A data-driven epidemic model with social structure for understanding the COVID-19 infection on a heavily affected Italian province

In this work, using a detailed dataset furnished by National Health Authorities concerning the Province of Pavia (Lombardy, Italy), we propose to determine the essential features of the ongoing COVID-19 pandemic in terms of contact dynamics. Our contribution is devoted to provide a possible planning of the needs of medical infrastructures in the Pavia Province and to suggest different scenarios about the vaccination campaign which possibly help in reducing the fatalities and/or reducing the number of infected in the population. The proposed research combines a new mathematical description of the spread of an infectious diseases which takes into account both age and average daily social contacts with a detailed analysis of the dataset of all traced infected individuals in the Province of Pavia. These information are used to develop a data-driven model in which calibration and feeding of the model are extensively used. The epidemiological evolution is obtained by relying on an approach based on statistical mechanics. This leads to study the evolution over time of a system of probability distributions characterizing the age and social contacts of the population. One of the main outcomes shows that, as expected, the spread of the disease is closely related to the mean number of contacts of individuals. The model permits to forecast thanks to an uncertainty quantification approach and in the short time horizon, the average number and the confidence bands of expected hospitalized classified by age and to test different options for an effective vaccination campaign with age-decreasing priority

Clinical and genetic Rett syndrome variants are defined by stable electrophysiological profiles

Abstract Background Rett Syndrome (RTT) is a complex neurodevelopmental disorder, frequently associated with epilepsy. Despite increasing recognition of the clinical heterogeneity of RTT and its variants (e.g Classical, Hanefeld and PSV(Preserved Speech Variant)), the link between causative mutations and observed clinical phenotypes remains unclear. Quantitative analysis of electroencephalogram (EEG) recordings may further elucidate important differences between the different clinical and genetic forms of RTT. Methods Using a large cohort (n = 42) of RTT patients, we analysed the electrophysiological profiles of RTT variants (genetic and clinical) in addition to epilepsy status (no epilepsy/treatment-responsive epilepsy/treatment-resistant epilepsy). The distribution of spectral power and inter-electrode coherence measures were derived from continuous resting-state EEG recordings. Results RTT genetic variants (MeCP2/CDLK5) were characterised by significant differences in network architecture on comparing first principal components of inter-electrode coherence across all frequency bands (p < 0.0001). Greater coherence in occipital and temporal pairs were seen in MeCP2 vs CDLK5 variants, the main drivers in between group differences. Similarly, clinical phenotypes (Classical RTT/Hanefeld/PSV) demonstrated significant differences in network architecture (p < 0.0001). Right tempero-parietal connectivity was found to differ between groups (p = 0.04), with greatest coherence in the Classical RTT phenotype. PSV demonstrated a significant difference in left-sided parieto-occipital coherence (p = 0.026). Whilst overall power decreased over time, there were no difference in asymmetry and inter-electrode coherence profiles over time. There was a significant difference in asymmetry in the overall power spectra between epilepsy groups (p = 0.04) in addition to occipital asymmetry across all frequency bands. Significant differences in network architecture were also seen across epilepsy groups (p = 0.044). Conclusions Genetic and clinical variants of RTT are characterised by discrete patterns of inter-electrode coherence and network architecture which remain stable over time. Further, hemispheric distribution of spectral power and measures of network dysfunction are associated with epilepsy status and treatment responsiveness. These findings support the role of discrete EEG profiles as non-invasive biomarkers in RTT and its genetic/clinical variants

Illness Severity, Social and Cognitive Ability, and EEG Analysis of Ten Patients with Rett Syndrome Treated with Mecasermin (Recombinant Human IGF-1)

Rett Syndrome (RTT) is a severe neurodevelopmental disorder characterized by an apparently normal development followed by an arrest and subsequent regression of cognitive and psychomotor abilities. At present, RTT has no definitive cure and the treatment of RTT represents a largely unmet clinical need. Following partial elucidation of the underlying neurobiology of RTT, a new treatment has been proposed, Mecasermin (recombinant human Insulin-Like Growth Factor 1), which, in addition to impressive evidence from preclinical murine models of RTT, has demonstrated safety in human studies of patients with RTT. The present clinical study examines the disease severity as assessed by clinicians (International Scoring System: ISS), social and cognitive ability assessed by two blinded, independent observers (RSS: Rett Severity Score), and changes in brain activity (EEG) parameters of ten patients with classic RTT and ten untreated patients matched for age and clinical severity. Significant improvement in both the ISS (p=0.0106) and RSS (p=0.0274) was found in patients treated with IGF1 in comparison to untreated patients. Analysis of the novel RSS also suggests that patients treated with IGF1 have a greater endurance to social and cognitive testing. The present clinical study adds significant preliminary evidence for the use of IGF-1 in the treatment of RTT and other disorders of the autism spectrum