Extending the signs that set off a PCR test for COVID-19 might assist spot around a 3rd more cases of the illness.
New research study led by scientists at King’s and released in the Journal of Infection recommends that limiting screening to the ‘classic triad’ of cough, fever, and loss of odor which is needed for eligibility for a PCR test through the NHS might have missed out on cases. Extending the list to consist of tiredness, aching throat, headache, and diarrhea would have identified 96% of symptomatic cases.
A group of scientists at King’s and the Coalition for Epidemic Preparedness Innovations (CEPI) examined information from more than 122,000 UK adult users of the ZOE COVID Symptom Study app. These users reported experiencing any possible COVID-19 signs, and 1,202 of those reported a favorable PCR test within a week of very first sensation ill.
While PCR swab screening is the most trustworthy method to inform whether somebody is contaminated with the SARS-CoV-2 coronavirus that triggers COVID-19, the analysis recommends the restricted list of 3 does not capture all favorable cases of COVID-19.
Testing individuals with any of the 3 ‘classic’ signs would have found 69% of symptomatic cases, with 46 individuals evaluating unfavorable for each individual screening favorable. However, screening individuals with any of 7 essential signs — cough, fever, anosmia, tiredness, headache, aching throat, and diarrhea — in the very first 3 days of disease would have identified 96% of symptomatic cases. In this case, for each individual with the illness recognized, 95 would evaluate unfavorable.
Researchers likewise discovered users of the Symptom Study App were most likely to choose headache and diarrhea within the very first 3 days of signs, and fever throughout the very first 7 days, which shows various timings of signs in the illness course. Data from the ZOE app reveals that 31% of individuals who are ill with COVID-19 don’t have any of the triad of signs in the early phases of the illness when most transmittable.
The scientists used a multi-objective evolutionary algorithm (MOEA) to produce a set of ideal sign mixes, each identified by a great compromise in between uniqueness and level of sensitivity. MOEA begins producing a population of random sign mixes and after that develops that population towards much better mixes ending with a set of ideal sign mixes. The option of the ideal mix to utilize depends upon the screening capability.
Cough or dyspnoea (shortness of breath) were reported by 46% of people favorable for COVID-19 within the very first 3 days of sign start. When users reported fever, the level of sensitivity increased to 60%, while logging anosmia/ageusia increased level of sensitivity to 69%. When headache and tiredness was included the percentage of COVID-19 cases increased to 92% however the tests per case doubled.
The findings might be important in circumstances where there is a minimal screening capability. Researchers recommend a variety of ideal sign mixes that might be utilized in vaccine effectiveness trials or in public health settings, when evaluating monetary and logistical resources.
“The identification of this combination of symptoms through the COVID Symptom Study app is another prime demonstration of the value of big data analytics and mobile health technology to support the management of this pandemic. Daily self-reported symptoms from a mobile application at the scale of an entire country has offered a new perspective for public health research and response towards the rapid spread of infectious diseases such as COVID-19.”
– Professor Sebastien Ourselin from the School of Biomedical Engineering & Imaging Sciences
Dr. Claire Steves, Reader at the School of Life Course Sciences, stated: “There are many symptoms which occur in acute COVID, including some like fatigue and headache which are also common in other conditions. Depending on the testing available, different symptom combinations can be used to be as sensitive or specific as possible. We hope these models are of use in a range of settings – from vaccine trials to detecting and treating COVID outbreaks going forward.”
Professor Tim Spector from the School of Life Course Sciences stated: “We’ve known since the beginning that just focusing testing on the classic triad of cough, fever and anosmia misses a significant proportion of positive cases. We identified anosmia as a symptom back in May and our work led to the government adding it to the list, it is now clear that we need to add more. By inviting any users who log any new symptoms to get a test, we confirmed that there are many more symptoms of COVID-19. This is especially important with new variants that may cause different symptoms. For us, the message for the public is clear: if you’re feeling newly unwell, it could be COVID and you should get a test.”
Dr. Jakob Cramer, Head of Clinical Development, at the Coalition for Epidemic Preparedness Innovations, stated: “Accurate diagnosis of COVID-19 cases is crucial when assessing the efficacy of COVID-19 vaccine candidates in large-scale studies, especially since the signs and symptoms associated with the disease are extensive and overlap with other common viral infections. The findings of this study provide important insights that will help optimize the choice of triggering symptoms for diagnostic work-up in COVID-19 vaccine-efficacy trials. We hope the findings of this study will not only aid CEPI’s COVID-19 vaccine-development partners but also the wider R&D community.”
Reference: “Optimal symptom combinations to aid COVID-19 case identification: analysis from a community-based, prospective, observational cohort” by M. Antonelli, PhD; J. Capdevila, PhD; A. Chaudhari, MD; J. Granerod, PhD; L.S. Canas, PhD; M.S. Graham, PhD; K. Klaser, MSc; M. Modat, PhD; E. Molteni, PhD; B. Murray, MSc; C.H. Sudre, PhD; R. Davies, MA; A. May, MA; L.H. Nguyen, MD; D.A. Drew, PhD; A. Joshi, PhD; A.T. Chan, MD; J.P. Cramer, MD; Professor T. Spector; J. Wolf, MA; Professor S. Ourselin; C.J. Steves, PhD and A.E. Loeliger, MD, 12 February 2021, Journal of Infection.
DOI: 10.1016/j.jinf.2021.02.015
