Corona peer reviewed artikelen +datavisualisatie & statistieken topic

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The temporal association of introducing and lifting non-pharmaceutical interventions with the time-varying reproduction number (R) of SARS-CoV-2: a modelling study across 131 countries

Research in context
Evidence before this study
The time-varying reproduction number (R; also known as Rt), defined by the expected number of secondary cases arising from a primary case infected at time t, is a metric that describes viral transmission at the population level. An R value above 1 indicates a growing outbreak, and an R value below 1 indicates a shrinking outbreak. We searched PubMed, medRxiv, and bioRxiv for studies that reported the effects of introducing and lifting non-pharmaceutical interventions (NPIs) on R of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) published between Jan 1 and Aug 5, 2020, using the keywords “COVID-19”, “SARS-CoV-2”, “intervention”, and “transmission”. No language restriction was applied. Studies in China, Hong Kong, South Korea, Singapore, and many European countries showed that several NPIs, including school closure, physical distancing, and lockdown, could reduce R substantially to near or below 1. However, little is known about the effects on R following the relaxation of these NPIs.
Added value of this study
To the best of our knowledge, this study is the first to explicitly quantify the effects of both introducing and lifting individual NPIs on R over time. By linking a global dataset of country-level daily R values with a global dataset of country-level policies on NPIs, we modelled the change in R values (as R ratio) from day 1 to day 28 following the introduction and relaxation of eight individual NPIs among 131 countries. We found that reopening schools, lifting bans on public events, lifting bans on public gatherings of more than ten people, lifting requirements to stay at home, and lifting internal movement limits were associated with an increase in R of 11–25% on day 28 following the relaxation. However, the effects of introducing and lifting NPIs were not immediate; it took a median of 8 days (IQR 6–9) following the introduction of NPIs to observe 60% of their maximum reduction in R and even longer (17 days [14–20]) following the relaxation to observe 60% of the maximum increase in R. A similar delay in response to the introduction and relaxation of NPIs was also identified using Google mobility data. We compared four different candidates of composite NPIs that countries might consider in response to a possible resurgence of COVID-19.
Implications of all the available evidence
We quantified the change in transmission of SARS-CoV-2, as measured by R, following the introduction and relaxation of individual NPIs, and found a delay of 1–3 weeks in observing the effects of introducing and lifting these NPIs. These findings provide additional evidence that can inform policy-maker decisions on which NPIs to introduce or lift and when to expect a notable effect following the introduction or the relaxation.

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Genomic evidence for reinfection with SARS-CoV-2: a case study
Summary
Background
The degree of protective immunity conferred by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently unknown. As such, the possibility of reinfection with SARS-CoV-2 is not well understood. We describe an investigation of two instances of SARS-CoV-2 infection in the same individual.
Methods
A 25-year-old man who was a resident of Washoe County in the US state of Nevada presented to health authorities on two occasions with symptoms of viral infection, once at a community testing event in April, 2020, and a second time to primary care then hospital at the end of May and beginning of June, 2020. Nasopharyngeal swabs were obtained from the patient at each presentation and twice during follow-up. Nucleic acid amplification testing was done to confirm SARS-CoV-2 infection. We did next-generation sequencing of SARS-CoV-2 extracted from nasopharyngeal swabs. Sequence data were assessed by two different bioinformatic methodologies. A short tandem repeat marker was used for fragment analysis to confirm that samples from both infections came from the same individual.
Findings
The patient had two positive tests for SARS-CoV-2, the first on April 18, 2020, and the second on June 5, 2020, separated by two negative tests done during follow-up in May, 2020. Genomic analysis of SARS-CoV-2 showed genetically significant differences between each variant associated with each instance of infection. The second infection was symptomatically more severe than the first.
Interpretation
Genetic discordance of the two SARS-CoV-2 specimens was greater than could be accounted for by short-term in vivo evolution. These findings suggest that the patient was infected by SARS-CoV-2 on two separate occasions by a genetically distinct virus. Thus, previous exposure to SARS-CoV-2 might not guarantee total immunity in all cases. All individuals, whether previously diagnosed with COVID-19 or not, should take identical precautions to avoid infection with SARS-CoV-2. The implications of reinfections could be relevant for vaccine development and application.

