From the onset of COVID-19 symptoms, each patient needs to have a confirmed diagnosis and, if positive, contacts traced and isolated within 3 days to have a chance of stopping the growth of an outbreak, according to findings from a mathematical model of coronavirus spread reported in Lancet Public Health (2020;5: e452-459).
The model predicted that for each person infected with severe acute respiratory syndrome coronarvirus 2 (SARS-CoV-2) there was, on average, transmission to more than one other person unless contact tracing and isolation was compete within the 3-day window of opportunity.
"This study reinforces findings from other modelling studies showing that contact tracing can be an effective intervention to prevent spread of the SARS-CoV-2 virus, but only if the proportion of contacts traced is high and the process is fast," said Mirjam Kretzschmar, PhD, from the University of Utrecht in the Netherlands, one of the lead authors of the study.
Speedy Testing & Tracing Needed
The model verified that COVID-19 testing and the isolation of positive cases was a powerful method of reducing the reproductive number, R (the average number of people to whom the infection is transmitted by each "index case"). The model found the R value could be reduced to 1.0 in a pandemic by prompt diagnosis and isolation of infected persons together with conventional epidemic mitigation measures including physical distancing.
But to reduce the R-value below 1.0 (with average transmission to less than one other person), it was also necessary to trace contacts-and to do this speedily-to identify secondary cases and isolate them, too.
Conventional tracing (by asking patients about recent contacts) was estimated to take at least 3 days-too long to help push the R-value below 1.0. So, the researchers modelled the use of mobile apps that were theoretically capable of tracing contacts instantaneously.
"Our study [shows] in detail what role each step in the process plays in making this approach successful," said Kretzschmar. Her group hoped these data would help policymakers understand where best to prioritize resources to maximize the chances of success.
"For example, we found that mobile apps can speed up the process of tracking down people who are potentially infected. But if testing is delayed by 3 days or more, even these technologies can't stop transmission of the virus."
Including contact tracing within the overall 3-day limit of total delays could push the reproduction number down to 0.8, according to the model. This could happen with mobile apps even if only 80 percent of the contacts were found, the study reported.
The findings implied that the most rapid diagnostic tests (bringing results in minutes, or hours if possible) were needed to control outbreaks-especially since many regions had begun relaxing mitigation measures with the intention of controlling outbreaks of coronavirus infection as, and when, they arose.
The mathematical model weighed the interrelationships and effects of a range of delays such as the interval between infection and the time an infected person became infectious to others, the interval of asymptomatic infectiousness, the length of time until confirmed diagnosis, and the time taken for all contacts to be traced. The analysis predicted that accumulated delay from these intervals influenced development of the epidemic by increasing transmission opportunities.
The model computed R-values resulting from combinations of delays in a population that was assumed to have already adopted physical distancing measures and isolation strategies.
The researchers concluded that shortening the time between symptom onset and a positive test result was the most important factor for reducing disease spread by contact tracing. Shortening the time taken to trace contacts was also a benefit, but was easily lost by excessive delay.
Although the value of contact tracing was predicted to decline as the proportion of the population using the app diminished, it was still more effective than conventional contact tracing because it was faster.
The overall findings favored mobile technology for minimizing delay and concluded that high levels of coverage in the population (of both testing and tracing) could "substantially reduce" the reproduction number and improve the chances of lifting mitigation measures without causing infections to increase.
Research Comments
Co-lead author, Marc J.M. Bonten, MD, PhD, from the Julius Center of Health Sciences and Primary Care and the Department of Medical Microbiology at the University of Utrecht, Netherlands, said the model predicted that minimizing testing delays had the largest impact on reducing transmission of the virus and was the most critical factor for the success of a contact tracing system.
"This means that as many infectious people as possible need to be tested, and policymakers might consider lowering the eligibility threshold for access to testing. This will lead to a large proportion of negative test results, however, and future studies should focus on identifying the optimal balance between the proportion of negative tests and the effectiveness of contact tracing."
In a comment article in The Lancet, Louise Ivers, MB, BCh, MD, MPH, Executive Director of the Massachusetts General Hospital Center for Global Health, and Daniel J. Weitzner, BA, Founding Director of MIT Internet Policy Research Initiative, noted crucial issues to be addressed. How accurately could smartphones locate their users and assess proximity to the infected person? Also, more understanding was needed on how mobile apps could integrate with contact tracing programs.
They also called for research to discover how to encourage people to trust tracing software with their personal and business data, and expressed concern about the potential impact of contact strategies on health disparities-potentially increasing them.
"As contact tracing remains a crucial component of the COVID-19 response, mobile apps offer promise, especially when considering the speed and scale required for tracing to be effective. However, understanding the potential impact of apps as part of a comprehensive integrated approach requires more evaluation of their use in real life and [on] multidisciplinary engagement of technologists, epidemiologists, public health experts, and the public," they stated.
Peter M. Goodwin is a contributing writer.