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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 陳秀熙 | zh_TW |
dc.contributor.advisor | Hsiu-Hsi Chen | en |
dc.contributor.author | 廖翎均 | zh_TW |
dc.contributor.author | Ling-Chun Liao | en |
dc.date.accessioned | 2023-03-03T17:02:43Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-03-03 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-02-16 | - |
dc.identifier.citation | Abu-Raddad, L. J., Chemaitelly, H., Ayoub, H. H., AlMukdad, S., Yassine, H. M., Al-Khatib, H. A., Smatti, M. K., Tang, P., Hasan, M. R., Coyle, P., Al-Kanaani, Z., Al-Kuwari, E., Jeremijenko, A., Kaleeckal, A. H., Latif, A. N., Shaik, R. M., Abdul-Rahim, H. F., Nasrallah, G. K., Al-Kuwari, M. G., . . . Bertollini, R. (2022). Effect of mRNA Vaccine Boosters against SARS-CoV-2 Omicron Infection in Qatar. N Engl J Med, 386(19), 1804-1816. https://doi.org/10.1056/NEJMoa2200797
Amanzio, M., Mitsikostas, D. D., Giovannelli, F., Bartoli, M., Cipriani, G. E., & Brown, W. A. Adverse events of active and placebo groups in SARS-CoV-2 vaccine randomized trials: A systematic review. (2666-7762 (Electronic)). Andrews, N., Stowe, J., Kirsebom, F., Toffa, S., Rickeard, T., Gallagher, E., Gower, C., Kall, M., Groves, N., O'Connell, A. M., Simons, D., Blomquist, P. B., Zaidi, A., Nash, S., Iwani Binti Abdul Aziz, N., Thelwall, S., Dabrera, G., Myers, R., Amirthalingam, G., . . . Lopez Bernal, J. (2022). Covid-19 Vaccine Effectiveness against the Omicron (B.1.1.529) Variant. N Engl J Med, 386(16), 1532-1546. https://doi.org/10.1056/NEJMoa2119451 Arbel, R., Sergienko, R., Friger, M., Peretz, A., Beckenstein, T., Yaron, S., Netzer, D., & Hammerman, A. (2022). Effectiveness of a second BNT162b2 booster vaccine against hospitalization and death from COVID-19 in adults aged over 60 years. Nat Med, 28(7), 1486-1490. https://doi.org/10.1038/s41591-022-01832-0 Backer, J. A., Eggink, D., Andeweg, S. P., Veldhuijzen, I. K., van Maarseveen, N., Vermaas, K., Vlaemynck, B., Schepers, R., van den Hof, S., Reusken, C. B., & Wallinga, J. Shorter serial intervals in SARS-CoV-2 cases with Omicron BA.1 variant compared with Delta variant, the Netherlands, 13 to 26 December 2021. LID - 10.2807/1560-7917.ES.2022.27.6.2200042 [doi] LID - 2200042. (1560-7917 (Electronic)). Barouch, D. H. (2022). Covid-19 Vaccines - Immunity, Variants, Boosters. N Engl J Med, 387(11), 1011-1020. https://doi.org/10.1056/NEJMra2206573 Bhattacharyya, R. P., & Hanage, W. P. (2022). Challenges in Inferring Intrinsic Severity of the SARS-CoV-2 Omicron Variant. N Engl J Med, 386(7), e14. https://doi.org/10.1056/NEJMp2119682 Biritwum, R. B., & Odoom, S. I. (1995). Application of Markov process modelling to health status switching behaviour of infants. Int J Epidemiol, 24(1), 177-182. https://doi.org/10.1093/ije/24.1.177 Bruxvoort, K. J., Sy, L. S., Qian, L., Ackerson, B. K., Luo, Y., Lee, G. S., Tian, Y., Florea, A., Aragones, M., Tubert, J. E., Takhar, H. S., Ku, J. H., Paila, Y. D., Talarico, C. A., & Tseng, H. F. (2021). Effectiveness of mRNA-1273 against delta, mu, and other emerging variants of SARS-CoV-2: test negative case-control study. Bmj, 375, e068848. https://doi.org/10.1136/bmj-2021-068848 Butt, A. A., Talisa, V. B., Shaikh, O. S., Omer, S. B., & Mayr, F. B. (2022). Relative Vaccine Effectiveness of a Severe Acute Respiratory Syndrome Coronavirus 2 Messenger RNA Vaccine Booster Dose Against the Omicron Variant. Clin Infect Dis, 75(12), 2161-2168. https://doi.org/10.1093/cid/ciac328 Carazo, S., Skowronski, D. M., Brisson, M., Barkati, S., Sauvageau, C., Brousseau, N., Gilca, R., Fafard, J., Talbot, D., Ouakki, M., Gilca, V., Carignan, A., Deceuninck, G., De Wals, P., & De Serres, G. (2023). Protection against omicron (B.1.1.529) BA.2 reinfection conferred by primary omicron BA.1 or pre-omicron SARS-CoV-2 infection among health-care workers with and without mRNA vaccination: a test-negative case-control study. Lancet Infect Dis, 23(1), 45-55. https://doi.org/10.1016/s1473-3099(22)00578-3 Carazo, S., Skowronski, D. M., Brisson, M., Sauvageau, C., Brousseau, N., Gilca, R., Ouakki, M., Barkati, S., Fafard, J., Talbot, D., Gilca, V., Deceuninck, G., Garenc, C., Carignan, A., De Wals, P., & De Serres, G. (2022). Estimated Protection of Prior SARS-CoV-2 Infection Against Reinfection With the Omicron Variant Among Messenger RNA-Vaccinated and Nonvaccinated Individuals in Quebec, Canada. JAMA Netw Open, 5(10), e2236670. https://doi.org/10.1001/jamanetworkopen.2022.36670 Chanda, D., Hines, J. Z., Itoh, M., Fwoloshi, S., Minchella, P. A., Zyambo, K. D., Sivile, S., Kampamba, D., Chirwa, B., Chanda, R., Mutengo, K., Kayembe, M. F., Chewe, W., Chipimo, P., Mweemba, A., Agolory, S., & Mulenga, L. B. (2022). COVID-19 Vaccine Effectiveness Against Progression to In-Hospital Mortality in Zambia, 2021-2022. Open Forum Infect Dis, 9(9), ofac469. https://doi.org/10.1093/ofid/ofac469 Chemaitelly, H., Tang, P., Hasan, M. R., AlMukdad, S., Yassine, H. M., Benslimane, F. M., Al Khatib, H. A., Coyle, P., Ayoub, H. H., Al Kanaani, Z., Al Kuwari, E., Jeremijenko, A., Kaleeckal, A. H., Latif, A. N., Shaik, R. M., Abdul Rahim, H. F., Nasrallah, G. K., Al Kuwari, M. G., Al Romaihi, H. E., . . . Abu-Raddad, L. J. (2021). Waning of BNT162b2 Vaccine Protection against SARS-CoV-2 Infection in Qatar. N Engl J Med, 385(24), e83. https://doi.org/10.1056/NEJMoa2114114 Chen, S. L., Jen, G. H., Hsu, C. Y., Yen, A. M., Lai, C. C., Yeh, Y. P., & Chen, T. H. (2022). A new approach to modeling pre-symptomatic incidence and transmission time of imported COVID-19 cases evolving with SARS-CoV-2 variants. Stoch Environ Res Risk Assess, 1-12. https://doi.org/10.1007/s00477-022-02305-z Chen, S. T., Park, M. D., Del