dataesr/scientific-paragraphs-categorization huggingface dataset:
Benford's Law has been widely used in forensic accounting to identify specific records that depart markedly from expectations  (Nigrini, 2012). A good illustration of this is the management cost dataset stemming from ten annual reports published by the regional government of Valencia, Spain between 2010 and 2019 describing the number of days (and fractions thereof) spent controlling different invasive alien species. These working days were converted to monetary values by multiplying them by an average constant daily rate of €128 across all reports  (Angulo et al. , 2021). However, the reported estimates of management expenditure do not conform to expectations under Benford's Law and out of the 123 records of €512 (equivalent to four workdays) in the InvaCost 4. 1 database, 48 (38. 7 %) stem from a single annual report published in 2014  (Generalitat Valenciana, 2014).
g. , having one day in the office a week) rather than the actual effort required to eradicate species. These types of factors known to influence imprecision, rounding, and heaping of management expenditure are unlikely to be unique to this dataset but it is in part the quantity and level of detail in these reports that have helped provide some insights into how management costs are sometimes estimated. Another example is the frequency with which the value 1,000,000 was reported in the InvaCost 4. 1 database. Of the 212 reported cases of costs valued at exactly 1,000,000, the majority (146) were from Japan. These data all stem from government statistics published in official reports or websites by the Japanese Ministry of Environment and the Ministry of Agriculture, Forestry and Fisheries (JUDGIT!, 2019;  Watari et al.
, 2021). These costs are associated with estimates of both damage and management at country as well as site scales and have been classed as reliable. However, the preponderance of values amounting to exactly ¥1,000,000 is not consistent with expectations under Benford's Law. While no claims of inappropriate behaviour are being made regarding the official government sources used in the InvaCost 4. 1 database, a deeper analysis on how such information is derived appears warranted. The sources of management costs in InvaCost are highly  heterogenous and include actual expenditures, estimates arising from research extrapolations, values that are estimated or planned budgets from government reports, and costs that are clearly coarse estimates (e. g. , with cost values often being preceded by "up to" or "at least").
Understanding the degree to which different types of management costs depart from Benford's Law expectations and which are the main sources of imprecision is necessary. For example, planned expenditures for invasive species management can be rounded or reworked to fit budget restrictions. In those cases, deviations from Benford's Law can be expected. The foregoing illustrates the value of applying Benford's Law to environmental cost data. The analytical approach, while simple, provides an efficient means to interrogate large datasets to identify potentially problematic data sources or statistical challenges (such as use of round numbers and heaping). So how much confidence should be placed in the current assessments of the costs of biological invasions? It is important to restate that there is no suggestion that the researchers developing InvaCost have deliberately manipulated cost data, since the analyses are all based on previously published sources.
Conclusions An integrated approach testing first-, second-and leading doubledigit distributions against null expectations from Benford's Law provides an objective and repeatable approach to assessing irregularities in cost estimates of environmental degradation. Using estimates of global costs of biological invasions as an example, it is evident that there can be quite large departures from null expectations. Detecting these irregularities using Benford's Law should become a standard basis for thorough auditing and quality assurance of large environmental datasets. These results also point to the absence of agreed data standards for the estimation of costs of the management or damage arising from invasive alien species and this limits the traceability of estimates and their comparability. Benford's Law can be used to pinpoint questionable sources that can then be examined in further detail and if necessary, rejected.
Funding: This study is partially funded by: the  Municipality of Paris  through the programme Emergence(s) to C. P. and B. F. ; the  Agence Nationale de la Recherche  project  DATA-REDUX  ( ANR-19-CE46-0008-03 ) to V. C. ;  ANRS-Maladies Infectieuses Émergentes project EMERGEN  ( ANRS0151 ) to V. C. ;  EU   Horizon 2020  grants  MOOD  ( H2020-874850 ) to V. C. , C. P. , P. Y. B. , M. U. G. K. , P. L. and  RECOVER  ( H2020-101003589 ) to V. C. ; the  ERC  grant  EvoComBac 949208  to F. B. ;  ERC  grant  ReservoirDOCS 725422  to P. L. ;  Institut des Sciences du Calcul et de la Donnée (ISCD).
