A Handy Glossary of COVID-19 Terms and Definitions

What do all these new terms mean? The COVID-19 outbreak has forced us all to take a crash course in infectious disease epidemiology. To help you understand what these terms mean, here is my glossary. I’ll be updating this every day or two.

Feel free to leave me a comment requesting a new term to be added to this list, or letting me know if something needs to be clarified.

Acute respiratory distress syndrome (ARDS)
Acute respiratory distress syndrome is a life-threatening medical crisis involving rapid onset of respiratory failure due to extensive inflammation and fluid buildup in the lungs. The main symptoms are shortness of breath, rapid breathing, and bluish skin color. ARDS is generally fatal without treatment. Medical management of ARDS involves the use of a mechanical ventilation (a machine that takes over breathing), medications, and special body positioning. Even with treatment, ARDS carries a high risk of death (35-50%). ARDS is a common occurrence in patients with severe illness related to COVID-19. On average, patients with severe disease develop ARDS between 8 to 12 days after symptoms start. Between 20% and 42% of patients hospitalized for COVID-19 will develop ARDS.
Asymptomatic/Pre-symptomatic transmission
In any infectious disease outbreak, it is critical to gain a clear understanding of the transmission dynamics, or how the disease is spreading. That includes understanding the incubation period, the mode of transmission and the timing of infectivity over the course of the disease. One of the most important aspects of transmission dynamics is whether the disease can be transmitted from an infected index case to a non-infected person prior to the onset of symptoms (also called secondary transmission). If that is happening, we refer to it as pre-symptomatic transmission and it’s a very big deal because nothing makes an infectious disease harder to control than this one feature. Pre-symptomatic transmission means there is a sufficient amount of virus being shed that transmission is possible after someone has been infected but before they themselves show symptoms. When true, conventional screening such as taking temperatures won’t work. More importantly, the disease is spread more rapidly when people don’t know to avoid contact with others and believe themselves to be unaffected. A related idea is asymptomatic transmission, which is similar in the sense that secondary infections can occur in those who have been infected but who never show any symptoms. I group them here because if either or both are present, the transmission dynamics will be very different. Evidence is increasing that both pre-symptomatic and asymptomatic transmission are occurring in the current pandemic and that this helps explain the patters we see.
Basic rate of reproduction (aka reproduction rate, R0, read: R-Sub-naught)
R0 has gone viral! This is for good reason because, in an epidemic, one of the most important numbers used to understand and predict the course of the outbreak is the basic rate of reproduction, or R0. The term has a technical meaning (I won’t bore you with that) and a more general one; it is the average expected number of secondary cases each primary infectious case will transmit the disease to (or, how many other people will a sick person infect). It is a measure of how contagious a disease is. This is not just about the pathogen, but has as much to do with the pattern of mixing behaviors that bring primary infected cases into contact with those who are susceptible. As a general rule, if R0 is greater than 1, an outbreak cycle will continue to propagate through chains of transmission until there are effective therapies or insufficient numbers of susceptible persons left to infect, and the epidemic cycle is broken. When R0 is less than 1, the outbreak can be contained and suppressed. And sorry British PM Boris Johnson, an R0<1.0 does NOT mean the epidemic will shrink. However, epidemic control measures (such as social distancing) are aimed at reducing R0 to below 1 so that health care systems can keep up and secondary cases can be prevented through contact tracing etc. The seasonal flu generally has an R0 of about 1.3 (each person with the flu gives it to 1.3 others). We still don’t know the R0 of this novel coronavirus with certainty, and we aren’t likely to know until well after this first global outbreak has ended. Initial estimates from China suggested a range of 2.2 to 3.6 assuming a population without immunity and no control measures. That makes this a fairly contagious disease with the potential to be up to twice as contagious as influenza. That is because we have vaccines and partial immunity to flu due to exposure to related strains. If we are to flatten-the-curve, we must put epidemic control measures in place to lower this number over time to a value below 1. However, it’s important not to turn R0 into a single-minded policy fetish. The basic rate of reproduction is a hypothetical number estimated with problematic data that is constantly changing. R0 is actually not very useful to health workers and planners on the ground. To track our progress and evaluate the success of those measures, we break the epidemic into shorter time segments using a related concept: theeffective reproductive number, or Rt.
