Covid19-Sources

Superspreading complicates the study of SARS-CoV-2 transmission. I propose a model for aggregated case data that accounts for superspreading and improves statistical inference. In a Bayesian...

While seasonal variation has a known influence on the transmission of several respiratory viral infections, its role in SARS-CoV-2 transmission remains unclear. As previous analyses have not accounted for the implementation of non-pharmaceutical interventions (NPIs) in the first year of the pandemic, they may yield biased estimates of seasonal effects. Building on two state-of-the-art observational models and datasets, we adapt a fully Bayesian method for estimating the association between seasonality and transmission in 143 temperate European regions. We find strong seasonal patterns, consistent with a reduction in the time-variable R t of 42.1% (95% CI: 24.7% – 53.4%) from the peak of winter to the peak of summer. These results imply that the seasonality of SARS-CoV-2 transmission is comparable in magnitude to the most effective individual NPIs but less than the combined effect of multiple interventions. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement J. T. Monrad acknowledges funding from the Augustinus Foundation, the Knud Hojgaard Foundation, the William Demant Foundation, the Aage and Johanne Louis-Hansen Foundation, and the Kai and Gunhild Lange Foundation. M. Sharma was supported by the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems (EP/S024050/1) and a grant from the EA Funds programme. S. Mindermann's funding for graduate studies was from Oxford University and DeepMind. G.Leech was supported by the UKRI Centre for Doctoral Training in Interactive Artificial Intelligence (EP/S022937/1). S.Bhatt acknowledges funding from the MRC Centre for Global Infectious Disease Analysis (MR/R015600/1), jointly funded by the U.K. Medical Research Council (MRC) and the U.K. Foreign, Commonwealth and Development Office (FCDO), under the MRC/FCDO Concordat agreement; is a part of the EDCTP2 program supported by the European Union; and acknowledges funding by Community Jameel. J.M. Brauner was supported by the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems (EP/S024050/1) and by Cancer Research UK. S. Bhatt acknowledges The UK Research and Innovation (MR/V038109/1), the Academy of Medical Sciences Springboard Award (SBF004/1080), The MRC (MR/R015600/1), The BMGF (OPP1197730), Imperial College Healthcare NHS Trust- BRC Funding (RDA02), The Novo Nordisk Young Investigator Award (NNF20OC0059309) and The NIHR Health Protection Research Unit in Modelling Methodology. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: No approval required for research on fully anonymised data, as per the Central University Research Ethics Committee (CUREC) of the University of Oxford. https://researchsupport.admin.ox.ac.uk/governance/ethics/apply All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Spring 2020 dataset has been published in Brauner et al.: "The effectiveness of eight nonpharmaceutical interventions against COVID-19 in 41 countries". Fall-winter 2020 dataset is currently available upon request. The entire implementation is publicly available on GitHub. [https://github.com/gavento/covid\_seasonal\_Brauner][1] [https://github.com/gavento/covid\_seasonal\_Sharma][2] [1]: https://github.com/gavento/covid_seasonal_Brauner [2]: https://github.com/gavento/covid_seasonal_Sharma

Background Understanding the global spatiotemporal pattern of seasonal influenza is essential for influenza control and prevention. Available data on the updated global spatiotemporal pattern of seasonal influenza are scarce. This study aimed to assess the spatiotemporal pattern of seasonal influenza after the 2009 influenza pandemic. Methods Weekly influenza surveillance data in 86 countries from 2010 to 2017 were obtained from FluNet. First, the proportion of influenza A in total influenza viruses (PA) was calculated. Second, weekly numbers of influenza positive virus (A and B) were divided by the total number of samples processed to get weekly positive rates of influenza A (RWA) and influenza B (RWB). Third, the average positive rates of influenza A (RA) and influenza B (RB) for each country were calculated by averaging RWA, and RWB of 52 weeks. A Kruskal-Wallis test was conducted to examine if the year-to-year change in PA in all countries were significant, and a universal kriging method with linear semivariogram model was used to extrapolate RA and RB in all countries. Results PA ranged from 0.43 in Zambia to 0.98 in Belarus, and PA in countries with higher income was greater than those countries with lower income. The spatial patterns of high RB were the highest in sub-Saharan Africa, Asia-Pacific region and South America. RWA peaked in early weeks in temperate countries, and the peak of RWB occurred a bit later. There were some temperate countries with non-distinct influenza seasonality (e.g., Mauritius and Maldives) and some tropical/subtropical countries with distinct influenza seasonality (e.g., Chile and South Africa). Conclusions Influenza seasonality is not predictable in some temperate countries, and it is distinct in Chile, Argentina and South Africa, implying that the optimal timing for influenza vaccination needs to be chosen with caution in these unpredictable countries.

