dataesr/scientific-paragraphs-categorization huggingface dataset:
The first approach to obtain eHOP data connected with ILI was to perform different manual queries to retrieve patients who had at least one document in their EHR that matched the following search criteria: (1) Queries directly connected with flu or ILI with the keywords "flu" or "ILI"; (2) Queries connected with flu symptoms with the keywords "fever", "pyrexia", "body aches" or "muscular pain"; (3) Queries connected with flu drugs with the keyword "Tamiflu"; (4) Queries with the ICD-10 terminology; (5) Queries connected with flu tests, positive or negative results. In total, we performed 34 manual queries. For each query, the eHOP search engine returned all documents containing the chosen keywords (often, several documents for one patient and one stay). For query aggregation, we kept the oldest document for one patient and one stay and then calculated, for each week, the number of stays with at least one document mentioning the keyword contained in the query.
From the CDW eHOP, we built a database containing the time series constructed from the structured data. In all, we have 1,335,347 time series. As Google Correlate, the Pearson correlation between each signal of each region and the time series from the database was calculated. In this way, for each region, the second approach was to retrieve the 100 most correlated signals to ILI signal. Because our test period is from 05 January 2015 to 20 February 2017, we calculated the correlation between January 2004 and December 2014. As a result, for each region, we obtained 134 variables from the CDW eHOP where there are at least 34 variables common to all regions (manual queries). We retrieved retrospective data in August 2018 for the period going from 03 January 2005 to 13 March 2017. Weather data.
We obtained region-specific weather data from the French climatological website Info Climat (https://www. infoclimat. fr). It has been shown in several studies that humidity is correlated with the spread of influenza  [32]. In the absence of humidity data on the Climat website, we retrieved precipitation and temperature data. This choice was made knowing that both variables  [33, 34] , can be used as a proxy for humidity since they are directly related by the Clausius-Clapeyron relation  [35]. We obtained temperatures and precipitations per day for the largest city of each region, and calculated the weekly mean for both temperature and precipitation. We retrieved climatic data in August 2018 for the time period going from 07 January 2008 to 13 March 2017. Twitter data. Geotag tweets were extracted as the national scale for France from Boston Children's Hospital Geotweet dataset with the following keywords pertaining to influenza ("grippe", "grippe", "syndrome grippal", "fièvre", "toux", "congestion", "malade", "faiblesse", "courbatures", "tamiflu", "la crève").
Statistical models The ARGO model. The ARGO model is a regularized regression dynamically calibrated weekly using the LASSO method  [36]  to combine multiple external data sources with historical flu information. We performed the LASSO regression with the R package caret and the associated function fit with the method glmnet  [37, 38]. We optimized the shrinkage parameter lambda via a ten-fold cross-validation. To test the stationarity and whiteness of residuals, we used Dickey Fuller's and Box-Pierce's tests available from the R packages tseries and stats  [39]. The formulation of our model is: • Real time estimates: y it ¼ X 52 j¼1 Z j y itÀ j þ X 10 k¼1 a k x kit þ X 10 l¼1 b l z lit þ X 11 p¼1 g p v pt þ X 2 m¼1 d m w mit þ � it • One-week ahead forecast: y itþ1 ¼ X 52 j¼1 Z j y itÀ j þ X 10 k¼1 a k x kit þ X 10 l¼1 b l z lit þ X 11 p¼1 g p v pt þ X 2 m¼1 d m w mit þ � itþ1 • Two-week ahead forecast: y itþ2 ¼ X 52 j¼1 Z j y itÀ j þ X 10 k¼1 a k x kit þ X 10 l¼1 b l z lit þ X 11 p¼1 g p v pt þ X 2 m¼1 d m w mit þ � itþ2 where y it corresponding to the flu incidence rate at time t for the region i, P 52 j¼1 Z j y itÀ j corresponding to the historical flu incidence rates for the region i, P 10 k¼1 a k x kit corresponding to the 10 most correlated variables from Google data for the region i, P 10 l¼1 b l z lit corresponding to the 10 most correlated variables from hospital data for the region i, P 11 p¼1 g p v pt corresponding to Twitter data, P 2 m¼1 d m w mit corresponding to climatic data for the region i, � t corresponding to residuals.
