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1.
BMC Med ; 20(1): 164, 2022 04 26.
Article in English | MEDLINE | ID: covidwho-1808370

ABSTRACT

BACKGROUND: Increasing age, male sex, and pre-existing comorbidities are associated with lower survival from SARS-CoV-2 infection. The interplay between different comorbidities, age, and sex is not fully understood, and it remains unclear if survival decreases linearly with higher ICU occupancy or if there is a threshold beyond which survival falls. METHOD: This national population-based study included 22,648 people who tested positive for SARS-CoV-2 infection and were hospitalized in Switzerland between February 24, 2020, and March 01, 2021. Bayesian survival models were used to estimate survival after positive SARS-CoV-2 test among people hospitalized with COVID-19 by epidemic wave, age, sex, comorbidities, and ICU occupancy. Two-way interactions between age, sex, and comorbidities were included to assess the differential risk of death across strata. ICU occupancy was modeled using restricted cubic splines to allow for a non-linear association with survival. RESULTS: Of 22,648 people hospitalized with COVID-19, 4785 (21.1%) died. The survival was lower during the first epidemic wave than in the second (predicted survival at 40 days after positive test 76.1 versus 80.5%). During the second epidemic wave, occupancy among all available ICU beds in Switzerland varied between 51.7 and 78.8%. The estimated survival was stable at approximately 81.5% when ICU occupancy was below 70%, but worse when ICU occupancy exceeded this threshold (survival at 80% ICU occupancy: 78.2%; 95% credible interval [CrI] 76.1 to 80.1%). Periods with higher ICU occupancy (>70 vs 70%) were associated with an estimated number of 137 (95% CrI 27 to 242) excess deaths. Comorbid conditions reduced survival more in younger people than in older people. Among comorbid conditions, hypertension and obesity were not associated with poorer survival. Hypertension appeared to decrease survival in combination with cardiovascular disease. CONCLUSIONS: Survival after hospitalization with COVID-19 has improved over time, consistent with improved management of severe COVID-19. The decreased survival above 70% national ICU occupancy supports the need to introduce measures for prevention and control of SARS-CoV-2 transmission in the population well before ICUs are full.


Subject(s)
COVID-19 , Hypertension , Aged , Bayes Theorem , COVID-19/epidemiology , Hospitalization , Humans , Male , SARS-CoV-2 , Switzerland/epidemiology
2.
Patterns (N Y) ; 2(8): 100310, 2021 Aug 13.
Article in English | MEDLINE | ID: covidwho-1763926

ABSTRACT

We discuss several issues of statistical design, data collection, analysis, communication, and decision-making that have arisen in recent and ongoing coronavirus studies, focusing on tools for assessment and propagation of uncertainty. This paper does not purport to be a comprehensive survey of the research literature; rather, we use examples to illustrate statistical points that we think are important.

3.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-324951

ABSTRACT

We discuss several issues of statistical design, data collection, analysis, communication, and decision making that have arisen in recent and ongoing coronavirus studies, focusing on tools for assessment and propagation of uncertainty. This paper does not purport to be a comprehensive survey of the research literature;rather, we use examples to illustrate statistical points that we think are important.

