Propagation Of Infectious Diseases Research Articles

The effects of evolutionary adaptations on spreading processes in complex networks

A common theme among previously proposed models for network epidemics is the assumption that the propagating object (e.g., a pathogen [in the context of infectious disease propagation] or a piece of information [in the context of information propagation]) is transferred across network nodes without going through any modification or evolutionary adaptations. However, in real-life spreading processes, pathogens often evolve in response to changing environments and medical interventions, and information is often modified by individuals before being forwarded. In this article, we investigate the effects of evolutionary adaptations on spreading processes in complex networks with the aim of 1) revealing the role of evolutionary adaptations on the threshold, probability, and final size of epidemics and 2) exploring the interplay between the structural properties of the network and the evolutionary adaptations of the spreading process.

The Effects of Health Spending on the Propagation of Infectious Diseases

Unraveling the relation of health spending and outcomes between bordering jurisdictions is a first-order concern to better coordinate public health provision. In this paper, we explore the rules governing the main federal grant to Brazilian municipalities, which varies discontinuously near population thresholds, to identify the effects of a subsequent exogenous increase in local health spending on bordering towns' health inputs and outcomes. We provide evidence that 1% increase in health spending reduces hospitalization for respiratory infectious diseases by 1 to 3%. Second, we find that both the smallest and largest neighbors benefit by reducing infant and elderly mortality and hospitalization for infectious diseases. Third, the only input that significantly increases is the number of physicians, corroborating that primary care by medical professionals helps to detain the proliferation an the worsening of infectious diseases. Finally, fiscal reactions are asymmetric: small jurisdictions reduce health spending while large neighbors spend more, mainly hiring more physicians.

Characterization of Aerosol Emission from Single-film Rupture in a Tube

ABSTRACT The generation of aerosols during “silent” tidal breathing via the bronchiole fluid film burst (BFFB) mechanism, which involves the rupturing of mucus meniscus or film in terminal bronchioles, has been described in recent studies. To replicate the BFFB mechanism and identify the characteristics of aerosol generation during normal breathing, this study set up a single-film generation system employing tubes ranging from 0.7 to 2.94 cm in diameter that simulated the bronchioles. A liquid film of artificial mucus or soap solution was applied on the bottom of each tube and moved upward by filtered carrier air, which eventually led to the rupturing of the film. The resultant airborne particles (> 7 nm) were then counted with a condensation particle counter, and the number size distributions (0.6–20 µm) were measured with an Aerodynamic Particle Sizer. The experimental results show that the film’s rising velocity, rise distance and surface tension in addition to the tube diameter all affected the total particle count and the size distribution. The total particle count increased with the rising velocity until the latter reached 3 cm s–1 and then decreased as the velocity continued growing—a phenomenon that was mainly due to the curvature of the film increasing with the velocity. Moreover, the larger the tube diameter, the higher the particle count. When a 0.9% NaCl solution was added to increase the surface tension of the film, the total particle count decreased as the surface tension increased, regardless of whether artificial mucus or soap solution was used. This approach to reducing the propagation of infectious diseases in healthcare facilities seems to merit further exploration.

A two-patch epidemic model with nonlinear reinfection

The propagation of infectious diseases and its impact on individuals play a major role in disease dynamics, and it is important to incorporate population heterogeneity into efforts to study diseases. As a simplistic but illustrative example, we examine interactions between urban and rural populations on the dynamics of disease spreading. Using a compartmental framework of susceptible–infected susceptible (SIS) dynamics with some level of immunity, we formulate a model that allows non linear reinfection. We investigate the effects of population movement in a simple scenario: a case with two patches, which allows us to model population movement between urban and rural areas. To study the dynamics of the system, we compute a basic reproduction number for each population (urban and rural). We also compute steady states, determine the local stability of the disease-free steady state, and identify conditions for the existence of endemic steady states. From our analysis and computational experiments, we illustrate that population movement plays an important role in disease dynamics. In some cases, it can be rather beneficial, as it can enlarge the region of stability of a disease-free steady state.

Stochastic Approach in Epidemic Modeling Using the SEIRS

The study of infectious diseases represents one of the oldest and richest sectors of biomathematics. The transmission dynamics of these diseases are still a major problem in mathematical epidemiology. In this work, we propose a stochastic version of a SEIRS epidemiological model for infectious diseases evolving in a random environment for the propagation of infectious diseases. This random model takes into account the rates of immigration and mortality in each compartment and the spread of these diseases follows a four-state Makovian process. We first study the stability of the model and then estimate the marginal parameters of each disease state over time. Real measles data are applied to the model.

