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.

Part 1: Information & Communication Technology-EurAsia Conference 2014, ICT-EurAsia 2014International audienceAgent-based modeling is a method to model a system by autonomous entities. The proposed framework models single persons with personal behavior, different health states and ability to spread the disease. Upon simulation, the epidemic emerges automatically. This approach is clear and easily understandable but requires extensive knowledge of the epidemic’s background. Such real-world model structures produce realistic epidemics, allowing detailed examination of the transmission process or testing and analyzing the outcome of interventions like vaccinations. Due to changed epidemic propagation, effects like herd immunity or serotype shift arise automatically. Beyond that, a modular structure splits the model into parts, which can be developed and validated separately. This approach makes development more efficient, increases credibility of the results and allows reusability and exchangeability of existing modules. Thus, knowledge and models can be easily and efficiently transferred, for example to compute scenarios for different countries and similar diseases

This Comparison investigates a classical population model for the spread of infection diseases (SIR ordinary differential equations model by Kermack and McKendrick) and an inhomogeneous spatial approach using cellular automata. An identification of parameters based on an abstract time discrete conceptual model is presented. The tasks of this comparison include the validation and analysis of this identification, an investigation on the impact of different spatial dynamics in the cellular automaton modelling approach and simulation scenarios for confining epidemic outbreaks that involve state-dependent interventions.

During the development of an agent-based simulation model, the model often has to be calibrated, which means adjusting the parameters such that a reference system can be reproduced. A major problem in calibrating an agent-based simulation model is the variability of the results, due to random choices made by the agents. To reduce the variability, the numbers of agents has to be increased, which in return increases the computation time of the simulation. An attempted solution to this problem consists of increasing the numbers of agents gradually. This approach is tested with two different calibration algorithm: simulated annealing and evolutionary algorithm. Different updating schedules are applied on a test model and examined in terms of their running time and their performance. It is shown that a evolutionary algorithm with an increasing agent count manages to produce similar results as a standard calibration using only half the computation time. To conclude, the best performing calibration process is used to calibrate an existing agent-based model simulating a well known past influenza epidemic.

Part 1: Information & Communication Technology-EurAsia Conference 2014, ICT-EurAsia 2014International audienceModeling of infectious diseases with a low number of infections is a task that often arises since most real epidemics affect only a small fraction of the population. Agent-based methods simulate individuals and their behavior. When the model is simulated, the epidemic automatically arises without being explicitly defined. Surprisingly, it is not easy to produce such epidemics with small infection numbers. Instead, it needs model improvements to accomplish that task. In this paper, we show different extensions, addressing the person’s behavior, the pathogen’s behavior and the environmental impacts. It turns out that the discussed improvements have different consequences. Hence, they need to be used deliberately to overcome modeling issues of a specific epidemic in an appropriate and valid way. Even more, these improvements address the underlying behavior of epidemics and hence have the ability to provide a deeper insight into the real spreading process of a disease

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In this work we present two structurally different mathematical models for the prognostic simulation of Austria's population: A time-continuous, macroscopic system dynamics approach and a time-discrete, microscopic agent-based approach. Both models were developed as case studies of a series of population concepts in order to support models for decision-support in Austria's health care system. In the present work we want to focus on the definition, the parametrisation as well as especially the validation process of both population-models. The latter was of special interest as it included a cross-model validation with Statistics Austria's own prognostic model SIKURS.