dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Age-structured transmission model and role of children. We used a stochastic discrete age-structured epidemic model  18  based on demographic and age profile data  19  of the region of Île-de-France. Four age classes were considered: 0-11, 11-19, 19-65, and 65+ years old. The first class includes ages of students in preschool and primary school, and the second class corresponds to students in middle and high school. We used social contact matrices measured in France in 2012  20  to account for the mixing, in the no interventions scenario, between individuals in these age groups, depending on the type of activity and place where contacts occur (household, school, workplace, transport, leisure, other) and the type of contact (physical or non-physical). Transmission dynamics follows a compartmental scheme specific for COVID-19 (Supplementary Fig.
Also, we did not consider seasonal behavior in viral transmission  40  , as this was still under investigation. Empirical contacts used in this study were measured in 2012, and no data are available to confirm that patterns have not been altered in more recent years. A reduction of 50% of contacts in adolescents, suggested by a recent study in the UK  41  , would mitigate the increase in the epidemic activity when middle and high schools are reopened, as adolescents would play a less important role in the transmission dynamics relatively to other age classes. We did not study reactive school closure as a means to slow down propagation  42, 43  , as our study was focused on the conditions allowing reopening for the last two months of the school calendar. We focused on Île-de-France region, following our previous work  4  , and did not consider spatially targeted reopening within the region  44.
Sensitivity analysis. We investigated social distancing measures based on moderate interventions coupled with a smaller rate of testing and isolation of cases (25%). We additionally performed the analysis also assuming that the reproduction number during lockdown is 10% lower or higher than the one estimated on current data. We evaluated the reopening of middle and high school starting June 8 (second set of scenarios), considering a full attendance of younger children starting May 11. Finally, we considered contact matrices with a 50% reduction of contacts in the [11,19) age class, to account for possible changes in the pattern of contacts measured in 2012, as suggested by a recent survey conducted in the UK  41. Ex-post assessment analysis. We retrospectively compared ICU admission data in the post-lockdown phase with different exit scenarios.
Acknowledgements This study is partially funded by:  ANR  projects  SPHINX  ( ANR-17-CE36-0008-05 ) and  DATAREDUX  ( ANR-19-CE46-0008-03 );  EU H2020  grants  RECOVER  ( H2020-101003589 ) and MOOD ( H2020-874850 ); REACTing COVID-19 modeling grant; INSERM-INRIA partnership on data science and public health. We thank  Chiara Poletto ,  Alain Barrat ,  Juliette Paireau , and  Santé publique France  for useful discussions.
At each stage, the probability of selecting an item (deme, cell type, genotype or event type) is proportional to the sum of event rates for that item, within the previous item. We measured elapsed time in terms of cell generations, where a generation is equal to the expected cell cycle time of the initial tumour cell. Sequencing data. We surveyed the multi-region and single-cell tumour sequencing literature to identify data sets suitable for comparison with our model results. 2] [73] [74]  ) were excluded as they were found to have insufficient sequencing depth for our purposes. 6] [77] [78] [79] [80]  ) because our model is not designed to correspond to such scenarios. The seven studies we chose to include in our comparison are characterized by either high-coverage multi-region sequencing or large-sample single-cell sequencing of several tumours.
From each of the seven cohorts, we obtained data for between three and eight tumours. In the ccRCC data set, we focused on the five tumours for which driver frequencies were reported in the original publication. For NSCLC, we used data for the five tumours for which at least six multi-region samples were sequenced. In mesothelioma, we selected the six tumours that had at least five samples taken. From the breast cancer multi-region study, we used data for the three untreated tumours that were subjected to multi-region sequencing. From the single-cell sequencing studies of uveal melanoma and breast cancer, we used all the published data (eight tumours in each case), and from the AML study, we selected a random sample of eight tumours. In multi-region sequencing data sets, it is uncertain whether all putative driver mutations were true drivers of tumour progression.
