Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userWKB calculation of an epidemic outbreak distribution
110
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We calculate both the exponential and pre-factor contributions in a WKB approximation of the master equation for a stochastic SIR model with highly oscillatory dynamics. Fixing the basic parameters of the model we investigate how the outbreak distribution changes with the population size. We show that this distribution rapidly becomes highly non-Gaussian, acquiring large tails indicating the presence of rare, but large outbreaks, as the population is made smaller. The analytic results are found to be in excellent agreement with simulations until the systems become so small that the dynamics are dominated by fade-out of the disease.
rate research
Read More
The COVID-19 pandemic has demonstrated how disruptive emergent disease outbreaks can be and how useful epidemic models are for quantifying risks of local outbreaks. Here we develop an analytical approach to calculate the dynamics and likelihood of outbreaks within the canonical Susceptible-Exposed-Infected-Recovered and more general models, including COVID-19 models, with fixed population sizes. We compute the distribution of outbreak sizes including extreme events, and show that each outbreak entails a unique, depletion or boost in the pool of susceptibles and an increase or decrease in the effective recovery rate compared to the mean-field dynamics -- due to finite-size noise. Unlike extreme events occurring in long-lived metastable stochastic systems, the underlying outbreak distribution depends on a full continuum of optimal paths, each connecting two unique non-trivial fixed-points, and thus represents a novel class of extreme dynamics.
Background: A deterministic model is developed for the spatial spread of an epidemic disease in a geographical setting. The disease is borne by vectors to susceptible hosts through criss-cross dynamics. The model is focused on an epidemic outbreak that initiates from a small number of cases in a small sub-region of the geographical setting. Methods: Partial differential equations are formulated to describe the interaction of the model compartments. Results: The partial differential equations of the model are analyzed and proven to be well-posed. The epidemic outcomes of the model are correlated to the spatially dependent parameters and initial conditions of the model. Conclusions: A version of the model is applied to the 2015-2016 Zika outbreak in the Rio de Janeiro Municipality in Brazil.
In times of outbreaks, an essential requirement for better monitoring is the evaluation of the number of undiagnosed infected individuals. An accurate estimate of this fraction is crucial for the assessment of the situation and the establishment of protective measures. In most current studies using epidemics models, the total number of infected is either approximated by the number of diagnosed individuals or is dependent on the model parameters and assumptions, which are often debated. We here study the relationship between the fraction of diagnosed infected out of all infected, and the fraction of infected with known contaminator out of all diagnosed infected. We show that those two are approximately the same in exponential models and across most models currently used in the study of epidemics, independently of the model parameters. As an application, we compute an estimate of the effective number of infected by the SARS-CoV-2 virus in various countries.
A pandemic caused by a new coronavirus (COVID-19) has spread worldwide, inducing an epidemic still active in Argentina. In this chapter, we present a case study using an SEIR (Susceptible-Exposed-Infected-Recovered) diffusion model of fractional order in time to analyze the evolution of the epidemic in Buenos Aires and neighboring areas (Region Metropolitana de Buenos Aires, (RMBA)) comprising about 15 million inhabitants. In the SEIR model, individuals are divided into four classes, namely, susceptible (S), exposed (E), infected (I) and recovered (R). The SEIR model of fractional order allows for the incorporation of memory, with hereditary properties of the system, being a generalization of the classic SEIR first-order system, where such effects are ignored. Furthermore, the fractional model provides one additional parameter to obtain a better fit of the data. The parameters of the model are calibrated by using as data the number of casualties officially reported. Since infinite solutions honour the data, we show a set of cases with different values of the lockdown parameters, fatality rate, and incubation and infectious periods. The different reproduction ratios R0 and infection fatality rates (IFR) so obtained indicate the results may differ from recent reported values, constituting possible alternative solutions. A comparison with results obtained with the classic SEIR model is also included. The analysis allows us to study how isolation and social distancing measures affect the time evolution of the epidemic.
Between pandemics, the influenza virus exhibits periods of incremental evolution via a process known as antigenic drift. This process gives rise to a sequence of strains of the pathogen that are continuously replaced by newer strains, preventing a build up of immunity in the host population. In this paper, a parsimonious epidemic model is defined that attempts to capture the dynamics of evolving strains within a host population. The `evolving strains epidemic model has many properties that lie in-between the Susceptible-Infected-Susceptible and the Susceptible-Infected-Removed epidemic models, due to the fact that individuals can only be infected by each strain once, but remain susceptible to reinfection by newly emerged strains. Coupling results are used to identify key properties, such as the time to extinction. A range of reproduction numbers are explored to characterize the model, including a novel quasi-stationary reproduction number that can be used to describe the re-emergence of the pathogen into a population with `average levels of strain immunity, analogous to the beginning of the winter peak in influenza. Finally the quasi-stationary distribution of the evolving strains model is explored via simulation.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
