dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Statistical evidence for the effect of COVID-19 containment measures on the weekly number of cases for each infection studied was assessed using generalised linear models. Analyses evaluated evidence for both step and linear slope changes in the levels of each infection (measured as cases per week) following the implementation of COVID-19 containment measures. These analyses were undertaken using combined data across countries and territories for S pneumoniae, H influenzae, and N meningitidis, limited to those countries and territories with data for all three pathogens. For S pneumoniae, in which numbers of cases were higher than for the other two pathogens, analyses used country-level data, with both country-specific analyses showing the pattern of effects across countries and combined analyses to evaluate the effect of interventions varying across countries in their intensity and timing (ie, school closures).
For S pneumoniae, models were first individually fitted to the dataset for each country using a Poisson distribution, which included a scaling factor to correct for overdispersion, and results summarised by meta-analysis. Second, models were fitted as a single mixed-effects negative binomial model. Country-specific interruption timepoints were based on Google COVID-19 Community Mobility Reports data for workplace mobility, selecting the week containing the midpoint of the decline in work-associated mobility (ie, the week containing the day halfway from baseline [maximum] to the new lower [minimum] value). No Google COVID-19 Community Mobility Reports data were available for Iceland or China. Therefore, we assigned Iceland the modal week of other European countries and we based China's interruption point (set at week 5) on news reports of policy decisions (appendix p 4).
Widespread school closures (ie, OxCGRT level 2 or 3) were used to model the effects of specific policy changes alongside general changes in behaviours indexed by the Google COVID-19 Community Mobility Reports workplace mobility data. A further country-specific interruption was modelled using the date of school closures and allowing both a step and slope change from that date. Models were individually checked for assumption violation. Sensitivity testing was done to assess whether a lag produced a better model fit. Analyses were also run on the combined weekly count data for each of the four pathogens. These analyses included only countries and territories that had submitted data for all of S pneumoniae, H influenzae, and N meningitidis to allow comparability. A common interruption week (ie, week 11, when the COVID-19 pandemic was declared by WHO) was applied and step and slope parameters were estimated.
China initiated stringent containment measures and public information campaigns in week 4, then reduced the measures slightly, and by week 20 increased to high stringency measures. Although the stringency of the imposed containment measures varied by country, there was a pronounced reduction in notifications of invasive disease due to S pneumoniae among all participating countries and this reduction was sustained up to the end of May, 2020. Similar trends were observed for H influenzae and N meningitidis, but not for S agalactiae (appendix pp 7-9). To assess the extent to which societal choices also influenced the reductions in invasive disease, we compared the movement of people within each country using Google COVID-19 Community Mobility Reports data (appendix p 10). All 26 countries and territories, regardless of the stringency of the containment measures imposed by their governments, experienced a decrease in workplace visits and an increase in time spent in residential areas.
The largest changes in movement (compared with baseline) occurred at the time WHO declared the COVID-19 pandemic and shortly thereafter. Countries that imple mented the most stringent COVID-19 containment measures also had the largest changes in movements around workplaces and residences, but even countries with moderate containment measures (ie, Finland, Iceland, and Sweden) experienced large changes in population movements in the same direction as other countries. Changes in the movement of people within Hong Kong and South Korea were less variable and occurred earlier. Countries in the southern hemisphere (ie, Brazil, New Zealand, and South Africa) had different patterns of movement in the early weeks of 2020, in part because of summer holidays. By the end of May, 2020, the mobility data in many countries were shifting back to baseline levels and movements around workplaces were beginning to increase above baseline in several countries; however, Brazil, Canada, Ireland, South Africa, Spain, and the UK had not yet reverted back to baseline levels of movement for either workplaces or residences.
