dataesr/scientific-paragraphs-categorization huggingface dataset:
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. [28]. In the absence of humidity data on the Climat website, we retrieved precipitation and temperatures data. This choice was November 19, 2019 4/109 made knowing that both variables,  [29, 30]  and temperature and precipitation can be used as a proxy for humidity since they are directly related by the Clausius-Clapeyron relation. [31]  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", "grippé", "syndrome grippal", "fièvre", "toux", "congestion", "malade", "faiblesse", "courbatures", "tamiflu", "la crève"). From there, we aggregated tweets to get weekly counts. In this way, we obtained 11 variables from Twitter. We retrieved Twitter data in December 2018 for the period going from 30 December 2013 to  13  March 2017.
Statistical models The ARGO model The ARGO model is a regularized regression dynamically calibrated weekly using the LASSO method  [32]  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  [33, 34]. We optimized the shrinkage parameter lambda via a 10-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  [35]. The formulation of our model is : • Real time estimates: y it = 52 j=1 η j y it-j + 100 k=1 α k x kit + 134 l=1 β l z lit + 11 p=1 γ p v pit + 2 m=1 δ m w mit + it • One-week ahead forecast: y it+1 = 52 j=1 η j y it-j + 100 k=1 α k x kit + 134 l=1 β l z lit + 11 p=1 γ p v pit + 2 m=1 δ m w mit + it+1 • Two-week ahead forecast: y it+2 = 52 j=1 η j y it-j + 100 k=1 α k x kit + 134 l=1 β l z lit + 11 p=1 γ p v pit + 2 m=1 δ m w mit + it+2 where y it corresponding to the flu incidence rate at time t for the region i,.
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, November 19, 2019 5/109 Figure  S1  (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 = 2 l=1 12 j=1 α j y jt-l + 12 j=1 j =i β j ŷjt + it • One-week ahead forecast: y it+1 = 2 l=1 12 j=1 α j y jt-l + 12 j=1 j =i β j ŷjt + it+1 • Two-week ahead forecast: y it+2 = 2 l=1 12 j=1 α j y jt-l + 12 j=1 j =i β j ŷjt + it+2 where y it corresponding to the flu incidence rate at time t for the region i, 2 l=1 12 j=1 α j y jt-l corresponding to two weeks of historical flu incidence rates for all regions, 12 j=1 j =i β j ŷjt corresponding to ARGO predictions for all regions excepted the region i to be predicted and t corresponding to residuals.
The Baseline 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 = 52 j=1 α j y it-j + it • One-week ahead forecast: y it+1 = 52 j=1 α j y it-j + it+1 November 19, 2019 6/109 • Two-week ahead forecast: y it+2 = 52 j=1 α j y it-j + it+2 where y it corresponding to the flu incidence rate at time t for the region i, 52 j=1 α j y it-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. The model was dynamically recalibrated every week.
Comparisons First, we assessed the importance of adding external data sources by comparing : • MSE and PCC of the autoregressive model and the ARGO model including historical data plus the 10 most correlated variables from hospital data and Google data. The individual contribution of hospital data and Google data has already been shown in a previous study  [24]. But, we added in appendices, two comparisons: A comparison with the 10 most correlated variables from hospital data and a comparison with the 10 most correlated variables from Google data. • MSE and PCC of the autoregressive model and the ARGO model including historical data plus climatic data.
Evaluation of Data Sources as Predictors In order to assess the predictive value of each and all external data source, we compared ARGO models that incrementally included external data sources with a baseline autoregressive model, AR(52), model that only uses historical information as input. As shown in the next sections, we found that all external data sources improve flu estimates, specially in the one-and two-week ahead forecasts. EHR Data and Google Data. Our first modeling experiment involved comparing ARGO models that use Google search and EHR data simultaneously with the baseline AR(52) in all French regions. A detailed analysis on the individual contribution of Google data and EHR data into predictions, separately, is provided for completeness in the supplementary materials. Our findings suggest that each of these data sources individually improves predictions in all time-horizons.
