Abstract
The rapid spread of SARS-CoV-2 and the consequent global COVID-19 pandemic has prompted the public administrations of different countries to establish health procedures and protocols based on information generated through predictive techniques and models, which, in turn, are based on technology such as artificial intelligence (AI) and machine learning (ML). This article presents some AI tools and computational models used to collaborate in the control and detection of COVID-19 cases. In addition, the main features of the Epidempredict project regarding COVID-19 in Panama are presented. This initiative consists of the planning and design of a digital platform, with cloud-based technology, to manage the ingestion, analysis, visualization and exportation of data regarding the evolution of COVID-19 in Panama. The methodology for the design of predictive algorithms is based on a hybrid model that combines the dynamics associated with population data of an SIR model of differential equations and extrapolation with recurrent neural networks. The technological solution developed suggests that adjustments can be made to the rules implemented in the expert processes that are considered. Furthermore, the resulting information is displayed and explored through user-friendly dashboards, contributing to more meaningful decision-making processes.
Highlights
In December 2019, the spread of the SARS-CoV-2 virus began worldwide, which caused the COVID-19 pandemic [1]
The rapid spread of SARS-CoV-2 and the consequent global COVID-19 pandemic has prompted the public administrations of different countries to establish health procedures and protocols based on information generated through predictive techniques and models, which, in turn, are based on technology such as artificial intelligence (AI) and machine learning (ML)
COVID-CAPS achieved an accuracy of 95.7%, sensitivity of 90%, specificity of 95.8%, and area under the curve (AUC) of 0.97, while having a far lower number of trainable parameters in comparison to its counterparts
Summary
In December 2019, the spread of the SARS-CoV-2 virus began worldwide, which caused the COVID-19 pandemic [1] This unprecedented crisis, which initially affected healthcare systems worldwide, has had an effect on every main sector of activity [2]. The context of the current healthcare crisis in which these types of public organizations operate, is associated with the so-called VUCA (volatile, uncertain, complex and ambiguous) environment [12,13]. For this scenario, the implementation of “disruptive” technologies allows for the maximization of the full potential of the latest advancements that are being applied to the intelligent management of large volumes of data (big data). Given the exceptional nature of the crisis that is being experienced in these organizational environments, it is important to implement solutions that provide significant value and enable knowledge management, as these will be the organizations’ most valuable resource [16]
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