ABSTRACT
Excess death estimates have great value in public health, but they can be sensitive to analytical choices. Here we propose a multiverse analysis approach that considers all possible different time periods for defining the reference baseline and a range of 1 to 4 years for the projected time period for which excess deaths are calculated. We used data from the Human Mortality Database on 33 countries with detailed age-stratified death information on an annual basis during the period 2009-2021. The use of different time periods for reference baseline led to large variability in the absolute magnitude of the exact excess death estimates. However, the relative ranking of different countries compared to others for specific years remained largely unaltered. Averaging across all possible analyses, distinct time patterns were discerned across different countries. Countries had declines between 2009 and 2019, but the steepness of the decline varied markedly. There were also large differences across countries on whether the COVID-19 pandemic years 2020-2021 resulted in an increase of excess deaths and by how much. Consideration of longer projected time windows resulted in substantial shrinking of the excess deaths in many, but not all countries. Multiverse analysis of excess deaths over long periods of interest can offer a more unbiased approach to understand comparative mortality trends across different countries, the range of uncertainty around estimates, and the nature of observed mortality peaks. SIGNIFICANCE STATEMENTExcess death estimates are the ultimate assessment of the impact of multiple diseases and forces on the mortality of a population. However, their calculation can be notoriously unstable because it depends on a multitude of analytical choices. Other scientific fields have started using multiverse analysis approaches where all possible analytical choices are considered. We developed a multiverse analysis approach for excess death estimation. The approach is demonstrated with data from 33 countries for the period 2009-2021. Multiverse analysis offers a standardized way to demonstrate the sensitivity of estimates to analytic assumptions, to understand the presence of time patterns (rather than arbitrarily prespecify them), and to reveal consistent patterns that characterize excess deaths in a comparative fashion between different countries.
ABSTRACT
The spread of SARS-CoV-2 has fueled the COVID-19 pandemic with its enduring medical and socioeconomic challenges due to subsequent waves and long-term consequences of great concern. Here we charted the molecular basis of COVID-19 pathogenesis, by analysing patients immune response at single-cell resolution across disease course and severity. This approach uncovered cell subpopulation-specific dysregulation in COVID-19 across disease course and severity and identified a severity-associated activation of the receptor for advanced glycation endproduct (RAGE) pathway in monocytes. In vitro experiments confirmed that monocytes bind the SARS-CoV-2 S1-RBD via RAGE and that RAGE-Spike interactions drive monocyte infection. Our results demonstrate that RAGE is a novel functional receptor of SARS-CoV-2 contributing to COVID-19 severity. One-Sentence SummaryMonocyte SARS-CoV-2 infection via the receptor for advanced glycation endproduct triggers severe COVID-19.
ABSTRACT
A pipeline involving data acquisition, curation, carefully chosen graphs and mathematical models, allows analysis of COVID-19 outbreaks at 3,546 locations world-wide (all countries plus smaller administrative divisions with data available). Comparison of locations with over 50 deaths shows all outbreaks have a common feature: H(t) defined as loge(X(t)/X(t-1)) decreases linearly on a log scale, where X(t) is the total number of Cases or Deaths on day, t (we use ln for loge). The downward slopes vary by about a factor of three with time constants (1/slope) of between 1 and 3 weeks; this suggests it may be possible to predict when an outbreak will end. Is it possible to go beyond this and perform early prediction of the outcome in terms of the eventual plateau number of total confirmed cases or deaths? We test this hypothesis by showing that the trajectory of cases or deaths in any outbreak can be converted into a straight line. Specifically Y(t) {equiv} -ln(ln(N / X (t)), is a straight line for the correct plateau value N, which is determined by a new method, Best-Line Fitting (BLF). BLF involves a straight-line facilitation extrapolation needed for prediction; it is blindingly fast and amenable to optimization. We find that in some locations that entire trajectory can be predicted early, whereas others take longer to follow this simple functional form. Fortunately, BLF distinguishes predictions that are likely to be correct in that they show a stable plateau of total cases or death (N value). We apply BLF to locations that seem close to a stable predicted N value and then forecast the outcome at some locations that are still growing wildly. Our accompanying web-site will be updated frequently and provide all graphs and data described here.
