dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability All data generated during this study are included in this published article (and its Supplementary Information files). All model code as well as diagnostic reports are available in the GitHub project code repository (https:// github. com/ ecohe altha llian ce/ sabre net-rouse ttus-dynam ics/, with a version on Zenodo https:// zenodo. org/ record/ 77097 16). | (2023) 13:15829 | https://doi. org/10. 1038/s41598-023-42938-w.
A bespoke compartmental susceptible-exposed-infectious-hospitalized-recovered-susceptible (SEIHRS) epidemiological model with a control policy is proposed, which also accounts for the influence of people's behavioral response as a result of the knowledge of adequate precautions. An accelerated Benders decompositionbased algorithm with tailored valid inequalities is offered to solve the model. Finally, we consider a realistic case study -the COVID-19 pandemic in France -to examine the computational proficiency of the decomposition method. The computational results reveal that the proposed decomposition method coupled with effective valid inequalities can solve large-sized test problems in a reasonable computational time and 9. 88 times faster than the commercial Gurobi solver. Moreover, the sharing mechanism reduces the total cost of the system and the unmet demand on the average up to 32.
In the first category, Anand et al. [49]  proposed a modified SIR model (MSIRM) with a case study in India, where infected individuals were tested and quarantined. Cooper et al. [50]  proposed an MSIRM with a dynamic susceptible population, where the proposed model was examined on different datasets, including China, South Korea, India, Australia, Italy, and the USA. Chen et al. [51]  addressed an MSIRM with a case study in China, in which recovery and transmission rates were time-dependent. Abou-Ismail  [52]  applied SIR and SEIR models and offered a susceptible-quarantinedquarantined-confirmed (SUQC) model. Wangping et al. [53]  extended the SIR model with a case study in Italy, where the transmission rate was time-dependent, and the Markov Chain Monte Carlo method was employed to estimate the basic reproductive number.
Knapsack inequalities The knapsack inequality is added to the master problem to accelerate the branch-and-bound process in the solver. Indeed, the convergence speed of Benders decomposition can be improved by adding the following cuts, which assist progressive solvers like Gurobi in deriving a range of valid inequalities  [87]. Consequently, the following cuts  (36)  are added in iteration n + 1 of the master problem. LB n ≤ j∈ J t∈ T OC j w jt + p∈ P m ∈ K t∈ T SC pm y pmt + ζ  (36)  where LB n signifies the best specified lower bound found so far, and ζ is an additional variable representing the total cost except ordering and setup costs. Finally, the PISP Benders reformulation is provided in Appendix F.
Computational experiments This section presents the computational results of the devised framework to deal with pandemic situations under dynamic demand. For this aim, Section 6. 1 designs a set of experiments based on a real case study, the COVID-19 pandemic in France. Next, Section 6. 2 provides a comparative analysis between the proposed ABD algorithm and the Gorubi solver without (GRB) and with the valid inequalities (GRB_VI). The comparison between GRB and GRB_VI helps to investigate how much the valid inequalities have been effective in reducing the computational time of the Gurobi solver. Finally, a set of comprehensive sensitivity analyses are presented in Section 6. 3 to investigate the behavior of the proposed PISP model to any changes in its input parameters.
To test the performance of the proposed ABD algorithm, a set of 25 test problems are derived from the case study, wherein the numbers of regions, suppliers, manufacturers, and hospitals are fixed, but the period increases from 4 weeks to 52 weeks. Since the SEIHRS model is run for each hospital for a time horizon of 365 days and it is also impossible and illogical to plan the PISP daily, each period of the PISP model represents a week and the demand of each period is the cumulative demand of the whole week. The PISP model and the ABD algorithm were coded in Python 3 using the Gurobi library, and all experiments were done on a server containing four Intel XEON processors with 5 GB of RAM running at 2. 3 GHz. Furthermore, two stopping criteria are considered when executing the accelerated Benders decomposition algorithm and the Gurobi solver: 1) a gap of 1% and 2) a CPU time of 7200s.