Case definition
As disease detectives, a critical first step in our investigation of an outbreak is to identify the crime. That involves formulating a case definition so that we can tell who is or is not infected. Defining a case requires knowing which symptoms or tests can be used to capture everyone who has disease (also known as sensitivity) and to rule out those who don’t have the disease we care about (also called specificity). It’s a bit technical, but to a disease detective, getting the case definition right is a top priority. COVID-19 illustrates how we sometimes latch onto the first clues we get in the case and don’t update when better information comes along. Based on a tiny study of 44 patients very early in the outbreak in China, the WHO and CDC used fever, cough and shortness of breath as the first case definition. The WHO has moved past this, but the CDC has not. We now know that fatigue is more important than shortness of breath and is often the first symptom. We know that as many as 1-in-5 patients don’t have a fever and that up to a third of patients have gastrointestinal rather than respiratory symptoms. Much more careful work is needed to refine our case definition in this pandemic.
Case fatality rate (CFR)
Epidemiologists consider the case fatality rate to be one of the most important parameters for understanding an outbreak. The CFR can be thought of as the risk of dying from the disease among those who are infected. It is determined by the lethality of the pathogen, but also the effectiveness of treatment. As is true for all outbreaks of a brand new pathogen, the estimation of CFR in COVID-19 has been very challenging. Current estimates range from 0.05% (5 out of 10,000) to 5.8% in Wuhan China during the initial outbreak (58 out of 1,000). The WHO published an estimate of 3.4% on March 3. Most epidemiologists believe this estimate is likely to be wrong. To accurately measure the CFR, we need two critical numbers: the number of deaths due to COVID-19, and the total number of infections. Any errors in either of these numbers can make the result wrong. Right now, we are certainly counting COVID-19 deaths with less error than we are counting infections. Due to lack of population-based testing in the US, we do not know the true number of infections. That’s because an estimated 50% of infections have no symptoms or mild symptoms, and because only a small fraction of Americans have yet been tested. If half of all cases are covert or hidden, the denominator will be wrong and our CFR estimate will be off. Until we do much more testing of the general population (not just those who are sick) we will not know what the CFR really is. Until then, the best we have is a crude death ratio (or CDR), which is the number of COVID-19 deaths divided by confirmed cases. My opinion is that the CFR will be between 3 and 15-times worse than seasonal flu, which translates to a mortality risk of between 3 out of 1,000 (0.3%) and 10 out of 1,000 (1.5%). This range represents plausible estimates that will need to be updated when more information is available.
Community mitigation strategies
In an infectious disease outbreak, community mitigation strategies are control measures put in place to reduce the negative impact of the outbreak once community transmission has started. For more information, see the CDC’s page on community mitigation strategies (PDF).
Community transmission
This refers to a key part of the epidemic cycle where an infectious disease enters a new area or population and begins to spread (or be transmitted) from person to person within that area after an initial period during which cases came from somewhere else. The start of community transmission is a very important tipping point in an outbreak that separates the containment phase (before) and the mitigation phase (after). You can think of community transmission as the point at which a disease becomes established and is ‘homegrown’, regardless of where it might have come from. The United States is now experiencing sustained community transmission of COVID-19.
Contact tracing
An outbreak control measure used during the containment phase and after peak incidence. It is a labor- and time-consuming process of identifying, locating and contacting any and all persons who might have had contact with infected persons who have brought the disease to a new area. The goal of contact tracing is to stop community transmission in its tracks by isolating those who might act as bridges between an outside outbreak and a new area. Contact tracing is generally not a good use of resources during the period of exponential case growth when community transmission is widespread. However, after the epidemic curve has peaked, contact tracing becomes an essential strategy to prevent sporadic cases from re-igniting a new wavelet of exponential growth. It is important to recognize that contact tracing is not new, it has been a well-established strategy at the heart of public health practice for decades. It is the first-line strategy in addressing diseases as diverse as measles, rabies, HIV and tuberculosis.
An outbreak control strategy designed to stop an epidemic cycle from taking hold in a particular place. Containment is used early in an outbreak when the people who were infected elsewhere come into a new population and community spread has not yet begun in earnest. This usually involves isolating infected persons and doing careful contact tracing to find and isolate anyone who has had contact with those who are infected.
There is nothing new about coronavirus. The term refers to a family of common viruses that have been known about for a very long time. The name refers to the ‘crown’-like shape seen on the outside of the virus under a very powerful microscope. There are seven different versions of coronavirus that can infect and cause illness in humans. In nature, these viruses live in bats and other animals, often without causing disease. Most coronaviruses cause only mild symptoms; a large percentage of ‘common colds’ every year are caused by a version of coronavirus.
