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1.
Nat Commun ; 11(1): 4507, 2020 09 09.
Article in English | MEDLINE | ID: covidwho-752501

ABSTRACT

Accurate estimates of the burden of SARS-CoV-2 infection are critical to informing pandemic response. Confirmed COVID-19 case counts in the U.S. do not capture the total burden of the pandemic because testing has been primarily restricted to individuals with moderate to severe symptoms due to limited test availability. Here, we use a semi-Bayesian probabilistic bias analysis to account for incomplete testing and imperfect diagnostic accuracy. We estimate 6,454,951 cumulative infections compared to 721,245 confirmed cases (1.9% vs. 0.2% of the population) in the United States as of April 18, 2020. Accounting for uncertainty, the number of infections during this period was 3 to 20 times higher than the number of confirmed cases. 86% (simulation interval: 64-99%) of this difference is due to incomplete testing, while 14% (0.3-36%) is due to imperfect test accuracy. The approach can readily be applied in future studies in other locations or at finer spatial scale to correct for biased testing and imperfect diagnostic accuracy to provide a more realistic assessment of COVID-19 burden.


Subject(s)
Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Bayes Theorem , Betacoronavirus/isolation & purification , Clinical Laboratory Techniques/methods , Coronavirus Infections/diagnosis , Coronavirus Infections/virology , Humans , Pandemics , Pneumonia, Viral/diagnosis , Pneumonia, Viral/virology , United States/epidemiology
2.
BMC Med ; 18(1): 271, 2020 09 04.
Article in English | MEDLINE | ID: covidwho-744985

ABSTRACT

BACKGROUND: New York City was the first major urban center of the COVID-19 pandemic in the USA. Cases are clustered in the city, with certain neighborhoods experiencing more cases than others. We investigate whether potential socioeconomic factors can explain between-neighborhood variation in the COVID-19 test positivity rate. METHODS: Data were collected from 177 Zip Code Tabulation Areas (ZCTA) in New York City (99.9% of the population). We fit multiple Bayesian Besag-York-Mollié (BYM) mixed models using positive COVID-19 tests as the outcome, a set of 11 representative demographic, economic, and health-care associated ZCTA-level parameters as potential predictors, and the total number of COVID-19 tests as the exposure. The BYM model includes both spatial and nonspatial random effects to account for clustering and overdispersion. RESULTS: Multiple regression approaches indicated a consistent, statistically significant association between detected COVID-19 cases and dependent children (under 18 years old), population density, median household income, and race. In the final model, we found that an increase of only 5% in young population is associated with a 2.3% increase in COVID-19 positivity rate (95% confidence interval (CI) 0.4 to 4.2%, p=0.021). An increase of 10,000 people per km2 is associated with a 2.4% (95% CI 0.6 to 4.2%, p=0.011) increase in positivity rate. A decrease of $10,000 median household income is associated with a 1.6% (95% CI 0.7 to 2.4%, p<0.001) increase in COVID-19 positivity rate. With respect to race, a decrease of 10% in White population is associated with a 1.8% (95% CI 0.8 to 2.8%, p<0.001) increase in positivity rate, while an increase of 10% in Black population is associated with a 1.1% (95% CI 0.3 to 1.8%, p<0.001) increase in positivity rate. The percentage of Hispanic (p=0.718), Asian (p=0.966), or Other (p=0.588) populations were not statistically significant factors. CONCLUSIONS: Our findings indicate associations between neighborhoods with a large dependent youth population, densely populated, low-income, and predominantly black neighborhoods and COVID-19 test positivity rate. The study highlights the importance of public health management during and after the current COVID-19 pandemic. Further work is warranted to fully understand the mechanisms by which these factors may have affected the positivity rate, either in terms of the true number of cases or access to testing.


