Modelling the Mobility Changes Caused by Perceived Risk and Policy Efficiency: A Case Study in Leeds (preprint)

In many countries, governments have implemented non-pharmaceutical techniques to limit COVID-19 transmission. Restricting human mobility is one of the most common interventions, including lockdown, travel restrictions, working from home, etc. However, due to the strong transmission ability of the virus variants, further rounds of interventions, including a strict lockdown, are not considered as effective as expected. The paper aims to understand how the lockdown policy and pandemics changed human mobility in the real scenario. Here we focus on understanding the mobility changes caused by compliance with restrictions and risk perceptions, using the mobility index from the Google report during three strict lockdown periods in Leeds, the largest city in the county of West Yorkshire, England from March 2020 to March 2021. The research proposed the time-varying z-scores and Principal Component Analysis (PCA) to simulate how local people dynamically process and perceive health risk based on multi-dimensional daily COVID-19 reports first. Further modelling highlights exponentially increasing policy non-compliance through the duration of lockdown, probably attributable to factors such as mental anxiety and economic pressures. Finally, the proposed nonlinear regression model examines the mobility changes caused by the population's dynamic risk perceptions and lockdown duration. The case study at Leeds fits data well and shows that the third lockdown policy took effect much slower than the first. At the same time, the negative impact of the epidemic on population mobility decayed 40% in the third lockdown period in contrast with the first lockdown. The risk perception estimation methods could reflect that the local population became increasingly accustomed to the COVID-19 situation, and local people rationally evaluated the risks of COVID in the third lockdown period. The results prove that simulated risk perceptions and policy decay could explain urban mobility behaviour during the mobility well during lockdown periods, which could be a reference for future decision-making processes.

Computational analysis suggests putative intermediate animal hosts of the SARS-CoV-2 (preprint)

The recent emerged SARS-CoV-2 may first transmit to intermediate animal host from bats before the spread to humans. The receptor recognition of ACE2 protein by SARS-CoVs or bat-originated coronaviruses is one of the most important determinant factors for the cross-species transmission and human-to-human transmission. To explore the hypothesis of possible intermediate animal host, we employed molecular dynamics simulation and free energy calculation to examine the binding of bat coronavirus with ACE2 proteins of 47 representing animal species collected from public databases. Our results suggest that intermediate animal host may exist for the zoonotic transmission of SARS-CoV-2. Furthermore, we found that tree shrew and ferret may be two putative intermediate hosts for the zoonotic spread of SARS-CoV-2. Collectively, the continuous surveillance of pneumonia in human and suspicious animal hosts are crucial to control the zoonotic transmission events caused by SARS-CoV-2.