dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: The last two authors are members of  Gruppo Nazionale per l'Analisi Matematica, la Probabilità e le loro Applicazioni (GNAMPA) of Istituto Nazionale di Alta Matematica (INdAM). The paper is partially supported by  PRIN 2017-Progetti di Ricerca di rilevante Interesse Nazionale , Nonlinear Differential Problems via Variational, Topological and Set-valued Methods. The authors thank the referees for careful reading and useful comments that helped to improve the paper.
1 , adapted from  [11] ). We developed an age-stratified disease transmission model to illustrate how possible scenarios in the short-and mid-term can define the industry decision landscape and review current knowledge of possible drivers of uncertainty in the long term. Further description of the model, parameters and assumptions can be found in the Supplementary material. The model code is available online (https://gitlab. com/SPMEGModels/covid19-model, R code). Briefly, this model, which expands on the standard Susceptible-E xposed-Infectious-Recovered (SEIR) model, allows us to take into account age specific contact-matrix, risk of hospitalizations, and risk of deaths to inform risk management strategies through scenario planning. We developed the model as a tool for uncertainty analysis that can be adapted to any settings as needed.
How stringent these measures will be is not yet known. We used data from France to address mid-term uncertainties. It is possible that, in the mid-term, as countries try out new socially and economically acceptable interventions, we will observe varying levels of control measures applied, varying from strong measures (that we modeled as aiming to reduce transmission within five days of implementation followed by a slow relaxing phase over a three month period, starting on thresholds below what was observed for first wave), to weak measures (modeled with a high threshold to start the implementation of control measures and a relaxing period over only a month). In our illustration, our base case scenario would fall in between, with a hypothesized reduction of 50% in population mobility over a 10-day period and considering a two-month relaxing phase starting on medium threshold.
Epidemiological data Epidemiological data were abstracted from daily reports from the Ministry of Health (MoH) of the DRC [8] detailing confirmed and suspected cases at the resolution of the health zones (Additional file 1). We reconstructed daily case data for the time period August 3, 2018, to July 21, 2019, from daily reports of the cumulative number of cases per health zone. We did so by subtracting case counts today from the previous day and did so going back to the start of the outbreak. When the difference was negative, we assumed there were no new cases on that day. Data can be directly downloaded from  [9].
Conflict variables We combined two databases containing information about conflict events in the DRC. We assessed pre-outbreak levels of conflict and violence by health zone using the Uppsala Conflict Data Program Georeferenced Events Dataset (UCDP GED)  [10]. The UCDP GED is an event dataset that disaggregates three types of organized violence (state-based conflict, non-state conflict, and one-sided violence) both spatially and temporally. The bases for this dataset are publicly reported news (including local radio stations such as Radio Okapi for the DRC), reports from non-governmental organizations, case studies, and historical archives. Each event is defined as an instance of organized violence with at least one fatality and includes additional information such as the date, geographical location (latitude and longitude), and identifiers that allow the dataset to be linked to and merged with other datasets.
Conflicts are categorized into battles, explosions/remote violence, protests, riots, strategic developments, and violence against civilians. A detailed description of the data and methodology can be found here  [10, 11]. We extracted data from the UCDP GED dataset for 29 years, from January 1, 1989, to July 31, 2018. From this dataset, we constructed a pre-outbreak conflict variable by aggregating numbers of conflicts and deaths at the health zone level. We assessed levels of conflict and violence over the course of the outbreak and by health zone using the Armed Conflict Location Events Dataset (ACLED). The ACLED data were abstracted from local, regional, national and continental media that were reviewed on a daily basis; reports from NGOs or international organizations used to supplement media reporting; selected social media accounts, including Twitter and Telegram; and information and data provided through partnerships with local conflict observatories in hardto-access cases.
The data were then coded to include information about the type of event (battles, explosions/remote violence, violence against civilians, protests, riots, and strategic developments), geographic information, time of event, and source. A full description of the methodology can be found here  [12]. We then aggregated daily conflicts to the health zone level in the DRC using data only from August 1, 2018, to July 26, 2019. Further, we aggregated data for all locations in the DRC that were outside the affected provinces. We created two distinct conflict variables, one which was simply the number of conflicts by category, health zone, and day and a second one that was the number of deaths by health zone and day that are directly related to conflict events. For a sensitivity analysis, we also divided the data into the different conflict types and recorded whether the event was directly related to the Ebola outbreak if the terms "Ebola" or "ebola" were mentioned in the report.