4.
Ann Intern Med ; 175(4): 523-532, 2022 04.
Article in English | MEDLINE | ID: covidwho-1662827

ABSTRACT

BACKGROUND: Excess mortality quantifies the overall mortality impact of a pandemic. Mortality data have been accessible for many countries in recent decades, but few continuous data have been available for longer periods. OBJECTIVE: To assess the historical dimension of the COVID-19 pandemic in 2020 for 3 countries with reliable death count data over an uninterrupted span of more than 100 years. DESIGN: Observational study. SETTING: Switzerland, Sweden, and Spain, which were militarily neutral and not involved in combat during either world war and have not been affected by significant changes in their territory since the end of the 19th century. PARTICIPANTS: Complete populations of these 3 countries. MEASUREMENTS: Continuous series of recorded deaths (from all causes) by month from the earliest available year (1877 for Switzerland, 1851 for Sweden, and 1908 for Spain) were jointly modeled with annual age group-specific death and total population counts using negative binomial and multinomial models, which accounted for temporal trends and seasonal variability of prepandemic years. The aim was to estimate the expected number of deaths in a pandemic year for a nonpandemic scenario and the difference in observed and expected deaths aggregated over the year. RESULTS: In 2020, the number of excess deaths recorded per 100 000 persons was 100 (95% credible interval [CrI], 60 to 135) for Switzerland, 75 (CrI, 40 to 105) for Sweden, and 155 (CrI, 110 to 195) for Spain. In 1918, excess mortality was 6 to 7 times higher. In all 3 countries, the peaks of monthly excess mortality in 2020 were greater than most monthly excess mortality since 1918, including many peaks due to seasonal influenza and heat waves during that period. LIMITATION: Historical vital statistics might be affected by minor completeness issues before the beginning of the 20th century. CONCLUSION: In 2020, the COVID-19 pandemic led to the second-largest infection-related mortality disaster in Switzerland, Sweden, and Spain since the beginning of the 20th century. PRIMARY FUNDING SOURCE: Foundation for Research in Science and the Humanities at the University of Zurich, Swiss National Science Foundation, and National Institute of Allergy and Infectious Diseases.


Subject(s)
COVID-19 , Pandemics , Humans , Mortality , Spain/epidemiology , Sweden/epidemiology , Switzerland/epidemiology
5.
Nat Commun ; 13(1): 482, 2022 01 25.
Article in English | MEDLINE | ID: covidwho-1655580

ABSTRACT

The impact of the COVID-19 pandemic on excess mortality from all causes in 2020 varied across and within European countries. Using data for 2015-2019, we applied Bayesian spatio-temporal models to quantify the expected weekly deaths at the regional level had the pandemic not occurred in England, Greece, Italy, Spain, and Switzerland. With around 30%, Madrid, Castile-La Mancha, Castile-Leon (Spain) and Lombardia (Italy) were the regions with the highest excess mortality. In England, Greece and Switzerland, the regions most affected were Outer London and the West Midlands (England), Eastern, Western and Central Macedonia (Greece), and Ticino (Switzerland), with 15-20% excess mortality in 2020. Our study highlights the importance of the large transportation hubs for establishing community transmission in the first stages of the pandemic. Here, we show that acting promptly to limit transmission around these hubs is essential to prevent spread to other regions and countries.


Subject(s)
Bayes Theorem , COVID-19/mortality , Pandemics/statistics & numerical data , SARS-CoV-2/isolation & purification , Adult , Aged , Aged, 80 and over , COVID-19/epidemiology , COVID-19/virology , Cause of Death , England/epidemiology , Female , Geography , Greece/epidemiology , Humans , Italy/epidemiology , Male , Middle Aged , Pandemics/prevention & control , SARS-CoV-2/physiology , Spain/epidemiology , Survival Rate , Switzerland/epidemiology
6.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-294847

ABSTRACT

This tutorial shows how to build, fit, and criticize disease transmission models in Stan, and should be useful to researchers interested in modeling the SARS-CoV-2 pandemic and other infectious diseases in a Bayesian framework. Bayesian modeling provides a principled way to quantify uncertainty and incorporate both data and prior knowledge into the model estimates. Stan is an expressive probabilistic programming language that abstracts the inference and allows users to focus on the modeling. As a result, Stan code is readable and easily extensible, which makes the modeler's work more transparent. Furthermore, Stan's main inference engine, Hamiltonian Monte Carlo sampling, is amiable to diagnostics, which means the user can verify whether the obtained inference is reliable. In this tutorial, we demonstrate how to formulate, fit, and diagnose a compartmental transmission model in Stan, first with a simple Susceptible-Infected-Recovered (SIR) model, then with a more elaborate transmission model used during the SARS-CoV-2 pandemic. We also cover advanced topics which can further help practitioners fit sophisticated models;notably, how to use simulations to probe the model and priors, and computational techniques to scale-up models based on ordinary differential equations.

