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1. Epidemiological Parameters of COVID-19: Case Series Study

Author

Ma, Shujuan, Zhang, Jiayue, Zeng, Minyan, Yun, Qingping, Guo, Wei, Zheng, Yixiang, Zhao, Shi, Wang, Maggie H, and Yang, Zuyao

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundThe estimates of several key epidemiological parameters of the COVID-19 pandemic are often based on small sample sizes or are inaccurate for various reasons. ObjectiveThe aim of this study is to obtain more robust estimates of the incubation period, serial interval, frequency of presymptomatic transmission, and basic reproduction number (R0) of COVID-19 based on a large case series. MethodsWe systematically retrieved and screened 20,658 reports of laboratory-confirmed COVID-19 cases released by the health authorities of China, Japan, and Singapore. In addition, 9942 publications were retrieved from PubMed and China National Knowledge Infrastructure (CNKI) through April 8, 2020. To be eligible, a report had to contain individual data that allowed for accurate estimation of at least one parameter. Widely used models such as gamma distributions were fitted to the data sets and the results with the best-fitting values were presented. ResultsIn total, 1591 cases were included for the final analysis. The mean incubation period (n=687) and mean serial interval (n=1015 pairs) were estimated to be 7.04 (SD 4.27) days and 6.49 (SD 4.90) days, respectively. In 40 cases (5.82%), the incubation period was longer than 14 days. In 32 infector-infectee pairs (3.15%), infectees’ symptom onsets occurred before those of infectors. Presymptomatic transmission occurred in 129 of 296 infector-infectee pairs (43.58%). R0 was estimated to be 1.85 (95% CI 1.37-2.60). ConclusionsThis study provides robust estimates of several epidemiological parameters of COVID-19. The findings support the current practice of 14-day quarantine of persons with potential exposure, but also suggest the need for additional measures. Presymptomatic transmission together with the asymptomatic transmission reported by previous studies highlight the importance of adequate testing, strict quarantine, and social distancing.

Published
2020
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3. Association of healthy lifestyle behaviours with incident irritable bowel syndrome: a large population-based prospective cohort study

Author

Ho, Fai Fai, primary, Sun, Hui, additional, Zheng, Hong, additional, Wong, David C N, additional, Gao, Yin-Yan, additional, Mao, Chen, additional, Cheung, Yin Ting, additional, Lam, Chun Sing, additional, Wang, Maggie H, additional, Wu, Irene Xin-Yin, additional, Wu, Justin C Y, additional, and Chung, Vincent C H, additional

Published
2024
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18. Frequent Genetic Mismatch between Vaccine Strains and Circulating Seasonal Influenza Viruses, Hong Kong, China, 1996-2012

Author

Chan, Martin C.W., Wang, Maggie H., Chen, Zigui, Hui, David S.C., Kwok, Angela K., Yeung, Apple C.M., Liu, Kun M., Yeoh, Yun Kit, Lee, Nelson, and Chan, Paul K.S.

Subjects
Influenza vaccines -- Surveys, Public health -- Surveys, Influenza -- Surveys, Vaccination -- Surveys, Health, World Health Organization -- Surveys
Abstract

Influenza is a major cause of illness and death worldwide. The human influenza virus spreads rapidly (average reproduction number of 1.28), typically infects 5%-10% of the population during seasonal epidemics, [...]

