EVENTS | VIEW CALENDAR
Tracking the transmission patterns of COVID-19
HONG KONG—The Lancet’s EClinicalMedicine journal recently published an article with original research by a team of scientists from the Joint Research Laboratory for Intelligent Disease Surveillance and Control (IDSC Lab) in Hong Kong. This research uncovers the underlying patterns of COVID-19 transmission by investigating social contacts among different age-groups and their associated disease transmission risks.
“Adopting the data-driven, computational methodology to understand the transmission patterns of COVID-19 provides an objective and scientifically sound way to assess the transmission risks and the effectiveness of intervention strategies,” commented Professor Xiao-Nong Zhou, an epidemiologist involved in this study. “This work has offered important and timely tools to other countries/regions for their intervention planning and operational responses.”
The researchers hope that their research will enable governments or policy makers to have a proactive grip on the development of COVID-19, throughout its different phases. Policy makers will be able to answer questions, like when will it be safe to bring work and life back to normal?
“The results show that the social contact-based analysis can readily explain the underlying disease transmission patterns as well as the associated risks (including both confirmed and unconfirmed cases),” states the article. “In Wuhan, the age-groups involving relatively intensive contacts in households and public/communities are dispersedly distributed. This can explain why the transmission of COVID-19 in the early stage mainly took place in public places and families in Wuhan.”
“The COVID-19 pandemic like a storm has hit the global economy. When the COVID-19 outbreak is contained, policy makers will need to think about when to safely bring work and life back to normal. This work responds to this pressing need by establishing a scientific ground for systematically planning the resumption of social/business activities near the end of the outbreak,” added Professor Yong Shi, a management scientist who also engaged in this study.
To determine the right timing for resuming work and life, the method developed by this team consists of a retrospective analysis of COVID-19 to gain an in-depth understanding of age-specific contact-based disease transmission. This is followed by a prospective analysis of different work resumption plans to assess the respective economic implications of the plans, and the associated disease transmission risks.
“[T]he surge in the number of new cases reported on Feb. 12-13 in Wuhan can readily be captured using our model, showing its ability in forecasting the potential/unconfirmed cases. We further estimate the disease transmission risks associated with different work resumption plans in these cities after the outbreak,” the article states. “The estimation results are consistent with the actual situations in the cities with relatively lenient control policies, such as Beijing, and those with strict control policies, such as Shenzhen.”
The articles also notes that “the model is not designed to provide a mathematical estimation/prediction that fits exactly to the number of reported cases, but to present an estimation of the risk to the community if certain measures are or are not exercised.”
The key to their method of COVID-19 transmission pattern characterization lies in modeling the interactions among people. The team considered four representative settings of social contacts that may cause the disease spread: households, schools, workplaces and public places. Researchers developed a computational method to measure the contact intensity between different age-groups in those social settings. With an in-depth characterization of social contact-based transmission, it is possible to analyze and explain the ins and outs of the COVID-19 outbreak, including the past and future risks, intervention effectiveness, and corresponding risks of restoring social activities.
“Significantly, our work has touched upon an important problem at a critical moment in time, and a deeper understanding of what may have happened in the outbreak is now overdue,” explained Professor Jiming Liu, a computer scientist who led this study. “Addressing this key question enables us to gain insights into not only retrospective, but more importantly prospective, situations of the outbreak and hence plan a series of measures accordingly for precision intervention.”