Data and materials availability: The findings of this study are based on metadata associated with a total of 1,735,675 sequences available on GISAID and submitted between 15 Aug 2020 and 1 Jun 2021 included and downloaded on 2 Jun 2021 via gisaid. org (GISAID: EPI_SET_230724tv). To view the contributors of each sequence associated with the metadata we used, visit https://doi. org/10. 55876/gis8. 230724tv. Proprietary airline data are commercially available from OAG and IATA databases. All other data used in the study are publicly available online at the links provided in the references. Source codes to reproduce the results of this study are publicly shared on GitHub at the link https://github. com/BenjaminFaucher/Alpha-dissemination/tree/main.
Introduction Consider the solution to the following stochastic equation with jumps in R d : X t (x) = x + t 0 b(X s (x))ds + t 0 E c(z, X s-(x)) N (ds, dz). (1. 1) Here E is a normed space, N (ds, dz) is a time homogeneous Poisson point measure on E with intensity measure μ(dz)ds and N (ds, dz) = N (ds, dz)μ(dz)ds denotes the compensated measure which is of infinite ✩ The research of the third author was supported by KAKENHI grants 20K03666, 20K03666 and 20K03666. This research is partly funded by the Bézout Labex, funded by ANR, reference ANR-10-LABX-58. L. C. is partially supported by the MIUR Excellence Department Project MatMod@TOV awarded to the Department of Mathematics, University of Rome Tor Vergata, and the Beyond Borders Project "Asymptotic Methods in Probability". activity near zero, i.
Acknowledgement. Part of this work has been carried out when the second author was developing the research period of the Doc-Course " Partial Differential Equations: Analysis, Numerics and Control " in the  Institute of Mathematics of the University of Seville (IMUS). The authors want to thank the  IMUS  for providing a great working framework. We would also like to thank  Prof. Assia Benabdallah  for her fruitful discussions that have helped to improve this work.
g. by developing generic reimbursement tariffs for categories of procedures and an additional list for hospital financing (as mentioned in the third proposal). Follow-up will be necessary to ensure the relevance of prescriptions (follow-up of RIHN activities in connection with a public or private hospitalisations). France has health databases that represent a unique source of exhaustive information for monitoring the use and quantifying the impact of innovative technologies directly in clinical practice. However, current coding/billing methods do not allow them to be fully used for diagnostic procedures, especially in hospitals. RIHN activities are only monitored in terms of volumes and are not linked to a given patient or a given hospitalisation, making it impossible to assess the indication for which the procedures were used or their impact on care.
g. : Plan France médecine génomique 2025). In addition to the direct benefits for individual patients, these analyses should generate data collectively to improve our knowledge of diseases, thus facilitating the discovery of new biomarkers and guiding future research. Although some registries or real-world databases have been set up (e. g. the ESME program launched by Unicancer), such initiatives are still very limited, often involving just a few centers. They should be rolled out more widely and endeavours undertaken to use the data generated as part of precision-medicine analyses (e. g. artificial intelligence tools).
Leveraging AI for mechanistic epidemiological models In this study, we used the EMULSION framework  (Picault et al. , 2019)  to develop human-readable mechanistic epidemiological models while reducing development time. EMULSION is an open-source software, distributed as a Python module (on the PyPI repository and from https://sourcesup. renater. fr/www/emulsion-public). This generic approach is based on the combination of two AI methods. First, the components of an epidemiological model (model structure, processes, epidemiological units, time scale, parameters, mathematical functions, initial conditions, observable variables…) are described as a structured text based on the YAML syntax  (Ben-Kiki et al. , 2009)  which looks like a bullet-point document (mainly based on lists and key-value pairs).
Funding This work was carried out with the financial support of  INRAE, Partnerships and Innovation Transfer Division  ( ATOM project ) and with a grant of the  Carnot institute France Futur Élevage  (project  SEPTIME ). This work has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 101000494  (DECIDE). This publication only reflects the authors' views and the  Research Executive Agency  is not responsible for any use that may be made of the information it contains. This work was also supported by the  French region Pays de la Loire ( PULSAR grant ). No funder was involved in the scientific or editorial choices made for this study.
