Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 13 de 13
Filter
1.
Sci Rep ; 13(1): 8360, 2023 05 24.
Article in English | MEDLINE | ID: covidwho-20239521

ABSTRACT

SARS-CoV-2 vaccines are useful tools to combat the Coronavirus Disease 2019 (COVID-19) pandemic, but vaccine reluctance threatens these vaccines' effectiveness. To address COVID-19 vaccine reluctance and ensure equitable distribution, understanding the extent of and factors associated with vaccine acceptance and uptake is critical. We report the results of a large nationwide study in the US conducted December 2020-May 2021 of 36,711 users from COVID-19-focused smartphone-based app How We Feel on their willingness to receive a COVID-19 vaccine. We identified sociodemographic and behavioral factors that were associated with COVID-19 vaccine acceptance and uptake, and we found several vulnerable groups at increased risk of COVID-19 burden, morbidity, and mortality were more likely to be reluctant to accept a vaccine and had lower rates of vaccination. Our findings highlight specific populations in which targeted efforts to develop education and outreach programs are needed to overcome poor vaccine acceptance and improve equitable access, diversity, and inclusion in the national response to COVID-19.


Subject(s)
COVID-19 Vaccines , COVID-19 , Humans , COVID-19/epidemiology , COVID-19/prevention & control , SARS-CoV-2 , Biological Transport , Educational Status
2.
Statistical Science ; 37(2):278, 2022.
Article in English | ProQuest Central | ID: covidwho-1862213

ABSTRACT

In this article, I will discuss my experience as a statistician involved in COVID-19 research in multiple capacities in the last two years, especially in the early phase of the pandemic. I will reflect on the challenges and the lessons I have learned in pandemic research regarding data collection and access, epidemic modeling and data analysis, open science and real time dissemination of research findings, implementation science, media and public communication, and partnerships between academia, government, industry and civil society. I will also make several recommendations on navigating the next stage of the pandemic and preparing for future pandemics.

3.
Nat Commun ; 13(1): 1837, 2022 04 05.
Article in English | MEDLINE | ID: covidwho-1778600

ABSTRACT

Large scale screening is a critical tool in the life sciences, but is often limited by reagents, samples, or cost. An important recent example is the challenge of achieving widespread COVID-19 testing in the face of substantial resource constraints. To tackle this challenge, screening methods must efficiently use testing resources. However, given the global nature of the pandemic, they must also be simple (to aid implementation) and flexible (to be tailored for each setting). Here we propose HYPER, a group testing method based on hypergraph factorization. We provide theoretical characterizations under a general statistical model, and carefully evaluate HYPER with alternatives proposed for COVID-19 under realistic simulations of epidemic spread and viral kinetics. We find that HYPER matches or outperforms the alternatives across a broad range of testing-constrained environments, while also being simpler and more flexible. We provide an online tool to aid lab implementation: http://hyper.covid19-analysis.org .


Subject(s)
COVID-19 , COVID-19 Testing , Humans , Mass Screening , Pandemics/prevention & control , SARS-CoV-2
4.
Bioinformatics ; 38(9): 2661-2663, 2022 04 28.
Article in English | MEDLINE | ID: covidwho-1730645