Although the benefit of population-level public facial masking to protect others during the COVID-19 pandemic has received a great deal of attention, we discuss for one of the first times the hypothesis that universal masking reduces the "inoculum" or dose of the virus for the mask-wearer, leading t …

The seasonality of respiratory diseases has been linked, among other factors, to low outdoor absolute humidity and low indoor relative humidity, which increase evaporation of water in the mucosal lining of the respiratory tract. We demonstrate that normal breathing results in an absorption-desorption cycle inside facemasks, in which supersaturated air is absorbed by the mask fibers during expiration, followed by evaporation during inspiration of dry environmental air. For double-layered cotton masks, which have considerable heat capacity, the temperature of inspired air rises above room temperature, and the effective increase in relative humidity can exceed 100%.

New data from a large healthcare organization in Israel reveal a reduction in new infections in an unvaccinated population in communities with rapid vaccination rollouts, suggesting that mass vaccination strategies confer cross-protection of unvaccinated individuals.

The study, based on anonymized Israeli medical records, showed that vaccination’s benefits extend to people who have not received a shot.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to evolve around the world, generating new variants that are of concern based on their potential for altered transmissibility, pathogenicity, and coverage by vaccines and therapeutics1–5. Here we report that 20 human sera, drawn 2 or 4 weeks after two doses of BNT162b2, neutralize engineered SARS-CoV-2 with a USA-WA1/2020 genetic background (a virus strain isolated in January 2020) and spike glycoproteins from the newly emerged B.1.617.1, B.1.617.2, B.1.618 (all first identified in India) or B.1.525 (first identified in Nigeria) lineages. Geometric mean plaque reduction neutralization titers against the variant viruses, particularly the B.1.617.1 variant, appear lower than the titer against USA-WA1/2020 virus, but all sera tested neutralize the variant viruses at titers of at least 40. The susceptibility of these newly emerged variants to BNT162b2 vaccine-elicited neutralization supports mass immunization as a central strategy to end the coronavirus disease 2019 (COVID-19) pandemic across geographies.

Moderna will take mRNA flu and HIV vaccines into Phase 1 trials this year, as well as beginning a pivotal Phase 3 study for its cytomegalovirus (CMV) vaccine candidate.

Scientists are starting to get insights into the lingering disorder that affects some people infected with SARS-CoV-2 — but many mysteries remain unsolved.

This work is concerned with the spatiotemporal dynamics of the coronavirus disease 2019 (COVID-19) in Germany. Our goal is twofold: first, we propose a novel spatial econometric model of the epidemic spread across NUTS-3 regions to identify the role played by commuting-to-work patterns for spatial disease transmission. Second, we explore if the imposed containment (lockdown) measures during the first pandemic wave in spring 2020 have affected the strength of this transmission channel. Our results from a spatial panel error correction model indicate that, without containment measures in place, commuting-to-work patterns were the first factor to significantly determine the spatial dynamics of daily COVID-19 cases in Germany. This indicates that job commuting, particularly during the initial phase of a pandemic wave, should be regarded and accordingly monitored as a relevant spatial transmission channel of COVID-19 in a system of economically interconnected regions. Our estimation results also provide evidence for the triggering role of local hot spots in disease transmission and point to the effectiveness of containment measures in mitigating the spread of the virus across German regions through reduced job commuting and other forms of mobility.