Statistical analysis In each health zone, we identified time periods of substantial uninterrupted EVD transmission defined as no more than 14 days between two consecutive cases and at least 20 cases (below did not yield robust results) in the whole time period and the time period is at least two generation times (> 28 days). There can be multiple time periods of substantial uninterrupted transmission in a given health zone. For each time period of substantial uninterrupted transmission, we reconstructed bi-weekly temporal trends in the instantaneous reproduction number using the EpiEstim package  [14] , excluding the first 14 days of the time period (i. e. , about one generation) as suggested by Cori et al. [14]. The reconstruction was performed under the assumption that the EVD serial interval had a Gamma distribution with mean 15.
Further, it will be important to include predictive maps of conflict when predicting the spatial and temporal spread of EVD in the future  [25]. This study had a number of limitations. First, conflict data are abstracted using a standard protocol but data was in part from online, self-reported sources which have inherent biases. Events that trigger reports are those that are larger and more severe. Those biases are related for example to the level of internet penetration and wealth which has been shown to be biased towards urban areas  [11]  and variation may be higher for small events compared to large events. We therefore assume that our estimates are biased towards larger population areas (e. g. , cities). Second, while an increase in the number of conflicts was observed in all regions, this increase was higher in regions affected by EVD.
We cannot rule out the possibility that this was due to increased reporting in these regions. Third, epidemiological data on cases are abstracted from daily reports and may be biased towards larger case clusters. Small clusters may have not been reported which bias estimates of the reproduction number upwards. Other biases include the potential delay in reporting and variation across different spatial locations, especially as data is abstracted from cumulative case reports rather than daily case counts. Genomic sequencing is done at a large scale during the outbreak, and we hope that estimates of the transmission dynamics from genomic analysis can be compared to those presented here  [26, 27]. Whether the drop in cases before the presidential election is influenced by reporting will need to be further investigated.
that includes different variables related to public health, economic interventions, public campaigns and research incentives for a vaccine. The data collection is based on news articles and government press releases and briefings. Their great and concomitant work leads to the creation of a stringency index from 0 to 100 for each country. Cheng et al. 6  created a government policy activity index ranging from 0 to 100 (https://www. coronanet-project. org/). The authors use an artificial intelligence company to scrap the press related to COVID-19 and to extract some information on the dates and scales of implementation of a wide range of public health measures, e. g. school or restaurant closures, but also mobilizations of volunteers, nurses and doctors. Research assistants then confront the results from the scrapping to web sources and manually coded the items in the CoronaNet dataset.
Zheng et al. 7  created the HIT-COVID tracker of 21 non-pharmaceutical interventions in 142 countries, structured in two levels, national or regional. They based their data collection on official sources and secondary sources such as media reports. They use two levels of interventions, partial or strong. Finally, Desvars-Larrive et al. 8  coded a structured dataset of non-pharmaceutical interventions divided in 8 themes and 63 categories in 56 countries, the Complexity Science Hub COVID-19 Control Strategies List (CCCSL). They based their data collection on public health institutions, official government sources, peer-reviewed and non-peer reviewed scientific papers, press releases, newspapers articles and social media. The supplementary information 1 gives detailed information on the differences between the various indicators and provides some comparisons on key measures.
The dataset can also be related to datasets tracking more specifically government interventions in the economy  5, 9. An example is Elgin et al. 9  who created a static index of economic policies to respond to COVID-19, by collecting information on the range of governments' fiscal and monetary interventions in the end of March 2020. The Response2covid19 dataset complements the efforts made by other researchers in different manners. First, the public health and economic measures considered are not exactly the same. Our dataset considers for example elections and enhanced surveillance as key variables, which are to the best of our knowledge not considered by the abovementioned datasets. Elections are an important moment in democracies, and postponing elections might be interpreted as the will of the governing power to influence the results of elections.
As countries are reopening the economy, mobile app or bracelet surveillance of contaminated individuals is increasingly required to avoid new lockdowns. These measures raise issues in terms of human rights. Second, our dataset covers economic measures to a larger extent than Hale et al. 5  We identify the implementation of six different fiscal policies and the change in the main policy rate. Our typology of economic policies can be used to specifically track the impact of cash transfers and wage support on consumption, or the relationship between credit schemes and companies' investments. Third, Response2covid19 covers 201 countries on public health measures and 198 on economic measures. It covers a wide range of countries. Only CoronaNet tracks interventions in so many countries. The resulting dataset provides some value added regarding its sources.
