The following is a set of basic figures about the epidemic as a snapshot, which will be updated in this document as we receive new information.
Reported cases underestimate the total number of cases due to the incubation period, asymptomatic or presymptomatic carriers, limited testing availability, and delays in receiving testing results after the initial onset of symptoms. The following resource is an excellent explanation of this phenomenon working from data publicly available in early March.
Based on previous epidemics, it is estimated that for every 1 case that is reported, there may be as many as 10-50 other cases that we don’t know about. So when you hear “confirmed cases,” mentally multiply that number by 10 to have an idea of the disease incidence (NYTimes U.S. Coronavirus Map, Lipsitch M. Presentation 3.16.20). One way to get a better estimate for the true prevalence of COVID-19 and future pandemics is ongoing surveillance testing (Lipsitch et al. NEJM 3.26.20). For more on these principles, here is a supplementary video on how to accurately estimate actual COVID-19 cases based on reported data.
Patients diagnosed with SARS-CoV-2 worldwide have a widely variable chance of death. Initial estimates from U.S. cases are 1.8-3.4% (see table below).
Overall case fatality rate (CFR) is difficult to discern because we are, for the most part, testing patients with more severe disease. The oft-quoted 2% CFR primarily comes from the largest epidemiologic report out of China that showed an overall CFR of 2.3% (1023/44672) (Zhang et al, CCDC 2020). The numerator here is the number of deaths in this cohort, which may be an underestimate because deaths lag behind new cases. The denominator is the number of CONFIRMED cases of COVID-19 in this cohort, which may also be an underestimate due to lack of testing of the general population and presence of asymptomatic cases. There is currently no way - particularly with how little surveillance screening is being performed - to determine the TRUE number of COVID-19 cases in this cohort or others like it, given that many patients are asymptomatic or mildly symptomatic and never present for care or testing.
Of note, reports from Italy suggest a much higher CFR than seen in China, hovering around 8% (Lazzerini et al., Lancet 2020). It is possible that this too is due to the lack of surveillance testing, but it may be due to inadequate medical resources from a healthcare system overburdened by the fast expansion of the epidemic in the region. The high disparity between the CFRs in China and in Italy warrant further attention as the US continues to respond to the pandemic. An additional point from the Chinese data is that there was a large disparity between the CFR in Hubei province (2.9%), which includes Wuhan, and outside of Hubei province (0.4%), suggesting that healthcare capacity is likely playing a role in the lethality of COVID-19. and is a major driver behind the “flatten the curve” movement, as described in the remainder of the module (Wu et al., JAMA 2020). It is important to note that since it takes 1-2 weeks for newly infected patients to develop symptoms, there is at least a corresponding 1-2 week delay to see the effect of new interventions on infection and mortality rates (Fauci et. al., 2/2020; Lipsitch M., Presentation 3/2020).
The CFR is widely variable along axes of age and race. Relatively low (0.01-0.02%) amongst youth and people in their 30’s and 40’s, case fatality rate begins to rise past age 50. By age 80 the case fatality rate is 15%, at least five times higher than the average (WHO, 2020).
Racial inequities that permeate the healthcare system have been brought to light, although they have existed long before the COVID-19 pandemic. Racial minorities, especially Black, Indigenous and Latinx populations are experiencing greater disease burden and mortality from COVID-19 pandemic. This is not a surprise - as early as March, epidemiologists warned that differential access to precautionary measures will disproportionately impact low-income communities and racial minorities, which are closely related social factors in the United States, due to racist policies.
If racial minority groups had died at the same rate of White Americans, at least 14,400 Black Americans, 1,200 Latino Americans and 200 Indigenous Americans would still be alive (APM Research Lab 2020). Navajo Nation’s COVID-19 infection rate is 10 times higher than the general population of Arizona. The Boston Globe reported that compared to prior years, there was a surge of excess death rates in communities with higher percentages of populations of color, especially Black folks, and independently with higher poverty, and higher crowded housing. Millett et al., (2020) found that counties with a higher proportion of Black residents had more COVID-19 diagnoses and deaths, even after adjusting for age, poverty, comorbidities and epidemic duration. That said, controlling for race, race-based co-morbidities, and area of residence, Black patients are not inherently more predisposed to COVID-19 morbidity and mortality (Boulware et al., JAMA, 2020).
Ajilore and Thames explore the biological consequences of structural racism and discrimination in the COVID-19 era, building upon a large body of work examining biological stress effects of racism (1,2,3). Although more studies are being done to show and explain racial inequities in the COVID-19 pandemic, the evidence is already clear that Blacks, Indigenous and People of Color are experiencing disproportionate morbidity and mortality. It is important to note that the higher disease risk cannot only be explained by race and other co-morbidities. Varying risk of exposure, for example, with a high percentage of Blacks and Latinos part of the essential work force, also exacerbates COVID-19 disease and mortality burden (Boulware et al., JAMA, 2020).