12), although the duration of the interaction effect was somewhat shorter (89 days; 99% CI: 66-98 days). Unlike with the A(H1N1)-RSV virusvirus pair, however, the effect of RSV on influenza B appears to be unidentifiable: the confidence interval for d2, the relative rate of loss of cross-protection against influenza after RSV, in particular, suggests a high degree of uncertainty, with the possible duration of the interaction ranging from negligible to lasting several years. Furthermore, the wide confidence interval found for θλ2 indicates that even the strength of the interaction during RSV infection is uncertain. Notably, these results do not necessarily signal that RSV has no impact on susceptibility to influenza B; rather, if this effect exists, it cannot be identified using this particular dataset.
We separated the data into seasons, such that seasons began in week 46 (mid-November) and continued through week 45 of the following year. This cutoff was chosen as earlier cutoffs often occurred mid-RSV epidemic, while later cutoffs were prone to occur during influenza epidemics. Because epidemics of influenza A(H3N2) often displayed multiple peaks per year, we do not consider influenza A(H3N2) here. We were therefore left with six seasons of data (2013-14 through 2018-19) for two virus-virus pairs (influenza A(H1N1)-RSV, and influenza B-RSV). Although data were not available for 2013, and therefore for the first seven weeks of the 2013-14 season, we retained this season in our dataset to maximize the number of seasons available for analysis. For each virus-virus pair, fitting was only performed for those seasons where greater than 2000 influenza cases were reported, as the impact of a virus-virus interaction is expected to be minimal in the absence of substantial circulation of both viruses.
Observation Model Due to the prevalence of mild and asymptomatic infections, many cases of influenza and RSV are never reported, and therefore do not show up in empirical datasets. (𝑡 -𝜙)) ) 𝐻 𝑖 (𝑡) 𝐼𝐿𝐼(𝑡) ) (2) Here, Hi(t) represents the modeled incidence of virus i at time t, while ILI(t) represents the observed proportion of ILI per consultation in Hong Kong at time t. The parameter ρi is a composite reporting rate and scaling parameter for virus i, and allows for a direct comparison to be made between the modeled output and the data  46, 79  (see Supplementary Methods). We account for possible seasonality in reporting and background consultation rates using a cosine function, where α represents the amplitude of this seasonality, and φ represents the time at which ρi is maximal.
With the developed Corona Dashboard the RIVM and Rijksoverheid collected this data to quicker adjust the policy measures to the current situation. Another aspect, which showed adaptive governance, is the rapid expansion of intensive care capacity. Within the initial months of the first phase, the ICU capacity was doubled. This measure played a vital role in supporting the implementation of the "intelligent lockdown" during the first phase, allowing for partial societal functioning and the continued operation of the economy  [27]. Besides and maybe even more important, the Dutch population developed herd immunity 1 to the COVID-19 virus, and its mutations, in the past three years. The establishment of herd immunity reduces the necessity for stringent containment measures to control the spread of the virus.
Dutch epidemiological COVID-19 indicators, we utilize publicly available real-time data from the Dutch National Institute for Public Health & Environment (RIVM). Economic data, such as GDP and unemployment rates, are obtained from Statistics Netherlands (CBS) via open databases. To account for documentation errors, we use smoothed data variables for daily new cases and daily new deaths. Our observation period spans from the first documented COVID-19 case in the Netherlands on February 27, 2020, to the end of February 2023. For economic data, the timeframe covers the first quarter of 2020 until the third quarter of 2022. This three-year observation period enables the analysis of three pandemic phases (see Fig. 1), which are visually distinguished using vertical dotted lines: -Phase I: February 27, 2020 to January 6, 2021; pre-vaccine with NPIs -Phase II: January 6, 2021 to January 7, 2022; vaccine rollout (until the curve of full-vaccinated people flattened down) with significant NPIs.
