We studied the possible role of the subways in the spread of SARS-CoV-2 in New York City during late February and March 2020. Data on cases and hospitalizations, along with phylogenetic analyses of viral isolates, demonstrate rapid community transmission throughout all five boroughs within days. The near collapse of subway ridership during the second week of March was followed within 1–2 weeks by the flattening of COVID-19 incidence curve. We observed persistently high entry into stations located along the subway line serving a principal hotspot of infection in Queens. We used smartphone tracking data to estimate the volume of subway visits originating from each zip code tabulation area (ZCTA). Across ZCTAs, the estimated volume of subway visits on March 16 was strongly predictive of subsequent COVID-19 incidence during April 1–8. In a spatial analysis, we distinguished between the conventional notion of geographic contiguity and a novel notion of contiguity along subway lines. We found that the March 16 subway-visit volume in subway-contiguous ZCTAs had an increasing effect on COVID-19 incidence during April 1–8 as we enlarged the radius of influence up to 5 connected subway stops. By contrast, the March 31 cumulative incidence of COVID-19 in geographically-contiguous ZCTAs had an increasing effect on subsequent COVID-19 incidence as we expanded the radius up to three connected ZCTAs. The combined evidence points to the initial citywide dissemination of SARS-CoV-2 via a subway-based network, followed by percolation of new infections within local hotspots. An accurate, thorough understanding of the rapid, widespread propagation of SARS-CoV-2 infection during the early phase of the massive outbreak in New York City is crucial to the successful control of future pandemic threats.To that end, we test three main hypotheses here. First, New York City's extensive public transport system, particularly its subways, played a critical role in the widespread dissemination of SARS-CoV-2 infection throughout the city during the end of February and the beginning of March 2020. Second, the ensuing marked decline in subway use was an important vehicle by which the public's growing perception of risk was translated into reduced community transmission of the virus. Third, those areas with an attenuated decline in subway use, we posit, subsequently became the loci for high-density clusters of viral infection in late March 2020.The Metropolitan Transportation Authority (MTA), a network of subways, buses and commuter rail cars serving the NYC area, is larger than all other metropolitan transport systems in the United States combined. While nearly 85% of U.S. workers drive to their jobs, according to the MTA, 80% of rush-hour commuters to the city's central business districts use transit . The MTAOur hypotheses are hardly novel. The role of transportation networks in the spread of SARS-CoV-2 has been supported by recent studies of the initial outbreak in Wuhan, China –6. One ssubway contiguity.What sets our study apart is its comprehensive, multidisciplinary approach. We rely on such diverse lines of evidence as phylogenetic analysis of early viral samples, public health data on confirmed COVID-19 cases, public transport data on turnstile entries, location-tracking data on the movements of smartphones, and census data on the prevalence of at-risk multi-generational households. Our spatial analysis of emerging case clusters distinguishes critically between the conventional notion of geographic contiguity and what we call boroughs-case-hosp-death, used to construct case-hosp-death, used in part to construct tests-by-zcta, used in part to construct The NYC health department's open data archive  was our Data on the total populations of zip code tabulation areas (ZCTAs) were derived from the Census Bureau's American Community Survey 5-year estimates for 2015–2019, accessed from the data server at the Missouri Census Data Center . Data onThe Metropolitan Transportation Authority (MTA) website for developers  was our Stata program geoinpoly  in New York City from the Census Bureau's website . We relieoinpoly , which ueoinpoly , 3A.Stata mapping software to verify that most CBGs were uniquely contained in a given ZCTA  for the 5-year period 2015–2019 . The datClade A2a GISAID IDs within in the spreadsheet Data File S2, posted in the Supplementary Materials of Gonzalez-Reiche et al. . This gave a total of 78 MSHS viral samples authored by Gonzalez-Reiche et al. within the A2a clade, including date, location and strain identifier. These 78 samples formed the database for the vertical bars in the figure.To construct e et al.  and (b) e et al. . We mergstrain in the merged file to identify the 17 virus samples specifically highlighted as sharing the ORF1b:A1844V mutation in the New York Cluster 1 in Figure 2C of Gonzalez-Reiche et al.  was shared by samples of infected persons residing in Manhattan, Queens, Brooklyn and Westchester County, collected during the space of only 5 days (March 14–18).Next, we used the variable e et al. . These 1The data on turnstile entries were similarly derived from the MTA's website for developers . Since sIn visit) had an origin and a destination. Each device's unique origin was the CBG where it regularly spent the night. Every CBG in which the device stopped for more than 1 min during a 24-h period was counted as the destination of a visit, but the duration of each visit was not recorded. The 1-min cutoff was chosen by SafeGraph; it was not under the researcher's control. For each calendar day and each CBG of origin, the database recorded the number of devices that visited each destination CBG. A destination CBG can be the same as the origin CBG.Our data on smartphone device movements come from the Social Distancing database maintained by SafeGraph . Every dsubway CBG visits, and the number of turnstile entries into that station. This comparison is illustrated for a particular subway station in We tested whether smartphone device movements whose destination CBG contained a subway station could serve as a proxy for subway turnstile entries. For each station, we compared two time series: the number of visits to the destination CBG containing that subway station, which we'll call n1, accumulated the total number of visits originating in that CBG with a destination at any one of the six stations during the months of January and February 2020 [The 74th Street–Broadway station on the 7 (Flushing) line shared the same CBG as the Jackson Heights–Roosevelt Ave. station on the intersecting 6th Avenue Local (M) line.]. The second count, which we denote n2, accumulated the total number of visits originating in the same CBG during the same interval with a destination at either the Queensboro Plaza or Court Square stops, two of the principal destinations within the Queens portion of the 7 (Flushing) line. We then ranked each origin CBG by the statistic nmin = min{n1, n2}, which captured trips to and from the Queens-Elmhurst yellow stations and the Queensboro Plaza–Court Square complex. In nmin ≥ 50, while the darker shaded CBGs correspond to nmin ≥ 100.We further investigated the origins of those smartphone devices whose destination CBGs contained one of the six key stations within the Queens-Elmhurst hot spot. For each CBG, we determined two visit counts. The first count, which we denote To estimate subway visits by ZCTA, we aggregated the number of device visits to all destination CBGs containing a subway station, and then further aggregated these CBG-specific counts of subway visits at the ZCTA level. Our reconstruction of the origins of subway visits from smartphone mobility data is to be distinguished from prior studies relying instead upon the SafeGraph Patterns Schema, a separate database which classifies visits by their destination points of interest , 29. TheWe relied upon the 5-year 2015–2019) public use microsample of the U.S. Census Bureau's American Community (ACS)  to estim–2019 pubM > 0 compact polygons in a two-dimensional plane, indexed by i = 1, …, M. No two ZCTAs share any interior points in common, but they can share boundary points. When ZCTAs i and j do share boundary points, we say that they are geographically contiguous, or g-contiguous. By contrast, when ZCTA j is the next stop after ZCTA i on some subway line in some direction, we say that ZCTAs i and j are contiguous in subway space, or s-contiguous. G-contiguity does not imply s-contiguity, nor does s-contiguity imply g-contiguity.Our concepts of geographic and subway contiguity, including an accompanying formal matrix algebra, are developed in detail in the 2)-contiguous with ZCTA 11415. Equivalently, the figure displays all ZCTAs within a geographic contiguity radius of 2. To illustrate compound s-contiguity, 2 + s3 + s4 + s5)-contiguous with ZCTA 11415, that is all ZCTAs that are either g-contiguous with that ZCTA or within a subway radius of five stops along the same or a connecting line. In general, compound g- and s-contiguity accommodate a variable radius.As further elaborated in detail in the y denote a M × 1 column vector of ZCTA-specific observations of incremental COVID-19 incidence during April 1–8, 2020  had a recorded origin and a destination census block group (CBG). p < 0.001.p = 0.006 or lower -contiguous if they are either g-contiguous or g2-contiguous. This situation is illustrated in 2)-contiguous with ZCTA 11415. Within this group, four ZCTAs are g-contiguous with ZCTA 11145, while the remaining 14 ZCTAs are situated effectively within a radius of 2 from the reference ZCTA 11415.As a further extension of the concept of compound contiguity, we say that two ZCTAs are (2 + s3 + s4 + s5)-contiguous with the reference ZCTA 11415 (again colored orange). ZCTA 11367 is exclusively g-contiguous with the reference ZCTA 1145. The remaining ZCTAs are accessible within five subway stops along the same or a connecting line. Thus, ZCTA 11101 in Queens is accessible via four stops on the E Line, while ZCTA 10065 in Manhattan is further accessible after a transfer at the Queens Plaza station to the R, N or W Lines.same ZCTA. By contrast, our spatial regression models  influenced subsequent COVID-19 incidence during April 1–8 within the We repeatedly estimated such between-ZCTA spatial effects as we varied the allowable radius of influence—from 1 to 3 in geographic space, and from 0 to 5 in subway space. As