dataesr/scientific-paragraphs-categorization huggingface dataset:
(15)  reported prevalence rates in children aged four year of 1. 34% (CI 95% : 1. 25-1. 44%), 1. 53% (CI 95% : 1. 43-1. 63%) and 1. 70% (CI 95% : 1. 61-1. 80%), for the years 2010, 2012, and 2014 respectively. Prevalence over time in children aged eight year old was also found in Myers et al. (21)  who reported a steady increase in prevalence from 0. 43% (CI 95% : 0. 32-0. 54%) in 2000 to 1. 41% (CI 95% : 1. 09-1. 82%) in 2008, which was in line with the estimates from the CDC who reported estimates of 0. 67% (CI 95% : 0. 45-0. 99%) and 1. 13% (CI 95% : 0. 48-2. 12%) for the same years  (14). Nevison et al. (27)  compared time trends in prevalence in three large US databases: the Individuals with Disabilities Education Act, ADDM Network, and California Department of Developmental Services.
The Individuals with Disabilities Education Act database collected data across 50 states in a cohort aged 5-17 year and the California Department of Developmental Services collected data for all individuals living in California meeting the DSM diagnostic criteria for autism. The California Department of Developmental Services was the longest running of the three datasets, reporting an increase in ASD prevalence from 0. 001% in the cohort born in 1931 to 1. 2% among the 5 year-olds born in 2012. All three datasets displayed a consistent upward trend over time  (27). Prevalence estimates for France and Italy were found in Delobel-Ayoub et al. (  16 ) and Narzisi et al. (17) , respectively. Both studies were part of the ASDEU project involving 14 European countries and which aimed at estimating the prevalence of ASD in children aged between 7 and 9 year in 2015.
5 (Asperger syndrome) and F84. 8 (other pervasive developmental disorders). Prevalence over time was reported in Yong et al. (22)  who analyzed time trends in ASD using the Clinical Practice Research Datalink, an electronic medical records database. The authors observed a steady increase in prevalence in children aged 5-12 and 13-17 years old between the years 2000 and 2014, progressing from 0. 27 to 1. 53% and 0. 13 to 1. 92% respectively. The M/F prevalence ratio was substantially higher in the 5-10 year old cohort (M/F ratio = 6. 5; male: 2. 5%, female: 0. 4%) compared to the 11-16 year old cohort (M/F ratio = 2. 7; male: 1. 8%; female: 0. 7%)  (22). Bachmann and colleagues estimated the prevalence of ASD in Germany by conducting a claims database analysis using the Allgemeine Ortskrankenkassen database for the period 2006-2012  (20).
Studies were heterogeneous in terms of study design, with a mix of cohort studies (n = 23), cross sectional studies (n = 4) and case-control studies (n = 6). Studies were also heterogeneous regarding the method considered to estimate the prevalence of co-morbidities: tools and scales (± combined with clinical assessment) were used in 18 studies while eight considered database or registries and seven relied on clinical expertise or caregivers report only. All studies considered cohorts with more than 30 autistic individuals, the lowest sample size being retrieved in Mosner et al. (29)  with 35 adolescents. In addition, case ascertainment was based on self-reporting in four studies  (30) (31) (32) (33)  and sample characteristics were missing in Polyak et al. (34). The number of studies per co-morbidity was as follows: ADHD (n = 17), anxiety (n = 13), depressive disorders (n = 12), epilepsy/seizure (n = 12), GI syndromes (n = 7), hearing impairment (n = 3), ID (n = 13), sleep disorder (n = 7), and vision impairment (n = 3).
ADHD Prevalence for ADHD in ASD children ranged from 0. 00% in children aged 3-4y  (35)  to 86. 00% (Figure  3  and Supplementary Material 6)  (36). Prevalence was further described by ADHD subtypes in Brookman-Frazee et al. (37) , Mansour et al. (36) , and Skwerer et al. (38)  with a prevalence of 11. 00, 21. 00, and 40. 00% respectively for the inattentive subtype, 4. 00, 1. 00, and 23. 10% for the impulsive subtype and 62. 00, 64. 00, and 18. 50% for both subtypes combined. In the US, Houghton et al. (39)  studied the prevalence of comorbidities in ASD using two insurance claims databases, one commercial database (Truven Health MarketScan R Commercial Database) and the Medicaid database. The authors reported the prevalence of ADHD for three age groups: 6. 89/14. 19% in 3-4y, 40. 46/47. 67% in 5-11y, and 47.