Published
2018
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19. Supplementary Table S2 from Epigenetic Silencing of miR-490-3p Reactivates the Chromatin Remodeler SMARCD1 to Promote Helicobacter pylori–Induced Gastric Carcinogenesis

Author

Shen, Jing, primary, Xiao, Zhangang, primary, Wu, William K.K., primary, Wang, Maggie H., primary, To, Ka F., primary, Chen, Yangchao, primary, Yang, Weiqin, primary, Li, May S.M., primary, Shin, Vivian Y., primary, Tong, Joanna H., primary, Kang, Wei, primary, Zhang, Lin, primary, Li, Minxing, primary, Wang, Lin, primary, Lu, Lan, primary, Chan, Ruby L.Y., primary, Wong, Sunny H., primary, Yu, Jun, primary, Chan, Matthew T.V., primary, Chan, Francis K.L., primary, Sung, Joseph J.Y., primary, Cheng, Alfred S.L., primary, and Cho, Chi H., primary

Published
2023
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20. Supplementary Figure S3 from Epigenetic Silencing of miR-490-3p Reactivates the Chromatin Remodeler SMARCD1 to Promote Helicobacter pylori–Induced Gastric Carcinogenesis

Author

Shen, Jing, primary, Xiao, Zhangang, primary, Wu, William K.K., primary, Wang, Maggie H., primary, To, Ka F., primary, Chen, Yangchao, primary, Yang, Weiqin, primary, Li, May S.M., primary, Shin, Vivian Y., primary, Tong, Joanna H., primary, Kang, Wei, primary, Zhang, Lin, primary, Li, Minxing, primary, Wang, Lin, primary, Lu, Lan, primary, Chan, Ruby L.Y., primary, Wong, Sunny H., primary, Yu, Jun, primary, Chan, Matthew T.V., primary, Chan, Francis K.L., primary, Sung, Joseph J.Y., primary, Cheng, Alfred S.L., primary, and Cho, Chi H., primary

Published
2023
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21. Data from Epigenetic Silencing of miR-490-3p Reactivates the Chromatin Remodeler SMARCD1 to Promote Helicobacter pylori–Induced Gastric Carcinogenesis

Author

Shen, Jing, primary, Xiao, Zhangang, primary, Wu, William K.K., primary, Wang, Maggie H., primary, To, Ka F., primary, Chen, Yangchao, primary, Yang, Weiqin, primary, Li, May S.M., primary, Shin, Vivian Y., primary, Tong, Joanna H., primary, Kang, Wei, primary, Zhang, Lin, primary, Li, Minxing, primary, Wang, Lin, primary, Lu, Lan, primary, Chan, Ruby L.Y., primary, Wong, Sunny H., primary, Yu, Jun, primary, Chan, Matthew T.V., primary, Chan, Francis K.L., primary, Sung, Joseph J.Y., primary, Cheng, Alfred S.L., primary, and Cho, Chi H., primary

Published
2023
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22. Supplementary Materials and Methods and Figure Legends from Epigenetic Silencing of miR-490-3p Reactivates the Chromatin Remodeler SMARCD1 to Promote Helicobacter pylori–Induced Gastric Carcinogenesis

Author

Shen, Jing, primary, Xiao, Zhangang, primary, Wu, William K.K., primary, Wang, Maggie H., primary, To, Ka F., primary, Chen, Yangchao, primary, Yang, Weiqin, primary, Li, May S.M., primary, Shin, Vivian Y., primary, Tong, Joanna H., primary, Kang, Wei, primary, Zhang, Lin, primary, Li, Minxing, primary, Wang, Lin, primary, Lu, Lan, primary, Chan, Ruby L.Y., primary, Wong, Sunny H., primary, Yu, Jun, primary, Chan, Matthew T.V., primary, Chan, Francis K.L., primary, Sung, Joseph J.Y., primary, Cheng, Alfred S.L., primary, and Cho, Chi H., primary

Published
2023
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23. Supplementary Figure Legends from Epigenetic Silencing of miR-490-3p Reactivates the Chromatin Remodeler SMARCD1 to Promote Helicobacter pylori–Induced Gastric Carcinogenesis