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The major genetic risk factor for severe COVID-19 is inherited from Neanderthals

Abstract

A recent genetic association study1 identified a gene cluster on chromosome 3 as a risk locus for respiratory failure after infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A separate study (COVID-19 Host Genetics Initiative)2 comprising 3,199 hospitalized patients with coronavirus disease 2019 (COVID-19) and control individuals showed that this cluster is the major genetic risk factor for severe symptoms after SARS-CoV-2 infection and hospitalization. Here we show that the risk is conferred by a genomic segment of around 50 kilobases in size that is inherited from Neanderthals and is carried by around 50% of people in south Asia and around 16% of people in Europe.

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Dexamethasone in hospitalised patients with COVID-19: addressing uncertainties

The impressive results of the RECOVERY trial established that a moderate dose of dexamethasone (6 mg daily for 10 days) reduced mortality in hospitalised patients with COVID-19 and respiratory failure who required therapy with supplemental oxygen or mechanical ventilation.
The data also indicated that dexamethasone might increase mortality in hospitalised patients who were not receiving oxygen. This landmark trial and the subsequent practice guidelines from several academic and health organisations recommending dexamethasone use in patients with severe COVID-19 have changed clinical practice for hospitalised patients on supplemental oxygen or mechanical ventilation
These favourable findings are supported by three other trialsof glucocorticoids for COVID-19, which stopped enrolment in early June, 2020, when the RECOVERY trial results were released. Each of these trials showed some evidence of benefit, although none had completed enrolment. A prospective meta-analysis of these and other trials, totalling 1703 participants (1007 [59%] from the RECOVERY trial), confirmed a reduction in 28-day mortality (summary odds ratio [OR] 0·66, 95% CI 0·53–0·82; p<0·001), with minimal heterogeneity across studies.6
While confirming beneficial effects of corticosteroids for critically ill hospitalised patients with COVID-19, some unanswered questions and issues remain that deserve discussion and should be addressed in future research.

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Early Hydroxychloroquine but not Chloroquine use reduces ICU admission in COVID-19 patients

Highlights

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After the global push for the use of Hydroxychloroquine and Chloroquine there is ongoing discussion about the effectivity of these drugs.
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Findings of this observational study provide crucial data on a potential protective effect of Hydroxychloroquine in non-ICU, hospitalized COVID-19 patients.
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Early treatment with HCQ on the first day of admission is associated with a reduced risk of 53% in transfer to the ICU for mechanical ventilation.
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This protective effect was not observed for Chloroquine, therefore these drugs cannot be regarded as interchangeable.

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Op maandag 2 november 2020 20:05 schreef Discombobulate het volgende:

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Nou, dat werd tijd na die hele media hetze tegen hydroxychloroquine. Het ging velen die voor HCQ waren namelijk om het zo vroeg mogelijk toedienen ervan, maar daar was tot voor kort weinig onderzoek naar gedaan.

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Race, Ethnicity, and Age Trends in Persons Who Died from COVID-19 — United States, May–August 2020

Summary

What is already known about this topic?

Persons aged ≥65 years and members of minority racial and ethnic groups are disproportionately represented among COVID-19–associated deaths.

What is added by this report?

Analysis of 114,411 COVID-19–associated deaths reported to National Vital Statistics System during May–August 2020, found that 51.3% of decedents were non-Hispanic White, 24.2% were Hispanic or Latino (Hispanic), and 18.7% were non-Hispanic Black. The percentage of Hispanic decedents increased from 16.3% in May to 26.4% in August.

What are the implications for public health practice?

These results can inform public health messaging and mitigation efforts focused on prevention and early detection of infection among disproportionately affected groups so as to minimize subsequent mortality.