We applied this model for each region. The model was dynamically recalibrated every week by incorporating all data available. In this way, the size of our training dataset increases every week. We obtained estimates from January 2011 to March 2017. The Net model. The Net model is a LASSO model dynamically calibrated weekly and using the relationship between the regions to know how synchronicity could improve forecasts. Indeed, S1 Fig in  S1  File (Heatmap of pairwise correlations between all regions) shows that the flu incidence rates of the different areas are correlated. For each region, we used historical data of all regions and estimates obtained with ARGO model for all regions expected the region to be predicted. The formulation of our model is: • Real time estimates: y it ¼ X 2 l¼1 X 12 j¼1 a j y jtÀ l þ X 12 j¼1 j6 ¼i b j ŷjt þ � it • One-week ahead forecast: y itþ1 ¼ X 2 l¼1 X 12 j¼1 a j y jtÀ l þ X 12 j¼1 j6 ¼i b j ŷjtþ1 þ � itþ1 • Two-week ahead forecast: y itþ2 ¼ X 2 l¼1 X 12 j¼1 a j y jtÀ l þ X 12 j¼1 j6 ¼i b j ŷjtþ2 þ � itþ2 where y it corresponding to the flu incidence rate at time t for the region i, P 2 l¼1 P 12 j¼1 a j y jtÀ l corresponding to two weeks of historical flu incidence rates for all regions, P 12 j¼1 j6 ¼i b j ŷjt corresponding to ARGO predictions for all regions excepted the region i to be predicted and � t corresponding to residuals.
We applied this model for each region. We used a two years' training dataset. We obtained estimates from January 2013 to March 2017. The ARGONet model. The ARGONet model is an ensemble approach combining the predictive power of ARGO and Net models. To combine the results of both models, we tested three methods: • First, for a given week, we choose ARGO's estimate if it leads to the lowest mean prediction error in the previous K weeks (compared to the Net model's estimate). If this is not true, we choose Net's estimate. The values of K were inspired by Lu et al. [9]  study and verified using cross-validation during the training time period. • A second method consists of calculating the mean value of the estimates produced by the ARGO and Net models for a given week. • In the final method, for a given week, ARGONet's estimate is built as a linear combination of estimates produced by ARGO and Net.
The coefficients are dynamically calculated each week to best predict new ground truth data available each week. The autoregressive model. To assess the importance of external data sources, we built an autoregressive model of order 52 (AR(52)). We used the LASSO regression with the previous 52 weeks of ILI incidence rates to predict the current week and the two weeks after. • Real time estimates: y it ¼ X 52 j¼1 a j y itÀ j þ � it • One-week ahead forecast: y itþ1 ¼ X 52 j¼1 a j y itÀ j þ � itþ1 • Two-week ahead forecast: y itþ2 ¼ X 52 j¼1 a j y itÀ j þ � itþ2 where y it corresponding to the flu incidence rate at time t for the region i, P 52 j¼1 a j y itÀ j corresponding to the previous 52 weeks, � t corresponding to residuals. We applied this model for each region. We used a six years' training dataset.
93) as seen in S1 Fig in  S1  File. We hypothesize that having access to region-specific EHR data, from all the french regions, will lead to prediction improvements across the board. Twitter data was collected at the National level given the sparsity of relevant flu-related Tweets at the regional level. This was the case as we only had access to the publicly available data shared by Twitter's API that only allows users to view up to 5% of all Geo-coded Tweets (themselves a small fraction of about 5% of the total corpus of all Tweets). We also suspect that gaining access to higher volumes of Tweets at the regional level could improve our forecasts. For climatic data, we only had a access to weekly local temperature and precipitation. Future studies may explore incorporating other climatic indicators known to be more directly related to the transmission of the virus, such as humidity  [32].
Funding: CP, MC, GB and AL were partially funded by the  French National Research Agency  inside the Integrating and Sharing Health Data for Research Project (Grant No. 452ANR-15-CE19-0024 ). MS and CP were partially funded by the  National Institute of General Medical Sciences  of the  National Institutes of Health  under Award Number  R01GM130668. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
3. Qualitative discussion of the results is given in §4. Conclusions are summarised in §5. This work is intended to be fully reproducible by independent researchers using the MANEAGE framework; it was produced using commit 086b149 of the live GIT repository https://codeberg. org/boud/subpoisson on a computer with Little Endian x86 64 architecture; the source is archived at zenodo. 5262698 and swh:1:rev:e217f397e9275763416018081f660337bc9462c0.