Subject(s)
Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Residence Characteristics , Socioeconomic Factors , Adolescent , Bayes Theorem , Betacoronavirus , Child , Female , Humans , Male , New York City/epidemiology , Pandemics , Poverty
3.
Eur J Epidemiol ; 35(8): 749-761, 2020 Aug.
Article in English | MEDLINE | ID: covidwho-743740

ABSTRACT

The global pandemic of the 2019-nCov requires the evaluation of policy interventions to mitigate future social and economic costs of quarantine measures worldwide. We propose an epidemiological model for forecasting and policy evaluation which incorporates new data in real-time through variational data assimilation. We analyze and discuss infection rates in the UK, US and Italy. We furthermore develop a custom compartmental SIR model fit to variables related to the available data of the pandemic, named SITR model, which allows for more granular inference on infection numbers. We compare and discuss model results which conducts updates as new observations become available. A hybrid data assimilation approach is applied to make results robust to initial conditions and measurement errors in the data. We use the model to conduct inference on infection numbers as well as parameters such as the disease transmissibility rate or the rate of recovery. The parameterisation of the model is parsimonious and extendable, allowing for the incorporation of additional data and parameters of interest. This allows for scalability and the extension of the model to other locations or the adaption of novel data sources.


Subject(s)
Coronavirus Infections/epidemiology , Forecasting , Pandemics , Pneumonia, Viral/epidemiology , Public Health Informatics/methods , Bayes Theorem , Betacoronavirus , Computer Simulation , Disease Outbreaks , Humans , Italy/epidemiology , Models, Biological , Models, Statistical , Quarantine , United Kingdom/epidemiology , United States/epidemiology
4.
Medicine (Baltimore) ; 99(35): e21927, 2020 Aug 28.
Article in English | MEDLINE | ID: covidwho-740206

ABSTRACT

BACKGROUND: The number of patients infected with novel coronavirus disease (COVID-19) has exceeded 10 million in 2020, and a large proportion of them are asymptomatic. At present, there is still no effective treatment for this disease. Traditional Chinese medicine (TCM) shows a good therapeutic effect on COVID-19, especially for asymptomatic patients. According to the search results, we found that although there are many studies on COVID-19, there are no studies targeting asymptomatic infections. Therefore, we design a network meta-analysis (NMA) to evaluate the therapeutic effect of TCM on asymptomatic COVID-19. METHODS: We will search Chinese and English databases to collect all randomized controlled trials (RCTs) of TCM combined with conventional western medicine or using only TCM to treat asymptomatic COVID-19 from December 2019 to July 2020. Then, two investigators will independently filter the articles, extract data, and evaluate the risk of bias. We will conduct a Bayesian NMA to evaluate the effects of different therapies. All data will be processed by Stata 16.0 and WinBUGS. RESULTS: This study will evaluate the effectiveness of various treatments for asymptomatic COVID-19. The outcome indicators include the time when the nucleic acid turned negative, the proportion of patients with disease progression, changes in laboratory indicators, and the side effects of drugs. CONCLUSION: This analysis will further improve the treatment of asymptomatic COVID-19. INPLASY REGISTRATION NUMBER: INPLASY202070022.


Subject(s)
Combined Modality Therapy/methods , Coronavirus Infections/therapy , Medicine, Chinese Traditional/methods , Pneumonia, Viral/therapy , Asymptomatic Infections/therapy , Bayes Theorem , Betacoronavirus/drug effects , Betacoronavirus/isolation & purification , Coronavirus Infections/drug therapy , Humans , Network Meta-Analysis , Pandemics , Research Design , Treatment Outcome
5.
Nat Commun ; 11(1): 4235, 2020 08 25.
Article in English | MEDLINE | ID: covidwho-738373

ABSTRACT

Bats are presumed reservoirs of diverse coronaviruses (CoVs) including progenitors of Severe Acute Respiratory Syndrome (SARS)-CoV and SARS-CoV-2, the causative agent of COVID-19. However, the evolution and diversification of these coronaviruses remains poorly understood. Here we use a Bayesian statistical framework and a large sequence data set from bat-CoVs (including 630 novel CoV sequences) in China to study their macroevolution, cross-species transmission and dispersal. We find that host-switching occurs more frequently and across more distantly related host taxa in alpha- than beta-CoVs, and is more highly constrained by phylogenetic distance for beta-CoVs. We show that inter-family and -genus switching is most common in Rhinolophidae and the genus Rhinolophus. Our analyses identify the host taxa and geographic regions that define hotspots of CoV evolutionary diversity in China that could help target bat-CoV discovery for proactive zoonotic disease surveillance. Finally, we present a phylogenetic analysis suggesting a likely origin for SARS-CoV-2 in Rhinolophus spp. bats.