The COVID-19 pandemic has taken a severe toll on the physical health of populations around the world, and its effects on community mental and emotional health are also becoming increasingly clear. Mental health challenges can arise from both the disease itself (being ill or knowing others suffering from the disease) and from the risk-mitigation measures set in place to control the outbreak of the SARS-CoV-2 virus (ex. social distancing). The following study from the CDC Morbidity and Mortality Weekly Report (8.14.20) presents survey results from 5,412 adults in the U.S., looking at their mental health, substance abuse, and suicidal ideation.
40.9% reported having at least “one adverse mental or behavioral health condition”.
30.9% reported symptoms of an anxiety disorder or a depressive disorder, with these symptoms being 3.15 times and 3.74 times more prevalent, respectively, than seen for the same time period in 2019.
26.3% reported symptoms of a “trauma and stressor-related disorder (TSRD) related to the pandemic”.
13.3% reported increasing their substance use to cope with stressors of the pandemic. This finding was most common among Black individuals (18.4%).
10.7% seriously considered committing suicide within the past 30 days. This finding was significantly higher among certain subsets of the population.
18-24 year olds: 25.5% considered suicide
Hispanic people: 18.6% considered suicide
Black people: 15.1% considered suicide
Unpaid caregivers for adults: 30.7% considered suicide
Essential workers: 21.7% considered suicide
While this study did not directly identify factors responsible for changes in mental health, several proposals include: impacts of social isolation, lack of school/work structure, unemployment, financial concerns, and violence/abuse (physical, emotional, mental, or sexual). These factors and their influence on individuals’ mental health during the COVID-19 pandemic are areas for future research.
Nearly everyone has been impacted by COVID-19 in some way, but certain populations of individuals in the U.S. have been disproportionately affected by adverse mental health conditions. These populations include young adults (ages 18-24), Hispanic people, Black people, unpaid caregivers for adults, and those who were receiving psychiatric treatment prior to the pandemic. Mental health thus provides a new lens through which health inequities in America can be identified and addressed. Several systemic approaches to reducing these disparities during the COVID-19 pandemic include providing increased financial support, address the impact of racism on peoples’ mental health, supporting those at risk for suicide, and providing services to those struggling with increased substance use.
Recall that the latent (presymptomatic) period, or time from exposure to transmissibility, can differ from the incubation period, or time from exposure to first symptoms (see figure above, from Wikimedia). Several sources of evidence suggest SARS-CoV-2 has a shorter latent period than incubation period, opening the door for presymptomatic transmission. Current estimates of the SARS-CoV-2 incubation period, from initial exposure to symptom onset, range from 1-14 days with a median of 5 days, an interquartile range from 4.5 to 5.8 days (Wiersinga et al., JAMA 2020), and 95th percentile of 12 days (Lauer et al., Ann Intern Med 2020; Li et al., NEJM 2020). Meanwhile, analyses of infector-infectee pairs show a mean serial interval of 4.0 days [95% CI 3.1, 4.9] (Nishiura et al., March 2020), though this varies by location; Shenzhen data, for example, had a serial interval of 6.3 days (Bi et al., medRxiv March 2020). Generation intervals in case clusters in Tianjin, China are estimated to be 3.95 [95% CI 3.01-4.91] days and in Singapore to be 5.20 [95% CI 3.68-6.78] days (Ganyani et al., Euro Surveill. March 6 2020). Put together, these estimates imply a period of presymptomatic transmissibility. (For more on the basic biology of transmission, see Module 1.)
It is becoming increasingly clear that most new confirmed COVID-19 cases (an estimated 79%, from models based on data from Wuhan) are likely spread by people that show mild or no symptoms of the disease (Ruiyan et al., Science 3/2020). Similarly, data from case clusters in Tianjin, China and Singapore give rise to estimates of 48-77% asymptomatic transition (Ganyani et al., Euro Surveill. March 6 2020). Social isolation, especially of young, healthy, asymptomatic people, will be critical to controlling disease spread.
Patients may be able to transmit the virus for a prolonged period of time, as SARS-CoV-2 RNA has been detected in patients for an average of 20 days, lasting as long as 37 days (Zhou et al., Lancet 2020). However, detection of RNA does not necessarily indicate the presence of live virus, and it is still unclear for how many days patients remain infectious.