Valle, D. M., Buckup, M., Tabachnikova, A., Thompson, R. C., Simons, N. W., Mouskas, K., Lee, B., Geanon, D., D'Souza, D., Dawson, T., Marvin, R., Nie, K., Zhao, Z., LeBerichel, J., Chang, C., Jamal, H., Akturk, G., . . . Merad, M. (2022). A shift in lung macrophage composition is associated with COVID-19 severity and recovery. Sci Transl Med, 14(662), eabn5168. https://doi.org/10.1126/scitranslmed.abn5168 Chiu, S. Y., Malila, N., Yen, A. M., Chen, S. L., Fann, J. C., & Hakama, M. Predicting the effectiveness of the Finnish population-based colorectal cancer screening programme. (1475-5793 (Electronic)). Chung, H., He, S., Nasreen, S., Sundaram, M. E., Buchan, S. A., Wilson, S. E., Chen, B., Calzavara, A., Fell, D. B., Austin, P. C., Wilson, K., Schwartz, K. L., Brown, K. A., Gubbay, J. B., Basta, N. E., Mahmud, S. M., Righolt, C. H., Svenson, L. W., MacDonald, S. E., . . . Kwong, J. C. (2021). Effectiveness of BNT162b2 and mRNA-1273 covid-19 vaccines against symptomatic SARS-CoV-2 infection and severe covid-19 outcomes in Ontario, Canada: test negative design study. Bmj, 374, n1943. https://doi.org/10.1136/bmj.n1943 Cook, R. J. (1999). A Mixed Model for Two-State Markov Processes under Panel Observation. Biometrics, 55(3), 915-920. http://www.jstor.org/stable/2533625 D.R.Cox, H. D. M. (1965). The Theory of Stochastic Processes. Dan, J. M., Mateus, J., Kato, Y., Hastie, K. M., Yu, E. D., Faliti, C. E., Grifoni, A., Ramirez, S. I., Haupt, S., Frazier, A., Nakao, C., Rayaprolu, V., Rawlings, S. A., Peters, B., Krammer, F., Simon, V., Saphire, E. O., Smith, D. M., Weiskopf, D., . . . Crotty, S. (2021). Immunological memory to SARS-CoV-2 assessed for up to 8 months after infection. Science, 371(6529), eabf4063. https://doi.org/10.1126/science.abf4063 Davis, R. A., Fokianos, K., Holan, S. H., Joe, H., Livsey, J., Lund, R., Pipiras, V., & Ravishanker, N. (2021). Count Time Series: A Methodological Review. Journal of the American Statistical Association, 116(535), 1533-1547. https://doi.org/10.1080/01621459.2021.1904957 Dean, N. E., Hogan, J. W., & Schnitzer, M. E. (2021). Covid-19 Vaccine Effectiveness and the Test-Negative Design. NEW ENGLAND JOURNAL OF MEDICINE, 385(15), 1431-1433. https://doi.org/10.1056/NEJMe2113151 Dickerman, B. A., Gerlovin, H., Madenci, A. L., Kurgansky, K. E., Ferolito, B. R., Figueroa Muñiz, M. J., Gagnon, D. R., Gaziano, J. M., Cho, K., Casas, J. P., & Hernán, M. A. (2022). Comparative Effectiveness of BNT162b2 and mRNA-1273 Vaccines in U.S. Veterans. N Engl J Med, 386(2), 105-115. https://doi.org/10.1056/NEJMoa2115463 Dona, M. G., Giuliani, M., Rollo, F., Vescio, M. F., Benevolo, M., Giglio, A., Giuliani, E., Morrone, A., & Latini, A. (2022). Incidence and clearance of anal high-risk Human Papillomavirus infection and their risk factors in men who have sex with men living with HIV. Sci Rep, 12(1), 184. https://doi.org/10.1038/s41598-021-03913-5 Dona, M. G., Vescio, M. F., Latini, A., Giglio, A., Moretto, D., Frasca, M., Benevolo, M., Rollo, F., Colafigli, M., Cristaudo, A., & Giuliani, M. (2016). Anal human papillomavirus in HIV-uninfected men who have sex with men: incidence and clearance rates, duration of infection, and risk factors. Clin Microbiol Infect, 22(12), 1004 e1001-1004 e1007. https://doi.org/10.1016/j.cmi.2016.08.011 Du, Z., Shao, Z., Bai, Y., Wang, L., Herrera-Diestra, J. L., Fox, S. J., Ertem, Z., Lau, E. H. Y., & Cowling, B. J. (2022). Reproduction number of monkeypox in the early stage of the 2022 multi-country outbreak. J Travel Med. https://doi.org/10.1093/jtm/taac099 Farahat, R. A., Abdelaal, A., Shah, J., Ghozy, S., Sah, R., Bonilla-Aldana, D. K., Rodriguez-Morales, A. J., McHugh, T. D., & Leblebicioglu, H. (2022). Monkeypox outbreaks during COVID-19 pandemic: are we looking at an independent phenomenon or an overlapping pandemic? Ann Clin Microbiol Antimicrob, 21(1), 26. https://doi.org/10.1186/s12941-022-00518-2 Feikin, D. R., Higdon, M. M., Abu-Raddad, L. J., Andrews, N., Araos, R., Goldberg, Y., Groome, M. J., Huppert, A., O'Brien, K. L., Smith, P. G., Wilder-Smith, A., Zeger, S., Deloria Knoll, M., & Patel, M. K. Duration of effectiveness of vaccines against SARS-CoV-2 infection and COVID-19 disease: results of a systematic review and meta-regression. (1474-547X (Electronic)). Ficiara, E., Crespi, V., Gadewar, S. P., Thomopoulos, S. I., Boyd, J., Thompson, P. M., Jahanshad, N., Pizzagalli, F., & Alzheimer's Disease Neuroimaging, I. (2021). Predicting Progression from Mild Cognitive Impairment to Alzheimer's Disease using MRI-based Cortical Features and a Two-State Markov Model. Proc IEEE Int Symp Biomed Imaging, 2021, 1145-1149. https://doi.org/10.1109/isbi48211.2021.9434143 Gani, R., & Leach, S. (2001). Transmission potential of smallpox in contemporary populations. Nature, 414(6865), 748-751. https://doi.org/10.1038/414748a GeurtsvanKessel, C. H., Geers, D., Schmitz, K. S., Mykytyn, A. Z., Lamers, M. M., Bogers, S., Scherbeijn, S., Gommers, L., Sablerolles, R. S. G., Nieuwkoop, N. N., Rijsbergen, L. C., van Dijk, L. L. A., de Wilde, J., Alblas, K., Breugem, T. I., Rijnders, B. J. A., de Jager, H., Weiskopf, D., van der Kuy, P. H. M., . . . de Vries, R. D. (2022). Divergent SARS-CoV-2 Omicron-reactive T and B cell responses in COVID-19 vaccine recipients. Sci Immunol, 7(69), eabo2202. https://doi.org/10.1126/sciimmunol.abo2202 Guan, W. J., Ni, Z. Y., Hu, Y., Liang, W. H., Ou, C. Q., He, J. X., Liu, L., Shan, H., Lei, C. L., Hui, D. S. C., Du, B., Li, L. J., Zeng, G., Yuen, K. Y., Chen, R. C., Tang, C. L., Wang, T., Chen, P. Y., Xiang, J., . . . China Medical Treatment Expert Group for, C. (2020). Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med, 382(18), 1708-1720. https://doi.org/10.1056/NEJMoa2002032 Gupta, R. K., & Topol, E. J. (2021). COVID-19 vaccine breakthrough infections. Science, 374(6575), 1561-1562. https://doi.org/doi:10.1126/science.abl8487 Haas, E. J., Angulo, F. J., McLaughlin, J. M., Anis, E., Singer, S. R., Khan, F., Brooks, N., Smaja, M., Mircus, G., Pan, K., Southern, J., Swerdlow, D. L., Jodar, L., Levy, Y., & Alroy-Preis, S. (2021). Impact and effectiveness of mRNA BNT162b2 vaccine against SARS-CoV-2 infections and COVID-19 cases, hospitalisations, and deaths following a nationwide vaccination campaign in Israel: an observational study using national surveillance data. Lancet, 397(10287), 1819-1829. https://doi.org/10.1016/S0140-6736(21)00947-8 Hannah Ritchie, E. M., Lucas Rodés-Guirao, Cameron Appel, Charlie Giattino, Esteban Ortiz-Ospina, Joe Hasell, Bobbie Macdonald, Diana Beltekian and Max Roser. (2020). Coronavirus Pandemic (COVID-19) https://ourworldindata.org/coronavirus Hart, W. S., Miller, E., Andrews, N. J., Waight, P., Maini, P. K., Funk, S., & Thompson, R. N. Generation time of the alpha and delta SARS-CoV-2 variants: an epidemiological analysis. (1474-4457 (Electronic)). Havers, F. P., Pham, H., Taylor, C. A., Whitaker, M., Patel, K., Anglin, O., Kambhampati, A. K., Milucky, J., Zell, E., Moline, H. L., Chai, S. J., Kirley, P. D., Alden, N. B., Armistead, I., Yousey-Hindes, K., Meek, J., Openo, K. P., Anderson, E. J., Reeg, L., . . . McMorrow, M. (2022). COVID-19-Associated Hospitalizations Among Vaccinated and Unvaccinated Adults 18 Years or Older in 13 US States, January 2021 to April 2022. JAMA Intern Med, 182(10), 1071-1081. https://doi.org/10.1001/jamainternmed.2022.4299 Hawkins, J. M. (2016). Markov process models of the dynamics of HIV reservoirs. Math Biosci, 275, 18-24. https://doi.org/10.1016/j.mbs.2016.02.009 Hay, J. A., Kennedy-Shaffer, L., Kanjilal, S., Lennon, N. J., Gabriel, S. B., Lipsitch, M., & Mina, M. J. (2021). Estimating epidemiologic dynamics from cross-sectional viral load distributions. Science, 373(6552), eabh0635. https://doi.org/doi:10.1126/science.abh0635 He, X., Lau, E. A.-O., Wu, P., Deng, X., Wang, J., Hao, X., Lau, Y. C., Wong, J. Y., Guan, Y., Tan, X., Mo, X., Chen, Y., Liao, B., Chen, W., Hu, F., Zhang, Q., Zhong, M., Wu, Y., Zhao, L., . . . Leung, G. A.-O. Temporal dynamics in viral shedding and transmissibility of COVID-19. (1546-170X (Electronic)). Heidarzadeh, A., Moridani, M. A., Khoshmanesh, S., Kazemi, S., Hajiaghabozorgi, M., & Karami, M. (2022). Effectiveness of COVID-19 vaccines on hospitalization and death in Guilan, Iran: a test negative case-control study. Int J Infect Dis. https://doi.org/10.1016/j.ijid.2022.12.024 Hitchings, M. D. T., Ranzani, O. T., Dorion, M., D'Agostini, T. L., de Paula, R. C., de Paula, O. F. P., de Moura Villela, E. F., Torres, M. S. S., de Oliveira, S. B., Schulz, W., Almiron, M., Said, R., de Oliveira, R. D., Silva, P. V., de Araújo, W. N., Gorinchteyn, J. C., Andrews, J. R., Cummings, D. A. T., Ko, A. I., & Croda, J. (2021). Effectiveness of ChAdOx1 vaccine in older adults during SARS-CoV-2 Gamma variant circulation in São Paulo. Nat Commun, 12(1), 6220. https://doi.org/10.1038/s41467-021-26459-6 Hsu, C. Y., Chang, J. C., Chen, S. L., Chang, H. H., Lin, A. T., Yen, A. M., & Chen, H. H. (2023). Primary and booster vaccination in reducing severe clinical outcomes associated with Omicron Naive infection. J Infect Public Health, 16(1), 55-63. https://doi.org/10.1016/j.jiph.2022.11.028 Intawong, K., Chariyalertsak, S., Chalom, K., Wonghirundecha, T., Kowatcharakul, W., Ayood, P., Thongprachum, A., Chotirosniramit, N., Noppakun, K., Khwanngern, K., Teacharak, W., Piamanant, P., & Khammawan, P. (2023). Reduction in severity and mortality in COVID-19 patients owing to heterologous third and fourth-dose vaccines during the periods of delta and omicron predominance in Thailand. Int J Infect Dis, 126, 31-38. https://doi.org/10.1016/j.ijid.2022.11.006 Ito, K., Piantham, C., & Nishiura, H. Estimating relative generation times and reproduction numbers of Omicron BA.1 and BA.2 with respect to Delta variant in Denmark. (1551-0018 (Electronic)). Kalbfleisch, J. D., & Lawless, J. F. (1985). The Analysis of Panel Data Under a Markov Assumption. Journal of the American Statistical Association, 80(392), 863-871. https://doi.org/10.2307/2288545 Kasper, M. R., Geibe, J. R., Sears, C. L., Riegodedios, A. J., Luse, T., Von Thun, A. M., McGinnis, M. B., Olson, N., Houskamp, D., Fenequito, R., Burgess, T. H., Armstrong, A. W., DeLong, G., Hawkins, R. J., & Gillingham, B. L. An Outbreak of Covid-19 on an Aircraft Carrier. (1533-4406 (Electronic)). Kiss, Z., Wittmann, I., Polivka, L., Surján, G., Surján, O., Barcza, Z., Molnár, G. A., Nagy, D., Müller, V., Bogos, K., Nagy, P., Kenessey, I., Wéber, A., Pálosi, M., Szlávik, J., Schaff, Z., Szekanecz, Z., Müller, C., Kásler, M., & Vokó, Z. (2022). Nationwide Effectiveness of First and Second SARS-CoV2 Booster Vaccines During the Delta and Omicron Pandemic Waves in Hungary (HUN-VE 2 Study). Front Immunol, 13, 905585. https://doi.org/10.3389/fimmu.2022.905585 Koelle, K. A.-O., Martin, M. A.-O., Antia, R. A.-O. X., Lopman, B. A.-O., & Dean, N. A.-O. The changing epidemiology of SARS-CoV-2. (1095-9203 (Electronic)). Kraemer, M. U. G., Tegally, H., Pigott, D. M., Dasgupta, A., Sheldon, J., Wilkinson, E., Schultheiss, M., Han, A., Oglia, M., Marks, S., Kanner, J., O'Brien, K., Dandamudi, S., Rader, B., Sewalk, K., Bento, A. I., Scarpino, S. V., de Oliveira, T., Bogoch, II, . . . Brownstein, J. S. (2022). Tracking the 2022 monkeypox outbreak with epidemiological data in real-time. Lancet Infect Dis, 22(7), 941-942. https://doi.org/10.1016/S1473-3099(22)00359-0 Lai, C. C., Hsu, C. Y., Jen, H. H., Yen, A. M., Chan, C. C., & Chen, H. H. (2021). The Bayesian Susceptible-Exposed-Infected-Recovered model for the outbreak of COVID-19 on the Diamond Princess Cruise Ship. Stoch Environ Res Risk Assess, 1-15. https://doi.org/10.1007/s00477-020-01968-w