Subject(s)
Chiroptera/virology , Coronavirus Infections/veterinary , Coronavirus/genetics , Evolution, Molecular , Zoonoses/transmission , Animals , Bayes Theorem , Betacoronavirus/classification , Betacoronavirus/genetics , Biodiversity , China , Chiroptera/classification , Coronavirus/classification , Coronavirus Infections/transmission , Coronavirus Infections/virology , Humans , Pandemics , Phylogeny , Phylogeography , Pneumonia, Viral/transmission , Pneumonia, Viral/virology , Zoonoses/virology
6.
Int J Health Geogr ; 19(1): 32, 2020 08 13.
Article in English | MEDLINE | ID: covidwho-714188

ABSTRACT

BACKGROUND: As of 13 July 2020, 12.9 million COVID-19 cases have been reported worldwide. Prior studies have demonstrated that local socioeconomic and built environment characteristics may significantly contribute to viral transmission and incidence rates, thereby accounting for some of the spatial variation observed. Due to uncertainties, non-linearities, and multiple interaction effects observed in the associations between COVID-19 incidence and socioeconomic, infrastructural, and built environment characteristics, we present a structured multimethod approach for analysing cross-sectional incidence data within in an Exploratory Spatial Data Analysis (ESDA) framework at the NUTS3 (county) scale. METHODS: By sequentially conducting a geospatial analysis, an heuristic geographical interpretation, a Bayesian machine learning analysis, and parameterising a Generalised Additive Model (GAM), we assessed associations between incidence rates and 368 independent variables describing geographical patterns, socioeconomic risk factors, infrastructure, and features of the build environment. A spatial trend analysis and Local Indicators of Spatial Autocorrelation were used to characterise the geography of age-adjusted COVID-19 incidence rates across Germany, followed by iterative modelling using Bayesian Additive Regression Trees (BART) to identify and measure candidate explanatory variables. Partial dependence plots were derived to quantify and contextualise BART model results, followed by the parameterisation of a GAM to assess correlations. RESULTS: A strong south-to-north gradient of COVID-19 incidence was identified, facilitating an empirical classification of the study area into two epidemic subregions. All preliminary and final models indicated that location, densities of the built environment, and socioeconomic variables were important predictors of incidence rates in Germany. The top ten predictor variables' partial dependence exhibited multiple non-linearities in the relationships between key predictor variables and COVID-19 incidence rates. The BART, partial dependence, and GAM results indicate that the strongest predictors of COVID-19 incidence at the county scale were related to community interconnectedness, geographical location, transportation infrastructure, and labour market structure. CONCLUSIONS: The multimethod ESDA approach provided unique insights into spatial and aspatial non-stationarities of COVID-19 incidence in Germany. BART and GAM modelling indicated that geographical configuration, built environment densities, socioeconomic characteristics, and infrastructure all exhibit associations with COVID-19 incidence in Germany when assessed at the county scale. The results suggest that measures to implement social distancing and reduce unnecessary travel may be important methods for reducing contagion, and the authors call for further research to investigate the observed associations to inform prevention and control policy.


Subject(s)
Built Environment , Communicable Diseases, Emerging/epidemiology , Coronavirus Infections/epidemiology , Environment , Pneumonia, Viral/epidemiology , Socioeconomic Factors , Spatial Analysis , Bayes Theorem , Betacoronavirus , Cross-Sectional Studies , Geographic Mapping , Germany/epidemiology , Humans , Incidence , Machine Learning , Pandemics , Risk Factors
7.
BMJ Case Rep ; 13(8)2020 Aug 11.
Article in English | MEDLINE | ID: covidwho-713638

ABSTRACT

Since the beginning of the COVID-19 pandemic, healthcare providers worldwide have faced many obstacles in the diagnostic evaluation of patients for severe acute respiratory syndrome coronavirus 2, the causative virus. Even with the application of statistical inference by Bayes' theorem to estimate the probability of a diagnosis, with and without testing capabilities, some cases may still carry a degree of uncertainty. This has important implications for limiting the spread of disease. The basis for isolation and quarantine is a known diagnosis. This case is an example of a diagnostic conundrum that required more thorough use of testing methods, particularly serological testing, to guide the isolation recommendations for a patient with COVID-19. This will be helpful to other diagnosticians by providing an example of how serological findings may be effectively applied in the course of individual COVID-19 management.