we enlarged the allowable radius of influence in geographic space, as shown in Our finding that subway volume as of March 16 exhibited increasingly contagious effects in subway space supports the conclusion that SARS-CoV-2 was being propagated via a subway-based network at least through March 16. Our finding that March 31 cumulative incidence exhibited increasingly contagious effects in geographic space supports the conclusion that percolation of new cases through local geographic spread had subsequently become the dominant mode of propagation by the end of March. Once local clusters developed, further percolation of new cases via transmission within multi-generational households  became dThe evidence presented here supports three distinct but not mutually exclusive hypotheses. First, the subway system played a critical role in the rapid, widespread community transmission of SARS-CoV-2 infection throughout New York City during late February and early March 2020. Second, the ensuing marked decline in subway travel was an important mechanism by which the public's growing perception of risk was translated into reduced community transmission of the virus. Third, those areas with an attenuated decline in subway use subsequently became hotspots of viral infection in late March and early April 2020.One alternative interpretation is that subway travel was no more than a proxy for other determinants of vulnerability to COVID-19. In higher-risk communities, so the argument goes, many residents had service jobs that could not be performed remotely. Such an interpretation, however, does not square with the spatial-effect findings in This last counterargument, however, does not square with the evidence on the known mechanisms of SARS-CoV-2 transmission. An infected person exhales moist air containing very small droplets loaded with the virus . A passeYet another counterargument draws upon conflicting studies of the transmission of other respiratory viruses in public transport. A study of the London Underground offered supporting evidence of the transmission of influenza-like illness , 56. Butpandemic . While apandemic . The relchanges in subway volume during the course of the COVID-19 outbreak. Less informative would be a study relating COVID-19 rates to static survey data on the proportion of individuals in each ZCTA regularly riding public transit prior to the epidemic. Our results also point to the importance of conducting tests of causation when baseline subway volume and COVID-19 incidence are high. A finding that coronavirus cases no longer relate to subway volume once subway use has plummeted to below 10% of baseline reveals little if anything about what happened back in March. The map of the Flushing Local line in The evidence presented here also highlights the methodological limitations of alternative approaches to studying the role of the subways in the propagation of SARS-CoV-2. The test conducted in If we are to successfully control future pandemic threats—and, for that matter, future outbreaks of COVID-19—we need to understand in exhaustive detail how SARS-CoV-2 first took hold and then established hot spots in major urban epicenters throughout the world. Considerable effort has been made to understand exactly what happened in Wuhan , 61. A sIf the subway system indeed played a critical role in the early propagation of SARS-CoV-2, as supported by the evidence assembled here, we need to understand that the conventional methods of personal contact tracing are less likely to be useful in halting future outbreaks. That means more sophisticated contact tracing through the pings of mobile devices and records of electronic transactions will be necessary , 71. To in situ transmission .The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: Open Science Framework (OSF), project entitled New York City COVID-19 Epidemic (The author confirms being the sole contributor of this work and has approved it for publication.This article represents the sole opinion of its author and does not necessarily represent the opinions of the Massachusetts Institute of Technology, Eisner Health, or any other organization or individual.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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To achieve global hepatitis elimination by 2030, it is critical to prevent the mother-to-child transmission (MTCT) of hepatitis B virus (HBV). Since 2009, the WHO has recommended administering hepatitis B vaccine to all neonates within 24 h of birth to prevent MTCT. However, many countries in sub-Saharan Africa only provide hepatitis B immunization at the age of 6, 10, and 14 weeks or 8, 12, and 16 weeks using a combined vaccine. To accelerate the introduction of the hepatitis B birth dose vaccine (HepB-BD) into sub-Saharan Africa, it is critical to establish to what extent the addition of HepB-BD can further reduce HBV transmission in areas where three-dose infant vaccination has been implemented. We therefore designed a study to evaluate the impact, acceptability, and cost-effectiveness of incorporating the HepB-BD into the routine immunization program in a real-life field condition in Burkina Faso, where the hepatitis B vaccination is currently scheduled at 8-12-16 weeks. Through a multidisciplinary approach combining epidemiology, anthropology, and health economics, the Neonatal Vaccination against Hepatitis B in Africa (NéoVac) study conducts a pragmatic stepped wedge cluster randomized controlled trial in rural areas of the Hauts-Bassins Region. The study was registered in ClinicalTrials.gov (identifier: NCT04029454). A health center is designated as a cluster, and the introduction of HepB-BD will be rolled out sequentially in 24 centers. Following an initial period in which no health center administers HepB-BD, one center will be randomly allocated to incorporate HepB-BD. Then, at a regular interval, another center will be randomized to cross from the control to the intervention period, until all 24 centers integrate HepB-BD. Pregnant women attending antenatal care will be systematically invited to participate. Infants born during the control period will follow the conventional immunization schedule (8-12-16 weeks), while those born in the interventional period will receive HepB-BD in addition to the routine vaccines (0-8-12-16 weeks). The primary outcome, the proportion of hepatitis B surface antigen (HBsAg) positivity in infants aged at 9 months, will be compared between children born before and after HepB-BD introduction. The study will generate data that may assist governments and stakeholders in sub-Saharan Africa to make evidence-based decisions about whether to add HepB-BD into the national immunization programs. Viral hepatitis is a serious global health concern. In 2015, the World Health Organization (WHO) estimated that 1.34 million people died from viral hepatitis ,7,8,9.To prevent MTCT, the WHO recommends that all neonates, regardless of maternal HBV infection, receive their first dose of the hepatitis B vaccine as soon as possible after birth, preferably within 24 h, as a monovalent formulation . This heMore importantly, the evidence base to support the WHO’s recommendation to start immunizing immediately after birth, rather than later at 6–8 weeks of life, is limited. First, most randomized controlled trials (RCTs) evaluating the efficacy of the vaccine series starting at birth have used a control group with a placebo or without any hepatitis B vaccine administration. A meta-analysis of RCTs indicated that among infants born to HBV-infected mothers, those who received the first dose at birth are 3.5 times less likely to be infected than those who did not receive any dose of the hepatitis B vaccine  . HoweverConsequently, we aim to conduct a stepped wedge cluster randomized controlled trial to assess the impact of incorporating the monovalent HepB-BD into the infant immunization program on the risk of HBV MTCT under real-life field conditions in Burkina Faso, where the hepatitis B vaccination is currently scheduled at 8-12-16 weeks of life. We also assessed the acceptability of the intervention and its cost-effectiveness by adopting a multidisciplinary approach combining epidemiology, anthropology, and health economics.Neonatal Vaccination against Hepatitis B in Africa study (NéoVac), funded by the Total Foundation, is a research program originally started in 2015 in Senegal, Burkina Faso, and Madagascar, where the hepatitis B vaccination series have been scheduled at 6-10-14 weeks  or 8-12-16 weeks (Burkina Faso), without HepB-BD. The aim of the program was to develop and evaluate a strategy to implement the timely administration of HepB-BD in these countries. The program was designed in two successive steps. Step 1, the formative phase, collects epidemiological, anthropological, and economic data in each country in order to develop a locally adapted, sustainable strategy for providing timely HepB-BD within 24 h of birth. This step is followed by Step 2 to evaluate the impact of the strategy to reduce the risk of HBV infection in infants. In Burkina Faso, Step 1 was conducted in Dafra District, Hauts-Bassins Region, in 2016–2017. The epidemiological study found approximately 90% of children born at health facilities, and 86% completed three doses of the pentavalent vaccine. The anthropological study observed that infant vaccination was widely accepted in the communities, and the economic study suggested that the costs of introducing HepB-BD would be relatively low compared to the introduction costs of other vaccines . These rThe study included three work packages  funded by the Total Foundation. Each has specific objectives described below. The NéoVac program has an ancillary study called “Performance of Diagnostic Test for Hepatitis B in Africa (PREDICT-B)” funded by Gilead Sciences and Abbott. This sub-study is described at the end of -To assess the effectiveness of introducing HepB-BD on the risk of MTCT, defined as positive HBsAg in infants aged 9 months;-To assess the impact of introducing HepB-BD on the risk of MTCT in subgroups of mother–child pairs defined by the maternal HBsAg;-To assess the impact of introducing HepB-BD on the risk of MTCT in subgroups of mother–child pairs defined by the maternal hepatitis B e antigen (HBeAg) and HBV DNA levels;-To explore the dose–response relationship between the number of hepatitis B vaccine