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Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis

Background

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 and is spread person-to-person through close contact. We aimed to investigate the effects of physical distance, face masks, and eye protection on virus transmission in health-care and non-health-care (eg, community) settings.
Methods

We did a systematic review and meta-analysis to investigate the optimum distance for avoiding person-to-person virus transmission and to assess the use of face masks and eye protection to prevent transmission of viruses. We obtained data for SARS-CoV-2 and the betacoronaviruses that cause severe acute respiratory syndrome, and Middle East respiratory syndrome from 21 standard WHO-specific and COVID-19-specific sources. We searched these data sources from database inception to May 3, 2020, with no restriction by language, for comparative studies and for contextual factors of acceptability, feasibility, resource use, and equity. We screened records, extracted data, and assessed risk of bias in duplicate. We did frequentist and Bayesian meta-analyses and random-effects meta-regressions. We rated the certainty of evidence according to Cochrane methods and the GRADE approach. This study is registered with PROSPERO, CRD42020177047.

Findings

Our search identified 172 observational studies across 16 countries and six continents, with no randomised controlled trials and 44 relevant comparative studies in health-care and non-health-care settings (n=25 697 patients). Transmission of viruses was lower with physical distancing of 1 m or more, compared with a distance of less than 1 m (n=10 736, pooled adjusted odds ratio [aOR] 0·18, 95% CI 0·09 to 0·38; risk difference [RD] −10·2%, 95% CI −11·5 to −7·5; moderate certainty); protection was increased as distance was lengthened (change in relative risk [RR] 2·02 per m; pinteraction=0·041; moderate certainty). Face mask use could result in a large reduction in risk of infection (n=2647; aOR 0·15, 95% CI 0·07 to 0·34, RD −14·3%, −15·9 to −10·7; low certainty), with stronger associations with N95 or similar respirators compared with disposable surgical masks or similar (eg, reusable 12–16-layer cotton masks; pinteraction=0·090; posterior probability >95%, low certainty). Eye protection also was associated with less infection (n=3713; aOR 0·22, 95% CI 0·12 to 0·39, RD −10·6%, 95% CI −12·5 to −7·7; low certainty). Unadjusted studies and subgroup and sensitivity analyses showed similar findings.

Interpretation

The findings of this systematic review and meta-analysis support physical distancing of 1 m or more and provide quantitative estimates for models and contact tracing to inform policy. Optimum use of face masks, respirators, and eye protection in public and health-care settings should be informed by these findings and contextual factors. Robust randomised trials are needed to better inform the evidence for these interventions, but this systematic appraisal of currently best available evidence might inform interim guidance.

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Bidirectional associations between COVID-19 and psychiatric disorder: retrospective cohort studies of 62 354 COVID-19 cases in the USA

Background
Adverse mental health consequences of COVID-19, including anxiety and depression, have been widely predicted but not yet accurately measured. There are a range of physical health risk factors for COVID-19, but it is not known if there are also psychiatric risk factors. In this electronic health record network cohort study using data from 69 million individuals, 62 354 of whom had a diagnosis of COVID-19, we assessed whether a diagnosis of COVID-19 (compared with other health events) was associated with increased rates of subsequent psychiatric diagnoses, and whether patients with a history of psychiatric illness are at a higher risk of being diagnosed with COVID-19.

Methods
We used the TriNetX Analytics Network, a global federated network that captures anonymised data from electronic health records in 54 health-care organisations in the USA, totalling 69·8 million patients. TriNetX included 62 354 patients diagnosed with COVID-19 between Jan 20, and Aug 1, 2020. We created cohorts of patients who had been diagnosed with COVID-19 or a range of other health events. We used propensity score matching to control for confounding by risk factors for COVID-19 and for severity of illness. We measured the incidence of and hazard ratios (HRs) for psychiatric disorders, dementia, and insomnia, during the first 14 to 90 days after a diagnosis of COVID-19.