88 times faster than the Gurobi solver. In addition, the effectiveness of the valid inequalities was also reported that helping the proposed model to be solved 1. 48 times faster using the Gurobi solver. Moreover, the sharing mechanism reduces the total cost of the system and the unmet demand, on average, up to 32. 98% and 20. 96%, respectively. Furthermore, the two parameters of maximum sharing capacity and allowable demand overload ratio are two valuable factors at hand for managers to regulate inventory and sharing decisions in the proposed sharing mechanism, which significantly influences the system's total cost and unmet demand. Some ideas for expanding the current study on the prediction and planning stages can be explored. The application of machine learning algorithms to estimate the parameters of the SEIHRS model during the prediction phase can improve demand prediction accuracy.
Acknowledgements F. Chamizo is partially supported by the  PID2020-113350GB-I00  grant of the  MICINN (Spain)  and by " Severo Ochoa Programme for Centres of Excellence in R&D"  ( CEX2019-000904-S ). B. Martin is supported by the joint  FWF-ANR  project  Arithrand: FWF: I 4945-N  and  ANR-20-CE91-0006. This work is the result of the visit of the first author to the  Université du Littoral Côte d'Opale. He appreciates the invitation, the support and the hospitality during this period. Funding Open Access funding provided thanks to the  CRUE-CSIC  agreement with Springer Nature. Data availibility Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
Acknowledgement We would like to thank  Brian Hopkins  and  Dennis Eichhorn. Their insightful comments on the early version of this paper help us to improve it. This work was supported by the  LABEX MILYON  ( ANR-10-LABX-0070 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR).
Statistical Analysis Data were analyzed on SPSS software version 25. A descriptive analysis was performed using absolute frequencies and percentages for categorical variables and means and standard deviations (SD) for quantitative measures. Construct validity of the knowledge, attitude, and practice scales was assessed using factor analysis with principal component analysis (PCA). The Promax rotation method was used, given that factors were correlated. The Kaiser-Meyer-Olkin (KMO) and Bartlett's test of sphericity was calculated to ensure the model's adequacy. Factors with Eigenvalues greater than one were retained, and the scree plot method was used to determine the number of components to extract  [24]. Furthermore, factors related to KAP were also correlated to assess convergent validity.
055). No significant association was found between education level and KAP in other age categories. The association between the source of information about cholera and the KAP scales is presented in Table  6. The results showed that higher knowledge scores were significantly associated with obtaining information from electronic databases, from the pharmacist, from the Ministry of Public Health (MOPH) brochures, and conferences. Low knowledge score was significantly associated with those refusing to seek such information. More careful attitude scores were significantly associated with obtaining information from social media platforms and the MOPH brochures. However, a more careless attitude score was significantly associated with those refusing to seek such information. Higher mean practice scores were significantly associated with those obtaining the information from the Ministry of Public Health brochures.
Multivariable Analysis A first linear regression taking the knowledge scale as the dependent variable showed that the female gender (β = 1. 74), easy access to healthcare (β = 2. 05), working as a full-time (β = 1. 24), being a healthcare professional (β = 2. 50), being a health student (β = 2. 39), knowing that cholera vaccine is available and effective (β = 1. 64), and obtain information about cholera from electronic databases (β = 1. 19) were significantly associated with a higher knowledge score. However, refusing to seek information (β = -1. 58) and smoking cigarettes (β = -1. 31) were significantly associated with lower knowledge scores (Table  7 , model 1). A second linear regression taking the attitude scale as the dependent variable showed that being a healthcare professional (β = 2.
Generally, less information seeking is reportedly associated with lower health literacy on various communicable and non-communicable illnesses  [31] [32] [33]. Furthermore, a better knowledge score was significantly associated with getting information on cholera from electronic databases. The role of electronic databases in promoting the health information system is well established. Digital data sources, including websites, were shown to be essential for collecting and mining information in the area of infectious diseases and early warning systems  [34]. A recent study explored the attitude of the Lebanese community toward cholera  [15]. Its main drawback is that it assessed knowledge of the transmission mode and causes of the infection rather than attitudes toward the disease and did not use validated scales.
