dataesr/scientific-paragraphs-categorization huggingface dataset:
These data show a pattern of near-ubiquitous presence in large swathes of West Africa, with more patchy presence in Central and East Africa, which in turn determine the gradient in transmission intensity seen in the model. Due to the very widespread YF presence in West Africa, the model lacks resolution for estimating the exact level of transmission intensity in this area. Under-reporting of cases and transmission intensity are estimated by fitting serological surveys data from Central and Eastern Africa. Unfortunately, high quality surveys from West Africa are lacking. Of note, our validation assessment revealed that the model produces reasonable predictions across settings exhibiting varying levels of seroprevalence (S2 Appendix). This may be seen as a case for the validity of our model across a range of transmission intensity levels.
Our model has several limitations but also some strengths compared with past work. We captured spatial heterogeneity in the disease using a statistical rather than a mechanistic approach. Our model does not explicitly represent the spatial distribution of vectors nor disease reservoirs unlike Shearer et al's model  [10]. However, the set of spatial predictors we used has been demonstrated to have good predictive power for both presence and seasonality of YF  [29]. Moreover, the environmental covariate we used as spatial predictor for case reports are frequently used to predict the spatial distribution of vectors or reservoirs  [30, 31]. To our knowledge, our model is the only one calibrated against serological data that captures the whole spectrum of disease severity. Other YF burden estimates stem from models calibrated on reported cases  [10, 32] , that may lead to underestimation due to under-reporting and misdiagnosis.
Background. The novel coronavirus (2019-nCoV) epidemic has spread to 23 countries from China. Local cycles of transmission already occurred in 7 countries following case importation. No African country has reported cases yet. The management and control of 2019-nCoV introductions heavily relies on country's health capacity. Here we evaluate the preparedness and vulnerability of African countries against their risk of importation of 2019-nCoV. Methods. We used data on air travel volumes departing from airports in the infected provinces in China and directed to Africa to estimate the risk of introduction per country. We determined the country's capacity to detect and respond to cases with two indicators: preparedness, using the WHO International Health Regulation Monitoring and Evaluation Framework; and vulnerability, with the Infectious Disease Vulnerability Index.
Methods The risk of importation of 2019-nCoV cases to Africa from China was estimated based on origin-destination air-travel flows from January 2019  [8, 11, 12] , number of cases in Chinese provinces, and the population in each of the Chinese provinces that report transmission. Case data were compiled as part of an international effort to build a database at individual level, and included all confirmed cases recorded until the 27th January 2020  [13]. Human population data per province  [14]  was used to estimate incidence in China. Province-level incidence data were linked to the three airports with the largest volumes in each province [12] (Fig. 1 ). The province of Hubei was not included among the possible locations that can export the virus, given the travel ban put in place by Chinese authorities on January 23rd and 24th  [5].
The importation risk per country in Africa measured the probability of importing a case from the infected provinces in China, accounting for the origin-destination travel flows originated from such provinces and for their different epidemic levels (see appendix for details). For each African country, the most likely origins of the potential case importation were identified. This was done by computing a country's exposure to each Chinese province, measuring the probability for a city in China to be the origin of a travelling case to the country. Similarity between exposure profiles of different countries was quantified with entropy-based metrics  [15] , and used to group countries with similar importation patterns via agglomerative clustering (see appendix for details). The 2018 SPAR database  [16]  contained a total of 20 indicator scores on a scale of 0 to 100, organized and grouped according to the following capacities (number between brackets is the number of indicators per capacity, see  [10] ): Legislation (2), IHR Coordination (2), Zoonoses (1), Food safety (1), Laboratory (3), Surveillance (2), Human resource (1), National health emergency framework (3), Health service provision (3), Communication (1), Points of Entry (2), Chemical events (1) and Radiation emergency  (1).
Acknowledgments This study was partially supported by the  ANR  project  DATAREDUX  ( ANR-19-CE46-0008-03 ) to VC; the  EU  grant  MOOD  ( H2020-874850 ) to MG, CP, MK, VC;  Branco Weiss Fellowship  to MK. We thank  Rajesh Sreedharan  for kind inputs on the use of IHR data, and REACTing (https://reacting. inserm. fr/)for useful discussions.