doses administered (0–4) and the risk of HBsAg positivity in infants;-To examine the association between the timeliness of the first dose of the hepatitis B vaccine and the risk of HBsAg positivity in infants;-To compare the immunological response between those who received HepB-BD and those who did not by titration of the antibody to HBsAg (anti-HBs) in infants;-To describe the coverage and timeliness of HepB-BD, pentavalent vaccines, and other vaccines recommended by the national infant immunization program in Burkina Faso;-To estimate the prevalence of HBsAg in mothers of children aged 9 months;-To estimate the prevalence of HBeAg in HBsAg-positive mothers;-To estimate the prevalence of HBV DNA levels ≥200,000 IU/mL in HBsAg-positive mothers.Work package (1) Epidemiology:-To evaluate the acceptability of adding HepB-BD into the infant immunization program among healthcare workers and community members.Work package (2) Anthropology:-To evaluate the public health benefits, the cost, and the cost-effectiveness of introducing HepB-BD into the infant immunization program in Burkina Faso versus the current situation.Work package (3) Health economics:The current protocol was developed in accordance with the CONSORT 2010 extension to cluster randomized trials  and the This is a pragmatic trial to evaluate a whole service delivery intervention that is “complex”. The intervention includes not only the administration of monovalent vaccines to newborns in a timely manner, but also the vaccine supply, storage, training of healthcare workers, and communication with pregnant women/mothers. The cluster design was chosen as it is logistically more convenient to provide the intervention at the CSPS level, rather than at the individual level. This design also enables us to limit the risk of contamination  that is more likely to happen when healthcare workers in the same facility simultaneously provide two different strategies .As discussed in the introduction, the evidence supporting the addition of HepB-BD to the conventional vaccination series is limited. Nevertheless, there has been an established WHO recommendation to administer HepB-BD to all newborns. The stepped wedge design has the advantage of making all CSPSs benefit from the intervention by the end of the trial, which is politically and ethically more acceptable than the parallel cluster randomized trials, in which half of clusters do not receive the intervention for the duration of the study . MoreoveThe trial will be conducted in two districts in the Hauts-Bassins Region, called Dô and Dafra, where an estimated 548,000 and 348,000 people lived in 2017, respectively . In Dô, CSPSs are eligible if they are located in the rural/semi-rural zone of the Dô and Dafra districts, and they are accessible from our research center  all-year-round, including the rainy season. We finally selected a total of 24 CSPSs: 16 from Dô and 8 from Dafra, that met the criteria above.All pregnant women of any age living in the area covered by the study CSPSs, who attended the antenatal care or gave birth at one of the study CSPSs, and who gave a written informed consent will be included. Exclusion criteria include a miscarriage, abortion, stillbirth, neonatal defect incompatible with life and any maternal or child health condition incompatible with the research activities. They will be also excluded if they had participated in other clinical trials that are incompatible with this trial.Before the start of the trial, all healthcare workers from the 24 study CSPSs will be invited to the Centre Muraz for a two-day training course on the epidemiology and clinical management of hepatitis B, the administration of HepB-BD, and the study protocol including how to obtain informed consent, to fill case report forms (CRF), and to obtain blood samples. Similarly, all medical doctors from the district hospitals of Dô and Dafra will be invited for a one-day training course on the clinical management of patients with chronic HBV infection and the study protocol.During the control period, there will be no supply of the monovalent vaccines to the CSPS; therefore, babies born during this period in the catchment area of this CSPS will not receive HepB-BD.Once a CSPS crosses from the control to the intervention period, the monovalent vaccines will be delivered from the district center to the CSPS and healthcare workers will start immunizing newborns. The delivery of HepB-BD will be integrated into a monthly supply chain for the routine infant vaccines. Babies born in the CSPS during the intervention period will receive HepB-BD immediately after birth. Those born at home during the intervention period will receive HepB-BD at the first contact with the CSPS at any time up to the time when the next dose of hepatitis B vaccine is scheduled  according to the WHO recommendation .All infants, irrespective of the study period, will receive three doses of pentavalent vaccine at 8-12-16 weeks as scheduled in the national immunization program. Screening of pregnant women for HBV infection is recommended in the country but not yet systematically conducted. In the study zone, none of the CSPSs and none of the district hospitals provide antenatal screening for HBV or the treatment of chronic HBV infection using antiviral therapy. Hepatitis B immune globulin (HBIG) is not available in the study area.-The risk of MTCT, defined as a prevalence of positive HBsAg in infants aged 9 months.-The coverage and timeliness of HepB-BD, pentavalent vaccines, and other vaccines recommended by the national infant immunization program.-The prevalence of anti-HBs ≥10 IU/L (or ≥100 IU/L) in infants aged 9 months.-The prevalence of HBsAg in mothers of children aged 9 months.-The prevalence of HBeAg in HBsAg-positive mothers.-The prevalence of HBV DNA levels ≥200,000 IU/mL in HBsAg-positive mothers.-Anthropology: the acceptability of adding HepB-BD into the infant immunization program among healthcare workers and community members.-Health economics: the public health benefits, the cost, and the cost-effectiveness of introducing HepB-BD into the infant immunization program in Burkina Faso versus the current situation.The participant flow is schematically presented in The second visit takes place at the first contact between a mother–infant pair and the CSPS. V2 will be at the time of child delivery for women who give birth at the CSPS. For women who give birth outside the CSPS, V2 will be the first postnatal care visit to the CSPS. At V2, HepB-BD will be administered to an infant who was born after his/her CSPS has crossed over to the intervention period. In contrast, HepB-BD will not be given to an infant born before his/her CSPS has started the intervention. Data on the index pregnancy, childbirth, child’s date of birth and sex, and postnatal care, including whether and when HepB-BD was administered, will be collected on the CRF for V2 .Mother–child pairs will be screened for HBsAg when the children return to the CSPS to receive the yellow fever vaccine and the first doses of the measles and rubella vaccines that are scheduled at the age of 9 months in Burkina Faso. Capillary blood will be obtained from mother–child pairs by finger-prick for a rapid diagnostic test (RDT) to detect HBsAg  ,31 and D(a)The average number of live births in a catchment area covered by each CSPS is about 22 per month.(b)Infant death or lost to follow-up occurs in 10% of children by the age of 9 months.(c)HBsAg) is estimated at 10%, but it can vary from 5% to 15% between the clusters [The prevalence of HBsAg in pregnant women (Pclusters .(d)HBeAg) is estimated at 15%, but it can vary from 10% to 20% between the clusters [The prevalence of HBeAg in HBsAg-positive pregnant women (Pclusters .(e)HBeAg-pos) is 60% [HBeAg-neg) is 5% [With three doses of the pentavalent vaccine (8-12-16 weeks) without timely administration of HepB-BD, the risk of MTCT from HBsAg-positive and HBeAg-positive pregnant women (R) is 60% ,36, and g) is 5% .(f)HBeAg-pos-BD) is 20% [HBeAg-neg-BD) is 0% [With the timely administration of HepB-BD followed by three doses of pentavalent vaccine, the risk of MTCT from HBsAg-positive and HBeAg-positive pregnant women (R) is 20% , and theD) is 0% ,37.The power calculation was made for the primary endpoint: the prevalence of positive HBsAg in infants aged 9 months. The study will be carried out in the rural areas of the Dafra health district  and Dô health district  . At eachUsing assumptions (a) and (b), the number of children evaluated at 9 months of age in each observation period (3 weeks) in each cluster (CSPS) was estimated at:1) and the control (π0) was estimated at:Using assumptions (c), (d), (e), and (f), the incidence of HBV infection among the infants aged 9 months in the intervention . The CV, the ratio of the standard deviation (SD) to the mean, was then estimated to be 0.40 in this study. Using the formula for the power calculation of the stepped wedge cluster randomized controlled trial [The coefficient of variation (CV) of the incidence of HBV infection in infants aged 9 months between control clusters (control observation periods) should be determined by the variation in the prevalence of HBV among pregnant women across the clusters. The prevalence of HBsAg in pregnant women is estimated to vary by a maximum of ±5% (5–15%), and the prevalence of HBeAg in HBsAg-positive pregnant women to vary by up to ±5% (10–20%) between the clusters. Consequently, the incidence of HBV infection in infants aged 9 months in the control periods would vary between 0.53% and 2.40% according to the CSPS, with a maximum variation of 1.07% around the estimated average of πed trial , the stu® version 15.0 . To minimize the imbalance between the intervention period and the control period, stratification will be performed by the districts . The list of the CSPS with its order will be kept in Paris, and will only be accessible by the study statistician. The intervention start date of each CSPS will be concealed from all the investigators, healthcare workers, and study participants, except the statistician. Two weeks prior to the date of the crossover from the control to the intervention, the statistician will inform the investigators about the next CSPS that will start the intervention. The study coordinator will then notify this CSPS of the intervention start date. This will ensure sufficient time for the delivery of the