dataesr/scientific-paragraphs-categorization huggingface dataset:
Egg masses were kept individually in test tubes capped with cotton to allow ventilation. Egg hatching was checked daily. After a period of at least 40 days with no additional hatching, the protective scales that cover PPM egg masses were removed to observe individual eggs under a binocular magnifier and collect data similar to that available in historical datasets: length of egg masses, number of eggs per egg mass (fecundity), and egg phenotype. First, hatched eggs were distinguished from unhatched eggs based on the presence of the characteristic large jagged exit hole from which the neonate left the egg, and an empty transparent shell. Then, unhatched eggs were dissected to assess the cause of mortality (parasitized, i. e. , eggs with a small parasitoid exit hole and/or containing a dead parasitoid and/or containing parasitoid meconium; aborted, i.
Climate data We used series of daily temperatures recorded (by the Institut National de Météorologie, INM, Tunis, Tunisia) in eight meteorological stations distributed within the PPM range in Tunisia (Fig. 1 ;   SM2 ). The similarity of both sources of data was evaluated using Pearson correlations tests for daily maximal and daily minimal temperatures in Tunis, where the data series from INM since 1990 was the most comprehensive. Daily maximal and minimal temperatures from both data sources were found to be strongly correlated (Pearson tests, r= 0. 95, p < 0. 001 and r = 0. 94, p < 0. 001, respectively). The two types of datasets where therefore combined in case of missing data in other INM series to reconstruct uninterrupted series for the period 1990-2019 (Table  SM2 ). Each site of egg sampling was assigned to the nearest meteorological station (< 100 km in all cases) among those situated at an elevation within 350 meters of the egg site, an arbitrary threshold we chose to mitigate potential climatic differences along elevation gradients (Table  1 , Figure  SM3 ).
ncbi. nlm. nih. gov/) and the International Subcommission on Trichoderma and Hypocrea Taxonomy (ISTH, www. isth. info) (Druzhinina et al. 2005; Kopchinskiy et al. 2005). Alignments of Algerian isolates sequences and the reference sequences of extype strains described by several authors  (Bissett et al. 2015; Jaklitsch and Voglmayr 2015; Chen and Zhuang 2017)  were performed with Clustalw program implemented in MEGA7. Phylogenetic trees were designed for the four genes studied using the neighbor-joining method (MEGA7) with 1000 bootstraps.
During incubation, the microplates were shaken at 175 rpm for 5 min every 10 min  (Joubert et al. 2010). Data were exported from Nephelostar Galaxy software in ASCII format and further analyzed with Microsoft Excel 2016 (version 16. 0. 12827. 20268) and R3. 4. 1 (R Core Team 2020). The lag phase and the maximal growth rate variables were calculated according to the method described by  Joubert et al. (2010). The initial relative nephelometric unit (RNU) value was calculated as the average of the 3 initial measurements and then subtracted from each curve value. For each point on the curve, a slope was calculated using measurements that were taken 2 h before and 2 h after this time. The lag phase was defined as the time required to obtain a slope value of 1 and the maximal growth rate was defined as the highest slope.
Statistical analysis To evaluate the biocontrol effect of Trichoderma spp. isolates on the development of B. cinerea, the data were subjected to analysis of variance (ANOVA), and when the data were not normally distributed (Shapiro normality test-Wilks, P < 0. 05), a non-parametric variance analysis (Kruskal-Wallis test) was performed, using Statistical Package for R3. 4. 1. (R Core Team 2020).
B. cinerea isolation and pathogenicity tests Thirty isolates of B. cinerea were obtained from tomato plants (10 isolates), vine plants (10 isolates), and strawberry plants (10 isolates). The survey was carried out over two successive years 2016 and 2017, in the northcentral and south-eastern region of Algeria (Table  S1 ; Supplementary data 1). Pathogenicity tests on halfapples revealed significant differences among the different isolates tested (Kruskal-Wallis test was done on diameters of the rot lesions, χ 2 = 114. 14, df = 29, P value < 0. 05). The most virulent isolate of each culture was chosen for the biocontrol tests, from vine (BCV02), tomato (BCT04), and BCFr11 (strawberry) (Fig. 1 ).
