ABSTRACT
Mathematical models play an important role in management of outbreaks of acute respiratory infections (ARI). While such models are generally used to study the spread of a solitary virus, in reality multiple viruses co-circulate in the population. These viruses have been studied in detail, including the course of infection and immune defense mechanisms. We developed an agent-based model, called ABM-ARI, assimilating heterogeneous data and theoretical knowledge into a biologically motivated system, that allows to reproduce the seasonal patterns of ARI incidence and simulate interventions. ABM-ARI uses city-specific data to create a synthetic population and to construct realistic contact networks in different activity settings. Characteristics of infection, immune protection and non-specific resistance were varied between individuals to account for the population heterogeneity. For the calibration, we minimised the normalised mean absolute error between simulated and observed epidemic curves. ABM-ARI was built based on the quantitative assessment of features of predominant respiratory viruses and epidemiological characteristics of the population. It provides a good fit to the observed epidemic curves for different age groups and viruses. We also simulated one-week school closures when student absences were at or above 10%, 20% or 30% and found that only 10% and 20% thresholds resulted in a reduction of the incidence. ABM-ARI has a great potential in tackling the challenge of emerging infections by simulating and evaluating the effectiveness of various interventions.
Subject(s)
Ecosystem , Respiratory Tract Infections , Humans , Mathematical Concepts , Models, Biological , Calibration , Disease Outbreaks , Respiratory Tract Infections/epidemiology , Respiratory Tract Infections/prevention & controlABSTRACT
After studying the dependence of acute respiratory diseases of all etiologies on air temperature and population immunity dynamics, the authors proposed that the air temperature and the immunity level can affect disease resistance. Knowledge of the relationship between these factors must clarify the mechanisms that determine morbidity dynamics.
ABSTRACT
This paper is dedicated to investigation of the impact of total antigenic load on immunosenescence. Data on pneumonia mortality have been simulated. The results of modelling revealed that antigenic load as well as probability of infecting could account for the shifting in mortality that takes place for different countries. Dynamics of immunosenescence is described by mathematical model for age related changes in T-cell populations. Numerical simulation of the model yields estimates for pneumonia severity at different ages. Probability function is used to describe relationship between pneumonia mortality and age related changes in disease severity.