* This work was done during the PhD thesis of the author under the supervision of  Jean-Stéphane Dhersin  and  Tran Viet Chi. The author was partially supported by the Chaire MMB ( Modélisation Mathématique et Biodiversité of Veolia-Ecole Polytechnique-Museum National d'Histoire Naturelle-Fondation X ) and by the  ANR Econet  ( ANR-18-CE02-0010 ).
Integration of the intra-host dynamics into outbreak simulations enhances realism to assess factors influencing outbreak initiation probability and scale We implemented a stochastic agent-based model (ABM) to assess the epidemiological impact of within-host ZIKV dynamicsin dose-dependent manner using the R package nosoi  [30]. Starting with one infected human in a population of susceptible hosts (humans and mosquitoes), the model simulates ZIKV transmissions by considering the dynamic nature of withinhuman virus loads and the associated probability of mosquito infection and virus transmission timeliness (i. e. , Extrinsic Incubation Period, EIP). The model was run 100 independent times over 365 days over a range of mean individual mosquito biting rates: 1, 5, 10 and 60 independent mosquitoes biting per person per day.
Zika virus viremia dynamic in human Data for ZIKV plasma loads in human over time. Venous serum samples from 35 symptomatic Zika virus patients were collected at different time points post-symptoms onset. All patients were infected during a period ranging from March to September 2016 in French Guiana in the course of the ZIKV outbreak in the American continent starting in 2015. Twentyone of these patients were included in a prospective study that described ZIKV genomic RNA load in sera samples collected sequentially from venous and skin capillary blood  [56]. Thirteen additional patients from whom sera were only collected from venous blood were added in this study (virus loads for these patients can be considered here as primary data)  [62]. ZIKV genomic RNA was extracted from 150 μL of each sample using the QIAamp Viral RNA kit (QIA-GEN, Hilden, Germany).
Target cell replenishment was ignored in the model because of the short-term nature of the infection. The model was fitted to observed data using a Bayesian approach implemented in Stan  [64]  through the Rstan package  [65]  (S3 and S4 Files). A fixed incubation period of 6 days was added to each time post-exposure in our data prior to fit the model in order to standardize the time scale between our data and the model (i. e. , our data are expressed in time post-symptom onset while the model start at the day of infection). This six-day incubation period corresponds to the rounded averaged median time of the ZIKV incubation period estimated from different independent studies (i. e. 5. 9 days (95% credible interval, 4. 4-7. 6 days) for Lessler J. et al. , 6. 2 (95% CI 5. 7--6. 6) for Krow-Lucal E.
Epidemiological modeling Model overview. To explore how variation in human viremia dynamics can impact the epidemic potential of ZIKV, we performed a series of stochastic agent-based epidemiological simulations using the R package nosoi  [82] , similar to the simulations performed in Fontaine et al. [41] , as a specific branch available on nosoi's GitHub page (https://github. com/slequime/ nosoi/tree/fontaine) (S2 File). This framework allows us to take into account within-host infection dynamics on transmission probability during mosquito-to-human and human-to-mosquito infectious contacts and place it in its full epidemiological context. We assumed that transmission could only happen between either an infected human and an uninfected mosquito or between an infected mosquito and an uninfected human.
Briefly, simulations started with one infected human and were run for 365 days or until the allowed number of infected individuals was reached (100,000 humans or 1,000,000 mosquitoes respectively). Each condition was run in 100 independent replicate simulations. S3 File. Database with patient identification number, the declared dates of symptoms, and blood sampling from the 35 patients infected with ZIKV included in our study. Quantification cycle (Cq) values for each sample and the corresponding ZIKV RNA concentration in copy/mL is provided when available. (TXT) S4 File. R code related to the intra-human ZIKV dynamic analysis. The file provides code lines used to analyze the experimental data and perform data visualization. This is a an R Markdown file created using RStudio, an open source Integrated Development Environment (IDE) for the R programming language.
