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In many regions of the world, the Alpha, Beta and Gamma SARS-CoV-2 Variants of Concern (VOCs) co-circulated during 2020-21 and fueled waves of infections. During 2021, these variants were almost completely displaced by the Delta variant, causing a third wave of infections worldwide. This phenomenon of global viral lineage displacement was observed again in late 2021, when the Omicron variant disseminated globally. In this study, we use phylogenetic and phylogeographic methods to reconstruct the dispersal patterns of SARS-CoV-2 VOCs worldwide. We find that the source-sink dynamics of SARS-CoV-2 varied substantially by VOC, and identify countries that acted as global hubs of variant dissemination, while other countries became regional contributors to the export of specific variants. We demonstrate a declining role of presumed origin countries of VOCs to their global dispersal: we estimate that India contributed <15% of all global exports of Delta to other countries and South Africa <1-2% of all global Omicron exports globally. We further estimate that >80 countries had received introductions of Omicron BA.1 100 days after its inferred date of emergence, compared to just over 25 countries for the Alpha variant. This increased speed of global dissemination was associated with a rebound in air travel volume prior to Omicron emergence in addition to the higher transmissibility of Omicron relative to Alpha. Our study highlights the importance of global and regional hubs in VOC dispersal, and the speed at which highly transmissible variants disseminate through these hubs, even before their detection and characterization through genomic surveillance. HighlightsO_LIGlobal phylogenetic analysis reveals relationship between air travel and speed of dispersal of SARS-CoV-2 variants of concern (VOCs) C_LIO_LIOmicron VOC spread to 5x more countries within 100 days of its emergence compared to all other VOCs C_LIO_LIOnward transmission and dissemination of VOCs Delta and Omicron was primarily from secondary hubs rather than initial country of detection during a time of increased global air travel C_LIO_LIAnalysis highlights highly connected countries identified as major global and regional exporters of VOCs C_LI
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BackgroundSARS-CoV-2 serologic surveys estimate the proportion of the population with antibodies against historical variants which nears 100% in many settings. New analytic approaches are required to exploit the full information in serosurvey data. MethodUsing a SARS-CoV-2 anti-Spike (S) protein chemiluminescent microparticle assay, we attained a semi-quantitative measurement of population IgG titres in serial cross-sectional monthly samples of routine blood donations across seven Brazilian state capitals (March 2021-November 2021). In an ecological analysis (unit of analysis: age-city-calendar month) we assessed the relative contributions of prior attack rate and vaccination to antibody titre in blood donors. We compared blood donor anti-S titre across the seven cities during the growth phase of the Delta variant of concern (VOC) and use this to predict the resulting age-standardized incidence of severe COVID-19 cases. ResultsOn average we tested 780 samples per month in each location. Seroprevalence rose to >95% across all seven capitals by November 2021. Driven proximally by vaccination, mean antibody titre increased 16-fold over the study. The extent of prior natural infection shaped this process, with the greatest increases in antibody titres occurring in cities with the highest prior attack rates. Mean anti-S IgG was a strong predictor (adjusted R2 =0.89) of the number of severe cases caused by the Delta VOC in the seven cities. ConclusionsSemi-quantitative anti-S antibody titres are informative about prior exposure and vaccination coverage and can inform on the potential impact of future SARS-CoV-2 variants. SummaryIn the face of near 100% SARS-CoV-2 seroprevalence, we show that average semi-quantitative anti-S titre predicted the extent of the Delta variants spread in Brazil. This is a valuable metric for future seroprevalence studies.
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SARS-CoV-2 virus genomes are currently being sequenced at an unprecedented pace. The choice of viral sequences used in genetic and epidemiological analysis is important as it can induce biases that detract from the value of these rich datasets. This raises questions about how a set of sequences should be chosen for analysis, and which epidemiological parameters derived from genomic data are sensitive or robust to changes in sampling. We provide initial insights on these largely understudied problems using SARS-CoV-2 genomic sequences from Hong Kong, China, and the Amazonas State, Brazil. We consider sampling schemes that select sequences uniformly, in proportion or reciprocally with case incidence and which simply use all available sequences (unsampled). We apply Birth-Death Skyline and Skygrowth methods to estimate the time-varying reproduction number (Rt) and growth rate (rt) under these strategies as well as related R0 and date of origin parameters. We compare these to estimates from case data derived from EpiFilter, which we use as a reference for assessing bias. We find that both Rt and rt are sensitive to changes in sampling whilst R0 and the date of origin are relatively robust. Moreover, we find that analysis using unsampled datasets, which reflect an opportunistic sampling scheme, result in the most biased Rt and rt estimates for both our Hong Kong and Amazonas case studies. We highlight that sampling strategy choices may be an influential yet neglected component of sequencing analysis pipelines. More targeted attempts at genomic surveillance and epidemic analyses, particularly in settings with limited sequencing capabilities, are necessary to maximise the informativeness of virus genomic datasets.