SARS-CoV-2 infection data Two obvious choices of a dataset for national daily SARS-CoV-2 counts would be those provided by the World Health Organization (WHO) 2  or those curated by the Wikipedia WikiProject COVID-19 Case Count Task Force 3  in medical cases chart templates (hereafter, C19CCTF). While WHO has published a wide variety of documents related to the COVID-19 pandemic, it does not appear to have published details of how national reports are communicated to it and collated. Given that most government agencies and systems of government procedures tend to lack transparency, despite significant moves towards forms of open government  (Yu & Robinson, 2012)  in many countries, data lineage tracing from national governments to WHO is likely to be difficult in many cases. In contrast, the curation of official government SARS-CoV-2 daily counts by the Wikipedia WikiProject COVID-19 Case Count Task Force follows a well-established technology of tracking data lineage.
The Wikipedia community high-tempo collaborative editing that has taken place in response to the COVID-19 pandemic is well quantified  (Keegan & Tan, 2020). The John Hopkins University Center for Systems Science and Engineering curated set of official COVID-19 data is discussed below. Unfortunately, it is clear that in the WHO data, there are several cases where two days' worth of detected infections appear to be listed by WHO as a sequence of two days j and j + 1 on which all the infections are allocated to the second of the two days, with zero infections on the first of the pair. There are also some sequences in the WHO data where the day listed with zero infections is separated by several days from a nearby day with double the usual amount of infections. This is very likely an effect of difficulties in correctly managing world time zones, or time zone and sleep schedule effects, in any of several levels of the chains of communication between health agencies and WHO.
In other words, there are several cases where a temporary sharp jump or drop in the counts appears in the data but is reasonably interpreted as a timing artefact. Whatever the reason for the effect, this effect will tend to confuse the epidemiological question of interest here: the aim is to globally characterise the noise and to highlight countries where unusual smoothing may have taken place. We quantify this jump/drop problem as follows. We consider a pair of days j, j + 1 for a given country to be a jump if the absolute difference in counts, |n i ( j + 1)n i ( j)|, is greater than the mean,  (n i ( j + 1) + n i ( j))/2. In the case of a pair in which one value is zero, the absolute difference is twice the mean, and the condition is necessarily satisfied. We evaluate the number of jumps N jump for both the WHO data and the C19CCTF medical cases chart data, starting, for any given country, from the first day with at least 50 infections.
Dates without data are omitted; this should have an insignificant effect on the analysis if these are due to low infection counts. Another global collection of daily SARS-CoV-2 counts that could be considered is the John Hopkins University Center for Systems Science and Engineering (JHU CSSE) git repository. Unfortunately, for several countries, the JHU CSSE data are provided for sub-national divisions rather than as official national statistics, making the dataset inhomogeneous for the purposes of this study. Artificial interference in the data at the national level will not be shown in data that is the sum of data obtained directly from sub-national geographical/political divisions. Moreover, detailed data provenance analysis (which exact government URL did a particular count come from? where is the archived version of the data of the original URL?) appears to be more difficult for the JHU CSSE data than for the C19CCTF data.
Nevertheless, for completeness, the JHU CSSE data is analysed using the same method as the main analysis, with results presented as tables in Appendix A. The full set of C19CCTF data includes many days, especially for countries or territories (as defined by the data source) of low populations, with low values, including zero and one. The standard deviation of a Poisson distribution (Poisson, 1837) of expectation value N is √ N, giving a fractional error of 1/ √ N. Even taking into account clustering or anticlustering of data, inclusion of these periods of close to zero infection counts would contribute noise that would overwhelm the signal from the periods of higher infection rates for the same or other countries. In the time sequences of SARS-CoV-2 infection counts, chaos in the administrative reactions to the initial stages of the pandemic will tend to create extra noise, so it is reasonable to choose a moderately high threshold at which the start and end of a consecutive sequence of days should be defined for analysis.
3. 1), shown as green X symbols; versus N i , the total number of officially recorded infections for that country. The horizontal axis is logarithmic. As discussed in the text ( §3. 2. 1), the Poisson point process is unrealistic for most of these data, while the φ i clustering model is consistent with the data for most countries. Plain-text table: zenodo. 5262698/phi N full. dat. country. Since the scales of interest vary logarithmically, the standard deviation of the best estimates of log 10 φ i for these numerical simulations is used as an estimate of σ (log 10 φ i ), the logarithmic standard error in φ i.
