A new method for accurate calculation of case fatality rates during a pandemic: Mathematical deduction based on population-level big data
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PROVIDER: S-EPMC10699681 | biostudies-literature | 2023 Mar
REPOSITORIES: biostudies-literature
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Project description:ObjectivesThe case fatality rate (CFR) of coronavirus disease 2019 (COVID-19) varies significantly between countries. We aimed to describe the associations between health indicators and the national CFRs of COVID-19.MethodsWe identified for each country health indicators potentially associated with the national CFRs of COVID-19. We extracted data for 18 variables from international administrative data sources for 34 member countries of the Organization for Economic Cooperation and Development (OECD). We excluded the collinear variables and examined the 16 variables in multivariable analysis. A dynamic web-based model was developed to analyse and display the associations for the CFRs of COVID-19. We followed the Guideline for Accurate and Transparent Health Estimates Reporting (GATHER).ResultsIn multivariable analysis, the variables significantly associated with the increased CFRs were percentage of obesity in ages >18 years (β = 3.26; 95%CI = 1.20, 5.33; p 0.003), tuberculosis incidence (β = 3.15; 95%CI = 1.09, 5.22; p 0.004), duration (days) since first death due to COVID-19 (β = 2.89; 95%CI = 0.83, 4.96; p 0.008), and median age (β = 2.83; 95%CI = 0.76, 4.89; p 0.009). The COVID-19 test rate (β = -3.54; 95%CI = -5.60, -1.47; p 0.002), hospital bed density (β = -2.47; 95%CI = -4.54, -0.41; p 0.021), and rural population ratio (β = -2.19; 95%CI = -4.25, -0.13; p 0.039) decreased the CFR.ConclusionsThe pandemic hits population-dense cities. Available hospital beds should be increased. Test capacity should be increased to enable more effective diagnostic tests. Older patients and patients with obesity and their caregivers should be warned about a potentially increased risk.
| S-EPMC7510430 | biostudies-literature
Project description:ObjectivesCoronavirus disease 2019 (COVID-19) is the most devastating pandemic to affect humanity in a century. In this article, we assessed tests as a policy instrument and policy enactment to contain COVID-19 and potentially reduce mortalities.Study designA model was devised to estimate the factors that influenced the death rate across 121 nations and by income group.ResultsNations with a higher proportion of people aged 65+ years had a higher fatality rate (P = 0.00014). Delaying policy enactment led to a higher case fatality rate (P = 0.0013). A 10% delay time to act resulted in a 3.7% higher case fatality rate. This study found that delaying policies for international travel restrictions, public information campaigns, and testing policies increased the fatality rate. Tests also impacted the case fatality rate, and nations with 10% more cumulative tests per million people showed a 2.8% lower mortality rate. Citizens of nations who can access more destinations without the need to have a prior visa have a significant higher mortality rate than those who need a visa to travel abroad (P = 0.0040).ConclusionTests, as a surrogate of policy action and earlier policy enactment, matter for saving lives from pandemics as such policies reduce the transmission rate of the pandemic.
| S-EPMC7434485 | biostudies-literature
Project description:Two main problems that arise in the context of hydrodynamic bead modeling are an inaccurate treatment of bead overlaps and the necessity of using volume corrections when calculating intrinsic viscosity. We present a formalism based on the generalized Rotne-Prager-Yamakawa approximation that successfully addresses both of these issues. The generalized Rotne-Prager-Yamakawa method is shown to be highly effective for the calculation of transport properties of rigid biomolecules represented as assemblies of spherical beads of different sizes, both overlapping and nonoverlapping. We test the method on simple molecular shapes as well as real protein structures and compare its performance with other computational approaches.
| S-EPMC6127458 | biostudies-literature
Project description:COVID-19 is a global pandemic with uncertain death rates. We examined county-level population morality rates (per 100,000) and case fatality rates by US region and rural-urban classification, while controlling for demographic, socioeconomic, and hospital variables. We found that population mortality rates and case fatality rates were significantly different across region, rural-urban classification, and their interaction. All significant comparisons had p < 0.001. Northeast counties had the highest population mortality rates (27.4) but had similar case fatality rates (5.9%) compared to other regions except the Southeast, which had significantly lower rates (4.1%). Population mortality rates were highest in urban counties but conversely, case fatality rates were highest in rural counties. Death rates in the Northeast were driven by urban areas (e.g., small, East Coast states), while case fatality rates tended to be highest in the most rural counties for all regions, especially the Southwest. However, on further inspection, high case fatality rate percentages in the Southwest, as well as in overall US counties, were driven by a low case number. This makes it hard to distinguish genuinely higher mortality or an artifact of a small sample size. In summary, coronavirus deaths are not homogenous across the United States but instead vary by region and population and highlight the importance of fine-scale analysis.
| S-EPMC7551952 | biostudies-literature
Project description:The computation of electric field in transcranial magnetic stimulation (TMS) is essentially a problem of gradient calculation for thin layers. This paper introduces a hybrid-order hybridizable discontinuous Galerkin finite element method (HDG-FEM) and systematically demonstrates its superiority in TMS computations. The discrete format of HDG-FEM employing hybrid orders for TMS is derived and, from a fundamental numerical principle perspective, this study provides the elucidation of why HDG-FEM exhibits superior gradient computation capabilities compared to the widely used CG-FEM. Furthermore, the exceptional performance of HDG-FEM in thin layer calculation is demonstrated on both modified head models and realistic head models, focusing on three aspects: calculation errors, utilization of hybrid order, and computational cost. For the calculation of E-field in thin-layer regions with parameter mutation, the L∞ norm error of the first-order HDG-FEM with the same tetrahedral mesh is comparable to the L∞ norm error of the second-order CG-FEM. The L2 norm error of the same-order HDG-FEM is smaller than that of the same-order CG-FEM. By utilizing the hybrid order, HDG-FEM achieves a rapid reduction in errors of thin layers without a significant increase in the computational cost. This study transforms the three-dimensional TMS problem into a special two-dimensional problem for computation, reducing computational complexity from p3 in three dimensions to p2 in two dimensions, while achieving significantly higher accuracy compared to the commonly used CG-FEM. The utilization of hybrid orders in thin layers of the head demonstrates significant flexibility, making HDG-FEM a new alternative choice for TMS computations.
