Viral Infections of Humans: Epidemiology and Control

Free download. Book file PDF easily for everyone and every device. You can download and read online Viral Infections of Humans: Epidemiology and Control file PDF Book only if you are registered here. And also you can download or read online all Book PDF file that related with Viral Infections of Humans: Epidemiology and Control book. Happy reading Viral Infections of Humans: Epidemiology and Control Bookeveryone. Download file Free Book PDF Viral Infections of Humans: Epidemiology and Control at Complete PDF Library. This Book have some digital formats such us :paperbook, ebook, kindle, epub, fb2 and another formats. Here is The CompletePDF Book Library. It's free to register here to get Book file PDF Viral Infections of Humans: Epidemiology and Control Pocket Guide.
See a Problem?
  • Manufacturing and Design. Understanding the principles of how things are made;
  • Viral Infections Of Humans: Epidemiology And Control.
  • Viral Infections of Humans: Epidemiology and Control by Alfred S. Evans -!
  • The Common Extremalities in Biology and Physics. Maximum Energy Dissipation Principle in Chemistry, Biology, Physics and Evolution?
  • The No-Nonsense Guide to Climate Change (No-Nonsense Guides).

Forgot password? Old Password.

Shop with confidence

New Password. Password Changed Successfully Your password has been changed. Returning user. Request Username Can't sign in? Forgot your username? Enter your email address below and we will send you your username. Viral Hepatitis. Descriptive Epidemiology. Pathogenesis and Immunity. Respiratory Syncytial Virus. Mechanisms and Routes of Transmission. CreutzfeldtJacob Disease.

VaricellaHerpes Zoster Virus. Biological Characteristics of the Virus That Affect. Unresolved Problems. Nasopharyngeal Carcinoma. Cervical Cancer. Derechos de autor. Evans Vista de fragmentos - Viral Infections of Humans Alfred S. We therefore decided to use the symptom scores as surrogate for pro-inflammatory cytokine levels.

The respiratory symptoms were modelled as:. Nonlinear mixed-effect models were used in order to take into account the inter-individual variability IIV and to compute individual parameters see Supplementary Material. Each parameter is described by a fixed effect determining the average value in the population and a random effect determining the IIV. Additionally, we estimated the full variance-covariance matrix for random effect and used the normalized covariance to describe the correlations between the parameters distributions.

Individual parameters are computed as empirical Bayes estimates.

Nipah virus

We simultaneously fitted the viral titre data, the systemic symptom score data and the respiratory symptom score data. When testing different models, we chose the model providing the smallest Bayesian information criteria BIC We performed a practical identifiability analysis as suggested by Brun et al. We computed the population and individual infectiousness and other epidemiological parameters incubation period and duration of symptomatic phase to predict how these parameters would vary within a large population.

We then simulated the individual viral kinetics, systemic and respiratory symptoms dynamics. We used these simulations to predict how individual infectiousness varies in time. We present the median and inter-quartile range Q1-Q3 for the parameters computed from the simulations. We assumed that infectiousness is a function of viral titre and respiratory symptoms. We adapted Chen et al. Indeed, infectious particles can also be produced during normal breathing 3.

R t and V t are the individual respiratory symptom score and viral titre at time t predicted by the within-host model. For symptomatic subjects, we computed the incubation period as the time from inoculation to reach a total symptom score i. Of the 44 subjects shedding influenza virus, 9 did not report systemic symptoms on any occasion after the challenge and 4 of these did not report respiratory symptoms either; these 4 subjects were therefore considered as asymptomatic.

The peak for total symptom i.

Time courses of virus titres and symptoms scores are summarized in Fig. The distribution of the maximal total symptom score in Fig. A Average observed dynamics for viral load V , respiratory symptoms R and systemic symptoms S. The dot represents the average time of maximal value, tmax. B Relationship between the symptom onset orange dot for respiratory symptoms, blue triangle for systemic symptoms and the onset of viral shedding.

Viral shedding onset is defined as the time between inoculation and the first sample above the limit of detection. C Distribution of the maximal total symptom scores. D Distribution of AUCV the area under the viral titre curve depending on the maximal total symptom scores. Parameters estimates are presented in Table 2. The model provides good fits to virus titre, systemic symptom score and respiratory symptom score Fig. The model predicts that the average virus titre increases sharply to a peak at 2.

The local and systemic cytokines levels are predicted to increase until 2. B Individual fits for the viral titre blue , systemic symptoms green and respiratory symptoms red for the 9 most infectious subjects. A Population fits for VK black , respiratory symptoms blue and systemic symptoms red.

Shop by category

B Average predictions for local cytokines purple line , systemic cytokines orange line and cytotoxic activity green line. Cytokine dynamics were scaled to show the relative proportions of local and systemic cytokines. Population parameters estimates are presented in Table 2 and individual parameter estimates in Table S1.

Account Options

Most parameters are accurately estimated with a relative standard error i. On average, the subjects shedding more virus have more symptoms. This shows that viral titre and total symptom score are correlated. From the simulations of subjects, we predict that For the symptomatic subjects, the incubation period lasts on average 1. Symptomatic subjects are 7.

