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. 2021 Sep 28;45(11):97.
doi: 10.1007/s10916-021-01772-1.

An Agent-based Decision Support for a Vaccination Campaign

Emilio Sulis  1 , Pietro Terna  2
Affiliations

An Agent-based Decision Support for a Vaccination Campaign

Emilio Sulis et al. J Med Syst. .

Abstract

We explore the Covid-19 diffusion with an agent-based model of an Italian region with a population on a scale of 1:1000. We also simulate different vaccination strategies. From a decision support system perspective, we investigate the adoption of artificial intelligence techniques to provide suggestions about more effective policies. We adopt the widely used multi-agent programmable modeling environment NetLogo, adding genetic algorithms to evolve the best vaccination criteria. The results suggest a promising methodology for defining vaccine rates by population types over time. The results are encouraging towards a more extensive application of agent-oriented methods in public healthcare policies.

Keywords: Agent-based modeling; Healthcare support system; Vaccination campaign.

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Conflict of interest statement

The authors declare that they have no conflict of interest. This article does not contain any studies with human participants performed by any of the authors.

Figures

Fig. 1
Fig. 1
A day in the simulation, with N repetition where N is the duration of a specific outbreak
Fig. 2
Fig. 2
The sequence of contagions in different cases: (a) without vaccinations (blue line for the starting point of the vaccination campaign, red line for the start of the effectiveness of the initial vaccinations); (b) without vaccinations, after day 413 (c) with vaccination campaign (vaccinated people still spreading the infection), after day 413; (d) GAs vaccination campaign, with vaccinated people still spreading the infection (best GAs strategy),, after day 413
Fig. 3
Fig. 3
GA vaccination sequence. On the y axis the number of vaccinated subjects of each group. If vaccination is complete, the line is horizontal
See this image and copyright information in PMC

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