SARS-CoV-2 cross-sectional seroprevalence study among public school staff in Metro Vancouver after the first Omicron wave in British Columbia, Canada.

OBJECTIVE: To determine the SARS-CoV-2 seroprevalence among school workers within the Greater Vancouver area, British Columbia, Canada, after the first Omicron wave. DESIGN: Cross-sectional study by online questionnaire, with blood serology testing. SETTING: Three main school districts (Vancouver, Richmond and Delta) in the Vancouver metropolitan area. PARTICIPANTS: Active school staff enrolled from January to April 2022, with serology testing between 27 January and 8 April 2022. Seroprevalence estimates were compared with data obtained from Canadian blood donors weighted over the same sampling period, age, sex and postal code distribution. PRIMARY AND SECONDARY OUTCOMES: SARS-CoV-2 nucleocapsid antibody testing results adjusted for test sensitivity and specificity, and regional variation across school districts using Bayesian models. RESULTS: Of 1850 school staff enrolled, 65.8% (1214/1845) reported close contact with a COVID-19 case outside the household. Of those close contacts, 51.5% (625/1214) were a student and 54.9% (666/1214) were a coworker. Cumulative incidence of COVID-19 positive testing by self-reported nucleic acid or rapid antigen testing since the beginning of the pandemic was 15.8% (291/1845). In a representative sample of 1620 school staff who completed serology testing (87.6%), the adjusted seroprevalence was 26.5% (95% CrI 23.9% to 29.3%), compared with 32.4% (95% CrI 30.6% to 34.5%) among 7164 blood donors. CONCLUSION: Despite frequent COVID-19 exposures reported, SARS-CoV-2 seroprevalence among school staff in this setting remained no greater than the community reference group. Results are consistent with the premise that many infections were acquired outside the school setting, even with Omicron.

Epidemiological inference for emerging viruses using segregating sites.

Epidemiological models are commonly fit to case and pathogen sequence data to estimate parameters and to infer unobserved disease dynamics. Here, we present an inference approach based on sequence data that is well suited for model fitting early on during the expansion of a viral lineage. Our approach relies on a trajectory of segregating sites to infer epidemiological parameters within a Sequential Monte Carlo framework. Using simulated data, we first show that our approach accurately recovers key epidemiological quantities under a single-introduction scenario. We then apply our approach to SARS-CoV-2 sequence data from France, estimating a basic reproduction number of approximately 2.3-2.7 under an epidemiological model that allows for multiple introductions. Our approach presented here indicates that inference approaches that rely on simple population genetic summary statistics can be informative of epidemiological parameters and can be used for reconstructing infectious disease dynamics during the early expansion of a viral lineage.

Predicting Mortality in Hospitalized COVID-19 Patients in Zambia: An Application of Machine Learning.

The coronavirus disease 2019 (COVID-19) has wreaked havoc globally, resulting in millions of cases and deaths. The objective of this study was to predict mortality in hospitalized COVID-19 patients in Zambia using machine learning (ML) methods based on factors that have been shown to be predictive of mortality and thereby improve pandemic preparedness. This research employed seven powerful ML models that included decision tree (DT), random forest (RF), support vector machines (SVM), logistic regression (LR), Naïve Bayes (NB), gradient boosting (GB), and XGBoost (XGB). These classifiers were trained on 1,433 hospitalized COVID-19 patients from various health facilities in Zambia. The performances achieved by these models were checked using accuracy, recall, F1-Score, area under the receiver operating characteristic curve (ROC_AUC), area under the precision-recall curve (PRC_AUC), and other metrics. The best-performing model was the XGB which had an accuracy of 92.3%, recall of 94.2%, F1-Score of 92.4%, and ROC_AUC of 97.5%. The pairwise Mann-Whitney U-test analysis showed that the second-best model (GB) and the third-best model (RF) did not perform significantly worse than the best model (XGB) and had the following: GB had an accuracy of 91.7%, recall of 94.2%, F1-Score of 91.9%, and ROC_AUC of 97.1%. RF had an accuracy of 90.8%, recall of 93.6%, F1-Score of 91.0%, and ROC_AUC of 96.8%. Other models showed similar results for the same metrics checked. The study successfully derived and validated the selected ML models and predicted mortality effectively with reasonably high performance in the stated metrics. The feature importance analysis found that knowledge of underlying health conditions about patients' hospital length of stay (LOS), white blood cell count, age, and other factors can help healthcare providers offer lifesaving services on time, improve pandemic preparedness, and decongest health facilities in Zambia and other countries with similar settings.

