dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgment: The work of  A. Chertock  was supported in part by  NSF  grants  DMS-1818684. P. Degond holds a visiting professor association with the  Department of Mathematics, Imperial College London, UK. The work of G. Dimarco was supported by the  Italian Ministry of Instruction, University and Research (MIUR)  under the  PRIN  Project  2017  (No. 2017KKJP4X ). M. Lukáčová-Medvid'ová and  A. Ruhi  were supported by the  German Science Foundation (DFG)  under the  Collaborative Research Center TRR 146 Multiscale Simulation Methods for Soft Matter Systems  (Project  C5 ). M. Lukáčová-.
Results: In total, 302 study participants (10. 72%) in the first round of the study and 404 (33. 92%) in the second round were positive for SARS-CoV-2-IgG antibodies. The prevalence of seropositivity observed after the second wave was 3. 16 times higher than after the first wave. We confirmed that direct, prolonged, and repeated contact with patients or their environment was a predominant seroconversion factor, but more unexpectedly, that this was the case for all HCWs and not only caregivers. Finally, the notion of high-risk contact seemed more readily identifiable in one's workplace than in one's private life. Conclusions: Our study confirmed that HCWs are at a significantly higher risk of contracting COVID-19 than the general population, and suggests that repeated contacts with at-risk patients, regardless of the HCWs' professions, represents the most important risk factor for seroconversion (Clinicaltrials.
be). During the first period, they were isolated for at least 7 days at home and for at least 10 days during the second period due to the Delta COVID-19 variant. Return to work was allowed on the condition that there was no fever for at least 3 days and that there was an improvement in the respiratory symptoms. The descriptive statistical evaluation of the questionnaire data, correlated with the laboratory results, used the following methods as appropriate: computation of the 95% confidence limits for proportions using the Wilson method  [13]  and uncorrected Pearson's Chi2 test for comparing proportions in independent samples (with two-tailed probability)  [14]. The study protocol was fully approved by the hospital's independent ethics committee and the Clinicaltrials. gov Identifier is NCT04723290.
Acknowledgments HA's work on this study was supported by research grants received from the  Future Education Research Institute , and the  Japan Society for the Promotion of Science  ( JSPS: Grant for Young Scientists ,  20J23507 ). EM's work on this research was supported by a  grant-in-aid  ( 20K20516 ) received from  JSPS. The authors express their gratitude to the students and teachers of  Gifu Shotoku Gakuen University Junior High School, Japan (especially Yukio Fuwa , the  Head Teacher)  and the staff of  Achieve Academy, Japan  (especially  Noriko Hanaki, the School Manager ) for their assistance in the collection and analysis of the data.
Unlike previous approaches that have focused on either selecting optimal combination schemes and weights or equally weighting a subset of best forecasters, our novelty ensemble procedure involves identifying the best holdout period for each model, selecting the best forecasting models and determining the optimal weights based on the out-of-sample forecasting performance for each dataset. To demonstrate empirically the robustness of our approach, we use monthly respiratory disease death data for 61 heterogeneous countries to estimate excess mortality during the COVID-19 pandemic. Excess mortality is the number of deaths attributable to all causes above and beyond mortality predictions under normal (baseline) circumstances for a given period in a population. Clearly, quantifying and analyzing excess mortality attributable to the coronavirus 2 (SARS-CoV-2) pandemic is of great relevance for policymakers, public health officials and epidemiologists  [29, 30]  and, in this sense, any improvement in such forecasts are to be welcomed.
Excess mortality is typically measured by national or supranational statistical agencies using the absolute, relative (P-score) or standardized (Z-score) number of ''excess'' deaths, where the benchmark is often computed quite naïvely, by using, for instance, the simple average of the previous year's deaths. The EuroMOMO project (https://www. euromomo. eu) is a notable example of this, with baseline mortality modeled using a generalized linear model corrected for over dispersion assuming that the number of deaths follows a Poisson distribution. However, this approach does not account for differences in the level, longterm secular trends, and seasonal patterns in all-cause mortality across countries and regions. Additionally, empirical studies show that it is hard to find a single, widely accepted forecasting method (if, indeed, one exists) that performs consistently well across all datasets and time horizons  [31].
As such, it could constitute a problem for the ensemble layer, were we to merge the models of different lengths. Thus, we need to force all the models selected to be of equal length, so that eventually the length of the ensemble is equal to the minimum length time series in our time series set. Although this windowing strategy provides each forecaster's best prediction and, therefore, the ensemble's best performance, it is clear that to obtain the best results, the length of all the time series should be sufficiently large and almost the same. Following  Ashofteh and Bravo (2021) , let each candidate model be denoted by M l , l = 1,. , L representing a set of probability distributions in which the ''true'' data-generating process is assumed to be included, comprehending the likelihood function L (y|θ l , M l ) of the observed data y in terms of model-specific parameters θ l and a set of prior probability densities for these parameters p (θ l |M l ).