This is consistent with the findings of a previous study conducted at the national-level and the French region of Brittany  [24] , where both Google and EHR information were found meaningful, but EHR data was shown to possess a stronger predictive power. The join contribution of both EHR and Google data on predictions is presented below. In real time (Table  1 ), in terms of correlation and error metrics, estimates produced using EHR data and Google data improve the accuracy for all the regions. The combination of both sources lead to correlation improvements of up to 5% and decreases in error of up to 30% for the region Bretagne. 3. Two-week ahead estimate -MSE and PCC for ARGO models including only historical data (AR(52)) and the 10 most correlated variables from hospital and Google data, for the period starting from January 2015 to March 2017 Climatic Data.
Real-time estimates. Figure  2      To assess the statistical significance of the improved prediction power of ARGONet, we constructed a 95% confidence interval for the relative efficiency of ARGONet compared to the autoregressive model (the error of ARGONet is in the denominator). 11. Real-time estimate -Relative efficiency being bigger than 1 suggests increased predictive power of ARGONet compared to the autoregressive model Figure  4  and Figure  5  show a typical example of plot and heatmap obtained for estimates in real time. The heatmap allows to visualize coefficients used for ARGO model. On these plots, we can see that all models have estimates close to the gold standard. However, for the autoregressive model, there is a time lag more important. On the heatmap, we can see that ARGO model uses mostly 5 variables including 2 variables from Google Data, 2 variables from Hospital Data and 1 variable from Historical Data.
As the time-horizon of prediction increases, the improvements of predictions are more evident, leading to up to 80% error reductions when comparing ARGONet with AR, and up to 60% error reduction of ARGONet over ARGO for one-week ahead predictions; and up to 80% (ARGONet vs AR) and 30% (ARGONet vs ARGO) respectively in two-week ahead predictions (Tables  11  and 12 ). Figures S2 through S13 show these results graphically. As expected, autoregressive approaches show "within-range" prediction values that consistently lag behind the observed disease activity and lead to under-predictions close to peak activity. We find that all external data sources contribute to improving local flu estimates, when compared to the baseline autoregressive model, specially for longer-term forecasts. Indeed, for the two-week ahead estimates, the combination of EHR data and Google data lead to correlation improvements of up to 25% and decreases in error of up to 60%.
For Climatic data, this improvement reaches 20% for correlation and 25% for the error. For Twitter data, it reaches 20% for both correlation and error. By analyzing heatmaps (Figures 5, 9 and 13 and in the Supplementary materials) obtained for ARGO models, we can see that the contributions of different predictors (data sources) change over time and time-horizon of prediction, but all data sources appear to posses predictive power. Indeed, the most important data sources are EHR data and Google data in real-time and for longer-term forecasts. Historical data is consistently used in real-time, but less used for longer-term forecasting. Conversely, Climatic data and Twitter data are used more prominently for longer-term forecasts than for real-time estimate. The fact that we could only access EHR data from Rennes University Hospital, and thus from the Brittany region, prevented us from being able to quantify the added valued of region-specific EHR information on flu predictions in their respective region.
93) as seen in Figure  S1. 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  [28].
Acknowledgments We would like to thank the  French National Research Agency  for partially funding this work inside the Integrating and Sharing Health Data for Research Project (Grant No. ANR-15-CE19-0024 ). We also thank the  French Sentinelles network and Google and Twitter services  for making their data publicly available. 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 content is solely the responsibility of the authors and does not necessarily represent the official views of the  National Institutes of Health.
In fact, in many countries, a significant portion of the so-called "banked population" was using informal non-bank financial services in lieu of the banking facilities. These households constitute a portion of so-called "underbanked" or "marginally banked" households, which has been regarded in the literature as equivalent to being financially excluded households  (Sarma, 2012). Demirguc-Kunt and Klapper (  2012 ) is a recent attempt to build up the Global Findex database based on conducting surveys of 150,000 adults in 148 countries during 2011. The initiative provides interesting indicators of financial inclusion from a micro perspective, i. e. for adult individuals classified by income group, gender and education levels of the respondents. These indicators consist of share of adults who have an account with a formal financial institution, of adults who saved and borrowed using a formal account, of adults who used informal methods to save and borrow and shares of adults with credit/debit cards, with mortgage and with a health insurance.