Discussion The IRIS Initiative rapidly established a peer-to-peer international network of laboratories to monitor changes in invasive bacterial diseases. We showed that in early 2020, the incidence of invasive disease due to S pneumoniae, H influenzae, and N meningitidis declined sharply in every country in the IRIS network, relative to rates in 2018 and 2019. These decreases corresponded with the timing of government responses to COVID-19, as measured by the OxCGRT, and changes in the movement of people, as measured by Google COVID-19 Community Mobility Reports. 17 By contrast, the inci dence of invasive disease due to S agalactiae (which is not transmitted by the respiratory route) in nine IRIS laboratories in 2020 did not differ significantly from the 2 previous years. These IRIS data showed a significant reduction in invasive diseases caused by three bacterial pathogens that are, like SARS-CoV-2, transmitted via the respiratory route.
The most plausible explanation for this observed reduction was the interruption of person-to-person bacterial respiratory transmission. Despite wide variations in the stringency of containment measures, the timing of these measures in all countries represented here coincided with a rapid reduction in the incidence of these invasive diseases. Mobility data suggest that people also voluntarily reduced their personal risks during the early stages of the pandemic. There might have also been a reduction in post-viral invasive bacterial diseases as a consequence of reductions in transmission of, and disease due to, respiratory viruses other than SARS-CoV-2. 18, 19 We, therefore, contend that the IRIS data were a proxy for the effectiveness of public health measures undertaken to reduce the transmission of respiratory pathogens.
The effect on bacterial transmission cannot be assumed to be identical to that on SARS-CoV-2, but it is likely to have followed a similar trajectory. As the ecological niche of S pneumoniae is typically the nasopharynx of children, we also assessed the extent to which school closures explained the significant reduction in invasive diseases caused by S pneumoniae. Adding parameters based on the week of enforced school closure did not significantly improve fit over the model with parameters for workplace mobility data as a general measure of changed behaviours. Therefore, although school closures would have contributed to the observed reductions in movement of people and reduced transmission of S pneumoniae, in addition to physical distancing and other measures, closing schools was not associated with a detectable additional reduction in invasive pneumococcal disease.
29 ur study highlights the crucial importance of active microbiological surveillance for public health and the fundamental role of reference laboratories. Surveillance is most effective when it is done consistently, provides high-quality data, and continues uninterrupted for many years so that emerging trends can be detected with confidence. Here, we have compared data for 2020 with data for 2018 and 2019 because the rates of disease were markedly altered in 2020, but most IRIS laboratories have been undertaking high-quality surveillance for many years, or even several decades. The strengths of the IRIS Initiative included the formation of a network of well established and experienced national laboratory and surveillance programmes, which rapidly provided high-quality invasive bacterial disease data.
The IRIS laboratories are leading comprehensive and sustained surveillance programmes that have been systematically collecting and testing micro biological data and samples for many years. The IRIS network includes 26 countries and territories across six continents and a large overall dataset. The consistency in the trends observed across all individual datasets provided confidence in our interpretations. The IRIS Initiative was established through existing international public health and academic networks in response to COVID-19, and the network of collaborators and the data produced will be an invaluable resource to observe and investigate future changes. Additional disease perturbations are to be expected as COVID-19-related containment measures are modified, which will allow greater insight into the effects of specific public health interventions.
The IRIS laboratories will have a central role in rapidly detecting further invasive disease perturbations in their respective countries and territories. Uniquely, we analysed invasive bacterial disease data, OxCGRT indices, and Google COVID-19 Community Mobility Reports data per country, which provided the means to assess the associations between country-specific containment measures, changes in the movements of human populations, and the corresponding reductions in the incidence of invasive diseases. Potential limitations in these analyses include the incompleteness of the submissions to individual laboratories, which would mitigate the observed reduction in invasive diseases. However, in most participating countries, invasive diseases due to these pathogens are legally notifiable and submissions are made routinely by well established systems, often to guide diagnosis or public health action, all of which continued throughout this period.