vaccines to the next CSPS and for the healthcare workers in this CSPS to be ready to start administering HepB-BD.The chronological order in which a CSPS crosses over to the intervention will be randomized by a statistician at the Institut Pasteur, Paris, using the STATAWhile a CSPS is in the control period, nobody will know when the intervention will begin in this CSPS. However, once CSPS has crossed over to the intervention, both the investigators and the healthcare workers in this CSPS will know that HepB-BD is now available and given in this CSPS. To limit the risk of selection bias during the enrolment of study participants, healthcare workers will inform pregnant women about the possibility of their child being vaccinated at birth, in the same way during the control and intervention period. However, despite such a precaution, if the pregnant woman spontaneously asks about the allocation status of the CSPS, the healthcare workers will tell her whether the intervention has already been assigned to this CSPS or not.At enrolment, a study identification number will be assigned to each woman. The anonymized data will be collected in a paper CRF at the CSPS, transferred to the research center at the Centre Muraz, and a double entry will be made in an electronic CRF built in the REDCap application . The datUsing an intention-to-treat analysis involving all mother–child pairs included in the study, the impact of introducing monovalent hepatitis B vaccines at birth on the risk of MTCT will be estimated by comparing the proportion of children positive for HBsAg at the age of 9 months between two groups: children born before the introduction of HepB-BD into their CSPS (control arm) and those born after their CSPS begins HepB-BD (intervention arm), regardless of whether or not they have received HepB-BD according to the assignment of the intervention to their CSPS. Our primary analysis will be “complete case analysis” excluding those with missing outcomes as it is unlikely that maternal HBV infection, the leading factor for the vaccine failure, is related with a loss to follow-up ,41. We w®.A per-protocol analysis will also be performed by restricting the analysis to children born before the introduction of HepB-BD into their CSPS and not having received HepB-BD, or to those born after the introduction of HepB-BD into their CSPS and who have received HepB-BD. To account for the calendar time and for the correlations between individuals within the same cluster, a logistic regression model with a random effect for clusters and a fixed effect for each step will be fitted . All theOnsite monitoring will be performed by the team of the clinical research associate. They will monitor whether the research is conducted according to the ICH Good Clinical Practice (GCP) guidelines, including participants’ consents, cold chain, vaccine storage and administration, handling and transport of blood samples, and data quality control. Adverse events will be systematically collected and reported according to the national system. Site reports and weekly meetings with clinical research associates will be held to identify and solve the problems. A monitoring plan will be regularly reviewed and risk-based monitoring will be carried out.-Comité de Recherche Clinique (CoRC), Institut Pasteur, Paris, France: approved on 25 October 2018.-IRB, Institut Pasteur, Paris, France: approved on 8 November 2018.-Comité d’éthique pour la recherche en santé (CERS), Ministère de la santé/Ministère de l’enseignement supérieur, de la recherche scientifique et de l’innovation, Burkina Faso: approved on 4 December 2018.-Comité d’éthique institutionnel du Centre MURAZ: approved on 4 April 2019.-Comité technique d’examen des demandes d’autorisation d’essais cliniques (CTEC): approved on 4 November 2019.-The project has also been registered in the ClinicalTrials.gov with the following ID (NCT04029454).The following committees rigorously evaluated the scientific, ethical, and regulatory aspects of the NéoVac study, including the PREDICT-B, and fully approved the conduct of the research:Any changes to the protocol will be submitted to these committees.The study findings will be disseminated by publishing in peer-reviewed journals, and they will be communicated locally at the steering committee with the Ministry of Health, national hepatitis program, civil society organizations, and professional associations, and internationally with key stakeholders, including the WHO.An exploratory qualitative study will be performed during the main trial to assess whether the HepB-BD strategy is likely to be accepted by communities and healthcare professionals and to explore potential barriers and facilitators to implementing the strategy. This study will use validated methods for qualitative analyses of interventions: semi-directive interviews, focus groups and participants’ observations ,43,44. TWe will assess the economic value (in USD per disability-adjusted life years (DALYs) averted) associated with the introduction of the HepB-BD strategy compared to the current vaccination schedule starting at 8 weeks (the reference strategy). The costing analysis will be performed from the health system perspective. Mathematical modelling will be used to estimate over the lifetime of the study population the health benefits  and the costs with and without the introduction of the HepB-BD. The cost-effectiveness of the HepB-BD strategy will be computed as the difference in costs between the two strategies divided by the difference in effectiveness (in terms of DALYs), and it will be compared to the country cost-effectiveness threshold to provide an indication of its cost-effectiveness. Health benefits will be estimated using a dynamic model that will be developed specially for the sub-Saharan African setting using standardized recommendations for the economic evaluation of immunization programs . The mod-◦Quantification of HBsAg (qHBsAg) and HBeAg (qHBeAg) using chemiluminescent immunoassay (CLIA) ,52;◦Detection of HBeAg using rapid diagnostic test (RDT) ,57,58,59◦Quantification of hepatitis B core-related antigen (HBcrAg) ,61,62;◦Semi-quantification of HBV DNA levels using hepatitis B loop-mediated isothermal amplification (HBV-LAMP) assay .To evaluate the performance of low-cost HBV markers to diagnose high-HBV DNA levels  quantified by real-time polymerase chain reaction (PCR) as a reference standard, in serum samples and dried blood spots (DBS) collected from HBsAg-positive mothers in Burkina Faso. The following alternative markers will be assessed:-To evaluate the performance of the low-cost HBV markers in HBsAg-positive mothers to predict the risk of MTCT, defined as positive HBsAg in infants aged 9 months.-To compare the performance of the low-cost HBV markers to diagnose high HBV DNA levels  by the sample type (serum versus DBS).-To assess the effectiveness and cost-effectiveness of antenatal screening strategies using the low-cost HBV markers and subsequent antiviral treatment during pregnancy to prevent HBV MTCT in sub-Saharan Africa using modelling.Using the samples collected through the main NéoVac study, this PREDICT-B study aims to assess the performance and cost-effectiveness of low-cost HBV markers to identify women with high viral load . We have the following objectives:This ancillary study will use serum samples and DBS collected from HBsAg-positive mothers when their infants reach the age of 9 months, and it will test for the following markers: qHBeAg, HBeAg RDT, qHBsAg, HBcrAg, and HBV-LAMP. Then, we will evaluate the performance of these markers to diagnose the clinically important HBV DNA level  using real-time PCR  as a reference test. We will also evaluate the performance of maternal HBV markers to predict MTCT (defined as HBsAg positivity of their infants at the age of 9 months). Finally, we will assess the costs and cost-effectiveness of alternative antenatal HBV screening strategies in pregnant women based on the low-cost HBV markers.In Burkina Faso, the vast majority of adults have been in contact with HBV during childhood, resulting in the prevalence of total hepatitis B core antibody (anti-HBc) exceeding 70–80% in adults . TherefoHBV viral replication remains stable after pregnancy in HBsAg-positive women; a large observational study in Australia did not find any significant difference in HBV DNA levels between samples collected during pregnancy and those collected 12 months after delivery .Although it would be ideal to assess maternal HBV markers using samples collected during their pregnancy, we will evaluate these HBV markers at 9 months postpartum using the NéoVac study platform. The risk of change in maternal HBV status between pregnancy and 9 months postpartum is minimal for the following reasons:The cost-effectiveness analysis will use real-life field data obtained from the NéoVac and PREDICT-B studies to assess the effectiveness and cost-effectiveness of alternative antenatal screening strategies using low-cost HBV markers, coupled with antiviral treatment during pregnancy. Using modelling, the effectiveness of each strategy will be estimated by the number of HBV infections averted in the cohort of children included in the NéoVac trial, as well as the number of life-years saved and DALYs saved. Costs will be estimated from a health system perspective in USD. A micro-costing study will be conducted in the study area to collect data on resources needed to implement the different screening strategies and their respective costs. The temporal horizon of the analysis will be the lifetime of the birth cohort. This stepped wedge cluster randomized controlled trial is designed to answer two major public health questions to better fight hepatitis B in sub-Saharan Africa. The first question is what the additional impact of HepB-BD would be in reducing HBsAg prevalence in children in a country where three doses of the infant hepatitis B vaccine have been scheduled at 8-12-16 weeks. The trial will provide strong evidence to support the WHO recommendation of universal HepB-BD and to assist African countries to seriously consider integrating HepB-BD into their immunization programs. The second question is whether timely administration of HepB-BD would be enough to eliminate HBV MTCT, or whether additional measures, such as HBIG or peripartum antiviral prophylaxis, should be implemented in sub-Saharan Africa. It is well established that HBV-infected mothers with HBV DNA levels of ≥200,000 IU/mL have a substantial risk of transmitting HBV to their offspring despite infant immunoprophylaxis using both HepB-BD and HBIG . As antiIn this study, we will assess the impact of universal HepB-BD, aiming to administer the monovalent hepatitis B vaccine to all neonates irrespective of maternal HBV sero-status. As an alternative strategy, selective HepB-BD, targeting neonates born to mothers who test positive for HBsAg, might be attractive and adapted to sub-Saharan Africa, because of the high uptake of antenatal care but low rate of institutional delivery in many African countries . São Tom


