The impact of spatial connectivity on NPIs effectiveness.

BACKGROUND: France implemented a combination of non-pharmaceutical interventions (NPIs) to manage the COVID-19 pandemic between September 2020 and June 2021. These included a lockdown in the fall 2020 - the second since the start of the pandemic - to counteract the second wave, followed by a long period of nighttime curfew, and by a third lockdown in the spring 2021 against the Alpha wave. Interventions have so far been evaluated in isolation, neglecting the spatial connectivity between regions through mobility that may impact NPI effectiveness. METHODS: Focusing on September 2020-June 2021, we developed a regionally-based epidemic metapopulation model informed by observed mobility fluxes from daily mobile phone data and fitted the model to regional hospital admissions. The model integrated data on vaccination and variants spread. Scenarios were designed to assess the impact of the Alpha variant, characterized by increased transmissibility and risk of hospitalization, of the vaccination campaign and alternative policy decisions. RESULTS: The spatial model better captured the heterogeneity observed in the regional dynamics, compared to models neglecting inter-regional mobility. The third lockdown was similarly effective to the second lockdown after discounting for immunity, Alpha, and seasonality (51% vs 52% median regional reduction in the reproductive number R0, respectively). The 6pm nighttime curfew with bars and restaurants closed, implemented in January 2021, substantially reduced COVID-19 transmission. It initially led to 49% median regional reduction of R0, decreasing to 43% reduction by March 2021. In absence of vaccination, implemented interventions would have been insufficient against the Alpha wave. Counterfactual scenarios proposing a sequence of lockdowns in a stop-and-go fashion would have reduced hospitalizations and restriction days for low enough thresholds triggering and lifting restrictions. CONCLUSIONS: Spatial connectivity induced by mobility impacted the effectiveness of interventions especially in regions with higher mobility rates. Early evening curfew with gastronomy sector closed allowed authorities to delay the third wave. Stop-and-go lockdowns could have substantially lowered both healthcare and societal burdens if implemented early enough, compared to the observed application of lockdown-curfew-lockdown, but likely at the expense of several labor sectors. These findings contribute to characterize the effectiveness of implemented strategies and improve pandemic preparedness.

Analysing the impact of electrification on rural attractiveness in Senegal with mobile phone data.

Reliable and affordable access to electricity has become one of the basic needs for humans and is, as such, at the top of the development agenda. It contributes to socio-economic development by transforming the whole spectrum of people's lives-food, education, healthcare. It spurs new economic opportunities, thus improving livelihoods. Using a comprehensive dataset of pseudonymized mobile phone records, we analyse the impact of electrification on attractiveness for rural areas in Senegal. We extract communication and mobility flows from call detail records and show that electrification is positively and specifically correlated with centrality measures within the communication network and with the volume of incoming visitors. This increased influence is however circumscribed to a limited spatial extent, creating a complex competition with nearby areas. Nevertheless, we found that the volume of visitors between any two sites could be well predicted from the level of electrification at the destination and the living standard at the origin. In view of these results, we discuss how to obtain the best outcomes from a rural electrification planning strategy. We determine that electrifying clusters of rural sites is a better solution than centralizing electricity supplies to maximize the development of specifically targeted sites.

Evaluating the effect of demographic factors, socioeconomic factors, and risk aversion on mobility during the COVID-19 epidemic in France under lockdown: a population-based study.

