Wardell, RebeccaPrem, K.Cowling, BenjaminCook, Alex R2021-10-042021-10-040950-2688http://hdl.handle.net/1885/250436Computer models can be useful in planning interventions against novel strains of influenza. However such models sometimes make unsubstantiated assumptions about the relative infectivity of asymptomatic and symptomatic cases, or conversely assume there is no impact at all. Using household-level data from known-index studies of virologically confirmed influenza A infection, the relationship between an individual's infectiousness and their symptoms was quantified using a discrete-generation transmission model and Bayesian Markov chain Monte Carlo methods. It was found that the presence of particular respiratory symptoms in an index case does not influence transmission probabilities, with the exception of child-to-child transmission where the donor has phlegm or a phlegmy coughR.W. was supported by a New Colombo Plan scholarship from the Australian Department of Foreign Affairs and Trade (DFAT). K.P. and A.R.C. were supported by funding from the Ministry of Education, Ministry of Health, Ministry of Defence, and the National University Health System, all Singapore (grant nos. CDPHRG/ 0009/2014, NUHSR0/2013/142IH7N9104, PROJECT MODUS 9014100379). B.J.C. was supported by the National Institute of General Medical Sciences (grant U54 GM088558), a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. T11-705/14N), and a commissioned grant from the Health and Medical Research Fund from the Government of the Hong Kong Special Administrative Region. The original household trial in Hong Kong was supported by the United States Centers for Disease Control and Prevention (grant no. 1 U01 CI000439).application/pdfen-AU© Cambridge University Press 2016https://creativecommons.org/licenses/by/4.0/Bayesian statisticsinfluenza Amodellingsymptomstransmission201710.1017/S09502688160027402020-11-23Creative Commons License (Attribution 4.0 International)