The disruptions caused by the Covid-19 pandemic might turn out to be greater than any other event since the Second World War. Governments and authorities around the world therefore have taken drastic measures since early 2020 to slow down the pandemic. Measures include the closing of schools and non-essential sales shops, mandatory face masks, travel and contact restrictions and even contact bans or curfews. It is no surprise that these measures encounter partially fierce protests by some politicians and parts of the public. It is high time to understand how effective the various measures were in reducing the spread of Covid-19 to be prepared for future epidemic and pandemic developments. This is the purpose of this paper.

Since the emerging of the "novel coronavirus" SARS-CoV-2 and the corresponding respiratory disease COVID-19, the virus has spread all over the world. Being one of the most affected countries in Europe, in March 2020, Germany established several nonpharmaceutical interventions to contain the virus spread, including the closure of schools and child day care facilities (March 16-18, 2020) as well as a full "lockdown" with forced social distancing and closures of "nonessential" services (March 23, 2020). The present study attempts to analyze whether these governmental interventions had an impact on the declared aim of "flattening the curve", referring to the epidemic curve of new infections. This analysis is conducted from a regional perspective. On the level of the 412 German counties, logistic growth models were estimated based on daily infections (estimated from reported cases), aiming at determining the regional growth rate of infections and the point of inflection where infection rates begin to decrease and the curve flattens. All German counties exceeded the peak of new infections between the beginning of March and the middle of April. In a large majority of German counties, the epidemic curve has flattened before the "lockdown" was established. In a minority of counties, the peak was already exceeded before school closures. The growth rates of infections vary spatially depending on the time the virus emerged. Counties belonging to states which established an additional curfew show no significant improvement with respect to growth rates and mortality. Furthermore, mortality varies strongly across German counties, which can be attributed to infections of people belonging to the "risk group", especially residents of retirement homes. The decline of infections in absence of the "lockdown" measures could be explained by 1) earlier governmental interventions (e.g., cancellation of mass events, domestic quarantine), 2) voluntary behavior changes (e.g., physical distancing and hygiene), 3) seasonality of the virus, and 4) a rising but undiscovered level of immunity within the population. The results raise the question whether formal contact bans and curfews really contribute to curve flattening within a pandemic.

Knowledge about the origin of SARS-CoV-2 is necessary for both a biological and epidemiological understanding of the COVID-19 pandemic. Evidence suggests that a

Emergence of SARS-CoV-2 variants, including the globally successful B.1.1.7 lineage, suggests viral adaptations to host selective pressures resulting in more efficient transmission. Although much effort has focused on Spike adaptation for viral entry and adaptive immune escape, B.1.1.7 mutations outside Spike likely contribute to enhance transmission. Here we used unbiased abundance proteomics, phosphoproteomics, mRNA sequencing and viral replication assays to show that B.1.1.7 isolates more effectively suppress host innate immune responses in airway epithelial cells. We found that B.1.1.7 isolates have dramatically increased subgenomic RNA and protein levels of Orf9b and Orf6, both known innate immune antagonists. Expression of Orf9b alone suppressed the innate immune response through interaction with TOM70, a mitochondrial protein required for RNA sensing adaptor MAVS activation, and Orf9b binding and activity was regulated via phosphorylation. We conclude that B.1.1.7 has evolved beyond the Spike coding region to more effectively antagonise host innate immune responses through upregulation of specific subgenomic RNA synthesis and increased protein expression of key innate immune antagonists. We propose that more effective innate immune antagonism increases the likelihood of successful B.1.1.7 transmission, and may increase in vivo replication and duration of infection. ### Competing Interest Statement The Krogan Laboratory has received research support from Vir Biotechnology and F. Hoffmann-La Roche. Nevan Krogan has consulting agreements with the Icahn School of Medicine at Mount Sinai, New York, Maze Therapeutics and Interline Therapeutics, is a shareholder of Tenaya Therapeutics and has received stocks from Maze Therapeutics and Interline Therapeutics. The A.G.-S. laboratory has received research support from Pfizer, Senhwa Biosciences, Kenall Manufacturing, Avimex, Johnson & Johnson, Dynavax, 7Hills Pharma, Pharmamar, ImmunityBio, Accurius, Nanocomposix and Merck. A.G.-S. has consulting agreements for the following companies involving cash and/or stock: Vivaldi Biosciences, Contrafect, 7Hills Pharma, Avimex, Vaxalto, Pagoda, Accurius, Esperovax, Farmak and Pfizer. A.G.-S. is inventor on patents and patent applications on the use of antivirals and vaccines for the treatment and prevention of virus infections, owned by the Icahn School of Medicine at Mount Sinai, New York.

