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Control the Infection and Spread

 

 

 

 

What are the Underlying Transmission Patterns of COVID-19 Outbreak? An Age-Specific Contact Characterisation

Prof  Jiming Liu

Prof Yang Liu

Department of Computer Science
Faculty of Science

 

 

 

This work has uncovered the ins and outs of COVID-19 outbreaks in China, which could have practical implications to the strategy planning in other countries.

Professor Yang Liu

Department of Computer Science

COVID-19 has spread to 6 continents. Now is opportune to gain a deeper understanding of what may have happened; the findings could potentially inform mitigation strategies and work-resumption schedules in other countries. This work addresses how COVID-19 spreads among different populations and the corresponding impacts on disease control, e.g., with/without interventions. We approach this question by examining age-specific social contact-based transmissions. We focus on 6 representative cities: Wuhan, plus Beijing, Tianjin, Hangzhou, Suzhou, and Shenzhen, from three major economic zones in China. The results show that the social contact-based analysis can readily explain the underlying disease transmission patterns and associated potential risks. This work has uncovered the ins and outs of COVID-19 outbreaks in China, which could have practical implications to the strategy planning in other countries.

 

 

 

 

 

Are the Transmission of COVID-19 Affected by Climate Conditions?

Dr. Meng Gao

Department of Geography
Faculty of Social Sciences

 

 

 

We aimed to use daily reported death to back-calculate number of infections across the globe, and to examine if climate conditions could determine the transmission of COVID-19.

Dr Meng Gao

Department of Geography

An environmental study of SARS-CoV-1 found reduced survival of the virus at higher temperature and humidity. Given the similarity between SARS-CoV-1 and SARS-CoV-2, the spread of COVID-19 might also be influenced by climate conditions. Due to the high fraction of infections not detected by health system and differences in testing policies, different proportions of infections were detected across time and countries. As a result, daily number of confirmed cases is not able to represent the realistic situation of infection. Reported deaths are likely to be more reliable than number of infection and can be used to back-calculate infection. In this study, we aimed to use daily reported death to back-calculate number of infections across the globe, and to examine if climate conditions could determine the transmission of COVID-19. Based on an infection-to-onset distribution and an onset-to-death distribution, we obtained daily number of infection, basic reproduction number and effective reproduction number varying with time. We used the basic reproduction number to represent the transmission speed and considered only countries with relatively good healthcare. The results indicate that transmission is relatively weaker in countries with high temperature (tropical regions) and colder climate (Nordic countries). In addition, transmission is stronger with higher RH. These results provide important implications on the regions of high transmission.

 

 

 

 

Battling the Invisible: Can Epidemic Spreading Model Help?

Prof Leihan Tang

Department of Physics
Faculty of  Science

 

We have developed a variant of the stochastic SEIR model with parameters calibrated against COVID-19 disease progression and transmission characteristics.

Professor Leihan Tang

Department of Physics

Globally, one of the biggest challenges to determining the appropriate policy response to the 2019 Coronavirus Disease (COVID-19) has been the uncertainty of pre-symptomatic transmission and the lack of tools to quantify and predict its impact. A number of studies suggested that this group of viral carriers could contribute significantly to the spread of the SARS-CoV-2 virus. In East Asian countries, mask-wearing, contact-tracing and testing combined have been effective in containing early outbreaks, while western countries taking a lax approach have witnessed prolonged exponential growth of the pandemic for more than two months [1]. Singapore, the city-state that adopted a mixed policy, withheld the first wave of imported cases but failed to prevent local outbreaks during the second wave that was 20 time stronger in the number of imported cases.

 

We have developed a variant of the stochastic SEIR model with parameters calibrated against COVID-19 disease progression and transmission characteristics[2]. Due to its linear nature, the model, when applied to a large population, affords analytical solutions. The efficacy of various prevention measures can be evaluated quantitatively. The seeding and initial growth of outbreaks, on the other hand, are dominated by chance events that require a different approach. Large social gatherings, for example, could set off an outbreak in an otherwise subcritical community. Our ongoing work quantifies risks associated with rare-event dominated transmission through numerical explorations of a discrete-time stochastic model and investigate preventative measures that need to be in place as economic and social activities resume.

 

  1. Jeremy Howard et al., “Face Masks Against COVID-19: An Evidence Review”, submitted to PNAS (https://www.preprints.org/manuscript/202004.0203/v2).
  2. Liang Tian, Xuefei Li, Fei Qi, Qian-Yuan Tang, Viola Tang, Jiang Liu, Zhiyuan Li, Xingye Cheng, Xuanxuan Li, Yingchen Shi, Haiguang Liu, Lei-Han Tang, “Calibrated Intervention and Containment of the COVID-19 Pandemic”, under review at Nature Communications (https://arxiv.org/abs/2003.07353 ).

 

BU-Trace: Bluetooth-Enabled Anonymous Contact Tracing

In this project, we leverage the short-range Bluetooth technology to develop an automated mobile contact tracing system, named BU-Trace.

Professor Jiangliang Xu

Department of Computer Science

Contact tracing, which aims to identify the people who have been in contact with an infected person, has been shown to be an effective measure to control the spread of COVID-19. Identified close contacts can be provided with early quarantine, diagnosis, and treatment so as to break the disease's transmission chain. However, the traditional manual contact tracing approach is inefficient since it is subject to a person’s ability to recall everyone they have had close contact with over the past two weeks.

 

In this project, we leverage the short-range Bluetooth technology to develop an automated mobile contact tracing system, named BU-Trace. The basic idea is to enable smartphones to exchange an anonymized identifier via Bluetooth connection when they are in close proximity. When a person is diagnosed positive for COVID-19, other users who have interacted with the patient's smartphone will be alerted for further action. By embracing a decentralized design and implementing enhanced security controls, BU-Trace preserves user privacy, ensures system integrity, and is highly scalable.  

 

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