Background: On March 17, 2020, French authorities implemented a nationwide lockdown to respond to the COVID-19 epidemic and curb the surge of patients requiring critical care. Assessing the effect of lockdown on individual displacements is essential to quantify achievable mobility reductions and identify the factors driving the changes in social dynamics that affected viral diffusion. We aimed to use mobile phone data to study how mobility in France changed before and during lockdown, breaking down our findings by trip distance, user age and residency, and time of day, and analysing regional data and spatial heterogeneities. Methods: For this population-based study, we used temporally resolved travel flows among 1436 administrative areas of mainland France reconstructed from mobile phone trajectories. Data were stratified by age class (younger than 18 years, 18-64 years, and 65 years or older). We distinguished between residents and non-residents and used population data and regional socioeconomic indicators from the French National Statistical Institute. We measured mobility changes before and during lockdown at both local and country scales using a case-crossover framework. We analysed all trips combined and trips longer than 100 km (termed long trips), and separated trips by daytime or night-time, weekdays or weekends, and rush hours. Findings: Lockdown caused a 65% reduction in the countrywide number of displacements (from about 57 million to about 20 million trips per day) and was particularly effective in reducing work-related short-range mobility, especially during rush hour, and long trips. Geographical heterogeneities showed anomalous increases in long-range movements even before lockdown announcement that were tightly localised in space. During lockdown, mobility drops were unevenly distributed across regions (eg, Île-de-France, the region of Paris, went from 585 000 to 117 000 outgoing trips per day). They were strongly associated with active populations, workers employed in sectors highly affected by lockdown, and number of hospitalisations per region, and moderately associated with the socioeconomic level of the regions. Major cities largely shrank their pattern of connectivity, reducing it mainly to short-range commuting (95% of traffic leaving Paris was contained in a 201 km radius before lockdown, which was reduced to 29 km during lockdown). Interpretation: Lockdown was effective in reducing population mobility across scales. Caution should be taken in the timing of policy announcements and implementation, because anomalous mobility followed policy announcements, which might act as seeding events. Conversely, risk aversion might be beneficial in further decreasing mobility in highly affected regions. We also identified socioeconomic and demographic constraints to the efficacy of restrictions. The unveiled links between geography, demography, and timing of the response to mobility restrictions might help to design interventions that minimise invasiveness while contributing to the current epidemic response. Funding: Agence Nationale de la Recherche, EU, REACTing.

META-GLARE: a shell for CIG systems.

In the last twenty years, many different approaches to deal with Computer-Interpretable clinical Guidelines (CIGs) have been developed, each one proposing its own representation formalism (mostly based on the Task-Network Model) execution engine. We propose META-GLARE a shell for easily defining new CIG systems. Using META-GLARE, CIG system designers can easily define their own systems (basically by defining their representation language), with a minimal programming effort. META-GLARE is thus a flexible and powerful vehicle for research about CIGs, since it supports easy and fast prototyping of new CIG systems.

Graphical representation of life paths to better convey results of decision models to patients.

The inclusion of patients' perspectives in clinical practice has become an important matter for health professionals, in view of the increasing attention to patient-centered care. In this regard, this report illustrates a method for developing a visual aid that supports the physician in the process of informing patients about a critical decisional problem. In particular, we focused on interpretation of the results of decision trees embedding Markov models implemented with the commercial tool TreeAge Pro. Starting from patient-level simulations and exploiting some advanced functionalities of TreeAge Pro, we combined results to produce a novel graphical output that represents the distributions of outcomes over the lifetime for the different decision options, thus becoming a more informative decision support in a context of shared decision making. The training example used to illustrate the method is a decision tree for thromboembolism risk prevention in patients with nonvalvular atrial fibrillation.

From decision to shared-decision: Introducing patients' preferences into clinical decision analysis.

OBJECTIVE: Taking into account patients' preferences has become an essential requirement in health decision-making. Even in evidence-based settings where directions are summarized into clinical practice guidelines, there might exist situations where it is important for the care provider to involve the patient in the decision. In this paper we propose a unified framework to promote the shift from a traditional, physician-centered, clinical decision process to a more personalized, patient-oriented shared decision-making (SDM) environment. METHODS: We present the theoretical, technological and architectural aspects of a framework that encapsulates decision models and instruments to elicit patients' preferences into a single tool, thus enabling physicians to exploit evidence-based medicine and shared decision-making in the same encounter. RESULTS: We show the implementation of the framework in a specific case study related to the prevention and management of the risk of thromboembolism in atrial fibrillation. We describe the underlying decision model and how this can be personalized according to patients' preferences. The application of the framework is tested through a pilot clinical evaluation study carried out on 20 patients at the Rehabilitation Cardiology Unit at the IRCCS Fondazione Salvatore Maugeri hospital (Pavia, Italy). The results point out the importance of running personalized decision models, which can substantially differ from models quantified with population coefficients. CONCLUSIONS: This study shows that the tool is potentially able to overcome some of the main barriers perceived by physicians in the adoption of SDM. In parallel, the development of the framework increases the involvement of patients in the process of care focusing on the centrality of individual patients.