Author

Shen, Jing, primary, Xiao, Zhangang, primary, Wu, William K.K., primary, Wang, Maggie H., primary, To, Ka F., primary, Chen, Yangchao, primary, Yang, Weiqin, primary, Li, May S.M., primary, Shin, Vivian Y., primary, Tong, Joanna H., primary, Kang, Wei, primary, Zhang, Lin, primary, Li, Minxing, primary, Wang, Lin, primary, Lu, Lan, primary, Chan, Ruby L.Y., primary, Wong, Sunny H., primary, Yu, Jun, primary, Chan, Matthew T.V., primary, Chan, Francis K.L., primary, Sung, Joseph J.Y., primary, Cheng, Alfred S.L., primary, and Cho, Chi H., primary

Published
2023
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29. Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges

Author

Daneshjou, Roxana, Wang, Yanran, Bromberg, Yana, Bovo, Samuele, Martelli, Pier L, Babbi, Giulia, Lena, Pietro Di, Casadio, Rita, Edwards, Matthew, Gifford, David, Jones, David T, Sundaram, Laksshman, Bhat, Rajendra Rana, Li, Xiaolin, Pal, Lipika R., Kundu, Kunal, Yin, Yizhou, Moult, John, Jiang, Yuxiang, Pejaver, Vikas, Pagel, Kymberleigh A., Li, Biao, Mooney, Sean D., Radivojac, Predrag, Shah, Sohela, Carraro, Marco, Gasparini, Alessandra, Leonardi, Emanuela, Giollo, Manuel, Ferrari, Carlo, Tosatto, Silvio C E, Bachar, Eran, Azaria, Johnathan R., Ofran, Yanay, Unger, Ron, Niroula, Abhishek, Vihinen, Mauno, Chang, Billy, Wang, Maggie H, Franke, Andre, Petersen, Britt-Sabina, Pirooznia, Mehdi, Zandi, Peter, McCombie, Richard, Potash, James B., Altman, Russ B., Klein, Teri E., Hoskins, Roger A., Repo, Susanna, Brenner, Steven E., and Morgan, Alexander A.

Published
2017
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30. The mechanism shaping the logistic growth of mutation proportion in epidemics at population scale.

Author

Shi Zhao, Inchi Hu, Jingzhi Lou, Chong, Marc K. C., Lirong Cao, Daihai He, Zee, Benny C. Y., and Wang, Maggie H.

Subjects
LOGISTICS, INFECTIOUS disease transmission, POPULATION dynamics, COVID-19 pandemic, EPIDEMIOLOGY
Abstract

Virus evolution is a common process of pathogen adaption to host population and environment. Frequently, a small but important fraction of virus mutations are reported to contribute to higher risks of host infection, which is one of the major determinants of infectious diseases outbreaks at population scale. The key mutations contributing to transmission advantage of a genetic variant often grow and reach fixation rapidly. Based on classic epidemiology theories of disease transmission, we proposed a mechanistic explanation of the process that between-host transmission advantage may shape the observed logistic curve of the mutation proportion in population. The logistic growth of mutation is further generalized by incorporating time-varying selective pressure to account for impacts of external factors on pathogen adaptiveness. The proposed model is implemented in real-world data of COVID-19 to capture the emerging trends and changing dynamics of the B.1.1.7 strains of SARS-CoV-2 in England. The model characterizes and establishes the underlying theoretical mechanism that shapes the logistic growth of mutation in population. [ABSTRACT FROM AUTHOR]

Published
2023
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31. Mucosal Antibody Response to SARS-CoV-2 in Paediatric and Adult Patients: A Longitudinal Study

Author

Chan, Renee W. Y., primary, Chan, Kate C. C., additional, Lui, Grace C. Y., additional, Tsun, Joseph G. S., additional, Chan, Kathy Y. Y., additional, Yip, Jasmine S. K., additional, Liu, Shaojun, additional, Yu, Michelle W. L., additional, Ng, Rita W. Y., additional, Chong, Kelvin K. L., additional, Wang, Maggie H., additional, Chan, Paul K. S., additional, Li, Albert M., additional, and Lam, Hugh Simon, additional