In the main text and below, we refer to this curve as corresponding to trees with an intermediate degree of branching. Specifically, this intermediate-branching curve is defined such that for every point below the curve (and with D > 1), there exist both linear trees and branching trees that have the corresponding values of n and D, whereas for every point above the curve there exist only branching trees. Derivation of the curve's equation is provided in Supplementary Information. A first-order approximation (accurate within 1% for n ≥ 2. 2) is D ≈ 9(2n -1)/8. To assess the extent to which clusters of points (n, D) are well separated, we calculated silhouette widths using the cluster R package  82. A positive mean silhouette width indicates that clusters are distinct. Other diversity indices.
Articles NaTUrE ECology & EvolUTioN To determine whether clonal turnover mostly occurred early, late or throughout tumour evolution, we calculated the weighted average T Θ = 1 max(t) ( ∑ t Θ(t)t / ∑ t Θ(t) ) , where max(t) denotes the final time of the simulation. This quantity takes values between 0 and 1, and is higher if clonal turnover occurs mostly late during tumour growth. If the rate of clonal turnover is constant over time, then T Θ ≈ 0. 55. Histology slide analysis to determine the number of cells per gland. We randomly selected five tumours of each of four cancer types (colorectal cancer, clear cell renal cancer, lung adenocarcinoma and breast cancer) from The Cancer Genome Atlas (TCGA) reference database (http://portal. gdc. cancer. gov). Using QuPath v0. 2. 0m4  88  , we manually delineated five representative groups of tumour cells in each image and automatically counted the number of cells in each group.
acknowledgements We thank  M. Hochberg ,  M. Lebid ,  K. Jahn ,  R. Neher ,  A. Deutsch ,  K. Korolev ,  C. -A. Weis ,  B. Werner ,  A. Sottoriva  and  A. Kaznatcheev  for helpful discussions, and  D. Minussi  for sharing data. R. N. and N. B. were supported by  ERC Synergy  Grant  609883. J. N. K. was supported by the  German Consortium for Translational Cancer Research (DKTK/DKFZ)  fellowship programme and by  RWTH Aachen START  grant  2018/691906. R. N. was also supported by the  National Cancer Institute of the National Institutes of Health  under Award Number  U54CA217376. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Acknowledgements We thank  Laura Capuano ,  Pietro Corvaja ,  Lionel Darondeau ,  Carlo Gasbarri ,  Ariyan Javanpeykar ,  Stefan Kebekus  and  Min Ru  for useful discussions. We thank  Min Ru  and  Paul Vojta  for providing us with an earlier copy of their paper. We are especially grateful to  Frédéric Campana  for all the discussions on this subject and especially his explanations on the construction of weakly special but not special varieties. We thank the referees for their comments and suggestions which greatly improved the paper. This collaboration was initiated at the conference " Topics On Nevanlinna Theory And Complex Hyperbolicities " at the  Shanghai Center for Mathematical Sciences ; we thank the organizers for making this possible. Part of this work was finalized during the visit of the first and second named author to the  Freiburg Institute for Advanced Studies ; they thank the  Institute  for providing an excellent working environment.
, 2021 ) , whereas other studies have used mobility data as early predictors of disease incidence  ( Funk et al. , 2020; Paireau et al. , 2021; Zebrowski et al. , 2021 ). For instance, mobile phone location data provided by Google Mobility Trends were used to calibrate individual movements  ( Pullano et al. , 2020 ). However, a limitation of such data is that they involve only individual movement but disease transmission requires at least two people. Here we use data from Facebook Inc. that inform us on colocation (or colocalization); that is, not just the position of a single individual but also possible interactions of individuals with respect to their administrative unit of residence (see 'Materials and methods' for details). Similar data were used to simulate metapopulation models and investigate the effect of interventions on the resulting network  ( Chang et al.
We also investigated the coverage of Facebook users with an assigned department of residence among the French population from the 2019 census data provided by the French National Institute of Statistics and Economic Studies (Insee; https://www. insee. fr ). To summarize temporal changes in the resulting weighted contact networks between French departments, we calculated for each week three node-based graph descriptors (clustering, strength and null links) and one edge-based graph descriptor (curvature). The node-based descriptors were calculated by constructing undirected, weighted graphs from colocation data. For clustering, we used the local clustering coefficient ('transitivity' function in the R package igraph ), which calculates for a given node all edge weights between the node neighbours relative to the maximum neighbourhood clique size.