Our study incorporated epidemiological parameters including daily cases, daily deaths, epidemic waves, emerging VOCs, and the impact of the natural decay of post-infectious SARS-CoV-2 immunity within the population. By investigating these factors, our study contributes to a comprehensive understanding of the SARS-CoV-2 pandemic in African countries, shedding light on the influence of immunity and viral variants on disease dynamics. The findings of this study can provide data for improving public health strategies and interventions tailored to the unique context of African countries.
e. f ( t )], we used four datasets acquired in Senegal between August 14, 2020 and August 17, 2021 (corresponding to epidemic waves 2, 3, and 4)  [9]. Data were normalized using technology and proteins. We divided the normalized kinetic data into discrete four-time groups: > 150 days; [50 , 150 days]; [23 , 50 days], and < 23 days, and performed a linear regression analysis, called the linear kinetic model, and a logistic regression analysis, called the non-linear kinetic model. A Each country is successively represented from top to bottom: the simulated data for daily cases, daily deaths, and daily hospitalizations per 100,000 individuals curves, the Google mobility curve, and the Oxford stringency index curve (OXCGRT Stringency Index) from the beginning of the epidemic to Oct. 2022. The simulated data for infected individuals ( I ) fit both the daily cases of SARS-CoV-2 and reported data from national seroprevalence surveys.
similar analysis was performed for immunoglobulin G antibodies against the N protein (Appendix Table  2  and Figure  7 ). The threshold below which recovered patients become susceptible was evaluated using a pre-pandemic database built in 2018 with sera from individuals who had never been infected with SARS-CoV-2 (Senegalese Dielmo 2018 cohort). We observed that the time required for immunity loss, T , falls within the range of 180-210 days, which is consistent with the findings of previous studies by Poll´an et al. [10]  and Dan et al. [11]. As the linear regression yielded an R2 > 0. 8 for all T 𝜏 (180 -210) days, we conducted a sensitivity analysis on r ( t, 𝜏) with respect to the range of 180 to 240 days. We incorporated daily case and death data from WHO databases for Senegal, Tunisia, and Madagascar in our model  3.
Results Our model was simulated using COVID-19 pandemic data collected from Senegal (for both linear and non-linear kinetic models) and from Tunisia and Madagascar (for linear kinetic models) between March 2020 and September 2022. We used antibody levels against S/RBD as a proxy for SARS-CoV-2 immunity acquired after natural infection or vaccination. The model exhibited a close match to the data on daily cases and daily deaths ( Figures  2 a-c ). Additionally, the simulated density of immune individuals, r ( t, 𝜏), in the pool of recovered individuals closely approximated the data from the first national seroprevalence surveys in Madagascar and Senegal ( Figures  3 g  and h ). The analysis of the epidemic in three countries enabled us to differentiate between the situations in Senegal and Tunisia, as well as the unique case of Madagascar.
The simulated data for the density of immune individuals ( r ( t, 𝜏)) fit both the daily cases of SARS-CoV-2 and the reported data from national seroprevalence surveys. Seroimmune waves are generated by epidemiological waves. The subsequent epidemiological wave ended in Tunisia and Senegal. In Senegal, W SE 2 and W SE 3 intersected slightly ( Figures  3 d-f ). In Madagascar, the seroimmune wave ended before the epidemiological wave ( Figure  3 e ). In Madagascar, a succession of independent seroimmune waves of a duration of 160 to180 days was observed. RBD, receptor binding domain. Senegal, Tunisia, and Madagascar experienced six epidemiological waves caused by the same event and VOC. However, the appearance of these waves creates different seroimmune responses. They intersect in Senegal, are very close to each other in Tunisia, and are very distinct in Madagascar.
The simulated data for the density of immune individuals ( r ( t, 𝜏)) fit both the daily cases of SARS-CoV-2 and the reported data from national seroprevalence surveys. Seroimmune waves are generated by epidemiological waves. The subsequent epidemiological wave ended in Tunisia and Senegal. In Senegal, W SE 2 and W SE 3 intersected slightly ( Figures3 d-f). In Madagascar, the seroimmune wave ended before the epidemiological wave ( Figure3 e). In Madagascar, a succession of independent seroimmune waves of a duration of 160 to180 days was observed. RBD, receptor binding domain.