73/51. 08% in 12-17y (commercial/Medicaid). The authors conducted a similar study in the UK and leveraged the Clinical Practice Research Datalink database to estimate the prevalence of multiple co-morbidities while considering the same age groups. Prevalence for ADHD was 0. 0% in children aged 3-4y, 10. 40% in 5-11y, and 17. 7% in the 12-17y group  (35). In a UK study, Salazar et al. (40)  reported the prevalence of ADHD to be 59. 10% for the total population and by sex 64. 40% (CI 95% : 50. 00-78. 70%) in male children aged 4-8 y and 38. 60% (CI 95% : 23. 90-53. 40%) in female children. The authors also calculated a statistically significant odds ratio (OR) of 2. 9 (CI 95% : 1. 2-6. 9), confirming the disorder was more prevalent in male individuals. In a similar manner, Stacy et al. (32)  also reported the prevalence of ADHD by sex, but also further stratified the disorder between "mild" and "moderate/severe" ADHD  (32).
Prevalence for mild ADHD in male/female children was 12. 80/15. 60% while prevalence of moderate/severe ADHD was 30. 60/20. 30%. Like in Salazar et al. the authors also calculated OR for each subtype, but none were statistically significant (OR mild ADHD = 1. 03 [CI 95% : 0. 39-2. 71]; OR moderate/severe ADHD = 0. 56 [CI 95% : 0. 25-1. 26]). Supekar et al. (41)  assessed the influence of sex and age on the prevalence rates of comorbidities in ASD leveraging the STRIDE database and reported rates of co-morbid ADHD of 46. 39 and 31. 25% in males and females aged 0 to 18 years old respectively. Overall, ADHD appears more prevalent in male adolescents as sex was identified as a risk factor in Salazar et al. (40) , and also confirmed in Brookman-Frazee et al. (37)  who found that boys were 3. 37 more likely to score positive for any ADHD disorder on the MINI-KID-P, while Soke et al.
Sleep Disorders Prevalence for sleep disorders varied from 2. 08% in ASD females aged 0-18y (41) to 72. 50% in ASD children with a mean age of 9y (Figure  10  and Supplementary Material 6)  (51). In the US, Houghton et al. (39)  reported prevalence of 6. 39/12. 79% in 3-4y, 5. 87/10. 22% in 5-11y and 5. 14/8. 85% in 12-17y (commercial/Medicaid). In the UK, Houghton et al. (  35 ) estimated prevalence at 6. 40% in children aged 3-4y, 13. 20% in children aged 5-11y and 14. 40% in 12-17y. Similar to previously mentioned co-morbidities, age was identified as a risk factor (adjusted OR = 1. 34 [CI 95% : 1. 15-1. 56]) for sleep abnormalities in Soke et al. (42)  which estimated prevalence of 26. 95% in children aged 4y and 37. 12% in children aged 8y. In a retrospective case-cohort study based on the military health system database, Elrod et al.
Similar to our findings for the prevalence of ASD, several factors should be considered when interpreting the heterogeneity in co-morbidity prevalence. First, the definition for a given comorbidity differed across studies. This was observed particularly for anxiety disorders where claim database studies such as Houghton et al. (39)  considered a wide array of disorders from anxiety states to dissociative disorders, whereas the prevalence reported in Stacy et al. (32)  corresponds to "anxiety" without further information. As such, the presence of subtypes within a single co-morbidity is a factor for heterogeneity. Second, study design and methodology should be considered when interpreting our results as our review included a mix of cohort, cross-sectional and case-control studies. Although informative, prevalence rates from surveys should be interpreted carefully as they are subject to multiple bias from parents who often must confirm or infer the presence of a co-morbidity based on a single question.
Similarly, claims database analyses do not provide information regarding the diagnostic process undertaken and instrument(s) used to confirm the presence of the comorbidity. Finally, diagnostic ambiguity should be considered when interpreting prevalence of co-morbidities as it is often challenging to distinguish a co-morbidity from the presentation of autism. As an example, deficit in communication/interaction in autism can be interpreted as an anxiety disorder while conversely it can prevent from reporting symptoms potentially attributable to a co-morbidity. Our study presents several limitations. First, a broad search term for comorbidity was considered and not terms for specific comorbidities. This approach allowed us to identify the most frequently observed comorbidities in ASD whose relevance was further confirmed by clinical experts.