Lauring, A. S., Tenforde, M. W., Chappell, J. D., Gaglani, M., Ginde, A. A., McNeal, T., Ghamande, S., Douin, D. J., Talbot, H. K., Casey, J. D., Mohr, N. M., Zepeski, A., Shapiro, N. I., Gibbs, K. W., Files, D. C., Hager, D. N., Shehu, A., Prekker, M. E., Erickson, H. L., . . . Self, W. H. (2022). Clinical severity of, and effectiveness of mRNA vaccines against, covid-19 from omicron, delta, and alpha SARS-CoV-2 variants in the United States: prospective observational study. Bmj, 376, e069761. https://doi.org/10.1136/bmj-2021-069761 Lipsitch, M., Grad, Y. H., Sette, A., & Crotty, S. (2020). Cross-reactive memory T cells and herd immunity to SARS-CoV-2. Nature Reviews Immunology, 20(11), 709-713. https://doi.org/10.1038/s41577-020-00460-4 Lopez Bernal, J., Andrews, N., Gower, C., Gallagher, E., Simmons, R., Thelwall, S., Stowe, J., Tessier, E., Groves, N., Dabrera, G., Myers, R., Campbell, C. N. J., Amirthalingam, G., Edmunds, M., Zambon, M., Brown, K. E., Hopkins, S., Chand, M., & Ramsay, M. Effectiveness of Covid-19 Vaccines against the B.1.617.2 (Delta) Variant. (1533-4406 (Electronic)). Lopez Bernal, J., Andrews, N., Gower, C., Robertson, C., Stowe, J., Tessier, E., Simmons, R., Cottrell, S., Roberts, R., O'Doherty, M., Brown, K., Cameron, C., Stockton, D., McMenamin, J., & Ramsay, M. (2021). Effectiveness of the Pfizer-BioNTech and Oxford-AstraZeneca vaccines on covid-19 related symptoms, hospital admissions, and mortality in older adults in England: test negative case-control study. Bmj, 373, n1088. https://doi.org/10.1136/bmj.n1088 Lopman, B., Armstrong, B., Atchison, C., & Gray, J. J. (2009). Host, weather and virological factors drive norovirus epidemiology: time-series analysis of laboratory surveillance data in England and Wales. PLoS One, 4(8), e6671. https://doi.org/10.1371/journal.pone.0006671 Marc, A. A.-O., Kerioui, M., Blanquart, F., Bertrand, J., Mitjà, O., Corbacho-Monné, M., Marks, M., & Guedj, J. A.-O. Quantifying the relationship between SARS-CoV-2 viral load and infectiousness. LID - 10.7554/eLife.69302 [doi] LID - e69302. (2050-084X (Electronic)). McConeghy, K. W., White, E. M., Blackman, C., Santostefano, C. M., Lee, Y., Rudolph, J. L., Canaday, D., Zullo, A. R., Jernigan, J. A., Pilishvili, T., Mor, V., & Gravenstein, S. (2022). Effectiveness of a Second COVID-19 Vaccine Booster Dose Against Infection, Hospitalization, or Death Among Nursing Home Residents - 19 States, March 29-July 25, 2022. MMWR Morb Mortal Wkly Rep, 71(39), 1235-1238. https://doi.org/10.15585/mmwr.mm7139a2 McMichael, A. J., Woodruff, R. E., & Hales, S. (2006). Climate change and human health: present and future risks. Lancet, 367(9513), 859-869. https://doi.org/10.1016/S0140-6736(06)68079-3 Meftahi, G. H., Jangravi, Z., Sahraei, H., & Bahari, Z. (2020). The possible pathophysiology mechanism of cytokine storm in elderly adults with COVID-19 infection: the contribution of "inflame-aging". Inflamm Res, 69(9), 825-839. https://doi.org/10.1007/s00011-020-01372-8 Meyerowitz, E. A.-O., Richterman, A. A.-O., Gandhi, R. T., & Sax, P. E. Transmission of SARS-CoV-2: A Review of Viral, Host, and Environmental Factors. (1539-3704 (Electronic)). Miura, F., van Ewijk, C. E., Backer, J. A., Xiridou, M., Franz, E., Op de Coul, E., Brandwagt, D., van Cleef, B., van Rijckevorsel, G., Swaan, C., van den Hof, S., & Wallinga, J. (2022). Estimated incubation period for monkeypox cases confirmed in the Netherlands, May 2022. Euro Surveill, 27(24). https://doi.org/10.2807/1560-7917.ES.2022.27.24.2200448 Monge, S., Rojas-Benedicto, A., Olmedo, C., Mazagatos, C., José Sierra, M., Limia, A., Martín-Merino, E., Larrauri, A., & Hernán, M. A. (2022). Effectiveness of mRNA vaccine boosters against infection with the SARS-CoV-2 omicron (B.1.1.529) variant in Spain: a nationwide cohort study. Lancet Infect Dis, 22(9), 1313-1320. https://doi.org/10.1016/s1473-3099(22)00292-4 Moss, P. (2022). The T cell immune response against SARS-CoV-2. Nat Immunol, 23(2), 186-193. https://doi.org/10.1038/s41590-021-01122-w Nasreen, S., Chung, H., He, S., Brown, K. A., Gubbay, J. B., Buchan, S. A., Fell, D. B., Austin, P. C., Schwartz, K. L., Sundaram, M. E., Calzavara, A., Chen, B., Tadrous, M., Wilson, K., Wilson, S. E., & Kwong, J. C. (2022). Effectiveness of COVID-19 vaccines against symptomatic SARS-CoV-2 infection and severe outcomes with variants of concern in Ontario. Nat Microbiol, 7(3), 379-385. https://doi.org/10.1038/s41564-021-01053-0 Pageaud, S., Eyraud-Loisel, A., Bertoglio, J. P., Bienvenüe, A., Leboisne, N., Pothier, C., Rigotti, C., Ponthus, N., Gauchon, R., Gueyffier, F., Vanhems, P., Iwaz, J., Loisel, S., Roy, P., & On Behalf Of The CovDyn Group Covid, D. (2022). Predicted Impacts of Booster, Immunity Decline, Vaccination Strategies, and Non-Pharmaceutical Interventions on COVID-19 Outcomes in France. Vaccines (Basel), 10(12). https://doi.org/10.3390/vaccines10122033 Pan, S. L., Wu, H. M., Yen, A. M., & Chen, T. H. (2007). A Markov regression random-effects model for remission of functional disability in patients following a first stroke: a Bayesian approach. Stat Med, 26(29), 5335-5353. https://doi.org/10.1002/sim.2999 Parker, S., Schultz, D. A., Meyer, H., & Buller, R. M. (2014). Smallpox and Monkeypox Viruses☆. In Reference Module in Biomedical Sciences. https://doi.org/10.1016/b978-0-12-801238-3.02665-9 Rearte, A., Castelli, J. M., Rearte, R., Fuentes, N., Pennini, V., Pesce, M., Barbeira, P. B., Iummato, L. E., Laurora, M., Bartolomeu, M. L., Galligani, G., Del Valle Juarez, M., Giovacchini, C. M., Santoro, A., Esperatti, M., Tarragona, S., & Vizzotti, C. (2022). Effectiveness of rAd26-rAd5, ChAdOx1 nCoV-19, and BBIBP-CorV vaccines for risk of infection with SARS-CoV-2 and death due to COVID-19 in people older than 60 years in Argentina: a test-negative, case-control, and retrospective longitudinal study. Lancet, 399(10331), 1254-1264. https://doi.org/10.1016/S0140-6736(22)00011-3 