Strengths and Limitations The current study has several strengths. The data were collected from districts all over Lebanon, which allowed for assessing the KAP toward cholera of the entire Lebanese community regardless of the sporadic outbreak regions. The sample size was adequate for all statistical analyses to be carried out and to determine the variables associated with better or poor KAP. To our knowledge, these are the first tools related to cholera KAP to be ever validated in a current complex crisis setting, combining socioeconomic hardship and sanitary decay. This fact might increase the usefulness of the suggested tools in developing countries with similar circumstances. The content and construct validity of the KAP tools were also confirmed, and the tools appeared consistent and reliable, which is expected to consolidate the validity of the results and decrease information bias.
Nevertheless, several limitations could not be avoided in this study. The cross-sectional design does not provide temporality but remains practical for capturing a snapshot amid outbreaks and rapidly identifying gaps, although causal associations cannot be confirmed. Furthermore, data were collected using a snowball technique (online questionnaire), which is a nonrandom technique that could have been associated with a possible selection bias as this data collection technique might have eliminated people with poor digital literacy. Nevertheless, it is believed that the likelihood of this bias is minimal as the survey was both in Arabic, the natively spoken language of Lebanon, and English, and almost all the people residing in Lebanon hold smartphones and are familiar with online access. However, some other people who were not interested or cooperative in these kinds of surveys were not enrolled in the study, which might have affected the results, as a possible correlation might be found between those interested and concerned about infectious diseases and KAPs and those who were not.
other *) 2. 394 0. 202 <0. 001 1. 183 3. 605 Work status (full-time vs. other *) 1. 243 0. 122 0. 009 0. 317 2. 170 Get information about cholera (electronic databases vs. others *) 1. 191 0. 114 0. 008 0. 314 2. 068 Get information about cholera (refuse to seek information vs. others *) -1. 582 -0. 100 0. 020 -2. 918 -0. 246 Cigarette smoking (Yes vs. No *) -1. 314 -0. 092 0. 039 -2. 562 -0. 067.
Imbert (2020)  estimates that across India, around 11 million inter-state migrant workers returned home after the first lockdown. In this paper, we provide new evidence on the effect of the pandemic on young migrant workers using novel longitudinal data and provide experimental evidence on re-connecting them with the labour market. We followed a cohort of 2,260 young workers from rural areas within Bihar and Jharkhand between 2019 and 2021. The respondents were recent participants in a large-scale national vocational training scheme called Deen Dayal Upadhyay Grameen Kaushal (DDU-GKY, henceforth). DDU-GKY provides trade-specific training for a duration of 3-12 months and places disadvantaged rural youth into formal salaried jobs, often located in other states. 1  In addition to the survey rounds used by  Chakravorty et al.
87% of the respondents of our sample completed the DDU-GKY training. We find stark differences between them and the remaining 13% who dropped out of the training. Pre-lockdown, 44% of training graduates had a salaried job, against 15% among training drop-out. By November-December 2021, training graduates were still twice as likely to hold a salaried job as the dropouts (28% against 14%). These results suggest that while employment opportunities worsened for all the youth in the sample, those who completed the DDU-GKY program did comparatively better. Given the policy challenge of (re)integrating youth into the labour market, the local government planned to ramp up the use of an app-based job platform (Yuvasampark) to encourage job search and application in the population. Yuvasampark is a privately owned and managed mobile app that was used by several state governments in India especially during the Covid-19 pandemic to help trainees search for and apply for jobs.
, 2021; Mahmud & Riley, 2021; Aggarwal et al. , 2022; Dang et al. , 2023). Using panel datasets from Bangladesh and Nepal,  Barker et al. (2020)  find a 15-25% decline in earnings and a fourfold greater prevalence of food insecurity among migrant households in the first few months of the pandemic. A longitudinal household survey study from Ethiopia, Malawi, Nigeria, and Uganda estimates that 77% of the population lives in households that have lost income during the pandemic  (Josephson et al. , 2021). Most households in Cambodia, the Lao People's Democratic Republic, Indonesia, Malaysia, Myanmar, the Philippines, Thailand, and Vietnam experienced significant declines in income and employment and having at least one person who lost their job or had reduced working time increased the likelihood of experiencing financial difficulties by 17 percentage points  (Morgan & Trinh, 2021).