Published
2022
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32. Suppression of Influenza Virus Infection by Rhinovirus Interference – at the Population, Individual and Cellular Levels

Author

Tao, Kin Pong, primary, Chong, Marc KC, additional, CHAN, Kathy Yuen Yee, additional, PUN, Jason Chun Sang, additional, TSUN, Joseph Gar Shun, additional, CHOW, Samuel Man Wai, additional, Ng, Calvin S.H., additional, Wang, Maggie H., additional, Chan, Paul K.S., additional, LI, Albert Martin Man Chim, additional, and Chan, Renee Wan Yi, additional

Published
2022
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39. Mucosal antibody response to SARS-CoV-2 in paediatric and adult patients: a longitudinal study

Author

Chan, Renee WY, primary, Chan, Kate CC, additional, Lui, Grace CY, additional, Tsun, Joseph GS, additional, Chan, Kathy YY, additional, Yip, Jasmine SK, additional, Liu, Shaojun, additional, Yu, Michelle WL, additional, Ng, Rita WY, additional, Chong, Kelvin KL, additional, Wang, Maggie H, additional, Chan, Paul KS, additional, Li, Albert M, additional, and Lam, Hugh S, additional

Published
2021
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40. A Bayesian method for synthesizing multiple diagnostic outcomes of COVID-19 tests

Author

Cao, Lirong, primary, Zhao, Shi, additional, Li, Qi, additional, Ling, Lowell, additional, Wu, William K. K., additional, Zhang, Lin, additional, Lou, Jingzhi, additional, Chong, Marc K. C., additional, Chen, Zigui, additional, Wong, Eliza L. Y., additional, Zee, Benny C. Y., additional, Chan, Matthew T. V., additional, Chan, Paul K. S., additional, and Wang, Maggie H., additional

Published
2021
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42. The changing patterns of COVID-19 transmissibility during the social unrest in the United States: A nationwide ecological study with a before-and-after comparison

Author

Ran, Jinjun, primary, Zhao, Shi, additional, Han, Lefei, additional, Chong, Marc K.C., additional, Qiu, Yulan, additional, Yang, Yiwei, additional, Wang, Jiayi, additional, Wu, Yushan, additional, Javanbakht, Mohammad, additional, Wang, Maggie H., additional, and He, Daihai, additional

Published
2021
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43. Figure S1 from A Bayesian method for synthesizing multiple diagnostic outcomes of COVID-19 tests

Author

Cao, Lirong, Zhao, Shi, Li, Qi, Ling, Lowell, Wu, William K. K., Zhang, Lin, Lou, Jingzhi, Chong, Marc K. C., Chen, Zigui, Wong, Eliza L. Y., Zee, Benny C. Y., Chan, Matthew T. V., Chan, Paul K. S., and Wang, Maggie H.

Abstract

Violin plot of the accuracy and precision for test #1, test #2 and the Bayesian probabilistic model. The pre-test probability is assumed from 0.001% to 5%; sensitivities and specificities for T1 are assumed from 70% - 100% and 80% - 100%, respectively; sensitivities and specificities for T2 are assumed from 90% - 100% and 50% - 100%, respectively.

Published
2021
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44. Additional file 1 of Real-time quantification of the transmission advantage associated with a single mutation in pathogen genomes: a case study on the D614G substitution of SARS-CoV-2

Author

Zhao, Shi, Lou, Jingzhi, Cao, Lirong, Zheng, Hong, Chong, Marc K. C., Chen, Zigui, Chan, Renee W. Y., Zee, Benny C. Y., Chan, Paul K. S., and Wang, Maggie H.

Subjects
body regions, viruses, fungi, skin and connective tissue diseases, respiratory tract diseases
Abstract

Additional file 1. The acknowledgement table of SARS-CoV-2 strain sequences used in this study.

