Demographic methods in epidemiology

2021 ◽  
pp. 351-362
Author(s):  
Petra Klepac ◽  
C. Jessica E. Metcalf

Demography is both shaped by and shapes infectious disease dynamics. Infectious pathogens can increase host mortality. Host birth rates introduce new susceptible individuals into the population, which allows infections to persist in the face of the depletion of susceptible individuals that can result from mortality or immunity that can follow infection. Many important processes in infectious disease epidemiology, from transmission to vaccination, vary as a function of age or life stage. Epidemiology thus requires demographic methods. This chapter introduces broad expectations for patterns emerging from the intersection between demography and epidemiology and presents a set of structured population modelling tools that can be used to dissect important processes, including next generation methods, and estimation of R0 in the context of stage structure and with important differences in time-scale between host demography and pathogen life cycle.

2019 ◽  
Vol 23 (3) ◽  
pp. 328-334
Author(s):  
E. Ya. Yanchevskaya ◽  
O. A. Mesnyankina

Mathematical modeling of diseases is an urgent problem in the modern world. More and more researchers are turning to mathematical models to predict a particular disease, as they help the most correct and accurate study of changes in certain processes occurring in society. Mathematical modeling is indispensable in certain areas of medicine, where real experiments are impossible or difficult, for example, in epidemiology. The article is devoted to the historical aspects of studying the possibilities of mathematical modeling in medicine. The review demonstrates the main stages of development, achievements and prospects of this direction.


2005 ◽  
pp. 1327-1362
Author(s):  
Susanne Straif-Bourgeois ◽  
Raoult Ratard

Author(s):  
Odo Diekmann ◽  
Hans Heesterbeek ◽  
Tom Britton

The basic reproduction number (or ratio) R₀ is arguably the most important quantity in infectious disease epidemiology. It is among the quantities most urgently estimated for infectious diseases in outbreak situations, and its value provides insight when designing control interventions for established infections. From a theoretical point of view R₀ plays a vital role in the analysis of, and consequent insight from, infectious disease models. There is hardly a paper on dynamic epidemiological models in the literature where R₀ does not play a role. R₀ is defined as the average number of new cases of an infection caused by one typical infected individual, in a population consisting of susceptibles only. This chapter shows how R₀ can be characterized mathematically and provides detailed examples of its calculation in terms of parameters of epidemiological models, culminating in a set of algorithms (or “recipes”) for the calculation for compartmental epidemic systems.


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