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Next-generation matrix
In epidemiology, the next-generation matrix is used to derive the basic reproduction number, for a compartmental model of the spread of infectious diseases. In population dynamics it is used to compute the basic reproduction number for structured population models. It is also used in multi-type branching models for analogous computations.
The method to compute the basic reproduction ratio using the next-generation matrix is given by Diekmann et al. (1990) and van den Driessche and Watmough (2002). To calculate the basic reproduction number by using a next-generation matrix, the whole population is divided into compartments in which there are
infected compartments. Let
be the numbers of infected individuals in the
infected compartment at time t. Now, the epidemic model is
-
, where
In the above equations, represents the rate of appearance of new infections in compartment
.
represents the rate of transfer of individuals into compartment
by all other means, and
represents the rate of transfer of individuals out of compartment
.
The above model can also be written as
where
and
Let be the disease-free equilibrium. The values of the parts of the Jacobian matrix
and
are:
and
respectively.
Here, and
are m × m matrices, defined as
and
.
Now, the matrix is known as the next-generation matrix. The basic reproduction number of the model is then given by the eigenvalue of
with the largest absolute value (the spectral radius of
. Next generation matrices can be computationally evaluated from observational data, which is often the most productive approach where there are large numbers of compartments.
See also
Sources
- Ma, Zhien; Li, Jia (2009). Dynamical Modeling and analysis of Epidemics. World Scientific. ISBN 978-981-279-749-0. OCLC 225820441.
- Diekmann, O.; Heesterbeek, J. A. P. (2000). Mathematical Epidemiology of Infectious Disease. John Wiley & Son.
- Heffernan, J. M.; Smith, R. J.; Wahl, L. M. (2005). "Perspectives on the basic reproductive ratio". J. R. Soc. Interface. 2 (4): 281–93. doi:10.1098/rsif.2005.0042. PMC 1578275. PMID 16849186.