Patients' involvement in e-health services quality assessment: a system for the automatic interpretation of SMS-based patients' feedback.

PURPOSE: Effective communication between patients and health services providers is a key aspect for optimizing and maintaining these services. This work describes a system for the automatic evaluation of users' perception of the quality of SmsCup, a reminder system for outpatient visits based on short message service (SMS). The final purpose is the creation of a closed-loop control system for the outpatient service, where patients' complaints and comments represent a feedback that can be used for a better implementation of the service itself. METHODS: SmsCup was adopted since about eight years by an Italian healthcare organization, with very good results in reducing the no-show (missing visits) phenomenon. During these years, a number of citizens, even if not required, sent a message back, with comments about the service. The automatic interpretation of the content of those SMS may be useful for monitoring and improving service performances.Yet, due to the complex nature of SMS language, their interpretation represents an ongoing challenge. The proposed system uses conditional random fields as the information extraction method for classifying messages into several semantic categories. The categories refer to appreciation of the service or complaints of various types. Then, the system analyzes the extracted content and provides feedback to the service providers, making them learning and acting on this basis. RESULTS: At each step, the content of the messages reveals the actual state of the service as well as the efficacy of corrective actions previously undertaken. Our evaluations showed that: (i) the SMS classification system has achieved good overall performance with an average F1-measure and an overall accuracy of about 92%; (ii) the notification of the patients' feedbacks to service providers showed a positive impact on service functioning. CONCLUSIONS: Our study proposed an interactive patient-centered system for continuous monitoring of the service quality. It has demonstrated the feasibility of a tool for the analysis and notification of the patients' feedback on their service experiences, which would support a more regular access to the service.

A system for the extraction and representation of summary of product characteristics content.

OBJECTIVE: Information about medications is critical in supporting decision-making during the prescription process and thus in improving the safety and quality of care. In this work, we propose a methodology for the automatic recognition of drug-related entities (active ingredient, interaction effects, etc.) in textual drug descriptions, and their further location in a previously developed domain ontology. METHODS AND MATERIAL: The summary of product characteristics (SPC) represents the basis of information for health professionals on how to use medicines. However, this information is locked in free-text and, as such, cannot be actively accessed and elaborated by computerized applications. Our approach exploits a combination of machine learning and rule-based methods. It consists of two stages. Initially it learns to classify this information in a structured prediction framework, relying on conditional random fields. The classifier is trained and evaluated using a corpus of about a hundred SPCs. They have been hand-annotated with different semantic labels that have been derived from the domain ontology. At a second stage the extracted entities are added in the domain ontology corresponding concepts as new instances, using a set of rules manually-constructed from the corpus. RESULTS: Our evaluations show that the extraction module exhibits high overall performance, with an average F1-measure of 88% for contraindications and 90% for interactions. CONCLUSION: SPCs can be exploited to provide structured information for computer-based decision support systems.

Information extraction from SMS text related to a reminder service for outpatients.

This work evaluates the users' satisfaction with an SMS-based reminder system that is being used since about six years by an Italian healthcare organization. The system was implemented for reducing dropouts. This goal has been achieved, as dropout decreased from 8% to 4%. During these years, a number of reminded citizens, even not required, sent an SMS message back, with comments about the service, further requirements, etc. We collected some thousands of them. Their analysis may represent a useful feedback to the healthcare organization. We used conditional random fields as the information extraction method for classifying messages into appreciation, critique, inappropriateness, etc. The classification system achieved a very good overall performance (F1-measure of 94%), thus it can be used from here on to monitor the users' satisfaction in time.