Methods Public health measures taken by governments. The coding of public health measures is based on cross-country information reported by the Assessment Capacities Project (ACAPS; https://www. acaps. org/cov-id19-government-measures-dataset), the International Institute for Democracy and Electoral Assistance (IDEA; https://www. idea. int/publications/catalogue/elections-and-covid-19) for elections, the International Monetary Fund (IMF; https://www. imf. org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19) and the United Nations Educational Scientific and Cultural Organization (UNESCO; https://en. unesco. org/covid19/educationresponse) for schools closures. The main source of information is the ACAPS. Their dataset is particularly useful in providing information on both the scale and the dates of the measures implemented.
The IMF tracker lists all the public health measures taken at the country-level and is particularly helpful to cross-check the information provided by ACAPS. IDEA lists elections postponed or maintained all around the world since the spread of COVID-19. UNESCO provides a worldwide dataset of school closures all around the world. Thirteen public health measures were considered -bans on mass gatherings, bans on sporting and recreational events, restaurant and bar closures, domestic lockdowns, international travel restrictions, domestic travel restrictions, curfew, declarations of states of emergency, public testing, enhanced surveillance, obligations to wear masks in the public space, school closures, and the postponement of elections. Each measure is coded 1 or 0, depending on whether it was implemented or not, and as a missing variable if the country is not covered.
Measures are coded on a daily basis. Before the implementation, the measure is coded 0; from the day of the implementation and until it is lifted, the measure is coded 1. The first eleven measures were manually coded from the ACAPS dataset. The format of their dataset does not allow us to directly merge their dataset with another one, as they textually report the measures implemented or discussed. Their dataset requires some reading to qualify whether, and at which scale, a given measure is implemented. School closures were directly taken from the UNESCO dataset which indicates on which scale schools are closed (local level, national-level or no school closures). The dataset was simply merged with our baseline dataset. Finally, the IDEA simply reports the postponed or maintained elections with their dates.
The coding of the dataset is based on two binary flags indicating the implementation of the measures, and whether measures are partial or strict. The two binary variables can be crossed to get an ordinal scale of the implementation of a given measure (e. g. 0 = no measure, 0. 5 = partial measure and 1 = strict measure). Such a scale is useful to reduce the potentiality of measurement error and increases the reliability of the coding. The thirteen public health measures can be used to create an index of the rigidity of governments' public health responses to COVID-19. While the scale of the measures is limited to three possibilities, the number of measures allows for a certain granularity of the indexes. Figures  1  and 2  are static maps of the rigidity index on April 15, 2020 and September 15, 2020, all around the world.
The index of economic measures is the mean of the seven variables coded and ranges between 0 and 1. Figures  3  and 4  are static maps of the index of economic measures on April 15, 2020 and September 15, 2020 all around the world. The index of rigidity of public health interventions and the index of economic measures can be plotted for each country or for the world. Figure  5  shows the evolution of the two indexes at the global level from January to October 2020. Custom codes used in the work. For all variables, except the variable school closures which is in a suitable dataset format by the UNESCO, the coding of the data is done directly in a Stata do file via lines of coding. Each line of coding identifies the measure to be coded, the scale of the measure, the targeted country and the period for which the measure is implemented.
The format of the coding is standard and easy to read for non-Stata users. For example, the national domestic lockdown in France started on March 17 and was lifted on May 11. The line coded is replace domestic = "Strict" if iso = "FRA" & tin(17mar2020, 10may2020). The spreadsheet data is created from the coding script, available on the online repository ("script. do"). There are many advantages to use such a methodology, instead of writing directly in the spreadsheet. 2. It reduces missing data as it forces the coders to fulfill the dates of implementation of the policies. It also gives more valuable knowledge of the dataset as we observe the changes in the intensity of the measures across time and the timing of the events; 3. When the coding script is run, it indicates for each line the number of changes written in the dataset.
Data Records We have established a Github repository (https://github. com/simonporcher/COVID-19-Governments-Responses) and an Open ICPSR repository  10  where new data is uploaded every month. The updated versions of the database can be downloaded from GitHub or OpenICPSR in Excel and Stata formats and can be imported into a variety of software programs. The file "Sources. xls" in the repository reports, for each country, lists the sources used for the economic and public health measures. Data visualizations are available at https://response2covid19. org/. As the situation regarding the COVID-19 outbreak is continuously evolving, the repositories list the former versions of the datasets with the Stata do files (script. do) tracking the updated changes for each version. Each row in the database represents the situation in a country at a given date between January 1, 2020 and October 1, 2020.
