Abstract
BackgroundEmerging 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.ResultsIn 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 well-defined 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.ConclusionsWe 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 even without advanced mathematical and computational skills. Moreover, the framework was implemented following the guidelines defined by Reproducible Bioinformatics Project so it guarantees reproducible analysis and makes simple the developed of new user-defined workflows.
Highlights
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
The framework was implemented following the guidelines defined by Reproducible Bioinformatics Project so it guarantees reproducible analysis and makes simple the developed of new user-defined workflows
Deterministic models can be exploited to answer questions such as: what fraction of individuals would be infected in an epidemic outbreak?, what conditions should be satisfied to prevent and control an epidemic?, what happens if individuals are mixed non-homogeneously? [1], while the stochastic ones address questions such as: how long is the disease likely to persist?, what is the probability of a major outbreak? [4]
Summary
Emerging and re-emerging infectious diseases such as Zika, SARS, ncovid and Pertussis, pose a compelling challenge for epidemiologists due to their significant impact on global public health. In the last twenty years the human ability to efficiently treat infectious diseases has greatly improved, the latest pandemics of SARS and the Swine Flu outbreak have clearly highlighted how these diseases can spread faster in today’s interconnected world In this context the computational epidemiology, a new multidisciplinary research field combining techniques from epidemiology, computer science, molecular biology and applied mathematics, makes extensive use of computational models for understanding and controlling spatio-temporal disease spread. The system population is divided into small groups namely compartments (or classes) typically representing specific epidemic statuses [1,2,3] These models are often formulated in terms of systems of differential equations (in continuous time) or difference equations (in discrete time), and produce an average description of the disease evolution at the population scale.
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