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The Delta variant of concern of SARS-CoV-2 has spread globally causing large outbreaks and resurgences of COVID-19 cases1-3. The emergence of Delta in the UK occurred on the background of a heterogeneous landscape of immunity and relaxation of non-pharmaceutical interventions4,5. Here we analyse 52,992 Delta genomes from England in combination with 93,649 global genomes to reconstruct the emergence of Delta, and quantify its introduction to and regional dissemination across England, in the context of changing travel and social restrictions. Through analysis of human movement, contact tracing, and virus genomic data, we find that the focus of geographic expansion of Delta shifted from India to a more global pattern in early May 2021. In England, Delta lineages were introduced >1,000 times and spread nationally as non-pharmaceutical interventions were relaxed. We find that hotel quarantine for travellers from India reduced onward transmission from importations; however the transmission chains that later dominated the Delta wave in England had been already seeded before restrictions were introduced. In England, increasing inter-regional travel drove Deltas nationwide dissemination, with some cities receiving >2,000 observable lineage introductions from other regions. Subsequently, increased levels of local population mixing, not the number of importations, was associated with faster relative growth of Delta. Among US states, we find that regions that previously experienced large waves also had faster Delta growth rates, and a model including interactions between immunity and human behaviour could accurately predict the rise of Delta there. Deltas invasion dynamics depended on fine scale spatial heterogeneity in immunity and contact patterns and our findings will inform optimal spatial interventions to reduce transmission of current and future VOCs such as Omicron.
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Mathematical models can provide insights into the control of pandemic COVID-19, which remains a global priority. The dynamics of directly-transmitted infectious diseases, such as COVID-19, are usually described by compartmental models where individuals are classified as susceptible, infected and removed. These SIR models typically assume homogenous transmission of infection, even in large populations, a simplification that is convenient but inconsistent with observations. Here we use original data on the dynamics of COVID-19 spread in a Brazilian city to investigate the structure of the transmission network. We find that transmission can be described by a network in which each infectious individual has a small number of susceptible contacts, of the order of 2-5, which is independent of total population size. Compared with standard models of homogenous mixing, this scale-free, fractal infection process gives a better description of COVID-19 dynamics through time. In addition, the contact process explains the geographically localized clusters of disease seen in this Brazilian city. Our scale-free model can help refine criteria for physical and social distancing in order to more effectively mitigate the spread of COVID-19. We propose that scale-free COVID-19 dynamics could be a widespread phenomenon, a topic for further investigation.
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Genomic sequencing provides critical information to track the evolution and spread of SARS-CoV-2, optimize molecular tests, treatments and vaccines, and guide public health responses. To investigate the spatiotemporal heterogeneity in the global SARS-CoV-2 genomic surveillance, we estimated the impact of sequencing intensity and turnaround times (TAT) on variant detection in 167 countries. Most countries submit genomes >21 days after sample collection, and 77% of low and middle income countries sequenced <0.5% of their cases. We found that sequencing at least 0.5% of the cases, with a TAT <21 days, could be a benchmark for SARS-CoV-2 genomic surveillance efforts. Socioeconomic inequalities substantially impact our ability to quickly detect SARS-CoV-2 variants, and undermine the global pandemic preparedness. One-Sentence SummarySocioeconomic inequalities impacted the SARS-CoV-2 genomic surveillance, and undermined the global pandemic preparedness.