It contains YAML metadata, markdown-formatted plain text, and chunks of R code that can be rendered using RStudio. (RMD) S5 File. Raw vector competence data. Each line represents an individual mosquito and the columns are the experimental conditions, and phenotypes. From left to right, the columns indicate the mosquito identification number, the mosquito population (M = Marseille, LR = La Reunion island), number of days post-exposure to the infectious blood meal (dpe), the infectious blood meal concentration (FFU/mL), the body infection status, the head infection status (1 = presence of virus), and the experiment. (TXT).
Acknowledgments We thank  Nathalie Wurtz ,  Muriel Militello , and  Jean-Marc Feuerstein  for their help and contributions concerning the development of the biological safety level 3 procedure settings concerning experimental mosquito exposure to ZIKV. We also thank  Joe ¨l Mosnier  and  Isabelle Fonta  for their help with the setting up of BSL3 experiments. We thank  Dr. Louis Lambrechts  for critically reading an earlier version of this manuscript and providing insightful comments. At last, we thank  FJCC  for its help with the Stan code on the Stan forum.
This study was funded by the  Direction Generale de l'Armement  (https://www. defense. gouv. fr/dga,grant no  PDH-2-NRBC-2-B-2113 , SB) and the  Direction Centrale du Service de Santedes Armees  (https://www. defense. gouv. fr/sante,grant agreement  2016RC10 , SB) and was supported by the  European Virus Archive goes Global  (EVAg, https://www. european-virus-archive. com )  project that has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  653316. The contents of this publication are the sole responsibility of the authors. SL was funded by a postdoctoral grant of the  Fonds Wetenschappelijk Onderzoek -Vlaanderen  (FWO, https://www. fwo. be). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Experimental: Simulation Design Simulation studies were processed to estimate and assess the proposed method type I and type II errors, i. e. , the false positive and false negative rates respectively. The type II error simulations were computed for the three equivalence methods (ABE, PBE, and BBE) in order to compare their ability to accurately prove equivalence. Simulations consisted of 10,000 replications for each combination of triplets (σ R , π BB , n Batches ). As detailed in Table  I , a wide range of different input parameters were used to generate the simulated dataset. These parameters, including the number of batches ranging from 3 to 10, RSD ranging from 5% to 40%, and the part of variability attributed to between-batch variability ranging from 10% to 90% were used to fit a large variety of real cases.
Second, the spray pattern (area) was measured using a Sprayview system (Proveris, USA). This parameter, describing the shape of the spray (  35 ) is known for its high variability  (36). The study has been performed at two distances (3 cm and 6 cm) as recommended in the guidance  (18)  with an actuation speed of 80 mm/s. Only data for Dv50 at a distance of 3 cm and a spray pattern area at a distance of 6 cm are presented here. The statistical comparison of the three methods (ABE, PBE, and BBE) was performed separately on batches of Flonase and Nasonex products. After checking for the Normality of the distribution, the raw data were not transformed for ABE and BBE, except for the PBE which requires a log transformation. The aim was to evaluate whether the three methods are able to adequately conclude in an equivalence context of each product.
1) converging as t → ∞ to the ansatz (1. 5) with parameters (λ, b) as in  (1. 11)  and c given by Lemma 3. In the next section, we recall coercivity results useful to apply the energy method in a neighborhood of the sum of decoupled solitons. In Section 3, we prove Theorem 1. 1. 4. Acknowledgements. JJ was partially supported by ANR-18-CE40-0028 project ESSED.
