Sentence examples for models for epidemic from inspiring English sources

Many stochastic models for epidemic populations have been developed in [6 17].

We then consider the possibility to use the surrogate data in numerical simulations of data-driven models for epidemic spread.

In this article, we study the generation of stochastic models for epidemic disease susceptible-infective-susceptible model.

Improved models for epidemic forecasting could be achieved by identifying signature features of epidemic growth, which could inform the design of models of disease spread and reveal important characteristics of the transmission process.

The increasing use of mathematical models for epidemic forecasting has highlighted the importance of designing models that capture the baseline transmission characteristics in order to generate reliable epidemic forecasts.

The increasing use of mathematical models for epidemic forecasting has highlighted the importance of designing reliable models that capture the baseline transmission characteristics of specific pathogens and social contexts.

To this aim, we consider the paradigmatic Susceptible-Infectious-Recovered (SIR) model for epidemic spreading.

First, the mathematical model for epidemic and endemic cholera is presented.

State Research Center of Virology and Biotechnology Vector (SRC VB Vector) is involved in construction of a universal model for epidemics adapted to the special pathogens causing infections, such as smallpox, anthrax, plague, and Ebola, Marburg, Lassa, and Crimean Congo hemorrhagic fevers.

The gravity model for directly transmitted diseases is currently one of the most parsimonious models for spatial epidemic spread.

The model used was based on Kermack and McKendrick (31 ), which has formed the basis of a number of models for both epidemic and pandemic influenza (32 – 34 ) and is implemented by using the set of differential equations given in equation 1. where α = 1/L = 0.5, γ = 1/PP = 0.4, λ = 1/IP = 2/3, and β = R 0/ PP + IP).