Consider a quantity of interest ∆ present in all models, such as the future observation of y. The marginal posterior distribution across all models is p(∆|y) = L ∑ l=1 p (∆|y, M l ) p (M l |y) (1) where p (∆|y, M l ) denotes the forecast PDF based on model M l , and p (M l |y) is the posterior probability of the model M l given the observed data with ∑ L l=1 p (M l |y) = 1. The weight assigned to each model M l is given by its posterior probability p (M l |y) = p (y|M l ) p (M l ) ∑ L l=1 p (y|M l ) p (M l ). (2) The workflow of our proposed method is presented in Fig. 2. To identify the model confidence set and compute model weights, we first set the different holdouts to be checked for each contributed model in each dataset. Let H = {h 1 , h 2 ,. , h k } represent the set of holdout periods to be considered in the estimation procedure (see Fig.
Fifth, the BMA forecasts are obtained based on the law of total probability (1) considering the model confidence set and the corresponding model weights (4). The sampling distribution of the ensemble forecast of the quantity of interest is a mixture of the individual model sampling distributions (see Table  1  -lines  [44] [45] [46] [47] [48] [49] [50] [51] [52] [53]. The pseudocode of the proposed methodology is listed in Table  1. In the interest of reproducible science, the dataset and all methods are publicly available  [39].
The models need to be robust to the outliers detected in the multiple panel members' (countries) datasets. In specifying the STL, we use a robust decomposition so that sporadic abnormal observations do not affect the estimates of the trend-cycle and seasonal components. The time series are tested for autocorrelation using the Ljung-Box test, considering the null hypothesis that the model exhibits appropriate goodness-of-fit. The method does not handle the calendar variation automatically, and it only provides facilities for additive decompositions, which could be considered a limitation of this approach. We use the two parameters t. window and s. window to control the speed at which the trend-cycle and seasonal components can change. Smaller values allow for more rapid changes, which we need especially for some time series with strong turning points.
Data selection and cleansing We use cause-of-death data from the World Health Organization's (WHO) mortality database  [50]  to empirically demonstrate the forecasting capacity of the methodology proposed. The database collects cause-of-death statistics from country civil registration systems and estimates from the United Nations Population Division for countries that do not regularly report population data. We use an Excel file  4  of this database to evaluate the data quality of each country and a CSV file that includes the death time series of each country by gender. The first of these files identifies the quality of data for each country, using five color categories -green, dark yellow, light yellow, dark red and light red. Countries classified as green have multiple years of national death registration data with high completeness and quality of cause-of-death assignment.
Estimates for these countries may be compared and time series may be used for priority setting and policy evaluation. However, this dataset only includes data for 2000, 2010, 2015, and 2016 and it is not complete for the time series. As a result, we used this dataset only to identify the countries reporting high-quality data to the WHO and ranked them according to their data quality. In line with the metadata of the dataset, the criteria used to rank the countries by data quality are shown in Table  3 , coinciding, that is, with the WHO descriptors. a The ETS method with automatic and ZZA parameter setting from the forecast statistical software R package  [48] , and the TBATS method, which includes Box-Cox transformation, ARMA errors, trend and seasonal components  [49]. We considered only those countries with a data quality corresponding to the first three categories and eliminated various islands due to a lack of data (e.
g. , Åland Islands). We also cleaned the dataset by removing the total column and various rows with unknown month data and/or zero deaths. Some countries reported total deaths for three months in a row during certain years. In such instances, we assumed a uniform distribution of deaths across the quarter and allocated the corresponding value to each month. We filtered the datasets for respiratory diseases and considered the death variable as a univariate time series with monthly sampling frequency. Table  4  shows the WHO codes classified as respiratory infections. To compute the number of deaths attributable to respiratory diseases, we aggregated codes 380 and 410 or, equivalently, codes 390, 400, and 410. We also corrected the names of some of the countries (Appendix A). In this way we were able to calculate the proportion of deaths attributable to respiratory diseases.
To estimate the number of monthly deaths caused by respiratory diseases, we multiplied the annual proportion by the total number of forecasted deaths each month. We used the fraction of annual deaths from respiratory diseases over the total number of deaths as a proportion of deaths in each month. This procedure provided us with a dataset with more than twelve thousand observations in a pool of a 61-member panel time series (countries) from 2000 to 2016  [39]  (see Table  1  lines 3-4). These panel time series cover possible situations of stationarity, non-stationarity, increasing trends, seasonality, and structural breaks so as to undertake a comprehensive evaluation of the improvement in accuracy of candidate and ensemble models in different scenarios. Given the varying data quality of countries/territories/areas as regards case detection, definitions, testing strategies, reporting practices, and lag times, missing values are expected in the time series dataset.