Since then, the database has been published every three years, by means of conducting nationally representative surveys of over 150,000 adults in more than 140 economies. The most recent Global Findex database published in 2017 shows that financial inclusion is on a rising trend at the global level. Specifically, 1. 2 billion adults have reportedly obtained a financial account since 2011, including 515 million since 2014. Between 2014 and 2017, the share of adults having an account with a financial institution or through a mobile money service sees an increase from 62% to 69% across the globe, and from 54% to 63% in the developing world. Nevertheless, the share of women having a bank account in developing economies remain 9 percentage points less than that of men  (Demirguc-Kunt, Klapper, Singer, Ansar, & Hess, 2018).
The indicators of financial inclusion, either at the macro level or micro level, provide insight into the inclusiveness of a financial system  (Sarma, 2012). However, when these indicators are used individually and separately, they provide only partial and thus incomplete information on the nature of inclusiveness of the financial system  (Sarma, 2012). Using individual indicators might also result in a misleading interpretation of the extent of financial inclusion in an economy as shown by  Sarma (2012). The second strand of literature falls on examining the determinants of financial inclusion (for instance,  Demirgu€c,-Kunt, Klapper, & Singer, 2013; Kumar, 2013; Fung a cov a & Weill, 2015; Allen, Demirgu€c,-Kunt, Klapper, & Peria, 2016; Zins & Weill, 2016). For instance, based on the 2012 World Bank Global Findex Database on 98 developing countries,  Demirgu€c,-Kunt et al.
(2013)  finds that gender matters for financial inclusion. The study shows evidence on a significant gender gap existing in account ownership, formal saving and formal credit. The likelihood of being financially excluded increases with being a woman. Zins and Weill (2016)  perform probit estimations on the World Bank's Global Findex database for 37 African countries. The empirical results indicate that being a man, richer, more educated, and older individuals, to a certain extent, are more likely to be financially included, with a higher influence of education and income. Basically mobile banking and traditional banking have the same determinants. On the other hand, the determinants of informal finance appear to be different from those of formal finance. Allenet al. (  2016 ) also utilised the 2012 World Bank Global Findex Database to explore the individual and country characteristics associated with financial inclusion on a global scale.
They find that greater financial inclusion is related to lower banking costs, greater proximity to financial intermediaries, and better institutions such as stronger legal rights, and more politically stable environments. Furthermore, being richer, more educated, older, urban, employed, married or separated individuals are shown to favour financial inclusion in terms of having an account at a formal financial institution. The same individual characteristics also linked with the increased probability of saving formally. Finally, the probability of borrowing formally is higher for older, educated, richer and married men. Fung a cov a and Weill (2015) used data from the World Bank Global Findex database for 2011 to study financial inclusion in China, and compare it with other BRICS countries.
Kim et al. (2018)  examines the linkage between financial inclusion and economic growth for Organization of Islamic Cooperation (OIC) countries. Five variables were employed to measure key factors of financial inclusion, namely: (1) automated teller machines per 100,000 adults, (2) bank branches per 100,000 adults, (3) deposit accounts with commercial banks per 1000 adults, (4) borrowers from commercial banks per 1000 adults, and (5) life insurance premium volume to GDP. Based on the results of dynamic panel estimations performed on a panel data for 55 OIC countries, the study finds that financial inclusion has a crucial role in promoting economic growth and there are mutual causalities between the two variables. While the study provides some interesting results, there are several limitations.
Data and variables This study attempts to develop a composite Financial Inclusion Index for Asia, taking into account different aspects as suggested by the existing literature. Principal component analysis (PCA) is then performed to assess which selected indicators have largest individual effects on this Index for Asia as a whole and for individual countries in the study sample. We collect data from the World Bank's Global Financial Development Database. In total, our country sample consists of 31 countries, belonging to different income country groups (Refer to Table  1 ). This study investigates whether financial inclusion is related to financial efficiency and financial sustainability for 31 Asian countries. The list of selected variables for constructing our three composite financial indicators, namely, financial inclusion, financial efficiency and financial sustainability are presented in Table  2.
mmx ¼ X i À X min X max À X min ð2Þ where, X min ¼ minimum data point. X max ¼ maximum data point. In the softmax normalization or normalized exponential function, the influence of extreme values or outliers in the data is reduced without removing them from the dataset. Because outliers are an important part of a dataset, they should be included in the dataset while we still preserve the significance of data within a standard deviation of the mean. The nonlinear transformation of the data is conducted using one of the sigmoidal functions. Softmax normalization calculates normalized score using exponential function and mean and standard deviation, as follows. softmax ¼ 1 1 þ exp ÀV ð3Þ where, V ¼ X i ÀX s s ¼ standard deviation. In the next stage, normalized data were processed by means of PCA, which assesses the impact of changes in the values of selected variable on final result.
