Estimating epidemiologic dynamics from cross-sectional viral load distributions.
Estimating epidemiologic dynamics from cross-sectional viral load distributions.
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DOI：
10.1126/science.abh0635
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发表时间：
2021-07-16
期刊：
影响因子：
--
通讯作者：
Mina MJ
中科院分区：
文献类型：
--
作者：
Hay JA;Kennedy-Shaffer L;Kanjilal S;Lennon NJ;Gabriel SB;Lipsitch M;Mina MJ
目前的流行病监测方法依赖于病例计数、检测阳性率以及报告的死亡或住院情况。然而，由于测试限制、不具代表性的抽样和报告延迟，这些指标提供的情况有限且常常有偏差。随机横断面病毒学调查可以通过提供感染流行情况的快照来克服其中一些偏差，但目前在不跨多个时间点采样的情况下，无法提供有关流行轨迹的信息。我们开发了一种新方法，利用逆转录定量聚合酶链反应 (RT-qPCR) 测试中循环阈值 (Ct) 值固有的信息，从阳性样本的多个甚至单个横截面稳健地估计流行轨迹。 Ct 值与病毒载量有关，病毒载量取决于感染后的时间；当感染和样本采集之间的时间较短时，Ct 值通常较低。尽管个体、样本和测试平台之间存在差异，但 Ct 值提供了感染后时间的概率度量。我们发现，单个时间点的阳性样本的 Ct 值分布反映了流行病的轨迹：流行病的增长必然有高比例的新近感染者具有高病毒载量，而流行病的下降将有更多的旧感染者，因此病毒载量较低。由于这些变化的比例，流行病轨迹或增长率应该可以从单个横截面中收集的 Ct 值的分布推断出来，并且多个连续的横截面应该能够识别长期发病率曲线。此外，了解样本病毒载量与流行病动态之间的关系可以进一步了解为什么监测测试中的病毒载量对于新兴病毒或变种可能显得较高，而对于正在减缓甚至个体水平病毒动力学没有变化的疫情爆发则较低。使用根据严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2) 病毒载量动力学的已知特征进行校准的人群水平病毒载量分布数学模型，我们发现随机样本中 Ct 值的中位数和偏度在流行病过程中发生变化。通过形式化这种关系，我们证明，病毒学测试的单个随机横截面的 Ct 值可以估计人群中病毒随时间变化的繁殖数量，我们使用从长期护理机构的全面 SARS-CoV-2 测试中收集的数据来验证这一点。使用更灵活的方法来建模感染发生率，我们还开发了一种方法，可以可靠地估计更复杂人群中的流行轨迹，随着时间的推移，可以实施并放松干预措施。该方法在使用常规入院 RT-qPCR 检测数据来估计马萨诸塞州的流行轨迹方面表现良好，准确复制了其他来源对整个州的估计。这项工作提供了一种估计流行病增长率的新方法，以及使用经常被简单丢弃的 RT-qPCR Ct 值进行稳健流行病监测的框架。通过部署单个或重复（但小）随机监测样本并充分利用半定量检测数据，我们可以实时估计流行轨迹，并避免非随机样本或检测实践随时间变化而产生的偏差。了解人群水平病毒载量与流行病状态之间的关系揭示了解释病毒学监测数据的重要含义和机会。它还强调了这种监视的必要性，因为这些结果展示了如何最有效地使用它。 Ct 值反映了流行轨迹，可用于估计发病率。 （A和B）流行病发病率上升还是下降将反映在感染后的时间分布上（A），进而影响监测样本中Ct值的分布（B）。 (C) 这些值可用于评估疫情是上升还是下降，并估计发病率曲线。估计流行病的轨迹对于制定针对传染病的公共卫生应对措施至关重要，但用于此类估计的病例数据因检测实践的变化而变得混乱。我们表明，在随机或基于症状的监测下观察到的病毒载量的群体分布（以从逆转录定量聚合酶链反应测试获得的循环阈值（Ct）的形式）在流行病期间发生变化。因此，即使是有限数量的随机样本的 Ct 值也可以提供对流行病轨迹的改进估计。组合来自多个此类样本的数据可以提高该估计的精度和稳健性。我们将我们的方法应用于严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2) 大流行期间在各种环境下进行的监测的 Ct 值，并为实时估计流行轨迹以进行疫情管理和响应提供替代方法。
Current approaches to epidemic monitoring rely on case counts, test positivity rates, and reported deaths or hospitalizations. These metrics, however, provide a limited and often biased picture as a result of testing constraints, unrepresentative sampling, and reporting delays. Random cross-sectional virologic surveys can overcome some of these biases by providing snapshots of infection prevalence but currently offer little information on the epidemic trajectory without sampling across multiple time points. We develop a new method that uses information inherent in cycle threshold (Ct) values from reverse transcription quantitative polymerase chain reaction (RT-qPCR) tests to robustly estimate the epidemic trajectory from multiple or even a single cross section of positive samples. Ct values are related to viral loads, which depend on the time since infection; Ct values are generally lower when the time between infection and sample collection is short. Despite variation across individuals, samples, and testing platforms, Ct values provide a probabilistic measure of time since infection. We find that the distribution of Ct values across positive specimens at a single time point reflects the epidemic trajectory: A growing epidemic will necessarily have a high proportion of recently infected individuals with high viral loads, whereas a declining epidemic will have more individuals with older infections and thus lower viral loads. Because of these changing proportions, the epidemic trajectory or growth rate should be inferable from the distribution of Ct values collected in a single cross section, and multiple successive cross sections should enable identification of the longer-term incidence curve. Moreover, understanding the relationship between sample viral loads and epidemic dynamics provides additional insights into why viral loads from surveillance testing may appear higher for emerging viruses or variants and lower for out-breaks that are slowing, even absent changes in individual-level viral kinetics. Using a mathematical model for population-level viral load distributions calibrated to known features of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral load kinetics, we show that the median and skewness of Ct values in a random sample change over the course of