论文标题
根据每周死亡率数据的主要成分分析评估大流行时期的过剩死亡率 - COVID-19
Assessing Excess Mortality in Times of Pandemics Based on Principal Component Analysis of Weekly Mortality Data -- The Case of COVID-19
论文作者
论文摘要
Covid-19的当前爆发称，人们对随着时间的流逝进行了声音统计分析的需求重新注意，以监测死亡率模式和趋势。已提出过多的死亡率是衡量大流行死亡率总负担的最合适指标。因此，在Covid-19-19大流行的头几个月中，过剩的死亡率已引起了很大的关注。以前的估计过剩死亡率的方法有些有限，因为它们不包括足够的长期趋势，不同的人口统计和地理群体之间的相关性以及死亡率时间序列中的自相关性。这可能会导致对死亡率过剩的估计有偏见，因为随机死亡率波动可能被误解为过量死亡率。我们提出了经典流行病学方法的融合，以估算非凡事件中的过剩死亡率，并具有既定的人口统计学方法，即死亡率预测，即李 - 卡特类型的模型，该模型涵盖了命名的限制，并涵盖了过多死亡率的更现实的情况。我们使用每周的19个国家 /年龄和性别的死亡率数据以及当前的COVID-19大流行作为案例研究来说明我们的方法。我们提出的模型提供了一个通用框架，可以应用于未来的大流行，并从特定的死亡原因中监测过多的死亡率。
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 autocorrelations in the mortality time series. This might lead to biased estimates of excess mortality, as random mortality fluctuations may be misinterpreted as excess mortality. We present a blend of classical epidemiological approaches to estimating excess mortality during extraordinary events with an established demographic approach in mortality forecasting, namely a Lee-Carter type model, which covers the named limitations and draws a more realistic picture of the excess mortality. We illustrate our approach using weekly age- and sex-specific mortality data for 19 countries and the current COVID-19 pandemic as a case study. Our proposed model provides a general framework that can be applied to future pandemics as well as to monitor excess mortality from specific causes of deaths.