4% (CI 95% 1 in 71 4. 4 (19) interviews with children 0. 70-1. 80%) Male: children 6. 8% Female: 4. 2% UK 2017 National Parent report and direct 5-19y 7,654 Total: 1. 2% (CI 95% 1 in 83 4. 8 interviews with children 0. 90-1. 40%) Male: children 1. 9% Female: 0. 4% UK 2017 National Parent report and direct 5-10y 3,597 Total: 1. 5% Male: 2. 5% 1 in 67 6. 5 interviews with children Female: 0. 4% children UK 2017 National Parent report and direct 11-16y 3,121 Total: 1. 2% Male: 1. 8% 1 in 83 2. 7 interviews with children Female: 0. 7% children UK 2017 National Parent report and direct 17-19y 936 Total: 0. 5% Male: 1. 0% 1 in 200 NR interviews with children Female: N/A children Bachmann Germany 2012 National Nationwide health 0-24y 6,400,000 Total: 0. 38% Male: 1 in 264 2. 7 et al. (20) insurance database 0.
Study Design and Participants This was a retrospective cross-sectional study of acceptance and hesitancy rates of the COVID-19 vaccine among parents of adolescents. In the overall cohort, we included young adolescents 12 to 15 years of age from Rawdat Al Khalil Health Centre (RAK-HC). The study used data collected from 17 May to 3 June 2021. RAK-HC is a leading governmental hospital in Qatar, dedicated as a COVID-19 institution since the beginning of the pandemic and has been the primary receiver of patients suspected of COVID-19 in Qatar. A list of eligible participants was obtained from two domains: (i) a database of adolescents aged 12-15-year-old registered at RAK-HC central region, or (ii) online registration via the MOPH website, in which parents were able to book vaccine appointments remotely for their adolescents.
Data Collection Parents of young adolescents were categorized into two main groups according to vaccine booking status: accepted or hesitant. For parents with multiple eligible adolescents, each response was handled and counted as a distinct participant irrespective of the sibling's number. Data related to adolescents' sociodemographic characteristics (i. e. , age, sex, nationality), health status (i. e. , history of any previous COVID-19 infection during the last 90 days previous to the call/booking, and if at high-risk category i. e. , having one or more comorbidities) in addition to vaccine status (acceptance or hesitancy) and VH reasons (i. e. , "wait and see", "definitely refused", "COVID-19 positive" or "currently sick" at the time of call/booking) conveyed by parents were extracted from the database maintained in an Excel sheet.
Institutional Review Board Statement: The ethical committee of Primary Health Care Corporation (PHCC) was consulted for a formal application, but the committee mentioned that an IRB is not required. Therefore, a data release was requested from the medical record of PHCC and was approved under the reference (PHCC/DCR/05/036). Informed Consent Statement: Patient consent was waived due to the retrospective nature of the study and the analysis used anonymous clinical data. Data Availability Statement: Data are available from the authors (S. M. , or I. D. ) upon reasonable request. Acknowledgments: Open access has been funded by Primary Health Care Corporation (PHCC), Doha, Qatar. The authors would like to extend their gratitude to the Ministry of Public Health of Qatar (MoPH) and PHCC for their commendable effort in the vaccine awareness campaign as well for making the COVID-19 vaccines accessible and totally free for the entire population of Qatar.
In this paper, we propose a new data-driven traffic state estimation model that estimates traffic flow based on average speed data only. The model is devised to implement a cost-effective framework that aggregates heterogeneous sources of vehicles' GPS and speed measurements to infer traffic flow using a novel triplet system called Conditionally Gaussian Observed Markov Fuzzy Switching Systems (CGOMFSM). Unlike its hard counterpart, CGOMFSM allows for a transient and gradual representation of traffic state transition and hence improves the estimation performance using a tractable scheme. The potential of the proposed model is illustrated through an application to the problem of traffic incident detection, particularly sporadic traffic congestion caused by unexpected road conditions. The performance of the proposed model is assessed using real traffic datasets from England highways.
Section III details the triplet model used to represent the traffic state evolution over time along with the underlying traffic state estimation algorithm. Section IV presents the derivation of the fuzzy parametrization of the triplet model. Section V details the parameter estimation scheme adapted for the proposed fuzzy parametrization and presents an application of the scheme to the problem of sporadic traffic incident detection. Section VI introduces an extension of the proposed estimation algorithm for a one-step ahead prediction of traffic state. In Section VII, we report the results of the experimental study using both real datasets and simulated traffic data for traffic event detection applications.
