Dr. Muge Cevik, an expert in Infectious Diseases and a Virology Clinician & Researcher at the University of St Andrews, Scotland (founded in 1413), is one of the eminent scientists performing research on and writing about COVID-19. She writes often, summarizing research findings from her own group and those of others, in easy to understand terms, with links to more advanced information. She even responds to questions that pop up in reader’s minds as they digest the information. She is a real busy-bee.
From her profile at www.st-andrews.ac.uk/… - “Her research interests focus on HIV, tuberculosis, viral hepatitis, emerging infections and tropical infections in LMICs. She provides scientific input to the Chief Medical Officer for Scotland on COVID-19, and provides expert input to the WHO Information Network for Epidemics about COVID-19 related infodemic.”
COVID-19 Transmission Rates
Her twitter thread from last week posed the questions —
- What are the actual probabilities of #COVID19 transmission (infection rate) in various environments and age groups?
- How does the probability of getting infected at home compare to that in a crowded setting?
- How would this info. help influence policies on social distancing and opening up the economy?
To search for answers, she examined a number for studies which have contact tracing and community testing data from various countries, including China, USA, France, Singapore, Taiwan, and Iceland. The contact tracing data allowed analysis of how infected individuals transmitted or not transmitted the virus to others in various environments and age groups. In a series of tweets, she presents nuggets of relevant information from these studies; many of the tweets contain statistical data and links to papers.
Here is a quick summary of her findings (not opinions). Please follow the twitter thread and associated links to dive deeper.
- Close & prolonged contact is required for #COVID19 transmission (this could have been phrased better; I think the author meant “is the main driver”).
- Casual, short interactions are not the main driver of the epidemic.
- The risk is highest in enclosed environments; household, long-term care facilities and public transport. (i.e, higher than outdoor activities).
- High infection rates seen in household, friend & family gatherings (e.g., birthday parties, church, funerals), and transport suggest that closed contacts in congregation is likely the key driver of productive transmission.
- Increased rates of infection seen in enclosed & connected environments is in keeping with high infection rates seen in megacities, deprived areas, and shelters.
- Susceptibility to infection increases with age (highest > 60y). Children are less susceptible, are infrequently responsible for household transmission, and are not the main drivers of this epidemic.
- “Most transmission is caused by close contact with a symptomatic case, highest risk within first 5d of symptoms.” [The paper where the 5-day number comes from did not examine infections during asymptotic or pre-symptomatic state; the main conclusion was that transmission decreases substantially 5 days after symptoms arise. Another paper she cites points out that pre-symptomatic cases were contributing to 40%-60% of infections, asymptotic cases contributed a small amount.]
- Similar high risk transmission pattern could be seen in other crowded & connected indoor environments such as crowded office spaces, other workplace environment, packed restaurants/cafes, cramped apartment buildings, homeless shelters, etc.
- The probability of getting infected when you are in close contact with an infected person tells us a lot, and puts things into perspective. In household, this risk is about 15-20% (so 1 in 5 chance), but in crowded closed places this can go up to 40% (super spreading events).
While many of these findings seem intuitive, they are based on real data gathered using contact tracing in over a dozen studies, and there are quantifiable metrics and probabilities that can be calculated based on the data.
Note that when Cevik states that “Casual, short interactions are not the main driver of the epidemic”, she is talking about low probability, not zero probability. Similarly for asymptomatic (never developed symptoms) vs pre-symptomatic (developed mild or severe symptoms eventually) vs symptomatic. The study she cites estimated asymptomatic case percentage of less than 20% (this excludes pre-symptomatic cases). The study does not say that asymptomatic individuals cannot spread disease, it’s just that they were not found to be the major source of transmission.
For example, she cites a Chinese study in which 2,147 close contacts of 157 #COVID19 cases were followed up:
- Overall infection rate was 6%,
- But higher infection rate among friends (22%) and household (18%),
- Main risk factors include contact in household (13%), transport (11%), and dining (7%).
Some more examples -
Note that the amount of COVID-19 virus needed to develop illness is not known yet. However, it is known from research on other virus, that small amounts of virus can be fought off by the immune system. Multiple exposures to the virus increases the probability of developing the infection.
She also makes the important point that contact tracing data is crucial to understanding these transmission dynamics.
She also has some thoughts on what we need to do going forward —
- Redesign our living/working spaces & rethink how to provide better, ventilated living/working environment for those who live in deprived & cramped areas.
- Avoid close, sustained contact indoors & in public transport, & maintain personal hygiene (including hand-washing and face coverings).
Here is a tip-of-the-hat from Sir Jeremy Farrar, the head of the Wellcome Trust and a member of the Scientific Advisory Group for Emergencies (Sage).
Other Corroborating Evidence
Nursing home hotspots -
Homeless shelters -
Buffets are a no no.
South Korea demonstrates the difficulty of opening up establishments in spite of having the best track record on testing and isolation.
Epilogue
There is a lot of research being done in understanding the dynamics of this virus and how it spreads; a lot still remains unknown. As a scientist, Dr. Cevik cautions readers that the analysis is based on limited data. Our understanding might change based on community testing and lifting of lockdown measures. As our knowledge improves, it should help guide how we open up our economy and what steps we should take to keep the probability of infection low.
Note that Cevik’s analysis does not imply that we could relax our social distancing policies in certain settings. Quite the contrary, she suggests that reasonable and easy-to-implement social distancing measures should be maintained to help keep the transmission probability low.
Of course, this is not the way republicans think or act; for them, politics, greed and “owning the libs” are the primary motivators, science and information are just obstacles in their path.
We can be hopeful that blue state governors, policy makers and residents will stay more informed and take prudent steps as they go about opening up their economies. Let’s do all we can to practice good social distancing and encourage others to do so.