Monitoring Complex Processes to Verify System Conformance: A Declarative Rule-Based Framework

Over the last 60 years, computers and software have favoured incredible advancements in every field. Nowadays, however, these systems are so complicated that it is difficult – if not challenging – to understand whether they meet some requirement or are able to show some desired behaviour or property. This dissertation introduces a Just-In-Time (JIT) a posteriori approach to perform the conformance check to identify any deviation from the desired behaviour as soon as possible, and possibly apply some corrections. The declarative framework that implements our approach – entirely developed on the promising open source forward-chaining Production Rule System (PRS) named Drools – consists of three components: 1. a monitoring module based on a novel, efficient implementation of Event Calculus (EC), 2. a general purpose hybrid reasoning module (the first of its genre) merging temporal, semantic, fuzzy and rule-based reasoning, 3. a logic formalism based on the concept of expectations introducing Event-Condition-Expectation rules (ECE-rules) to assess the global conformance of a system. The framework is also accompanied by an optional module that provides Probabilistic Inductive Logic Programming (PILP). By shifting the conformance check from after execution to just in time, this approach combines the advantages of many a posteriori and a priori methods proposed in literature. Quite remarkably, if the corrective actions are explicitly given, the reactive nature of this methodology allows to reconcile any deviations from the desired behaviour as soon as it is detected. In conclusion, the proposed methodology brings some advancements to solve the problem of the conformance checking, helping to fill the gap between humans and the increasingly complex technology.

Le pressioni sui servizi sanitari (demografiche, epidemiologiche, sociali, pandemia COVID-19) e la risposta del sistema ospedaliero. Il caso studio del Policlinico S. Orsola di Bologna

I sistemi sanitari sono messi sotto stress da fattori diversi che possono essere sintetizzati schematizzando il problema in pressioni sistemiche e pressioni pandemiche leggendole secondo due vettori paralleli: fattori modificabili e fattori non modificabili. I fattori non modificabili sono legati alla condizione socio-demografica di una popolazione (reddito pro-capite, livello di istruzione) e alle caratteristiche individuali dei pazienti che accedono ai servizi (condizioni di moltimorbidità, fragilità, età, sesso) mentre i fattori modificabili sono legati al modello organizzativo del servizio regionale e Aziendale. I fattori modificabili sono quelli che leggendo i fattori non modificabili possono adattarsi al contesto specifico e con gradi di flessibilità variabile rispondere alle domande emergenti. Il tradizionale approccio ospedaliero, ancora in gran parte basato su modelli organizzativi funzionalmente e strutturalmente chiusi, costruiti attorno alle singole discipline, non si è rivelato in grado di rispondere adeguatamente da solo a questi bisogni di salute complessi che necessitano di una presa in carico multidisciplinare e coordinata tra diversi setting assistenziali. La pandemia che ha portato in Italia ad avere più di 8 milioni di contagiati ha esacerbato problemi storici dei sistemi sanitari. Le Regioni e le Aziende hanno fronteggiato un doppio binario di attività vedendo ridursi l’erogazione di servizi per i pazienti non Covid per far fronte all’incremento di ricoveri di pazienti Covid. Il Policlinico S. Orsola ha in questa congiuntura storica sviluppato un progetto di miglioramento del percorso del paziente urgente coinvolgendo i professionisti e dando loro strumenti operativi di analisi del problema e metodi per identificare risposte efficaci. Riprendendo infine la distinzione tra pressioni modificabili e non modificabili il lavoro mostra che dall’analisi delle cause profonde dei nodi critici del percorso del paziente si possono identificare soluzioni che impattino sugli aspetti organizzativi (modificabili) personalizzando l’approccio per il singolo paziente (non modificabile) in un’ottica patient centred.

Data surveillance for infectious diseases: models, complex networks and machine learning

In this thesis, we investigate the role of applied physics in epidemiological surveillance through the application of mathematical models, network science and machine learning. The spread of a communicable disease depends on many biological, social, and health factors. The large masses of data available make it possible, on the one hand, to monitor the evolution and spread of pathogenic organisms; on the other hand, to study the behavior of people, their opinions and habits. Presented here are three lines of research in which an attempt was made to solve real epidemiological problems through data analysis and the use of statistical and mathematical models. In Chapter 1, we applied language-inspired Deep Learning models to transform influenza protein sequences into vectors encoding their information content. We then attempted to reconstruct the antigenic properties of different viral strains using regression models and to identify the mutations responsible for vaccine escape. In Chapter 2, we constructed a compartmental model to describe the spread of a bacterium within a hospital ward. The model was informed and validated on time series of clinical measurements, and a sensitivity analysis was used to assess the impact of different control measures. Finally (Chapter 3) we reconstructed the network of retweets among COVID-19 themed Twitter users in the early months of the SARS-CoV-2 pandemic. By means of community detection algorithms and centrality measures, we characterized users’ attention shifts in the network, showing that scientific communities, initially the most retweeted, lost influence over time to national political communities. In the Conclusion, we highlighted the importance of the work done in light of the main contemporary challenges for epidemiological surveillance. In particular, we present reflections on the importance of nowcasting and forecasting, the relationship between data and scientific research, and the need to unite the different scales of epidemiological surveillance.