Regev-Yochay, G., Gonen, T., Gilboa, M., Mandelboim, M., Indenbaum, V., Amit, S., Meltzer, L., Asraf, K., Cohen, C., Fluss, R., Biber, A., Nemet, I., Kliker, L., Joseph, G., Doolman, R., Mendelson, E., Freedman, L. S., Harats, D., Kreiss, Y., & Lustig, Y. (2022). Efficacy of a Fourth Dose of Covid-19 mRNA Vaccine against Omicron. N Engl J Med, 386(14), 1377-1380. https://doi.org/10.1056/NEJMc2202542 Reynolds, C. J., Pade, C., Gibbons, J. M., Otter, A. D., Lin, K. M., Muñoz Sandoval, D., Pieper, F. P., Butler, D. K., Liu, S., Joy, G., Forooghi, N., Treibel, T. A., Manisty, C., Moon, J. C., Semper, A., Brooks, T., McKnight, Á., Altmann, D. M., Boyton, R. J., . . . Moon, J. C. (2022). Immune boosting by B.1.1.529 (Omicron) depends on previous SARS-CoV-2 exposure. Science, 377(6603), eabq1841. https://doi.org/10.1126/science.abq1841 Riley, S., Fraser, C., Donnelly, C. A., Ghani, A. C., Abu-Raddad, L. J., Hedley, A. J., Leung, G. M., Ho, L. M., Lam, T. H., Thach, T. Q., Chau, P., Chan, K. P., Lo, S. V., Leung, P. Y., Tsang, T., Ho, W., Lee, K. H., Lau, E. M., Ferguson, N. M., & Anderson, R. M. (2003). Transmission dynamics of the etiological agent of SARS in Hong Kong: impact of public health interventions. Science, 300(5627), 1961-1966. https://doi.org/10.1126/science.1086478 Rogers, A. A.-O., Rooke, E. A.-O. X., Morant, S. A.-O., Guthrie, G. A.-O., Doney, A. A.-O., Duncan, A. A.-O. X., Mackenzie, I. A.-O., Barr, R. A.-O., Pigazzani, F. A.-O. X., Zutis, K., & MacDonald, T. A.-O. Adverse events and overall health and well-being after COVID-19 vaccination: interim results from the VAC4COVID cohort safety study. (2044-6055 (Electronic)). Schwarzinger, M., Watson, V., Arwidson, P., Alla, F., & Luchini, S. (2021). COVID-19 vaccine hesitancy in a representative working-age population in France: a survey experiment based on vaccine characteristics. The Lancet Public Health, 6(4), e210-e221. https://doi.org/10.1016/S2468-2667(21)00012-8 Sethuraman, N., Jeremiah, S. S., & Ryo, A. (2020). Interpreting Diagnostic Tests for SARS-CoV-2. JAMA, 323(22), 2249-2251. https://doi.org/10.1001/jama.2020.8259 Sinclair, J. E., Mayfield, H. J., Short, K. R., Brown, S. J., Puranik, R., Mengersen, K., Litt, J. C. B., & Lau, C. L. (2022). A Bayesian network analysis quantifying risks versus benefits of the Pfizer COVID-19 vaccine in Australia. NPJ Vaccines, 7(1), 93. https://doi.org/10.1038/s41541-022-00517-6 Sonabend, R., Whittles, L. K., Imai, N., Perez-Guzman, P. N., Knock, E. S., Rawson, T., Gaythorpe, K. A. M., Djaafara, B. A., Hinsley, W., FitzJohn, R. G., Lees, J. A., Kanapram, D. T., Volz, E. M., Ghani, A. C., Ferguson, N. M., Baguelin, M., & Cori, A. (2021). Non-pharmaceutical interventions, vaccination, and the SARS-CoV-2 delta variant in England: a mathematical modelling study. Lancet, 398(10313), 1825-1835. https://doi.org/10.1016/s0140-6736(21)02276-5 Ssentongo, P., Ssentongo, A. E., Voleti, N., Groff, D., Sun, A., Ba, D. M., Nunez, J., Parent, L. J., Chinchilli, V. M., & Paules, C. I. (2022). SARS-CoV-2 vaccine effectiveness against infection, symptomatic and severe COVID-19: a systematic review and meta-analysis. BMC Infect Dis, 22(1), 439. https://doi.org/10.1186/s12879-022-07418-y Suarez Rodriguez, B., Guzman Herrador, B. R., Diaz Franco, A., Sanchez-Seco Farinas, M. P., Del Amo Valero, J., Aginagalde Llorente, A. H., de Agreda, J., Malonda, R. C., Castrillejo, D., Chirlaque Lopez, M. D., Chong Chong, E. J., Balbuena, S. F., Garcia, V. G., Garcia-Cenoz, M., Hernandez, L. G., Montalban, E. G., Carril, F. G., Cortijo, T. G., Bueno, S. J., . . . Sierra Moros, M. J. (2022). Epidemiologic Features and Control Measures during Monkeypox Outbreak, Spain, June 2022. Emerg Infect Dis, 28(9), 1847-1851. https://doi.org/10.3201/eid2809.221051 Surie, D., DeCuir, J., Zhu, Y., Gaglani, M., Ginde, A. A., Douin, D. J., Talbot, H. K., Casey, J. D., Mohr, N. M., Zepeski, A., McNeal, T., Ghamande, S., Gibbs, K. W., Files, D. C., Hager, D. N., Ali, H., Taghizadeh, L., Gong, M. N., Mohamed, A., . . . Self, W. H. (2022). Early Estimates of Bivalent mRNA Vaccine Effectiveness in Preventing COVID-19-Associated Hospitalization Among Immunocompetent Adults Aged ≥65 Years - IVY Network, 18 States, September 8-November 30, 2022. MMWR Morb Mortal Wkly Rep, 71(5152), 1625-1630. https://doi.org/10.15585/mmwr.mm715152e2 Suryawanshi, R., & Ott, M. (2022). SARS-CoV-2 hybrid immunity: silver bullet or silver lining? Nat Rev Immunol, 22(10), 591-592. https://doi.org/10.1038/s41577-022-00771-8 Tao, K., Tzou, P. L., Nouhin, J., Gupta, R. K., de Oliveira, T., Kosakovsky Pond, S. L., Fera, D., & Shafer, R. W. (2021). The biological and clinical significance of emerging SARS-CoV-2 variants. Nat Rev Genet, 22(12), 757-773. https://doi.org/10.1038/s41576-021-00408-x team, T. G. h. (2022). Monkeypox 2022 global epidemiology;Report 2022-08-05. Retrieved Augest 7 from https://www.monkeypox.global.health/ Tenforde, M. W., Self, W. H., Gaglani, M., Ginde, A. A., Douin, D. J., Talbot, H. K., Casey, J. D., Mohr, N. M., Zepeski, A., McNeal, T., Ghamande, S., Gibbs, K. W., Files, D. C., Hager, D. N., Shehu, A., Prekker, M. E., Frosch, A. E., Gong, M. N., Mohamed, A., . . . Patel, M. M. (2022). Effectiveness of mRNA Vaccination in Preventing COVID-19-Associated Invasive Mechanical Ventilation and Death - United States, March 2021-January 2022. MMWR Morb Mortal Wkly Rep, 71(12), 459-465. https://doi.org/10.15585/mmwr.mm7112e1 Thompson, M. G., Stenehjem, E., Grannis, S., Ball, S. W., Naleway, A. L., Ong, T. C., DeSilva, M. B., Natarajan, K., Bozio, C. H., Lewis, N., Dascomb, K., Dixon, B. E., Birch, R. J., Irving, S. A., Rao, S., Kharbanda, E., Han, J., Reynolds, S., Goddard, K., . . . Klein, N. P. (2021). Effectiveness of Covid-19 Vaccines in Ambulatory and Inpatient Care Settings. NEW ENGLAND JOURNAL OF MEDICINE, 385(15), 1355-1371. https://doi.org/10.1056/NEJMoa2110362 Thornhill, J. P., Barkati, S., Walmsley, S., Rockstroh, J., Antinori, A., Harrison, L. B., Palich, R., Nori, A., Reeves, I., Habibi, M. S., Apea, V., Boesecke, C., Vandekerckhove, L., Yakubovsky, M., Sendagorta, E., Blanco, J. L., Florence, E., Moschese, D., Maltez, F. M., . . . Group, S. H.