This clustering coefficient is 1 if the node is contained in a clique (i. e. the node and all its neighbours are connected to each other). The strength of a node is calculated by summing the edge weights incident on the node ('strength' function in igraph ). The number of null links rep-resents the number of incident edges with zero weight and is bounded from above by d -1. Curvature was calculated in the sense of discrete Ollivier-Ricci curvature  Ollivier (2007)  with a Python code freely available at https://github. com/saibalmars/ GraphRicciCurvature. The Ollivier-Ricci curvature of an edge xy compares the distance between two nodes x and y with the optimal transport cost W between uniform measures on unit balls centred at x and y : curvature (x, y ) = 1 - W (B 1 (x ) , B 1 (y )) d(x, y ).
Numerical studies showed that edges with positive curvature tend to be part of a cluster, whereas edges with negative curvature tend to act as bridges between clusters  ( Jost and Liu, 2013; Ni et al. , 2015; 2019 ). Therefore, a decrease in curvature can be seen as an indicator for connectivity breakdown. For comparison, we also incorporated Google Community Mobility Report data ( www. google. com/covid19/mobility/ ), from which we obtained the daily change as a percentage with respect to a pre-pandemic baseline in visits to grocery stores, parks, workplaces, residential areas, transit stations and retail stores. We calculated cumulative weekly changes matching the dates of the weekly recorded colocation data. Since Google Community Mobility Reports were not available at the department level, we used these data only for national-level analyses.
We downloaded the positive testing rate (i. e. the ratio of positive tests to all tests) from https://ourworldindata. org. To remove reporting bias, we calculated the rolling average with a rightaligned 7-day window and calculated the weekly positive test rate matching the dates of the weekly recorded colocation data. We downloaded daily minimum, maximum and average temperature data at department level provided by Open Data Réseaux Énergies via the Web portal https://www. data. gouv. fr ; see also Table  1. The data were aggregated into weekly minimum, maximum and average temperature. For the same data, we also calculated quantiles for national analysis. COVID-19 hospital data for France were downloaded from https: //www. data. gouv. fr. The data comprised daily hospital admissions, ICU admissions and deaths in hospitals by department.
To match the Facebook Inc. colocation data, we considered only departments in continental France spanning the period from 24 March 2020 to 30 March 2021. For the country-wide analysis, we summed the daily incidence in all departments and calculated right-aligned 7day rolling averages to remove reporting bias. Finally, we calculated the cumulative weekly incidence matching the dates of the weekly recorded colocation data. The weekly incidence data were log-transformed before the time series analysis. The time series analysis was performed with the R package forecast. For each week, we trained and tuned ARIMA models on historical data starting from 24 March 2020 and performed forecasting for 1-week and 2-week horizons. Model parsimony during tuning was determined by the Akaike information criterion.
Predicting national-level hospital incidence The time series analysis showed that predictions made using only incidence data from past time points led to an average mean error of 1632 hospitalizations per week and 238 deaths per week (regressor group 'none' in Fig. 3  ). Including a single exogenous regressor across all time points clearly reduced the prediction error by about 13% for hospital admissions and by 50% for hospital deaths ( Fig. 3  ). Quantiles from Facebook Inc. colocation data, such as network strength, clustering or null links, were among regressors that reduced prediction errors most. Google Community Mobility Report data related to recreation and residential movement also greatly improved predictions regarding deaths but not hospital admissions. Positive testing rate appeared to improve predictions only with 2-week horizons.