A Tutorial on Modern Bayesian Methods in Clinical Trials.

Clinical trials continue to be the gold standard for evaluating new medical technologies. New advancements in modern computation power have led to increasing interest in Bayesian methods. Despite the multiple benefits of Bayesian approaches, application to clinical trials has been limited. Based on insights from the survey of clinical researchers in drug development conducted by the Drug Information Association Bayesian Scientific Working Group (DIA BSWG), insufficient knowledge of Bayesian approaches was ranked as the most important perceived barrier to implementing Bayesian methods. Results of the same survey indicate that clinical researchers may find the interpretation of results from a Bayesian analysis to be more useful than conventional interpretations. In this article, we illustrate key concepts tied to Bayesian methods, starting with familiar concepts widely used in clinical practice before advancing in complexity, and use practical illustrations from clinical development.

Understanding COVID-19: comparison of spatio-temporal analysis methods used to study epidemic spread patterns in the United States.

This article examines three spatiotemporal methods used for analyzing of infectious diseases, with a focus on COVID-19 in the United States. The methods considered include inverse distance weighting (IDW) interpolation, retrospective spatiotemporal scan statistics and Bayesian spatiotemporal models. The study covers a 12-month period from May 2020 to April 2021, including monthly data from 49 states or regions in the United States. The results show that the spread of COVID-19 pandemic increased rapidly to a high value in winter of 2020, followed by a brief decline that later reverted into another increase. Spatially, the COVID-19 epidemic in the United States exhibited a multi-centre, rapid spread character, with clustering areas represented by states such as New York, North Dakota, Texas and California. By demonstrating the applicability and limitations of different analytical tools in investigating the spatiotemporal dynamics of disease outbreaks, this study contributes to the broader field of epidemiology and helps improve strategies for responding to future major public health events.

Spatial genetic structure of 2009 H1N1 pandemic influenza established as a result of interaction with human populations in mainland China.

Identifying the spatial patterns of genetic structure of influenza A viruses is a key factor for understanding their spread and evolutionary dynamics. In this study, we used phylogenetic and Bayesian clustering analyses of genetic sequences of the A/H1N1pdm09 virus with district-level locations in mainland China to investigate the spatial genetic structure of the A/H1N1pdm09 virus across human population landscapes. Positive correlation between geographic and genetic distances indicates high degrees of genetic similarity among viruses within small geographic regions but broad-scale genetic differentiation, implying that local viral circulation was a more important driver in the formation of the spatial genetic structure of the A/H1N1pdm09 virus than even, countrywide viral mixing and gene flow. Geographic heterogeneity in the distribution of genetic subpopulations of A/H1N1pdm09 virus in mainland China indicates both local to local transmission as well as broad-range viral migration. This combination of both local and global structure suggests that both small-scale and large-scale population circulation in China is responsible for viral genetic structure. Our study provides implications for understanding the evolution and spread of A/H1N1pdm09 virus across the population landscape of mainland China, which can inform disease control strategies for future pandemics.

Does mobility restriction significantly control infectious disease transmission? Accounting for non-stationarity in the impact of COVID-19 based on Bayesian spatially varying coefficient models.

