Abstract
The computational modelling has been applied in several works, which exert considerable positive impact, particularly in epidemiological field. However, modelling epidemics is very sensitive where selecting appropriate feature and model structure is challenging task for experts and epidemiologists. To overcome this limitation, we presented in previous work a methodology combining computational modelling and decision tree techniques. The approach has been validated on tuberculosis case study. Therefore, as comparative study, we propose here to apply association rules algorithms. The results indicate the epidemiological relevance of the extracted rules. Thus, the enhanced Bio-PEPA model demonstrates the robustness of the proposed approach.
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Notes
- 1.
SEMEP: Service d’Epidémiologie et MEdecine Préventive.
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Appendix: Bio-PEPA Formalism
Appendix: Bio-PEPA Formalism
A Bio-PEPA model is described by a set of species, which execute a set of activities. The latter define the dynamic behaviour of the species. Conventionally, Bio-PEPA formalism is described by the following syntax:
S :: = (α, κ) op S | S + S | C
op = << | >> | (+) | (−) | (.)
PÂ ::Â =Â PÂ ><Â PÂ |Â S(x)
For more clarity, we explain the above syntax through an example of a generic SEIR (Susceptible Exposed Infected Recovered) compartmental disease model taken from [24] and illustrated in the program code as below:
As defined in [24], a Bio-PEPA model is structured by defining a set of numeric rates (e.g. crw), functional rates (introduced by kineticLawOf) and species definitions (S, E, I and R). The activities executed by the species are contact, incubation and recover. The contact activity, decreases (resp. increases) the level of species S (resp. E), by using the operator ≪(resp.)≫. The incubation activity decreases (resp. increases) the level of species E (resp. I) and finally, the recover activity decreases (resp. increases) the level of species I (resp. R). The operator (.), used in I, indicates that I participates in contact, but this does not affect its level. The operator ‘+’, allows a choice between activities (contact, incubation and recover) based on rate (faster activities are more likely to occur). The last line of the model is the model component, defining interaction between species (* means activities are shared where possible) and their initial levels.
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Hamami, D., Atmani, B. (2016). Obtaining Optimal Bio-PEPA Model Using Association Rules: Approach Applied to Tuberculosis Case Study. In: DÃaz, P., Bellamine Ben Saoud, N., Dugdale, J., Hanachi, C. (eds) Information Systems for Crisis Response and Management in Mediterranean Countries. ISCRAM-med 2016. Lecture Notes in Business Information Processing, vol 265. Springer, Cham. https://doi.org/10.1007/978-3-319-47093-1_6
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