SARS-CoV-2 lineage B.1.1.7 viruses are more transmissible, may lead to greater clinical severity, and result in modest reductions in antibody neutralization. subgenomic RNA (sgRNA) is produced by discontinuous transcription of the SARS-CoV-2 genome and is a crucial step in the SARS-CoV-2 life cycle. Applying our tool (periscope) to ARTIC Network Oxford Nanopore genomic sequencing data from 4400 SARS-CoV-2 positive clinical samples, we show that normalised sgRNA expression profiles are significantly increased in B.1.1.7 infections (n=879). This increase is seen over the previous dominant circulating lineage in the UK, B.1.177 (n=943), which is independent of genomic reads, E gene cycle threshold and days since symptom onset at sampling. A noncanonical sgRNA which could represent ORF9b is found in 98.4% of B.1.1.7 SARS-CoV-2 infections compared with only 13.8% of other lineages, with a 16-fold increase in median expression. We hypothesise that this is a direct consequence of a triple nucleotide mutation in nucleocapsid (28280:GAT>CAT, D3L) creating a transcription regulatory-like sequence complementary to a region 3’ of the genomic leader. These findings provide a unique insight into the biology of B.1.1.7 and support monitoring of sgRNA profiles in sequence data to evaluate emerging potential variants of concern. One Sentence Summary The recently emerged and more transmissible SARS-CoV-2 lineage B.1.1.7 shows greater subgenomic RNA expression in clinical infections and enhanced expression of a noncanonical subgenomic RNA near ORF9b. ### Competing Interest Statement The authors have declared no competing interest.

The rapid spread of the B.1.1.7 variant — also called Alpha — might be linked to its ability to dampen the body’s initial immune response.

Research and data to make progress against the world’s largest problems

Throughout 2020, the notion that the novel coronavirus leaked from a lab was off-limits. Those who dared to push for transparency say toxic politics and hidden agendas kept us in the dark.

A proportion of patients surviving acute COVID-19 infection develop post-COVID syndrome (long COVID) encompassing physical and neuropsychiatric symptoms lasting longer than 12 weeks. Here we studied a prospective cohort of individuals with long COVID (the ADAPT study) compared to age/gender matched subjects without long COVID, healthy donors and individuals infected with other non-SARS CoV2 human coronaviruses (the ADAPT-C study). We found an elevated diffuse serum inflammatory cytokine profile in symptomatic long COVID subjects that was maintained at 8 months post-infection and was not observed in asymptomatic COVID-19 survivors. This inflammatory profile consisted of 15 cytokines that positively correlated; revealing an apparent diffuse, potentially coordinated, low level up regulation of a spectrum of immune and inflammatory mediators. In addition, we found an absence of subsets of un-activated naїve T and B cells in peripheral blood of long COVID subjects, that did not reconstitute over time. In contrast, individual serum cytokines from the interferon I and III classes, T cell activation markers and plasma ACE2, while elevated in the serum of people previously infected with SARS-CoV-2 were not further elevated in subjects with long COVID symptoms. This work defines immunological parameters associated with long COVID and suggests future opportunities to prevention and treatment. ### Competing Interest Statement The authors have declared no competing interest. ### Clinical Trial ACTRN12620000554965 ### Funding Statement We appreciate grant support from the St Vincents Clinic Foundation - the Curran Foundation, the Rapid Response Research Fund (UNSW) - the Medical Research Futures Fund (Australia) - NHMRC programme grant APP 1055214 (LMB) - Medical Research Future Fund award GNT 1175865 - - Austin Medical Research Foundation Grant - the Victorian Government - MRFF Award (2005544) - NHMRC program grant (1149990) - NHMRC Fellowships 1136322 and 1123673 ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: - The ADAPT study was approved by the St Vincents Hospital Research Ethics Committee (2020/ETH00964) and is a registered trial (ACTRN12620000554965) - ADAPT-C sub study was approved by the same committee (2020/ETH01429) - All data were stored using REDCap electronic data capture tools - Unexposed healthy donors were recruited through St Vincents Hospital and was approved by St Vincents Hospital Research Ethics Committee (HREC/13/SVH/145) - The University of Melbourne unexposed donors were approved by Medicine and Dentistry HESC-Study ID 2056689. All participants gave written informed consent. All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Source data files will be uploaded upon submission to intended journal