COVID-19 is the most severe health crisis of the 21st century. COVID-19 presents a threat to almost all countries worldwide. The restriction of human mobility is one of the strategies used to control the transmission of COVID-19. However, it has yet to be determined how effective this restriction is in controlling the rise in COVID-19 cases, particularly in small areas. Using Facebook's mobility data, our study explores the impact of restricting human mobility on COVID-19 cases in several small districts in Jakarta, Indonesia. Our main contribution is showing how the restriction of human mobility data can give important information about how COVID-19 spreads in different small areas. We proposed modifying a global regression model into a local regression model by accounting for the spatial and temporal interdependence of COVID-19 transmission across space and time. We applied Bayesian hierarchical Poisson spatiotemporal models with spatially varying regression coefficients to account for non-stationarity in human mobility. We estimated the regression parameters using an Integrated Nested Laplace Approximation. We found that the local regression model with spatially varying regression coefficients outperforms the global regression model based on DIC, WAIC, MPL, and R2 criteria for model selection. In Jakarta's 44 districts, the impact of human mobility varies significantly. The impacts of human mobility on the log relative risk of COVID-19 range from -4.445 to 2.353. The prevention strategy involving the restriction of human mobility may be beneficial in some districts but ineffective in others. Therefore, a cost-effective strategy had to be adopted.

Can ship travel contain COVID-19 outbreak after re-opening: a Bayesian meta-analysis.

Large gatherings of people on cruise ships and warships are often at high risk of COVID-19 infections. To assess the transmissibility of SARS-CoV-2 on warships and cruise ships and to quantify the effectiveness of the containment measures, the transmission coefficient (ß), basic reproductive number (R0), and time to deploy containment measures were estimated by the Bayesian Susceptible-Exposed-Infected-Recovered model. A meta-analysis was conducted to predict vaccine protection with or without non-pharmaceutical interventions (NPIs). The analysis showed that implementing NPIs during voyages could reduce the transmission coefficients of SARS-CoV-2 by 50%. Two weeks into the voyage of a cruise that begins with 1 infected passenger out of a total of 3,711 passengers, we estimate there would be 45 (95% CI:25-71), 33 (95% CI:20-52), 18 (95% CI:11-26), 9 (95% CI:6-12), 4 (95% CI:3-5), and 2 (95% CI:2-2) final cases under 0%, 10%, 30%, 50%, 70%, and 90% vaccine protection, respectively, without NPIs. The timeliness of strict NPIs along with implementing strict quarantine and isolation measures is imperative to contain COVID-19 cases in cruise ships. The spread of COVID-19 on ships was predicted to be limited in scenarios corresponding to at least 70% protection from prior vaccination, across all passengers and crew.

Dynamics of SARS-CoV-2 infection hospitalisation and infection fatality ratios over 23 months in England.

The relationship between prevalence of infection and severe outcomes such as hospitalisation and death changed over the course of the COVID-19 pandemic. Reliable estimates of the infection fatality ratio (IFR) and infection hospitalisation ratio (IHR) along with the time-delay between infection and hospitalisation/death can inform forecasts of the numbers/timing of severe outcomes and allow healthcare services to better prepare for periods of increased demand. The REal-time Assessment of Community Transmission-1 (REACT-1) study estimated swab positivity for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection in England approximately monthly from May 2020 to March 2022. Here, we analyse the changing relationship between prevalence of swab positivity and the IFR and IHR over this period in England, using publicly available data for the daily number of deaths and hospitalisations, REACT-1 swab positivity data, time-delay models, and Bayesian P-spline models. We analyse data for all age groups together, as well as in 2 subgroups: those aged 65 and over and those aged 64 and under. Additionally, we analysed the relationship between swab positivity and daily case numbers to estimate the case ascertainment rate of England's mass testing programme. During 2020, we estimated the IFR to be 0.67% and the IHR to be 2.6%. By late 2021/early 2022, the IFR and IHR had both decreased to 0.097% and 0.76%, respectively. The average case ascertainment rate over the entire duration of the study was estimated to be 36.1%, but there was some significant variation in continuous estimates of the case ascertainment rate. Continuous estimates of the IFR and IHR of the virus were observed to increase during the periods of Alpha and Delta's emergence. During periods of vaccination rollout, and the emergence of the Omicron variant, the IFR and IHR decreased. During 2020, we estimated a time-lag of 19 days between hospitalisation and swab positivity, and 26 days between deaths and swab positivity. By late 2021/early 2022, these time-lags had decreased to 7 days for hospitalisations and 18 days for deaths. Even though many populations have high levels of immunity to SARS-CoV-2 from vaccination and natural infection, waning of immunity and variant emergence will continue to be an upwards pressure on the IHR and IFR. As investments in community surveillance of SARS-CoV-2 infection are scaled back, alternative methods are required to accurately track the ever-changing relationship between infection, hospitalisation, and death and hence provide vital information for healthcare provision and utilisation.