With more than 82 million cases worldwide and almost two million deaths, the Covid-19 global pandemic shows little sign of abating. However, its effect on quality of life (QoL) in skin cancer patients has not been systematically evaluated to date. Given that QoL impairments may be associated with increased psychological morbidity, and may interfere with engagement with cancer therapy and follow-up, we prospectively evaluated quality of life in skin cancer patients using the Covid-19 Emotional Impact Survey (C-19EIS) and the EORTC QLQ-C30 questionnaires. 101 patients  completed both questionnaires. The mean C-19EIS score was 3.8 on a scale from 0 (no impact) to 12 (severe impact). Patients undergoing systemic therapy showed significantly impaired physical (p = 0.006) and social functioning (p = 0.003). However, when compared to the published normative EORTC QLQ-C30 data, there was no evidence that the Covid-19 pandemic had significantly impacted upon overall quality of life. Subscales of the EORTC QLQ-C30 were significantly inversely correlated with the C-19EIS, validating its use in skin cancer patients. Despite the Covid-19 pandemic, skin cancer patients in our tertiary referral center were surprisingly resilient. However, given the geographical variations in the rates of Sars-CoV-2 infection it is possible that the low incidence in Northern Germany may have resulted in a lack of general QoL impairments. Multi-center studies are required to further determine the impact of Covid-19 on psychological wellbeing in skin cancer patients in order to develop supportive interventions and to ensure that engagement with cancer care services is maintained in order to enable early detection of cancer progression and/or recurrence. The SARS-CoV-2 virus is a member of the coronavirus family and is responsible for the development of Covid-19 disease. Initially considered to be primarily a pulmonary disease, it is now clear that Covid-19 is a multi-system infection that can be associated with significant morbidity and mortality. With more than 82 million cases of SARS-CoV-2 infection worldwide since the beginning of the pandemic, Covid-19 represents a major and ongoing global healthcare challenge –5.This first wave of the COVID-19 pandemic was associated with a significant impact upon psychosocial wellbeing . Whilst Patients suffering from cancer are clearly a vulnerable group in the Covid-19 pandemic. Due to an immunocompromised/-suppressed status and dependent on the underlying tumor disease and burden, cancer patients may be at an increased risk of developing severe Covid-19 disease and requiring treatment in an intensive care setting . KudererHowever, the advent of immunotherapy has dramatically improved both overall and progression-free survival for patients suffering from melanoma and other skin cancers –13. The st and 30th of November 2020 which coincided with the second national lockdown (lockdown-light) in Germany, a period marked by a dramatic increase in the number of daily new infections. All patients were recruited from the dermato-oncology outpatient setting, either attending for routine follow-up (group 1) or systemic therapy (group 2). The systemic therapies were primarily immunotherapy (immune-checkpoint inhibition) but also included patients undergoing targeted (BRAF and MEK inhibition) or chemotherapy (dacarbazine). The sample size reflected the number of skin cancer patients attending the outpatient clinic or immunotherapy unit in November 2020 who were willing to participate in our study. Only patients aged over 18 years were eligible to participate.In order to provide a robust assessment of the impact of Covid-19 on quality of life two questionnaires were used. Both were offered to patients to complete on a voluntary basis after written informed consent was obtained according to the Declaration of Helsinki principles. Ethical approval was obtained from the University of Lübeck ethics committee (Reference number: 20–363). The questionnaires were distributed and completed between the 1Given that no specific questionnaire has been developed to determine the impact of COVID-19 on quality of life in patients with skin cancer, we used the Covid-19 Emotional Impact Survey (C-19EIS) developed by Falcone et al. 2020. Not only was the C-19EIS developed in Italy, a country severely affected during the first wave of the pandemic, but was also designed for use in patients with cancer . MoreoveIn addition to the Covid-19 impact survey, patients were asked to complete the EORTC QLQ-C30 questionnaire. This is a well-validated and recognized questionnaire to measure the quality of life in patients with cancer , 18 It cThe Spearman rank correlation was used to initially determine whether there was a significant correlation between responses to the Covid-19 Emotional Impact Survey and the QLQ-C 30. Both parametric and non-parametric tests were used. Analyses were then performed using the unpaired t-test or Man-Whitney Test depending on the normal distribution and variances. All data were analyzed using GraphPad Prism version 8, Microsoft Excel (2017), and the R software package. P values < 0.05 are were considered significant.st until the 30th of November). Incomplete questionnaires were excluded from further analysis. For the final analysis, 102 fully completed C-19EIS questionnaires and 101 QLQ-C 30 were evaluated. The mean age of our patients was 65 +/-14.4 years . The overall mean score for the Covid-19 impact survey was 3.8 +/-2.2 (range: 0–12). There was no significant difference between the scores of patients in group 1 and 2 (p = 0.3). When the results were analyzed according to the presence of risk factors for severe Covid-19  Ski Ski19] STo determine whether differences in quality of life were associated with treatment, we further analyzed the EORTC data according to treatment group . Again, Finally, to validate the Covid-19 Emotional Impact Survey in patients with skin cancer we compared the results with those of the EORTC QLQ-C30 using spearman´s rho correlation coefficient . Covid-1https://www.who.int/news-room/fact-sheets/detail/cancer).The Covid-19 pandemic continues to affect physical and mental health on a global and unprecedented scale , 23. DesTherefore, we sought to ascertain and chart the impact of the Covid-19 pandemic on patients with skin cancer in a tertiary referral center during the second nationwide lockdown in Germany. To our surprise, the overall C-19EIS score was low, at 3.8. Moreover, there was no significant difference in the Covid-19 quality of life impact score between patients who were attending routine out-patient skin cancer follow-up appointments and those attending for systemic anti-cancer therapy. The C-19EIS was developed and validated by Falcone et al.  in ItalySurprisingly, our patients scored significantly better on the EORTC QLQ-C30 measure of global health when compared to cancer patients in general. In fact, their levels of global health were similar to those in the general population. Whilst this is reassuring, it should be borne in mind that the normative data for the EORTC QLQ-C30 for both cancer patients and healthy individuals was not obtained during a global pandemic but is historic in nature. In addition, patients scored also significantly better on the measure of social functioning and emotional functioning in comparison to all cancers at all stages and the improved emotional functioning was maintained when only patients with melanoma were compared . The higNo details were obtained regarding the patients’ direct experience with Covid-19. It is conceivable that patients who had recovered from Sars-CoV-2 infection may have been less anxious. On the other hand, patients who had direct experience with friends or family members who were seriously ill with, or had died due to, Covid-19 infection may have expressed an increased impact on quality of life. This did not seem to be the case in our cohort given that there we no significant overall quality of life impairments.Moreover, although melanoma is a highly aggressive and life-threatening tumor, depending on the stage of the disease and melanoma subtype  we may hNevertheless, physical, role and social functioning were impaired in our cohort compared to the healthy population. Furthermore, scores of physical and social function were significantly worse in the patients attending routine skin cancer follow-up care than in those attending for systemic therapy. It is likely that the impact on social functioning reflected the national recommendations on social distancing and dramatically reducing social contact. This can only be confirmed by repeating the questionnaire when the “lockdown” recommendations have been eased.Finally, we were able to essentially replicate the results of Falcone et. al  that theIn summary, in one of the first comprehensive assessments of the effect of the Covid-19 pandemic on patients with skin cancer, we were able to show that overall measures of global health were largely unaffected. In fact, when compared to the published EORTC QLC published data for all cancer patients, our cohort reported significantly higher levels of global health. However, physical, role, and social functioning were markedly impaired when compared to healthy individuals. Until widespread vaccination and subsequent immunity is reached, and given that periodic “lockdowns” are likely to continue on a local and national level, there is a pressing need to fully understand the impact of the Covid-19 pandemic on quality of life in skin cancer patients in order to plan and provide psychosocial support and ensure that engagement with cancer care services is maintained in order to enable early detection of cancer progression and/or recurrence.