To deal with this problem, we tested the Kalman, seasplit and seadec algorithms to impute the missing values. Of the three, the seasplit algorithm performed best as regards saving both the trend and the seasonality for our dataset (see Table  1  -line 19). We only imputed missing values within the time series, but not at the beginning of the time series with a start date after 2000. As a result, rather than changing the first year of the time series to our base year 2000, we used the latest year available (see Table  1  -line 17-18). To avoid the error caused by combining time series of different lengths in an ensemble model, we adapted the R code to handle different start years. The same problem arises as a result of the procedure adopted to select the best holdout for each model, which may ultimately lead to model combinations considering forecasts based on different holdouts, i.
The models with SMAPE values higher than that of the introduced indicator are considered poor forecasters and eliminated from the ensemble forecast. Table  5  presents the results aggregated across all countries, with individual country results available as supplementary material in a Mendeley dataset  [39]. The results in Table  5  show that the accuracy of the BMA approach improves in robustness when pursuing the selection approach for each holdout, with the composite model now ranking first among all the methods tested. With a fixed holdout of 3 for all models, which is the classical approach, the BMA has a SMAPE value of 0. 112. For the same holdout, but with model selection, this SMAPE value improves (0. 103). In the third approach -''model selection plus dynamic holdouts'' -that is, a combination of approaches one and two -the percentage error improves again (0.
000), which can be attributed to the higher quality of the official statistics in these countries. Ashofteh and Bravo (2020)  have shown there to be significant variation in the quality of the COVID-19 datasets reported worldwide, albeit a recent study suggests that data science and new technologies can be expected to play a significant role in improving data quality from national statistical offices in the future  [54]. The comparison of death forecasts attributable to respiratory diseases and COVID-19 deaths is shown in Figs. 5  and 6. For most countries, COVID-19 deaths can be said to have ''replaced'' the respiratory deaths that would have occurred based on extrapolations of past respiratory disease trends. Here, a study of.
Acknowledgments The authors are grateful to the anonymous reviewers for their constructive comments. Afshin Ashofteh  and  Jorge M. Bravo  were supported by Portuguese national science funds made available through the  FCT  under project  UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC). Additionally, Mercedes Ayuso is grateful to the  Spanish Ministry of Science and Innovation for funding received  under grant  PID2019-105986GB-C21  and to the  Secretaria d'Universitats i Recerca del departament d'Empresa i Coneixement de la Generalitat de Catalunya  for funding received under grant  2020-PANDE-00074  (research project directly related to COVID-19 and economy). ■.
Introduction 2] [3]  As countries have implemented these interventions, aggregated location data from mobile phones have become an important source of real-time information about human mobility and behavioural changes on a population level. ] [12]  Human activity measured using mobile phones reflects the aggregate behaviour of a subset of people, and although metrics of mobility are related to patterns of social contact that spread SARS-CoV-2, they do not provide a direct measure of metrics of epidemiological interest such as the contact rate. In March, 2020, during the first lockdowns due to COVID-19 in cities like New York and Seattle, the early epicentres of transmission in the USA, substantial changes in mobility were observed in many different data streams from mobile operators and social media.
16 COVID-19 case notifications, hospitalisations, and deaths provide observable measures of epidemic severity and trend, but these observations provide only a delayed and incomplete picture of the number of incident infections happening at any point in time. R t is not always straightforward to estimate, and case notifications are unreliable and probably biased in different ways in different locations. 16 Efforts to nowcast the effective reproductive number have relied on available data and subject matter expertise on data generation processes. In the simplest epidemi ological models, the reproduction number is linearly related to the contact rate, for which mobility metrics are a proxy. 17 We set out to estimate the relationship between human mobility metrics and estimated R t for 1396 US counties between Jan 22 and July 9, 2020.
Study population In this population-based study, we collected data on 1396 counties across the USA that had at least 100 cases of COVID-19 on June 30, 2020. 18 We grouped these counties into six ordinal categories, defined by the National Center for Health Statistics (NCHS) and graded from urban to rural (figure  1A ). 19 For our analysis, we considered NCHS category 1 (large central metro) to be the most urban counties and NCHS category 6 (non-core) to be the most rural counties.
