Abstract
This paper is concerned with the efficient stochastic simulation of multiple scenarios of an infectious disease as it propagates through a population. In particular, we propose a simple “green” method to speed up the simulation of disease transmission as we vary the probability of infection of the disease from scenario to scenario. After running a baseline scenario, we incrementally increase the probability of infection, and use the common random numbers variance reduction technique to avoid re-simulating certain events in the new scenario that would not otherwise have changed from the previous scenario. A set of Monte Carlo experiments illustrates the effectiveness of the procedure. We also propose various extensions of the method, including its use to estimate the sensitivity of propagation characteristics in response to small changes in the infection probability.
Highlights
Successive global influenza pandemics have attracted a great deal of attention from society and public heath officials
We show how to use the methodology for purposes of sensitivity analysis—e.g., what is the derivative of the expected number of infected individuals as a function of p? The latter application combines a simulation variance reduction technique with our green technology
That p.m.f. is characterized by distinct chunks of pseudo-random number (PRN) corresponding to the first few days of the pandemic; and we find that B = 355.7 and SB = 310.7
Summary
Successive global influenza pandemics have attracted a great deal of attention from society and public heath officials. In terms of potential drawback (i), it has come to pass that the use of simulation to model pandemic disease spread is widely accepted, even if some of the modeling elements are sometimes regarded as ad hoc To address this latter issue, there has been a great deal of work in the literature that incorporates sophisticated population modeling techniques—including population mixing and movement of individuals from location to location—along with a variety of mitigation strategies; see Andradóttir et al [11] for additional motivation and references.
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