Adaptive Preferential Sampling in Phylodynamics With an Application to SARS-CoV-2.
Adaptive Preferential Sampling in Phylodynamics With an Application to SARS-CoV-2.
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在系统动力学中的自适应优先采样，并应用于SARS-COV-2。
DOI：
10.1080/10618600.2021.1987256
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发表时间：
2022
期刊：
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快速演变的病毒和病原体的纵向分子数据提供了有关疾病传播的信息，并补充了基于病例计数数据的传统监测方法。该合并器用于对表示样本祖先关系的系谱进行建模。基本假设是，合并事件的发生速度与有效种群规模ne(T)成反比，有效种群规模ne(T)是遗传多样性的一种时变衡量标准。当采样过程(随时间收集的样本)依赖于Ne(T)时，可以对合并和采样过程进行联合建模以改进对Ne(T)的估计。如果做不到这一点，可能会由于型号错误而导致偏差。然而，抽样过程取决于有效人口大小的方式可能会随着时间的推移而变化。我们介绍了一种方法，将抽样过程建模为速率等于Ne(T)和时变系数的乘积的非齐次Poisson过程，通过马尔可夫随机场先验对其函数形状做出最小假设。我们提供了有效的推理算法，在仿真研究中展示了模型相对于其他方法的性能，并将我们的模型应用于来自洛杉矶和圣克拉拉县的SARS-CoV-2序列。该方法已在R包Adapref中实现并可用。本文的补充文件可在网上找到。
Longitudinal molecular data of rapidly evolving viruses and pathogens provide information about disease spread and complement traditional surveillance approaches based on case count data. The coalescent is used to model the genealogy that represents the sample ancestral relationships. The basic assumption is that coalescent events occur at a rate inversely proportional to the effective population size Ne(t), a time-varying measure of genetic diversity. When the sampling process (collection of samples over time) depends on Ne(t), the coalescent and the sampling processes can be jointly modeled to improve estimation of Ne(t). Failing to do so can lead to bias due to model misspecification. However, the way that the sampling process depends on the effective population size may vary over time. We introduce an approach where the sampling process is modeled as an inhomogeneous Poisson process with rate equal to the product of Ne(t) and a time-varying coefficient, making minimal assumptions on their functional shapes via Markov random field priors. We provide efficient algorithms for inference, show the model performance vis-a-vis alternative methods in a simulation study, and apply our model to SARS-CoV-2 sequences from Los Angeles and Santa Clara counties. The methodology is implemented and available in the R package adapref. Supplementary files for this article are available online.
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DOI：
10.1126/science.aaf5036
发表时间：
2016-04-15
期刊：
Science (New York, N.Y.)
影响因子：
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作者：
Faria NR;Azevedo RDSDS;Kraemer MUG;Souza R;Cunha MS;Hill SC;Thézé J;Bonsall MB;Bowden TA;Rissanen I;Rocco IM;Nogueira JS;Maeda AY;Vasami FGDS;Macedo FLL;Suzuki A;Rodrigues SG;Cruz ACR;Nunes BT;Medeiros DBA;Rodrigues DSG;Queiroz ALN;da Silva EVP;Henriques DF;da Rosa EST;de Oliveira CS;Martins LC;Vasconcelos HB;Casseb LMN;Simith DB;Messina JP;Abade L;Lourenço J;Alcantara LCJ;de Lima MM;Giovanetti M;Hay SI;de Oliveira RS;Lemos PDS;de Oliveira LF;de Lima CPS;da Silva SP;de Vasconcelos JM;Franco L;Cardoso JF;Vianez-Júnior JLDSG;Mir D;Bello G;Delatorre E;Khan K;Creatore M;Coelho GE;de Oliveira WK;Tesh R;Pybus OG;Nunes MRT;Vasconcelos PFC
通讯作者：
Vasconcelos PFC
影响因子：
3.4
作者：
Heled, Joseph;Drummond, Alexei J.
通讯作者：
Drummond, Alexei J.
影响因子：
4.3
作者：
Karcher MD;Carvalho LM;Suchard MA;Dudas G;Minin VN
通讯作者：
Minin VN
DOI：
10.1016/s1473-3099(20)30562-4
发表时间：
2020-11
期刊：
The Lancet. Infectious diseases
影响因子：
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作者：
Meredith LW;Hamilton WL;Warne B;Houldcroft CJ;Hosmillo M;Jahun AS;Curran MD;Parmar S;Caller LG;Caddy SL;Khokhar FA;Yakovleva A;Hall G;Feltwell T;Forrest S;Sridhar S;Weekes MP;Baker S;Brown N;Moore E;Popay A;Roddick I;Reacher M;Gouliouris T;Peacock SJ;Dougan G;Török ME;Goodfellow I
通讯作者：
Goodfellow I
DOI：
10.1126/science.1259657
发表时间：
2014-09-12
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Gire SK;Goba A;Andersen KG;Sealfon RS;Park DJ;Kanneh L;Jalloh S;Momoh M;Fullah M;Dudas G;Wohl S;Moses LM;Yozwiak NL;Winnicki S;Matranga CB;Malboeuf CM;Qu J;Gladden AD;Schaffner SF;Yang X;Jiang PP;Nekoui M;Colubri A;Coomber MR;Fonnie M;Moigboi A;Gbakie M;Kamara FK;Tucker V;Konuwa E;Saffa S;Sellu J;Jalloh AA;Kovoma A;Koninga J;Mustapha I;Kargbo K;Foday M;Yillah M;Kanneh F;Robert W;Massally JL;Chapman SB;Bochicchio J;Murphy C;Nusbaum C;Young S;Birren BW;Grant DS;Scheiffelin JS;Lander ES;Happi C;Gevao SM;Gnirke A;Rambaut A;Garry RF;Khan SH;Sabeti PC
通讯作者：
Sabeti PC