Research in context Evidence before this study In early 2020, at the onset of the SARS-CoV-2 pandemic in the USA, various advertisement technology companies made anonymised mobility data available to researchers. These data have been used extensively to understand how population movement affects the transmission dynamics of the virus. On March 26, 2021, we used Google Scholar and PubMed and searched using key search terms ("mobility" AND "Ad-tech providers" AND "public health" OR "epidemiology") to identify articles of interest published in all languages between Jan 1, 2019, and Dec 31, 2020. Early studies showed promise, suggesting a role for these metrics as proxies for transmission parameters of interests such as the effective reproduction number (R t ). Subsequent research and replication studies signalled a weakening utility of mobility metrics as a viable proxy; however, these data continue to be used extensively in public health research.
Added value of this study We collected a nationally representative dataset of anonymised mobility metrics and evaluated their temporal and spatially varying relationship with R t. We show that the relationship between mobility metrics and R t vary considerably by urbanicity, and that this relationship changes over epidemic period, waning over the course of our study period of interest. Given the continued use of mobility metrics at face value by policy makers and researchers, our study urges nuance in interpretation, and transparency in the data generation process of these mobility metrics.
Data sources and metrics For each county, we collected epidemiological data on COVID-19 cases and deaths from the COVID-19 data repository from the Center for Systems Science and Engineering at Johns Hopkins University, as well as human mobility metrics collated by advertisement technology that was derived from global positioning systems (GPS) trace data provided by Camber Systems (Washington, DC, USA), an aggregator of mobility data from various providers in the USA (figure  2 ). 18, 20 Due to the geographical heterogeneity in initial outbreaks, varying amounts of data are available by state (figure  1B ). We collected case data from Feb 15 to June 15, 2020, for the case data and we collected the R t estimates from March 1 to June 15, 2020. With the advertisement technology user data, we calculated mobility metrics including the percentage change in movement into counties, percentage change in movement out of counties, and percentage change in movement within counties, as well as the average radius of gyration, average number of locations users visited, and average entropy of movement for users across all counties, every day from March 1 to June 15, 2020.
Movement was categorised in 8-h blocks of time with a vector of movement defined as occurring from the county in which an individual spent most of their time in the preceding 8-h block to the county in which an individual spent most of their time in the current 8-h block. We used the average movement in the month preceding the first day for each county for which we have data as our baseline conditioning for time of day and day of the week. Descriptions of these metrics and their uses have been previously described. 20 All metrics were calculated using GPS coordinates published by mobile phone applications for commercial purposes (advertise ment technology). Further descriptions of derived metrics are provided in the appendix (p 1).
Statistical analysis We used day-specific and county-specific estimates of the effective reproductive number, generated using a Bayesian nowcasting procedure that incorporated the effects of reporting delays and change in how cases were defined over time and in different regions. 21, 22 We aggregated all of our data points to their average log transformed value for each epidemiological week, in which epidemiological week is as defined by the US Centres for Disease Control and Prevention. 23 We specified models to predict the modelled R t values using the mobility data. First, we evaluated the six candidate mobility metrics and ensured that no highly correlated pairs of metrics remained in our model. Given the nature of the data generation process for both the independent variables and the outcome, we used a linear model that allowed for autoregressive correlated errors.
Results We collected epidemiological and mobility data from 1396 US counties between March 1 and June 15, 2020, including 1 907 098 epidemiological cases from Jan 22 to June 9, 2020 (from which we calculated the R t ). In initial data exploration we identified that weekly percentage change in movement into counties and percentage change in movement out of counties were very highly correlated, and so we removed percentage change out from our analyses (appendix p 2). We also included an interaction term between weekly average radius of gyration and entropy as, in practice, these metrics are difficult to interpret individually. Our full model, therefore, includes the estimated R t as the dependent variable and percentage change of movement into counties, percentage change of movement within counties, average radius of gyration, average number of locations users visited, and average entropy of movement as our independent variables.
Finally, we used the CovidEstim package to calculate R t , which incorporates death data into its algorithm. This includes the implicit assumption these data will not face biases in reporting lags and changing definitions that the case data faced early in the pandemic. Nevertheless, exogenous factors may affect the validity of the death data which may bias our estimate of R t. An abundance of human mobility metrics from various providers are now available and, in some cases, actively integrated into both modelling and public policy efforts. These data provide information on human behaviour at a level previously reserved for the data generators and their commercial interests. Our study shows that the integration of these metrics into retrospective modelling efforts can be fruitful in some cases, linking these metrics directly with public health outcomes of interest such as the effective reproductive number.
Contributors NK, ART, PEJ, NV, TC, COB, and NAM contributed to the ideation, analysis, and writing of this manuscript. All authors had access to all of the derived datasets. NV, as a representative of Camber Systems, had direct access to the raw GPS mobility dataset. This data was not directly available to other authors as a guarantee of individual privacy for users who contributed mobility data. All authors had access to all derived datasets. NV and NK verified the derived datasets.