Methods for detecting probable COVID-19 cases from large-scale survey data also reveal probable sex differences in symptom profiles.
Methods for detecting probable COVID-19 cases from large-scale survey data also reveal probable sex differences in symptom profiles.
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DOI：
10.3389/fdata.2022.1043704
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发表时间：
2022
影响因子：
3.1
通讯作者：
Smarr, Benjamin L.
中科院分区：
文献类型：
--
作者：
Klein, Amit;Puldon, Karena;Dilchert, Stephan;Hartogensis, Wendy;Chowdhary, Anoushka;Anglo, Claudine;Pandya, Leena S.;Hecht, Frederick M.;Mason, Ashley E.;Smarr, Benjamin L.
通过基于网络的症状调查工具收集的每日症状报告具有改善疾病监测的潜力。这种筛查工具可能不仅能够区分急性疾病和非疾病状态，而且还可以利用额外的人口统计信息，以便确定不同群体之间的疾病可能有何不同，例如生理性别。这些能力可能在未来疾病暴发的背景下发挥重要作用。使用通过每日基于网络的症状调查工具收集的数据，开发可以区分COVID-19和其他疾病的贝叶斯模型，并对该模型进行改进，以确定因生物性别而不同的疾病概况。我们使用每日症状概况来绘制COVID-19、流感和普通感冒的症状进展情况。然后，我们建立了一个贝叶斯网络，根据日常症状报告来区分这三种疾病。我们进一步将COVID-19队列分为自我报告的女性和男性亚组，以观察与性别相关的症状的差异。我们使用逻辑回归模型确定了男性和女性中有助于预测COVID-19的关键症状。尽管贝叶斯模型在识别COVID-19诊断方面表现一般（真阳性率为71.6%），但该模型显示出能够区分COVID-19、流感和普通感冒，以及急性疾病期与非疾病期的希望。此外，COVID-19症状在生理性别之间存在差异；具体而言，在识别男性随后感染COVID-19时，发烧是比女性更重要的症状。基于网络的症状调查工具有望成为识别疾病的工具，并可能有助于协调疾病爆发的反应。将人口统计学因素（如生理性别）纳入预测模型，可能会阐明对疾病检测有影响的症状特征的重要差异。
Daily symptom reporting collected via web-based symptom survey tools holds the potential to improve disease monitoring. Such screening tools might be able to not only discriminate between states of acute illness and non-illness, but also make use of additional demographic information so as to identify how illnesses may differ across groups, such as biological sex. These capabilities may play an important role in the context of future disease outbreaks. Use data collected via a daily web-based symptom survey tool to develop a Bayesian model that could differentiate between COVID-19 and other illnesses and refine this model to identify illness profiles that differ by biological sex. We used daily symptom profiles to plot symptom progressions for COVID-19, influenza (flu), and the common cold. We then built a Bayesian network to discriminate between these three illnesses based on daily symptom reports. We further separated out the COVID-19 cohort into self-reported female and male subgroups to observe any differences in symptoms relating to sex. We identified key symptoms that contributed to a COVID-19 prediction in both males and females using a logistic regression model. Although the Bayesian model performed only moderately well in identifying a COVID-19 diagnosis (71.6% true positive rate), the model showed promise in being able to differentiate between COVID-19, flu, and the common cold, as well as periods of acute illness vs. non-illness. Additionally, COVID-19 symptoms differed between the biological sexes; specifically, fever was a more important symptom in identifying subsequent COVID-19 infection among males than among females. Web-based symptom survey tools hold promise as tools to identify illness and may help with coordinated disease outbreak responses. Incorporating demographic factors such as biological sex into predictive models may elucidate important differences in symptom profiles that hold implications for disease detection.
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影响因子：
4.6
作者：
Smarr BL;Aschbacher K;Fisher SM;Chowdhary A;Dilchert S;Puldon K;Rao A;Hecht FM;Mason AE
通讯作者：
Mason AE
影响因子：
11.8
作者：
Dawson, Patrick;Rabold, Elizabeth M.;Kirking, Hannah L.
通讯作者：
Kirking, Hannah L.
影响因子：
4.6
作者：
Mason AE;Hecht FM;Davis SK;Natale JL;Hartogensis W;Damaso N;Claypool KT;Dilchert S;Dasgupta S;Purawat S;Viswanath VK;Klein A;Chowdhary A;Fisher SM;Anglo C;Puldon KY;Veasna D;Prather JG;Pandya LS;Fox LM;Busch M;Giordano C;Mercado BK;Song J;Jaimes R;Baum BS;Telfer BA;Philipson CW;Collins PP;Rao AA;Wang EJ;Bandi RH;Choe BJ;Epel ES;Epstein SK;Krasnoff JB;Lee MB;Lee SW;Lopez GM;Mehta A;Melville LD;Moon TS;Mujica-Parodi LR;Noel KM;Orosco MA;Rideout JM;Robishaw JD;Rodriguez RM;Shah KH;Siegal JH;Gupta A;Altintas I;Smarr BL
通讯作者：
Smarr BL
影响因子：
4
作者：
Day, Suzanne;Mason, Robin;Rochon, Paula A.
通讯作者：
Rochon, Paula A.
影响因子：
7.8
作者：
Mason AE;Kasl P;Hartogensis W;Natale JL;Dilchert S;Dasgupta S;Purawat S;Chowdhary A;Anglo C;Veasna D;Pandya LS;Fox LM;Puldon KY;Prather JG;Gupta A;Altintas I;Smarr BL;Hecht FM
通讯作者：
Hecht FM