A bitstring approach for implementing agent-based epidemiological models

Rizzi, R.; Kaizer, Wesley Luciano; Rizzi, Claudia Brandelero; Galante, Guilherme; Coelho, Flávio Codeço

doi:10.3233/mgs-170275

Cited by 2 publications

(1 citation statement)

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“…The sensitivity of agent-based simulations to small changes in the parameter values was evaluated by using massive parallel computation and interactive data visualization [48]. Rizzi et al [49,50] have developed computationally efficient ways of simulating large epidemiological models by using a bit-string approach. Considerable work has been carried out to determine the best way to set up these models and how to scale them [33,[51][52][53][54].…”

Section: Literature Review On Agent-based Simulationsmentioning

confidence: 99%

Using Paired Agent-Based Simulations To Test Strategies For Limiting The Effects Of Epidemics

Armstrong

Fc²

2019

Preprint

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Agent-based simulations are widely used nowadays in public health research for comparing different strategies for mitigating epidemics and for planning appropriate responses in the aftermath of crises in large urban areas because they can capture fine scale heterogeneities that may have important non-linear effects on the results. Given the costs of implementing strategies, decision-makers have to be convinced that the proposed treatment/procedure leads to a statistically significant improvement. This paper presents an innovative method for constructing paired agent-based simulations where exactly the same set of random effects is applied to simulations with and without the treatment/procedure. Statistical Analysis of Variance distinguishes the sum of squares between groups (BSS) from the sum of squares within groups (WSS). Our aim was to filter out the within sum of squares (WSS) leaving only the sum of squares between the control group and the treatment group (BSS). We propose to filter out the WSS by constructing paired simulations because as is well known, when paired t-tests can be used, they are much more powerful than ordinary t-tests. Pearson's Chi-squared goodness of fit, the Kolmogorov-Smirnov statistic and the Kullback-Leibler Divergence are then used to test whether the effect is statistically significant. This procedure has been tested on a case-study on the propagation of the Zika epidemic in Rio de Janeiro in 2015.

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