{"id":5779,"date":"2023-06-18T12:53:28","date_gmt":"2023-06-18T06:53:28","guid":{"rendered":"https:\/\/www.isrt.ac.bd\/?post_type=tribe_events&p=5779"},"modified":"2023-06-18T12:53:28","modified_gmt":"2023-06-18T06:53:28","slug":"applied-statistics-seminar-on-pairwise-accelerated-failure-time-models-for-infectious-disease-transmission-within-and-between-households","status":"publish","type":"tribe_events","link":"https:\/\/isrt.ac.bd\/event\/applied-statistics-seminar-on-pairwise-accelerated-failure-time-models-for-infectious-disease-transmission-within-and-between-households\/","title":{"rendered":"Applied Statistics Seminar on “Pairwise Accelerated Failure Time Models for Infectious Disease Transmission Within and Between Households”"},"content":{"rendered":"
Abstract<\/b>:\u00a0Pairwise survival analysis handles dependent happenings in infectious disease transmission data by analyzing failure times in ordered pairs of individuals. The contact interval in the pair ij is the time from the onset of infectiousness in i to infectious contact from i to j, where an infectious contact is sufficient to infect j if he or she is susceptible. The contact interval distribution determines transmission probabilities and the infectiousness profile of infected individuals. Many important questions in infectious disease epidemiology involve the effects of covariates (e.g., age or vaccination status) on transmission. Here, we generalize earlier pairwise methods in two ways: First, we introduce an accelerated failure time model that allows the contact interval rate parameter to depend on infectiousness covariates for i, susceptibility covariates for j, and pairwise covariates. Second, we show how internal infections (caused by individuals under observation) and external infections (caused environmental or community sources) can be handled simultaneously. In simulations, we show that these methods produce valid point and interval estimates and that accounting for external infections is critical to consistent estimation. Finally, we use these methods to analyze household surveillance data from Los Angeles County during the 2009 influenza A(H1N1) pandemic.<\/div>\n
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Speaker<\/b>:\u00a0Yushuf Sharker, Ph.D., Takeda Pharmaceuticals, USA<\/div>\n
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Abstract:\u00a0Pairwise survival analysis handles dependent happenings in infectious disease transmission data by analyzing failure times in ordered pairs of individuals. 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