Theoretically, artificial intelligence can provide an accurate automatic solution to measure right ventricular (RV) ejection fraction (RVEF) from cardiovascular magnetic resonance (CMR) images, despite the complex RV geometry. However, in our recent study, commercially available deep learning (DL) algorithms for RVEF quantification performed poorly in some patients. The current study was designed to test the hypothesis that quantification of RV function could be improved in these patients by using more diverse CMR datasets in addition to domain-specific quantitative performance evaluation metrics during the cross-validation phase of DL algorithm development.We identified 100 patients from our prior study who had the largest differences between manually measured and automated RVEF values. Automated RVEF measurements were performed using the original version of the algorithm (DL1), an updated version (DL2) developed from a dataset that included a wider range of RV pathology and validated using multiple domain-specific quantitative performance evaluation metrics, and conventional methodology performed by a core laboratory (CORE). Each of the DL-RVEF approaches was compared against CORE-RVEF reference values using linear regression and Bland–Altman analyses. Additionally, RVEF values were classified into 3 categories: ≤ 35%, 35–50%, and ≥ 50%. Agreement between RVEF classifications made by the DL approaches and the CORE measurements was tested.CORE-RVEF and DL-RVEFs were obtained in all patients (feasibility of 100%). DL2-RVEF correlated with CORE-RVEF better than DL1-RVEF (r = 0.87 vs. r = 0.42), with narrower limits of agreement. As a result, DL2 algorithm also showed increasing accuracy from 0.53 to 0.80 for categorizing RV function.The use of a new DL algorithm cross-validated on a dataset with a wide range of RV pathology using multiple domain-specific metrics resulted in a considerable improvement in the accuracy of automated RVEF measurements. This improvement was demonstrated in patients whose images were the most challenging and resulted in the largest RVEF errors. These findings underscore the critical importance of this strategy in the development of DL approaches for automated CMR measurements. Right ventricular (RV) function is an important predictor of outcomes in patients with heart disease –3. HowevNevertheless, manual segmentation of the RV cavity is time-consuming and has greater inter-reader variability than is seen for the LV cavity –14. The Most currently available software that automatically quantifies RV size and function from CMR images are complex algorithms that use a form of artificial intelligence referred to as deep learning (DL) , 17. SucData and materials used in this study will not be made publicly available.From our prior study , in whicAll subjects had previously signed informed consent to be included in a CMR registry permitting their images, clinical data, and outcomes to be used for future research. Patient demographics and clinical history were extracted from the electronic medical records. The protocol was approved by the Institution Review Board.The DL1 segmentation model, included in cvi42 , is founded on a convolutional neural network (CNN) based on the U-net architecture. The CNN is trained to associate pixel intensities of a CMR image to segmentation maps corresponding to the desired ventricular contours. During the training stage, the model parameters of CNN were optimized to reduce an energy function computed using the pixel-wise cross-entropy loss function, which penalizes the CNN when it does not correctly predict the segmentation label of a given pixel.The initial machine learning-based RV contouring CNN was trained on multiple datasets summarized in Table DL2, included in cvi42  was trained using the exact same CNN architecture as DL1. Prior to training DL2, 9745 new images were added to the Clinical dataset and the dataset was resampled such that roughly 20% of the cases were used in training and the remainder were kept for cross-validation. 460 additional tetralogy of Fallot images and 9202 additional images from UKBB were also added to the cross-validation dataset. Importantly, the cross-validation process was performed on this dataset and model selection was done in a manner that was blind to the cases on which it was tested and reported on in this study.Additional quantitative performance metrics were used to assess the performance of the algorithm during the cross-validation phase as a spatial overlap metric, such as DSC fails to capture potential biases in the predicted contours . In addiVasodilator contrast-enhanced stress perfusion CMR imaging was performed using a 1.5-T scanner  with a five-element phased array cardiac coil. Retrospectively gated cine images were obtained following stress perfusion imaging using a balanced steady-state free precession (bSSFP) sequence, during approximately 5-s breath holds . Standard long-axis views were obtained, including four-chamber, two-chamber, and three-chamber images. In addition, six to ten short-axis slices were obtained from the LV and RV base to the apex .The cine-CMR images were analyzed semi-automatically using commercial software  by an experienced expert reader in our core lab, who was blinded to clinical information. Using the short-axis cine images, the RV endocardial boundary was automatically detected by the software and then corrected by the expert in end-diastolic and end-systolic frames. RV papillary muscles and trabecular tissue were included in the blood pool volume. The RV outflow tract was included in the RV volume to the level of the pulmonary valve. When the boundary between the atrium and ventricle was unclear, long axis views and other frames of the cardiac cycle were reviewed to differentiate between the two chambers. Simpson’s method of disks was used to calculate RV end-diastolic volume (RVEDV) and end-systolic volume (RVESV), and RVEF. All volume measurements were indexed to body surface area. The RV measurements made by the core lab expert were considered as the reference standard . CORE-RVEF were used to divide the patient cohort into three clinically relevant groups –21: seveThe RVEF was determined using two different fully automated DL-algorithms DL1 and DL2, described above. Cine images were uploaded into the software and fully automated segmentation was then performed without any further user input. A plane demarcating the ventricular base was identified automatically. Using the entire short axis stack and standard long axis views, both DL1 and DL2 generated time-volume curves, from which end-diastole and end-systole were automatically determined and used to calculate RVEDV, RVESV, and RVEF.Continuous variables were tested for normal distribution. Continuous variables that were not normally distributed, were presented as the median with interquartile range (IQR). Categorical variables were presented as absolute numbers with percentages. Inter-technique comparisons included linear regression analysis with Pearson correlation coefficients and Bland–Altman analyses of biases and limits of agreement. This included the agreement between each of the DL techniques (DL1 and DL2) against the CORE reference. Histograms displaying the difference in RVEF measurements between the DL algorithms and the core lab were generated. Confusion matrices were generated for each DL-RVEF algorithm to display the concordance/discordance with the CORE-RVEF for each of the RVEF categories . The sensitivity, specificity, and accuracy of DL1-RVEF and DL2-RVEF algorithms’ ability to correctly categorize RV function were also calculated. Reproducibility was tested for the 2 DL algorithms on 20 randomly selected patients and by manual analyses by two expert readers, including inter- and intra-observer reproducibility. Inter- and intra- observer variability was assessed using intraclass correlation coefficients (ICC) and coefficients of variation (CoV). P-values < 0.05 were considered significant. Analyses were performed using SPSS software .Patient characteristics for Group 1 are shown in Table Compared to CORE-RVEF, the correlations of DL1-RVEF and DL2-RVEF were 0.42 and 0.87, respectively , 21 had mildly to moderately reduced RVEF (35–50%), and 68 had preserved RVEF (≥ 50%)  vasodilator stress is a common indication for CMR imaging; (2) we suspected that the algorithms may not perform as well in this situation, since the images used to quantify EF are usually acquired after the administration of a gadolinium-based contrast agent; and (3) the previous study, in which the original algorithm did not perform well in this subset of patients, was performed on their stress CMR images, and therefore testing the hypothesis that the retrained algorithm would perform better had to be done on the same images.The use of a diverse dataset during the cross-validation phase of DL algorithm development resulted in a considerable improvement in the accuracy of the automated analysis of RV volumes and RVEF in patients in whom the previous version of the software did not perform well. However, despite the improvement, the current fully automated algorithm is still prone to errors when compared to a core laboratory measurement due to intrinsic anatomical challenges and therefore requires verification by an experienced reader.