Trichoderma identification and phylogenetic analysis Fifteen isolates with macroscopic and microscopic characteristics of the genus Trichoderma were isolated from the different samples collected. Ten isolates were obtained from tomato; strawberries, vines rhizosphere and 5 isolates were obtained from Bio-compost®. Four genomic regions from all of these isolates were sequenced (Table  S2 ; Supplementary data 1). BLAST search of the sequences obtained during this work was performed and also, they were submitted to the ISTH TrichOKey (http://isth. info/tools/molkey/ index. php) and TrichoBlast programs (http://isth. info/ tools/blast/index. php). The results with the highest similarity percentages to the 15 sequences obtained in this study were selected for species identification. Results revealed that the isolates TAS2, TAS4, TAS5, and TAS8 belong to Harzianum clade, and presented a (99%) of nucleotide identity with the reference sequences of the specie T.
TBS1 showed (99%) of nucleotide identity with the reference sequences of T. brevicompactum species, for the tef1 sequence (EU338292, EU338283) and for ITS sequence, it was identified as T. brevicompactum and revealed (100%) nucleotide identity with several species of the Brevicompactum clade. Phylogenetic trees were designed for each of the 4 gene regions studied, with the sequences of the 15 Algerian isolates and the reference sequences downloaded from GenBank. Thereby, the trees of the tef1, rpb2, and acl1 genes revealed the same phylogenetic distribution of the Algerian sequences obtained during this work and the presence of 4 distinct clades (Fig. 2 ) (Fig. S1, S2, S3 ; Supplementary data 2). The first one was the clade Harzianum, containing the isolates TAS2, TAS4, TAS5, TAS8, TBeC1, and TLiC8, the first 4 isolates were closely related to the reference strain of specie T.
The app-based COVID Symptom Study was launched in Sweden in April 2020 to contribute to realtime COVID-19 surveillance. We enrolled 143,531 study participants (≥18 years) who contributed 10. 6 million daily symptom reports between April 29, 2020 and February 10, 2021. Data from 19,161 self-reported PCR tests were used to create a symptom-based model to estimate the individual probability of symptomatic COVID-19, with an AUC of 0. 78 (95% CI 0. 74-0. 83) in an external dataset. These individual probabilities were used to estimate daily regional COVID-19 prevalence, which were in turn used together with current hospital data to predict next week COVID-19 hospital admissions. We found that this hospital prediction model demonstrated a lower median absolute percentage error (MdAPE: 25. 9%) across the five most populated regions in Sweden during the first pandemic wave than a model based on case notifications (MdAPE: 30.
Descriptive characteristics In this nationwide study on COVID-19 during the first year of the pandemic, we included data from 143,531 COVID Symptom Study Sweden (CSSS) participants ≥18 years from April 29, 2020 to February 10, 2021, who had contributed with at least one daily report in the app (Table  1 ). The median duration of study participation was 151 days (inter-quartile range [IQR] 52-252), with a median of 43 days with submitted reports (IQR 13-119). Of all participants, 30% reported at least one COVID-19 PCR test, and 6% of women and 4% of men reported at least one positive test result. The cohort included a larger proportion of women, fewer people ≥65 years, and fewer smokers than the general population, while the prevalence of obesity was similar. Participants further resided in postal code areas with less deprivation, similar proportions of inhabitants with foreign background, and higher population densities, as compared to the general population.
Step 5. Validation of the hospitalization prediction model in England We sought to validate the CSSS-based hospitalization prediction model in England by repeating Steps 2 and 3 and parts of Step 4. The English dataset encompassed daily reports from 2,638,536 ZOE COVID Study (CSS UK) English study participants from March 30, 2020 to January 31, 2021 (study population characteristics and regional participation rates are available in Supplementary Tables  3  and 4 ). We extracted information on all COVID-19 hospital admissions (n=318,232) in individuals ≥18 years across the seven English healthcare regions from April 6, 2020 through February 7, 2021 from National Health Service England data. The number of new daily COVID-19 hospital admissions ranged from 0-958 across the English regions during this period.
