XX, y cómo esos factores, junto con las representaciones sobre la enfermedad que predominaban en el discurso de la elite gobernante, incidieron en las políticas públicas para combatir las dolencias de la época. Para ello se consultaron diversos documentos escritos y fotográficos que permitieron analizar las modificaciones del discurso y las políticas públicas implementadas.

ABSTRACT From the late 19th century to the beginning of the 20th, the province of Mendoza presented problematic sanitary conditions due to rapid demographic and urban growth, the scarcity of public services, and the poor state of the old colonial city (destroyed by the 1861 earthquake), which facilitated the spread of various infectious diseases. The objective of this article is to inquire into the ways in which the healthcare system in the province of Mendoza both expanded and became increasingly professionalized from the late 19th to early 20th century. We explore how these factors, along with the predominant social representations of disease that permeated the discourses of governing elites, influenced public policy aimed at combating the diseases of the time. To that end, we consulted a wide range of written documents and photographic material that allowed us to analyze changes in discourse as well as public policy.

Incorporating Contact Network Uncertainty in Individual Level Models of Infectious Disease using Approximate Bayesian Computation.

Infectious disease transmission between individuals in a heterogeneous population is often best modelled through a contact network. However, such contact network data are often unobserved. Such missing data can be accounted for in a Bayesian data augmented framework using Markov chain Monte Carlo (MCMC). Unfortunately, fitting models in such a framework can be highly computationally intensive. We investigate the fitting of network-based infectious disease models with completely unknown contact networks using approximate Bayesian computation population Monte Carlo (ABC-PMC) methods. This is done in the context of both simulated data, and data from the UK 2001 foot-and-mouth disease epidemic. We show that ABC-PMC is able to obtain reasonable approximations of the underlying infectious disease model with huge savings in computation time when compared to a full Bayesian MCMC analysis.

Local risk perception enhances epidemic control.

As infectious disease outbreaks emerge, public health agencies often enact vaccination and social distancing measures to slow transmission. Their success depends on not only strategies and resources, but also public adherence. Individual willingness to take precautions may be influenced by global factors, such as news media, or local factors, such as infected family members or friends. Here, we compare three modes of epidemiological decision-making in the midst of a growing outbreak using network-based mathematical models that capture plausible heterogeneity in human contact patterns. Individuals decide whether to adopt a recommended intervention based on overall disease prevalence, the proportion of social contacts infected, or the number of social contacts infected. While all strategies can substantially mitigate transmission, vaccinating (or self isolating) based on the number of infected acquaintances is expected to prevent the most infections while requiring the fewest intervention resources. Unlike the other strategies, it has a substantial herd effect, providing indirect protection to a large fraction of the population.

Desirable BUGS in models of infectious diseases.

Bayesian inference using Gibbs sampling (BUGS) is a set of statistical software that uses Markov chain Monte Carlo (MCMC) methods to estimate almost any specified model. Originally developed in the late 1980s, the software is an excellent introduction to applied Bayesian statistics without the need to write a MCMC sampler. The software is typically used for regression-based analyses, but any model that can be specified using graphical nodes are possible. Advanced topics such as missing data, spatial analysis, model comparison and dynamic infectious disease models can be tackled. Three examples are provided; a linear regression model to illustrate parameter estimation, the steps to ensure that the estimates have converged and a comparison of run-times across different computing platforms. The second example describes a model that estimates the probability of being vaccinated from cross-sectional and surveillance data, and illustrates the specification of different models, model comparison and data augmentation. The third example illustrates estimation of parameters within a dynamic Susceptible-Infected-Recovered model. These examples show that BUGS can be used to estimate parameters from models relevant for infectious diseases, and provide an overview of the relative merits of the approach taken.

Current strategy for local- to global-level molecular epidemiological characterisation of global antimicrobial resistance surveillance system pathogens.

The prime goal of molecular epidemiology is to identify the origin and evolution of pathogens, which can potentially influence the public health worldwide. Traditional methods provide limited information which is not sufficient for outbreak investigation and studying transmission dynamics. The recent advancement of next-generation sequencing had a major impact on molecular epidemiological studies. Currently, whole-genome sequencing (WGS) has become the gold standard typing method, especially for clinically significant pathogens. Here, we aimed to describe the application of appropriate molecular typing methods for global antimicrobial resistance surveillance system pathogens based on the level of discrimination and epidemiological settings. This shows that sequence-based methods such as multi-locus sequence typing (MLST) are widely used due to cost-effectiveness and database accessibility. However, WGS is the only method of choice for studying Escherichia coli and Shigella spp. WGS is shown to have higher discrimination than other methods in typing Klebsiella pneumoniae, Acinetobacter baumannii and Salmonella spp. due to its changing accessory genome content. For Gram positives such as Streptococcus pneumoniae, WGS would be preferable to understand the evolution of the strains. Similarly, for Staphylococcus aureus, combination of MLST, staphylococcal protein A or SCCmec typing along with WGS could be the choice for epidemiological typing of hospital- and community-acquired strains. This review highlights that combinations of different typing methods should be used to get complete information since no one standalone method is sufficient to study the varying genome diversity.

Introduction to particle Markov-chain Monte Carlo for disease dynamics modellers.

The particle Markov-chain Monte Carlo (PMCMC) method is a powerful tool to efficiently explore high-dimensional parameter space using time-series data. We illustrate an overall picture of PMCMC with minimal but sufficient theoretical background to support the readers in the field of biomedical/health science to apply PMCMC to their studies. Some working examples of PMCMC applied to infectious disease dynamic models are presented with R code.