Importantly, numbers of submissions of invasive S agalactiae cases over the same period were as expected. Furthermore, although we could not completely exclude the possibility that people with these invasive diseases were less likely to seek health care because of the pandemic, given that invasive disease is a medical emergency and the data presented here were based on bacteria detected from clinical specimens taken in hospital (eg, blood and cerebrospinal fluid), the significant and sustained reductions in bacterial invasive disease reported in every country were unlikely to be due to reduced hospital admissions. Invasive meningococcal isolates were reported to be variably affected by COVID-19 lockdown measures in France, depending on their phenotypes and genotypes. 16 These data suggest that highly invasive and highly transmitted meningococci were affected by lockdown to a greater extent than other meningococci.
Acknowledgments We thank all the hospitals and clinical microbiology laboratories who submitted data and isolates to the reference laboratories as part of their surveillance programmes. We acknowledge the assistance of laboratory personnel who made important contributions to the microbiological data in this study:  Wendy Keijzers ,  Ilse Schuurman , and  Agaath Arends  in the  Netherlands Reference Laboratory for Bacterial Meningitis, Amsterdam , The Netherlands;  Marie-Cécile Ploy ,  Carole Grelaud , and all the microbiologists of the  French Regional Observatory of Pneumococci Network (Limoges, France) ;  Alice Enefer ,  Anna Lewis ,  Karina Micah ,  Samuel Rose ,  Chenchal Dhami , and  Roger Daniel  at the  Public Health England Respiratory and Vaccine Preventable Bacteria Unit, London, UK ;  Xilian Bai ,  Aiswarya Lekshmi ,  Jay Lucidarme ,  Andrew Walker ,  Lloyd Walsh , and  Laura Willerton  at the Public Health England Meningococcal Reference Unit in Manchester, UK;  Ana Paula Silva de Lemos  and  Samanta Cristine Grassi Almeida  at the  National Laboratory for Meningitis and Pneumococcal Infections ,  São Paulo , Brazil; and the technicians from the  Department of Bacteria, Parasites and Fungi, Statens Serum Institut, Copenhagen, Denmark.
The infrastructure for the  IRIS Initiative  was funded by a  Wellcome Trust   Investigator Award  to ABB (grant number  206394/Z/17/Z ) and a  Wellcome Trust   Biomedical Resource Grant  to MJCM, ABB, and  KAJ  (grant number  218205/Z/19/Z ). The German National Reference Center for Streptococci receives financial support from the  Robert Koch Institute  with funds from the  Federal Ministry of Health  (funding code  1369-235 ),  Pfizer , and  Merck. The  German National Reference Laboratory for Meningococci and Haemophilus influenzae  is supported by the  Robert Koch Institute  with funds from the  Federal Ministry of Health  (funding code  1369-237 ). The Irish Meningitis and Sepsis Reference Laboratory has received support from  Children's Health Ireland at Temple Street, Royal College of Surgeons, Health Protection Surveillance Centre , and the  SpID-Net  project, which has received funding from the  European Centre for Disease Prevention and Control and Horizon 2020.
Children's  Health Ireland at Temple Street has previously received research funds  from  Pfizer  to partially support this work. The Polish data collection was partially supported by the  Ministry of Health  within the framework of the  National Programme of Antibiotic Protection , by the  Ministry of Science and Higher Education  ( Mikrobank 2 Programme ), and by a research grant from  Pfizer. Funding for materials, equipment, and human resources associated with the laboratory in Catalonia (Spain) was provided by  Agencia de Salut Pública de Catalunya and Sant Joan de Deu Foundation. BH-N  received funding from the  Knut and Alice Wallenberg Foundation , the  Swedish Research Council , and  Region Stockholm. The Swiss National Reference Centre for Invasive Pneumococci is funded by the  Federal Office of Public Health of Switzerland.
By generating data rapidly, and disseminating them continuously as open data on the Open Science Framework (OSF), the international research network behind the COVIDiSTRESS project aims to yield rapid insights for the use of practitioners such as health communicators, government officials and policy makers at all levels, as well as members of the academic community. Currently, there are many psychologists and social scientists making a great effort to better understand the psychological effects and implications of the COVID-19 pandemic (e. g. [1] [2] [3] ) and provide suggestions for practitioners (e. g. [4] [5] [6] ). To our knowledge, there is currently no other project comparing human responses to COVID-19 across such a large number of countries. Further, the COVIDiSTRESS project specifically attempted to leverage the convenience of unpaid online populations to quickly acquire a large number of responses from 48 countries by using a snowball sample via online and traditional media platforms, rather than pre-specified samples from paid subject pools.
