Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 7 de 7
Filter
1.
Nature ; 610(7930): 154-160, 2022 10.
Article in English | MEDLINE | ID: covidwho-1991629

ABSTRACT

The SARS-CoV-2 Delta (Pango lineage B.1.617.2) variant of concern spread globally, causing resurgences of COVID-19 worldwide1,2. The emergence of the Delta variant in the UK occurred on the background of a heterogeneous landscape of immunity and relaxation of non-pharmaceutical interventions. Here we analyse 52,992 SARS-CoV-2 genomes from England together with 93,649 genomes from the rest of the world 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. Using analysis of human movement, contact tracing and virus genomic data, we find that the geographic focus of the expansion of Delta shifted from India to a more global pattern in early May 2021. In England, Delta lineages were introduced more than 1,000 times and spread nationally as non-pharmaceutical interventions were relaxed. We find that hotel quarantine for travellers reduced onward transmission from importations; however, the transmission chains that later dominated the Delta wave in England were seeded before travel restrictions were introduced. Increasing inter-regional travel within England drove the nationwide dissemination of Delta, with some cities receiving more than 2,000 observable lineage introductions from elsewhere. Subsequently, increased levels of local population mixing-and not the number of importations-were associated with the faster relative spread of Delta. The invasion dynamics of Delta depended on spatial heterogeneity in contact patterns, and our findings will inform optimal spatial interventions to reduce the transmission of current and future variants of concern, such as Omicron (Pango lineage B.1.1.529).


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/transmission , COVID-19/virology , Cities/epidemiology , Contact Tracing , England/epidemiology , Genome, Viral/genetics , Humans , Quarantine/legislation & jurisprudence , SARS-CoV-2/genetics , SARS-CoV-2/growth & development , SARS-CoV-2/isolation & purification , Travel/legislation & jurisprudence
2.
BMJ Glob Health ; 6(4)2021 04.
Article in English | MEDLINE | ID: covidwho-1476465

ABSTRACT

INTRODUCTION: Little 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 São Paulo state, Brazil, and show how vulnerability to COVID-19 is shaped by socioeconomic inequalities. METHODS: We conducted a cross-sectional study using hospitalised severe acute respiratory infections notified from March to August 2020 in the Sistema de Monitoramento Inteligente de São Paulo database. We examined the risk of hospitalisation and death by race and socioeconomic status using multiple data sets for individual-level and spatiotemporal analyses. We explained these inequalities according to differences in daily mobility from mobile phone data, teleworking behaviour and comorbidities. RESULTS: Throughout the study period, patients living in the 40% poorest areas were more likely to die when compared with patients living in the 5% wealthiest areas (OR: 1.60, 95% CI 1.48 to 1.74) and were more likely to be hospitalised between April and July 2020 (OR: 1.08, 95% CI 1.04 to 1.12). Black and Pardo individuals were more likely to be hospitalised when compared with White individuals (OR: 1.41, 95% CI 1.37 to 1.46; OR: 1.26, 95% CI 1.23 to 1.28, respectively), and were more likely to die (OR: 1.13, 95% CI 1.07 to 1.19; 1.07, 95% CI 1.04 to 1.10, respectively) between April and July 2020. Once hospitalised, patients treated in public hospitals were more likely to die than patients in private hospitals (OR: 1.40%, 95% CI 1.34% to 1.46%). Black individuals and those with low education attainment were more likely to have one or more comorbidities, respectively (OR: 1.29, 95% CI 1.19 to 1.39; 1.36, 95% CI 1.27 to 1.45). CONCLUSIONS: Low-income and Black and Pardo communities are more likely to die with COVID-19. This is associated with differential access to quality healthcare, ability to self-isolate and the higher prevalence of comorbidities.


Subject(s)
COVID-19/ethnology , COVID-19/mortality , Hospital Mortality/ethnology , Pneumonia, Viral , Poverty Areas , Residence Characteristics/statistics & numerical data , Adult , Aged , Aged, 80 and over , Brazil/epidemiology , Cross-Sectional Studies , Female , Health Status Disparities , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/epidemiology , SARS-CoV-2 , Seroepidemiologic Studies , Socioeconomic Factors
3.
Epidemics ; 36: 100478, 2021 09.
Article in English | MEDLINE | ID: covidwho-1274235

ABSTRACT

National influenza pandemic plans have evolved substantially over recent decades, as has the scientific research that underpins the advice contained within them. While the knowledge generated by many research activities has been directly incorporated into the current generation of pandemic plans, scientists and policymakers are yet to capitalise fully on the potential for near real-time analytics to formally contribute to epidemic decision-making. Theoretical studies demonstrate that it is now possible to make robust estimates of pandemic impact in the earliest stages of a pandemic using first few hundred household cohort (FFX) studies and algorithms designed specifically for analysing FFX data. Pandemic plans already recognise the importance of both situational awareness i.e., knowing pandemic impact and its key drivers, and the need for pandemic special studies and related analytic methods for estimating these drivers. An important next step is considering how information from these situational assessment activities can be integrated into the decision-making processes articulated in pandemic planning documents. Here we introduce a decision support tool that directly uses outputs from FFX algorithms to present recommendations on response options, including a quantification of uncertainty, to decision makers. We illustrate this approach using response information from within the Australian influenza pandemic plan.