ABSTRACT

SUMMARY: Amidst the continuing spread of coronavirus disease-19 (COVID-19), real-time data analysis and visualization remain critical the general public to track the pandemic's impact and to inform policy making by officials. Multiple metrics permit the evaluation of the spread, infection and mortality of infectious diseases. For example, numbers of new cases and deaths provide easily interpretable measures of absolute impact within a given population and time frame, while the effective reproduction rate provides an epidemiological measure of the rate of spread. By evaluating multiple metrics concurrently, users can leverage complementary insights into the impact and current state of the pandemic when formulating prevention and safety plans for oneself and others. We describe COVID-19 Spread Mapper, a unified framework for estimating and quantifying the uncertainty in the smoothed daily effective reproduction number, case rate and death rate in a region using log-linear models. We apply this framework to characterize COVID-19 impact at multiple geographic resolutions, including by US county and state as well as by country, demonstrating the variation across resolutions and the need for harmonized efforts to control the pandemic. We provide an open-source online dashboard for real-time analysis and visualization of multiple key metrics, which are critical to evaluate the impact of COVID-19 and make informed policy decisions. AVAILABILITY AND IMPLEMENTATION: Our model and tool are publicly available as implemented in R and hosted at https://metrics.covid19-analysis.org/. The source code is freely available from https://github.com/lin-lab/COVID19-Rt and https://github.com/lin-lab/COVID19-Viz. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , SARS-CoV-2 , Pandemics/prevention & control , Software
5.
BMJ Open ; 12(2): e053635, 2022 02 21.
Article in English | MEDLINE | ID: covidwho-1704364

ABSTRACT

OBJECTIVE: To develop simple but clinically informative risk stratification tools using a few top demographic factors and biomarkers at COVID-19 diagnosis to predict acute kidney injury (AKI) and death. DESIGN: Retrospective cohort analysis, follow-up from 1 February through 28 May 2020. SETTING: 3 teaching hospitals, 2 urban and 1 community-based in the Boston area. PARTICIPANTS: Eligible patients were at least 18 years old, tested COVID-19 positive from 1 February through 28 May 2020, and had at least two serum creatinine measurements within 30 days of a new COVID-19 diagnosis. Exclusion criteria were having chronic kidney disease or having a previous AKI within 3 months of a new COVID-19 diagnosis. MAIN OUTCOMES AND MEASURES: Time from new COVID-19 diagnosis until AKI event, time until death event. RESULTS: Among 3716 patients, there were 1855 (49.9%) males and the average age was 58.6 years (SD 19.2 years). Age, sex, white blood cell, haemoglobin, platelet, C reactive protein (CRP) and D-dimer levels were most strongly associated with AKI and/or death. We created risk scores using these variables predicting AKI within 3 days and death within 30 days of a new COVID-19 diagnosis. Area under the curve (AUC) for predicting AKI within 3 days was 0.785 (95% CI 0.758 to 0.813) and AUC for death within 30 days was 0.861 (95% CI 0.843 to 0.878). Haemoglobin was the most predictive component for AKI, and age the most predictive for death. Predictive accuracies using all study variables were similar to using the simplified scores. CONCLUSION: Simple risk scores using age, sex, a complete blood cell count, CRP and D-dimer were highly predictive of AKI and death and can help simplify and better inform clinical decision making.


Subject(s)
Acute Kidney Injury , COVID-19 , Renal Insufficiency, Chronic , Acute Kidney Injury/complications , Acute Kidney Injury/diagnosis , Adolescent , COVID-19 Testing , Cohort Studies , Hospitals , Humans , Male , Middle Aged , Renal Insufficiency, Chronic/complications , Renal Insufficiency, Chronic/diagnosis , Retrospective Studies , Risk Assessment , Risk Factors , SARS-CoV-2
6.
J Am Stat Assoc ; 116(536): 1561-1577, 2021.
Article in English | MEDLINE | ID: covidwho-1585585