Subject(s)
Betacoronavirus , Coronavirus Infections/blood , Coronavirus Infections/diagnosis , Pneumonia, Viral/blood , Pneumonia, Viral/diagnosis , Serologic Tests/methods , Bayes Theorem , Diagnosis, Differential , Female , Humans , Middle Aged , Pandemics , Polymerase Chain Reaction , Reproducibility of Results
8.
BMC Med Res Methodol ; 20(1): 209, 2020 08 12.
Article in English | MEDLINE | ID: covidwho-712996

ABSTRACT

BACKGROUND: As the whole world is experiencing the cascading effect of a new pandemic, almost every aspect of modern life has been disrupted. Because of health emergencies during this period, widespread fear has resulted in compromised patient safety, especially for patients with cancer. It is very challenging to treat such cancer patients because of the complexity of providing care and treatment, along with COVID-19. Hence, an effective treatment comparison strategy is needed. We need to have a handy tool to understand cancer progression in this unprecedented scenario. Linking different events of cancer progression is the need of the hour. It is a huge challenge for the development of new methodology. METHODS: This article explores the time lag effect and makes a statistical inference about the best experimental arm using Accelerated Failure Time (AFT) model and regression methods. The work is presented as the occurrence of other events as a hazard rate after the first event (relapse). The time lag effect between the events is linked and analysed. RESULTS: The results were presented as a comprehensive analytical strategy by joining all disease progression. An AFT model applied with the transition states, and the dependency structure between the gap times was used by the auto-regression model. The effects of arms were compared using the coefficient of auto-regression and accelerated failure time (AFT) models. CONCLUSIONS: We provide the solutions to overcome the issue with intervals between two consecutive events in motivating head and neck cancer (HNC) data. COVID-19 is not going to leave us soon. We have to conduct several cancer clinical trials in the presence of COVID-19. A comprehensive analytical strategy to analyse cancer clinical trial data during COVID-19 pandemic is presented.


Subject(s)
Algorithms , Coronavirus Infections/prevention & control , Head and Neck Neoplasms/therapy , Medical Oncology/methods , Models, Theoretical , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Bayes Theorem , Betacoronavirus/physiology , Coronavirus Infections/complications , Coronavirus Infections/virology , Disease Progression , Head and Neck Neoplasms/complications , Head and Neck Neoplasms/diagnosis , Humans , Kaplan-Meier Estimate , Markov Chains , Monte Carlo Method , Neoplasm Recurrence, Local , Pneumonia, Viral/complications , Pneumonia, Viral/virology
10.
Epidemiol Infect ; 148: e166, 2020 08 05.
Article in English | MEDLINE | ID: covidwho-697050

ABSTRACT

Following the importation of coronavirus disease (COVID-19) into Nigeria on 27 February 2020 and then the outbreak, the question is: How do we anticipate the progression of the ongoing epidemic following all the intervention measures put in place? This kind of question is appropriate for public health responses and it will depend on the early estimates of the key epidemiological parameters of the virus in a defined population.In this study, we combined a likelihood-based method using a Bayesian framework and compartmental model of the epidemic of COVID-19 in Nigeria to estimate the effective reproduction number (R(t)) and basic reproduction number (R0) - this also enables us to estimate the initial daily transmission rate (ß0). We further estimate the reported fraction of symptomatic cases. The models are applied to the NCDC data on COVID-19 symptomatic and death cases from 27 February 2020 and 7 May 2020.In this period, the effective reproduction number is estimated with a minimum value of 0.18 and a maximum value of 2.29. Most importantly, the R(t) is strictly greater than one from 13 April till 7 May 2020. The R0 is estimated to be 2.42 with credible interval: (2.37-2.47). Comparing this with the R(t) shows that control measures are working but not effective enough to keep R(t) below 1. Also, the estimated fraction of reported symptomatic cases is between 10 and 50%.Our analysis has shown evidence that the existing control measures are not enough to end the epidemic and more stringent measures are needed.