Subject(s)
Influenza, Human , Australia , Humans , Influenza, Human/epidemiology , Pandemics/prevention & control , Policy
4.
Science ; 371(6530): 708-712, 2021 02 12.
Article in English | MEDLINE | ID: covidwho-1066806

ABSTRACT

The United Kingdom's COVID-19 epidemic during early 2020 was one of world's largest and was unusually well represented by virus genomic sampling. We determined the fine-scale genetic lineage structure of this epidemic through analysis of 50,887 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes, including 26,181 from the UK sampled throughout the country's first wave of infection. Using large-scale phylogenetic analyses combined with epidemiological and travel data, we quantified the size, spatiotemporal 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 tended to be larger and more dispersed. Lineage importation and regional lineage diversity declined after lockdown, whereas lineage elimination was size-dependent. We discuss the implications of our genetic perspective on transmission dynamics for COVID-19 epidemiology and control.


Subject(s)
COVID-19/epidemiology , COVID-19/virology , Genome, Viral , SARS-CoV-2/genetics , COVID-19/prevention & control , COVID-19/transmission , Chain of Infection , Communicable Disease Control , Communicable Diseases, Imported/epidemiology , Communicable Diseases, Imported/virology , Epidemics , Humans , Phylogeny , Travel , United Kingdom/epidemiology
5.
Nat Med ; 26(12): 1829-1834, 2020 12.
Article in English | MEDLINE | ID: covidwho-834900

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic is straining public health systems worldwide, and major non-pharmaceutical interventions have been implemented to slow its spread1-4. During the initial phase of the outbreak, dissemination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was primarily determined by human mobility from Wuhan, China5,6. Yet empirical evidence on the effect of key geographic factors on local epidemic transmission is lacking7. In this study, we analyzed highly resolved spatial variables in cities, together with case count data, to investigate the role of climate, urbanization and variation in interventions. We show that the degree to which cases of COVID-19 are compressed into a short period of time (peakedness of the epidemic) is strongly shaped by population aggregation and heterogeneity, such that epidemics in crowded cities are more spread over time, and crowded cities have larger total attack rates than less populated cities. Observed differences in the peakedness of epidemics are consistent with a meta-population model of COVID-19 that explicitly accounts for spatial hierarchies. We paired our estimates with globally comprehensive data on human mobility and predict that crowded cities worldwide could experience more prolonged epidemics.


Subject(s)
COVID-19/epidemiology , COVID-19/etiology , Crowding , Pandemics , China/epidemiology , Cities/epidemiology , Contact Tracing , Demography/standards , Demography/statistics & numerical data , Disease Outbreaks , Forecasting/methods , Geography , Human Activities/statistics & numerical data , Humans , Physical Distancing , Population Density , Public Policy/trends , SARS-CoV-2/physiology , Travel/statistics & numerical data
6.
Nat Hum Behav ; 4(8): 856-865, 2020 08.
Article in English | MEDLINE | ID: covidwho-690410

ABSTRACT

The first case of COVID-19 was detected in Brazil on 25 February 2020. We report and contextualize epidemiological, demographic and clinical findings for COVID-19 cases during the first 3 months of the epidemic. By 31 May 2020, 514,200 COVID-19 cases, including 29,314 deaths, had been reported in 75.3% (4,196 of 5,570) of municipalities across all five administrative regions of Brazil. The R0 value for Brazil was estimated at 3.1 (95% Bayesian credible interval = 2.4-5.5), with a higher median but overlapping credible intervals compared with some other seriously affected countries. A positive association between higher per-capita income and COVID-19 diagnosis was identified. Furthermore, the severe acute respiratory infection cases with unknown aetiology were associated with lower per-capita income. Co-circulation of six respiratory viruses was detected but at very low levels. These findings provide a comprehensive description of the ongoing COVID-19 epidemic in Brazil and may help to guide subsequent measures to control virus transmission.


Subject(s)
Betacoronavirus/isolation & purification , Coronavirus Infections , Disease Transmission, Infectious , Influenza, Human , Pandemics , Pneumonia, Viral , Adult , Aged , Brazil/epidemiology , COVID-19 , COVID-19 Testing , Child , Clinical Laboratory Techniques/methods , Clinical Laboratory Techniques/statistics & numerical data , Coinfection/epidemiology , Coronavirus Infections/diagnosis , Coronavirus Infections/drug therapy , Coronavirus Infections/mortality , Coronavirus Infections/therapy , Coronavirus Infections/transmission , Disease Transmission, Infectious/prevention & control , Disease Transmission, Infectious/statistics & numerical data , Female , Hospitalization/statistics & numerical data , Humans , Infant , Influenza, Human/diagnosis , Influenza, Human/epidemiology , Influenza, Human/virology , Male , Mortality , Pneumonia, Viral/diagnosis , Pneumonia, Viral/mortality , Pneumonia, Viral/therapy , Pneumonia, Viral/transmission , SARS-CoV-2 , Socioeconomic Factors
7.
Sci Data ; 7(1): 106, 2020 03 24.
Article in English | MEDLINE | ID: covidwho-15533

ABSTRACT

Cases of a novel coronavirus were first reported in Wuhan, Hubei province, China, in December 2019 and have since spread across the world. Epidemiological studies have indicated human-to-human transmission in China and elsewhere. To aid the analysis and tracking of the COVID-19 epidemic we collected and curated individual-level data from national, provincial, and municipal health reports, as well as additional information from online reports. All data are geo-coded and, where available, include symptoms, key dates (date of onset, admission, and confirmation), and travel history. The generation of detailed, real-time, and robust data for emerging disease outbreaks is important and can help to generate robust evidence that will support and inform public health decision making.


Subject(s)
Betacoronavirus , Coronavirus Infections/epidemiology , Coronavirus Infections/virology , Pneumonia, Viral/epidemiology , Pneumonia, Viral/virology , COVID-19 , China , Epidemics , Geographic Mapping , Geography , Humans , Pandemics , Public Health , SARS-CoV-2
SELECTION OF CITATIONS
SEARCH DETAIL