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J.A.P. Heesterbeek

Researcher at Utrecht University

Publications -  96
Citations -  16803

J.A.P. Heesterbeek is an academic researcher from Utrecht University. The author has contributed to research in topics: Population & Basic reproduction number. The author has an hindex of 43, co-authored 95 publications receiving 15191 citations. Previous affiliations of J.A.P. Heesterbeek include Wageningen University and Research Centre & Centrum Wiskunde & Informatica.

Papers
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On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations

TL;DR: It is shown that in certain special cases one can easily compute or estimate the expected number of secondary cases produced by a typical infected individual during its entire period of infectiousness in a completely susceptible population.

On the definition and the computation of the basic reproduction ratio : $R_ 0$ in models for infectious diseases in heterogeneous populations

TL;DR: In this paper, the expected number of secondary cases produced by a typical infected individual during its entire period of infectiousness in a completely susceptible population is defined as the dominant eigenvalue of a positive linear operator.
Book

Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis and Interpretation

TL;DR: The Basic Reproduction Ratio (RBR) as mentioned in this paper is a variant of the Kermack-McKendrick ODE model, and it has been used to model population dynamics at the Demographic Time Scale.
Journal ArticleDOI

The construction of next-generation matrices for compartmental epidemic models.

TL;DR: An elementary but complete proof that ℛ0 defined as the dominant eigenvalue of the NGM for compartmental systems and the Malthusian parameter r, the real-time exponential growth rate in the early phase of an outbreak, are connected by the properties.
Journal ArticleDOI

Stability in real food webs: weak links in long loops.

TL;DR: It is shown and explained mathematically that patterning enhances stability, because it reduces maximum “loop weight” and thus reduces the amount of intraspecific interaction needed for matrix stability.