Assessing racial inequality in COVID-19 testing with Bayesian threshold tests

通过中介变量分布偏移评估医疗支出中的种族差异

Assessing Racial Disparities in Healthcare Expenditures via Mediator Distribution Shifts

本研究提出一个框架，通过中介变量分布的变化分解医疗支出中的种族差异，并应用机器学习方法分析MEPS数据，发现社会经济地位和健康状况是主要贡献因素。

Assessing epidemic curves for evidence of superspreading

The expected number of secondary infections arising from each index case, referred to as the reproduction or $R$ number, is a vital summary statistic for understanding and managing epidemic diseases. There are many methods for estimating $R$; however, few explicitly model heterogeneous disease reproduction, which gives rise to superspreading within the population. We propose a parsimonious discrete-time branching process model for epidemic curves that incorporates heterogeneous individual reproduction numbers. Our Bayesian approach to inference illustrates that this heterogeneity results in le…

结合干预措施的扩展SEIRD疫苗接种模型：在卡塔尔COVID-19中的应用

Incorporating Interventions to an Extended SEIRD Model with Vaccination: Application to COVID-19 in Qatar

本文提出一种结合干预措施的扩展SEIRDV分区模型，利用贝叶斯框架进行参数估计与预测，以评估政府干预和疫苗接种对卡塔尔COVID-19疫情动态的影响，并量化因疫苗避免的超额死亡人数。

Assessing Excess Mortality in Times of Pandemics Based on Principal Component Analysis of Weekly Mortality Data -- The Case of COVID-19

The current outbreak of COVID-19 has called renewed attention to the need for sound statistical analysis for monitoring mortality patterns and trends over time. Excess mortality has been suggested as the most appropriate indicator to measure the overall burden of the pandemic on mortality. As such, excess mortality has received considerable interest during the first months of the COVID-19 pandemic. Previous approaches to estimate excess mortality are somewhat limited, as they do not include sufficiently long-term trends, correlations among different demographic and geographic groups, and the a…

On assessing excess mortality in Germany during the COVID-19 pandemic

Coronavirus disease 2019 (COVID-19) is associated with a very high number of casualties in the general population. Assessing the exact magnitude of this number is a non-trivial problem, as relying only on officially reported COVID-19 associated fatalities runs the risk of incurring in several kinds of biases. One of the ways to approach the issue is to compare overall mortality during the pandemic with expected mortality computed using the observed mortality figures of previous years. In this paper, we build on existing methodology and propose two ways to compute expected as well as excess mor…

利用不一致的种族感知测试种族偏见

Testing for racial bias using inconsistent perceptions of race

本研究提出一种基于同一个体的种族感知差异测试种族偏见的新方法，规避了不同个体比较的干扰，经警察交通拦截数据验证有效，可推广至多类身份感知类数据集的偏见检测。

评估巴西COVID-19有效再生数的时间演变

Assessing the Time Evolution of COVID-19 Effective Reproduction Number in Brazil

本文采用贝叶斯方法估计巴西不同地理层级的COVID-19有效再生数R(t)，并与每日报告感染数移动平均比较，发现局部监测更能反映差异并支撑非药物干预政策设计。

Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy

Anonymized smartphone-based mobility data has been widely adopted in devising and evaluating COVID-19 response strategies such as the targeting of public health resources. Yet little attention has been paid to measurement validity and demographic bias, due in part to the lack of documentation about which users are represented as well as the challenge of obtaining ground truth data on unique visits and demographics. We illustrate how linking large-scale administrative data can enable auditing mobility data for bias in the absence of demographic information and ground truth labels. More precisel…

利用交互驱动的时间模型评估人群中的个体风险与 COVID-19 的潜在传播

Assessing individual risk and the latent transmission of COVID-19 in a population with an interaction-driven temporal model

本文通过引入会议持续时间和个体疾病进展来丰富交互驱动的 COVID-19 传播模型，从而在群体和个体层面预测感染风险，并揭示无症状传播仅在稀疏社区中具有显著影响。

Multi-Objective Allocation of COVID-19 Testing Centers: Improving Coverage and Equity in Access

At the time of this article, COVID-19 has been transmitted to more than 42 million people and resulted in more than 673,000 deaths across the United States. Throughout this pandemic, public health authorities have monitored the results of diagnostic testing to identify hotspots of transmission. Such information can help reduce or block transmission paths of COVID-19 and help infected patients receive early treatment. However, most current schemes of test site allocation have been based on experience or convenience, often resulting in low efficiency and non-optimal allocation. In addition, the …

Assessing the impact of the COVID-19 shock on a stochastic multi-population mortality model

We aim to assess the impact of a pandemic data point on the calibration of a stochastic multi-population mortality projection model and its resulting projections for future mortality rates. Throughout the paper we put focus on the Li & Lee mortality model, which has become a standard for projecting mortality in Belgium and the Netherlands. We calibrate this mortality model on annual deaths and exposures at the level of individual ages. This type of mortality data is typically collected, produced and reported with a significant delay of -- for some countries -- several years on a platform such …

泊松自回归的贝叶斯时空扩展：建模英格兰COVID-19疾病感染率

A Bayesian Spatio-Temporal Extension to Poisson Auto-Regression: Modeling the Disease Infection Rate of COVID-19 in England

本文提出一种贝叶斯时空泊松自回归扩展模型，用于刻画英格兰COVID-19感染率的时空动态，并通过模拟和真实数据验证，评估政策与移动指数的影响。

Exposure Density and Neighborhood Disparities in COVID-19 Infection Risk: Using Large-scale Geolocation Data to Understand Burdens on Vulnerable Communities

This study develops a new method to quantify neighborhood activity levels at high spatial and temporal resolutions and test whether, and to what extent, behavioral responses to social distancing policies vary with socioeconomic and demographic characteristics. We define exposure density as a measure of both the localized volume of activity in a defined area and the proportion of activity occurring in non-residential and outdoor land uses. We utilize this approach to capture inflows/outflows of people as a result of the pandemic and changes in mobility behavior for those that remain. First, we …

探索教育公平：基于机器学习方法揭示佐治亚州的学业成就差距

Exploring Educational Equity: A Machine Learning Approach to Unravel Achievement Disparities in Georgia

本研究利用机器学习分析佐治亚州K-12学生数据，揭示疫情期间英语和数学成绩下降，社会经济地位是主要影响因素，并提出针对性资源分配以缓解教育差距。