7.
Stat Med ; 40(27): 6209-6234, 2021 11 30.
Article in English | MEDLINE | ID: covidwho-1396957

ABSTRACT

This tutorial shows how to build, fit, and criticize disease transmission models in Stan, and should be useful to researchers interested in modeling the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and other infectious diseases in a Bayesian framework. Bayesian modeling provides a principled way to quantify uncertainty and incorporate both data and prior knowledge into the model estimates. Stan is an expressive probabilistic programming language that abstracts the inference and allows users to focus on the modeling. As a result, Stan code is readable and easily extensible, which makes the modeler's work more transparent. Furthermore, Stan's main inference engine, Hamiltonian Monte Carlo sampling, is amiable to diagnostics, which means the user can verify whether the obtained inference is reliable. In this tutorial, we demonstrate how to formulate, fit, and diagnose a compartmental transmission model in Stan, first with a simple susceptible-infected-recovered model, then with a more elaborate transmission model used during the SARS-CoV-2 pandemic. We also cover advanced topics which can further help practitioners fit sophisticated models; notably, how to use simulations to probe the model and priors, and computational techniques to scale-up models based on ordinary differential equations.


Subject(s)
COVID-19 , SARS-CoV-2 , Bayes Theorem , Humans , Monte Carlo Method , Workflow
8.
Lancet Public Health ; 6(9): e683-e691, 2021 09.
Article in English | MEDLINE | ID: covidwho-1305339

ABSTRACT

BACKGROUND: The inverse care law states that disadvantaged populations need more health care than advantaged populations but receive less. Gaps in COVID-19-related health care and infection control are not well understood. We aimed to examine inequalities in health in the care cascade from testing for SARS-CoV-2 to COVID-19-related hospitalisation, intensive care unit (ICU) admission, and death in Switzerland, a wealthy country strongly affected by the pandemic. METHODS: We analysed surveillance data reported to the Swiss Federal Office of Public Health from March 1, 2020, to April 16, 2021, and 2018 population data. We geocoded residential addresses of notifications to identify the Swiss neighbourhood index of socioeconomic position (Swiss-SEP). The index describes 1·27 million small neighbourhoods of approximately 50 households each on the basis of rent per m2, education and occupation of household heads, and crowding. We used negative binomial regression models to calculate incidence rate ratios (IRRs) with 95% credible intervals (CrIs) of the association between ten groups of the Swiss-SEP index defined by deciles (1=lowest, 10=highest) and outcomes. Models were adjusted for sex, age, canton, and wave of the epidemic (before or after June 8, 2020). We used three different denominators: the general population, the number of tests, and the number of positive tests. FINDINGS: Analyses were based on 4 129 636 tests, 609 782 positive tests, 26 143 hospitalisations, 2432 ICU admissions, 9383 deaths, and 8 221 406 residents. Comparing the highest with the lowest Swiss-SEP group and using the general population as the denominator, more tests were done among people living in neighbourhoods of highest SEP compared with lowest SEP (adjusted IRR 1·18 [95% CrI 1·02-1·36]). Among tested people, test positivity was lower (0·75 [0·69-0·81]) in neighbourhoods of highest SEP than of lowest SEP. Among people testing positive, the adjusted IRR was 0·68 (0·62-0·74) for hospitalisation, was 0·54 (0·43-0·70) for ICU admission, and 0·86 (0·76-0·99) for death. The associations between neighbourhood SEP and outcomes were stronger in younger age groups and we found heterogeneity between areas. INTERPRETATION: The inverse care law and socioeconomic inequalities were evident in Switzerland during the COVID-19 epidemic. People living in neighbourhoods of low SEP were less likely to be tested but more likely to test positive, be admitted to hospital, or die, compared with those in areas of high SEP. It is essential to continue to monitor testing for SARS-CoV-2, access and uptake of COVID-19 vaccination and outcomes of COVID-19. Governments and health-care systems should address this pandemic of inequality by taking measures to reduce health inequalities in response to the SARS-CoV-2 pandemic. FUNDING: Swiss Federal Office of Public Health, Swiss National Science Foundation, EU Horizon 2020, Branco Weiss Foundation.