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BackgroundBrazil is one of the countries worst affected by the COVID-19 pandemic with over 20 million cases and 557,000 deaths reported. Comparison of real-time local COVID-19 data between areas is essential for understanding transmission, measuring the effects of interventions and predicting the course of the epidemic, but are often challenging due to different population sizes and structures. MethodsWe describe the development of a new app for the real-time visualisation of COVID-19 data in Brazil at the municipality level. In the CLIC-Brazil app, daily updates of case and death data are downloaded, age standardised and used to estimate reproduction number (Rt). We show how such platforms can perform real-time regression analyses to identify factors associated with the rate of initial spread and early reproduction number. We also use survival methods to predict the likelihood of occurrence of a new peak of COVID-19 incidence. FindingsAfter an initial introduction in Sao Paulo and Rio de Janeiro states in early March 2020, the epidemic spread to Northern states and then to highly populated coastal regions and the Central-West. Municipalities with higher metrics of social development experienced earlier arrival of COVID-19 (decrease of 11{middle dot}1 days [95% CI:13{middle dot}2,8{middle dot}9] in the time to arrival for each 10% increase in the social development index). Differences in the initial epidemic intensity (mean Rt) were largely driven by geographic location and the date of local onset. InterpretationThis study demonstrates that platforms that monitor, standardise and analyse the epidemiological data at a local level can give useful real-time insights into outbreak dynamics that can be used to better adapt responses to the current and future pandemics. FundingThis project was supported by a Medical Research Council UK (MRC-UK) -Sao Paulo Research Foundation (FAPESP) CADDE partnership award (MR/S0195/1 and FAPESP 18/14389-0)
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BackgroundThe city of Manaus, north Brazil, was stricken by a second epidemic wave of SARS-CoV-2 despite high seroprevalence estimates, coinciding with the emergence of the Gamma (P.1) variant. Reinfections were postulated as a partial explanation for the second surge. However, accurate calculation of reinfection rates is difficult when stringent criteria as two time-separated RT-PCR tests and/or genome sequencing are required. To estimate the proportion of reinfections caused by the Gamma variant during the second wave in Manaus and the protection conferred by previous infection, we analyzed a cohort of repeat blood donors to identify anti-SARS-CoV-2 antibody boosting as a means to infer reinfection. MethodsWe tested serial blood samples from unvaccinated repeat blood donors in Manaus for the presence of anti-SARS-CoV-2 IgG antibody. Donors were required to have three or more donations and at least one donation during each epidemic wave. Donors were tested with two assays that display waning in early convalescence, enabling the detection of reinfection-induced boosting. The serial samples were used to divide donors into six groups defined based on the inferred sequence of infection and reinfection with non-Gamma and Gamma variants. ResultsFrom 3,655 repeat blood donors, 238 met all inclusion criteria, and 223 had enough residual sample volume to perform both serological assays. Using a strict serological definition of reinfection, we found 13.6% (95% CI 7.0% - 24.5%) of all presumed Gamma infections that were observed in 2021 were reinfections. If we also include cases of probable or possible reinfections, these percentages increase respectively to 22.7% (95% CI 14.3% - 34.2%) and 39.3% (95% CI 29.5% - 50.0%). Previous infection conferred a protection against reinfection of 85.3% (95% CI 71.3% - 92.7%), decreasing to respectively 72.5% (95% CI 54.7% - 83.6%) and 39.5% (95% CI 14.1% - 57.8%) if probable and possible reinfections are included. ConclusionsReinfection due to Gamma is common and may play a significant role in epidemics where Gamma is prevalent, highlighting the continued threat variants of concern pose even to settings previously hit by substantial epidemics.
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Characterisation of SARS-CoV-2 genetic diversity through space and time can reveal trends in virus importation and domestic circulation, and permit the exploration of questions regarding the early transmission dynamics. Here we present a detailed description of SARS-CoV-2 genomic epidemiology in Ecuador, one of the hardest hit countries during the early stages of the COVID-19 pandemic. We generate and analyse 160 whole genome sequences sampled from all provinces of Ecuador in 2020. Molecular clock and phylgeographic analysis of these sequences in the context of global SARS-CoV-2 diversity enable us to identify and characterise individual transmission lineages within Ecuador, explore their spatiotemporal distributions, and consider their introduction and domestic circulation. Our results reveal a pattern of multiple international importations across the country, with apparent differences between key provinces. Transmission lineages were mostly introduced before the implementation of non-pharmaceutical interventions (NPIs), with differential degrees of persistence and national dissemination.
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Cases of SARS-CoV-2 infection in Manaus, Brazil, resurged in late 2020, despite high levels of previous infection there. Through genome sequencing of viruses sampled in Manaus between November 2020 and January 2021, we identified the emergence and circulation of a novel SARS-CoV-2 variant of concern, lineage P.1, that acquired 17 mutations, including a trio in the spike protein (K417T, E484K and N501Y) associated with increased binding to the human ACE2 receptor. Molecular clock analysis shows that P.1 emergence occurred around early November 2020 and was preceded by a period of faster molecular evolution. Using a two-category dynamical model that integrates genomic and mortality data, we estimate that P.1 may be 1.4-2.2 times more transmissible and 25-61% more likely to evade protective immunity elicited by previous infection with non-P.1 lineages. Enhanced global genomic surveillance of variants of concern, which may exhibit increased transmissibility and/or immune evasion, is critical to accelerate pandemic responsiveness. One-Sentence SummaryWe report the evolution and emergence of a SARS-CoV-2 lineage of concern associated with rapid transmission in Manaus.