The collected speed data are used to estimate the average velocity denoted hereafter as v s n , where s refers to the road segment and n to the period I n. For GPS and speed data, we consider the following model: < t n , L n , l n , ω n , d n >, where t n refers to the timestamp, L n to the longitude, l n to the latitude, ω n to the speed and d n to the direction of the vehicle movement during I n. The GPS coordinates of the vehicle are mapped to the road segment using the map matching procedure described in  [56]. It is worth mentioning that the issues related to network connectivity such as latency and those related to power consumption are not considered in the scope of this research. We mainly focus on the data-driven model and its ability to infer complete traffic state from a single traffic variable.
Once the location of a vehicle is matched to the road segment of interest, the vehicle speed is recorded throughout the journey. As soon as the period I n is elapsed, the vehicle speed data are averaged to v n. Whenever possible, traffic flow data during I n , denoted as f n , are recorded as a training set for the estimation module and used afterwards to infer traffic state using speed data only, as will be presented later. For the sake of simplification, we assume that connectivity related issues incurred by transmission lags are not considered. Hence, we assume that the time intervals I n are sufficiently large so that communication delays do not impact the accuracy of the model. We distinguish between two phases (Figure  1 ): • Training phase: During this phase, both flow and speed data are recorded on a regular basis spanning a certain period of time T.
). Ideally, the trained model could be directly transposed to road sections that share the same features. The underlying dependency between traffic speed and flow, as well as the transient transition between traffic conditions over time, suggest the use of a model that (i) supports discrete-time discontinuities in a continuous fashion and (ii) represents the dependencies (supposed here linear) between the stochastic processes F N 1 and V N 1. To achieve a time-dependent representation of traffic state, we introduce a latent process denoted as S N  1 and referring to the traffic switch over time. Hence, the random variable s n represents the traffic state during the time interval I n during which the speed measure v n has been recorded. The problem can be formulated as follows: given P measures of average speed v P 1 and traffic flow f P 1 at a given road section spanning a time interval T = P n=0 I n , where I n are time periods of equal length, the goal is to determine a model that can be used to (i) explicitly represent the dependence between the stochastic processes F and V using an auxiliary process S and (ii) construct a tractable procedure to estimate the traffic state from the speed data V observed during a monitoring time interval.
A. TRAFFIC STATE MODELING USING CGOMFSM We assume that the average speed data are measured and recorded on a regular basis, while the total carriageway flow data are not necessarily observable. The main rationale for considering such assumption is that it is easy to aggregate traffic speed from sampled measures while it is more difficult to determine the traffic volume unless necessary nay intrusive equipment has been installed. The proposed traffic modeling framework is composed of two main stages:. Z n+1 n (s n+1 n ) = (s n+1 n ) (s n+1 n ) (s n+1 n ) (s n+1 n ) , (6) where ( s n+1 n ) = V (s n+1 n ) b(s n+1 n ) b(s n+1 n ) F (s n+1 n ) and (s n+1 n ) = a(s n+1 n ) d(s n+1 n ) e(s n+1 n ) c(s n+1 n ) B. TRAFFIC STATE ESTIMATION As mentioned earlier, the traffic state is assumed to be modeled by a continuous state-space = [0, 1] in which the boundary conditions correspond to 0 for unoccupied road and to 1 for extremely congested.
A. EXPERIMENTS WITH REAL DATASETS In this part of the experimental study, we focus on the validation of the proposed model using ground truth datasets. The goal is to measure the extent to which the fuzzy approximation of non-linear systems pertains to highways traffic data. For this purpose, we selected the England highways datasets publicly available through the Motorway Incident Detection and Automatic Signalling (MIDAS) system. 1 Each MIDAS site reports, on a regular basis of 15 minutes, the traffic volume that corresponds to different categories of vehicles depending on their lengths, as well as the average speed. For the sake of simplicity, we assume the following:  • The traffic is homogeneous, i. e. we consider only one category of vehicles corresponding to 0-520 cm. • The traffic is stationary, i.