At the end of March 2020, the World Health Organization released a scientific brief detailing the main routes of transmission of COVID-19 (WHO, 27 Mar 2020). In the brief, there is a clear distinction made between large respiratory particles (>5-10 microns in diameter) and small respiratory particles (<5 microns in diameter) (FIG. 1). Large particles are often referred to as “respiratory droplets” while small particles are often referred to as “aerosols” or “droplet nuclei.” At the time, the WHO, along with the CDC, believed that COVID-19 was spread via large respiratory droplets (>5-10 microns) at a very close range (1-2 meters) (Roberts, 6 Oct 2020). Airborne transmission via small particles was considered highly unlikely except for a few specific circumstances (e.g. endotracheal intubation in the ICU). Given these assumptions, both organizations recommended 2-meter social distancing guidelines and frequent hand-washing in order to decrease the probability of infection from “large” respiratory droplets. The public health authorities hoped that these non-pharmaceutical interventions would reduce community spreading events to help to flatten the curve.
FIGURE 1. Droplet Transmission vs Airborne Transmission Dichotomy (modified from Roberts, 2020)
Though oft-repeated, the 5-10 micron cutoff between large and small particles is arbitrary. The large vs. small droplet dichotomy and the 2-meter social distancing rule are based on science from the 1950s and earlier (Wilson et al., 20 Aug 2020). In reality, respiratory particles occur along a wide continuum of sizes that depend strongly on both the particle generation procedure (e.g. sneezing, talking breathing) and on the ambient conditions (e.g. temperature, humidity, ventilation). In early July of 2020, a group of 239 scientists released an open letter requesting that public health organizations recognize the potential for airborne COVID-19 transmission (Morawska and Milton, 6 Jul 2020). Most of the coronavirus prevention measures were focused on large droplet precautions, and the authors argued that although such measures were necessary, they would be insufficient if airborne transmission was also a possibility. Though our understanding of COVID-19 is still incomplete, laboratory studies, computational models, and retrospective case studies all suggest the potential for aerosol transmission of SARS-CoV-2 (Stadnytskyi et al., 4 May 2020 and Chen et al., 10 Apr 2020 and Miller et al., 26 Sep 2020).
Eventually, the WHO and CDC recognized that airborne transmission was possible, but widespread support for and adoption of appropriate countermeasures has been lagging (WHO, 09 Jul 2020 and WHO, 23 Jul 2020 and CDC, 05 Oct 2020). Airborne transmission naturally has a number of important implications for public health guidelines, particularly for indoor settings (Morawska and Cao, Jun 2020). For example, infectious individuals without symptoms may generate fewer large droplets if they aren’t sneezing and coughing, but they can still generate large volumes of small particles through normal breathing and speaking. Such aerosols can accumulate indoors and also spread well beyond the currently-prescribed 2-meter (6 feet) distancing guidelines. Factors like indoor occupancy, duration of contact, adherence to masking, adherence to distancing, mode of respiratory particle generation procedure, and level of indoor ventilation all substantially affect the probability of a successful transmission event (and therefore the R0 value as well).
FIGURE 2. Masks reduce droplet and airborne transmission (modified from Prather et al., 6 Jun 2020)
Overall, appropriate mask-wearing has emerged as one of the most consistently-effective measures to reduce COVID-19 spread (Prather et al., 6 Jun 2020 and Peeples, 6 Oct 2020) (FIG. 2). Exposure is maximized when neither infectious nor susceptible individuals are wearing masks, while exposure is limited when both infectious and susceptible individuals are wearing correctly-fitted masks. Given the multi-faceted nature of the problem, some authors have argued for a more nuanced framework to describe COVID-19 transmission risk (Jones et al., 25 Aug 2020). If we assume that symptomatic individuals (with high viral load and violent respiratory events) are appropriately self-isolating, the probability of transmission between asymptomatic/presymptomatic individuals and susceptible individuals will vary according to metrics like occupancy, ventilation, masking, and type of respiratory event (FIG. 3). Thus, universal adoption of masking, optimization of ventilation, and de-densification strategies should be combined with other containment/mitigation strategies to reduce the spread of COVID-19.
FIGURE 3. COVID-19 Transmission Risk in Different Circumstances (modified from Jones et al. 25 Aug 2020)
In an epidemic, superspreaders are individuals who are more likely to infect other people, compared to the average infected person. The question is: how many individuals can a superspreader infect? Let’s first turn to how epidemiologists define superspreaders.
While many loose definitions of super-spreading events (SSEs) exist, Lloyd-Smith et al. published a protocol to identify superspreading events. According to this protocol, one needs to:
Estimate the R for the disease and population being studied
Construct a Poisson distribution with mean R and expected range Z due to stochasticity without individual variation
Define an SSE as any infected person who infects Z(n) others, where Z(n) is the nth percentile of the Poisson distribution.