Mortality patterns of selected European countries and regions, week ending 3 January 2020 (Week 1) to week ending 1 January 2021 (Week 53).

Introduction This observational study looked at 255 COVID19 patients who required invasive mechanical ventilation (IMV) during the first two months of the US pandemic. Through comprehensive, longitudinal evaluation and new consideration of all the data, we were able to better describe and understand factors affecting outcome after intubation. Methods All vital signs, laboratory values, and medication administrations (time, date, dose, and route) were collected and organized. Further, each patient’s prior medical records, including PBM data and available ECG, were reviewed by a physician. These data were incorporated into time-series database for statistical analysis. Results By discharge or Day 90, 78.2% of the cohort expired. The most common pre-existing conditions were hypertension, (63.5%), diabetes (59.2%) and obesity (50.4%). Age correlated with death. Comorbidities and clinical status on presentation were not predictive of outcome. Admission markers of inflammation were universally elevated (>96%). The cohort’s weight range was nearly 7-fold. Causal modeling establishes that weight-adjusted HCQ and AZM therapy improves survival by over 100%. QTc prolongation did not correlate with cumulative HCQ dose or HCQ serum levels. Discussion This detailed approach gives us better understanding of risk factors, prognostic indicators, and outcomes of Covid patients needing IMV. Few variables were related to outcome. By considering more factors and using new methods, we found that when increased doses of co-administered HCQ and AZM were associated with >100% increase in survival. Comparison of absolute with weight-adjusted cumulative doses proves administration ≥80 mg/kg of HCQ with > 1 gm AZM increases survival in IMV-requiring Covid patients by over 100%. According to our data, HCQ is not associated with prolongation. Studies, which reported QTc prolongation secondary to HCQ, need to be re-evaluated more stringently and with controls. The weight ranges of Covid patient cohorts are substantially greater than those of most antibiotic RCTs. Future clinical trials need to consider the weight variance of hospitalized Covid patients and need to study therapeutics more thoughtfully. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement No external funding. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Saint Barnabas Medical Center is a 557-bed, teaching medical center in Livingston, New Jersey. Using the hospital's discharge coding data, we identified 255 Covid patients, who were admitted by May 1, 2020 and required invasive mechanical ventilation (IMV). Ethical approval for the study was granted by the hospital's Institutional Review Board. All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Data were extracted and combined into a database. Each patient's data were combined into a dataset containing relevant time, date, result, and dosing information. In addition, each chart was reviewed to extract past medical history, prior medical attention, and outpatient medication use. These are available upon request in large format time series datasets in CSV filetype.

Most of the major life-threatening symptoms of COVID-19 disease are associated with the host's generation of an exacerbated pro-inflammatory immune response. Many studies have correlated the intensity of the immune response with the severity of disease.

