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1.
BMJ Glob Health ; 7(6)2022 06.
Artículo en Inglés | MEDLINE | ID: mdl-35760438

RESUMEN

The COVID-19 pandemic has underlined the need to partner with the community in pandemic preparedness and response in order to enable trust-building among stakeholders, which is key in pandemic management. Citizen science, defined here as a practice of public participation and collaboration in all aspects of scientific research to increase knowledge and build trust with governments and researchers, is a crucial approach to promoting community engagement. By harnessing the potential of digitally enabled citizen science, one could translate data into accessible, comprehensible and actionable outputs at the population level. The application of citizen science in health has grown over the years, but most of these approaches remain at the level of participatory data collection. This narrative review examines citizen science approaches in participatory data generation, modelling and visualisation, and calls for truly participatory and co-creation approaches across all domains of pandemic preparedness and response. Further research is needed to identify approaches that optimally generate short-term and long-term value for communities participating in population health. Feasible, sustainable and contextualised citizen science approaches that meaningfully engage affected communities for the long-term will need to be inclusive of all populations and their cultures, comprehensive of all domains, digitally enabled and viewed as a key component to allow trust-building among the stakeholders. The impact of COVID-19 on people's lives has created an opportune time to advance people's agency in science, particularly in pandemic preparedness and response.


Asunto(s)
COVID-19 , Ciencia Ciudadana , Participación de la Comunidad , Recolección de Datos , Humanos , Pandemias
2.
BMC Bioinformatics ; 20(1): 439, 2019 Aug 27.
Artículo en Inglés | MEDLINE | ID: mdl-31455214

RESUMEN

Following publication of the original article [1], the author noticed that the following lines were missing from the published article. The original article has been corrected.

3.
BMC Bioinformatics ; 20(1): 312, 2019 Jun 11.
Artículo en Inglés | MEDLINE | ID: mdl-31185887

RESUMEN

BACKGROUND: Mathematical and computational models are widely used to study the transmission, pathogenicity, and propagation of infectious diseases. Unfortunately, complex mathematical models are difficult to define, reuse and reproduce because they are composed of several concerns that are intertwined. The problem is even worse for computational models because the epidemiological concerns are also intertwined with low-level implementation details that are not easily accessible to non-computing scientists. Our goal is to make compartmental epidemiological models easier to define, reuse and reproduce by facilitating implementation of different simulation approaches with only very little programming knowledge. RESULTS: We achieve our goal through the definition of a domain-specific language (DSL), Kendrick, that relies on a very general mathematical definition of epidemiological concerns as stochastic automata that are combined using tensor-algebra operators. A very large class of epidemiological concerns, including multi-species, spatial concerns, control policies, sex or age structures, are supported and can be defined independently of each other and combined into models to be simulated by different methods. Implementing models does not require sophisticated programming skills any more. The various concerns involved within a model can be changed independently of the others as well as reused within other models. They are not plagued by low-level implementation details. CONCLUSIONS: Kendrick is one of the few DSLs for epidemiological modelling that does not burden its users with implementation details or required sophisticated programming skills. It is also currently the only language for epidemiology modelling that supports modularity through clear separation of concerns hence fostering reproducibility and reuse of models and simulations. Future work includes extending Kendrick to support non-compartmental models and improving its interoperability with existing complementary tools.


Asunto(s)
Algoritmos , Métodos Epidemiológicos , Lenguaje , Modelos Teóricos , Animales , Simulación por Computador , Culicidae/fisiología , Vectores de Enfermedades , Interacciones Huésped-Parásitos , Reproducibilidad de los Resultados , Procesos Estocásticos
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