Acknowledgements We thank  Wiji Arulampalam ,  Gaurav Chiplunkar ,  Santhosh Mathew ,  Rohini Pande ,  Simon Quinn ,  Chris Woodruff  for useful comments at many stages of the projects. This project has been funded by  International Growth Centre ,  EQUIP-ESRC ,  CAGE Univer-sity of Warwick , and  British Academy. This project would not have been possible without the amazing collaboration of  Mr Sanjay Kumar  ( BRLPS ),  Mr Abhinav Bakshi  ( JSLPS ) and the  Ministry of Rural Development. Chakravorty  is affiliated with GLO,  Bhatiya  with CAGE,  Imbert  with BREAD, CEPR, EUDN, and  J-PAL , Rathelot with CREST, CEPR, and  J-PAL.
Therefore, we are unable to determine the impact of demographic factors like age and sex on infectiousness variation. Also, we cannot disentangle the host behaviors from biological factors. Previous analysis suggested no evidence of the impact of age on infectiousness of cases  (2, 9). In addition, we could not include factors affecting susceptibility to infection. Our estimates of infectiousness variation should be interpreted in light of these limitations: they capture heterogeneity in infectiousness due to demographic, host and biological factors. If we were able to account for these factors thanks to the analysis of more detailed datasets, our estimates of infectiousness variations would likely be lower. However, in one study that included susceptibility component in the estimation of individual infectiousness, substantial heterogeneity remained with 20% of cases estimated to contribute to 80% of transmission  (22).
The relative infectiousness of case i compared with case j is exp( ! )/exp( " ). Since the data were extracted from publication, the infection time of cases was unavailable. Also, the individual infectiousness parameters  ! for cases were augmented variables. Therefore, we conducted our inference under a Bayesian framework with using data-augmentation Markov chain Monte Carlo (MCMC) algorithm to joint estimate the model parameters, the missing infection time and augmented variables with using metropolis-hasting algorithm. We separately. fitted this model to identified studies. We assessed the model adequacy by comparing the observed and expected number of infections in households by household size. We compared the model with or without the random effect for variation of individual infectiousness by using Deviance Information Criterion (DIC)  (54).
Systematic review We conducted a systematic review following the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) statement  (62). A standardized search was done in PubMed, Embase and Web of Science, using the search term "("COVID-19" OR "SARS-CoV-2") AND ("household" OR "family") AND ("transmission")". The search was done on 22 June, 2022, with no language restrictions. Additional relevant articles from the reference sections were also reviewed. Two authors (XH and CW) independently screened the titles and extracted data from the included studies, with disagreement resolved by consensus together with a third author (TKT). Studies identified from different databases were de-duplicated.
Inference, model comparison and validation The infection time for cases were unavailable in the data extracted from publication. Also, the individual infectiousness parameter  ! was an augmented variable. Therefore, we conducted our inference under a Bayesian framework with using dataaugmentation Markov chain Monte Carlo (MCMC) algorithm to joint estimate the model parameters, the missing infection time and the relative individual infectiousness with using metropolis-hasting algorithm. For the parameter that only takes positive value, we used a vague Uniform(0,10) prior. For the parameter for relationship between number of household contacts and transmission, we used Uniform(0,1). The algorithm runs for 50000 iterations after a burn-in of 50000 iterations. Converge was visually assessed. One run takes about 3 hours on our typical desktop computer.
We fitted the model to all studies, but only 14 of them could converge and provide robust estimates of individual infectiousness. We assessed the model adequacy by comparing the observed and expected number of infections in households by household size. We simulated 10000 datasets with a structure identical to that of the observed data (in terms of number of household contacts), with parameters randomly drawn from the posterior distribution of model parameter. We compared the model with or without the random effect for variation of individual infectiousness by using Deviance Information Criterion (DIC)  (54). Smaller DIC indicated a better model fit. DIC difference >5 was considered as substantial improvement  (55). Since the likelihood of observed data could not be directly evaluated for a given model  (65) , we used an importance sampling approach to estimate the likelihood for the observed data and evaluate the DIC  (66, 67).