Methods A global survey study was conducted with the purpose of mapping the psychological and behavioural responses to the COVID-19 pandemic across countries, and how these have impacted social behaviour and trust in government efforts to slow the spread of the coronavirus. The COVIDiSTRESS survey (COVIDiSTRESS global survey network, 2020-available at https://osf. io/z39us/) consists of two parts. The first part collected general demographic data including proximate effects of the COVID-19 pandemic (e. g. isolation status, first-hand experience, attenuated risk), personality traits and self-reported variables, such as loneliness and perceived stress  [57] , daily behaviours including compliance with general and social preventive measures. The second part contains sets of more specific items related to distress, coping and social provisions.
Key independent and dependent variables The COVIDiSTRESS global survey contains a number of variables (full survey at https://osf. io/z39us/). The Royal Society Open Science registration files including the in principle accepted stage one analysis protocol and electronic supplementary material are available at the Open Science Framework https:// osf. io/vz73d/. The final version of the cleaned and preprocessed dataset is available at https://osf. io/ cjxua/. This urgent call RR focuses on 10 hypotheses (see § §1 and 4) and comparisons between countries. Table  1  gives an overview of all variables relevant for our hypotheses.
Categorical variable (factor). [SPS] available social provisions in critical/distressing situations Social Provisions scale short form SPS-10. 10 items. 6-point Likert scale (1 = strongly disagree, 6 = strongly agree). Validated language versions where available. Back-translated where necessary. Mean scores will be computed. Continuous variable. Steigen & Bergh  [47]  [population_size]). [GDP]). [edu_attainment]). [unemployment]). [gini_coefficient] country data -Population size. -GDP per capita. -Education attainment. -Unemployment rate. -Income inequality (GINI coefficients). Continuous variables. Country demographic variables from OECD). [61] ). We added country-level variables (population size, GDP per capita, education attainment, unemployment rate, and GINI coefficient) as control variables to predict the DVs.
Number of observations to be collected and rule of termination Given that this large-scale COVIDiSTRESS survey was spread by numerous researchers all over the world, we had limited control over the total number of responses per country. Thus, we defined a set of stopping rules that are practically feasible. Statistical power in multilevel design is rather complex and greatly depends on the nested structure of the data  [67]. Therefore, a general rule of thumb (i. e. 30-30 rule;  [68] ) is unlikely to be applicable to our data structure. Thus, we sought to refer to a more specific guideline to plan sample sizes according to the estimates acquired from pre-existing databases. Due to a lack of previous research that could be used for reasonable power analyses, we ran a planned multilevel analysis for H5 (specified in §9), as there was a dataset at hand, which we perceived as sufficiently suitable.
We used the 2018 European Social Survey (ESS) database to estimate the effect sizes of fixed and random effects as well as intraclass coefficients (ICCs). The ESS database consists of 43 215 respondents from 23 European countries (on average 1878 respondents per country). Although the variables available in the ESS database are not identical to those in the COVIDiSTRESS survey, given their similarity in the underlying constructs, we used the health condition variable (a higher value indicating poorer health condition) and satisfaction about the government in the ESS data as proxies for stress, compliance and trust in government efforts for our original variables of interest, respectively. The ICC from the observed ESS data was 0. 26, indicating that 26% of variability comes from between-country differences.
Inclusion criteria All participating researchers received the same task of starting to distribute the survey in their language on 30 March, 2020 (exception: Denmark started on 26 March, 2020, see §7). All possible channels were allowed, e. g. social media, panels, e-mails to friends or organizations, use of media contacts such as the newspaper or TV, websites of organizations such as universities or NGOs involved in health communication. Moreover, we also asked all participants to help spread the survey after they finished their own responses. Data was extracted each Monday and made available at the Open Science Framework. In order to be considered for the present analysis, however, a country needed at least 30 respondents at the time of acceptance of the RR stage 1 (i. e. the most recent data extracted at time of acceptance).