The probability of emergence p e after the introduction of a single infected individual can also be expressed as a function of these birth and death rates. With vector borne transmission this probability of emergence depends on which infected host is introduced (Figure E in S1 Text). Here we give the probability of emergence after the introduction of a single human exposed to the pathogen, E V , and where the index i refers to the four consecutive states of the pathogen life cycle (see sections 2 and 3 of S1 Text). https://doi. org/10. 1371/journal. pcbi. 1007954. g001 professor. He also thanks the Centre Henri Lebesgue ANR-11-LABX-0020-01 for creating an attractive mathematical environment. This project has received financial support from the CNRS through the 80|Prime program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Partially supported by  Serbian Min-Partially  supported by  ANR  project  Stint  under reference  ANR-13-BS02-0007  and 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). Partially supported by  EPSRC  grant  EP/N0196660/1 , and  Serbian Ministry of Education and Science projects 174033  and  III44006.
Acknowledgements This work of HYC was supported in part by  Ministry of Science and Technology (MOST)  through the grant  107 -2112-M-002-008 -. The work of TK was supported in part by  JSPS   Grant-in-Aid for Scientific Research  (No. JP17K18090 ), the  MEXT-Supported Program for the Strategic Research Foundation at Private Universities "Topological Science"  (No. S1511006 ),  JSPS   Grant-in-Aid for Scientific Research on Innovative Areas "Topological Materials Science"  (No. JP15H05855 ), " Discrete Geometric Analysis for Materials Design " (No. JP17H06462 ), and also by the  French "Investissements d'Avenir" program , project  ISITE-BFC  (No. ANR-15-IDEX-0003 ). The work of NL is supported by  Simons Center for Geometry and Physics and State of New York. HYC and NL are also grateful to the hospitality of  Keio University  during the completion of this work.
Acknowledgements. This research was supported by the  Swiss National Science Foundation. The second author is grateful to the first author, her doctoral advisor, for introducing her to the beautiful topics and techniques of bounded cohomology and simplicial volume. The authors thank  Inanç Baykur  for pointing out his interesting construction of surface bundles with non-zero signature. They also thank  Dieter Kotschick  for noticing an inaccuracy in a previous statement of Proposition 1. 2 and  Pierre de la Harpe  for.
Maximum Likelihood Method Here, we adopt a general statistical point view. We consider the RE-G models and investigate the estimation of the models parameters by a very efficient estimation method: the maximum likelihood method, for complete sample only. Let x 1 ,. , x n be n independent observations of a random variable having the pdf of the RE-G family, i. e. , given by (3). Then, the log-likelihood function is defined by (α, β, ξ) = n ∑ i=1 log[ f (x i ; α, β, ξ)] = n log(2) + (β -1) n ∑ i=1 log[G(x i ; ξ)] + n ∑ i=1 log[g(x i ; ξ)] + n ∑ i=1 log[β + (β -α)G(x i ; ξ) α ] -2 n ∑ i=1 log[1 + G(x i ; ξ) α ]. Hence, the maximum likelihood estimates (MLEs) of α, β and ξ, say α, β and ξ, respectively, are defined by (α, β, ξ) = argmax α,β,ξ (α, β, ξ). This maximization can be performed either directly by using any statistical software such as R (with the package AdequacyModel) or SAS (with the procedure PROC NLMIXED), or by solving the nonlinear likelihood equations obtained by differentiating (α, β, ξ) with respect to the model parameters.
In addition, all the general theoretical properties presented in the above section can be applied without efforts. Naturally, one can consider other special members of the RE-G family by choosing other baseline distributions, depending on the context of the study. In particular, the conjoint actions of the parameters α and β can be of interest to modulate the properties of the tails of distributions somehow rigid on this aspect. We may think to the ("only" right heavy-tailed) Pareto distribution, whose extensions has been the object of all the attentions these last years. We may refer to  [9, 17] , and the references therein. The next of the study focus on the numerical aspects of the REW model. The software R is used (see  [18] ), with the package AdequacyModel, allowing to analyze the adequacy of statistical models via several statistics for a given data set.
Data Fitting Application of the REW model is provided by the means of the following three practical data sets. Data set 1: Carbon dioxide. The first data set represents the annual mean growth rate of carbon dioxide at Mauna Loa (Hawaii). The measurement are given in parts per million year. The data are available at the earth system research laboratory website, from the following electronic link: https://www. esrl. noaa. gov/gmd/ccgg/trends/gr. html We have analyzed these data from the period of 1959 to 2014. Data set 2: Annual flood discharges. The second data set was originally reported by  [20]. It represents the maximum annual flood discharges of the North Saskachevan of the North Saskachevan River at Edmonton, over a period of 47 years. The measurement are given in 1000 cubic feet per second.
