Using symptom-based measures for tracking COVID-19

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Reports of COVID-19 based on symptoms may help provide a more complete picture of coronavirus infection in a population, especially when used in combination with testing for virus and antibody. In addition, symptom-based reporting measures can provide advance warning of increases or decreases in the level of infection in a population.

Testing for COVID-19

There are two main forms of testing for COVID-19. Diagnosis of current infection uses a test based on detection of the virus genetic material using a technique called PCR (polymerase chain reaction) whilst past infection is assessed using antibody testing. Both are important tools in helping track the course of COVID-19 infection.[1] However, they have limitations. With PCR testing for active infection, not everyone with symptoms suggestive of coronavirus is tested, so results will underestimate the true number of infections. A typical example is where, despite known community transmission, diagnostic tests are principally used only for those admitted to hospital or symptomatic health workers.[1] Both virus and antibody tests also suffer from time lags between infection, testing and reporting a result. So COVID-19 testing, while of vital importance, can only ever provide a partial, historical snapshot of infection progression.

Symptom-based measures of COVID-19

Symptom-based reports are a different way of estimating the number of people in the population that may be infected with a disease and can help with advance planning as well as with tracking the spread of an outbreak. They are useful and widely recommended[2][3] but have not yet been systematically used to track COVID-19. They rely on health-related symptoms and clinical signs reported by patients and doctors. Potentially useful symptom-based measures of COVID-19 include specific family doctor consultations (for instance those coded as influenza-like-Illness), calls to medical telephone helplines with symptoms such as breathing difficulties, internet searches relating to fever and cough, along with mobile phone apps developed specifically for monitoring coronavirus symptoms.[4]

Symptom-based measures could provide better estimates of the true number of COVID-19 cases in the population, if they could be calibrated against reliable test results. Influenza-like illness surveillance data on outpatients was used by the US Centers for Disease Control (CDC) in early March. This indicated that the true burden of COVID-19 was likely to be 100 times that reported by testing results.[5] Reports of influenza-like illnesses and acute respiratory infections from general practice in France are used for similar purposes.[6] To interpret these reports, statistical methods are used to compare symptom-based measures of COVID-19 with coronavirus testing results. This helps to determine, for example, the proportion of calls to a medical telephone helpline for difficulty with breathing, that are likely to be attributable to COVID-19.

Symptom-based measures can also be used for forecasting. Usually, there is at least a week between a patient first experiencing symptoms and being admitted to hospital.[7] Thus an increase (or decrease) in typical COVID-19 symptom measures may provide advance warning of a likely increase (or decrease) in hospital admissions. Developers of a COVID-19 symptom monitoring app[4] estimate that the symptoms reported by their users provide two weeks’ advance notice of when changes in hospital demand will occur.

Like all other monitoring tools, symptom-based measures have their limitations. Not all people who call medical telephone helplines with symptoms of influenza-like illness will have COVID-19 – some may indeed have flu or another respiratory illness.[5] Media accounts of COVID-19 may influence the reporting of symptoms by the public, adding to the difficulty of identifying true changes in coronavirus activity.[8] In addition, not all members of the public use helplines or monitoring apps. Further questions about interpreting the data arise when a change to public health advice occurs. For example, on 12 March 2020 the UK public were advised that they should no longer call the NHS 111 telephone helpline if they developed symptoms of COVID-19.

Symptom-based measures are just one more tool that can be used to give advance warning of COVID-19, and to track and to contain COVID-19 cases. The Real Time Surveillance Team at Public Health England routinely collects a suite of measures on symptoms via NHS 111 telephone helpline data, GP consultations and Emergency Department attendances.[9] These data will help decide which symptom-based measures, in combination with enhanced testing for virus and antibody, are most helpful in managing the COVID-19 pandemic.

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  1. Laboratory testing strategy recommendations for COVID-19: Interim guidance. World Health Organization. 2020 Mar. WHO/2019-nCoV/lab_testing/2020.1

  2. Operational considerations for COVID-19 surveillance using GISRS: Interim guidance. World Health Organization. 2020 Mar. WHO/2019-nCoV/Leveraging_GISRS/2020.1

  3. European Centre for Disease Prevention and Control. Strategies for the surveillance of COVID-19. ECDC. 2020 Apr.

  4. COVID Symptom Tracker. ZOE (App). Accessed: 2020 Apr 21.

  5. Silverman JD, Hupert N, Washburne AD. Using ILI surveillance to estimate state-specific case detection rates and forecast SARS-CoV-2 spread in the United States. medRxiv. 2020 Apr. DOI: 10.1101/2020.04.01.20050542.

  6. Boëlle P, Sentinelles syndromic and viral surveillance group. Excess cases of Influenza like illnesses in France synchronous with COVID19 invasion. medRxiv. 2020 Mar. DOI: 10.1101/2020.03.14.20035741.

  7. Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet. 2020 Feb;395(10223):497-506. DOI: 10.1016/S0140-6736(20)30183-5.

  8. Elliot AJ, Hughes HE, Astbury J, et al. The potential impact of media reporting in syndromic surveillance: an example using a possible Cryptosporidium exposure in North West England, August to September 2015. Euro Surveillance. 2016 Oct;21(41) DOI: 10.2807/1560-7917.ES.2016.21.41.30368.

  9. Smith GE, Elliot AJ, Lake I, et al. Syndromic surveillance: two decades experience of sustainable systems - its people not just data! Epidemiology and Infection. 2019 Jan;147:e101. DOI: 10.1017/S0950268819000074.

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