COVIDici was updated daily using databases published by the national public health agency, Santé Publique France  [13]  until 2022 when it was halted after the emergence of the Omicron variant [14] led to decreased interest in epidemic forecasting by French authorities, partly driven by the belief that Omicron BA. 1 would represent our way out of the pandemic and the last wave  [15]. Here, we briefly summarise the structure of the underlying compartmental model, the statistical procedure for the parameter inference and describe communication via the web dashboard. We present an evaluation of COVIDici's forecasts for ICU occupancy up to the four-week horizon at the regional and national levels using standard metrics for continuous variables as well as a binarized version representing ICU overload to focus on model performance in anticipating wave peaks.
METHODS This section presents a summary of COVIDici and a retrospective evaluation. COVIDici is based on a pre-existing discrete-time deterministic agestructured transmission model tailored to capture the dynamics of the hospital indicators of the epidemic in France, which was modified to include vaccination. Derivations of the flows and notational details are provided in  [16]. Further technical details regarding the implementation of COVIDici as well as all the baseline models is accessible via the Supplementary Materials. The scripts and data used to perform the analysis and generate this manuscript are available on GitLab (https://gitlab. in2p3. fr/ete/covidici_public) and archived in Zenodo  [17].
g. the increased transmissibility of the Alpha  [22]  and then of the Delta variant  [23] , but also to public health interventions (e. g. lockdowns, curfews, limitations on businesses, etc. ), social factors (e. g. school holidays), improvement in COVID-19 patient care, and variation in patient profiles. To account for this, we allowed for some parameters to be timedependent by partitioning the time since the beginning of the epidemic and allowing each period to be associated with its own parameter set. Parameter estimations were optimised based on the daily COVID-19-related critical care admissions from the COVID-19 hospital activity database (SI-VIC)  [13]. In France, critically ill patients can be hospitalised either in intensive care units, continuous care units, or acute care units, the three forming the critical care capacities  [24].
Communication An automated cluster computing workflow refit the COVIDici model using daily updates of hospital, vaccination and testing data downloaded from the SI-VIC database, allowing a Shiny web application (see Figure  2  for screenshot, link in Introduction or  [17]  for source code) to communicate real-time results to the public. The original 2021 production version permitted users to visualise the combined past and future model fit by national, regional or departmental administrative unit for multiple epidemiological parameters, including ICU admissions, ICU occupancy, mortality (cumulative and daily), temporal reproductive number (R(t)), infections (cumulative, daily and current), vaccination coverage and incidence for positive tests. In 2022, a post-mortem version of the interface was deployed to allow for retrospective inspection of past forecasts with respect to a historical reference date.
The study covers the first 6 months of the coronavirus disease 2019  epidemics in 56 African countries (February 2020-August 2020). It links epidemiological parameters (incidence, case fatality) with demographic parameters (population density, urbanization, population concentration, fertility, mortality, and age structure), with economic parameters (gross domestic product [GDP] per capita, air transport), and with public health parameters (medical density). Epidemiological data are cases and deaths reported to the World Health Organization, and other variables come from databases of the United Nations agencies. Results show that COVID-19 spread fairly rapidly in Africa, although slower than in the rest of the world: In 3 months, all countries were affected, and in 6 months, approximately 1.
Introduction For most infectious diseases, the relationships between disease prevalence, incidence or mortality, and the level of economic and social development are negative. The more advanced a country is in economic development and in the demographic transition, the more effective it is in controlling infectious diseases, and the less frequent and the less fatal are infectious diseases  (Preston, 1976). However, this is not always the case for emerging diseases. The case of HIV/AIDS struck people's minds in the early years of the epidemic: The more advanced countries were often more affected, such as the United States on the American continent or South Africa on the African continent  (WHO database, 2020). In addition, at the micro-level (at the individual level), in the first phase of the epidemic, HIV/AIDS was more prevalent among wealthier, more educated, more urban people than among others, whether in Europe, America, or Africa  (Fortson, 2008; Mishra, Assche, Greener, et al.