Estimation of local time-varying reproduction numbers in noisy surveillance data.

A valuable metric in understanding local infectious disease dynamics is the local time-varying reproduction number, i.e. the expected number of secondary local cases caused by each infected individual. Accurate estimation of this quantity requires distinguishing cases arising from local transmission from those imported from elsewhere. Realistically, we can expect identification of cases as local or imported to be imperfect. We study the propagation of such errors in estimation of the local time-varying reproduction number. In addition, we propose a Bayesian framework for estimation of the true local time-varying reproduction number when identification errors exist. And we illustrate the practical performance of our estimator through simulation studies and with outbreaks of COVID-19 in Hong Kong and Victoria, Australia. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Estimation of local time-varying reproduction numbers in noisy surveillance data.

A valuable metric in understanding local infectious disease dynamics is the local time-varying reproduction number, i.e. the expected number of secondary local cases caused by each infected individual. Accurate estimation of this quantity requires distinguishing cases arising from local transmission from those imported from elsewhere. Realistically, we can expect identification of cases as local or imported to be imperfect. We study the propagation of such errors in estimation of the local time-varying reproduction number. In addition, we propose a Bayesian framework for estimation of the true local time-varying reproduction number when identification errors exist. And we illustrate the practical performance of our estimator through simulation studies and with outbreaks of COVID-19 in Hong Kong and Victoria, Australia.

Prediction of genome-wide effects of single nucleotide variants on transcription factor binding.

Single nucleotide variants (SNVs) located in transcriptional regulatory regions can result in gene expression changes that lead to adaptive or detrimental phenotypic outcomes. Here, we predict gain or loss of binding sites for 741 transcription factors (TFs) across the human genome. We calculated 'gainability' and 'disruptability' scores for each TF that represent the likelihood of binding sites being created or disrupted, respectively. We found that functional cis-eQTL SNVs are more likely to alter TF binding sites than rare SNVs in the human population. In addition, we show that cancer somatic mutations have different effects on TF binding sites from different TF families on a cancer-type basis. Finally, we discuss the relationship between these results and cancer mutational signatures. Altogether, we provide a blueprint to study the impact of SNVs derived from genetic variation or disease association on TF binding to gene regulatory regions.

Viscoelastic properties and dynamics of porcine gastric mucin.

Gastric mucin is a glycoprotein known to undergo a pH-dependent sol-gel transition that is crucial to the protective function of the gastric mucus layer in mammalian stomachs. We present microscope-based dynamic light scattering data on porcine gastric mucin at pH 6 (solution) and pH 2 (gel) with and without the presence of tracer particles. The data provide a measurement of the microscale viscosity and the shear elastic modulus as well as an estimate of the mesh size of the gel formed at pH 2. We observe that the microscale viscosity in the gel is about 100-fold lower than its macroscopic viscosity, suggesting that large pores open up in the gel reducing frictional effects. The data presented here help to characterize physiologically relevant viscoelastic properties of an important biological macromolecule and may also serve to shed light on diffusive motion of small particles in the complex heterogeneous environment of a polymer gel network.