Ageing impairs the airway epithelium defence response to SARS-CoV-2

Age-dependent differences in the clinical response to SARS-CoV-2 infection is well-documented1-3 however the underlying molecular mechanisms involved are poorly understood. We infected fully differentiated human nasal epithelium cultures derived from healthy children (1-12 years old), young adults (26-34 years old) and older adults (56-62 years old) with SARS-COV-2 to identify age-related cell-intrinsic differences that may influence viral entry, replication and host defence response. We integrated imaging, transcriptomics, proteomics and biochemical assays revealing age-related changes in transcriptional regulation that impact viral replication, effectiveness of host responses and therapeutic drug targets. Viral load was lowest in infected older adult cultures despite the highest expression of SARS-CoV-2 entry and detection factors. We showed this was likely due to lower expression of hijacked host machinery essential for viral replication. Unlike the nasal epithelium of young adults and children, global host response and induction of the interferon signalling was profoundly impaired in older adults, which preferentially expressed proinflammatory cytokines mirroring the "cytokine storm" seen in severe COVID-194,5. In silico screening of our virus-host-drug network identified drug classes with higher efficacy in older adults. Collectively, our data suggests that cellular alterations that occur during ageing impact the ability for the host nasal epithelium to respond to SARS-CoV-2 infection which could guide future therapeutic strategies.

Real-time time-series modelling for prediction of COVID-19 spread and intervention assessment

Substantial amount of data about the COVID-19 pandemic is generated every day. Yet, data streaming, while considerably visualized, is not accompanied with advanced modelling techniques to provide real-time insights. This study introduces a unified platform which integrates visualization capabilities with advanced statistical methods for predicting the virus spread in the short run, using real-time data. The platform is backed up by advanced time series models to capture any possible non-linearity in the data which is enhanced by the capability of measuring the expected impact of preventive interventions such as social distancing and lockdowns. The platform enables lay users, and experts, to examine the data and develop several customized models with different restriction such as models developed for specific time window of the data. Our policy assessment of the case of Australia, shows that social distancing and travel ban restriction significantly affect the reduction of number of cases, as an effective policy.