COVID-19 lethality reduction using artificial intelligence solutions derived from telecommunications systems

In this chapter, we propose to bridge two worlds as we suggest to repurpose artificial intelligence solutions originally developed for telecommunications systems in the field of fighting against a pandemic. The objective is to provide solutions supporting the global effort to fight the coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The European Telecommunications Standards Institute has published an architecture group specification that introduces a number of building blocks and a general high-level approach enabling automated data analysis and related decision-making in the context of large-scale communication systems. We illustrate how the available structure can be adapted to the needs and analysis of patient allocation in the context of a pandemic and scarce resources. Thus learning and decision-making processes may be applied for the extraction of information related to the pandemic evolution and forecast, as well as the health system optimization. An artificial intelligence (AI) system is indeed ideally suited to extract relevant information from a data pool and to train decision-making algorithms for modeling the spread of a pandemic. Instead of using AI tools in medical diagnostics for a single patient, we propose to extend the approach to a country-wide optimization to optimally allocate patients and available medical resources and show how discrete optimization tools may provide an optimum mapping under the given constraints such as limited medical resources. As a consequence, the analysis and processing of such data by public services is optimized considerably building on state-of-the-art data analytics as originally developed in a different field.