Scientific responses to 3 common myths about COVID-19

Kyle Tretina, Ph.D.

Kyle Tretina, Ph.D., Genomics Application Scientist
Posted on July 8th, 2020

MYTH #1: COVID-19 is only about as deadly as the flu

One of the most common ways of measuring the deadliness of an infectious disease is the case fatality ratio (CFR), which is the number of confirmed deaths divided by the number of confirmed cases. What we really want to look at to answer this question, is the infection fatality rate (IFR), which uses the total number of deaths divided by the total number of cases. The main trouble in comparing COVID-19 to the flu is that the total number of cases of COVID-19 is not known and can only be estimated. Importantly, CFR is often very different from the IFR, and in the case of COVID-19, there is likely a vast underreporting of cases.

Accurately measuring the deadliness of COVID-19 is far more difficult than you might initially think, since it depends on the number of total cases and the number of deaths attributed to the infection. Each of these numbers is susceptible to biases associated with surveillance, data synthesis, diagnosis and reporting. When looking through the literature, you should note that any reported CFR should include information about the time frame and geographic location under consideration, since CFR can vary dramatically by country, region, population age, population sex ratio, as the response of a government evolves throughout a pandemic, and many other factors. Instead of diving into a discussion of how various countries report the data leading to their CFR, let’s look at the data from a few sources using selected countries where the data are believed to be of the best quality and see how they compare.

According to the European CDC, the COVID-19 CFR has changed significantly by country throughout the course of the pandemic, ranging from 1.4% (Australia) to 18.5% (France) (Figure 1). Some studies have tried to improve on this reporting methodology to account for potential underreporting and publication bias, estimating a global CFR of 7.4%[1]. Another alternate method tried to capture the total number of cases more comprehensively by looking for SARS-CoV-2-reactive antibodies in various populations and estimated a CFR of 0.02% to 0.78% (median 0.25%)[2]. According to one of the most popular COVID-19 dashboards[3], as of 06/26/2020, there have been 9,682,414 cases of COVID-19 and 491,113 deaths, resulting which would correspond to a CFR of 5%. This is essentially identical to the COVID-19 CFR in the U.S.A. (124,749 deaths and 2,446,706 cases, CFR = 5%). For various technical reasons[4], many public health experts prefer measures other than CFR, such as case fatality risk (measures in deaths per 100,000 cases or infections). A CFR of 5% would correspond to a risk of 5,000 deaths per 100,000 cases.


Figure 1. The COVID-19 case fatality ratio of selected countries based on data from the European CDC[5].

Comparing these numbers to data available for flu deaths, however, is even more complicated[6], because the data are acquired using very different methods[7] and therefore most clinicians and statisticians that weigh in on the topic would likely be hesitant to make any strong statements to compare COVID-19 to influenza. A recent review in the respected medical journal The Lancet reported a global 2017 IFR for influenza of 1.9 deaths per 100,000 cases (range:1.3 to 2.6)[8]. Importantly, This value will depend on how the number of cases is reported[9], and an equivalent measure of fatality must be used in each disease for a proper comparison. The best estimates of the CDC reporting CFR are about 0.1% for 2017[10]. This would make COVID-19 have a CFR 50 times higher than the flu. Although this is not a good measure of actual risk of death from the infection, the common consensus is that COVID-19 is deadlier than the flu[11].


Table 1. Selected viruses, viral diseases and their estimated case fatality ratios.

MYTH #2: Thermal scanners are enough to detect COVID-19

Many businesses are using only thermal scanners as a screening tool for worksite re-entry, but there are several problems with that approach. First, a recent study from the UK indicated that 71.5%[18] of the 13,863 individuals testing COVID-19 positive indicated fever as a symptom, which means that ~30% of symptomatic positive cases would be missed if only fever is used as a screening tool. In addition, other research indicates that the time from exposure to the virus to symptoms can extend to 14 days, with a median time of 4–5 days[19]. There is also an issue of sensitivity, as demonstrated by one study that found that self-reported fever had a sensitivity of 75.0% and a specificity 84.7%, indicating that even in cases where the person has a fever, a thermal scanner may not detect it. In all of these cases, thermal scanners will miss potentially infectious employees.

MYTH #3: COVID-19 is only dangerous to older people with underlying medical conditions

While people over age 85 are most likely to die from COVID-19, mortality rates for people age 55 and younger can still reach up to 4%[20] and account for almost a quarter of total deaths[21] in some places like New York City. Young people contribute a significant amount of transmission in endemic areas[22,23], but they may have hidden complications that don’t show as obvious symptoms that could be a problem later. This is a pattern that is only beginning to be described, but doctors are reporting[24] that they are getting COVID-19 patients with very low blood oxygen saturation levels, but seem to be breathing well. Instead of the acute respiratory distress syndrome (ARDS) that typifies many older COVID-19 patients, they have reduced lung function, but don’t realize it yet. This finding simply underscores how little we know[25] about the long-term effects of SARS-CoV-2 infection, even in asymptomatic cases.