Subject(s)
COVID-19/therapy , Healthcare Disparities/statistics & numerical data , Social Class , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/mortality , COVID-19 Testing/statistics & numerical data , Child , Child, Preschool , Female , Hospitalization/statistics & numerical data , Humans , Infant , Infant, Newborn , Intensive Care Units , Male , Middle Aged , Switzerland/epidemiology , Young Adult
10.
PLoS Comput Biol ; 17(2): e1008728, 2021 02.
Article in English | MEDLINE | ID: covidwho-1154072

ABSTRACT

Large-scale serological testing in the population is essential to determine the true extent of the current SARS-CoV-2 pandemic. Serological tests measure antibody responses against pathogens and use predefined cutoff levels that dichotomize the quantitative test measures into sero-positives and negatives and use this as a proxy for past infection. With the imperfect assays that are currently available to test for past SARS-CoV-2 infection, the fraction of seropositive individuals in serosurveys is a biased estimator of the cumulative incidence and is usually corrected to account for the sensitivity and specificity. Here we use an inference method-referred to as mixture-model approach-for the estimation of the cumulative incidence that does not require to define cutoffs by integrating the quantitative test measures directly into the statistical inference procedure. We confirm that the mixture model outperforms the methods based on cutoffs, leading to less bias and error in estimates of the cumulative incidence. We illustrate how the mixture model can be used to optimize the design of serosurveys with imperfect serological tests. We also provide guidance on the number of control and case sera that are required to quantify the test's ambiguity sufficiently to enable the reliable estimation of the cumulative incidence. Lastly, we show how this approach can be used to estimate the cumulative incidence of classes of infections with an unknown distribution of quantitative test measures. This is a very promising application of the mixture-model approach that could identify the elusive fraction of asymptomatic SARS-CoV-2 infections. An R-package implementing the inference methods used in this paper is provided. Our study advocates using serological tests without cutoffs, especially if they are used to determine parameters characterizing populations rather than individuals. This approach circumvents some of the shortcomings of cutoff-based methods at exactly the low cumulative incidence levels and test accuracies that we are currently facing in SARS-CoV-2 serosurveys.


Subject(s)
COVID-19 Serological Testing/methods , COVID-19/diagnosis , COVID-19/epidemiology , Models, Statistical , Pandemics , SARS-CoV-2 , Antibodies, Viral/blood , Asymptomatic Infections/epidemiology , COVID-19/immunology , COVID-19 Serological Testing/statistics & numerical data , Computational Biology , Computer Simulation , Confidence Intervals , False Negative Reactions , False Positive Reactions , Humans , Incidence , Likelihood Functions , Pandemics/statistics & numerical data , ROC Curve , Reproducibility of Results , SARS-CoV-2/immunology , Sensitivity and Specificity
11.
Swiss Med Wkly ; 150: w20457, 2020 12 14.
Article in English | MEDLINE | ID: covidwho-979197

ABSTRACT

In the wake of the pandemic of coronavirus disease 2019 (COVID-19), contact tracing has become a key element of strategies to control the spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Given the rapid and intense spread of SARS-CoV-2, digital contact tracing has emerged as a potential complementary tool to support containment and mitigation efforts. Early modelling studies highlighted the potential of digital contact tracing to break transmission chains, and Google and Apple subsequently developed the Exposure Notification (EN) framework, making it available to the vast majority of smartphones. A growing number of governments have launched or announced EN-based contact tracing apps, but their effectiveness remains unknown. Here, we report early findings of the digital contact tracing app deployment in Switzerland. We demonstrate proof-of-principle that digital contact tracing reaches exposed contacts, who then test positive for SARS-CoV-2. This indicates that digital contact tracing is an effective complementary tool for controlling the spread of SARS-CoV-2. Continued technical improvement and international compatibility can further increase the efficacy, particularly also across country borders.


Subject(s)
COVID-19/transmission , Contact Tracing/methods , Disease Notification/methods , Mobile Applications , Smartphone , COVID-19/epidemiology , COVID-19/prevention & control , Confidentiality , Humans , SARS-CoV-2 , Switzerland/epidemiology , Wireless Technology
12.
PLoS Med ; 17(7): e1003189, 2020 07.
Article in English | MEDLINE | ID: covidwho-690567

