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Individual behavioral response to the spreading of an epidemic plays a crucial role in the progression of the epidemic itself. The risk perception induces individuals to adopt a protective behavior, as for instance reducing their social contacts, adopting more restrictive hygienic measures or undergoing prophylaxis procedures. In this paper, starting with a previously developed lattice-gas SIR model, we construct a coupled behavior-disease model for influenza spreading with spontaneous behavioral changes. The focus is on self-initiated behavioral changes that alter the susceptibility to the disease, without altering the contact patterns among individuals. Three different mechanisms of awareness spreading are analyzed: the local spreading due to the presence in the neighborhood of infective individuals; the global spreading due to the news published by the mass media and to educational campaigns implemented at institutional level; the local spreading occurring through the “thought contagion” among aware and unaware individuals. The peculiarity of the present approach is that the awareness spreading model is calibrated on available data on awareness and concern of the population about the risk of contagion. In particular, the model is validated against the A(H1N1) epidemic outbreak in Italy during the season, by making use of the awareness data gathered by the behavioral risk factor surveillance system (PASSI). We find that, increasing the accordance between the simulated awareness spreading and the PASSI data on risk perception, the agreement between simulated and experimental epidemiological data improves as well. Furthermore, we show that, within our model, the primary mechanism to reproduce a realistic evolution of the awareness during an epidemic, is the one due to globally available information. This result highlights how crucial is the role of mass media and educational campaigns in influencing the epidemic spreading of infectious diseases. [ABSTRACT FROM AUTHOR]

The dynamics of infectious diseases that are spread through direct contact have been proven to depend on the strength of community structure or modularity within the underlying network. It has been recently shown that weighted networks with similar modularity values may exhibit different mixing styles regarding the number of connections among communities and their respective weights. However, the effect of mixing style on epidemic behavior was still unclear. In this paper, we simulate the spread of disease within networks with different mixing styles: a dense-weak style (i.e., many edges among the communities with small weights) and a sparse-strong style (i.e., a few edges among the communities with large weights). Simulation results show that, with the same modularity: 1) the mixing style significantly influences the epidemic size, speed, pattern and immunization strategy; 2) the increase of the number of communities amplifies the effect of the mixing style; 3) when the mixing style changes from sparse-strong to dense-weak, there is a ‘saturation point’, after which the epidemic size and pattern become stable. We also provide a mean-field solution of the epidemic threshold and size on weighted community networks with arbitrary external and internal degree distribution. The solution explains the effect of the second moment of the degree distribution, and a symmetric effect of internal and external connections (incl. degree distribution and weight). Our study has both potential significance for designing more accurate metrics for the community structure and exploring diffusion dynamics on metapopulation networks. [ABSTRACT FROM AUTHOR]

In the Susceptible–Infectious–Recovered (SIR) model of disease spreading, the time to extinction of the epidemics happens at an intermediate value of the per-contact transmission probability. Too contagious infections burn out fast in the population. Infections that are not contagious enough die out before they spread to a large fraction of people. We characterize how the maximal extinction time in SIR simulations on networks depend on the network structure. For example we find that the average distances in isolated components, weighted by the component size, is a good predictor of the maximal time to extinction. Furthermore, the transmission probability giving the longest outbreaks is larger than, but otherwise seemingly independent of, the epidemic threshold. [ABSTRACT FROM AUTHOR]

Background: Norovirus (NoV) is a major cause of gastroenteritis during the autumn and winter seasons in Japan as well as in other temperate climate regions. Most outbreaks are thought to occur by secondary attacks through person-to-person infection by fecal-oral route. Severe cases are found in young children or patients with chronic diseases. Clarifying the patterns of epidemic diffusion is important for considering effective monitoring and surveillance as well as possible prevention. Methods: We considered the predominant viral genotype from the laboratory result obtained from Infectious Agents Surveillance Report (IASR) of National Institute of Infectious Diseases (NIID). We investigated the increase of NoV cases nationwide for the 2006–07 to 2008–09 seasons using sentinel gastroenteritis data collected from about 3000 pediatric clinics on National Epidemiological Surveillance of Infectious Diseases (NESID) acquired from the kriging method in the geographic information system (GIS). Results: During these three seasons, the majority of the detected virus was GII.4, which ranged from 60.4 to 88.9%. The number of cases (per sentinel site) at the peak week was 22.81 in the 2006–07 season and it decreased in the following seasons. NoV cases began to increase earlier in the southern areas and gradually extended into the northern areas, similarly, over the seasons. The average period from when the increase of cases was detected in the southern area to when it reached the northern area was 12.7 weeks. Conclusion: The decrease of the number of sentinel cases at the peak week may suggest the development of herd immunity after a period of high prevalence. Although the NoV epidemic is thought to be associated with cold weather, its cases first increased in the southern area with relatively warm temperature, indicating there are other climate factors involved. Geographic study using the sentinel data could enhance the monitoring and surveillance of and preparedness against epidemics. [ABSTRACT FROM AUTHOR]

The spread of certain diseases can be promoted, in some cases substantially, by prior infection with another disease. One example is that of HIV, whose immunosuppressant effects significantly increase the chances of infection with other pathogens. Such coinfection processes, when combined with nontrivial structure in the contact networks over which diseases spread, can lead to complex patterns of epidemiological behavior. Here we consider a mathematical model of two diseases spreading through a single population, where infection with one disease is dependent on prior infection with the other. We solve exactly for the sizes of the outbreaks of both diseases in the limit of large population size, along with the complete phase diagram of the system. Among other things, we use our model to demonstrate how diseases can be controlled not only by reducing the rate of their spread, but also by reducing the spread of other infections upon which they depend. [ABSTRACT FROM AUTHOR]