Losartan in hospitalized patients with COVID-19 in North America: An individual participant data meta-analysis.

BACKGROUND: Angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers (ARBs) have been hypothesized to benefit patients with COVID-19 via the inhibition of viral entry and other mechanisms. We conducted an individual participant data (IPD) meta-analysis assessing the effect of starting the ARB losartan in recently hospitalized COVID-19 patients. METHODS: We searched ClinicalTrials.gov in January 2021 for U.S./Canada-based trials where an angiotensin-converting enzyme inhibitors/ARB was a treatment arm, targeted outcomes could be extrapolated, and data sharing was allowed. Our primary outcome was a 7-point COVID-19 ordinal score measured 13 to 16 days post-enrollment. We analyzed data by fitting multilevel Bayesian ordinal regression models and standardizing the resulting predictions. RESULTS: 325 participants (156 losartan vs 169 control) from 4 studies contributed IPD. Three were randomized trials; one used non-randomized concurrent and historical controls. Baseline covariates were reasonably balanced for the randomized trials. All studies evaluated losartan. We found equivocal evidence of a difference in ordinal scores 13-16 days post-enrollment (model-standardized odds ratio [OR] 1.10, 95% credible interval [CrI] 0.76-1.71; adjusted OR 1.15, 95% CrI 0.15-3.59) and no compelling evidence of treatment effect heterogeneity among prespecified subgroups. Losartan had worse effects for those taking corticosteroids at baseline after adjusting for covariates (ratio of adjusted ORs 0.29, 95% CrI 0.08-0.99). Hypotension serious adverse event rates were numerically higher with losartan. CONCLUSIONS: In this IPD meta-analysis of hospitalized COVID-19 patients, we found no convincing evidence for the benefit of losartan versus control treatment, but a higher rate of hypotension adverse events with losartan.

Hybrid prediction of infections and deaths due to COVID-19 in two Colombian data series.

The prediction of the number of infected and dead due to COVID-19 has challenged scientists and government bodies, prompting them to formulate public policies to control the virus' spread and public health emergency worldwide. In this sense, we propose a hybrid method that combines the SIRD mathematical model, whose parameters are estimated via Bayesian inference with a seasonal ARIMA model. Our approach considers that notifications of both, infections and deaths are realizations of a time series process, so that components such as non-stationarity, trend, autocorrelation and/or stochastic seasonal patterns, among others, must be taken into account in the fitting of any mathematical model. The method is applied to data from two Colombian cities, and as hypothesized, the prediction outperforms the obtained with the fit of only the SIRD model. In addition, a simulation study is presented to assess the quality of the estimators of SIRD model in the inverse problem solution.

Declining trends in vaccine confidence across sub-Saharan Africa: A large-scale cross-sectional modeling study.