ABSTRACT

BACKGROUND: As of 16 May 2020, more than 4.5 million cases and more than 300,000 deaths from disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been reported. Reliable estimates of mortality from SARS-CoV-2 infection are essential for understanding clinical prognosis, planning healthcare capacity, and epidemic forecasting. The case-fatality ratio (CFR), calculated from total numbers of reported cases and reported deaths, is the most commonly reported metric, but it can be a misleading measure of overall mortality. The objectives of this study were to (1) simulate the transmission dynamics of SARS-CoV-2 using publicly available surveillance data and (2) infer estimates of SARS-CoV-2 mortality adjusted for biases and examine the CFR, the symptomatic case-fatality ratio (sCFR), and the infection-fatality ratio (IFR) in different geographic locations. METHOD AND FINDINGS: We developed an age-stratified susceptible-exposed-infected-removed (SEIR) compartmental model describing the dynamics of transmission and mortality during the SARS-CoV-2 epidemic. Our model accounts for two biases: preferential ascertainment of severe cases and right-censoring of mortality. We fitted the transmission model to surveillance data from Hubei Province, China, and applied the same model to six regions in Europe: Austria, Bavaria (Germany), Baden-Württemberg (Germany), Lombardy (Italy), Spain, and Switzerland. In Hubei, the baseline estimates were as follows: CFR 2.4% (95% credible interval [CrI] 2.1%-2.8%), sCFR 3.7% (3.2%-4.2%), and IFR 2.9% (2.4%-3.5%). Estimated measures of mortality changed over time. Across the six locations in Europe, estimates of CFR varied widely. Estimates of sCFR and IFR, adjusted for bias, were more similar to each other but still showed some degree of heterogeneity. Estimates of IFR ranged from 0.5% (95% CrI 0.4%-0.6%) in Switzerland to 1.4% (1.1%-1.6%) in Lombardy, Italy. In all locations, mortality increased with age. Among individuals 80 years or older, estimates of the IFR suggest that the proportion of all those infected with SARS-CoV-2 who will die ranges from 20% (95% CrI 16%-26%) in Switzerland to 34% (95% CrI 28%-40%) in Spain. A limitation of the model is that count data by date of onset are required, and these are not available in all countries. CONCLUSIONS: We propose a comprehensive solution to the estimation of SARS-Cov-2 mortality from surveillance data during outbreaks. The CFR is not a good predictor of overall mortality from SARS-CoV-2 and should not be used for evaluation of policy or comparison across settings. Geographic differences in IFR suggest that a single IFR should not be applied to all settings to estimate the total size of the SARS-CoV-2 epidemic in different countries. The sCFR and IFR, adjusted for right-censoring and preferential ascertainment of severe cases, are measures that can be used to improve and monitor clinical and public health strategies to reduce the deaths from SARS-CoV-2 infection.


Subject(s)
Coronavirus Infections/mortality , Pneumonia, Viral/mortality , Age Factors , Betacoronavirus/isolation & purification , COVID-19 , China/epidemiology , Coronavirus Infections/transmission , Coronavirus Infections/virology , Europe/epidemiology , Humans , Models, Statistical , Pandemics , Pneumonia, Viral/transmission , Pneumonia, Viral/virology , SARS-CoV-2
13.
Euro Surveill ; 25(4)2020 01.
Article in English | MEDLINE | ID: covidwho-278

ABSTRACT

Since December 2019, China has been experiencing a large outbreak of a novel coronavirus (2019-nCoV) which can cause respiratory disease and severe pneumonia. We estimated the basic reproduction number R0 of 2019-nCoV to be around 2.2 (90% high density interval: 1.4-3.8), indicating the potential for sustained human-to-human transmission. Transmission characteristics appear to be of similar magnitude to severe acute respiratory syndrome-related coronavirus (SARS-CoV) and pandemic influenza, indicating a risk of global spread.


Subject(s)
Betacoronavirus/pathogenicity , Coronavirus Infections/transmission , Disease Outbreaks/statistics & numerical data , Pneumonia, Viral/transmission , Severe Acute Respiratory Syndrome/transmission , Virus Replication , COVID-19 , China/epidemiology , Coronavirus Infections/epidemiology , Global Health , Humans , Infection Control , Influenza A virus/pathogenicity , Influenza, Human/transmission , Pandemics , Pneumonia, Viral/epidemiology , Risk , SARS Virus/pathogenicity , SARS-CoV-2
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