e. we discard traffic states that correspond to conspicuous outliers. To evaluate the accuracy of the flow estimation, we considered the two error measures MAPE and RMSE defined as follows: MAPE = 1 T T i=1 xi -x i x i RMSE = 1 T T i=1 xi -x i 2 , where x i represents the observed flow (x i = f i ) or the recorded speed (x i = v i ), xi represents the estimated flow (x i = fi ) or the predicted speed ( xi = vi ) at time point i, and T is the observation interval. Figure  6  shows two monitoring points situated at A1 highway corresponding to the sites A1/9547A and A1/9542A. Data were collected over a time interval spanning from 01/01/2019 00:14 to: 31/12/2019 23:44, corresponding to 27418 entries of which 26000 where used for the parameter estimation and the remainder (1418 entries) were used to test the estimation and the prediction algorithms.
Traffic state estimation. For any time point n > N , only speed data are recorded from a variable number of road users. The aggregated speed data V n are hence used to extrapolate the traffic volume F n |V n 1 , the current switch S n |V n 1 using a recursive filtering scheme and to predict the next measurement V n+1 |V n 1 , S n 1. Within the CGOMFSM framework, let us denote by Z n ,Z n+1 (s for which both traffic volume and average speed data are avail- able. Z N 1 = (F N 1 , V N 1 ) data are used to estimate the CGOMFSM parameters and the associated switch pro- cess S N 1. n+1 n ) the covariance matrix of the vector Z n Z n+1 • Stage 1: Traffic model parameter estimation. At this level, historic traffic data are utilized to infer the CGOMFSM model parameters and fit the fuzzy parametrization to a particular dataset.
Data and indicators We ran a face-saving-also labeled as a 'guilt-free strategy' throughout the rest of the studyexperiment in twelve countries: Australia, Austria, Brazil, France, Germany, Italy, New Zealand, Poland, Spain, Sweden, the United Kingdom and the United States. After outlining the study's details, we obtained respondents' written consent to participate in the survey. The data were analyzed anonymously, while ethical approval for the project "Citizens' Attitudes Under COVID-19 Pandemic" was received from TSE-IAST (Toulouse School of Economics). The online surveys were conducted by three different data collection firms: IPSOS (for all countries except Australia, United States and Spain), CSA (for Australia and the United States) and Netquest (Spain). While these different platforms do not entail major differences, having different firms involved in data collection reduces the risk of bias due to potential "house effects.
For this analysis, we rely on a pooled dataset that includes data from all countries. All in all, we find that the face-saving strategy is effective. In total, 47 out of 48 effects are positive, and, their effects are substantive, ranging from 9 to 16 percentage points. Moreover, the impact of the treatment effect is fairly homogenous. As shown in Fig  2 , it does not seem to be conditioned by individuals' gender, age or level of education. We made sure that our conclusions were robust in several ways. First, although we should be cautious about randomization checks in an experimental context  [23] , we verified that both control and treatment groups were similar in terms of age, gender and education. The average age across treated and untreated respondents was identical (at 47 years of age); 52% of respondents in the control group were women compared to 51% in the treatment group, and means for the education variable were the same at.
60. Second, using weights for age, gender, education and region did not alter our conclusions. More specifically, we replicated Figs 1 and 2 for the weighted dataset (see Figs D. 1 and D. 2 in the S1 File). One interaction out of twelve reached p <. 05, that is, the interaction between age and treatment for the 'no mask' behaviour. Focusing on the coefficients rather than p-values, our findings are very similar. Third, we made sure that results from our tests of heterogeneous treatment effects were not driven by unobserved characteristics of particular countries by estimating the effects with a model that included country fixed effects. Figs D. 3 and D. 4 in S1 File replicate and the results of this test and lead to essentially the same conclusion. Fourth, we tackle the possibility that our results are in fact a "false positive"  [24].
Acknowledgments We thank all everyone involved in the data collection process, including:  Pavlos Vasilopoulos  ( University of York ),  Vincenzo Galasso  ( Bocconi University ),  Eric Kerrouche  (Sciences Po-CEVIPOF),  Sandra Leon Alfonso  ( University Carlos III ),  Vincent Pons  ( Harvard Business School ),  Hanspeter Kriesi  (EUI),  Dominique Reynie(Sciences Po-CEVIPOF). We also thank  Eric Kennedy  ( York University ) who provided the opportunity to present a first version of this research as part of the  COVID-19 Survey Research Working Group seminars , and we are grateful for participants' comments. Any error is ours.