Country level Active dissemination of the survey and calls for participation were carried out via online and traditional media platforms in Afghanistan, Argentina, Australia, Austria, Bangladesh, Belgium, Bosnia, Brazil, Bulgaria, Canada, China, Colombia, Croatia, Republic of Cyprus, Czech Republic, Denmark, Finland, France, Germany, Greece, India, Indonesia, Israel, Italy, Japan, South Korea, Lithuania, Malaysia, Mexico, The Netherlands, Pakistan, The Philippines, Poland, Portugal, Russia, Serbia, Singapore, South Africa, Spain, Switzerland, Taiwan, Turkey, United Kingdom, United States of America and Vietnam. Countries that failed to generate 30 respondents by the time of extraction were not included in the final dataset. Sections 5 and 9 provide a detailed justification for the exclusion of countries that did not reach 30 respondents.
The measurement invariance test was required royalsocietypublishing. org/journal/rsos R. Soc. Open Sci. 8: 200589 since we wanted to compare scores measured by the aforementioned scales across different countries. Further details regarding the measurement invariance test procedures are described in the next section 1. We implemented the examination of the equivalence of measures at the suggestion of a reviewer. Given that the number of countries to be analysed directly influences the power of the planned multilevel modelling, we conducted the multilevel modelling analyses as follows: 1. If our planned analyses can be conducted without any issue (e. g. failed convergence) with the dataset that passed the measurement invariance test, then we would use composite scores and report the findings.
General data analysis approach -The main analysis used the lavaan, tidyverse and brms R packages. -Descriptive statistics were computed for all variables/scales (i. e. M, s. d. , α, rs). -Multilevel models (MLMs) were run using SAS PROC MIXED with restricted maximum-likelihood (REML) and Kenward-Roger denominator degrees of freedom. comparisons to examine the between-country difference. Then, we performed Bayesian MLM with Model 2 with brms to confirm whether the effect was significantly different from zero (table  2 ;  [86] ). Once we found that the effect of the country is non-zero, we performed a post hoc test with Scheffe's method to compare the dependent variables between countries. -Similarly, while testing H2 to H5, to identify the best model, we compared the following five models for each hypothesis.
≤ BF < 10 The effect is positively supported by evidence but not strongly. 1/3 < BF < 3 The current evidence is insufficient to make any decisive decision although the non-zero effect is likely to exist. 1/3 < BF < 3 The current evidence is insufficient to make any decisive decision although the null hypothesis (effect = 0) is likely to be the case. 1/10 < BF ≤ 1/3 The null hypothesis (effect = 0) is supported by evidence but not strongly. ≤ 1/10 The null hypothesis (effect = 0) is strongly supported by evidence. royalsocietypublishing. org/journal/rsos R. Soc. Open Sci. 8: 200589 squared correlation between the actual outcome and predicted outcome by the predictive models  [87]. Moreover, to increase the power of multilevel modelling, we performed the same analyses with two different datasets: one before and one after the respondent-level exclusion screening (see §7 for criteria).
We primarily presented the results of MLM with the whole dataset and those with the screened dataset in the appendix in the Open Science Framework repository for reference. We performed MLM with the whole dataset before the screening to maximize the statistical power by retaining as many samples as possible. -For Bayesian analyses, Bayes factor ≥ 10 (BF10 ≥ 10) was employed, which indicates the presence of strong evidence supporting H1 against H0 for Bayesian testing in general  [86, 88]. In addition to this main Bayes factor criterion, we used Bayes factor ≥ 3 (BF10 ≥ 3) to indicate the presence of positive but not strong evidence supporting our hypothesis auxiliary. -We interpreted the outcomes based on both p-values and Bayes factors. Further details regarding how to make decisions are presented in table 2.