-n. C. (2022). Monkeypox Virus Infection in Humans across 16 Countries - April-June 2022. N Engl J Med, 387(8), 679-691. https://doi.org/10.1056/NEJMoa2207323 Tregoning, J. A.-O., Flight, K. E., Higham, S. L., Wang, Z. A.-O. X., & Pierce, B. A.-O. Progress of the COVID-19 vaccine effort: viruses, vaccines and variants versus efficacy, effectiveness and escape. (1474-1741 (Electronic)). Turner, J. S., Kim, W., Kalaidina, E., Goss, C. W., Rauseo, A. M., Schmitz, A. J., Hansen, L., Haile, A., Klebert, M. K., Pusic, I., O'Halloran, J. A., Presti, R. M., & Ellebedy, A. H. (2021). SARS-CoV-2 infection induces long-lived bone marrow plasma cells in humans. Nature, 595(7867), 421-425. https://doi.org/10.1038/s41586-021-03647-4 Turner, J. S., O'Halloran, J. A., Kalaidina, E., Kim, W., Schmitz, A. J., Zhou, J. Q., Lei, T., Thapa, M., Chen, R. E., Case, J. B., Amanat, F., Rauseo, A. M., Haile, A., Xie, X., Klebert, M. K., Suessen, T., Middleton, W. D., Shi, P. Y., Krammer, F., . . . Ellebedy, A. H. (2021). SARS-CoV-2 mRNA vaccines induce persistent human germinal centre responses. Nature, 596(7870), 109-113. https://doi.org/10.1038/s41586-021-03738-2 Watson, O. J., Barnsley, G., Toor, J., Hogan, A. B., Winskill, P., & Ghani, A. C. (2022). Global impact of the first year of COVID-19 vaccination: a mathematical modelling study. Lancet Infect Dis, 22(9), 1293-1302. https://doi.org/10.1016/S1473-3099(22)00320-6 Wherry, E. J., & Barouch, D. H. (2022). T cell immunity to COVID-19 vaccines. Science, 377(6608), 821-822. https://doi.org/10.1126/science.add2897 WHO. (2020). Statement on the second meeting of the International Health Regulations (2005) Emergency Committee regarding the outbreak of novel coronavirus (2019-nCoV). https://www.who.int/news-room/detail/30-01-2020-statement-on-the-second-meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-outbreak-of-novel-coronavirus-(2019-ncov) WHO. (2022, July 25, 2022). Multi-country monkeypox outbreak: situation update. World Health Organization. Retrieved 7 Augest from https://www.who.int/emergencies/disease-outbreak-news/item/2022-DON396 WHO TEAM, I., Vaccines and Biologicals. (2021). Evaluation of COVID-19 vaccine effectiveness (W. H. Organization, Ed.) https://www.who.int/publications/i/item/WHO-2019-nCoV-vaccine_effectiveness-measurement-2021.1 Woldaregay, A. Z., Launonen, I. K., Albers, D., Igual, J., Årsand, E., & Hartvigsen, G. (2020). A Novel Approach for Continuous Health Status Monitoring and Automatic Detection of Infection Incidences in People With Type 1 Diabetes Using Machine Learning Algorithms (Part 2): A Personalized Digital Infectious Disease Detection Mechanism. J Med Internet Res, 22(8), e18912. https://doi.org/10.2196/18912 Wong, C. A.-O., Lau, K. T. K., Xiong, X. A.-O., Au, I. A.-O., Lai, F. A.-O., Wan, E. A.-O., Chui, C. S. L., Li, X. A.-O., Chan, E. A.-O., Gao, L. A.-O., Cheng, F. A.-O., Tang, S. A.-O., & Wong, I. A.-O. Adverse events of special interest and mortality following vaccination with mRNA (BNT162b2) and inactivated (CoronaVac) SARS-CoV-2 vaccines in Hong Kong: A retrospective study. (1549-1676 (Electronic)). Wu, Y., Kang, L., Guo, Z., Liu, J., Liu, M., & Liang, W. (2022). Incubation Period of COVID-19 Caused by Unique SARS-CoV-2 Strains: A Systematic Review and Meta-analysis. JAMA Network Open, 5(8), e2228008-e2228008. https://doi.org/10.1001/jamanetworkopen.2022.28008 Yan, V. K. C., Wan, E. Y. F., Ye, X., Mok, A. H. Y., Lai, F. T. T., Chui, C. S. L., Li, X., Wong, C. K. H., Li, P. H., Ma, T., Qin, S., Wong, V. K. C., Tsang, T. C., Tsui, S. H., Chui, W. C. M., Cowling, B. J., Leung, G. M., Lau, C. S., Wong, I. C. K., & Chan, E. W. Y. (2022). Effectiveness of BNT162b2 and CoronaVac vaccinations against mortality and severe complications after SARS-CoV-2 Omicron BA.2 infection: a case-control study. Emerg Microbes Infect, 11(1), 2304-2314. https://doi.org/10.1080/22221751.2022.2114854 Yen, A. M., Auvinen A Fau - Schleutker, J., Schleutker J Fau - Wu, Y.-Y., Wu Yy Fau - Fann, J. C.-Y., Fann Jc Fau - Tammela, T., Tammela T Fau - Chen, S. L.-S., Chen Sl Fau - Chiu, S. Y.-H., Chiu Sy Fau - Chen, H.-H., & Chen, H. H. Prostate cancer screening using risk stratification based on a multi-state model of genetic variants. (1097-0045 (Electronic)). ZEGER, S. L. (1988). A regression model for time series of counts. Biometrika, 75(4), 621-629. https://doi.org/10.1093/biomet/75.4.621 Zhang, Z., Ling, X., Liu, L., Xi, M., Zhang, G., & Dai, J. (2022). Natural History of Anal Papillomavirus Infection in HIV-Negative Men Who Have Sex With Men Based on a Markov Model: A 5-Year Prospective Cohort Study. Front Public Health, 10, 891991. https://doi.org/10.3389/fpubh.2022.891991 | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83302 | - |
dc.description.abstract | 研究背景
