Quantifying the information in noisy epidemic curves
Quantifying the information in noisy epidemic curves
复制标题
量化嘈杂流行曲线中的信息
DOI：
10.1101/2022.05.16.22275147
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Parag K
中科院分区：
文献类型：
--
作者：
Parag K
从嘈杂的监测数据中可靠地估计传染病的动态是现代流行病学中的一个持久问题。关键参数往往是从事件时间序列中推断出来的，目的是让决策者了解疫情的增长速度，或检验关于公共卫生干预措施有效性的假设。然而，这些推论的可靠性严重依赖于报告错误和时间序列固有的延迟。在这里，我们开发了一个分析框架，以量化报告感染的漏报和延迟引起的不确定性，以及对监测数据信息性进行排名的指标。我们将此度量应用于两个主要数据源，用于推断瞬时繁殖数：流行病例和死亡曲线。我们发现，死亡曲线更可靠的假设，通常是针对急性传染病，如COVID-19和流感，并不明显，在许多情况下可能是不真实的。我们的框架阐明和量化了由于监测限制而丢失的有关病原体传播性的可操作信息。
Reliably estimating the dynamics of transmissible diseases from noisy surveillance data is an enduring problem in modern epidemiology. Key parameters are often inferred from incident time series, with the aim of informing policy-makers on the growth rate of outbreaks or testing hypotheses about the effectiveness of public health interventions. However, the reliability of these inferences depends critically on reporting errors and latencies innate to the time series. Here, we develop an analytical framework to quantify the uncertainty induced by under-reporting and delays in reporting infections, as well as a metric for ranking surveillance data informativeness. We apply this metric to two primary data sources for inferring the instantaneous reproduction number: epidemic case and death curves. We find that the assumption of death curves as more reliable, commonly made for acute infectious diseases such as COVID-19 and influenza, is not obvious and possibly untrue in many settings. Our framework clarifies and quantifies how actionable information about pathogen transmissibility is lost due to surveillance limitations.
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影响因子：
3.8
作者：
Ali, Sheikh Taslim;Kadi, A. S.;Ferguson, Neil M.
通讯作者：
Ferguson, Neil M.
DOI：
10.1056/nejmoa1411100
发表时间：
2014-10-16
期刊：
The New England journal of medicine
影响因子：
--
作者：
WHO Ebola Response Team;Aylward B;Barboza P;Bawo L;Bertherat E;Bilivogui P;Blake I;Brennan R;Briand S;Chakauya JM;Chitala K;Conteh RM;Cori A;Croisier A;Dangou JM;Diallo B;Donnelly CA;Dye C;Eckmanns T;Ferguson NM;Formenty P;Fuhrer C;Fukuda K;Garske T;Gasasira A;Gbanyan S;Graaff P;Heleze E;Jambai A;Jombart T;Kasolo F;Kadiobo AM;Keita S;Kertesz D;Koné M;Lane C;Markoff J;Massaquoi M;Mills H;Mulba JM;Musa E;Myhre J;Nasidi A;Nilles E;Nouvellet P;Nshimirimana D;Nuttall I;Nyenswah T;Olu O;Pendergast S;Perea W;Polonsky J;Riley S;Ronveaux O;Sakoba K;Santhana Gopala Krishnan R;Senga M;Shuaib F;Van Kerkhove MD;Vaz R;Wijekoon Kannangarage N;Yoti Z
通讯作者：
Yoti Z
影响因子：
3.8
作者：
De Angelis, Daniela;Presanis, Anne M.;Birrell, Paul J.;Tomba, Gianpaolo Scalia;House, Thomas
通讯作者：
House, Thomas
影响因子：
1.9
作者：
BARTLETT, MS
通讯作者：
BARTLETT, MS
影响因子：
3
作者：
Casella, Francesco
通讯作者：
Casella, Francesco