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With the emergence of SARS-CoV-2 variants that may increase transmissibility and/or cause escape from immune responses1-3, there is an urgent need for the targeted surveillance of circulating lineages. It was found that the B.1.1.7 (also 501Y.V1) variant first detected in the UK4,5 could be serendipitously detected by the ThermoFisher TaqPath COVID-19 PCR assay because a key deletion in these viruses, spike {Delta}69-70, would cause a "spike gene target failure" (SGTF) result. However, a SGTF result is not definitive for B.1.1.7, and this assay cannot detect other variants of concern that lack spike {Delta}69-70, such as B.1.351 (also 501Y.V2) detected in South Africa6 and P.1 (also 501Y.V3) recently detected in Brazil7. We identified a deletion in the ORF1a gene (ORF1a {Delta}3675-3677) in all three variants, which has not yet been widely detected in other SARS-CoV-2 lineages. Using ORF1a {Delta}3675-3677 as the primary target and spike {Delta}69-70 to differentiate, we designed and validated an open source PCR assay to detect SARS-CoV-2 variants of concern8. Our assay can be rapidly deployed in laboratories around the world to enhance surveillance for the local emergence spread of B.1.1.7, B.1.351, and P.1.
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BackgroundLittle evidence exists on the differential health effects of COVID-19 on disadvantaged population groups. Here we characterise the differential risk of hospitalisation and death in Sao Paulo state, Brazil and show how vulnerability to COVID-19 is shaped by socioeconomic inequalities. MethodsWe conducted a cross-sectional study using hospitalised severe acute respiratory infections (SARI) notified from March to August 2020, in the Sistema de Monitoramento Inteligente de Sao Paulo (SIMI-SP) database. We examined the risk of hospitalisation and death by race and socioeconomic status using multiple datasets for individual-level and spatio-temporal analyses. We explained these inequalities according to differences in daily mobility from mobile phone data, teleworking behaviour, and comorbidities. FindingsThroughout the study period, patients living in the 40% poorest areas were more likely to die when compared to patients living in the 5% wealthiest areas (OR: 1{middle dot}60, 95% CI: 1{middle dot}48 - 1{middle dot}74) and were more likely to be hospitalised between April and July, 2020 (OR: 1{middle dot}08, 95% CI: 1{middle dot}04 - 1{middle dot}12). Black and Pardo individuals were more likely to be hospitalised when compared to White individuals (OR: 1{middle dot}37, 95% CI: 1{middle dot}32 - 1{middle dot}41; OR: 1{middle dot}23, 95% CI: 1{middle dot}21 - 1{middle dot}25, respectively), and were more likely to die (OR: 1{middle dot}14, 95% CI: 1{middle dot}07 - 1{middle dot}21; 1{middle dot}09, 95% CI: 1{middle dot}05 - 1{middle dot}13, respectively). InterpretationLow-income and Black and Pardo communities are more likely to die with COVID-19. This is associated with differential access to healthcare, adherence to social distancing, and the higher prevalence of comorbidities. FundingThis project was supported by a Medical Research Council-Sao Paulo Research Foundation (FAPESP) CADDE partnership award (MR/S0195/1 and FAPESP 18/14389-0) (http://caddecentre.org/). This work received funding from the U.K. Medical Research Council under a concordat with the U.K. Department for International Development.
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The UKs COVID-19 epidemic during early 2020 was one of worlds largest and unusually well represented by virus genomic sampling. Here we reveal the fine-scale genetic lineage structure of this epidemic through analysis of 50,887 SARS-CoV-2 genomes, including 26,181 from the UK sampled throughout the countrys first wave of infection. Using large-scale phylogenetic analyses, combined with epidemiological and travel data, we quantify the size, spatio-temporal origins and persistence of genetically-distinct UK transmission lineages. Rapid fluctuations in virus importation rates resulted in >1000 lineages; those introduced prior to national lockdown were larger and more dispersed. Lineage importation and regional lineage diversity declined after lockdown, whilst lineage elimination was size-dependent. We discuss the implications of our genetic perspective on transmission dynamics for COVID-19 epidemiology and control.
