Va2 Gathering Information by Comparison of Different Dynamic Modeling Approaches for Epidemic Models

Zauner, Günther; Popper, Nikolas; Miksch, Florian; Urach, Christoph; Einzinger, Patrick; Endel, Gottfried; Schiller-Frühwirth, I; Breitenecker, Felix

doi:10.1111/j.1524-4733.2010.00791_2.x

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An individual-based simulation allows one to include and track patient characteristics and medical history, evaluation of subgroup specific interventions, calculating specific outcomes and distributions of these outcomes as well as modeling an open cohort. A specific distinguishing feature of ABM compared to other individual-based modeling approaches is that the behavior of agents and respective emerging behavior of the epidemics [56–58] can be captured. Changing the behavior of specific groups of agents is possible with less programming effort.…”

Section: Discussionmentioning

confidence: 99%

Why should we apply ABM for decision analysis for infectious diseases?—An example for dengue interventions

Miksch

Jahn²,

Espinosa

et al. 2019

PLoS ONE

View full text Add to dashboard Cite

For the evaluation of infectious-diseases interventions, the transmissible nature of such diseases plays a central role. Agent-based models (ABM) allow for dynamic transmission modeling but publications are limited. We aim to provide an overview of important characteristics of ABM for decision-analytic modeling of infectious diseases. A case study of dengue epidemics illustrates model characteristics, conceptualization, calibration and model analysis. First, major characteristics of ABM are outlined and discussed based on ISPOR and ISPOR-SMDM Good Practice guidelines. Second, in our case study, we modeled a dengue outbreak in Cebu City (Philippines) to assess the impact interventions to control the relative growth of the mosquito population. Model outcomes include prevalence and incidence of infected persons. The modular ABM simulates persons and mosquitoes over an annual time horizon considering daily time steps. The model was calibrated and validated. ABM is a dynamic, individual-level modeling approach that is capable to reproduce direct and indirect effects of interventions for infectious diseases. The ability to replicate emerging behavior and to include human behavior or the behavior of other agents is a distinguishing modeling characteristic (e.g., compared to Markov models). Modeling behavior may, however, require extensive calibration and validation. The analyzed hypothetical effectiveness of dengue interventions showed that a reduced human-mosquito ratio of 1:2.5 during rainy seasons leads already to a substantial decrease of infected persons. ABM can support decision-analyses for infectious diseases including disease dynamics, emerging behavior, and providing a high level of reusability due to modularity.

show abstract

Section: Discussionmentioning

confidence: 99%