A description of the fields in the database is shown below and is available through a data dictionary on the repositories. • country, name of the country or the territory; • geoid, two-letters country code; • iso, three-letters country code; • d, date of the observation;  • cases, number of cases reported on the given day by the European Centre for Disease Prevention and Control; • deaths, number of deaths reported on the given day by the European Centre for Disease Prevention and Control; • school, binary variable equal to1 if schools were closed and 0 otherwise; • school_local, binary flag to distinguish localized school closures from other cases. 1 denotes that school closures were implemented at the local level and 0 denotes that school closures were not implemented at the local level (either at the national level or no school closures).
The data on the scale of school closures is imported from the UNESCO. The interaction of school and school_local allows researchers to create three levels of measures, no school closures (school = 0 and school_local = 0), localized school closures (school = 1 and school_local = 1) or national school closures (school = 1 and school_local = 0); • domestic, binary variable equal to 1 if there was a domestic lockdown and 0 otherwise; • domestic_local, binary variable to distinguish localized domestic lockdowns from other cases. 1 denotes that domestic lockdowns were implemented at the local level and 0 means that domestic lockdowns were not implemented at the local-level (either at the national level or not implemented). The nature of the domestic lockdown is based on our reading of the measures reported by the ACAPS.
Technical Validation The database was checked on a rolling basis using two complementary methodologies. One was manual and the other was machine enabled. In the first version of the dataset, the principal investigator manually entered the information in an excel file and exchanged with a research assistant and manually checked the accuracy of the coding of the data for each country on a rolling basis. The dataset was sent via email to the research assistant who checked that the coding was accurate. The checks on the previous versions of the dataset were done on March 30, April 17, 2020, between May 25 and May 29, between June 29 and July 3 and between July 24 and 31, 2020. For economic measures, the research assistant went through the two sources (IGC if the country was coded in their dataset, IMF if the country was not coded in the IGC) and orally listed for each country the economic measures implemented.
The principal investigator controlled in the dataset, using the "summarize" function in Stata 14 (https://www. stata. com/), that the coded measures were the same as the ones reported by the sources. When the coding was not accurate, we checked that the policy was not implemented after the last verification in the ACAPS dataset, and updated the dataset with a measure change or modified the mistake. The same manual methodology was used for public health measures: the research assistant filtered the ACAPS dataset on the different categories of interventions (lockdowns, movement restrictions, public health measures, and social distancing) and orally listed the measures taken at the country-level with their dates. As the ACAPS coding is sometimes anomalous, the research assistant had to go through all the lines for each country when needed.
We checked that the coding was accurate using the "tabulate" function in Stata for all coded measures. The tabulate function allows us to summarize information for a given country at a given date. As the ACAPS dataset records the dates of policy measures, modifications were directly made -if necessary -for measures implemented between the two rounds of data verification. The verifications on the first versions of the dataset are listed in the Stata do file "verif. do" in the former version of the dataset in the GitHub repository. Since the end of June, in order to be swifter in the verification of the different changes, we created a Stata do file which directly codes the spreadsheet from lines of coding (script. do). The script gathers both the former verifications and the first version of the coding which was directly done in the spreadsheet.
It allows us to rapidly update dates and scales when measures are extended, relaxed or lifted. The dataset is now updated every month and the checks are divided in two rounds of manual verifications. The principal investigator coded the potential policy updates in the latest version of the ACAPS dataset between October 15 and October 25, 2020. A research assistant double checked manually, re-reading all the lines reported in ACAPS country per country, that the coded measures were accurate between October 25 and November 6, 2020. A research assistant updated the economic measures reported by the IMF between October 25 and October 30, 2020. The principal investigator manually checked that the coding was accurate, based on his reading of the IMF measures, and did the latest update in the first week of November.
Usage Notes The dataset codes thirteen public health measures and seven economic policies taken by governments to respond to COVID-19. The coding of the dataset is based on two binary variables for each measure to flag whether it is implemented and the scale of the implementation. We are conscious that it limits the nuances of the policies taken and that it removes part of the information reported in the sources. The raison d' être of the dataset is not to cross-check that the different sources are identically tagged or to control for the quality of the sources. Such an effort has been undertaken for public health measures by the World Health Organization (WHO; https://www. who. int/emergencies/diseases/novel-coronavirus-2019/phsm). The WHO PHSM resource harmonizes the coding of public health measures listed by a variety of core sources, including ACAPS, and can be leveraged to control the information reported by the core sources.