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The herd immunity threshold is the proportion of a population that must be immune to an infectious disease, either by natural infection or vaccination such that, in the absence of additional preventative measures, new cases decline and the effective reproduction number falls below unity. This fundamental epidemiological parameter is still unknown for the recently-emerged COVID-19, and mathematical models have predicted very divergent results. Population studies using antibody testing to infer total cumulative infections can provide empirical evidence of the level of population immunity in severely affected areas. Here we show that the transmission of SARS-CoV-2 in Manaus, located in the Brazilian Amazon, increased quickly during March and April and declined more slowly from May to September. In June, one month following the epidemic peak, 44% of the population was seropositive for SARS-CoV-2, equating to a cumulative incidence of 52%, after correcting for the false-negative rate of the antibody test. The seroprevalence fell in July and August due to antibody waning. After correcting for this, we estimate a final epidemic size of 66%. Although non-pharmaceutical interventions, plus a change in population behavior, may have helped to limit SARS-CoV-2 transmission in Manaus, the unusually high infection rate suggests that herd immunity played a significant role in determining the size of the epidemic.
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BackgroundWith more than 50000 accumulated cases, Panama has one of the highest incidences of SARS-CoV-2 in Central America, despite the fast implementation of disease control strategies. We investigated the early transmission patterns of the virus and the outcomes of mitigation measures in the country. MethodsWe collected information from epidemiological surveillance, including contact tracing, and genetic data from SARS-CoV-2 whole genomes, of the first five weeks of the outbreak. These data were used to estimate the exponential growth rate, doubling time and the time-varying effective reproductive number (Rt) using date of symptom onset in a Bayesian framework. The time of most recent ancestor for the introduced and circulating lineages was estimated by Bayesian analysis. FindingsA total of 4210 subjects were SARS-CoV-2 positive during the period evaluated, of them we sequenced 313 cases, detecting the circulation of 10 SARS-CoV-2 lineages. Whole genomes analysis identified the local transmission of one cryptic lineage as early as 2 weeks before it was detected by surveillance systems. Analysis of transmission dynamics showed that lockdown reduced Rt and increased the doubling time, however, these measures did not stop the circulation of this lineage in the country. InterpretationThese results demonstrate the value of epidemiological modeling and genome surveillance to assess mitigation strategies. At the same time, an active search for cryptic transmission clusters is crucial to interrupt local transmission of SARS-CoV-2 in a region. FundingMinistry of Health, Contribution from private donors and Secretaria Nacional de Ciencia y Tecnologia. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSIn May 2020, we searched for published studies in PubMed and web of Science related to genetic variability and dynamics of SARS-CoV-2 transmission in Latin America, and there was none. On July 2020, there was one study of this type on SARS-CoV-2 transmission in Brazil and none in Central America. We were particularly interested in SARS-CoV-2 cryptic transmission that could allow the virus spread through locals without being detected by respiratory health system surveillance, and no publication was reported. On July 2020, seven papers (five in preprint) were about SARS-CoV-2 cryptic transmission, one in China, another in UK and five in the US. None in Central America. All of them showed the importance of genomic surveillance to detect different lineage introductions, cryptic transmission and its role in early spread in a region or in health-care setting. Added value of this studyWe integrate data collected from tested individual during national surveillance of COVID19 suspected cases or contact of cases, as part of the National COVID19 Laboratory network. This data was used to estimate epidemiological parameters of the outbreak as well as the effect of mitigation measures on the epidemic dynamic. We sequence the whole genome of SARS-COV-2 of 7.4% of RT-PCR confirmed cases at the national level, and with phylogenetic analysis we identified SARS-CoV-2 lineages introduced in the country and estimate date of their introductions. Epidemiological and genetic data was compared and we observed the cryptic transmission of one introduced lineage and the rise of a local lineage that was not detected by the active contact tracing implemented by the health system surveillance. This cryptic lineage could explain the fact that early implementation measures decreased the transmission rate and the increased the doubling time, however they were not able to eliminate totally the virus spread. Implications of all the available evidenceThis is the first study that analyzed the epidemiology and transmission dynamics of the early COVID19 epidemic in a Central American country using both epidemiological and genomic surveillance. Our findings suggest that strict containment measures and movement restrictions in Panama might have contributed to decrease the early spread of the virus, but that cryptic local transmission allowed a continual basal virus diffusion that could explain, in part, the high incidence of cases in the country. More broadly, our findings are crucial to inform intervention policy in real-time, for countries in similar situations and the importance of constant monitoring of SARS-CoV-2 lineages to understand its transmission in a region.