新興傳染病與再現傳染病之監測,首重於在族群層次,評估疾病大規模傳播特性與流行爆發風險。另外感染病毒於個人層次所造成之動態變化,由於病毒特性與人體免疫機轉,亦對於受感染宿主在不同病毒量之下所造成的疾病傳播風險、宿主在感染後由於不同病毒量負擔所可能造成之疾病傳播與感染擴散,以及感染後產生之臨床照護需求息息相關。有效精準防治策略的發展,有賴於對前述在個人與族群層次之疾病傳播特性,以及各種防治策略,包含公衛防疫措施、族群疫苗施打策略,以及抗病毒藥物治療策略等,對於預防感染與疾病進展可達到效益之評估結果而規劃。然而傳統對於傳染病之監測評估,多著重於族群感染層次運用,如時間序列方法與傳染病傳播隔間模型,配合傳播基礎再生數指標,評估新興及再現傳染病對族群可能造成之流行風險。然而考慮前述傳染病之病毒與宿主交互影響所造成之動態變化,以及感染後所造成之疾病進展風險,傳統以族群為層次之傳染病評估架構有其限制。 研究目標 (1)在疾病傳播動態模型下拓展貝氏網絡DAG方法,建立新興與再現傳染病爆發流行之風險族群監測實證基礎。 (2)建構疾病進展多階段隨機過程貝氏網絡DAG模型運用於評估病毒以及宿主特性在個人層次對於感染後病毒量變化之影響監測。 (3)運用貝氏網絡DAG方法結合廣義線性階層模式建立大流行與區域流行評估架構,並且拓展為含括傳染病疾病進展狀態之介入效益評估方法。 材料與方法 本研究運用新冠肺炎以及猴痘全球流行為新興傳染病與再現傳染病實證資料,結合所發展之貝氏網絡模型進行實證評估。對於新冠肺炎全球流行,本研究摘取包含全球各國通報個案、住院、重症,以及死亡等公開資料。對於台灣之流行資料,本研究摘取疫苗施打以及通報個案與其發展為中症、重症,以及死亡及年齡公開資料。對於病毒量對於疾病進展之影響,本研究運用台灣某縣市之疫情調查與檢測資料。猴痘全球流行本研究運用全球各國於2022全球各國通報個案進行評估。 本研究運用貝氏網絡結合有向無環圖模型在考量傳染病傳播特性下,分別運用傳染病動態傳播模型、多階段馬可夫隨機過程,以及廣義線性迴歸模型在前述實證資料支持下,以貝氏馬可夫蒙地卡羅演算法產生前述模型中之參數事後分佈,並據以對新興傳染病以及再現傳染病之疾病傳播特性、不同防治策略,包含疫苗施打、公衛防疫措施,以及抗病毒藥物可達到之保護效果,以及感染後病毒量動態階段變化進行評估。 結果 運用貝氏網絡傳染病動態模型,結合鑽石公主郵輪侷限空間情境之新冠肺炎評估結果,顯示新冠肺炎之基礎再生數(R0)為5.70 (95% CI: 4.23-7.79)。施行鑽石公主郵輪之防疫措施對於疾病傳播可達到37% (95% CI: 33-40%)之效益。在此侷限空間情境下,若可提前5日施行防疫措施則可達到53% (95% CI: 44-62%)之疾病傳播防治效益,使新冠肺炎個案數由761人減少為403人。運用此貝氏網絡動態模型於再現傳染病猴痘2022年全球流行顯示,全球之疾病傳播再生數由初期之1.001 (95% CI: 0.986-1.150)增加至1.459 (95% CI: 1.370-1.507),隨著介入措施包含公衛防疫措施以及天花疫苗施打達到31% (95% CI: 27.0-33.6%)之介入效益,此再現傳染病之再生數下降至1.027 (95% CI: 1.026-1.026),傳播逐漸受到控制。本研究亦對各國之猴痘傳播流行再生數變化以及對應之疾病傳播防治策略進行評估。 運用貝氏網絡多階段隨機過程,本研究評估Alpha與Omicron變異株之病毒量,對於新冠肺炎感染個案由無症狀進展為臨床狀態、持續無症狀個案之比例,以及進展發生臨床症狀之時間中位數。評估結果顯示於Alpha時期多數感染者由症狀前期個案期進展成為症狀之路徑,僅有少數依循持續無症狀路徑(發生率24.9,95% CI: 15.6-35.1)。而Omicron時期依循無症狀路徑者則增加達271.4 (95% CI: 240.4-303.7)。Alpha與Omicron變異株感染發生臨床症狀之中位數時間分別為4.07天(95% CI: 3.33-4.84)以及1.22天(95% CI: 1.12-1.33)。在考慮宿主特性與接觸模式後,病毒量對於受到Alpha變異株感染個案是否產生臨床症狀以及其病程進展皆具有顯著影響,然而對於Omicron感染個案病毒量高低所造成的影響則相對減低。病毒量對於Alpha與Omicron感染後之疾病進展模式亦有所差異。病毒量於Alpha感染個案之進展呈現劑量效應,但對於Omicron感染個案病毒量影響較小,多數個案在1-2天之內由症狀前期個案期進展之發生臨床症狀。疫苗施打與否對於疾病進展亦呈現不同的模式。完整施打疫苗者受病毒量影響較小,但未施打疫苗者其感染病毒量對於其臨床動態進展則會有所影響。 運用貝氏網絡階層廣義線性迴歸DAG模型,分析新冠肺炎實證資料結果顯示,新冠肺炎之流行無法達到地方流行之平衡態,而將以四週遞延之模式受到變異株病毒傳播特性為主要影響下爆發流行。就不同防疫措施之評估結果,疫苗施打對於症狀感染可達到55% (95% CI: 38-67%)之保護效果,其中mRNA疫苗施打可提供較佳之保護效果(63%,95% CI: 40-77%),此保護效果在年輕族群(未滿50歲)優於年長族群(大於50歲)。疫苗對於死亡、重症,以及中症之保護方面,完成追加劑施打分別可提供73.9% (95% CI: 72.7-75.1%)、73.9% (95% CI: 73.0-74.8%),以及72.5 (95% CI: 71.8-73.2%)之效益,完成基礎劑之保護效益則分別為52.7% (95% CI: 49.6-55.6%)、54.5 (95% CI: 50.0-54.5%),以及51.9% (95% CI: 50.3-53.6%)。公衛防疫措施則可提供約12%-20%之保護效益。口服抗病毒藥物約可降低中症風險約12%。 結論 本研究運用隨機過程與DAG模型建立創新方法,結合多階段馬可夫模式運用以全球以及臺灣地區實證資料,發展新興與再現傳染病由族群至個人之監測架構與介入效益評估貝氏DAG階層模型。所提出多層次之傳染病監測方法有助於傳染病傳播風險完整評估以及發展個人化防治策略。 | zh_TW |
dc.description.abstract | Background
The evaluation for the risk of outbreak and transmission at large scale has been the first and foremost goal in the surveillance of emerging and reemerging infectious diseases at population level. In addition to this purpose, the risk of disease evolution in terms of viral shedding and clinical severity resulting from the characteristics of pathogen, viral load, and host immunity has also been an important aspect of surveillance at individual level. The development of effective and individual-tailored containment strategies including non-pharmaceutical interventions (NPIs), mass vaccination, and antiviral therapies is highly dependent on the empirical evidence taking into account these factors with multilevel characteristics. Conventional approaches for surveillance of infection disease including the use of time-series models and basic reproductive number (R0) derived from compartment models, both focusing on the risk at population level. However, the characteristics of multilevel and multiple outcomes regarding the surveillance of infectious disease render the use of these conventional approaches intractable. Aim The aims of this study are (1)to develop a Bayesian network (BN) analysis with DAG model supported by the compartment model to characterize the dynamics of disease evolution and to assess the risk of disease outbreak at population level; (2)to apply the BN DAG model with stochastic process underpinning for the surveillance of disease evolution associated with viral load and viral characteristics at individual level; (3)to develop a BN hierarchical DAG in conjunction with generalized linear regression model to assess the risk of pandemic and endemic and the effectiveness of containment strategies at different level. Materials and Methods A series of BN DAG models were developed with the support from the information contained in the empirical data of emerging (COVID-19) and reemerging (monkeypox) infectious diseases. Regarding the data on COVID-19 pandemic, global open data with the information on country and region, reported cases, hospitalized cases, cases admitted for intensive care were collected from open data. Taiwan outbreak data on the clinical severities including moderate, severe, and death, age, and vaccination history were abstracted from surveillance report. The empirical data on contact tracing and viral load measured by Ct level collected from a county in Taiwan were used for assessing disease evolution at individual level. The open data on global monkeypox outbreak surveillance in 2022 were used for assessing the risk of outbreak of re-emerging infectious disease. A series of BN DAG model in conjunction with dynamic compartment model, multistate Markov process, and generalized linear regression model taking into account disease characteristics were developed. By using the Bayesian Markov Chain Monte Carlo (MCMC) algorithm, information from the empirical data mentioned above were used for the derivation of posterior distributions of the parameters of the proposed BN DAG models. Results For the risk of COVID-19 outbreak in the confined space of cruise ship, the R¬0 derived from BN DAG susceptible, exposed, infected, and recovered (SEIR) were estimated as 5.70 (95% CI: 4.23-7.79). The effectiveness of containment measures implemented on board was estimated as 37% (95% CI: 33-40%). On the basis of the posterior distributions of the BN DAG SEIR model, the implementation of containment measured 5-days earlier can enhance the effectiveness to 53% (95% CI: 44-62%) and reduce the COVID-19 cases from 761 to 403 in such a confined space of cruise ship. Regarding the re-emerging disease of monkeypox, the R0 increased from 1.001 (95% CI: 0.986-1.150) in the early stage to 1.459 (95% CI: 1.370-1.507) in the latter stage, suggesting the risk of transmission at large scale and outbreak. The effectiveness of