Current WHO/UNICEF estimates of routine childhood immunization coverage reveal the largest sustained decline in uptake in three decades with pronounced setbacks across Africa. Although the COVID-19 pandemic has induced significant supply and delivery disruptions, the impact of the pandemic on vaccine confidence is less understood. We here examine trends in vaccine confidence across eight sub-Saharan countries between 2020 and 2022 via a total of 17,187 individual interviews, conducted via a multi-stage probability sampling approach and cross-sectional design and evaluated using Bayesian methods. Multilevel regression combined with poststratification weighting using local demographic information yields national and sub-national estimates of vaccine confidence in 2020 and 2022 as well as its socio-demographic associations. We identify declines in perceptions toward the importance of vaccines for children across all eight countries, with mixed trends in perceptions toward vaccine safety and effectiveness. We find that COVID-19 vaccines are perceived to be less important and safe in 2022 than in 2020 in six of the eight countries, with the only increases in COVID-19 vaccine confidence detected in Ivory Coast. There are substantial declines in vaccine confidence in the Democratic Republic of Congo and South Africa, notably in Eastern Cape, KwaZulu-Natal, Limpopo, and Northern Cape (South Africa) and Bandundu, Maniema, Kasaï-Oriental, Kongo-Central, and Sud-Kivu (DRC). While over 60-year-olds in 2022 have higher vaccine confidence in vaccines generally than younger age groups, we do not detect other individual-level socio-demographic associations with vaccine confidence at the sample sizes studied, including sex, age, education, employment status, and religious affiliation. Understanding the role of the COVID-19 pandemic and associated policies on wider vaccine confidence can inform post-COVID vaccination strategies and help rebuild immunization system resilience.

Estimating fine age structure and time trends in human contact patterns from coarse contact data: The Bayesian rate consistency model.

Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), large-scale social contact surveys are now longitudinally measuring the fundamental changes in human interactions in the face of the pandemic and non-pharmaceutical interventions. Here, we present a model-based Bayesian approach that can reconstruct contact patterns at 1-year resolution even when the age of the contacts is reported coarsely by 5 or 10-year age bands. This innovation is rooted in population-level consistency constraints in how contacts between groups must add up, which prompts us to call the approach presented here the Bayesian rate consistency model. The model can also quantify time trends and adjust for reporting fatigue emerging in longitudinal surveys through the use of computationally efficient Hilbert Space Gaussian process priors. We illustrate estimation accuracy on simulated data as well as social contact data from Europe and Africa for which the exact age of contacts is reported, and then apply the model to social contact data with coarse information on the age of contacts that were collected in Germany during the COVID-19 pandemic from April to June 2020 across five longitudinal survey waves. We estimate the fine age structure in social contacts during the early stages of the pandemic and demonstrate that social contact intensities rebounded in an age-structured, non-homogeneous manner. The Bayesian rate consistency model provides a model-based, non-parametric, computationally tractable approach for estimating the fine structure and longitudinal trends in social contacts and is applicable to contemporary survey data with coarsely reported age of contacts as long as the exact age of survey participants is reported.

Ecosystem degradation and the spread of Covid-19.

The linkages between the emergence of zoonotic diseases and ecosystem degradation have been widely acknowledged by the scientific community and policy makers. In this paper we investigate the relationship between human overexploitation of natural resources, represented by the Human Appropriation of Net Primary Production Index (HANPP) and the spread of Covid-19 cases during the first pandemic wave in 730 regions of 63 countries worldwide. Using a Bayesian estimation technique, we highlight the significant role of HANPP as a driver of Covid-19 diffusion, besides confirming the well-known impact of population size and the effects of other socio-economic variables. We believe that these findings could be relevant for policy makers in their effort towards a more sustainable intensive agriculture and responsible urbanisation.

Dynamic calibration with approximate Bayesian computation for a microsimulation of disease spread.