Rapid growth of the SARS-CoV-2 variant B.1.617.2 has been observed in many countries. The factors driving the recent rapid growth of COVID-19 cases could be attributed to shorten generation intervals or higher transmissibility (effective reproduction number, R), or both. Establishing the reasons for the observed rapid growth is key for outbreak control. In this study, we analysed the serial interval of household transmission pairs infected with SARS-CoV-2 B.1.617.2 variant and compared with those who were infected prior to the occurrence of the major global SARS-CoV-2 variants. After controlling for confounding factors, our findings suggest no significant changes in the serial intervals for SARS-CoV-2 cases infected with the B.1.617.2 variant. This, in turn, lends support for the hypothesis of a higher R in B.1.617.2 cases. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The following funding sources are acknowledged as providing funding for the named authors. Singapore Ministry of Health (RP). This research was partly funded by the National Institute for Health Research (NIHR) using UK aid from the UK Government to support global health research. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK Department of Health and Social Care (NIHR200908: AJK). Wellcome Trust (206250/Z/17/Z: AJK). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: London School of Hygiene and Tropical Medicine Ethics Committee All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All code and data to reproduce the analysis can be found at https://github.com/rachaelpung/serial\_interval\_covid_b.1.617.2 [https://github.com/rachaelpung/serial\_interval\_covid_b.1.617.2][1] [1]: https://github.com/rachaelpung/serial_interval_covid_b.1.617.2

