Supplementary material from "Evaluating a transfer gradient assumption in a fomite-mediated microbial transmission model using an experimental and Bayesian approach"
Posted on 2020-06-09 - 08:48
Current microbial exposure models assume microbial exchange follows a concentration gradient during hand-to-surface contacts. Our objectives were to evaluate this assumption using transfer efficiency experiments and to evaluate a model's ability to explain concentration changes using Approximate Bayesian Computation (ABC) on these experimental data. Experiments were conducted with two phages (MS2, ΦX174) simultaneously to study bidirectional transfer. Concentrations on the fingertip and surface were quantified before and after fingertip-to-surface contacts. Prior distributions for surface and fingertip swabbing efficiencies and transfer efficiency were used to estimate concentrations on the fingertip and surface post-contact. To inform posterior distributions, Euclidean distances were calculated for predicted detectable concentrations (log10 PFU/cm2) on the fingertip and surface post-contact in comparison to experimental values. To demonstrate posterior distributions' usefulness in calibrated model applications, posterior transfer efficiencies were used to estimate rotavirus infection risks for a fingertip-to-surface and subsequent fingertip-to-mouth contact. Experimental findings supported the transfer gradient assumption. Through ABC, the model explained concentration changes more consistently when concentrations on the fingertip and surface were similar. Future studies evaluating microbial transfer should consider accounting for differing fingertip-to-surface and surface-to-fingertip transfer efficiencies and extend this work for other microbial types.
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Wilson, Amanda M.; King, Marco-Felipe; López-García, Martín; Weir, Mark H.; Sexton, Jonathan D.; Canales, Robert A.; et al. (2020). Supplementary material from "Evaluating a transfer gradient assumption in a fomite-mediated microbial transmission model using an experimental and Bayesian approach". The Royal Society. Collection. https://doi.org/10.6084/m9.figshare.c.5013515.v1
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AUTHORS (10)
AW
Amanda M. Wilson
MK
Marco-Felipe King
ML
Martín López-García
MW
Mark H. Weir
JS
Jonathan D. Sexton
RC
Robert A. Canales
GK
Georgiana E. Kostov
TJ
Timothy R. Julian
CN
Catherine J. Noakes
KR
Kelly A. Reynolds