References

1. Case fatality rate in COVID-19: a systematic review and meta-analysis. doi:10.37473/dac/10.1101/2020.04.01.20050476

2. Ioannidis J. The infection fatality rate of COVID-19 inferred from seroprevalence data. doi:10.1101/2020.05.13.20101253

3. COVID-19 Map. In: Johns Hopkins Coronavirus Resource Center [Internet]. [cited 26 Jun 2020]. Available: https://coronavirus.jhu.edu/map.html

4. Mortality Risk of COVID-19 — Statistics and Research. In: Our World in Data [Internet]. [cited 26 Jun 2020]. Available: https://ourworldindata.org/mortality-risk-covid

5. Mortality Risk of COVID-19 — Statistics and Research. In: Our World in Data [Internet]. [cited 26 Jun 2020]. Available: https://ourworldindata.org/mortality-risk-covid

6. Walker M. COVID-19 No Worse Than the Flu? Hardly. In: MedpageToday [Internet]. 27 Apr 2020 [cited 26 Jun 2020]. Available: https://www.medpagetoday.com/infectiousdisease/covid19/86176

7. Walker M. COVID-19 Deaths Far Outpace Flu in “Apples-to-Apples” Comparison. In: MedpageToday [Internet]. 14 May 2020 [cited 26 Jun 2020]. Available: https://www.medpagetoday.com/infectiousdisease/covid19/86504

8. GBD 2017 Influenza Collaborators. Mortality, morbidity, and hospitalisations due to influenza lower respiratory tract infections, 2017: an analysis for the Global Burden of Disease Study 2017. Lancet Respir Med. 2019;7: 69–89.

9. Wong JY, Kelly H, Ip DKM, Wu JT, Leung GM, Cowling BJ. Case fatality risk of influenza A (H1N1pdm09): a systematic review. Epidemiology. 2013;24: 830–841.

10. Estimated Influenza Illnesses, Medical visits, Hospitalizations, and Deaths in the United States — 2017–2018 influenza season | CDC. 22 Nov 2019 [cited 26 Jun 2020]. Available: https://www.cdc.gov/flu/about/burden/2017-2018.htm

11. Resnick B. Covid-19 is way, way worse than the flu. In: Vox [Internet]. Vox; 5 May 2020 [cited 26 Jun 2020]. Available: https://www.vox.com/science-and-health/2020/5/5/21246567/coronavirus-flu-comparisons-fatality-rate-contagiousness

12. Roser M, Ritchie H, Ortiz-Ospina E, Hasell J. Coronavirus Pandemic (COVID-19). Our World in Data. 2020 [cited 26 Jun 2020]. Available: https://ourworldindata.org/coronavirus

13. COVID-19 pandemic. In: European Centre for Disease Prevention and Control [Internet]. [cited 26 Jun 2020]. Available: https://www.ecdc.europa.eu/en/covid-19-pandemic

14. Venkatesh S, Memish ZA. SARS: the new challenge to international health and travel medicine. East Mediterr Health J. 2004;10: 655–662.

15. Munster VJ, Koopmans M, van Doremalen N, van Riel D, de Wit E. A Novel Coronavirus Emerging in China — Key Questions for Impact Assessment. N Engl J Med. 2020;382: 692–694.

16. Estimated Influenza Illnesses, Medical visits, Hospitalizations, and Deaths in the United States — 2018–2019 influenza season | CDC. 9 Jan 2020 [cited 26 Jun 2020]. Available: https://www.cdc.gov/flu/about/burden/2018-2019.html#:~:text=CDC%20estimates%20that%20the%20burden,from%20influenza%20(Table%201).

17. Shultz JM, Espinel Z, Espinola M, Rechkemmer A. Distinguishing epidemiological features of the 2013–2016 West Africa Ebola virus disease outbreak. Disaster Health. 2016. pp. 78–88. doi:10.1080/21665044.2016.1228326

18. Menni C, Valdes AM, Freidin MB, Sudre CH, Nguyen LH, Drew DA, et al. Real-time tracking of self-reported symptoms to predict potential COVID-19. Nat Med. 2020. doi:10.1038/s41591–020–0916–2

19. Lauer SA, Grantz KH, Bi Q, Jones FK, Zheng Q, Meredith HR, et al. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Ann Intern Med. 2020;172: 577–582.

20. Shekerdemian LS, Mahmood NR, Wolfe KK, Riggs BJ, Ross CE, McKiernan CA, et al. Characteristics and Outcomes of Children With Coronavirus Disease 2019 (COVID-19) Infection Admitted to US and Canadian Pediatric Intensive Care Units. JAMA Pediatr. 2020. doi:10.1001/jamapediatrics.2020.1948

21. NYC. Coronavirus Disease 2019 (COVID-19) Daily Data Summary. In: NYC.gov [Internet]. [cited 26 Jun 2020]. Available: https://www1.nyc.gov/assets/doh/downloads/pdf/imm/covid-19-daily-data-summary-deaths-05132020-1.pdf

22. Davies NG, Klepac P, Liu Y, Prem K, Jit M, CMMID COVID-19 working group, et al. Age-dependent effects in the transmission and control of COVID-19 epidemics. Nat Med. 2020. doi:10.1038/s41591–020–0962–9

23. Liu Y, Gu Z, Xia S, Shi B, Zhou X-N, Shi Y, et al. What are the underlying transmission patterns of COVID-19 outbreak? An age-specific social contact characterization. EClinicalMedicine. 2020. p. 100354. doi:10.1016/j.eclinm.2020.100354

24. Pappas S. “Silent hypoxia” may be killing COVID-19 patients. But there’s hope. 2020 [cited 26 Jun 2020]. Available: https://www.livescience.com/silent-hypoxia-killing-covid-19-coronavirus-patients.html

25. Levitan R. Opinion | The Infection That’s Silently Killing Coronavirus Patients. 20 Apr 2020 [cited 26 Jun 2020]. Available: https://www.nytimes.com/2020/04/20/opinion/sunday/coronavirus-testing-pneumonia.html



Kyle Tretina, Ph.D.

Kyle Tretina, Ph.D.

Kyle has extensive research expertise and interest in the area of genomics, microbiology and immunology. He received his Ph.D. from the University of Maryland, Baltimore working at the Institute for Genome Sciences and came to Meenta from a postdoc at Yale University.

The Definitive COVID-19 Cheat Sheet
July 8th, 2020
Kyle Tretina, Ph.D. Genomics Application Scientist