The emergence and spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and subsequent mitigation measures have caused widespread social disruption. These disruptions have also affected community transmission of endemic diseases and the seasonal circulation patterns of other respiratory viruses. In both the Northern and Southern hemispheres, within-season influenza activity has been at historically low levels since 2020 ([ 1 ][1], [ 2 ][2]). Additionally, the circulation of human metapneumovirus, enterovirus, adenovirus, respiratory syncytial virus (RSV), and rhinovirus has been substantially reduced ([ 3 ][3]). These reductions in respiratory virus infections are linked to changes in health care–seeking behaviors and limited surveillance capacity, but mostly to the widespread implementation of nonpharmacological interventions (NPIs) to control SARS-CoV-2 transmission. How this will affect the transmission patterns of endemic respiratory viruses remains unknown. NPIs such as face mask use, increased handwashing practices, social distancing, and restrictions of global mobility have been key measures in reducing circulation of other respiratory viruses. As NPIs are relaxed and vaccination programs increase to control SARS-CoV-2 infections, countries have started to report increases in activity and circulation of certain viruses, such as RSV and rhinoviruses, with atypical timing ([ 3 ][3]–[ 6 ][4]). It is unclear why similar trends of resurgence (off-seasonal increases) have not been observed so far in other respiratory viruses, such as influenza, following relaxation of NPI measures. Currently, questions remain as to what the downstream impact of the COVID-19 pandemic and our response to it will be on circulation patterns of endemic respiratory viruses. What can be expected once this pandemic subsides and NPIs are lifted? If there is a reduction of population-level immunity, endemic respiratory viruses could resurge with atypical patterns and/or with high attack rates (higher risk of infection during a specific time period) owing to the large susceptible population. Current disruption in respiratory virus circulation could also lead to changes in their epidemiology—for example, changes in age distribution or disease severity. Moreover, it is unclear how many years it would take to reestablish regular seasonal patterns and whether new pandemic threats can be expected, especially considering the unpredictability of influenza virus evolution and the role of animal reservoirs (see the figure). Modeling studies have started to explore the impact of an increase in population susceptibility due to minimal RSV and influenza virus infections in 2020–2021 on the magnitude of subsequent seasons ([ 7 ][5]). RSV is a common respiratory virus that often circulates during cold months in temperate countries, causing mostly mild disease in the general population but with a risk for severe disease in infants and the elderly. Contrary to influenza viruses, RSV has no known animal reservoir. Two main antigenic groups (A and B) present variability that may contribute to the ability of RSV to establish reinfections throughout a life span. Data from surveillance systems have recently identified off-season circulation of RSV in both Northern and Southern hemispheres, albeit of lower magnitude than in previously documented RSV seasons and despite some NPIs still in use. This increased circulation could have been driven by an increased susceptibility in the very young and waning of immunity among adults ([ 5 ][6]). Periodic circulation of RSV, even if limited, may minimize the pool of susceptible population in the long term and prevent large outbreaks in the future ([ 6 ][4]). For influenza viruses, the overall modeling conclusions are less robust than for RSV ([ 7 ][5]). The rapid evolution and the dynamics of host immunity associated with influenza virus infections add further uncertainty and complexity to the modeling forecast. Although initial modeling analyses ([ 7 ][5]) help illustrate broad scenarios of the possible impact of the COVID-19 pandemic on endemic respiratory diseases, they also highlight the gaps in data and knowledge on viral interference theories (which explain how an individual infected by a virus becomes resistant to infection by a second virus), environmental and temperature effects on virus seasonality, and the role of immunity in transmission at the population level. Theoretically, in the case of influenza virus, limited community transmission, as documented in the last seasons, could present less opportunity for viral mutations ([ 8 ][7]) through antigenic drift (a process of gradual accumulation of mutations in the surface glycoproteins, or antigens, of the influenza virus). Overall, the lack of new mutation opportunities could limit the variability of circulating influenza viruses ([ 9 ][8], [ 10 ][9]). In turn, those viruses accumulating mutations could face limited antigenic selection due to a lower immunological pressure because there is a reduction in population-wide immunity, despite the increased influenza vaccination coverage observed in 2020 in various countries ([ 11 ][10]). The pool of susceptible individuals could also change qualitatively, with children becoming especially vulnerable during future influenza epidemics if the rest of the population maintains cross-protection from infection with previous seasonal strains. The implication of this scenario is the possibility of future (larger) influenza seasonal outbreaks affecting clinically different subpopulations. Nonetheless, if more homogeneous populations of viruses are observed, disease could be controlled through well-matched vaccines. Conversely, reduced population-wide immunity could allow for the emergence of variant strains with pandemic potential, including those possibly introduced from other species. This is observed, for example, with H3N2v viruses, which are often detected during summertime in the US from exposure to swine in agricultural fairs ([ 12 ][11]). These variant strains mostly affect children because population immunity from other H3N2 circulating viruses may be controlling their spread among the adult population ([ 13 ][12]). Further research into the underlying mechanisms determining the epidemiological features of specific respiratory viruses that considers viral evolution, interactions among viruses, and between virus and host immunity is needed. This will help identify emerging pandemic threats as well as better prepare for the long-term management of future outbreaks and epidemics. The evolution of SARS-CoV-2 and the appearance of variants threatening the effectiveness of newly authorized vaccines have underlined the importance and limitations of genomic surveillance networks globally. The uncertainty in future scenarios for other respiratory viruses in the post–COVID-19 period, including possible surges off-season and changes in clinical burden distribution, raises the need for an improved and comprehensive approach to respiratory disease and viral genomic surveillance. Widespread virus genomic surveillance embedded as part of national disease surveillance efforts and with links to clinical and epidemiological data could not only help monitor evolution but also identify those changes in strains associated with increased disease severity or vaccine breakthrough. It could improve current and new vaccine targets by refining vaccine strain selection against COVID-19 and influenza. ![Figure][13] Patterns of respiratory virus infections During the pandemic, circulation patterns of respiratory viruses other than severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been disrupted. This could mean a future shift in the epidemiology of respiratory diseases, potential for new epidemic threats, or larger outbreaks than previously observed. It is also unknown how long it will take for seasonal circulation patterns to return to prepandemic levels. Graphs illustrate trends in detection of respiratory viruses. Data are from respiratory illness surveillance in participating sites of the Global Influenza Hospital Surveillance Network ([ 14 ][14]). GRAPHIC: N. DESAI/ SCIENCE The COVID-19 pandemic has exposed the extent to which countries are still ill-prepared to monitor the emergence of new viruses, to assess their potential public health risk and the effectiveness of public health responses. The World Health Organization's Global Influenza Surveillance and Response System (GISRS) network, though providing much needed surveillance coverage, has limited linkages to clinical data, and global hospital-based surveillance networks once heavily supported by the US Centers for Disease Control and Prevention have suffered from disinvestments in recent years. Ensuring effective and real-time data sharing, expanding geographical coverage, and integrating genomic data of identified viruses with clinical data will require dedicated financing mechanisms and a stronger scientific collaboration between diagnostic and pharmaceutical companies, public health authorities, and academic institutions. The Global Influenza Hospital Surveillance Network (GIHSN) has provided a proof of concept for the expansion of current systems ([ 14 ][14]). This public-private partnership initiative was built 8 years ago to improve surveillance of influenza viruses and covers more than 100 hospitals across over 20 countries. Centers are asked to identify episodes of severe acute respiratory illness among hospitalized patients, testing primarily for influenza virus but covering other selected respiratory viruses, such as SARS-CoV-2, as resources allow. The network then ensures that virus whole-genome sequencing data are linked to epidemiologic and clinical data. The genomic sequences are uploaded into GISAID, a global data-sharing platform that has become the largest database of SARS-CoV-2 genomic data ([ 15 ][15]). These i