PCA is a standard technique for simplifying a dataset by extracting data for hidden features and relationships, and removing data with excessive information. By so doing, PCA reduces the dimensionality of data for analysis. PCA basis vectors depend on the dataset and PCA does not have a fixed set of basis vectors, unlike other linear transformation methods. Furthermore, the additional advantage of PCA lies on its ability to identify the similarity and difference of the various models created  (Yoshino & Taghizadeh-Hesary, 2015). PCA is widely used for explanatory data analysis. As a method of projections, it reveals the structure of data and explains the variations  (Jolliffe, 2011). In the existing literature, PCA has rarely been used in quantification of financial inclusion. However, the use of PCA in the analysis of the phenomena affected by a variety of financial variables has been acceptable (see  Adu, Marbuah, & Mensah, 2013; Ang, 2010; Ang & McKibbin, 2007; Le, Kim, & Lee, 2016; Muhammad Adnan Hye, 2011).
Data The SNDS comprehensively collects individual outpatient data, as well as healthcare prescriptions and procedures reimbursed, but it does not provide any clinical data. Nevertheless, it includes information on the presence of long-term chronic diseases (LTD) eligible for 100% reimbursement of healthcare expenditure. All this information is linked to data concerning public and private hospital stays: short stay hospitals (SSH), rehabilitation (Rehab) and hospital at home (HaH) and a specific database indicating whether or not the person is a resident of a skilled nursing home (SNH). Hospital discharge diagnoses and LTD diagnoses are coded according to the International Classification of Diseases 10 th revision (ICD 10). Causes of death (ICD 10) are linked in the SNDS using an indirect matching procedure.
Means were calculated only for those people with at least one reimbursement during the period considered. The total and mean lengths of stay during the year per patient in the same type of hospital were determined. Chi-square test, ANOVA, Student's t test, Wilcoxon's test and Kruskal-Wallis test were used for comparison. A Sankey diagram was used to illustrate patient flow according to the presence and the types of hospitalisation during the year or during the last 28 days before death. SAS software (version 7. 11, SAS Institute Inc. , Cary, NC, USA) was used for statistical analysis and R software (3. 4. 3. ) was used for Sankey diagrams.
What Is Information? From Data to Decision-Making Information economics as developed 20 years ago by Shapiro and Varian (9) consider information as "anything that is or can be digitized" and that has value for consumers. Recent frameworks for information system analysis propose a fundamental distinction between data, information and knowledge, or intelligence, then fuelling decision-making  (10, 11). Data will be considered here as anything that can be measured or characterized qualitatively and expressed in a way that can be transmitted and processed. Data is thus the raw material to produce information, which has the basic property of being meaningful for a user. Once integrated in a set of information and logically analyzed by human thinking, this information contributes to the gradual building of knowledge.
Therefore, the value derived from the continuity, time-points density and timeframe of datasets has to be acknowledged. These features of information intervene in trade-offs with other qualities and values and are subject to strategic and operational choices in active surveillance. In particular, dense and complete follow-up datasets will be needed to ascertain the absence of events, as opposed to an hypothesized one. This may be critical if the link between two or more outbreaks detected at different timepoints has to be interpreted, in absence or presence of data during the intervals. Although the trend is a central value added of surveillance, its intrinsic value lacks a framework, for which we may propose the following basis. Let us consider a decision-maker facing followup information within its corrective and reflexive assignments, i.
The Consolidated Microbiology Networks The consolidated microbiology networks (CMNs) are an example of another complex health system. They are existing non-agentspecific infrastructures that constitute an emerging structure within healthcare systems, where the provision of diagnostic services is centralized for multiple healthcare providers into a single, new servicing unit  (62). The emergence of CMNs has been made possible through the technological advances in the field of microbiology, allowing the simultaneous access to information and processes, in real-time, and by many system users; as well as the ability to multiplex technical components providing a highly automated and standardized laboratory. It is anticipated that in the future such CMNs would play an increasing role in the routine health surveillance networks and act as a major informational conduit, where well-defined and structured data will be collected, interpreted and distributed for downstream decision-making.