Subject(s)
Basic Reproduction Number/statistics & numerical data , Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Epidemics/prevention & control , Pandemics/prevention & control , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Public Health Practice , Bayes Theorem , Humans , Likelihood Functions , Nigeria/epidemiology
11.
PLoS One ; 15(8): e0237126, 2020.
Article in English | MEDLINE | ID: covidwho-696225

ABSTRACT

The Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has highlighted the need for performing accurate inference with limited data. Fundamental to the design of rapid state responses is the ability to perform epidemiological model parameter inference for localised trajectory predictions. In this work, we perform Bayesian parameter inference using Markov Chain Monte Carlo (MCMC) methods on the Susceptible-Infected-Recovered (SIR) and Susceptible-Exposed-Infected-Recovered (SEIR) epidemiological models with time-varying spreading rates for South Africa. The results find two change points in the spreading rate of COVID-19 in South Africa as inferred from the confirmed cases. The first change point coincides with state enactment of a travel ban and the resultant containment of imported infections. The second change point coincides with the start of a state-led mass screening and testing programme which has highlighted community-level disease spread that was not well represented in the initial largely traveller based and private laboratory dominated testing data. The results further suggest that due to the likely effect of the national lockdown, community level transmissions are slower than the original imported case driven spread of the disease.


Subject(s)
Bayes Theorem , Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Algorithms , Coronavirus Infections/diagnosis , Coronavirus Infections/transmission , Coronavirus Infections/virology , Humans , Markov Chains , Monte Carlo Method , Pandemics , Pneumonia, Viral/diagnosis , Pneumonia, Viral/transmission , Pneumonia, Viral/virology , South Africa/epidemiology
14.
PLoS One ; 15(7): e0236860, 2020.
Article in English | MEDLINE | ID: covidwho-690729

ABSTRACT

Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to global health. The rapid spread of the virus has created pandemic, and countries all over the world are struggling with a surge in COVID-19 infected cases. There are no drugs or other therapeutics approved by the US Food and Drug Administration to prevent or treat COVID-19: information on the disease is very limited and scattered even if it exists. This motivates the use of data integration, combining data from diverse sources and eliciting useful information with a unified view of them. In this paper, we propose a Bayesian hierarchical model that integrates global data for real-time prediction of infection trajectory for multiple countries. Because the proposed model takes advantage of borrowing information across multiple countries, it outperforms an existing individual country-based model. As fully Bayesian way has been adopted, the model provides a powerful predictive tool endowed with uncertainty quantification. Additionally, a joint variable selection technique has been integrated into the proposed modeling scheme, which aimed to identify possible country-level risk factors for severe disease due to COVID-19.


Subject(s)
Betacoronavirus , Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Global Health/trends , Pneumonia, Viral/epidemiology , Pneumonia, Viral/transmission , Bayes Theorem , Coronavirus Infections/virology , Humans , Models, Theoretical , Pandemics , Pneumonia, Viral/virology , Prognosis , Risk Factors , Travel , Uncertainty
15.
Science ; 369(6508): 1255-1260, 2020 09 04.
Article in English | MEDLINE | ID: covidwho-675945

ABSTRACT

Brazil currently has one of the fastest-growing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemics in the world. Because of limited available data, assessments of the impact of nonpharmaceutical interventions (NPIs) on this virus spread remain challenging. Using a mobility-driven transmission model, we show that NPIs reduced the reproduction number from >3 to 1 to 1.6 in São Paulo and Rio de Janeiro. Sequencing of 427 new genomes and analysis of a geographically representative genomic dataset identified >100 international virus introductions in Brazil. We estimate that most (76%) of the Brazilian strains fell in three clades that were introduced from Europe between 22 February and 11 March 2020. During the early epidemic phase, we found that SARS-CoV-2 spread mostly locally and within state borders. After this period, despite sharp decreases in air travel, we estimated multiple exportations from large urban centers that coincided with a 25% increase in average traveled distances in national flights. This study sheds new light on the epidemic transmission and evolutionary trajectories of SARS-CoV-2 lineages in Brazil and provides evidence that current interventions remain insufficient to keep virus transmission under control in this country.


Subject(s)
Betacoronavirus/genetics , Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Pneumonia, Viral/epidemiology , Pneumonia, Viral/transmission , Basic Reproduction Number , Bayes Theorem , Betacoronavirus/classification , Brazil/epidemiology , Cities/epidemiology , Clinical Laboratory Techniques , Coronavirus Infections/diagnosis , Coronavirus Infections/prevention & control , Coronavirus Infections/virology , Europe , Evolution, Molecular , Genome, Viral , Humans , Models, Genetic , Models, Statistical , Pandemics/prevention & control , Phylogeny , Phylogeography , Pneumonia, Viral/prevention & control , Pneumonia, Viral/virology , Spatio-Temporal Analysis , Travel , Urban Population
16.
Viruses ; 12(8)2020 07 24.
Article in English | MEDLINE | ID: covidwho-670832