Findings
In patients with no previous psychiatric history, a diagnosis of COVID-19 was associated with increased incidence of a first psychiatric diagnosis in the following 14 to 90 days compared with six other health events (HR 2·1, 95% CI 1·8–2·5 vs influenza; 1·7, 1·5–1·9 vs other respiratory tract infections; 1·6, 1·4–1·9 vs skin infection; 1·6, 1·3–1·9 vs cholelithiasis; 2·2, 1·9–2·6 vs urolithiasis, and 2·1, 1·9–2·5 vs fracture of a large bone; all p<0·0001). The HR was greatest for anxiety disorders, insomnia, and dementia. We observed similar findings, although with smaller HRs, when relapses and new diagnoses were measured. The incidence of any psychiatric diagnosis in the 14 to 90 days after COVID-19 diagnosis was 18·1% (95% CI 17·6–18·6), including 5·8% (5·2–6·4) that were a first diagnosis. The incidence of a first diagnosis of dementia in the 14 to 90 days after COVID-19 diagnosis was 1·6% (95% CI 1·2–2·1) in people older than 65 years. A psychiatric diagnosis in the previous year was associated with a higher incidence of COVID-19 diagnosis (relative risk 1·65, 95% CI 1·59–1·71; p<0·0001). This risk was independent of known physical health risk factors for COVID-19, but we cannot exclude possible residual confounding by socioeconomic factors.
Interpretation
Survivors of COVID-19 appear to be at increased risk of psychiatric sequelae, and a psychiatric diagnosis might be an independent risk factor for COVID-19. Although preliminary, our findings have implications for clinical services, and prospective cohort studies are warranted.

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Age-specific mortality and immunity patterns of SARS-CoV-2

Estimating the size and infection severity of the SARS-CoV-2 epidemic is made challenging by inconsistencies in available data. The number of COVID-19 deaths is often used as a key indicator for the epidemic size, but observed deaths represent only a minority of all infections1,2. Additionally, the heterogeneous burden in nursing homes and variable reporting of deaths in elderly individuals can hamper direct comparisons across countries of the underlying level of transmission and mortality rates3. Here we use age-specific COVID-19 death data from 45 countries and the results of 22 seroprevalence studies to investigate the consistency of infection and fatality patterns across multiple countries. We find that the age distribution of deaths in younger age groups (<65 years) is very consistent across different settings and demonstrate how this data can provide robust estimates of the share of the population that has been infected. We estimate that the infection-to-fatality ratio (IFR) is lowest among 5-9 years old, with a log-linear increase by age among individuals older than 30 years. Population age-structures and heterogeneous burdens in nursing homes explain some but not all of the heterogeneity between countries in infection-fatality ratios. Among the 45 countries included in our analysis, we estimate approximately 5% of these populations had been infected by the 1st of September 2020, with much higher transmission likely to have occurred in a number of Latin American countries. This simple modelling framework can help countries assess the progression of the pandemic and can be applied wherever reliable age-specific death data exists.

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Mobility network models of COVID-19 explain inequities and inform reopening

The COVID-19 pandemic dramatically changed human mobility patterns, necessitating epidemiological models which capture the effects of changes in mobility on virus spread1. We introduce a metapopulation SEIR model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in 10 of the largest US metropolitan statistical areas. Derived from cell phone data, our mobility networks map the hourly movements of 98 million people from neighborhoods (census block groups, or CBGs) to points of interest (POIs) such as restaurants and religious establishments, connecting 57k CBGs to 553k POIs with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in population behavior over time. Our model predicts that a small minority of “superspreader” POIs account for a large majority of infections and that restricting maximum occupancy at each POI is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups2–8 solely from differences in mobility: we find that disadvantaged groups have not been able to reduce mobility as sharply, and that the POIs they visit are more crowded and therefore higher-risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more effective and equitable policy responses to COVID-19.

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The 40 health systems, COVID-19 (40HS, C-19) study

Background

The health, social and economic consequences of the severe acute respiratory syndrome coronavirus (SARS-CoV-2, henceforth COVID-19) pandemic have loomed large as every national government made decisions about how to respond. The 40 Health Systems, COVID-19 (40HS, C-19) study aimed to investigate relationships between governments’ capacity to respond (CTR), their response stringency, scope of COVID-19 testing and COVID-19 outcomes.

