A computational framework for modeling and studying pertussis epidemiology and vaccination
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RESEARCH
Open Access
A computational framework for modeling and studying pertussis epidemiology and vaccination Paolo Castagno1† , Simone Pernice1† , Gianni Ghetti2 , Massimiliano Povero2 , Lorenzo Pradelli2 , Daniela Paolotti3 , Gianfranco Balbo1 , Matteo Sereno1 and Marco Beccuti1* Annual Meeting of the Bioinformatics Italian Society (BITS 2019) Palermo, Italy. 26-28 June 2019 *Correspondence: [email protected] † Paolo Castagno and Simone Pernice contributed equally to this work. 1 Department of Computer Science, University of Turin, Turin, Italy Full list of author information is available at the end of the article
Abstract Background: Emerging and re-emerging infectious diseases such as Zika, SARS, ncovid19 and Pertussis, pose a compelling challenge for epidemiologists due to their significant impact on global public health. In this context, computational models and computer simulations are one of the available research tools that epidemiologists can exploit to better understand the spreading characteristics of these diseases and to decide on vaccination policies, human interaction controls, and other social measures to counter, mitigate or simply delay the spread of the infectious diseases. Nevertheless, the construction of mathematical models for these diseases and their solutions remain a challenging tasks due to the fact that little effort has been devoted to the definition of a general framework easily accessible even by researchers without advanced modelling and mathematical skills. Results: In this paper we describe a new general modeling framework to study epidemiological systems, whose novelties and strengths are: (1) the use of a graphical formalism to simplify the model creation phase; (2) the implementation of an R package providing a friendly interface to access the analysis techniques implemented in the framework; (3) a high level of portability and reproducibility granted by the containerization of all analysis techniques implemented in the framework; (4) a welldefined schema and related infrastructure to allow users to easily integrate their own analysis workflow in the framework. Then, the effectiveness of this framework is showed through a case of study in which we investigate the pertussis epidemiology in Italy. Conclusions: We propose a new general modeling framework for the analysis of epidemiological systems, which exploits Petri Net graphical formalism, R environment, and Docker containerization to derive a tool easily accessible by any researcher (Continued on next page)
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