dataesr/scientific-paragraphs-categorization huggingface dataset:
Data sharing All data used in this study is saved in a static repository. Access to these data is available to researchers once they have established a data use agreement (DUA) with Camber Systems. These datasets include daily estimates of the mobility metrics described in this study for all counties in the USA over our study period of interest. Once a DUA is established, researchers can access the static repository of data used in this research or access the dynamic, up-to-date repository made available by Camber Systems. All code used in the pre-processing, modelling, and production of figures will be available in a static code repository.
Results The COVID-19 pandemic is a truly global crisis with over 200 countries reporting infections. To understand the variables that account for public health support around the globe, we launched a collaborative, international project in April 2020 collecting large-scale data from as many nations as possible. In Study 1, we collected a large sample consisting of citizens from 67 countries. We analyzed a sample of 49,968 participants (see Fig. 1 ). See "Methods" for details about the sample (all reported materials and data are available at: https://osf. io/y7ckt/). We analyzed these data using multi-level models in which persons were treated as nested within countries  51. We also included a measurement level to control for individual differences in how consistently people responded to items that were meant to measure the same construct.
001). Taken together, the three predictors accounted for 8% of the person-level variance of the contact measure, for 8% of the person-level variance of the hygiene   The mean score for each scale is presented along with the variance explained within and between participants and the scale reliability (alpha). There is no alpha for ideology since it is a one-item measure. Higher scores reflect greater support for each measure (and stronger right-wing political beliefs in the case of ideology). To this end, we relied on two publicly available datasets: the World Values Survey  52  and the COVID-19 Google Community Mobility Reports  53  which indicate how people's physical movement has changed in response to COVID-19 policies (available at www. google. com/covid19/mobility/). We created an index of NI using the two relevant items from the World Value Survey (i.
e. , national pride and closeness to their nation) and an index of physical mobility by averaging community movement across all available places (i. e. , retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential). See "Methods" for details about the sample and measures. We examined whether countries with higher average NI prior to the pandemic predicted a stronger change in mobility in response to COVID-19 restrictions during April and May 2020 (This period mirrored when we collected most of the samples in Study 1). We conducted our analysis for the full sample of 42 countries in which aggregate data which was publicly available for both for the NI and the mobility scores. Replicating the pattern of results from Study 1, NI was associated with reduced spatial mobility, r = -0.
All the worldmaps were produced using R packages. The map is from the package 'rworldmap' and is licensed-free from South, A. (2011). rworldmap: A New R package for Mapping Global Data. The R Journal, 3, 35-43. understand public health behaviours. However, work in the United States has found that threats to national identity can lead to less support for public health initiatives  57. As such, mobilizing people around a shared national identity might require considerable nuance. Future work should examine the impact of different types of identity appeals during a pandemic and isolate the causal influence of national identity on real behaviour. There is reason to believe that other forms of group identification can undercut public health. For instance, partisanship within countries (i. e. , when people strongly identify with a specific political party) is associated with risky behaviour  25, 26, 58.
One major strength of our paper is the scope of nations we included in our samples. The first study included data from 67 nations and the second study included data from 42 countries. The vast majority of published research in psychology and social sciences has been conducted in so-called WEIRD cultures  66  , typically restricted to the narrow western and educational setting of American or European university students, and nonrepresentative participants from industrialized, rich and democratic countries. The COVID-19 pandemic, however, is a truly global issue underscoring the importance of gathering samples outside these WEIRD cultures. Moreover, it was striking to see that the same person-level association between NI and our public health measures was in the same direction in almost every country we studied.
Methods In Study 1, we launched a call using social media to collect data all over the world on psychological factors that might be related to COVID-19 pandemic response, with public health support as the primary outcome in April 2020. Each team was asked to collect data from at least 500 participants, representative with respect to gender and age, in their own country or territory. We created a survey in English (see below) that we sent to each team. The survey was approved by the ethics board at the University of Kent (each research team was allowed to include additional items after the main survey under their own institutional protocol). We have complied with all relevant ethical regulations and all participants were asked to give informed consent. Where necessary, each team translated the survey into the local language, using the standard forward-backward translation method, and then collected the data.
The datasets were then collated and analyzed using multi-level models. We report how we determined our sample size, all data exclusions (if any), all manipulations, and all measures in the study (see Supplementary Information). All materials and data are available at: https://osf. io/y7ckt/. Raw data we obtained from all collaborators were cleaned to exclude any duplicate answers as well as those younger than 18 years or older than 100 years. We then excluded data from two participants from Puerto Rico and 313 participants recruited from the UEA where it was difficult to establish participant nationality. This resulted in a sample of 51,089 participants. For the current analysis, we also excluded participants who had missing data on all six key variables of interest. We were left with a sample of 49,968 for analyses (Mean age = 43; Gender = 52% females).