We applied the exact same model that was developed in Step 1 in CSSS to estimate daily individual probability of COVID-19 in the English dataset, and then estimated the daily age-and sex-weighted COVID-19 prevalence across the English regions. We further applied the same iterative time-updated prediction model as in the Swedish dataset to predict hospital admissions the following week in the seven English regions. We used available outcome data up to April 27, 2020 to tune the parameters and to predict admissions a week later on May 4, 2020 and then repeated this daily throughout the study period (until February 7, 2021). Across the seven English regions, we observed an MdAPE of 22. 3% for the part of the first English pandemic wave captured in the data (April 6 to June 19, 2020) and an MdAPE of 19.
However, although the use of a smart device app was intended to minimize barriers to enrolment  16  , a lesser proportion of CSSS participants were male, ≥65 years, or smokers, or obese, as compared to the general population, indicating an overrepresentation of healthy individuals. Further, owing to limited resources the CSSS app was in Sweden only available in Swedish, which precluded participation of non-Swedish speakers, a group that may be at higher risk of COVID-19 infection  17, 18. We also observed that the CSSS participants resided in postal code areas with on average less deprivation than the general public. Together, these factors may have limited the ability of the CSSS to detect local outbreaks in vulnerable neighbourhoods, areas where lower community testing rates were also observed during the pandemic  12.
We sought to reduce this risk by excluding the first seven days of data collected for each participant, but we cannot exclude the risk of residual participation bias. Secondly, all data collected in the COVID Symptom Study app are self-reported. We had no means of linking the self-reported data to national population or health registers, so we could not validate information on COVID-19 tests or baseline health survey questions. We were able to overcome this limitation by validating the model predicting COVID-19 PCR test positivity in the independent CRUSH Covid dataset. Lastly, some questions in the COVID Symptom Study app were updated during the study period, potentially influencing data collection and analyses. The baseline health survey questions that were modified and/or updated during this time were, however, not included in our analyses and did not affect the prevalence estimates or the accuracy of the hospitalization prediction model.
COVID Symptom Study Sweden (CSSS) COVID Symptom Study Sweden (CSSS) was launched in Sweden on April 29, 2020 to provide COVID-19 syndromic surveillance data and to build a large-scale repeated measures database for COVID-19 research. More than 166,000 participants (2. 4% of the adult population) joined CSSS in the first five weeks after launch. The non-commercial mobile application used in the study was initially developed by health data science company ZOE Limited in partnership with King's College London and Massachusetts General Hospital  7, 8  , and adapted for use in Sweden by ZOE Limited in collaboration with Lund University and Uppsala University. The app has been used to study the contemporary disease burden and to predict consequences of COVID-19  9, 13, 28. All individuals ≥18 years living in Sweden with access to a smart device were eligible to participate in the CSSS after downloading the app and providing informed consent.
Study population and data management In this study, we included data from April 29, 2020 to February 10, 2021. Participants were in this study excluded from the analyses if they: 1) never submitted a daily report (n=5,931), 2) had missing age or reported an age <18 years or >99 years (n=801), or 3) stated their sex as other or intersex (n=236) as this sample size was insufficient for a further analysis (Supplementary Table  7 ). Participants whose last report was within seven days of joining the study (n=45,483) or did not provide a valid postal code (n=7,310) were excluded from the prevalence estimation, but included in model training if they reported a PCR test and submitted at least one symptomatic daily report within seven days preceding or on the test date (n=967). The final study population consisted of 143,531 individuals (Figure  6 ).
Representativeness of the study population We assessed the representativeness of the CSSS study population as a sample of the Swedish general population. We obtained data from the Living Condition Surveys in Sweden from 2018-2019 on prevalence of current smoking and BMI in the general adult population  29. We further extracted aggregate adult population sociodemographic information for all postal code areas in Sweden from Statistics Sweden, which included proportion of women, proportion of inhabitants ≥65 years, highest achieved education level, proportion engaged in work or studies in the population 20-64 years, median yearly net income for the population ≥20 years, and proportion of population ≥18 years with foreign background. Foreign background is here in defined as birth in a country other than Sweden, and/or birth in Sweden but with both parents born in a country other than Sweden.
