An Innovative System to Understand the Development of Epidemics Using GIS Spatial Analysis and Based on AI and Big Data

Abstract

The advent of the COVID-19 pandemic (C19) has put a strain on the tightness of the epidemiological forecasting algorithms. These predictive models are traditionally based on SIR (Susceptible, Infected, Removed) and its updates. However, they did not provide reliable answers, especially in the first delicate phase, in which governments must take rapid decisions that are deemed to affect deeply the development and the outcome of the outbreak. This inadequacy derives not only from the model itself; it is also and undoubtedly generated by the lack of correct and timely data. Moreover, on the onset of a new pandemic, the disease is not known or it is only partially known. The first problem is the attitude of predicting it a priori, assuming the trend starting from a known mathematical curve. This approach is flawed, because it is impossible to provide a truthful forecast at the beginning of the epidemics (or of a new wave of infections), when, however, it is necessary to act promptly. Though as expected, as the epidemic progresses and the situation becomes homogeneous, mathematical models of pure interpolation and also SIR give more and more correct results. But during an epidemic, producing precise diffusion forecasts, including information on the structure of the wave front and its speed, is of paramount importance to organize an effective containment response.

Failure to produce reliable previsions is secondary to three major issues: model, data, and methodology. Hereby we propose the adoption of a digital data collection strategy and analysis model to simultaneously satisfy the needs of clinical qualification and tracking of the territorial care and those of monitoring and forecasting services invested in response's prioritization and coordination. The system we propose is not a “personal model” that can be installed on a personal computer or a server; it requires connections from multiple inputs, the processing of different data and the ability to “learn” from the data, “listening to” what’s going on in the territory, “following” the spread, and calibrating the parameters of the model by making it run with different hypotheses and learning at the same time from them. So, it is a complex system that requires resources, minds, and time to be ready.

We are aware that in “happy times” we do not care about pandemics so that spending time and resources for a such complex system may appear inadequate. But we have a lot of clues in recent times—avian fever, SARS, C19, Ebola—that this attitude is self-destructive. Besides, the recent pandemic shows us that the world is so interconnected that diseases cannot be contained by arbitrary geographical boundaries.