The global COVID-19 pandemic brought considerable public and policy attention to the field of infectious disease modelling. A major hurdle that modellers must overcome, particularly when models are used to develop policy, is quantifying the uncertainty in a model's predictions. By including the most recent available data in a model, the quality of its predictions can be improved and uncertainties reduced. This paper adapts an existing, large-scale, individual-based COVID-19 model to explore the benefits of updating the model in pseudo-real time. We use Approximate Bayesian Computation (ABC) to dynamically recalibrate the model's parameter values as new data emerge. ABC offers advantages over alternative calibration methods by providing information about the uncertainty associated with particular parameter values and the resulting COVID-19 predictions through posterior distributions. Analysing such distributions is crucial in fully understanding a model and its outputs. We find that forecasts of future disease infection rates are improved substantially by incorporating up-to-date observations and that the uncertainty in forecasts drops considerably in later simulation windows (as the model is provided with additional data). This is an important outcome because the uncertainty in model predictions is often overlooked when models are used in policy.

Bayesian Noise Modelling for State Estimation of the Spread of COVID-19 in Saudi Arabia with Extended Kalman Filters.

The epistemic uncertainty in coronavirus disease (COVID-19) model-based predictions using complex noisy data greatly affects the accuracy of pandemic trend and state estimations. Quantifying the uncertainty of COVID-19 trends caused by different unobserved hidden variables is needed to evaluate the accuracy of the predictions for complex compartmental epidemiological models. A new approach for estimating the measurement noise covariance from real COVID-19 pandemic data has been presented based on the marginal likelihood (Bayesian evidence) for Bayesian model selection of the stochastic part of the Extended Kalman filter (EKF), with a sixth-order nonlinear epidemic model, known as the SEIQRD (Susceptible-Exposed-Infected-Quarantined-Recovered-Dead) compartmental model. This study presents a method for testing the noise covariance in cases of dependence or independence between the infected and death errors, to better understand their impact on the predictive accuracy and reliability of EKF statistical models. The proposed approach is able to reduce the error in the quantity of interest compared to the arbitrarily chosen values in the EKF estimation.

Unraveling the socio-environmental drivers during the early COVID-19 pandemic in China.

The effect of environmental and socioeconomic conditions on the global pandemic of COVID-19 had been widely studied, yet their influence during the early outbreak remains less explored. Unraveling these relationships represents a key knowledge to prevent potential outbreaks of similar pathogens in the future. This study aims to determine the influence of socioeconomic, infrastructure, air pollution, and weather variables on the relative risk of infection in the initial phase of the COVID-19 pandemic in China. A spatio-temporal Bayesian zero-inflated Poisson model is used to test for the effect of 13 socioeconomic, urban infrastructure, air pollution, and weather variables on the relative risk of COVID-19 disease in 122 cities of China. The results show that socioeconomic and urban infrastructure variables did not have a significant effect on the relative risk of COVID-19. Meanwhile, COVID-19 relative risk was negatively associated with temperature, wind speed, and carbon monoxide, while nitrous dioxide and the human modification index presented a positive effect. Pollution gases presented a marked variability during the study period, showing a decrease of CO. These findings suggest that controlling and monitoring urban emissions of pollutant gases is a key factor for the reduction of risk derived from COVID-19.

Distribution of COVID-19 cases and health resources in Brazil's Amazon region: a spatial analysis. / Distribuição da COVID-19 e dos recursos de saúde na Amazônia Legal: uma análise espacial.

Spatial analysis can help measure the spatial accessibility of health services with a view to improving the allocation of health care resources. The objective of this study was to analyze the spatial distribution of COVID-19 detection rates and health care resources in Brazil's Amazon region. We conducted an ecological study using data on COVID-19 cases and the availability of health care resources in 772 municipalities during two waves of the pandemic. Local and global Bayesian estimation were used to construct choropleth maps. Moran's I was calculated to detect the presence of spatial dependence and Moran maps were used to identify disease clusters. In both periods, Moran's I values indicate the presence of positive spatial autocorrelation in distributions and spatial dependence between municipalities, with only a slight difference between the two estimators. The findings also reveal that case rates were highest in the states of Amapá, Amazonas, and Roraima. The data suggest that health care resources were inefficiently allocated, with higher concentrations of ventilators and ICU beds being found in state capitals.