The COVID-19 pandemic has had a profound effect on all aspects of life, including physical fitness and well-being of the general population. In the present study, we assessed the effect of the pandemic on the subjective and objective fitness of high-level nonelite runners.  The MASTERS Athletic Study is an ongoing survey of training and health habits of high-level nonelite runners, the majority of whom compete in marathons and other extreme endurance events. We invited participants to a web-based questionnaire regarding training and fitness during the COVID-19 pandemic. Comparisons were made between subjective and objective fitness as well as well as the relationship of prepandemic training volume and history of COVID-19 on change in fitness during the pandemic, using Mann–Whitney rank-sum tests and chi-square tests for nonparametric and categorical variables, respectively.  A total of 189 runners completed the survey, of whom 26 (13.8%) reported prior diagnosis with COVID-19. In terms of the subjective sense of fitness compared to before the pandemic, 49 (25.9%) reported they were less fit, 55 (29.1%) reported they were more fit, and 85 (45.0%) reported their fitness was unchanged. These assessments correlated well with objective measurement of training volume in MET-min/week. Runners with improved fitness at present had a lower calculated training volume before the pandemic versus those who reported unchanged or worsened fitness. There was no relationship between the report of prior COVID-19 and either subjective or objective measures of fitness.  The COVID-19 pandemic has had a variable effect on the fitness of high-level nonelite runners. We found an inverse relationship between baseline training volume and the likelihood of runners reporting improved fitness and no relationship between a history of COVID-19 and change in fitness through the pandemic. Understanding the effect of the COVID-19 pandemic on athletic fitness will help guide strategies to maintain physical health and wellness through future public health crises. The COVID-19 pandemic and public health response to this crisis have dramatically changed multiple aspects of everyday life, including physical fitness and well-being. Several recent studies have revealed confirmatory evidence that the pandemic has had a significant effect on the overall fitness and resultant health status of a large number of individuals . In addiA number of studies have attempted to quantify the effect of the COVID-19 pandemic on both subjective and objective measures of physical fitness among regular exercisers, including a spectrum of athletes from casual to college-level athletes –8. At leThe current study was an online, secure, HIPAA-compliant survey regarding training habits, subjective fitness, and COVID-19 history among runners. An invitation to participate in the survey was sent to runners who had participated in the MASTERS Athletic Study beginning in 2014 . The MASThe current study consisted of 25 questions comparing training volume and pace prior to the start of the COVID-19 pandemic in March 2020 and at the time of the survey, February 2021. The survey included questions about average weekly mileage, average training pace, and subjective sense of fitness at both time points. In addition, runners who were diagnosed with COVID-19 were asked additional questions regarding training and medical care during their illness. The survey is included in the Appendix.Weekly training volume in MET-min/week was calculated from reported weekly training mileage and pace . Changesp value less than 0.05 was considered significant.Statistical analysis was performed using Sigmastat 4.0 software . Nonparametric data were compared using Mann–Whitney rank sum tests, and categorical data were compared using chi-square tests. For all comparisons, a The study has been approved by the Institutional Review Board of Geisinger Health System.p < 0.005). We also calculated change in training volume from before the pandemic to the current time. We defined increased training volume as ≥10% increase in MET-min/week and decreased training volume as ≥10% decrease in MET-min/week over the pandemic period. When we compared reported subjective fitness to change in training volumes, we found that subjective fitness correlated very well with training volume changes, confirming that runners' subjective sense of their fitness correlated well with their actual reported training volume (p < 0.001 comparing reported fitness with change in training volume during the pandemic).A total of 189 runners (10% of those contacted by e-mail) completed the survey in full, with no omitted answers. 49 (25.9%) reported they were less fit, 55 (29.1%) reported they were more fit, and 85 (45.0%) reported their fitness was unchanged to the question “How is your fitness now compared to prior to the pandemic?” Comparisons between these 3 groups are shown in Of the 189 runners who completed the survey, 26 (13.8%) reported being diagnosed with COVID-19. Characteristics of the infectious events reported by study respondents are summarized in We compared both subjective fitness and change in training volume during the pandemic across those runners who did versus those who did not report being infected with COVID-19 during the pandemic. Despite the history of COVID-19 and significant presence of continued symptomology among respondents, there were no significant differences in either the categorical distribution of self-reported fitness  or averaA growing body of data is helping to shape our understanding of the effects of the COVID-19 pandemic and resultant public health responses on fitness, among both the general population and recreational athletes. The present study has several notable findings which add to this knowledge base. First, the effect of the COVID-19 pandemic on the fitness of high-level nonelite runners has been variable, with nearly equal respondents reporting an increase or decrease in the subjective sense of overall fitness. Second, this subjective sense of fitness correlated well with a more objective measure of running fitness, specifically weekly training volume defined by MET-min/week. Third, among variables we examined, we found that those runners who reported an increase in fitness over the pandemic period tended to have a lower baseline training volume versus those who reported no change or a worsening of fitness. Fourth, we found no significant relationship between runners who suffered COVID-19 and those who did not in terms of subjective fitness or training volume. These findings add to the current knowledge base concerning effects of the COVID-19 pandemic on runners and are, to the best of our knowledge, the first to examine a very high-volume cohort of athletes, with runners performing up to 6,000 MET-min/week, an exercise volume that is achieved by less than 15% of recreational athletes .A number of studies have examined the effect of the COVID-19 pandemic and resultant restrictions on physical well-being. Several of these studies documented overall decreased physical activity among the general population following pandemic restrictions . Recent The fact that we did not find a negative relationship between runners suffering COVID-19 and the sense of fitness or training volume is somewhat surprising, especially given the fact that most runners who reported a positive COVID-19 diagnosis indicated this event was a recent event and the majority also reported at least one continued symptom related to the illness –18. ClooOur study suffers from significant shortcomings, and the most obvious is that it is a relatively small and based on survey respondent data only. Recall survey data are prone to bias, and the possibility of bias affecting our results should be noted. The introduction of selection bias is a possibility given that runners who felt the COVID-19 pandemic had a substantial effect on their endurance fitness may have been particularly motivated to participate. We did not confirm either the test status of the runners who reported COVID-19 or the reported training volumes of all participants. Objective measures of exercise tolerance changes following COVID-19, such as results of cardiopulmonary stress testing, would obviously provide more concrete measures of the true effect of infection on fitness across a spectrum of athletes. Regardless, the present study represents, to the best of our knowledge, the first examination of the effects of the COVID-19 pandemic on high-level nonelite endurance athletes of a wide age range.The present study demonstrates that the COVID-19 pandemic has had heterogeneous effects on the fitness of high-level nonelite runners, independent of whether or not these athletes had COVID-19. Athletes reporting the highest training volume before the pandemic suffered the greatest subjective and objective loss of fitness during the period of pandemic-related restrictions. Further study of the effect of the pandemic on overall fitness across a spectrum of baseline fitness will add to our understanding of how to direct efforts to maintain health and physical well-being in challenging times to come.


Social media can be used to monitor the adverse effects of vaccines. The goal of this project is to develop a machine learning and natural language processing approach to identify COVID-19 vaccine adverse events (VAE) from Twitter data. Based on COVID-19 vaccine-related tweets (1 December 2020–1 August 2021), we built a machine learning-based pipeline to identify tweets containing personal experiences with COVID-19 vaccinations and to extract and normalize VAE-related entities, including dose(s); vaccine types ; and symptom(s) from tweets. We further analyzed the extracted VAE data based on the location, time, and frequency. We found that the four most populous states  in the US witnessed the most VAE discussions on Twitter. The frequency of Twitter discussions of VAE coincided with the progress of the COVID-19 vaccinations. Sore to touch, fatigue, and headache are the three most common adverse effects of all three COVID-19 vaccines in the US. Our findings demonstrate the feasibility of using social media data to monitor VAEs. To the best of our knowledge, this is the first study to identify COVID-19 vaccine adverse event signals from social media. It can be an excellent supplement to the existing vaccine pharmacovigilance systems. As of December 2021, the COVID-19 pandemic has claimed over five million lives worldwide ,4. As diTraditionally, social media data have been used for outbreak surveillance, i.e., using search records  or the discussion of symptoms to track and predict the development of infectious disease outbreaks . Flu suThe same approaches can be leveraged in detecting and surveilling adverse events associated with medication, which is called pharmacovigilance . AccordiThe ongoing COVID-19 pandemic necessitates the quick development, governmental approval, and rollout of different COVID-19 vaccines on a global scale. Today, over 8 billion doses of COVID-19 vaccines have been administered worldwide , SoberanWe collected COVID-19 vaccine-related discussions from Twitter using the Twitter streaming API. Tweets containing personal experiences postvaccination were identified  using a rule-based approach. We then randomly selected 5600 tweets for manual annotation. This annotated subset served as the training and test datasets for (1) machine-learning-based classification  and (2) named entity recognition . Next, we leveraged CLAMP, a state-of-the-art natural language processing (NLP) pipeline, for entity normalization . FinallyData Collection and Rule-Based Filtering: We used the Twitter streaming API to collect vaccine-related discussions from December 2020 to August 2021 based on a set of keywords . Since the Twitter data contained a high percentage of noise (tweets not related to COVID-19 vaccine adverse events), we then used rule-based approaches to remove the noise and identify the content related to personal experiences of COVID-19 vaccine adverse events. First, we removed retweets and quotes and only kept the original tweets so that the VAEs were not artificially inflated. Second, we removed the tweets from users with more than 10,000 followers, which are considered by Twitter as “super follows” , to focuMachine Learning-Based Filtering: We built machine learning-based approaches to further select tweets that contained personal VAE experiences after COVID-19 vaccination. A set of commonly used machine learning algorithms, including support vector machine (SVM), logistic regression, random forest, extra trees, and gradient boosting, implemented using the scikit-learn package were evaluated . Named Entity Recognition (NER): For tweets containing individual vaccine experiences, we further built a machine learning-based named the entity recognition (NER) model to extract information on (1) vaccine type ; (2) dose ; and (3) adverse event  from tweets. We leveraged CLAMP , an inteAnnotation and preprocessing: After rule-based filtering, we had 111,229 tweets that may contain COVID-19 VAE experiences. Three annotators coded 5600 tweets that were randomly selected to decide if a tweet contained personal VAE. The annotation agreement (measured as a F-1 score) was 0.96. We excluded 2216 tweets from this step, because they did not contain personal VAE. We further annotated the remaining 3384 tweets in terms of vaccine type, dose, and adverse events. Cohen’s kappa was calculated to measure the intercoder reliability. The agreement scores were 0.83, 0.84, and 0.82, respectively, which indicated strong agreement [Evaluation: The annotated tweets were divided into training, validation, and testing sets with the proportion of 7:1:2 for both the text classification tasks and named entity recognition tasks. We calculated the precision, recall, and F-1 scores for the two tasks. These three measures are commonly used in evaluating the performance of machine learning algorithms. Precision calculates the percentage of correct positive predictions out of all the positive predictions ). Recall is the percentage of positive prediction from all the positive cases in the data ). The F-1 score is the harmonic mean of precision and recall, where F-1 is 2*precision*recall/.Named Entity Recognition and Normalization: For the tweets containing personal vaccine experiences included in this study, we leveraged a trained CRF algorithm and a few lexicon-based rules through CLAMP. n = 23,657) included geographic information. We extracted the geographic locations of these tweets and analyzed the longitudinal trends of the VAE-related tweet volumes. Even though our data included 66,499 VAE mentions, only around 30% of them (We further calculated the top 10 most frequently discussed adverse events for each of the three COVID-19 vaccines available in the United States see a. Sore tAccording to the Pew Research Center, seven out of ten Americans used social media in 2021 . The pubUsing both machine learning and named entity recognition, our approach can relatively accurately identify the vaccine type, dose, and vaccine adverse events from COVID-19 vaccine-related tweets based on a small training dataset. A comparison of the SVM, extra trees, random forest, logistic regression, and gradient boosting algorithms showed that random forest has the best performance. Our results showed that the four most populous states in the US also had the most discussions of VAEs on Twitter. The trends of VAE-related tweets are also consistent with the US vaccination progress, which further demonstrates the validity of our machine learning-based pipeline.In comparing our results to the data reported in the Vaccine Adverse Event Reporting System, we found that “sore to touch” was the most common VAE identified through Twitter data but did not show up in the VAERS. This is probably because “sore to touch” is such a minor yet common adverse event that individuals do not bother to report it to physicians and through the governmental portal, but they nevertheless talk about it in describing their vaccine experiences on Twitter. This discrepancy shows the relative strength and weakness of identifying VAEs through social media content that people share informally and through more formal report portals operated by the government and pharmaceutical companies. Using Twitter data to monitor VAEs is not without its limitations. Twitter has been the most often used social media platform for outbreak surveillance and adverse effect surveillance in recent years  due to tFurthermore, research has shown that a fear of vaccine adverse effects contributes to vaccine hesitancy and called for a “positive framing of mild adverse effects” and “balancing risk and benefit information” . UnderstThe ongoing COVID-19 pandemic is the most significant threat to global health, and vaccines are one of the most effective tools in our collective fight against COVID-19. Vaccine safety is of paramount importance, and the perceived lack of safety is a major barrier to vaccine rollout. In addition to/instead of reporting their advertise events to their physicians and the portals created by the government and pharmaceutical companies, individuals often share their vaccine experiences and adverse events on social media. Our proposed system could be a good supplement to existing vaccine pharmacovigilance systems.