ABSTRACT

Modeling infectious disease dynamics has been critical throughout the COVID-19 pandemic. Of particular interest are the incidence, prevalence, and effective reproductive number (Rt). Estimating these quantities is challenging due to under-ascertainment, unreliable reporting, and time lags between infection, onset, and testing. We propose a Multilevel Epidemic Regression Model to Account for Incomplete Data (MERMAID) to jointly estimate Rt, ascertainment rates, incidence, and prevalence over time in one or multiple regions. Specifically, MERMAID allows for a flexible regression model of Rt that can incorporate geographic and time-varying covariates. To account for under-ascertainment, we (a) model the ascertainment probability over time as a function of testing metrics and (b) jointly model data on confirmed infections and population-based serological surveys. To account for delays between infection, onset, and reporting, we model stochastic lag times as missing data, and develop an EM algorithm to estimate the model parameters. We evaluate the performance of MERMAID in simulation studies, and assess its robustness by conducting sensitivity analyses in a range of scenarios of model misspecifications. We apply the proposed method to analyze COVID-19 daily confirmed infection counts, PCR testing data, and serological survey data across the United States. Based on our model, we estimate an overall COVID-19 prevalence of 12.5% (ranging from 2.4% in Maine to 20.2% in New York) and an overall ascertainment rate of 45.5% (ranging from 22.5% in New York to 81.3% in Rhode Island) in the United States from March to December 2020. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

7.
JAMA ; 323(19): 1915-1923, 2020 May 19.
Article in English | MEDLINE | ID: covidwho-1441893

ABSTRACT

IMPORTANCE: Coronavirus disease 2019 (COVID-19) has become a pandemic, and it is unknown whether a combination of public health interventions can improve control of the outbreak. OBJECTIVE: To evaluate the association of public health interventions with the epidemiological features of the COVID-19 outbreak in Wuhan by 5 periods according to key events and interventions. DESIGN, SETTING, AND PARTICIPANTS: In this cohort study, individual-level data on 32 583 laboratory-confirmed COVID-19 cases reported between December 8, 2019, and March 8, 2020, were extracted from the municipal Notifiable Disease Report System, including patients' age, sex, residential location, occupation, and severity classification. EXPOSURES: Nonpharmaceutical public health interventions including cordons sanitaire, traffic restriction, social distancing, home confinement, centralized quarantine, and universal symptom survey. MAIN OUTCOMES AND MEASURES: Rates of laboratory-confirmed COVID-19 infections (defined as the number of cases per day per million people), across age, sex, and geographic locations were calculated across 5 periods: December 8 to January 9 (no intervention), January 10 to 22 (massive human movement due to the Chinese New Year holiday), January 23 to February 1 (cordons sanitaire, traffic restriction and home quarantine), February 2 to 16 (centralized quarantine and treatment), and February 17 to March 8 (universal symptom survey). The effective reproduction number of SARS-CoV-2 (an indicator of secondary transmission) was also calculated over the periods. RESULTS: Among 32 583 laboratory-confirmed COVID-19 cases, the median patient age was 56.7 years (range, 0-103; interquartile range, 43.4-66.8) and 16 817 (51.6%) were women. The daily confirmed case rate peaked in the third period and declined afterward across geographic regions and sex and age groups, except for children and adolescents, whose rate of confirmed cases continued to increase. The daily confirmed case rate over the whole period in local health care workers (130.5 per million people [95% CI, 123.9-137.2]) was higher than that in the general population (41.5 per million people [95% CI, 41.0-41.9]). The proportion of severe and critical cases decreased from 53.1% to 10.3% over the 5 periods. The severity risk increased with age: compared with those aged 20 to 39 years (proportion of severe and critical cases, 12.1%), elderly people (≥80 years) had a higher risk of having severe or critical disease (proportion, 41.3%; risk ratio, 3.61 [95% CI, 3.31-3.95]) while younger people (<20 years) had a lower risk (proportion, 4.1%; risk ratio, 0.47 [95% CI, 0.31-0.70]). The effective reproduction number fluctuated above 3.0 before January 26, decreased to below 1.0 after February 6, and decreased further to less than 0.3 after March 1. CONCLUSIONS AND RELEVANCE: A series of multifaceted public health interventions was temporally associated with improved control of the COVID-19 outbreak in Wuhan, China. These findings may inform public health policy in other countries and regions.