Published
2021
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45. Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges

Author

Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Daneshjou, Roxana, Wang, Yanran, Bromberg, Yana, Bovo, Samuele, Martelli, Pier L, Babbi, Giulia, Di Lena, Pietro, Casadio, Rita, Edwards, Matthew D, Gifford, David K, Jones, David T, Sundaram, Laksshman, Bhat, Rajendra, Li, Xiaolin, Pal, Lipika R., Kundu, Kunal, Yin, Yizhou, Moult, John, Jiang, Yuxiang, Pejaver, Vikas, Pagel, Kymberleigh A., Li, Biao, Mooney, Sean D., Radivojac, Predrag, Shah, Sohela, Carraro, Marco, Gasparini, Alessandra, Leonardi, Emanuela, Giollo, Manuel, Ferrari, Carlo, Tosatto, Silvio C E, Bachar, Eran, Azaria, Johnathan R., Ofran, Yanay, Unger, Ron, Niroula, Abhishek, Vihinen, Mauno, Chang, Billy, Wang, Maggie H, Franke, Andre, Petersen, Britt-Sabina, Pirooznia, Mehdi, Zandi, Peter, McCombie, Richard, Potash, James B, Altman, Russ, Klein, Teri E., Hoskins, Roger, Repo, Susanna, Brenner, Steve E, Morgan, Alexander A, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Daneshjou, Roxana, Wang, Yanran, Bromberg, Yana, Bovo, Samuele, Martelli, Pier L, Babbi, Giulia, Di Lena, Pietro, Casadio, Rita, Edwards, Matthew D, Gifford, David K, Jones, David T, Sundaram, Laksshman, Bhat, Rajendra, Li, Xiaolin, Pal, Lipika R., Kundu, Kunal, Yin, Yizhou, Moult, John, Jiang, Yuxiang, Pejaver, Vikas, Pagel, Kymberleigh A., Li, Biao, Mooney, Sean D., Radivojac, Predrag, Shah, Sohela, Carraro, Marco, Gasparini, Alessandra, Leonardi, Emanuela, Giollo, Manuel, Ferrari, Carlo, Tosatto, Silvio C E, Bachar, Eran, Azaria, Johnathan R., Ofran, Yanay, Unger, Ron, Niroula, Abhishek, Vihinen, Mauno, Chang, Billy, Wang, Maggie H, Franke, Andre, Petersen, Britt-Sabina, Pirooznia, Mehdi, Zandi, Peter, McCombie, Richard, Potash, James B, Altman, Russ, Klein, Teri E., Hoskins, Roger, Repo, Susanna, Brenner, Steve E, and Morgan, Alexander A

Abstract

Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype–phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype–phenotype relationships.

Published
2021

49. Modelling COVID-19 outbreak on the Diamond Princess ship using the public surveillance data.

Author

Shi Zhao, Peihua Cao, Daozhou Gao, Zian Zhuang, Weiming Wang, Jinjun Ran, Kai Wang, Lin Yang, Einollahi, Mohammad R., Yijun Lou, Daihai He, and Wang, Maggie H.

Subjects
COVID-19 pandemic, PUBLIC health, MEDICAL care, INFECTIOUS disease transmission, QUARANTINE
Abstract

The novel coronavirus disease 2019 (COVID-19) outbreak on the Diamond Princess (DP) ship has caused over 634 cases as of February 20, 2020. We model the transmission process on DP ship as a stochastic branching process, and estimate the reproduction number at the innitial phase of 2.9 (95%CrI: 1.7e7.7). The epidemic doubling time is 3.4 days, and thus timely actions on COVID-19 control were crucial. We estimate the COVID-19 transmissibility reduced 34% after the quarantine program on the DP ship which was implemented on February 5. According to the model simulation, relocating the population at risk may sustainably decrease the epidemic size, postpone the timing of epidemic peak, and thus relieve the tensive demands in the healthcare. The lesson learnt on the ship should be considered in other similar settings. [ABSTRACT FROM AUTHOR]

Published
2022
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