Thus, a 99th-percentile SSE would be a case that causes more infections than would occur in 99% of case histories in a homogeneous population.
SSEs, therefore, depend on the disease and the population. During the 2003 SARS outbreak, epidemiologists defined a super-spreader as an individual that could transmit SARS to at least 8 other individuals (Shen et al., Emerging Infectious Diseases 2004). In one of the most famous superspreading cases of the SARS outbreak, a physician who had treated SARS patients traveled to the Hotel Metropole in Hong Kong and infected at least 13 other people on his floor. These individuals left the country, spreading the disease to Canada, Vietnam, and Singapore. The single imported case to Canada resulted in 128 cases at a Toronto hospital. During the 2014-2015 Ebola outbreak, SSEs were also found to play a key role in sustaining the epidemic. One study by Lau et al. found that 3% of cases were responsible for 61% of infections. SSEs were also reported in the 2015 MERS outbreak, when a 68-year-old male traveled to Bahrain, the United Arab Emirates, Saudi Arabia, and Qatar before returning to South Korea. This index patient led to 29 secondary infections. Two of these secondary cases were shown to be responsible for 106 subsequent infections, out of 166 cases known at the time. Approximately 75% of cases in the MERS-CoV outbreak in South Korea could be traced back to these three superspreaders.
As SARS-CoV-2 continues to spread, several reports of SSEs have emerged. In South Korea, a cluster of cases was linked to a 61-year-old woman known as “Patient 31,” who attended church services in the city of Daegu. By February 20, 2020, South Korea reported a total of 82 infections, of which 37 cases were linked to her. The Korean Centre for Disease Control (KCDC) called this event a super-spreader event. (Read more about this case in the South Korea case study.) Several other countries have also reported SSEs during the COVID-19 outbreak. According to the WHO, a British man who traveled from a conference in Singapore to a ski trip in the French Alps in late January reportedly infected 11 other people in the ski village. The New York Times reported a case of super-spreading in the suburb of Westport, Connecticut, after 50 guests gathered on March 5 for a 50th birthday party. On the day of the party, Westport did not have a single known COVID-19 case. By March 23, Westport had 85 cases, reflecting a 40-fold change in cases in 11 days. In Massachusetts, the annual leadership meeting of the pharmaceutical company, Biogen, February 26-27th was found to be responsible for 99 in-state cases, as well as the first confirmed cases in several other states.
One of the most significant SSEs in the US has been in nursing homes (see Module 3 for more information on infection spread in nursing homes). The New York Times reports that residents and staff of long-term care facilities account for almost 40 percent -- around 68,000 -- of all COVID-19-related deaths in the US as of September 5th. In Massachusetts, nursing home deaths account for over 60% of all deaths in the state, per the Boston Herald in August 2020. Data from the CDC shows that over half of all COVID-19 fatalities in the US are in the over-75 population as of September 2020. Research suggests that older people have paid a higher toll in COVID-19-related mortality than other age groups due more severe clinical presentations caused by frailty (Maltese et al., Journal of Clinical Medicine 2020). To prevent further spreading of SARS-CoV-2 in long-term care facilities, states like Maryland, as highlighted by the Washington Post in September 2020, are deploying thousands of rapid SARS-CoV-2 antigen tests to mass screen nursing home populations. This response is a part of a novel effort to adapt response models typically used for natural disasters and deploy teams of responders (including medical workers, emergency responders, etc.) to help nursing homes handle outbreaks in their facilities, according to the New York Times in August 2020. Maryland is but one of several states such as Massachusetts, Florida, Texas, New Jersey, Ohio, Wisconsin, and Tennessee who are also adapting such large-scale epidemiological response methods.
SSEs play a critical role in both the early explosive growth of epidemics and sustained transmission in later stages. SSEs are worrisome because they are inherently difficult to predict and therefore, even more difficult to prevent. The best prevention and mitigation strategies rely on quickly recognizing and studying SSEs. According to a study by Frieden et al. (2020), several actions can help to reduce the impact of SSEs to better control COVID-19, including: 1) understanding their transmission dynamics, 2) identifying and mitigating high-risk settings for SSEs, 3) strictly adhering to protocols for infection prevention in healthcare settings, and 4) quickly implementing nonpharmaceutical interventions, such as closing schools, canceling public events, and imposing city lockdowns. Interventions such as these seek to reduce the impact of SSEs, as well as reduce the risk of future SSEs. Thought Questions:
Given what you already know about SARS-CoV-2, why might it be giving rise to so many super-spreading events? In your answer, incorporate epidemiological concepts defined at the beginning of the Module.