ABSTRACT
To find out the circumstances under which airborne transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) would happen, we conducted mechanistic and systematic modelling of two Coronavirus disease 2019 (COVID-19) outbreaks, i.e., Hunan 2-bus outbreak and Luk Chuen House outbreak (the horizontal cluster). Computational fluid dynamics (CFD) simulations, multi-zone airflow modelling, multi-route mechanistic modelling, and dose-response estimation were carried out selectively according to the transmission characteristics in each outbreak. Our results revealed that poorly ventilated bus indoor environments bred the Hunan 2-bus outbreak in which airborne transmission predominates;prevailing easterly background wind and probable door opening behaviour led to the secondary infections across the corridor in Luk Chuen House outbreak. Measures to facilitate sufficient ventilation indoors and positive pressure in the housing building corridor may help minimise infection risk. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.
ABSTRACT
The continued outbreak of the novel coronavirus pneumonia (COVID-19) has had a huge impact on people's lives. In the context of the ongoing epidemic and the limited distribution capacity due to the multi-regional epidemic closure, it has become an urgent reality to minimise the damage caused to people's daily lives under the epidemic and other emergencies, and to implement safe, fair and economical dispatch of emergency supplies for the epidemic area. The problem. Based on this, a mixed integer linear programming model is constructed to maximise the fairness and minimise the transportation cost of emergency material dispatch. © 2023 SPIE.
ABSTRACT
The global tourism industry is struggling to recover from the COVID-19 pandemic. During the COVID-19 pandemic, daily tourism forecasting is more critical than ever before in supporting decisions and planning. Considering the changes in tourist psyche and behaviour caused by COVID-19, this study attempts to investigate whether the statistical modelling methods can work reliably under the new normal when travel restrictions are eased or lifted. To this end, we first compare the predictivity of daily tourism demand data before and during COVID-19, and observe heterogeneous impacts across different geographical scales. Then an improved multivariate & multiscale decomposition-ensemble framework is proposed to forecast daily tourism demand. The empirical study indicates the superiority and practicability of the proposed framework before and during COVID-19. Finally, we call for more research on the comparability of tourism demand forecasting.
ABSTRACT
Based on the TVP-VAR-DY and TVP-VAR-BK models, this article examines the characteristics and mechanisms of systemic risk contagion in the Chinese industries under geopolitical events by selecting data spans from 1 January 2010 to 31 August 2022. First, dynamic analysis of full-sample risk contagion shows that there is a significant climb in total risk during geopolitical events. Then the static analysis of risk contagion in the full sample specifically shows the correlation between risk contagion and industry chain between the financial and real sectors. Besides, the sub-sample analysis illustrates that during geopolitical events such as the Sino-US Trade War, the COVID-19 Pandemic and the Russia-Ukraine Conflict, Chinese industrial stock indexes show short-term risk spillovers from key industries related to geopolitical events, and gradually spread along the industrial chain in the long run compared to the Chinese ‘Stock Market Crash'. Through further mechanistic tests, we find that the irrational behaviour of investors in the market exacerbates short-term risk contagion, while the financial distress of real firms due to financing constraints exacerbates long-term risk contagion. In addition, geopolitical risk, economic uncertainty, and policy uncertainty as macro variables also have an impact on the short-run and long-run risk contagion. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
ABSTRACT
Short-range exposure to expired aerosols or droplet nuclei has been considered as the predominant route for SARS-CoV-2. The observed effect of mask wearing, and social distancing suggests the importance of expired jet in the spread of COVID-19. The well-known steady-state dilution model is no longer valid for the interrupted expiratory jet. We reanalysed the existing interrupted jet data and proposed a simple dilution model of expired jet using the two-stage jet model. The interrupted jet consists of two stages, i.e., the jet-like and puff-like stage. Results show dilution factor grows linearly with the distance at the jet-like stage but increases with the cubic of the increasing distance in the puff-like stage. Dilution factor at any distance for the puff-like stage decreases as the activity intensifies, which is still much larger than that estimated via the steady jet model. The findings can be further applied into the short-range airborne exposure assessment. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.
ABSTRACT
Objective: To estimate the latent period and incubation period of Omicron variant infections and analyze associated factors. Methods: From January 1 to June 30, 2022, 467 infections and 335 symptomatic infections in five local Omicron variant outbreaks in China were selected as the study subjects. The latent period and incubation period were estimated by using log-normal distribution and gamma distribution models, and the associated factors were analyzed by using the accelerated failure time model (AFT). Results: The median (Q1, Q3) age of 467 Omicron infections including 253 males (54.18%) was 26 (20, 39) years old. There were 132 asymptomatic infections (28.27%) and 335 (71.73%) symptomatic infections. The mean latent period of 467 Omicron infections was 2.65 (95%CI: 2.53-2.78) days, and 98% of infections were positive for nucleic acid test within 6.37 (95%CI: 5.86-6.82) days after infection. The mean incubation period of 335 symptomatic infections was 3.40 (95%CI: 3.25-3.57) days, and 97% of them developed clinical symptoms within 6.80 (95%CI: 6.34-7.22) days after infection. The results of the AFT model analysis showed that compared with the group aged 18-49 years old, the latent period [exp(ß)=1.36 (95%CI: 1.16-1.60), P<0.001] and incubation period [exp(ß)=1.24 (95%CI: 1.07-1.45), P=0.006] of infections aged 0-17 years old were prolonged. The latent period [exp(ß)=1.38 (95%CI: 1.17-1.63), P<0.001] and the incubation period [exp(ß)=1.26 (95%CI: 1.06-1.48), P=0.007] of infections aged 50 years old and above were also prolonged. Conclusion: The latent period and incubation period of most Omicron infections are within 7 days, and age may be a influencing factor of the latent period and incubation period.