ABSTRACT

The aim of this study is the characterization and genomic tracing by phylogenetic analyses of 59 new SARS-CoV-2 Italian isolates obtained from patients attending clinical centres in North and Central Italy until the end of April 2020. All but one of the newly-characterized genomes belonged to the lineage B.1, the most frequently identified in European countries, including Italy. Only a single sequence was found to belong to lineage B. A mean of 6 nucleotide substitutions per viral genome was observed, without significant differences between synonymous and non-synonymous mutations, indicating genetic drift as a major source for virus evolution. tMRCA estimation confirmed the probable origin of the epidemic between the end of January and the beginning of February with a rapid increase in the number of infections between the end of February and mid-March. Since early February, an effective reproduction number (Re) greater than 1 was estimated, which then increased reaching the peak of 2.3 in early March, confirming the circulation of the virus before the first COVID-19 cases were documented. Continuous use of state-of-the-art methods for molecular surveillance is warranted to trace virus circulation and evolution and inform effective prevention and containment of future SARS-CoV-2 outbreaks.


Subject(s)
Betacoronavirus/classification , Betacoronavirus/genetics , Coronavirus Infections/epidemiology , Coronavirus Infections/virology , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/virology , Bayes Theorem , Betacoronavirus/isolation & purification , Epidemiological Monitoring , Genome, Viral , Humans , Italy/epidemiology , Likelihood Functions , Molecular Epidemiology , Molecular Typing , Mutation , Phylogeny , Time Factors , Whole Genome Sequencing
17.
J R Soc Interface ; 17(168): 20200144, 2020 07.
Article in English | MEDLINE | ID: covidwho-665024

ABSTRACT

A novel coronavirus (SARS-CoV-2) emerged as a global threat in December 2019. As the epidemic progresses, disease modellers continue to focus on estimating the basic reproductive number [Formula: see text]-the average number of secondary cases caused by a primary case in an otherwise susceptible population. The modelling approaches and resulting estimates of [Formula: see text] during the beginning of the outbreak vary widely, despite relying on similar data sources. Here, we present a statistical framework for comparing and combining different estimates of [Formula: see text] across a wide range of models by decomposing the basic reproductive number into three key quantities: the exponential growth rate, the mean generation interval and the generation-interval dispersion. We apply our framework to early estimates of [Formula: see text] for the SARS-CoV-2 outbreak, showing that many [Formula: see text] estimates are overly confident. Our results emphasize the importance of propagating uncertainties in all components of [Formula: see text], including the shape of the generation-interval distribution, in efforts to estimate [Formula: see text] at the outset of an epidemic.


Subject(s)
Basic Reproduction Number , Betacoronavirus , Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Disease Outbreaks , Models, Biological , Pneumonia, Viral/epidemiology , Pneumonia, Viral/transmission , Basic Reproduction Number/statistics & numerical data , Bayes Theorem , China/epidemiology , Disease Outbreaks/statistics & numerical data , Epidemics/statistics & numerical data , Humans , Markov Chains , Monte Carlo Method , Pandemics , Probability , Uncertainty
18.
Swiss Med Wkly ; 150: w20313, 2020 07 13.
Article in English | MEDLINE | ID: covidwho-651678

ABSTRACT

The reproduction number is broadly considered as a key indicator for the spreading of the COVID-19 pandemic. Its estimated value is a measure of the necessity and, eventually, effectiveness of interventions imposed in various countries. Here we present an online tool for the data-driven inference and quantification of uncertainties for the reproduction number, as well as the time points of interventions for 51 European countries. The study relied on the Bayesian calibration of the SIR model with data from reported daily infections from these countries. The model fitted the data, for most countries, without individual tuning of parameters. We also compared the results of SIR and SEIR models, which give different estimates of the reproduction number, and provided an analytical relationship between the respective numbers. We deployed a Bayesian inference framework with efficient sampling algorithms, to present a publicly available graphical user interface (https://cse-lab.ethz.ch/coronavirus) that allows the user to assess and compare predictions for pairs of European countries. The results quantified the rate of the disease’s spread before and after interventions, and provided a metric for the effectiveness of non-pharmaceutical interventions in different countries. They also indicated how geographic proximity and the times of interventions affected the progression of the epidemic.