Akaike and Bayesian information criteria In each case we calculate the  Akaike (1974)  and Bayesian  (Schwarz, 1978)  information criteria, defined AIC := 2k -2 ∑ i ln L i (4) BIC := ln(N days ) k -2 ∑ i ln L i , (5) respectively. The number of free parameters k is defined as the number of countries satisfying the criteria for a sequence to be analysable ( §2. 1), since there is one free parameter allowed to vary individually for each country. The number of data points for BIC is set to the total number of days N days in the sequences over all k countries. The φ ′ i model, and the logarithmic median and negative binomial alternatives, each have the same values of k and N days. The 2-day and 3-day alternatives can be expected to have slightly smaller numbers of countries k whose sequences satisfy the analysis criteria, and much smaller numbers N days of 'days', since in reality these no longer represent single days.
5. There are clearly a wider overall scatter and bigger error bars compared to Figs 5 and 6; a low φ 7 i is a noisier indicator than φ 28 i and φ 14 i for individual countries. Plain-text table: zenodo. 5262698/phi N 07days. dat. Table  5. Akaike (1974)  and Bayesian  (Schwarz, 1978)   show the countries with the least noisy sequences as determined by φ 28 i , φ 14 i and φ 7 i , respectively. Tables  2  and 3  show that the lists of countries with the strongest anti-clustering are similar to one another. Thus, Fig. 8  shows the SARS-CoV-2 counts curves for countries with the lowest φ 28 i , and Fig. 9  the curves for those with the lowest φ 7 i. Both figures exclude countries with total counts N i ≤ 10000, in which low total counts tend to give low clustering. It is clear in these figures that several countries have subsequences that are strongly sub-Poissonian -with some form of anti-clustering, whether natural or artificial.
, 2020) ), so comparison of the clustering behaviour on these different time scales might help to separate out some of these contributions. 6 )) using the logarithmic median model of the expected full-sequence counts ( §2. 4. 1) versus ψ i for the primary analysis. B: Normalised clustering ψ NB i := ω i / √ N i for the negative binomial model (see Eqs (2), (3)) versus ψ i. A line shows ψ LM i = ψ i and ψ NB i = ψ i , respectively. The data point for Algeria, with log 10 ψ i = -2. 69 ± 0. 05, log 10 ψ NB i = -5. 69 ± 0. 93, lies below the displayed area in the right-hand panel. Plain-text table: zenodo. 5262698/phi N full. dat. 10 -3 10 -2 10 -1 10 0 i and ψ 3d i (Eq. (  6 )) for counts summed in successive pairs (A) and triplets (B) of days, respectively, versus that for the primary analysis.
Neither Poissonian nor super-Poissonian The negative binomial model φ NB i ( §3. 2. 3) rejects the possibility of Algeria having a super-Poissonian noise distribution at P KS i = 0. 01. The Poissonian model for Algeria is similarly rejected with P Poiss i = 0. 005. However, the φ i model does model the Algeria data adequately, with a modest probability of P KS i = 0. 17. Figure  8  dramatically shows the least noisy 28-day sequence for Algeria. Only two days of SARS-CoV-2 recorded infections during this period appear to have diverged towards the edge of the Poissonian 68% band, rather than about nine, the expected number that should be outside this band for a Poissonian distribution. Almost all of the points appear to stick extremely closely to the median model. It is difficult to imagine a natural process for obtaining noise that is this strongly sub-Poissonian, especially in the context where most countries have super-Poissonian daily counts.
information criteria for the φ ′ i and alternative analyses; plain-text version: zenodo. 5262698/AIC BIC full. dat. Kendall τ statistic and its significance (two-sided) P τ for the null hypothesis of no correlation between the ranking of PFI 2020 and φ i or ψ i for the full data or subsequences; plain-text version: zenodo. 5262698/pfi correlations table. dat.
ACKNOWLEDGEMENTS Thank you to  Marius Peper ,  Taha Rouabah ,  Dmitry Borodaenko , anonymous colleagues, and to  Niayesh Afshordi  and the two other referees for several useful comments and to the Maneage developers for the Maneage framework in general and for several specific comments on this work. This project used computing resources of the  Poznań Supercomputing and Networking Center (PSNC)  provided under computational grant  314. SOFTWARE ACKNOWLEDGEMENTS This research was partly done using the following free-licensed software packages:  Boost 1. 73. 0 , Bzip2 1. 0. 6 ,cURL 7. 71. 1,Dash 0. 5. 10. 2,Discoteq flock 0. 2. 3, Eigen 3. 3. 7,Expat 2. 2. 9, File 5. 39,  Fontconfig 2. 13. 1, FreeType 2. 10. 2,Git 2. 28. 0 , GNU Autoconf 2. 69. 200-babc,GNU Automake.
