Régularisation spatiale de représentations distribuées de mots

Stimulée par l’usage intensif des téléphones mobiles, l’exploitation conjointe des don-nées textuelles et des données spatiales présentes dans les objets spatio-textuels (p. ex. tweets)est devenue la pierre angulaire à de nombreuses applications comme la recherche de lieux d’attraction. Du point de vue scientifique, ces tâches reposent de façon critique sur la représentation d’objets spatiaux et la définition de fonctions d’appariement entre ces objets. Dans cet article,nous nous intéressons au problème de représentation de ces objets. Plus spécifiquement, confortés par le succès des représentations distribuées basées sur les approches neuronales, nous proposons de régulariser les représentations distribuées de mots (c.-à-d. plongements lexicaux ou word embeddings), pouvant être combinées pour construire des représentations d’objets,grâce à leurs répartitions spatiales. L’objectif sous-jacent est de révéler d’éventuelles relations sémantiques locales entre mots ainsi que la multiplicité des sens d’un même mot. Les expérimentations basées sur une tâche de recherche d’information qui consiste à retourner le lieu physique faisant l’objet (sujet) d’un géo-texte montrent que l’intégration notre méthode de régularisation spatiale de représentations distribuées de mots dans un modèle d’appariement de base permet d’obtenir des améliorations significatives par rapport aux modèles de référence

Geospatial model of COVID-19 spreading and vaccination with event Gillespie algorithm

We have developed a mathematical model and stochastic numerical simulation for the transmission of COVID-19 and other similar infectious diseases that accounts for the geographic distribution of population density, detailed down to the level of location of individuals, and age-structured contact rates. Our analytical framework includes a surrogate model optimization process to rapidly fit the parameters of the model to the observed epidemic curves for cases, hospitalizations, and deaths. This toolkit (the model, the simulation code, and the optimizer) is a useful tool for policy makers and epidemic response teams, who can use it to forecast epidemic development scenarios in local settings (at the scale of cities to large countries) and design optimal response strategies. The simulation code also enables spatial visualization, where detailed views of epidemic scenarios are displayed directly on maps of population density. The model and simulation also include the vaccination process, which can be tailored to different levels of efficiency and efficacy of different vaccines. We used the developed framework to generate predictions for the spread of COVID-19 in the canton of Geneva, Switzerland, and validated them by comparing the calculated number of cases and recoveries with data from local seroprevalence studies

Impact of Screening and Treatment for Hepatitis C Virus (HCV) Infection in Switzerland. A Comprehensive Mathematical Model of the Swiss HCV Epidemic