2) We are expecting the #DeltaVariant to continue growing in the US, outpacing all other variants just like it has in India 🇮🇳 and UK 🇬🇧, and become dominant in US by end of July, if not earlier. Here is the prediction track - via @TWenseleers from @GISAID data. https://t.co/VRWL9abxZd

They Knew. They Ignored. The Reckoning, One Year In. Welcome to the first article of Uncharted Territories! In the next few ones, we’re going to draw lessons from the last year of the pandemic while I start introducing new topics I’m very excited about! For example, there’s an in-depth look at remote work, and another one about how everything you’ve ever been told about History is false. Through the regular articles, I’ll make sure you get a good sense for the premium ones. And, as always, all articles related to the management of COVID will be free. For today, we’re going to expose the failures, expose the excuses, expose the lies, expose what we knew one year ago that we didn’t learn fast enough, and the true reasons why the West failed. If you are receiving this from a friend, feel free to subscribe!

Concerns surrounding new strains of SARS-CoV-2 (hCoV-19), the virus behind the COVID-19 pandemic, have been developing. This report outlines the prevalence of the B.1.617.2 lineage in the world, how it is changing over time, and how its prevalence varies across different locations.

Comparing the impact of the COVID-19 pandemic between countries or across time is difficult because the reported numbers of cases and deaths can be strongly affected by testing capacity and reporting policy. Excess mortality, defined as the increase in all-cause mortality relative to the expected mortality, is widely considered as a more objective indicator of the COVID-19 death toll. However, there has been no global, frequently-updated repository of the all-cause mortality data across countries. To fill this gap, we have collected weekly, monthly, or quarterly all-cause mortality data from 94 countries and territories, openly available as the regularly-updated World Mortality Dataset. We used this dataset to compute the excess mortality in each country during the COVID-19 pandemic. We found that in several worst-affected countries (Peru, Ecuador, Bolivia, Mexico) the excess mortality was above 50% of the expected annual mortality. At the same time, in several other countries (Australia, New Zealand) mortality during the pandemic was below the usual level, presumably due to social distancing measures decreasing the non-COVID infectious mortality. Furthermore, we found that while many countries have been reporting the COVID-19 deaths very accurately, some countries have been substantially underreporting their COVID-19 deaths (e.g. Nicaragua, Russia, Uzbekistan), sometimes by two orders of magnitude (Tajikistan). Our results highlight the importance of open and rapid all-cause mortality reporting for pandemic monitoring. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement DK was supported by the Deutsche Forschungsgemeinschaft (BE5601/4-1 and the Cluster of Excellence "Machine Learning \---| New Perspectives for Science", EXC 2064, project number 390727645), the Federal Ministry of Education and Research (FKZ 01GQ1601 and 01IS18039A) and the National Institute of Mental Health of the National Institutes of Health under Award Number U19MH114830. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Non applicable. All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The dataset is available at https://github.com/akarlinsky/world_mortality The analysis code is available at https://github.com/dkobak/excess-mortality

Even as the pandemic appears ready to recede in the United States, dropping below an average of 30,000 new cases daily, it will take years to more fully understand the way the virus afflicts the brain.