Ka Chun Chong1, Benny Chung Ying Zee2, Maggie Haitian Wang3. 1. Division of Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China; Clinical Trials and Biostatistics Laboratory, Shenzhen Research Institute, The Chinese University of Hong Kong, Hong Kong, China. Electronic address: marc@cuhk.edu.hk. 2. Division of Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China; Clinical Trials and Biostatistics Laboratory, Shenzhen Research Institute, The Chinese University of Hong Kong, Hong Kong, China. Electronic address: bzee@cuhk.edu.hk. 3. Division of Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China; Clinical Trials and Biostatistics Laboratory, Shenzhen Research Institute, The Chinese University of Hong Kong, Hong Kong, China. Electronic address: maggiew@cuhk.edu.hk.
Abstract
BACKGROUND: In an influenza pandemic, arrival times of cases are a proxy of the epidemic size and disease transmissibility. Because of intense surveillance of travelers from infected countries, detection is more rapid and complete than on local surveillance. Travel information can provide a more reliable estimation of transmission parameters. METHOD: We developed an Approximate Bayesian Computation algorithm to estimate the basic reproduction number (R0) in addition to the reporting rate and unobserved epidemic start time, utilizing travel, and routine surveillance data in an influenza pandemic. A simulation was conducted to assess the sampling uncertainty. The estimation approach was further applied to the 2009 influenza A/H1N1 pandemic in Mexico as a case study. RESULTS: In the simulations, we showed that the estimation approach was valid and reliable in different simulation settings. We also found estimates of R0 and the reporting rate to be 1.37 (95% Credible Interval [CI]: 1.26-1.42) and 4.9% (95% CI: 0.1%-18%), respectively, in the 2009 influenza pandemic in Mexico, which were robust to variations in the fixed parameters. The estimated R0 was consistent with that in the literature. CONCLUSIONS: This method is useful for officials to obtain reliable estimates of disease transmissibility for strategic planning. We suggest that improvements to the flow of reporting for confirmed cases among patients arriving at different countries are required.
BACKGROUND: In an influenza pandemic, arrival times of cases are a proxy of the epidemic size and disease transmissibility. Because of intense surveillance of travelers from infected countries, detection is more rapid and complete than on local surveillance. Travel information can provide a more reliable estimation of transmission parameters. METHOD: We developed an Approximate Bayesian Computation algorithm to estimate the basic reproduction number (R0) in addition to the reporting rate and unobserved epidemic start time, utilizing travel, and routine surveillance data in an influenza pandemic. A simulation was conducted to assess the sampling uncertainty. The estimation approach was further applied to the 2009 influenza A/H1N1 pandemic in Mexico as a case study. RESULTS: In the simulations, we showed that the estimation approach was valid and reliable in different simulation settings. We also found estimates of R0 and the reporting rate to be 1.37 (95% Credible Interval [CI]: 1.26-1.42) and 4.9% (95% CI: 0.1%-18%), respectively, in the 2009 influenza pandemic in Mexico, which were robust to variations in the fixed parameters. The estimated R0 was consistent with that in the literature. CONCLUSIONS: This method is useful for officials to obtain reliable estimates of disease transmissibility for strategic planning. We suggest that improvements to the flow of reporting for confirmed cases among patients arriving at different countries are required.
Authors: Shi Zhao; Xiujuan Tang; Xue Liang; Marc K C Chong; Jinjun Ran; Salihu S Musa; Guangpu Yang; Peihua Cao; Kai Wang; Benny C Y Zee; Xin Wang; Daihai He; Maggie H Wang Journal: Infect Drug Resist Date: 2020-06-17 Impact factor: 4.003
Authors: Amna Tariq; Juan M Banda; Pavel Skums; Sushma Dahal; Carlos Castillo-Garsow; Baltazar Espinoza; Noel G Brizuela; Roberto A Saenz; Alexander Kirpich; Ruiyan Luo; Anuj Srivastava; Humberto Gutierrez; Nestor Garcia Chan; Ana I Bento; Maria-Eugenia Jimenez-Corona; Gerardo Chowell Journal: PLoS One Date: 2021-07-21 Impact factor: 3.240