An estimated 17% of COVID-19 cases show no symptoms
An estimated 17% of COVID-19 cases show no symptoms A new meta-analysis (a study of studies) suggests that roughly 1-in-6 cases of COVID-19 present without symptoms
A new meta-analysis (a study of studies) suggests that roughly 1-in-6 cases of COVID-19 present without symptoms
Questions surrounding the nature of asymptomatic cases of coronavirus disease 2019 (COVID-19) have been present since near the beginning of the pandemic. Anecdotally, it was reported as early as January of 2020 that one woman spread the virus to a colleague 6 days before she herself showed symptoms. In recent months, evidence has arisen suggesting that even without developing any of the telltale symptoms of COVID-19 such as fever, coughing, and anosmia (loss of smell), asymptomatic patients can still develop damage to their heart and lung tissue in the form of inflammation.
First, in order to understand asymptomatic transmission, researchers must have a reliable way of identifying asymptomatic cases. Over a year after the first case of COVID-19 was identified, the true rates of asymptomatic COVID-19 cases have still remained elusive, with estimates from various studies ranging from as low as 5% to as high as 80%. The difficulty in determining the proper percentage of asymptomatic cases can be explained in part that asymptomatic carriers of the virus are, by the very nature of the definition, hard to find. Without presenting any symptoms, these patients are largely unaware that they have COVID-19, and thus will be unlikely to get tested and identified in COVID-19 statistics.
A recent study from researchers at a Bond University, the University of Sydney, and the University of New South Wales Sydney in Australia have attempted to elucidate the proportion of COVID-19 cases that are asymptomatic by conducting a meta-analysis of published research in which asymptomatic cases were likely to be tested and identified. A meta-analysis is a type of secondary analysis in which the results of several similar scientific studies are combined in order to gain a more robust picture of whatever the researchers are aiming to study.
In this case, studies included tests of nursing home residents, close contacts of confirmed COVID-19 cases, and a district surveillance program in Italy in which the majority of a population of an Italian town were selected. The researchers also specifically selected studies in which participants were tested regardless of the presence or absence of symptoms, and with sufficient follow-up periods of participants to ensure that they were in fact asymptomatic rather than just pre-symptomatic. In sum, a total of thirteen articles were included, which accounted for 21,708 tested participants which were close contacts of 849 confirmed COVID-19 cases. These studies were selected across seven countries: China, the United States, Taiwan, Brunei, Korea, France, and Italy.
After combining the data from these studies, the researchers estimated that 17% of COVID-19 cases were asymptomatic, or in other words, roughly 1-in-6 COVID-19 cases had no actual symptoms of the disease. The estimated asymptomatic rates were 16% for outside of nursing homes (where the average age was 31) and 20% inside of care homes (where the average age was 75), indicating a slightly higher rate of asymptomatic COVID-19 in higher age groups.
The researchers from Australia also aimed to identify disease transmission from asymptomatic cases of COVID-19, or more simply, how likely asymptomatic COVID-19 patients were to spread the virus to others. Through analysing the data on secondary infections, they estimated the relative risk of asymptomatic transmission to be 42% lower than that of symptomatic transmission, meaning that while patients with asymptomatic cases of COVID-19 can still transmit the virus, they appear to be 42% less likely to do so compared to patients that presented with actual COVID-19 symptoms.
An understanding of the percentage of asymptomatic cases, as well as the likelihood of asymptomatic spread, allows researchers to generate more accurate models of COVID-19 case growth. These types of models allow public health experts to predict how people’s behaviour will contribute to the growth or decline of COVID-19 cases in local populations. With more information, people in local populations can also gain more informed expectations for what to expect from the pandemic as it progresses. Now approaching the one year anniversary since the World Health Organization officially declared COVID-19 to be a global pandemic, insights into the disease’s presentation and transmissibility are needed more than ever.