Histogram of weekly dip w i (Eq. (9)) in national daily SARS-CoV-2 counts. Values below unity indicate a dip; values above unity indicate a bump. Plain-text full list of w i : zenodo. 5262698/phi N full. dat. 20 15 N 10 5 0 0. 75 1. 00 1. 25 w 1. 50 1. 75 Figure 17. 21/30. International me- dia interest in Russian COVID-19 data has mostly focussed on controversy related to COVID-19 death.
An optimization was performed on the parameters a i in order to minimize the quadratic error ε. The function θ(t) is computed numerically from data of Figure  2  and the optimized function f are plotted in Figure  8  as functions of θ(t). The diagram in Figure  9  is an implementation of relation (8) where function f is given by relation  (9). Such a diagram is used for simulation of the density of adsorbed disks as a function of time. A comparison of RSA density data and the response of model (  8 ) is proposed in Figure  10. The parameters a 0 , a 1 , a 2 , a 3 , and a 4 given in Table  3  were obtained using the MATLAB function fmincon. Both on the long time and on all the data, the errors are lower than the first approach with ε. Even if the error on the long time is greater than when the optimization done only on the long time (see Table  2 ), the compromise is better.
ACKNOWLEDGEMENTS We thank the horse stable  Les Peupliers (Balan)  for their authorization and support during the trapping, as well as  Angéline Clair ,  Laetitia Averty , and  Julie Ullman , who managed the acclimation and breeding of the mice. This work was supported by the  Fédération de Recherche BioEnviS -FR3728  of  University Claude Bernard Lyon 1.
We use these to define extinction risk scenarios, i. e. , scenario CTC for NbF ≤ 100 and EDG for NbF ≤ 1000 breeding females. As future population size is unknown, we define a function, Risk NbF,t,b ∈ [0,1],representing the probability that the population of a livestock breed b being below the population status NbF in year t. To avoid redundancy we need only consider NbF as the NbM/NbF ratio will hold for any NbF in the dataset we used. Thus, by setting an arbitrary accepted level of risk (r) ∈ [0,1], we can determine the year in which the endangered breed material should be collected to prevent extinction, the latter being where the number of individuals is below the critical population reproduction threshold. Numbered steps in Fig. 1  illustrate the modelled ex situ collection decision criteria based on r and Risk NbF,t,b (1).
Data Census data were provided by the National Information System of Spanish Livestock Breeds (ARCA) held by the Ministry of Agriculture, Fisheries and Food (MAPA 2020) containing times series for NbF and NbM of all autochthonous breeds from 2009 to 2018. The data and the geographic distribution of the animals is uploaded to the ARCA web portal annually by the breed associations (See Table  S2  in De Oliveira  Silva et al. (2020) ). The data cover all Spanish autochthonous breeds for seven livestock species (the number of breeds classified as nonendangered/endangered for each species in parentheses), cattle (8/ 31), sheep (36/8), goats (20/3), pigs (3/12), chicken (1/16), equine (horse and donkey) (1/14). A survey was also distributed to 8 autonomous community gene banks and another 10 gene banks selected by geographical location and/ or biological material stored.
The survey gathered information on species germplasm and breeds and cost data, including fixed maintenance cost of cryotanks and for upgrading their capacity, labour costs, materials and equipment. Information is summarised in Table  3. To plan ex situ collections from 2018 to 2060 we used data for 210 livestock breeds, of which 179 are Spanish autochthonous breeds, 18 gene banks based in 15 different locations. Each bank can collect samples from 50 in situ locations (Spanish provinces). We used census data from 2009 to 2018 for the linear regression (Parameters G t,sb,c and Risk t, sb ) and the production bounds calculated using Matlab  (Radi and El Hami 2018). Gene bank storage dynamics are simulated from 2009, while collections start from the reference year of 2018 (most recent data). The deterministic version of the SCLP model consisted of 7.
Acknowledgments We acknowledge the participating institutions that provided cost data for this work and to the  Spanish Ministry of Agriculture, Fisheries and Food. This work received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under the grant agreement n • 677353 for the  IMAGE  project  (Innovative Management of Animal Genetic Resources ). Rafael Silva  acknowledges  The University of Edinburgh 's  Data-Driven Innovation Chancellors fellowship.