containment strategies including NPIs and vaccination was estimated as 31% (95% CI: 27.0-33.6%), which bring the R0 down to 1.027 (95% CI: 1.026-1.026), indicating the containment of outbreak. By using the BN DAG with stochastic process, a four-state disease progression model was constructed for the surveillance of COVID-19 evolution at individual level regarding Alpha and Omicron variants of concern (VOC) infection. The results demonstrated the difference between subjects infected by these two VOCs. While the small fraction of subjects infected by Alpha VOC turned into asymptomatic (incidence: 24.9, 95% CI: 15.6-35.1), a high incidence for asymptomatic infection (271.4, 95% CI: 240.4-303.7) was estimated for Omicron infection. The median time from pre-symptomatic to symptom phase for Alpha and Omicron VOC infection was estimated as 4.07 (95% CI: 3.33-4.84) and 1.22(95% CI: 1.12-1.33) days, a significant short period for Omicron infection. After adjusting for host characteristics and contact pattern, there was different roles of viral load for clinical evolution for Alpha and Omicron infection. A signification impact of viral load on the clinical progression for subject infection by Alpha VOC with a dose-response pattern was observed. For those infected by Omicron VOC, although viral load remains a significant effect on clinical evolution, especially for the unvaccinated population, a lower extent was observed compared with those infected by Alpha VOC The results derived by applying GLM with BN DAG show the outbreak of COVID-19 will reach equilibrium in the long run. Recurrent outbreaks affected mainly by the characteristics of dominant VOC are expected with the optimal lag function spanned over four weeks. By using the BN DAG with GLM, the effectiveness of vaccination against symptomatic infection of Omicron was estimated as 55% (95% CI: 38-67%), with a higher protective effectiveness conveyed by mRNA -based vaccine (63%,95% CI: 40-77%). Regarding the effectiveness of vaccination against death, severe, and moderate disease, the estimated results were 73.9% (95% CI: 72.7-75.1%)、73.9% (95% CI: 73.0-74.8%), and 72.5 (95% CI: 71.8-73.2%) for booster vaccination. The corresponding figures for primary series were estimated as 52.7% (95% CI: 49.6-55.6%)、54.5 (95% CI: 50.0-54.5%), and 51.9% (95% CI: 50.3-53.6%). The protective effectiveness resulting from NPIs and antiviral therapy were estimated as 12-20% and 12%, respectively. Conclusion By using Bayesian DAG approach with stochastic process underpinning, a series of novel applications to surveillance of infectious disease were developed. The proposed Bayesian DAG framework considering the effect at population level and individual level can facilitate the surveillance of emergence and re-emergence infectious disease in terms of outbreak risk assessment and the development of individual-tailored containment strategies. | en |
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dc.description.tableofcontents | 目錄
口試委員會審定書 I 誌謝 II 中文摘要 III Abstract VI 圖目錄 XII 表目錄 XIII 第一章 研究背景 1 1.1 傳統傳染流行病學模式限制 1 1.2 新冠肺炎及猴痘疫情 1 1.3 研究目的 4 第二章 文獻回顧 5 2.1 傳染病疾病特性 5 2.1.2 猴痘疾病傳播特性 5 2.1.1 新冠肺炎疾病傳播特性 5 2.2 新冠肺炎疫苗保護效益評估 6 2.2.1 新冠肺炎疫苗效益評估研究 6 2.2.2 新冠肺炎疫苗效益研究設計 7 2.2.3 時間對疫苗效益之影響 9 2.2.4 其他的介入措施影響效益評估 10 2.3 傳染病傳播與防治評估方法 10 2.3.1 廣義線性模型 10 2.3.2 廣義自我回歸模型 11 2.3.3 兩階段馬可夫過程與傳染病流行平衡態 13 2.3.4 多階段隨機過程用於疾病狀態預測 17 2.3.5 貝氏有向無環圖(Directed Acyclic Graphic,DAG)模型與多階段馬可夫過程 20 第三章 研究方法 21 3.1 傳染傳播隨機過程模式貝氏DAG模型 21 3.1.1 貝氏微分方程SEIR模型 21 3.1.2 傳染傳播隨機過程貝氏DAG模型與馬可夫鏈蒙地卡羅演算法 23 3.2 傳染病疾病進展隨機過程貝氏DAG模型 29 3.2.1 疾病進展多階段隨機過程貝氏網絡(BN)模型建構 29 3.2.2 傳染病多階段DAG模型馬可夫蒙地卡羅演算法 34 3.3 平穩過程之時間序列資料(Stationary Process of Time-series Data) 36 3.3.1 自我回歸過程Autoregressive process 36 3.3.2 移動平均過程 Moving Average process (MA) 39 3.3.3 廣義自我回歸模型Generalized Autoregressive Model 40 3.4 貝氏網絡(BN)架構模型 40 3.4.1 新冠肺炎臨床疾病狀態貝氏網絡架構建立 40 3.4.2 全球新冠肺炎傳播模型 42 3.4.3 台灣新冠感染保護效益評估模型 44 3.4.4 階層貝氏廣義線性DAG模型馬可夫蒙地卡羅學習演算法 45 第四章 新興與再現傳染病傳播與進展資料 51 4.1 猴痘國際傳播資料 51 4.2 新冠肺炎疾病傳播資料 51 4.2.1 國際新冠疫情評估資料 51 4.2.2 台灣新冠肺炎疫苗、NPIs與抗病毒藥保護效益實證資料 51 4.2.3 新冠肺炎於局限空間傳播實證資料 52 4.2.4 多階段新冠肺炎進展實證資料 53 第五章 結果 53 5.1 猴痘傳播評估 53 5.2 新冠肺炎基礎傳播再生數 84 5.3 多階段新冠感個人化動態病毒量曲線 90 5.3.1 不同變異株的爆發個案情形 90 5.3.2 病毒載量相依與共變數特定的狀態轉換 90 5.3.3 病毒量和相關共變數的多階段感染動態過程 91 5.4 全球新冠疫情貝氏網絡傳播評估 104 5.5 新冠疫苗對感染保護台灣效益評估 109 5.6 疫苗對新冠肺炎臨床嚴重程度保護台灣效益評估 111 第六章 討論 116 6.1 猴痘病毒傳播 116 6.2 COVID-19病毒郵輪傳播 116 6.3 多階段馬可夫感染過程 118 6.3.1 以實證資料為基礎的多階段馬可夫感染過程 118 6.3.2 個人化多階段感染過程 118 6.4 資料模擬自我相關函數對於傳染病觀察資料之影響 119 6.5. 採用貝氏分層模型評估疫苗有效性與NPIs與抗病毒藥物影響 120 6.5.1 大規模施打疫苗對於預防Omicron感染重症與死亡之有效性 121 6.5.2 新冠疫苗引發的T-cell 調節免疫 121 6.6 結論 122 參考文獻 126 | - |
dc.language.iso | zh_TW | - |
dc.title | 貝氏非循環隨機模型於傳染病監測應用 | zh_TW |
dc.title | Stochastic Processes Applications to Surveillance of Infectious Disease with Bayesian Directed Acyclic Graphic (DAG) Approach | en |
dc.title.alternative | Stochastic Processes Applications to Surveillance of Infectious Disease with Bayesian Directed Acyclic Graphic (DAG) Approach | - |
dc.type | Thesis | - |
dc.date.schoolyear | 111-1 | - |
dc.description.degree | 博士 | - |
dc.contributor.oralexamcommittee | 陳祈玲;陳立昇;潘信良;邱月暇;莊紹源;盧子彬 | zh_TW |
dc.contributor.oralexamcommittee | Chi-Ling Chen;Li-Sheng Chen;Shin-Liang Pan;Yueh-Hsia Chiu;Shao-Yuan Chuang;Tzu-pin Lu | en |
dc.subject.keyword | 隨機過程,貝氏有向無環圖模型,新興與再現傳染病,新冠肺炎,猴痘,病毒量,臨床疾病進展, | zh_TW |
dc.subject.keyword | stochastic process,Bayesian Directed Acyclic Graphs(DAGs),emerging and reemerging infectious diseases,COVID-19,monkeypox,viral load,clinical disease progression, | en |
dc.relation.page | 145 | - |
dc.identifier.doi | 10.6342/NTU202300419 | - |
dc.rights.note | 同意授權(全球公開) | - |
dc.date.accepted | 2023-02-16 | - |
dc.contributor.author-college | 公共衛生學院 | - |
dc.contributor.author-dept | 流行病學與預防醫學研究所 | - |
顯示於系所單位: | 流行病學與預防醫學研究所 |
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