The Kendrick modelling platform: language abstractions and tools for epidemiology

BackgroundMathematical and computational models are widely used to study the transmission, pathogenicity, and propagation of infectious diseases. Unfortunately, complex mathematical models are difficult to define, reuse and reproduce because they are composed of several concerns that are intertwined. The problem is even worse for computational models because the epidemiological concerns are also intertwined with low-level implementation details that are not easily accessible to non-computing scientists. Our goal is to make compartmental epidemiological models easier to define, reuse and reproduce by facilitating implementation of different simulation approaches with only very little programming knowledge.ResultsWe achieve our goal through the definition of a domain-specific language (DSL), Kendrick, that relies on a very general mathematical definition of epidemiological concerns as stochastic automata that are combined using tensor-algebra operators. A very large class of epidemiological concerns, including multi-species, spatial concerns, control policies, sex or age structures, are supported and can be defined independently of each other and combined into models to be simulated by different methods. Implementing models does not require sophisticated programming skills any more. The various concerns involved within a model can be changed independently of the others as well as reused within other models. They are not plagued by low-level implementation details.ConclusionsKendrick is one of the few DSLs for epidemiological modelling that does not burden its users with implementation details or required sophisticated programming skills. It is also currently the only language for epidemiology modelling that supports modularity through clear separation of concerns hence fostering reproducibility and reuse of models and simulations. Future work includes extending Kendrick to support non-compartmental models and improving its interoperability with existing complementary tools.

Extracellular vesicles in renal disease.

Extracellular vesicles, such as exosomes and microvesicles, are host cell-derived packages of information that allow cell-cell communication and enable cells to rid themselves of unwanted substances. The release and uptake of extracellular vesicles has important physiological functions and may also contribute to the development and propagation of inflammatory, vascular, malignant, infectious and neurodegenerative diseases. This Review describes the different types of extracellular vesicles, how they are detected and the mechanisms by which they communicate with cells and transfer information. We also describe their physiological functions in cellular interactions, such as in thrombosis, immune modulation, cell proliferation, tissue regeneration and matrix modulation, with an emphasis on renal processes. We discuss how the detection of extracellular vesicles could be utilized as biomarkers of renal disease and how they might contribute to disease processes in the kidney, such as in acute kidney injury, chronic kidney disease, renal transplantation, thrombotic microangiopathies, vasculitides, IgA nephropathy, nephrotic syndrome, urinary tract infection, cystic kidney disease and tubulopathies. Finally, we consider how the release or uptake of extracellular vesicles can be blocked, as well as the associated benefits and risks, and how extracellular vesicles might be used to treat renal diseases by delivering therapeutics to specific cells.

Traveling waves for a diffusive SEIR epidemic model with non-local reaction

• This paper is the first study of the traveling waves of diffusive SEIR model with non-local reaction. • We give a more precise estimation on the traveling wave speed of infectious diseases. • Our results are of great practical value in the control of the propagation of infectious diseases. This paper is devoted to the existence of the traveling waves of the equations describing a diffusive SEIR model with non-local reaction between the infected and the susceptible. The existence of traveling waves depends on the basic reproduction rate S − ∞ β / γ and the minimal speed. We use the Schauder fixed point theorem and the Laplace transform to get the existence and non-existence of traveling waves in our paper. We also give some numerical results of the minimal wave speed.

Reach and speed of judgment propagation in the laboratory

In recent years, a large body of research has demonstrated that judgments and behaviors can propagate from person to person. Phenomena as diverse as political mobilization, health practices, altruism, and emotional states exhibit similar dynamics of social contagion. The precise mechanisms of judgment propagation are not well understood, however, because it is difficult to control for confounding factors such as homophily or dynamic network structures. We introduce an experimental design that renders possible the stringent study of judgment propagation. In this design, experimental chains of individuals can revise their initial judgment in a visual perception task after observing a predecessor's judgment. The positioning of a very good performer at the top of a chain created a performance gap, which triggered waves of judgment propagation down the chain. We evaluated the dynamics of judgment propagation experimentally. Despite strong social influence within pairs of individuals, the reach of judgment propagation across a chain rarely exceeded a social distance of three to four degrees of separation. Furthermore, computer simulations showed that the speed of judgment propagation decayed exponentially with the social distance from the source. We show that information distortion and the overweighting of other people's errors are two individual-level mechanisms hindering judgment propagation at the scale of the chain. Our results contribute to the understanding of social-contagion processes, and our experimental method offers numerous new opportunities to study judgment propagation in the laboratory.

Contact infection of infectious disease onboard a cruise ship.