Methods

Data over March and April 2020 were extracted for 40 national health systems on prepandemic government CTR (Global Competitiveness Index), stringency measures (Oxford COVID-19 Government Response Tracker Stringency Index), approach to COVID-19 testing and COVID-19 cases and deaths (Our-World-in-Data). Multidimensional scaling (MDS) and cluster analysis were applied to examine latent dimensions and visualize country similarities and dissimilarities. Outcomes were tested using multivariate and one-way analyses of variances and Kruskal–Wallis H tests.

Results

The MDS model found three dimensions explaining 91% of the variance and cluster analysis identified five national groupings. There was no association between national governments’ prepandemic CTR and the adoption of early stringent public health measures or approach to COVID-19 testing. Two national clusters applied early stringency measures and reported significantly lower cumulative deaths. The best performing national cluster (comprising Australia, South Korea, Iceland and Taiwan) adopted relatively early stringency measures but broader testing earlier than others, which was associated with a change in disease trajectory and the lowest COVID-19 death rates. Two clusters (one with high CTR and one low) both adopted late stringency measures and narrow testing and performed least well in COVID-19 outcomes.

Conclusion

Early stringency measures and intrinsic national capacities to deal with a pandemic are insufficient. Extended stringency measures, important in the short term, are not economically sustainable. Broad-based testing is key to managing COVID-19.

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Transmission of SARS-CoV-2 on mink farms between humans and mink and back to humans

Abstract

Animal experiments have shown that non-human primates, cats, ferrets, hamsters, rabbits and bats can be infected by SARS-CoV-2. In addition, SARS-CoV-2 RNA has been detected in felids, mink and dogs in the field. Here, we describe an in-depth investigation using whole genome sequencing of outbreaks on 16 mink farms and the humans living or working on these farms. We conclude that the virus was initially introduced from humans and has since evolved, most likely reflecting widespread circulation among mink in the beginning of the infection period several weeks prior to detection. Despite enhanced biosecurity, early warning surveillance and immediate culling of infected farms, transmission occurred between mink farms in three big transmission clusters with unknown modes of transmission. Sixty-eight percent (68%) of the tested mink farm residents, employees and/or contacts had evidence of SARS-CoV-2 infection. Where whole genomes were available, these persons were infected with strains with an animal sequence signature, providing evidence of animal to human transmission of SARS-CoV-2 within mink farms.

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Persistence and Evolution of SARS-CoV-2 in an Immunocompromised Host

The coronavirus can mutate swiftly in one person’s body

The new coronavirus resurged again and again in the body of an infected man, eventually killing him while showing evidence of fast-paced evolution.

Manuela Cernadas and Jonathan Li at Brigham and Women’s Hospital in Boston, Massachusetts, and their colleagues followed the course of COVID-19 in a 45-year-old man with a long-standing autoimmune disorder, who was on a medication regimen that included powerful immunosuppressants (B. Choi et al. N. Engl. J. Med. https://doi.org/fhv8; 2020). Roughly 40 days after the man first tested positive for SARS-CoV-2, follow-up tests indicated that the virus was dwindling — but it surged back, despite antiviral treatment.

The man’s infection subsided and then returned twice more before he died, five months after his first COVID-19 diagnosis. Genomic analysis showed that the man had not been infected multiple times. Instead, the virus had lingered and quickly mutated in his body.

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Ranking the effectiveness of worldwide COVID-19 government interventions

Assessing the effectiveness of non-pharmaceutical interventions (NPIs) to mitigate the spread of SARS-CoV-2 is critical to inform future preparedness response plans. Here we quantify the impact of 6,068 hierarchically coded NPIs implemented in 79 territories on the effective reproduction number, Rt, of COVID-19. We propose a modelling approach that combines four computational techniques merging statistical, inference and artificial intelligence tools. We validate our findings with two external datasets recording 42,151 additional NPIs from 226 countries. Our results indicate that a suitable combination of NPIs is necessary to curb the spread of the virus. Less disruptive and costly NPIs can be as effective as more intrusive, drastic, ones (for example, a national lockdown). Using country-specific ‘what-if’ scenarios, we assess how the effectiveness of NPIs depends on the local context such as timing of their adoption, opening the way for forecasting the effectiveness of future interventions