an epidemic. By formalizing this relationship, we demonstrate that Ct values from a single random cross section of virologic testing can estimate the time-varying reproductive number of the virus in a population, which we validate using data collected from comprehensive SARS-CoV-2 testing in long-term care facilities. Using a more flexible approach to modeling infection incidence, we also develop a method that can reliably estimate the epidemic trajectory in even more-complex populations, where interventions may be implemented and relaxed over time. This method performed well in estimating the epidemic trajectory in the state of Massachusetts using routine hospital admissions RT-qPCR testing data—accurately replicating estimates from other sources for the entire state. This work provides a new method for estimating the epidemic growth rate and a framework for robust epidemic monitoring using RT-qPCR Ct values that are often simply discarded. By deploying single or repeated (but small) random surveillance samples and making the best use of the semiquantitative testing data, we can estimate epidemic trajectories in real time and avoid biases arising from nonrandom samples or changes in testing practices over time. Understanding the relationship between population-level viral loads and the state of an epidemic reveals important implications and opportunities for interpreting virologic surveillance data. It also highlights the need for such surveillance, as these results show how to use it most informatively. Ct values reflect the epidemic trajectory and can be used to estimate incidence. (A and B) Whether an epidemic has rising or falling incidence will be reflected in the distribution of times since infection (A), which in turn affects the distribution of Ct values in a surveillance sample (B). (C) These values can be used to assess whether the epidemic is rising or falling and estimate the incidence curve. Estimating an epidemic’s trajectory is crucial for developing public health responses to infectious diseases, but case data used for such estimation are confounded by variable testing practices. We show that the population distribution of viral loads observed under random or symptom-based surveillance—in the form of cycle threshold (Ct) values obtained from reverse transcription quantitative polymerase chain reaction testing—changes during an epidemic. Thus, Ct values from even limited numbers of random samples can provide improved estimates of an epidemic’s trajectory. Combining data from multiple such samples improves the precision and robustness of this estimation. We apply our methods to Ct values from surveillance conducted during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in a variety of settings and offer alternative approaches for real-time estimates of epidemic trajectories for outbreak management and response.
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影响因子：
9.3
作者：
Hellewell J;Russell TW;SAFER Investigators and Field Study Team;Crick COVID-19 Consortium;CMMID COVID-19 working group;Beale R;Kelly G;Houlihan C;Nastouli E;Kucharski AJ
通讯作者：
Kucharski AJ
影响因子：
4.3
作者：
Hay, James A.;Minter, Amanda;Riley, Steven
通讯作者：
Riley, Steven
影响因子：
11.8
作者：
Bullard, Jared;Dust, Kerry;Poliquin, Guillaume
通讯作者：
Poliquin, Guillaume
影响因子：
--
作者：
Jombart, Thibaut;van Zandvoort, Kevin;Edmunds, W John
通讯作者：
Edmunds, W John
DOI：
10.1126/science.abh2644
发表时间：
2021-05-21
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Faria NR;Mellan TA;Whittaker C;Claro IM;Candido DDS;Mishra S;Crispim MAE;Sales FCS;Hawryluk I;McCrone JT;Hulswit RJG;Franco LAM;Ramundo MS;de Jesus JG;Andrade PS;Coletti TM;Ferreira GM;Silva CAM;Manuli ER;Pereira RHM;Peixoto PS;Kraemer MUG;Gaburo N Jr;Camilo CDC;Hoeltgebaum H;Souza WM;Rocha EC;de Souza LM;de Pinho MC;Araujo LJT;Malta FSV;de Lima AB;Silva JDP;Zauli DAG;Ferreira ACS;Schnekenberg RP;Laydon DJ;Walker PGT;Schlüter HM;Dos Santos ALP;Vidal MS;Del Caro VS;Filho RMF;Dos Santos HM;Aguiar RS;Proença-Modena JL;Nelson B;Hay JA;Monod M;Miscouridou X;Coupland H;Sonabend R;Vollmer M;Gandy A;Prete CA Jr;Nascimento VH;Suchard MA;Bowden TA;Pond SLK;Wu CH;Ratmann O;Ferguson NM;Dye C;Loman NJ;Lemey P;Rambaut A;Fraiji NA;Carvalho MDPSS;Pybus OG;Flaxman S;Bhatt S;Sabino EC
通讯作者：
Sabino EC