We then calculated a neighbourhood deprivation index (NDI, lowest corresponds to most disadvantaged) for each postal code area based on the proportion of adult inhabitants employed or studying, the proportion with a university education, and the median yearly net income  30. We also calculated population density for all postal code areas in Sweden as the total number of inhabitants per square kilometres. The surface area of each postal code was estimated using shapefiles originating from an external company, Postnummerservice Norden AB, that contained detailed information on five-digit postal code area boundaries.
NOVUS We compared overall daily symptom prevalence in CSSS during the study period with symptom data collected by NOVUS, a private company that conducts opinion polls and other surveys using panels recruited by random sampling of the Swedish population  31. Since March 2020, NOVUS has carried out repeated surveys on COVID-19-related symptoms and healthcare contacts, not including PCR test results, with a response rate of approximately 70% 32. While in the CSSS participants report symptoms on the same day they experience them, NOVUS participants report any symptoms experienced over the past 14 days even if these reflect their baseline health status (for details, see Supplementary Material).
Step 1. Training of model for estimation of individual probability of COVID-19 We trained a model to estimate the individual probability of a symptomatic COVID-19, defined as having symptoms and a positive PCR test result. The model training set consisted of data from 19,161   participants who reported at least one PCR test result (of which 2,588 were positive) between April 29 and December 31, 2020, and who reported at least one reported candidate symptom within seven days before or on the test date. As we observed that a higher proportion of study participants reported symptoms within the first week of joining the study than thereafter (Supplementary Figure  8 ), we excluded all reports submitted during the first week to reduce participation bias from increased motivation among symptomatic individuals in the general population.
After the loss of smell and/or taste symptom question was branched (November 4, 2020) into loss of smell and/or taste and altered smell and/or taste, we combined these two new questions into a single variable, which we subsequently used interchangeably with the original loss of smell and/or taste variable. As the primary aim was to provide individual probabilities of symptomatic COVID-19, rather than creating a classification rule, we followed the TRIPOD explanation and elaboration document (https://www. equator-network. org/reporting-guidelines/tripod-statement/) guidelines to report discrimination and calibration for the model. Discrimination and calibration were internally evaluated by applying nested tenfold cross-validation within the dataset from April 29 to December 31, 2020. We further assessed discrimination and calibration in CSSS data (1,753 participants, of which 339 tested positive) from January 1 to February 10, 2021, the period not included in model training.
The nested tenfold cross-validation is described in detail in the Supplementary Material. Discrimination was quantified using the ROC area under the curve (AUC) as this metric is robust to differences in prevalence. Model calibration was assessed by plots with estimated probabilities divided into deciles. To externally validate the model, we used data from the CRUSH Covid study, which invited all individuals (≥18 years) to complete a symptom survey when they did a COVID-19 PCR test in Region Uppsala, the fifth largest healthcare region in Sweden. Using data from October 18, 2020 to February 10, 2021, the classification ability of the model for individual probability of symptomatic COVID-19 was assessed using ROC analysis among individuals who had completed the survey on the day of the test, reported at least one symptom, and had a conclusive test result (n=943; see Supplementary Material and Supplementary Table  9 ).
Step 3. Daily regional age-and sex-weighted COVID-19 prevalence estimates We estimated the daily regional prevalence of symptomatic COVID-19 infection in real-time using a weighted mean of individual predicted probabilities for each of the 21 administrative healthcare regions in Sweden re-weighted by age (<50 and ≥50 years) and sex in the total adult population using direct standardization. Demographic data from 2021 was available from Statistics Sweden. The 95% confidence intervals (95% CI) for predictions were generated using the function ageadjust. direct from the epitools package in R 33 , using the method of Fay and Feuer  34. This function accommodates the sum of the model-generated probabilities, number of participants for each of the four age-and sex strata on a given day, and the total population of Sweden.