| S-EPMC11612496 | biostudies-literature
Project description:The severe acute respiratory syndrome coronavirus 2 originated in Wuhan, China at the end of 2019 and rapidly spread in more than 100 countries. Researchers in different fields have been working on finding explanations for the unequal impact of the virus and deaths from the associated coronavirus disease 2019 (COVID-19) across geographical areas. Demographers and other social scientists have hinted at the importance of demographic factors, such as age structure and intergenerational relationships. Our aim is to reflect on the possible link between intergenerational relationships and spread and lethality of COVID-19 in a critical way. We show that with available aggregate data it is not possible to draw robust evidence to support these links. In fact, despite a higher prevalence of intergenerational coresidence and contacts that is broadly positively associated with COVID-19 case fatality rates at the country level, the opposite is generally true at the subnational level. While this inconsistent evidence demonstrates neither the existence nor the absence of a causal link between intergenerational relationships and the severity of COVID-19, we warn against simplistic interpretations of the available data, which suffer from many shortcomings. We conclude by arguing that intergenerational relationships are not only about physical contacts between family members. Theoretically, different forms of intergenerational relationships may have causal effects of opposite sign on the diffusion of COVID-19. Policies should also take into account that intergenerational ties are a source of instrumental and emotional support, which may favor compliance to the lockdown and "phase-2" restrictions and may buffer their negative consequences on mental health.
| S-EPMC7431085 | biostudies-literature
Project description:It is of fundamental interest in statistics to test the significance of a set of covariates. For example, in genome-wide association studies, a joint null hypothesis of no genetic effect is tested for a set of multiple genetic variants. The minimum p-value method, higher criticism, and Berk-Jones tests are particularly effective when the covariates with nonzero effects are sparse. However, the correlations among covariates and the non-Gaussian distribution of the response pose a great challenge towards the p-value calculation of the three tests. In practice, permutation is commonly used to obtain accurate p-values, but it is computationally very intensive, especially when we need to conduct a large amount of hypothesis testing. In this paper, we propose a Gaussian approximation method based on a Monte Carlo scheme, which is computationally more efficient than permutation while still achieving similar accuracy. We derive non-asymptotic approximation error bounds that could vanish in the limit even if the number of covariates is much larger than the sample size. Through real-genotype-based simulations and data analysis of a genome-wide association study of Crohn's disease, we compare the accuracy and computation cost of our proposed method, of permutation, and of the method based on asymptotic distribution.
| S-EPMC6530914 | biostudies-literature
Project description: Not available
| S-EPMC7270730 | biostudies-literature
Project description:ObjectiveReports of disparities in COVID-19 mortality rates are emerging in the public health literature as the pandemic continues to unfold. Alcohol misuse varies across the US and is related to poorer health and comorbidities that likely affect the severity of COVID-19 infection. High levels of pre-pandemic alcohol misuse in some counties may have set the stage for worse COVID-19 outcomes. Furthermore, this relationship may depend on how rural a county is, as access to healthcare in rural communities has lagged behind more urban areas. The objective of this study was to test for associations between county-level COVID-19 mortality, pre-pandemic county-level excessive drinking, and county rurality.MethodWe used national COVID-19 data from the New York Times to calculate county-level case fatality rates (n = 3,039 counties and county equivalents; October 1 -December 31, 2020) and other external county-level data sources for indicators of rurality and health. We used beta regression to model case fatality rates, adjusted for several county-level population characteristics. We included a multilevel component to our model and defined state as a random intercept. Our focal predictor was a single variable representing nine possible combinations of low/mid/high alcohol misuse and low/mid/high rurality.ResultsThe median county-level COVID-19 case fatality rate was 1.57%. Compared to counties with low alcohol misuse and low rurality (referent), counties with high levels of alcohol and mid (β = -0.17, p = 0.008) or high levels of rurality (β = -0.24, p<0.001) demonstrated significantly lower case fatality rates.ConclusionsOur findings highlight the intersecting roles of county-level alcohol consumption, rurality, and COVID-19 mortality.
| S-EPMC8211222 | biostudies-literature
Project description:Affymetrix GeneChip PM-MM probe pair is designed with the intension of measuring non-specific binding. Though the rationale behind the design id that a PM probe is expected to have a larger value than that of the MM probe, there are many exceptions in actual data. We gave an explanation for this inconsistency based on the assumption of functional states of a gene ‘ON/OFF’. Our hypothesis on PM-MM probe pairs is that the logarithmic of PM and MM values have the same distribution when gene is in OFF state. It means that the probability of MM > PM is expected to be equal to that of MM < PM for OFF genes. The validity of the hypothesis was given by inter-platform comparisons using common targets among three different types of platforms. Keywords: Affymetrix Gene Chip; Binomial distribution; Mathematical modeling; Oligonucleotide microarray; ON/OFF genes
2007-12-31 | GSE8353 | GEO