Subject(s)
Basic Reproduction Number/statistics & numerical data , Coronavirus Infections , Disease Transmission, Infectious/statistics & numerical data , Epidemiological Monitoring , Pandemics , Pneumonia, Viral , Bayes Theorem , Betacoronavirus/isolation & purification , Communicable Disease Control/methods , Communicable Disease Control/statistics & numerical data , Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Coronavirus Infections/transmission , Disease Transmission, Infectious/prevention & control , Epidemiologic Measurements , Europe/epidemiology , Humans , Pandemics/prevention & control , Pandemics/statistics & numerical data , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Pneumonia, Viral/transmission , Uncertainty
19.
Comput Math Methods Med ; 2020: 4296806, 2020.
Article in English | MEDLINE | ID: covidwho-647157

ABSTRACT

In the current scenario, the outbreak of a pandemic disease COVID-19 is of great interest. A broad statistical analysis of this event is still to come, but it is immediately needed to evaluate the disease dynamics in order to arrange the appropriate quarantine activities, to estimate the required number of places in hospitals, the level of individual protection, the rate of isolation of infected persons, and among others. In this article, we provide a convenient method of data comparison that can be helpful for both the governmental and private organizations. Up to date, facts and figures of the total the confirmed cases, daily confirmed cases, total deaths, and daily deaths that have been reported in the Asian countries are provided. Furthermore, a statistical model is suggested to provide a best description of the COVID-19 total death data in the Asian countries.


Subject(s)
Coronavirus Infections/epidemiology , Models, Statistical , Pneumonia, Viral/epidemiology , Algorithms , Asia , Bayes Theorem , Betacoronavirus , Data Interpretation, Statistical , Hospitals , Humans , Kaplan-Meier Estimate , Likelihood Functions , Pandemics , Patient Isolation , Quarantine
20.
Bone Joint J ; 102-B(9): 1256-1260, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-633815

ABSTRACT

AIMS: The risk to patients and healthcare workers of resuming elective orthopaedic surgery following the peak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has been difficult to quantify. This has prompted governing bodies to adopt a cautious approach that may be impractical and financially unsustainable. The lack of evidence has made it impossible for surgeons to give patients an informed perspective of the consequences of elective surgery in the presence of SARS-CoV-2. This study aims to determine, for the UK population, the probability of a patient being admitted with an undetected SARS-CoV-2 infection and their resulting risk of death; taking into consideration the current disease prevalence, reverse transcription-polymerase chain reaction (RT-PCR) testing, and preassessment pathway. METHODS: The probability of SARS-CoV-2 infection with a false negative test was calculated using a lower-end RT-PCR sensitivity of 71%, specificity of 95%, and the UK disease prevalence of 0.24% reported in May 2020. Subsequently, a case fatality rate of 20.5% was applied as a worst-case scenario. RESULTS: The probability of SARS-CoV-2 infection with a false negative preoperative test was 0.07% (around 1 in 1,400). The risk of a patient with an undetected infection being admitted for surgery and subsequently dying from the coronavirus disease 2019 (COVID-19) is estimated at approximately 1 in 7,000. However, if an estimate of the current global infection fatality rate (1.04%) is applied, the risk of death would be around 1 in 140,000, at most. This calculation does not take into account the risk of nosocomial infection. Conversely, it does not factor in that patients will also be clinically assessed and asked to self-isolate prior to surgery. CONCLUSION: Our estimation suggests that the risk of patients being inadvertently admitted with an undetected SARS-CoV-2 infection for elective orthopaedic surgery is relatively low. Accordingly, the risk of death following elective orthopaedic surgery is low, even when applying the worst-case fatality rate. Cite this article: Bone Joint J 2020;102-B(9):1256-1260.


Subject(s)
Asymptomatic Diseases , Cause of Death , Coronavirus Infections/epidemiology , Elective Surgical Procedures/adverse effects , Pandemics/statistics & numerical data , Pneumonia, Viral/epidemiology , Postoperative Complications/mortality , Bayes Theorem , Clinical Laboratory Techniques , Cohort Studies , Coronavirus Infections/diagnosis , Coronavirus Infections/prevention & control , Elective Surgical Procedures/mortality , False Negative Reactions , Female , Humans , Incidence , Male , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Postoperative Complications/physiopathology , Risk Assessment , Survival Rate , Treatment Outcome , United Kingdom
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