To ensure our results were not confounded with the pandemic rate across countries, we measured the total COVID-19 infection and mortality cases (as well as the infection and mortality rate per capita) in each country at the start of data collection for this project. Our main findings did not vary as a function of total infections and deaths as well as infections and deaths per capita at the start of data collection for this project  70  (April 17, 2020). These variables had very little impact on the results and are not discussed further. All measures will be made publicly available upon publication at the Open Science Framework website. We conceptualized the data as a multi-level data structure in which persons were nested within countries, and we analyzed the data with a series of multi-level models (MLM) using the programme HLM 71 (see ref.
51  for a description of using MLM to analyze data from multinational studies). The analyses examined withincountry (person-level) relationships between behavioural health-protective responses to COVID-19 (i. e. , spatial distancing, physical hygiene, and policy support) and individual differences in collective concerns (i. e. , NI, NN, and PI). We also examined the moderating effects of country-level differences on these personlevel relationships. For instance, we examined if these person-level relationships between collective concerns and health-protective measures varied as a function of between-country differences in overall human development as measured by the HDI or national rates of COVID-19 infections and mortality. Before examining relations between COVID-19 protection and socio-political attitudes, we examined the reliability of our measures (with the exception of PI, which was measured with only one item).
e. , means for all three measures were above 8, on scales ranging from 0 to 10). Moreover, although the majority of variance in NI, NN, and PI was within-country, there was also notable between-country variance. This justified further analyses of relations between country-level means of these measures and HDI. We calculated scale means and used Mplus  75  to estimate multilevel correlations for person-level measures, controlling for the nested structure of the data (see Table  1 ). The next set of analyses examined relations between scores on the HDI and the means of the person-level measures. This model was a variant of the unconditional model. HDI scores were entered as a predictor in the country-level model presented above (level 3). MLM analyses do not estimate standardized coefficients, and to simplify the interpretation of the results, HDI scores were standardized prior to analysis (and, therefore, were entered uncentered).
002, functionally 0). We note that these effects were not statistically significant at the p < 0. 001 threshold we used for Study 1 so we recommend interpreting them with caution. In Study 2, we accessed data from two publicly available datasets: the World Values Survey  52  and the COVID-19 Google Community Mobility Reports  53  which indicate how people's physical movement has changed in response to COVID-19 policies (available at www. google. com/covid19/mobility/). We examined whether countries with higher average NI would also show stronger change in mobility in response to COVID-19 restrictions during April and May 2020. We created an index of NI using the two relevant items from the World Value Survey (i. e. , national pride and closeness to their nation) and an index of physical mobility by averaging community movement across all available places (i.
e. , retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential). We analyzed all 42 countries in which aggregate data was publicly available for both for NI and mobility scores. The study was approved by the ethics board at the University of Kent. All materials and data are available at: https:// osf. io/y7ckt/. NI was computed based on indices from the first release of data from Wave 7 of the World Value Survey. The surveys were conducted between early 2017 to mid-2020. All countries employed random probability representative samples of the adult population (We computed country averages using default weights applied in the World Values Survey dataset. However, our results are very similar whether are not these weights are applied). Our analysis focused on two indices.
Acknowledgment. This work is partially supported by Grant  ANR-14-CE25-0014  (GRAAL). The author also acknowledges partial support from the  Isaac Newton Institute for Mathematical Sciences  where part of this work was conducted. We thank  Guillaume Chapuy  and  Maciej Dołęga  for stimulating discussions and for sharing their paper [CD17] with us, as well as  Grégory Miermont  for encouraging discussions, especially about further developments on the subject of nonorientable Brownian surfaces. We thank  Élie de Panafieu  and  Emmanuel Guitter  for discussions on the enumeration application. Finally, we thank anonymous referees for interesting comments leading to an improvement of our presentation.
originated from Gujarat (26%), which has one of the largest populations of migrant workers in India  (12). In this study, we investigated the introduction and transmission of SARS-CoV-2 in Gujarat during the first wave of the COVID-19 epidemic in India by using a combination of epidemiologic and genomic data. Through our analyses, we characterized the epidemiologic and lineage dynamics of SARS-CoV-2, evaluated key drivers of virus importation and transmission, and assessed whether major changes in human movement (i. e. , because of lockdown and transport of migrant workers) shaped virus transmission dynamics in Gujarat. Our study demonstrates the limitations of current datasets and highlight the need for greater investment in virus genomic surveillance and collection of human mobility data to support comprehensive investigations into the origins and dissemination of future viral outbreaks.