Subject(s)
Betacoronavirus , Communicable Disease Control/methods , Coronavirus Infections/epidemiology , Pandemics/prevention & control , Pneumonia, Viral/epidemiology , Adolescent , Adult , Aged , COVID-19 , Child , China/epidemiology , Communicable Disease Control/statistics & numerical data , Coronavirus Infections/prevention & control , Disease Outbreaks , Disease Transmission, Infectious/prevention & control , Female , Health Policy , Humans , Incidence , Male , Middle Aged , Pneumonia, Viral/prevention & control , SARS-CoV-2 , Young Adult
9.
BMC Public Health ; 21(1): 1007, 2021 05 28.
Article in English | MEDLINE | ID: covidwho-1247583

ABSTRACT

BACKGROUND: Identifying county-level characteristics associated with high coronavirus 2019 (COVID-19) burden can help allow for data-driven, equitable allocation of public health intervention resources and reduce burdens on health care systems. METHODS: Synthesizing data from various government and nonprofit institutions for all 3142 United States (US) counties, we studied county-level characteristics that were associated with cumulative and weekly case and death rates through 12/21/2020. We used generalized linear mixed models to model cumulative and weekly (40 repeated measures per county) cases and deaths. Cumulative and weekly models included state fixed effects and county-specific random effects. Weekly models additionally allowed covariate effects to vary by season and included US Census region-specific B-splines to adjust for temporal trends. RESULTS: Rural counties, counties with more minorities and white/non-white segregation, and counties with more people with no high school diploma and with medical comorbidities were associated with higher cumulative COVID-19 case and death rates. In the spring, urban counties and counties with more minorities and white/non-white segregation were associated with increased weekly case and death rates. In the fall, rural counties were associated with larger weekly case and death rates. In the spring, summer, and fall, counties with more residents with socioeconomic disadvantage and medical comorbidities were associated greater weekly case and death rates. CONCLUSIONS: These county-level associations are based off complete data from the entire country, come from a single modeling framework that longitudinally analyzes the US COVID-19 pandemic at the county-level, and are applicable to guiding government resource allocation policies to different US counties.


Subject(s)
COVID-19 , Social Segregation , Humans , Pandemics , Rural Population , SARS-CoV-2 , United States/epidemiology
10.
Intensive Care Med ; 47(7): 761-771, 2021 07.
Article in English | MEDLINE | ID: covidwho-1241594

ABSTRACT

PURPOSE: Acute respiratory distress syndrome (ARDS) is accompanied by a dysfunctional immune-inflammatory response following lung injury, including during coronavirus disease 2019 (COVID-19). Limited causal biomarkers exist for ARDS development. We sought to identify novel genetic susceptibility targets for ARDS to focus further investigation on their biological mechanism and therapeutic potential. METHODS: Meta-analyses of ARDS genome-wide association studies were performed with 1250 cases and 1583 controls in Europeans, and 387 cases and 387 controls in African Americans. The functionality of novel loci was determined in silico using multiple omics approaches. The causality of 114 factors potentially involved in ARDS development was assessed using Mendelian Randomization analysis. RESULTS: There was distinct genetic heterogeneity in ARDS between Europeans and African Americans. rs7967111 at 12p13.2 was functionally associated with ARDS susceptibility in Europeans (odds ratio = 1.38; P = 2.15 × 10-8). Expression of two genes annotated at this locus, BORCS5 and DUSP16, was dynamic but ultimately decreased during ARDS development, as well as downregulated in immune cells alongside COVID-19 severity. Causal inference implied that comorbidity of inflammatory bowel disease and elevated levels of C-reactive protein and interleukin-10 causally increased ARDS risk, while vitamin D supplementation and vasodilator use ameliorated risk. CONCLUSION: Our findings suggest a novel susceptibility locus in ARDS pathophysiology that implicates BORCS5 and DUSP16 as potentially acting in immune-inflammatory processes. This locus warrants further investigation to inform the development of therapeutic targets and clinical care strategies for ARDS, including those induced by COVID-19.