Background and objective: An estimated 40,000 people were chronically infected with Hepatitis C virus (HCV) in Switzerland in 2016. HCV is one of the leading causes of liver disease, but a considerable proportion of the infected people may remain unaware of their infections until the onset of severe symptoms. A few years ago, the new effective therapy with direct acting antivirals (DAA) became available, and since October 2017, all HCV infected patients in Switzerland are eligible to be treated. The aim of our project was to estimate the effect of various screening strategies on identifying the currently undiagnosed patients, and to project the number of annual new diagnoses, treated patients achieving sustained virological response (SVR), liver related deaths among HCV infected people, and the size of the HCV viremic population, between 2018 and 2029. We compared the following screening interventions with the current practice of screening (baseline scenario): intensified testing of current injection drug users (IDU); screening of former IDU; screening of people originating in high prevalence regions (South Europe, Asia, Africa); screening of people born 1951-1985; and universal screening of the entire population. Methods: We developed a mathematical model of HCV disease progression that simulates individual patients from HCV infection until death. The progression of the disease is represented using health states that account for the current stage of liver disease (F0-F4, decompensated cirrhosis, hepatocellular carcinoma, transplanted liver) and stage of the infection and care (acute, chronic undiagnosed, diagnosed, on treatment, SVR/cured). Patients are assigned demographic and behavioral baseline characteristics. Transition times between health states are sampled from hazard functions, which were parameterized based on a comprehensive literature search and consulting experts. Because of uncertainty in input parameters, we conducted four alternative analyses, combining two assumptions about the rate of fibrosis progression (dynamic age- and stage-dependent vs. constant) and past diagnosis rate among IDU (low increasing vs. constant high). The outputs of the model were converted into the assumed HCV infected population of Switzerland by giving each simulated patient a weight based on his/her baseline characteristics, corresponding to the representativeness of this simulated patient among the true infected population. We used the notification database of the Federal Office of Public Health and the data collected by the Swiss Hepatitis C Cohort Study to estimate the distribution of the characteristics among the individuals diagnosed by 2016. We estimated the size of the undiagnosed population in 2016 by assuming a total infected population of 40,000 individuals. We assumed that the distribution of the characteristics was the same among the individuals infected in a particular year regardless of being diagnosed or not by 2016, and that the number of annual new cases of HCV would continue in the future on the same level as in the recent years. We also conducted sensitivity analyses where we either increased or decreased the total size of the infected population, or the proportion of individuals with high-risk behavior among the undiagnosed, or increased the liver related mortality rate. Results: In this summary, we present the results comparing the future strategies from the main analysis assuming dynamic fibrosis progression and low diagnosis rate among IDU in the past (see Section 6 and Appendix E of the full report for the other analyses). The expected number of new diagnoses in 2018 was about 700 in the baseline scenario, which represents a substantial drop from 2017 due to the decreasing number of undiagnosed patients in the easy-to-identify population groups (Figure i). Afterwards, the annual new diagnoses continued to slightly decrease. More intensive screening of current IDU did not considerably change the number of new diagnoses. With origin based screening, the new diagnoses were slightly above the baseline scenario, with similar pattern across the years. The number of diagnoses in 2018 was considerably higher with birth cohort screening (3,000) and universal screening (3,900). After the first years, the diagnoses decreased rapidly. The model predicted in the baseline scenario that over 7,000 patients would achieve SVR in 2018. Afterwards, the number decreased fast, with only about 200 patients achieving SVR in 2029. The differences in annual number of SVR across the scenarios followed those of the new diagnoses. In particular universal and birth cohort screening scenarios will be able to cure over 1,000 patients more than the baseline scenario in the first years

Clusters of sub-Saharan African countries based on sociobehavioural characteristics and associated HIV incidence

HIV incidence varies widely between sub-Saharan African (SSA) countries. This variation coincides with a substantial sociobehavioural heterogeneity, which complicates the design of effective interventions. In this study, we investigated how sociobehavioural heterogeneity in sub-Saharan Africa could account for the variance of HIV incidence between countries

The psychological impact of quarantine on coronavirus disease 2019 (COVID-19)

Comment on: Brooks SK, Webster RK, Smith LE, Woodland L, Wessely S, Greenberg N, Rubin GJ. The psychological impact of quarantine and how to reduce it: rapid review of the evidence. Lancet. 2020 Mar 14;395(10227):912-920. doi: 10.1016/S0140-6736(20)30460-8. Epub 2020 Feb 26. PMID: 32112714; PMCID: PMC7158942

Future scenarios for the SARS-CoV-2 epidemic in Switzerland: an age-structured model

The recent lifting of COVID-19 related restrictions in Switzerland causes uncertainty about the future of the epidemic. We developed a compartmental model for SARS-CoV-2 transmission in Switzerland and projected the course of the epidemic until the end of year 2020 under various scenarios. The model was age-structured with three categories: children (0-17), adults (18-64) and seniors (65- years). Lifting all restrictions according to the plans disclosed by the Swiss federal authorities by mid-May resulted in a rapid rebound in the epidemic, with the peak expected in July. Measures equivalent to at least 76% reduction in all contacts were able to eradicate the epidemic; a 54% reduction in contacts could keep the intensive care unit occupancy under the critical level and delay the next wave until October. In scenarios where strong contact reductions were only applied in selected age groups, the epidemic could not be suppressed, resulting in an increased risk of a rebound in July, and another stronger wave in September. Future interventions need to cover all age groups to keep the SARS-CoV-2 epidemic under control