Sample Collection, Library Preparation, Sequencing, and Data Analysis During April-July 2020, we generated 434 SARS-CoV-2 genome sequences for investigation. We collected nasopharyngeal/oropharyngeal swab specimens from COVID-19-positive persons after obtaining informed consent and ethics approval. We transported samples and processed them for sequencing as described  (13). In brief, we used the Ion AmpliSeq SARS-CoV-2 Research Panel and the Ion AmpliSeq Library Kit Plus (both from Thermo Fisher Scientific) for the library preparation. We performed sequencing on the Ion Torrent S5Plus system (Thermo Fisher Scientific) by using a 530 chip with 400-bp chemistry. We used a referencebased genome assembly, as described by Joshi et al. (13) , to obtain whole-genome sequences. In brief, we used PRINSEQ-lite version 0.
Epidemiologic Analysis We obtained COVID-19 case data from a crowdsourced initiative in India (https://www. covid19india. org and https://api. covid19india. org). These data were curated by volunteers from different data sources, such as state press bulletins and the Ministry of Health and Family Welfare of the Government of Gujarat. The source code is available in the GitHub repository (https://github. com/covid19india/api). We estimated the instantaneous effective reproduction number at time t, R t (median and 95% equal tailed Bayesian credible intervals), by using the Epi-Filter method  (15). This approach applies optimal recursive smoothing techniques to minimize the mean squared error in inferring R t from the incidence of cases under a renewal transmission model. For all analyses, we assumed that the SARS-CoV-2 generation time distribution is well approximated by the serial interval distribution from  (16) , and we applied a weekly averaging filter to daily incidence data to Epidemiology of SARS-CoV-2 Transmission Dynamics reduce weekend effects and inconsistent reporting, which probably corrupts the incidence time series.
Identifying Transmission Lineages On the basis of lineages detected in the Gujarat SARS-CoV-2 dataset, we collated a representative global dataset comprising 10,000 genome sequences sampled evenly by week and country. To maximize our power to detect within-India dissemination, we added all sequences from India on GISAID (https://www. gisaid. org) as of November 5, 2020, to this dataset, for a final dataset size of 12,180 sequences (including samples from Gujarat). We followed a similar pipeline to that outlined in du Plessis et al. (1)  and used Thorney-Beast (https://beast. community/thorney_beast) to estimate the posterior molecular clock tree. In brief, we estimated a maximum-likelihood tree by using iqtree (https://www. iqtree. org), and a Juke-Cantor substitution model with Wuhan/WH04/2020 as an outgroup.
Drivers of Virus Transmission To investigate the drivers of virus transmission, we focused on the 7 largest transmission lineages identified in the global dataset. Each dataset ranged from 11 to 75 sequences. We collated data on key predictors (population size and density, number of cases, geographic distance) at the district level. For each transmission lineage dataset, we used a separate exponential coalescent prior, while sharing a SRD06 substitutional model  (19) , a strict molecular clock model with a fixed rate (0. 001 substitutions/site/year), and a generalized linear phylogeographic model  (20). We executed 8 chains of 50 million steps (logged every 5,000 steps), then combined and thinned the chains by a factor of 10 after discarding the first 10% as burn-in.
We show the role of international connectedness and intraregional demographics in shaping virus lineage dynamics in India, and we highlight the limitations of investigating virus movement and origins because of comparatively low genomic surveillance in this region. All of these factors will need to be taken into consideration as part of evaluating the emergence and epidemiology of SARS-CoV-2 variants. All data used in this study were collated from public databases and are freely available. Regional case data, SARS-CoV-2 genome sequences, and analysis files used in this study are available (https://github. com/jnarag/ covid-gujarat).
For the estimation of α, we have performed the same kind of simulations for the three models in R 2. The results and conclusions are similar. In the following we only present the details for the Gaussian-type DPP but the code used to produce all results is provided as supplementary material and at https://github. com/APoinas/MLEDPP. We consider realizations of the Gaussian-type DPP with true parameters ρ * = 100 and α * ∈ {0. 01, 0. 03, 0. 05}, when the observation window W is either [0, 1] 2 , [0, 2] 2 or [0, 3] 2. When ρ * = 100, α can take values in ]0, (10 √ π) -1 ≈ 0. 056[ since the process exists if and only if πρα 2  1. Therefore, α * = 0. 01 corresponds to a weakly repulsive point process, close to a Poisson point process, while α * = 0. 03 corresponds to a mildly repulsive DPP and α * = 0.
05 corresponds to a strongly repulsive DPP. Examples of realizations are shown in Figure  2. We estimate α * by the approximate MLE defined in  (3. 11)  and compare it to its edge-corrected version defined in (3. 12) as well as MCEs based on the pcf or Ripley's Moreover we truncate the series defining L 0 (see Table  2 ) to the minimal value of n 50 such that all remainder terms in the series become less than 10 -4 times the first term. This choice leads to n 10 for most values of α and to n = 50 only for α > 0. 9α max , where α max = 1/ √ πρ. All realizations have been generated in R  [25]  using the spatstat  [3]  package and both MCEs were computed by the function dppm of the same package. The tuning parameters for these MCEs were r min = 0. 01, r max being one quarter of the side length of the window and q = 0.