Subject(s)
COVID-19 , Respiratory Distress Syndrome , Genome-Wide Association Study , Humans , Respiratory Distress Syndrome/genetics , SARS-CoV-2 , White People/genetics
11.
Nat Hum Behav ; 4(9): 972-982, 2020 09.
Article in English | MEDLINE | ID: covidwho-733521

ABSTRACT

Despite the widespread implementation of public health measures, coronavirus disease 2019 (COVID-19) continues to spread in the United States. To facilitate an agile response to the pandemic, we developed How We Feel, a web and mobile application that collects longitudinal self-reported survey responses on health, behaviour and demographics. Here, we report results from over 500,000 users in the United States from 2 April 2020 to 12 May 2020. We show that self-reported surveys can be used to build predictive models to identify likely COVID-19-positive individuals. We find evidence among our users for asymptomatic or presymptomatic presentation; show a variety of exposure, occupational and demographic risk factors for COVID-19 beyond symptoms; reveal factors for which users have been SARS-CoV-2 PCR tested; and highlight the temporal dynamics of symptoms and self-isolation behaviour. These results highlight the utility of collecting a diverse set of symptomatic, demographic, exposure and behavioural self-reported data to fight the COVID-19 pandemic.


Subject(s)
Betacoronavirus , Clinical Laboratory Techniques/statistics & numerical data , Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Adult , Asymptomatic Diseases/epidemiology , COVID-19 , COVID-19 Testing , Coronavirus Infections/diagnosis , Coronavirus Infections/prevention & control , Coronavirus Infections/psychology , Female , Humans , Longitudinal Studies , Male , Mobile Applications , Models, Statistical , Pandemics/prevention & control , Pandemics/statistics & numerical data , Pneumonia, Viral/diagnosis , Pneumonia, Viral/prevention & control , Pneumonia, Viral/psychology , SARS-CoV-2 , United States/epidemiology
12.
Nature ; 584(7821): 420-424, 2020 08.
Article in English | MEDLINE | ID: covidwho-649530

ABSTRACT

As countries in the world review interventions for containing the pandemic of coronavirus disease 2019 (COVID-19), important lessons can be drawn from the study of the full transmission dynamics of its causative agent-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)- in Wuhan (China), where vigorous non-pharmaceutical interventions have suppressed the local outbreak of this disease1. Here we use a modelling approach to reconstruct the full-spectrum dynamics of COVID-19 in Wuhan between 1 January and 8 March 2020 across 5 periods defined by events and interventions, on the basis of 32,583 laboratory-confirmed cases1. Accounting for presymptomatic infectiousness2, time-varying ascertainment rates, transmission rates and population movements3, we identify two key features of the outbreak: high covertness and high transmissibility. We estimate 87% (lower bound, 53%) of the infections before 8 March 2020 were unascertained (potentially including asymptomatic and mildly symptomatic individuals); and a basic reproduction number (R0) of 3.54 (95% credible interval 3.40-3.67) in the early outbreak, much higher than that of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS)4,5. We observe that multipronged interventions had considerable positive effects on controlling the outbreak, decreasing the reproduction number to 0.28 (95% credible interval 0.23-0.33) and-by projection-reducing the total infections in Wuhan by 96.0% as of 8 March 2020. We also explore the probability of resurgence following the lifting of all interventions after 14 consecutive days of no ascertained infections; we estimate this probability at 0.32 and 0.06 on the basis of models with 87% and 53% unascertained cases, respectively-highlighting the risk posed by substantial covert infections when changing control measures. These results have important implications when considering strategies of continuing surveillance and interventions to eventually contain outbreaks of COVID-19.


Subject(s)
Coronavirus Infections/transmission , Models, Biological , Pneumonia, Viral/transmission , COVID-19 , China/epidemiology , Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Epidemiological Monitoring , Female , Humans , Male , Pandemics/prevention & control , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Reproducibility of Results , Stochastic Processes
SELECTION OF CITATIONS
SEARCH DETAIL