We also find that all parameters are significantly different between symptomatic and asymptomatic. The increased cytotoxicity induces a rapid halt in the virus life-cycle, which reduces the subsequent inflammation responsible for symptoms. The peak of infectiousness occurs 1. We predict from our model fits that a limited number of subjects are producing the great majority of infectious particles. We tested several models for the infectiousness, with different values for b and s.

  • Israel.
  • Fixed Point Theorems and Their Applications.
  • Frontiers | Epidemiology and Immune Pathogenesis of Viral Sepsis | Immunology?
  • Mad Men and Philosophy: Nothing Is as It Seems (The Blackwell Philosophy and Pop Culture Series).
  • Under the Influence: Questioning the Comparative in Medieval Castile (Medieval and Early Modern Iberian World);

These subjects were the most infectious with the different values of b and s Fig. From the simulated subjects, we predict that This raises the question of the identification of the most infectious subjects. Understanding the temporal dynamics of influenza infectiousness and the factors driving it is relevant to understanding influenza epidemiology and designing effective mitigation measures. The parameter estimates and predictions from our model are broadly consistent with previous findings.

The wide range of estimates can be explain by the threshold used to detect symptomatic subjects. Indeed we predict that depending on the threshold used, the proportion of asymptomatic subjects in a given sample varies between Most of the estimated parameters are 2.

Other host and virus factors can also play a role such as the age or virus strain Children are more prone to be infected than any age group during seasonal influenza epidemics and to develop severe case or complications 28 whereas the majority of deaths due to influenza are observed in elderly in developed countries Our predicted duration of symptomatic phase of 1.

Using our model to predict infectiousness, we show high heterogeneity in infectiousness.

Viral Infections of Humans. Epidemiology and Control. 3rd Edition

Indeed, our model predicts that This heterogeneity in infectiousness adds another level of heterogeneity for influenza transmission, in addition to population structure 31 and contacts Matthews et al. Whereas in E. We did not identify a bimodal distribution for infectiousness produced in the present work see Fig.

However, the high degree of heterogeneity of viral shedding implies that targeting the most infectious subjects would have disproportionate benefits. Consequently, this suggest that highly infectious subjects could remain undetected and would therefore be difficult to target for mitigation measures. Our study has two main limitations.

Bacterial Infections of Humans: Epidemiology and Control - Semantic Scholar

First the VK and SD data come from experimental infections with a single viral strain of a homogenous population of healthy volunteers with low influenza antibody titres. The applicability of our model to natural infection in adults depends on the pathogenicity of the viral strain as well as pre-existing immunity associated with past influenza exposure and vaccination. Moreover, increased attack rate of influenza among children may reflect their higher susceptibility and infectiousness 36 , To describe the variability of shedding patterns in the general population, it would therefore be necessary to adapt the model to take into account how the VK parameters changes with age.

Second, in our model, the different pro-inflammatory cytokines interferons, interleukins, TNF are represented by a single variable, as we did not have data to distinguish them. With the addition of such data, we could provide more refined models for the interaction between the virus and the host and possibly identify more precisely the biomarkers associated with increased infectiousness.

If informative biomarkers were identified, highly infectious subjects could be preferentially targeted for interventions. Second, symptom scores were self-reported and data describing thoroughly the number of coughs or sneezes were not available. Particles count and size distribution can vary substantially for different respiratory symptoms and several respiratory symptoms can occur simultaneously in influenza infected subjects. Therefore, to thoroughly describe the impact of the different respiratory symptoms, a daily monitoring of particles production would be necessary. As this information was unavailable in the present study, we used the average respiratory symptom score to predict infectiousness.

In summary, we have developed a new model combining infection and symptom dynamics and used unique data describing the time course of infection and infectiousness and their variability in healthy adults. Our predictions suggest a high degree of heterogeneity in virus shedding by infected subjects. Our work supports targeting mitigation measures at highly infectious individuals to efficiently reduce transmission.

Effectiveness of public health interventions would depend on accurate identification of highly infectious subjects and on how quickly control measures can be applied.

Viral Infections of Humans: Epidemiology and Control Viral Infections of Humans: Epidemiology and Control
Viral Infections of Humans: Epidemiology and Control Viral Infections of Humans: Epidemiology and Control
Viral Infections of Humans: Epidemiology and Control Viral Infections of Humans: Epidemiology and Control
Viral Infections of Humans: Epidemiology and Control Viral Infections of Humans: Epidemiology and Control
Viral Infections of Humans: Epidemiology and Control Viral Infections of Humans: Epidemiology and Control
Viral Infections of Humans: Epidemiology and Control Viral Infections of Humans: Epidemiology and Control
Viral Infections of Humans: Epidemiology and Control Viral Infections of Humans: Epidemiology and Control
Viral Infections of Humans: Epidemiology and Control Viral Infections of Humans: Epidemiology and Control

Related Viral Infections of Humans: Epidemiology and Control

Copyright 2019 - All Right Reserved