The method assumes that the sum of the model-generated probabilities is Poisson-distributed using an approximation based on the gamma distribution. The odds ratios for all variables in the prediction model were assumed to be generalizable to the background population. Because the model was trained in a dataset with higher prevalence of COVID-19 compared to the general population, the intercept was inflated. We therefore recalibrated the intercept of the model generated in Step 1 until the nationwide app-based predictions for May 27, 2020, matched the estimated nationwide prevalence of 0. 3% (95% CI 0. 1-0. 5%) from a survey performed by the Public Health Agency of Sweden between May 25-28, 2020  11. In that survey, selfsampling nasal and throat swabs with saliva samples were delivered to a random sample of 2,957 individuals (details provided in the Supplementary Material).
We assumed both the sensitivity and the proportion of symptomatic COVID-19 in the Public Health Agency of Sweden survey to be 70%  35, 36. Although the method of Fay and Feuer 34 used to calculate the confidence intervals may be Consistent with the main model, we also performed nested tenfold cross-validation, described in detail in the Supplementary Material, and independently validated the model in the CRUSH Covid dataset. Step 4. Predictions of regional COVID-19 hospital admissions the following week We assessed the ability of the CSSS prevalence estimates to predict the following week's COVID-19 hospitalizations during the first and second pandemic waves in Sweden. We defined the end of the first pandemic wave as when the rate of daily new hospital admissions in Sweden dropped below 0.
5 individuals per 100,000 inhabitants ≥18 years (July 3, 2020), and the beginning of the second wave as when the hospital admission rate again rose above that threshold (October 19, 2020). We utilized data from the entire study period in Swedish data to assess which variables to include in the model predicting hospital admission seven days ahead. The candidate variables were: daily regional CSSS prevalence estimate on day 0 or -1; current regional rate of COVID-19 hospitalizations per 100,000 inhabitants ≥18 years on day 0 or -1; weekday of hospitalization (Monday through Sunday); mean age in region; and mean regional Neighbourhood Deprivation Index (NDI). The final model was a weighted linear regression model, including daily regional CSSS prevalence estimate on day 0 and daily regional rate of COVID-19 hospitalizations per 100,000 inhabitants ≥18 years on day 0, assuming both variables to have a linear relationship with the outcome.
We then calculated the median absolute percentage error (MdAPE) between predicted and observed number of hospitalizations across the first wave (May 11 to July 3, 2020), the summer period (July 4 to October 18, 2020), and the second wave (October 19 to November 29, 2020), for each of the 21 healthcare regions as well as for the five most populated regions combined (Stockholm, Västra Götaland, Skåne, Östergötland and Uppsala). If a region had zero new hospitalizations on any day, that data point was excluded when calculating the absolute percentage error. For comparison, we further created a second regression model, where we instead of the regional CSSS prevalence estimates inserted the daily regional case notification rates as registered in SmiNet (on day 0) in the equation. We evaluated both date of PCR test and date of registration in SmiNet in the model.
When using date of PCR test, we only counted tests that were registered before or on day 0 in each time-updated iteration. Using the date of registration provided better predictions. We found the same density and weights suitable for the SmiNet model as for the CSSS-based model. Model fit was repeated daily across the same period. We further plotted the relative error in % per day and region in hospital admission predictions over the range of observed admissions. We concluded by seeking replication of Step 4 by applying Eq. 1 in the English dataset using the equivalent variables used in the analysis of CSSS data. We trained the model in available outcome data up to the first day 0 (April 27, 2020) to derive the coefficients β0, βprevalence, and βhospital. We inserted the ZOE COVID Study prevalence estimate and daily regional rate of COVID-19 hospitalizations per 100,000 inhabitants ≥18 years on April 27, 2020 into the equation, applying the derived coefficients to predict hospitalization rates on May 4, 2020.
We then repeated the model fit and prediction from May 5, 2020 to February 11, 2021, with past data influencing the daily new intercept as well as the daily new two betas. 2-5. We provide mock CSSS individual level datasets, that can be used together with the code. These data do not represent real observations. The Source Data that support the findings of this study are available on GitHub at https://github. com/ulfha881/App-based-COVID-19-syndromic-surveillanceand-prediction-of-hospital-admissions-The-COVID-Symptom-S, or, in archived form, at  40.