O método de análise espacial permite mensurar a acessibilidade espacial dos serviços de saúde para alocação dos recursos de forma eficiente e eficaz. Diante disso, o objetivo deste estudo foi analisar a distribuição espacial das taxas de COVID-19 e dos recursos de saúde na Amazônia Legal. Estudo ecológico realizado com casos de COVID-19 e os recursos de saúde nos 772 municípios em dois picos da pandemia. Utilizou-se o método bayesiano global e local para elaboração de mapas coropléticos, com cálculo do índice de Moran para análise da dependência espacial e utilização do Moran map para identificação dos clusters da doença. Os índices de Moran calculados para os dois períodos demonstraram autocorrelação espacial positiva dessa distribuição e dependência espacial entre os municípios nos dois períodos, sem muita diferença entre os dois estimadores. Evidenciaram-se maiores taxas da doença nos estados do Amapá, Amazonas e Roraima. Em relação aos recursos de saúde, observou-se alocação de forma ineficiente, com maior concentração nas capitais.

Identification of major depression patients using machine learning models based on heart rate variability during sleep stages for pre-hospital screening.

With the COVID-19 pandemic causing challenges in hospital admissions globally, the role of home health monitoring in aiding the diagnosis of mental health disorders has become increasingly important. This paper proposes an interpretable machine learning solution to optimise initial screening for major depressive disorder (MDD) in both male and female patients. The data is from the Stanford Technical Analysis and Sleep Genome Study (STAGES). We analyzed 5-min short-term electrocardiogram (ECG) signals during nighttime sleep stages of 40 MDD patients and 40 healthy controls, with a 1:1 gender ratio. After preprocessing, we calculated the time-frequency parameters of heart rate variability (HRV) based on the ECG signals and used common machine learning algorithms for classification, along with feature importance analysis for global decision analysis. Ultimately, the Bayesian optimised extremely randomized trees classifier (BO-ERTC) showed the best performance on this dataset (accuracy 86.32%, specificity 86.49%, sensitivity 85.85%, F1-score 0.86). By using feature importance analysis on the cases confirmed by BO-ERTC, we found that gender is one of the most important factors affecting the prediction of the model, which should not be overlooked in our assisted diagnosis. This method can be embedded in portable ECG monitoring systems and is consistent with the literature results.

Age-specific SARS-CoV-2 infection fatality rates derived from serological data vary with income and income inequality.

The ongoing COVID-19 pandemic has killed at least 1.1 million people in the United States and over 6.7 million globally. Accurately estimating the age-specific infection fatality rate (IFR) of SARS-CoV-2 for different populations is crucial for assessing and understanding the impact of COVID-19 and for appropriately allocating vaccines and treatments to at-risk groups. We estimated age-specific IFRs of wild-type SARS-CoV-2 using published seroprevalence, case, and death data from New York City (NYC) from March to May 2020, using a Bayesian framework that accounted for delays between key epidemiological events. IFRs increased 3-4-fold with every 20 years of age, from 0.06% in individuals between 18-45 years old to 4.7% in individuals over 75. We then compared IFRs in NYC to several city- and country-wide estimates including England, Switzerland (Geneva), Sweden (Stockholm), Belgium, Mexico, and Brazil, as well as a global estimate. IFRs in NYC were higher for individuals younger than 65 years old than most other populations, but similar for older individuals. IFRs for age groups less than 65 decreased with income and increased with income inequality measured using the Gini index. These results demonstrate that the age-specific fatality of COVID-19 differs among developed countries and raises questions about factors underlying these differences, including underlying health conditions and healthcare access.