Binge-eating refers to episodes of uncontrolled eating accompanied by a perceived loss of control, which can be common in the general population. Given the profound negative consequences of persistent binge-eating such as weight and eating disorders, it is vital to determine what makes someone more vulnerable than others to engage in such a conduct. A total of 42 normal-weight individuals (21 with binge-eating episodes and 21 without binge-eating episodes) underwent a structural magnetic resonance imaging measurement and Voxel-based morphometry (VBM) was used to assess between-group differences in terms of gray matter volume (GMV), together with self-report impulsivity and binge-eating measures. The results showed binge-eating individuals as characterized by higher trait impulsivity and greater regional GMV in the left middle frontal gyrus: however, the GMV in this region appeared to be positively correlated only with measures of binge-eating but not with trait impulsivity measures. These findings provide novel insights on the neurobiological roots of BE in normal-weight individuals and highlight how this behavior can be associated with brain morphometric changes within prefrontal regions also in a non-clinical population. Overall, this study provides a further characterization of the neural correlates of binge-eating and novel insights into the treatment of its more severe pathological forms. With the worldwide rise of overeating and overweight in the past few decades, researchers have put considerable attention on trying to understand the possible predisposing factors that may contribute to the development of obesity and binge-eating disorder (BED) . The terPreliminary insights into binge-eating derive from a recent line of research according to which there would be a shared behavioral and neural substrate between overeating and substance compulsive use ,5; in boA point worth noticing is that, since the majority of the evidence stems from the study of adults with obesity, overweight, and/or full-blown eating disorders, it becomes hard to disentangle possible inconsistencies in the directionality of results and to elucidate whether changes in these brain regions either act as possible neural markers and risk factors for increased propensity to disordered eating and weight gain ,17 or raThe novelty of this study stands in the investigation of a non-clinical normal-weight population, reporting episodes of binge-eating (BE). We hypothesized that GMV differences could characterize these individuals (in respect to non-BE), even if a full-blown eating or weight disorder is diagnosed. In more detail, we expect differences between the groups to be mainly located in self-control related regions, such as the DLPFC areas, which have been repeatedly associated with impulsiveness linked to overconsumption . EvidencWe enrolled normal-weight male and females (from 20 to 35 years old) through local advertisements at the University of Padua, and divided them in two groups taking the declared presence/absence of binge-eating episodes as a key criterion. Binge-eating status was certified by means of the Eating Attitude Test (EAT 26) , assessi2) for participants of both groups had to range from 18.5 to 24.9  and were right-handed according to the Edinburgh Handedness Inventory [Body mass index  : A 25-itBarratt Impulsiveness Scale (BIS-11) : It alloBehavioral Inhibition/Behavioral Activation Scale (BIS/BAS) : InvestiParticipants of both groups completed a behavioral assessment that included: MRI data were collected with a whole body 1.5 T scanner  equipped with a standard Siemens eight-channels coil. High-resolution T1-weighted images were acquired  320 × 320; Matrix 320 × 295; Flip Angle (FA) 20°; Repetition Time (TR) 20 ms; Echo Time (TE) 4.89 ms; Bandwidth = 130 Hz).http://dbm.neuro.uni-jena.de/cat/, accessed on 31 August 2021), an extension of SPM12  implemented in MATLAB 2011a environment . Default settings provided by CAT12 were adopted. Preprocessing of MR images included (i) normalization according to the Montreal Neurological Institute (MNI) template; and (ii) segmentation into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). After these steps, (iii) a quality check was performed and, the images were smoothed with a Gaussian kernel of 8 × 8 × 8 mm Full Width at Half Maximum (FWHM). The total intracranial volume (TIV) and percentage of global tissue volumes  of each subject were computed with CAT 12 and compared between the two groups (BE and non-BE) with the software Statistical Package for Social Sciences, version 23 .MRI data preprocessing and analysis were performed using a VBM approach . Preproct-tests within a General Linear Model (GLM) in SPM 12, using a Whole Brain approach. In order to control for the possible influence of difference in TIV and age, these variables were included as covariates of no interest in the statistical model. Besides, given that sex/gender differences in both brain activity and structure has been well documented in healthy adults and individuals with obesity (for review see [The assessment of between-group differences focused only on GMV: we used GM images of each participant and performed voxel-wise two-samples view see ), we alsIn order to investigate the possible relations between brain morphometry, trait impulsivity, and binge-eating behavior, the mean GMV values were extracted from the cluster showing differential GMV between the groups  with the SPM toolbox “MarsBaR” , and corPercentage of global volumes of GM, WM, CSF, and TIV did not differ between the groups .Increased GMV in the left middle and superior frontal gyrus  were observed in the BE group when compared to the non-BE. The opposite comparison (non-BE > BE) did not yield any significant result ; Table 3The correlations between GMV in the left MFG and scores of the BIS-11 questionnaires  did not yield any significant results in both groups .The correlational analysis revealed a significant positive correlation between the extracted GMV within the cluster in the left MFG and the scores of the BES  in the BThe present research aimed at exploring the possibility that normal-weight BE—compared to non-BE—would be characterized by differences in GMV, especially in PFC, assumed to have a role in behavior regulation .BE, compared to non-BE, showed higher BIS-11 scores, indicating high general trait impulsivity, and higher scores in the BES and YFAS, suggesting the presence of loss of control and addictive-like tendencies toward food. In addition, BE showed lower scores for the BIS subscale of the BIS/BAS questionnaire . This suWith regard to brain morphometry, results indicated that BE and non-BE did not differ in terms of global volumes but differed in the regional GMV. In line with our hypotheses, BE—compared to non-BE—showed higher GMV in the left MFG and partially, the SFG. The MFG—together with the SFG—belongs to the DLPFC, a brain area involved in executive control processes . DLPFC iThe involvement of the left DLPFC has been previously described in overeating as a key region for inhibitory control processes ; for insContrary to our expectations, we did not find any significant correlation between the GMV in the left MFG and the BIS-11 scores, while a positive correlation with BES scores was observed. This result suggests that the binge-eating status may—at least partially—account for the structural differences between BE and non-BE in the left MFG. Although the directionality of these results needs to be interpreted with caution and deserves further investigation, our result points toward the intriguing possibility of a close connection between the tendency to overeat and structural GMV changes in left DLPFC in BE, even if in a condition of normal weight. On the other hand, the lack of significant correlations with general impulsivity might lie on the self-report measure we adopted: even though the BIS-11 is a reliable tool to assess trait impulsivity, impulsivity is known to be a multidimensional construct . Thus, tOverall, BE—compared to non-BE—showed higher GMV in the left MFG, positively correlated with binge-eating behavior. These results, together with the involvement of prefrontal regions in the characterization of overeating conditions , may add