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Using 65 transmission pairs of SARS-CoV-2 reported to the Brazilian Ministry of Health we estimate the mean and standard deviation for the serial interval to be 2.97 and 3.29 days respectively. We also present a model for the serial interval probability distribution using only two parameters.
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Social distancing measures have emerged as the predominant intervention for containing the spread of COVID-19, but evaluating adherence and effectiveness remains a challenge. We assessed the relationship between aggregated mobility data collected from mobile phone users and the time-dependent reproduction number R(t), using severe acute respiratory illness (SARI) cases reported by Sao Paulo and Rio de Janeiro. We found that the proportion of individuals staying home all day (isolation index) had a strong inverse correlation with R(t) (rho<-0.7) and was predictive of COVID-19 transmissibility (p<0.0001). Furthermore, indexs of 46.7% had the highest accuracy (93.9%) to predict R(t) below one. This metric can be monitored in real time to assess adherence to social distancing measures and predict their effectiveness for controlling SARS-CoV-2 transmission. One Sentence SummaryMobility data to monitoring social distancing in the COVID-19 outbreak
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BackgroundThe first case of COVID-19 was detected in Brazil on February 25, 2020. We report the epidemiological, demographic, and clinical findings for confirmed COVID-19 cases during the first month of the epidemic in Brazil. MethodsIndividual-level and aggregated COVID-19 data were analysed to investigate demographic profiles, socioeconomic drivers and age-sex structure of COVID-19 tested cases. Basic reproduction numbers (R0) were investigated for Sao Paulo and Rio de Janeiro. Multivariate logistic regression analyses were used to identify symptoms associated with confirmed cases and risk factors associated with hospitalization. Laboratory diagnosis for eight respiratory viruses were obtained for 2,429 cases. FindingsBy March 25, 1,468 confirmed cases were notified in Brazil, of whom 10% (147 of 1,468) were hospitalised. Of the cases acquired locally (77{middle dot}8%), two thirds (66{middle dot}9% of 5,746) were confirmed in private laboratories. Overall, positive association between higher per capita income and COVID-19 diagnosis was identified. The median age of detected cases was 39 years (IQR 30-53). The median R0 was 2{middle dot}9 for Sao Paulo and Rio de Janeiro. Cardiovascular disease/hypertension were associated with hospitalization. Co-circulation of six respiratory viruses, including influenza A and B and human rhinovirus was detected in low levels. InterpretationSocioeconomic disparity determines access to SARS-CoV-2 testing in Brazil. The lower median age of infection and hospitalization compared to other countries is expected due to a younger population structure. Enhanced surveillance of respiratory pathogens across socioeconomic statuses is essential to better understand and halt SARS-CoV-2 transmission. FundingSao Paulo Research Foundation, Medical Research Council, Wellcome Trust and Royal Society.
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COVID-19 is caused by the SARS-CoV-2 coronavirus and was first reported in central China in December 2019. Extensive molecular surveillance in Guangdong, Chinas most populous province, during early 2020 resulted in 1,388 reported RNA positive cases from 1.6 million tests. In order to understand the molecular epidemiology and genetic diversity of SARS-CoV-2 in China we generated 53 genomes from infected individuals in Guangdong using a combination of metagenomic sequencing and tiling amplicon approaches. Combined epidemiological and phylogenetic analyses indicate multiple independent introductions to Guangdong, although phylogenetic clustering is uncertain due to low virus genetic variation early in the pandemic. Our results illustrate how the timing, size and duration of putative local transmission chains were constrained by national travel restrictions and by the provinces large-scale intensive surveillance and intervention measures. Despite these successes, COVID-19 surveillance in Guangdong is still required as the number of cases imported from other countries is increasing. HighlightsO_LI1.6 million molecular diagnostic tests identified 1,388 SARS-CoV-2 infections in Guangdong Province, China, by 19th March 2020 C_LIO_LIVirus genomes can be recovered using a variety of sequencing approaches from a range of patient samples. C_LIO_LIGenomic analyses reveal multiple virus importations into Guangdong Province, resulting in genetically distinct clusters that require careful interpretation. C_LIO_LILarge-scale epidemiological surveillance and intervention measures were effective in interrupting community transmission in Guangdong C_LI
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HighlightThe global outbreak caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been declared a pandemic by the WHO. As the number of imported SARS-CoV-2 cases is on the rise in Brazil, we use incidence and historical air travel data to estimate the most important routes of importation into the country.