While the association between pathogen traits and virulence has already been explored, to the best of our knowledge, how pathogen features shape age-dependent virulence has not been investigated, yet. Here, we try to fill this gap. To this end, we used a large dataset on age-specific infection-induced mortality that has been recently compiled  [20]. Glynn and Moss  [20]  showed that the severity of most infectious diseases was lowest for school-age children. We went a step further and for each infectious disease included in the dataset, we added the following information: 1) whether the infectious agent is a virus or a bacterium; 2) the duration of the incubation period; 3) the duration of the disease symptoms; 4) whether the infection is localized or systemic; 5) whether the infectious agent can be transmitted from human-tohuman; 6) whether the infectious agent has an animal reservoir; 8) whether the infection agent has a recent history of human infection (i.
Data We used data on age-specific mortality following infection with human pathogens that have been compiled by  [20]  and include 32 infectious diseases. These data are based on different mortality metrics and do not always report the number of infected cases and deaths. To allow a quantitative comparison, we therefore only used data for which numbers of infected people and number of deaths were reported. This restricted the dataset to 28 infectious diseases. For some of these infectious diseases, the data were derived from different countries or geographical regions and different periods, representing replicated datasets. In particular, for infectious diseases for which treatments and vaccines are available now,  [20]  sought older studies in order to have estimates of untreated CFR. Consequently, the data included spanned a large period, from the first half of the 19 th century (cholera) to 2020.
Sex was only reported for a small fraction of diseases and therefore it was not possible to systematically compare CFR between women and men across different ages. Accordingly, when reported, CFRs of women and men were combined in a unique value per age class. For COVID-19, we also reported sex-specific results. To this initial dataset on age-dependent CFR, we added 11 variables referring to different clinical and ecological aspects of the disease: pathogen type (virus or bacterium), duration of the incubation period (three ordered classes), duration of symptoms/illness (three ordered classes), systemic/local infection (two classes), length of the human-pathogen association (two classes), animal reservoir (two classes), human-to-human transmission (two classes), transmission via ingestion (two classes), transmission via inhalation (two classes), transmission via contact with body fluids (two classes), transmission via vector (two classes).
Finally, for transmission mode, we used four 0/1 scores: ingestion, inhalation, body fluids and vector. This allowed having multiple 1 for pathogens that can be transmitted by multiple pathways (e. g. , Lassa fever, tuberculosis). S1 Table  reports  the list of diseases, the full description of the original information used to attribute scores for each variable, and all the sources the original information comes from. The complete dataset (the age-specific CFRs and all the explanatory variables used for the statistical models) is available in S1 Data.
Statistical analyses Age and infection-induced mortality can vary in a non-linear way. Therefore, we first used generalized additive models (GAMs) to assess the shape of the relationship between CFR and age for each infectious disease. GAMs decompose the linear and non-linear relationship between variables, using smoothing spline optimization functions. CFR was modelled as a binomial response variable (number of deaths/number of infected) and age was included as the independent variable. This allowed us to infer the shape of the relationship and to assess the significance of the linear and non-linear components. GAMs were run using PROC GAM in SAS  (14. 3). Having characterized the shape of the relationship for each disease, we next implemented a finite mixture model with a beta-binomial distribution of errors (PROC FMM, SAS 14.
Therefore for model 1, we compared 19 models; for model 2 and 3, we compared 35 models each. Models with the lowest BIC values were considered as the most parsimonious ones. For each set of models, the ΔBIC (difference between the focal model and the model with the lowest BIC) was always higher than 2. Compared to the Akaike Information Criterion (AIC), BICs penalize complex models more strongly, and therefore provide more conservative results. S2, S3, S4, S6 Tables reports all the models run and the corresponding BICs, AICs, Pearson's statistics, and number of parameters. The SAS codes used to run GAMs and FMMs are also reported in S1 Text.
Age-dependent CFR increased more for bacterial diseases and for emerging pathogens (Table  3 , Fig  3 ). However, it should be noted that among the 28 infectious diseases considered here, all the emerging ones involve viruses. We, therefore, tested whether emerging diseases exert a higher mortality burden in the oldest age classes using a restricted dataset with viruses only. Again, the model with the lowest BIC included the interaction between age and length of human-pathogen association, with emerging viruses producing the steepest increase in age-dependent CFR (Fig 3, S4 and S5 Tables). Finally, model 3 included four variables describing the mode of pathogen transmission. Among the 35 competing models, the one with the lowest BIC value only included the main terms (S6 Table ), and all models with the interactions between age (and squared age) and the explanatory variables were rejected by the model selection procedure.