, x (n) are the ordered observations, y i = F(x (i) ), where F(x) denotes the corresponding cdf of the model defined with the corresponding MLEs for the parameters and F n (x) denotes the random empirical cdf. Details on these measures can be found in  [26]. We also refer to  [27]  concerning the KS test. For a given data set and model, the values of these measures are listed in the output of the command goodness. fit of the R package AdequacyModel. The rule is universal: a lower value of (-ˆ ), AIC, W, AD and KS, and a larger value of the p-value of the KS test, indicate a better fit. The obtained results for the considered models are given in Tables 6-8 for Carbon dioxide, Annual flood discharges and Rainfall, respectively. For the three data sets, Tables  6 7 8 indicate that the REW model has the smallest value for -ˆ , AIC, BIC, W, AS and K-S, and the largest value of the p-value for the KS test (which is near optimal in each case, in the sense that p-value ≈ 1), indicating that it provides the best fits among those of the concurrence.
Statistical analyses We calculated in-hospital COVID-19 incidence, mortality and lethality (or case-fatality) rates for each administrative department of metropolitan France. We mapped these indicators and the deviations from their mathematical expectation, and estimated spatial autocorrelation using Moran's I statistic for areal data (Queen criterion of contiguity)  (45). In order to analyse factors associated with epidemiologic indicators, and avoid the curse of dimensionality and multiple collinearities, we initially summarized a multidimensional factor using a non-supervised classification technique: hierarchical ascendant classification (HAC) on principal component analysis (PCA)  (46, 47). To classify department into similar age-pyramid profiles, we performed a PCA on the relative composition of the five-year age and sex population groups, and then included the coordinates of each variable in the first 25 principal components, in a HAC.
This strongly suggests that lockdown was an effective way to control the diffusion of the epidemic wave across the country, in spite of high social and economic costs. This effect of the lockdown strategy is in line with other publications based on modelling or observational studies. Among them, Salje  (23)  and Di Domenico  (53)  estimated that the lockdown resulted in a 77% and 81% reduction in transmission in France and Ile-de-France respectively; based on databases across 149 countries, Islam estimated that early implementation of lockdown was associated with a larger incidence rate reduction  (54). As expected, mortality was also strongly associated with the incidence of in-hospital COVID-19 cases, even after adjusting for the interaction between incidence and the relative lag to lockdown.
Besides, the number of intensive-care beds available in 2018 was not significantly associated with mortality and lethality. This suggests that hospitals could manage to scale up their intensive-care capacity or organize medical evacuations to less affected departments when necessary. This scale-up capacity may have been higher in departments with important hospitals, as indicated by the significant negative association between lethality rate and usual incidence of hospital stays. Unfortunately, no compiled database with the total number of beds activated during the epidemic wave has been made publicly available to check this hypothesis. Nevertheless, the absence of significant associations between basal population health and healthcare services, and the incidence and mortality reinforces the assumption of an important hospital adaptation to the epidemic wave.
After the number of cases began to subside, distancing measures were progressively relaxed from 8 May onward until a local resurgence of cases from 5 July brought their subsequent reintroduction (and maintenance at the time of writing). For this study we collected information on 1,038 cases identified in Hong Kong up to 28 April. The majority (51. 3%, 533/1,038) of SARS-CoV-2 infections confirmed during the study period (23  January-28 April) were associated with at least 1 of 137 clusters. Cases were linked to clusters (≥2 confirmed cases) based on the reported contact histories between cases (Methods). The median cluster size was 2 and the largest involved 106 cases (Extended Data Fig. 1 ). Of the cluster cases, 220 (41. 3%, 220/533) belonged to 22 (22/137,16. 0%) clusters initiated by another local case, compared to 89 (89/533, 16.