Cruise tourism has become more popular. Long-term personal contact, complex population flows, a lack of medical care facilities, and defective infrastructure aboard most cruise ships is likely to result in the ship becoming an incubator for infectious diseases. In this paper, we use a cruise ship as a research scenario. Taking into consideration personal behavior, the nature and transfer route of the virus across different surfaces, virus reproduction, and disinfection, we studied contact infection of infectious disease on a cruise ship. Using gastroenteritis caused by the norovirus as an example, we analyzed the characteristics of infectious disease propagation based on simulation results under different conditions. We found hand washing are the most important factors affecting virus propagation and passenger infection. It also decides either the total number of virus microorganisms or the virus distribution in different functional areas. The transfer rate between different surfaces is a key factor influencing the concentricity of the virus. A high transfer rate leads to high concentricity. In addition, the risk of getting infected is effectively reduced when the disinfection frequency is above a certain threshold. The efficiency of disinfection of functional areas is determined by total virus number and total contact times of surfaces.

Self-Propelled Pedestrian Dynamics Model for Studying Infectious Disease Propagation during Air-Travel

Self-Propelled Pedestrian Dynamics Model for Studying Infectious Disease Propagation during Air-Travel

Dynamic population flow based risk analysis of infectious disease propagation in a metropolis

Knowledge on the characteristics of infectious disease propagation in metropolises plays a critical role in guiding public health intervention strategies to reduce death tolls, disease incidence, and possible economic losses. Based on the SIR model, we established a comprehensive spatiotemporal risk assessment model to compute infectious disease propagation within an urban setting using Beijing, China as a case study. The model was developed for a dynamic population distribution using actual data on location, density of residences and offices, and means of public transportation (e.g., subways, buses and taxis). We evaluated four influencing factors including biological, behavioral, environmental parameters and infectious sources. The model output resulted in a set of maps showing how the four influencing factors affected the trend and characteristics of airborne infectious disease propagation in Beijing. We compared the scenarios for the long-term dynamic propagation of infectious disease without governmental interventions versus scenarios with government intervention and hospital coordinated emergency responses. Lastly, the sensitivity of the average number of people at different location in spreading infections is analyzed. Based on our results, we provide valuable recommendations to governmental agencies and the public in order to minimize the disease propagation.

Epidemiological implications of mobility between a large urban centre and smaller satellite cities

An SIR infectious disease propagation model is considered that incorporates mobility of individuals between a large urban centre and smaller satellite cities. Because of the difference in population sizes, the urban centre has standard incidence and satellite cities have mass action incidence. It is shown that the general basic reproduction number {mathcal {R}}_{0} acts as a threshold between global asymptotic stability of the disease free equilibrium and disease persistence. The case of Winnipeg (MB, Canada) and some neighbouring satellite communities is then considered numerically to complement the mathematical analysis, highlighting the importance of taking into account not only {mathcal {R}}_{0} but also other measures of disease severity. It is found that the large urban centre governs most of the behaviour of the general system and control of the spread is better achieved by targeting it rather than reducing movement between the units. Also, the capacity of a satellite city to affect the general system depends on its population size and its connectivity to the main urban centre.

Modeling the evolutionary demographic processes for geomedicine

We describe the principles for constructing evolutionary demographic models for geomedical statistics. Several variants of evolutionary models are proposed: (1) a model of the evolution of a closed population taking into account distribution by age, (2) a model that takes into account the morbidity and difference in mortality for groups of patients and healthy individuals, (3) a model that takes into account the distribution of different age groups by fertile ability, (4) a migration model that takes into account the population exchange between several localities, and (5) a model of the propagation of infectious diseases. Each model depends on a group of parameters determined from the medical and demographic state of the population. We discuss the possible application of the proposed evolutionary models to geomedical statistics.

The contagious nature of imprisonment: an agent-based model to explain racial disparities in incarceration rates.

We build an agent-based model of incarceration based on the susceptible–infected–suspectible (SIS) model of infectious disease propagation. Our central hypothesis is that the observed racial disparities in incarceration rates between Black and White Americans can be explained as the result of differential sentencing between the two demographic groups. We demonstrate that if incarceration can be spread through a social influence network, then even relatively small differences in sentencing can result in large disparities in incarceration rates. Controlling for effects of transmissibility, susceptibility and influence network structure, our model reproduces the observed large disparities in incarceration rates given the differences in sentence lengths for White and Black drug offenders in the USA without extensive parameter tuning. We further establish the suitability of the SIS model as applied to incarceration by demonstrating that the observed structural patterns of recidivism are an emergent property of the model. In fact, our model shows a remarkably close correspondence with California incarceration data. This work advances efforts to combine the theories and methods of epidemiology and criminology.