Discussion Numerous studies have reported potential insights that can be gained from mobile phone usage data so as to understand the spread of infectious diseases. However, these data are usually analysed from an individual perspective, by following where the users are and how they move. The Facebook Inc. data differ in that they contain colocation data. We hypothesized that such data could be particularly suited to understanding the transmission of respiratory infections, which involves close contact between individuals. To address this question, we developed an explicit network-based analysis of relevant summary statistics regarding colocation data. Our analysis indicates that human mobility and contact data improve time series prediction of French COVID-19 hospital admissions and deaths by up to 50%.
Acknowledgment The authors thank the Data for Good initiative by  Facebook Inc. , which provided the colocalization data through a data sharing agreement with the  University of Montpellier. They also acknowledge the i-Trop high-performance computing cluster (a member of the  South Green bioinfomatics platform) at IRD Montpellier  for providing high-performance computing resources that contributed to the research results reported in this study ( https://bioinfo. ird. fr/ ).
Numerical implementation We implemented and simulated all the models using the pomp package  [35]  in R version 3. 6. 3 [36]. For the global sensitivity analysis, we used the mgcv package  [34]  to fit the GAMs and the ggeffects package  [37]  to plot the marginal effect of each input parameter. Finally, we used the renv package to keep track of all packages' version and to increase the results' reproducibility  [38].
Acknowledging all these limitations, our simple model could serve as a building block for further research on epidemiological interactions. In conclusion, our results show that the inherently complex, non-linear dynamic of respiratory viruses makes the interpretation of seemingly intuitive measures of interaction difficult, if not impossible. Despite these pitfalls, other statistical or mathematical methods based on longitudinal data should enable epidemiological research on pathogen interactions. Indeed, with increasing evidence that SARS-CoV-2 and other pathogens do not circulate in isolation but within polymicrobial systems, such research should remain a priority.
Schools were largely closed in 2020-2021 to counter COVID-19 spread, impacting students' education and wellbeing. With highly contagious variants expanding in Europe while vaccine hesitancy persists, safe options to maintain schools open are urgently needed. We developed an agent-based model of SARS-CoV-2 transmission in school. We used empirical contact data measured in a primary and a secondary school in France, and field estimates for adherence to screening from 683 schools during the spring 2021 wave. Examining different screening protocols, we performed a cost-benefit analysis for varying epidemic conditions and vaccination scenarios. In a partially immunized school population, weekly screening would reduce the number of cases on average by 24% in the primary and 53% in the secondary school compared to symptom-based testing alone, if R=1.
METHODS Empirical patterns of contacts. We used empirical data describing time-resolved face-to-face proximity contacts between individuals in two educational settings, collected using wearable RFID sensors in a pre-pandemic period. The Primary school dataset describes the contacts among 232 students and 10 teachers during two days in October 2009 in a primary school in Lyon, France  [18]. The school is composed of 5 grades, each of them comprising two classes. The Secondary school dataset describes the contacts between 325 students of 9 different classes during one week in December 2013 in a secondary school in Marseilles, France  [19]. Data collection concerned high school students of the second year of classes specific to the French schooling system, called "classes préparatoires". These classes gather students for 2-year studies at the end of the standard curriculum to prepare for entry exams at specific Universities.
Classes are divided in three groups, each focusing on a specialization (mathematics and physics; physics, chemistry, engineering studies; biology). Datasets are available as lists of contacts over time between anonymized individuals, with a classification by class and in terms of students/teachers. From the raw data, we built the corresponding temporal contact networks, composed of nodes representing individuals, and links representing empirically measured proximity contacts occurring at a given time (Figure  1 ). As each dataset covers only a few days, we developed an approach to temporally extend the datasets by generating synthetic networks of contacts that reproduce the main features of the empirical data (class structures, within-vs. between-classes links, density of links, heterogeneity of contact durations, and similarity of contacts between consecutive days).
The secondary school synthetic network was further extended to generate a synthetic first year (to consider the full curriculum of the "classes préparatoires") including teachers whose contacts were inferred from an additional dataset for the same school. The resulting network for the secondary school was composed of 650 students and 18 teachers. Details are provided in the Appendix. Field data on adherence to screening in schools in France. In response to a rising third wave in France in the spring 2021, authorities started offering the screening at schools on a voluntary basis to limit cases and avoid closures. We used data on adherence to screening collected in 683 schools between March 8 and June 7, 2021, in the Auvergne-Rhône-Alpes region of France. Participating schools included different levels: 94 pre-schools, 427 primary schools, 158 middle schools, and 4 high schools, for a total of 209,564 students and 18,019 personnel.
