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With the start of the Sars-CoV-2 outbreak, which is the strain of virus that causes the COVID-19 respiratory illness, and after it became clear that it was going to turn into a pandemic, Dr. Ghina Mumtaz, assistant research professor of infectious disease epidemiology at the Faculty of Health Sciences (FHS), embarked on an endeavor to research the infection using various quantitative methods, including mathematical modelling.
As the pandemic was unfolding, several questions were raised: What characterizes the spread of this emerging infection? Who are the populations who are most at risk of getting infected and seriously ill? Is Lebanon and the region going to face an epidemic of the same scale as what was observed in China and Europe? Will our healthcare system have the capacity needed to deal with the burden of infections? What interventions will need to be put in place to curb spread? Will a vaccine offer a solution to the pandemic?
“Many of these questions can be addressed and predicted by the use of mathematical models, through simulating the infection's transmission dynamics," Mumtaz explained.
Mumtaz joined forces with her long-standing collaborators at Weill Cornell Medicine in Qatar, to work on this research and make local applications. “The first step was to build a sophisticated mathematical model for SARS-CoV-2 that could be used to answer some of these questions in a series of research studies," she said.
The team first applied this model to the epidemic in China and found that there are large age differences in the biological susceptibility to SARS-CoV-2 infection.1 Susceptibility was limited among children, intermediate among young adults and those mid-age, but high among those over 50 years of age. If this observation held true elsewhere, this suggested that the average age of populations may explain why the epidemic has spread differently in different parts of the world.
To illustrate this hypothesis, the team simulated the natural course of the epidemic in 159 countries worldwide.2 “We predicted that countries with sizable adult/elderly populations and smaller children populations could experience large and rapid epidemics in absence of interventions," Mumtaz explained. “Meanwhile, countries with predominantly younger age cohorts, such as most of the Arab world and African continent, would experience smaller and slower epidemics, and a smaller disease burden."
With evidence mounting that even most seriously affected settings may still be far from reaching herd immunity naturally, it has become almost certain that a vaccine would offer the most reliable solution to the pandemic. “With over 110 vaccine candidates at various stages of development and testing, we wanted to provide the scientific evidence that can inform vaccine development, licensure, decision-making, and administration strategies," she added.
Using their mathematical model, the team found that a vaccine that reduces susceptibility to contracting the infection by at least 70% is needed to eliminate the infection.3 However, infection spread can be controlled with a vaccine with lower effectiveness if used in conjunction with moderate social distancing, or if a significant number of people who were infected during this current wave of the virus become immune.
“These findings offer a glimmer of hope that even a moderately effective vaccine could be enough to control the infection, save lives, and resume economic and normal life activities, and at high cost effectiveness," Mumtaz said.
The studies reporting these findings are currently under review but have been released as pre-prints in commitment to the principles set out in the 2016 Statement on Data Sharing in Public Health Emergencies. “Data sharing and rapid dissemination of findings is very important in such circumstances as our understanding of the infection is evolving very rapidly, and research studies need to be revised and up-to-date with most recent developments," she explained.
Application in Lebanon
The mathematical modeling tools developed as part of this collaboration could also be very useful in making predictions for local epidemics. “We have started applying the model to the Lebanon epidemic, however we were faced by many challenges pertaining mainly to the public availability of relevant and reliable data, uncertainty around our denominator in the absence of a census, and uncertainty on the extent of community transmission with still relatively limited testing," Mumtaz said.
“As more data might become available, useful predictions could be made to inform the public health response in the short and long term, especially given the dire economic situation calling for easing restrictions and the repercussions these measures will have on epidemic spread." she added.
About the researcher
Before the COVID-19 pandemic, Dr. Ghina Mumtaz had been studying the epidemiology of HIV and other sexually transmitted infections in the Middle East and North Africa using the same methods.
Her research work has informed policy and programming decisions at key international organizations such as the World Health Organization, UNAIDS, and the World Bank.
Dr. Mumtaz holds a PhD in infectious disease epidemiology from the London School of Hygiene and Tropical Medicine (LSHTM). She is currently working on RECAP, a UK Research and Innovation funded project focusing on improving the response to humanitarian crises and epidemics, in collaboration with the LSHTM.
- Ayoub HH, Chemaitelly H, Mumtaz GR, et al. Characterizing key attributes of the epidemiology of COVID-19 in China: Model-based estimations. Found at: https://www.medrxiv.org/content/10.1101/2020.04.08.20058214v1. LAst accessed April 25, 2020.
- Ayoub HH, Chemaitelly H, Seedat S, Mumtaz GR, Makhoul M, Abu-Raddad LJ. Age could be driving variable SARS-CoV-2 epidemic trajectories worldwide. Found at: https://www.medrxiv.org/content/10.1101/2020.04.13.20059253v1. Last accessed April 25, 2020.
- Makhoul M, Ayoub HH, Chemaitelly H, et al. Epidemiological impact of SARS-CoV-2 vaccination: mathematical modeling analyses. Available:
https://www.medrxiv.org/content/10.1101/2020.04.19.20070805v1. Accessed: 30 April 2020.