论医疗机构的传染病防控职能

Infectious disease is a major global public issue and social medical problem to human health. Our country implements a policy of putting the emphasis on prevention, combining prevention with treatment to infectious disease. Medical institutions is the main source of information on infectious diseases. The medical institutions monitor the epidemic situation of infectious disease for early detection and timely accurate report of infectious disease, which provides an important scientific basis for the prevention and control of infectious disease. Medical treatment for infectious disease is carried in medical institutions, and patients with infectious diseases were isolated to prevent and control the propagation of infectious diseases. An in-depth study of epidemic area and accurate diagnosis and treatment provide scientific basis for the prevention of infectious diseases. Medical institution is an effective way for health education of infectious diseases prevention. We should strengthen the legal responsibility for the prevention and treatment of infectious diseases in medical institutions, establish and improve the mechanism of prevention and control of infectious diseases, give full play to the functions of medical institutions, due to medical staff’s less awareness of infectious diseases prevention.

Inferring epidemic network topology from surveillance data.

The transmission of infectious diseases can be affected by many or even hidden factors, making it difficult to accurately predict when and where outbreaks may emerge. One approach at the moment is to develop and deploy surveillance systems in an effort to detect outbreaks as timely as possible. This enables policy makers to modify and implement strategies for the control of the transmission. The accumulated surveillance data including temporal, spatial, clinical, and demographic information, can provide valuable information with which to infer the underlying epidemic networks. Such networks can be quite informative and insightful as they characterize how infectious diseases transmit from one location to another. The aim of this work is to develop a computational model that allows inferences to be made regarding epidemic network topology in heterogeneous populations. We apply our model on the surveillance data from the 2009 H1N1 pandemic in Hong Kong. The inferred epidemic network displays significant effect on the propagation of infectious diseases.

Individual Decision Making Can Drive Epidemics: A Fuzzy Cognitive Map Study

Existing studies on the propagation of infectious diseases have not sufficiently considered the uncertainties that are related to individual behavior and its influence on individual decision making to prevent infections, even though it is well known that changes in behavior can lead to variations in the macrodynamics of the spread of infectious diseases. These influencing factors can be categorized into emotion-related and cognition-related components. We present a fuzzy cognitive map (FCM) denotative model to describe how the factors of individual emotions and cognition influence each other. We adjust the weight matrix of causal relationships between these factors by using a so-called nonlinear Hebbian learning method. Based on this FCM model, we can implement individual decision rules against possible infections for disease propagation studies. We take the simulation of influenza A [H1N1] spreading on a campus as an example. We find that individual decision making against infections (frequent washing, respirator usage, and crowd contact avoidance) can significantly decrease the at-peak number of infected patients, even when common policies, such as isolation and vaccination, are not deployed.

Estimation of leaf wetness duration for greenhouse roses using a dynamic greenhouse climate model in Zimbabwe

Leaf wetness duration (LWD) is one of the most critical parameters involved in the development of plant diseases, since many pathogens require the presence of free water on plant organs to infect foliar tissue. For this reason LWD monitoring is extremely important in crop protection, particularly through the use of weather-related disease forecasting models. Because of the difficulties involved in the measurement of leaf wetness duration, simulation models, based on agrometeorological parameters, are often used as alternatives to field measurement. Furthermore, in greenhouse crop production, these LWD models can be used in predictive control of the greenhouse climate as to suppress the development and propagation of infectious plant diseases. In this study, the Gembloux Dynamic Greenhouse Climate Model (GDGCM) was applied to derive estimates of LWD and hence potential disease incidence, for a rose crop in a naturally ventilated Azrom type greenhouse in Zimbabwe. The model LWD estimates were compared both with data measured by sensors and with visual inspections of LWD conducted during the period May 2007–April 2008. When the GDGCM outputs were used to estimate LWD, the best agreement between measured and predicted LWDs (RMSE=3.2hd−1) were obtained when LWD was calculated as the number of hours that the dew point depression of the air remained below 2.3°C (for wetness onset) and 2.5°C (for drying off). The good agreement between measured and predicted LWD showed that the GDGCM can be used to predict LWD with acceptable accuracy. This could be of considerable value in helping to predict possible outbreaks of certain climate related diseases, like downy mildew, powdery mildew and botrytis. This information can be used by growers to decide on the optimal precautions to take to prevent possible epidemics of these diseases.

Next Steps in Simulating High-risk Infectious Disease Propagation Networks

Simulating HIV transmission networks using agent-based systems has consistently shown promise during the last ten years. In this position paper we briefly discuss the state of the art, as elaborated on our previous work, and propose next steps that incorporate not only current HIV infection propagation approaches but also promising prevention strategies that include sociological markers. We stress that throughout translational validation will be key for approaching real impact in clinical outcomes. Finally, we place our proposed approach in the context of a real-life cohort that provides significant and relevant data on high-risk populations, HIV+ drug abusing patients that circulate through the U.S. jail system, and propose that the use of more complex sociological prevention markers may greatly enrich currently used biomarker-based approaches and provide with more sophisticated and nuanced results.