We varied test sensitivity after the prodromal phase between 70% and 100% to assess the impact of sampling accuracy (e. g. samples collected by non-professionals). We considered that teachers are required to show proof of a negative PCR test when returning to school after infection. Adherence to regular testing was informed from field data on the testing initiatives in France in the spring 2021, and then further explored in a range between 10% and 75%. Simulation details and analysis. Results for each scenario were obtained from 1,000 (primary school) to 2,000 (secondary school) stochastic simulations over the course of a school trimester (90 days). We computed medians and 95% confidence intervals (CI) for the quantities of interest.
(f) Daily average time that an individual spends in interaction for teachers and students in the primary school. In the three bottom panels, histogram bars refer to the empirical networks. Points and error bars refer to the synthetic networks (errors represent 95% confidence intervals). In panels d and e for the secondary school, there is an increase of average number of contacts and their duration in the synthetic networks compared to their empirical counterparts, due to the ad hoc addition of contacts between school years. In panel f, only values from the synthetic networks are shown, as teachers did not participate to the data collection, and their contact behavior was inferred from another dataset (see Methods).
ACKNOWLEDGMENTS We thank the  Assistance Publique -Hôpitaux de Paris and Santé publique France  for useful discussions;  Philippe Vanhems ,  Elisabeth Bothello-Nevers ,  Olivier Epaulard ,  Jean Beytout ,  Annabelle Ravni  and the  Academie of the Auvergne-Rhône-Alpes region  for the school screening initiatives. This study was partially funded by:  ANR  projects  COSCREEN  ( ANR-21-CO16-0005 ) and  DATAREDUX  ( ANR-19-CE46-0008-03 );  EU   H2020 grants MOOD  ( H2020-874850 ) and  RECOVER  ( H2020-101003589 ); REACTing COVID-19 grant.
(e) Daily average time that an individual spends in interaction within the same class or in different classes, in the primary and secondary school. (f) Daily average time that an individual spends in interaction for teachers and students in the primary school. In the three bottom panels, histogram bars refer to the empirical networks. Points and error bars refer to the synthetic networks (errors represent 95% confidence intervals). In panels d and e for the secondary school, there is an increase of average number of contacts and their duration in the synthetic networks compared to their empirical counterparts, due to the ad hoc addition of contacts between school years. In panel f, only values from the synthetic networks are shown, as teachers did not participate to the data collection, and their contact behavior was inferred from another dataset (see Methods).
INTRODUCTION After spreading from China to other Asian countries in late 2019, COVID-19 appeared in Western Europe in January 2020 where it rapidly led to overwhelmed hospitals and an exponential increase in deaths (COVID-19 data repository of the Johns Hopkins Centre for Systems Science and Engineering, Baltimore, MD, USA). While most European countries adopted lockdown measures only several weeks after the first COVID-19 cases were reported (eg, a 6-week delay in Italy and a 7-week delay in France), many sub-Saharan African (SSA) countries decided to act sooner before the outbreak spread.
METHODS AND ANALYSIS Population This study includes adults (18 years old and over) living in all 30 villages of the rural Niakhar area covered by the Niakhar Health and Demographic Surveillance System (HDSS). 23 The Niakhar HDSS, which is the oldest HDSS in Senegal (created in 1962) and one of the oldest in Western Africa, gathers regular data for the population covered, including demographic and health data. The Niakhar area itself is located 135 km East of Dakar and covers 203 km 2 with a population of 50 355 inhabitants (January 2018 census). More specifically, it is located in the 'department' (an administrative area) of Fatick (there are 45 departments in Senegal) which covers three different healthcare districts (including the Niakhar healthcare district). Most of the population (96.