For each household, we simulated 2000 datasets, with parameters randomly drawn from the posterior distribution. Then we compared the observed data and simulated data. The contribution to the likelihood of a household was equal to the proportion of simulated data with infection status that exactly matched the observed data, for all household members. To avoid the problem of 0-valued likelihood, we used the approach developed by Cauchemez et al  (66) , and assumed the sensitivity and specificity for diagnosing a case were both 99. 99%.
Funding: This project was supported by the  Health and Medical Research Fund, Food and Health Bureau, Government of the Hong Kong Special Administrative Region  (grant no. COVID190118 ;  BJC ) and the  Collaborative Research Fund  (Project No. C7123-20G ; BJC) of the  Research Grants Council  of the  Hong Kong SAR Government. BJC is supported by the  AIR@innoHK program of the Innovation and Technology Commission of the Hong Kong SAR Government. SC acknowledges financial support from the  Investissement d'Avenir program , the  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases program  (grant  ANR-10-LABX-62-IBEID ), the  EMERGEN  project ( ANRS0151 ), the  INCEPTION  project ( PIA/ANR-16-CONV-0005 ), the  European Union 's  Horizon 2020 research and innovation program  under grant  101003589  (RECOVER) and 874735 (VEO),  AXA and Groupama.
Since the beginning of the COVID-19 pandemic, counting infected people has underestimated asymptomatic cases. This literature scoping review assessed the seroprevalence progression in general populations worldwide over the first year of the pandemic. Seroprevalence studies were searched in PubMed, Web of Science and medRxiv databases up to early April 2021. Inclusion criteria were a general population of all ages or blood donors as a proxy. All articles were screened for the title and abstract by two readers, and data were extracted from selected articles. Discrepancies were resolved with a third reader. From 139 articles (including 6 reviews), the seroprevalence estimated in 41 countries ranged from 0 to 69%, with a heterogenous increase over time and continents, unevenly distributed among countries (differences up to 69%) and sometimes among regions within a country (up to 10%).
Eligibility We included original articles and reviews written in English language that reported data for the general population living in a defined geographical area. We considered a general population as a population-based sample, a described general population sample, or the inclusion of men and women of unselected categories of all ages in the population, preferably (but not exclusively) obtained by a random sampling technique from a large population (survey or database). We also included blood donors as a proxy for the general population. We excluded studies of health care workers, people attending a clinic or a hospital, a professional branch (industry, factory, farmers, university) and a particular population (students, nursing home) as well as modelling studies.
Search strategy The strategy consisted of searching PubMed and medRxiv with "(Covid OR SARS-CoV-2) AND seroprevalence" and Web of Science with "covid AND seroprevalence". Articles were selected by reading titles and abstracts. All article titles were first read by two readers (CM, AB), then by a third one (FG or TL), which allowed for discussion in case of discrepancies after abstract reading to obtain consensus. Full-article reading allowed for the final selection of relevant studies and data extraction.
Acknowledgements This work has been conducted as part of a doctoral research (see [4]) under the supervision of  Elisha Falbel  and  Maxime Wolff  to whom I am deeply grateful for their commitment and their friendship. The idea of using Lefschetz fibrations was suggested by  Misha Kapovich. I also thank  Pascal Dingoyan ,  Laurent Koelblen  and  Frédéric Le Roux  for helpful discussions.
Acknowledgements D. M. and K. M. thank  Robert Seiringer  for helpful discussions. Funding Financial support from the  Agence Nationale de la Recherche (ANR)  through the projects  ANR-17-CE40-0016 ,  ANR-17-CE40-0007-01 ,  ANR-17-EURE-0002  (J. L. ) and from the  European Union 's  Horizon 2020 research and innovation programme  under the  Maria Sk lodowska-Curie  grant agreement No. 665386  (K. M. ) is gratefully acknowledged.