Covert cases
People who are infected with a pathogen, but who have mild symptoms or no symptoms at all are referred to as covert cases. In the COVID-19 pandemic, identifying and counting covert cases remains a top priority. It is likely that covert cases are driving the epidemic because even though these people have mild or no symptoms, we now believe they are capable of spreading the disease to others. Covert cases are not the same as pre-clinical cases. It is now estimated that up to 60% of all infections are covert cases. This is a major reason the R0 value for COVID-19 is higher than 1 and why the disease is spreading so fast. For details, see the commentary in Nature Microbiology.
Refers to the disease in humans caused by a version of coronavirus that recently jumped from animals to humans in the fall of 2019. More specifically, the disease is caused by SARS-CoV-2. It is closely related to an earlier version of coronavirus that caused an epidemic (but not pandemic) of illness in 2003 called SARS. COVID-19 is more easily transmitted (contagious) and more severe than regular flu or the common cold. The main symptoms are fever, cough and shortness of breath. Not everyone who has the disease has all these symptoms. Only 88% of people with COVID-19 had a fever. Muscle pain, runny nose, congestion, nausea and sore throat are less common symptoms. The majority of cases involve only mild symptoms, but some develop a severe lung infection (pneumonia).
Crude death ratio (CDR)
The case fatality rate (or CRF) of an infectious disease is a critically important number. However, early in an outbreak of a new disease, estimating CFR is highly problematic when a complete enumeration of all cases of infection is not possible. All we are left with is an estimate based on the number of deaths divided by the number of confirmed cases we know about. I am calling this the crude death ratio (CDR), while others prefer crude fatality ratio or infection fatality rate (IFR). The main point is that when a high proportion of cases are mild or asymptomatic, the denominator (all cases of infection in a population) is dramatically under-estimated, and the resulting CDR is exaggerated. Until the true population prevalence is known, the denominator will be biased and CDR will not be a good proxy for CFR.
Effective reproduction rate (Rt), aka Re, effective R
The family of R statistics describe the estimated average number of secondary infections that will arise from each person who becomes infected with a communicable disease. While much emphasis has been placed on the basic reproduction rate or R0 during the pandemic, its utility is limited (see above). In order to track and evaluate the success of community mitigation strategies, we prefer to use the Effective reproduction rate or Rt. The technical aspects of how this number is computed are not important here, but the main idea is that it estimates a more narrow quantity by breaking time into shorter segments, rather than estimating an overall global rate of reproduction. Epidemiologists use different names for this quantity including Rt, Reor just “effective R”.
The occurrence of disease in a particular time and place in excess of what is expected. A disease that is consistent at expected levels is referred to as endemic (meaning it is ‘normal’ part of a given place). An epidemic is an outbreak of disease that is greater than expected and indicates that something about a disease, a place or the people has changed.
Epidemic curve
An epidemic curve is a basic tool used by epidemiologists to visualize the course of an epidemic outbreak. It is created by graphing new cases by day, hour or month. The shape of the curve gives important clues to help identify likely modes of transmission, incubation periods and the source of the outbreak. The figure below is an example of an epidemic curve from an outbreak of yellow fever from Angola in 2016.


Epidemic cycle
In infectious disease outbreaks, the epidemic cycle refers to the time between the start and end of an outbreak, during which the disease tries to survive and spread itself, and the population tries to prevent the spread and defeat the illness. This tug-of-war leads to a dynamic process that is well-understood to the science of epidemiology. We track the progress of an epidemic cycle by counting new cases over time (by hour, weeks or days), creating an epidemic curve that shows how the disease is moving through the population. That curve takes on a predictable shape determined by the number of infected persons, the number of people who are susceptible to become infected, and the contact between them. How fast the outbreak occurs, and when it stops is determined by the pathogen, the place, and by people’s behavior. Outbreaks tend to end on their own even if we do nothing to stop them because, at some point, there are not enough susceptible persons left to keep the cycle going and the outbreak fizzles. Social distancing and other control measures are designed to alter the shape of the epidemic curve and to slow the cycle of the epidemic.
The science of how diseases occur in populations and how to prevent and treat them. It is a main arm of public health and a branch of medical science. Epidemiologists are ‘disease detectives’ who use data, models and science to understand where diseases come from and how to prevent or stop them. Within the field, infectious disease epidemiology is the particular subfield that specializes in diseases like COVID-19.
This has become one of the hottest memes to emerge from the COVID-19 pandemic. That’s good because it is a very important idea. However, the term is often not well-understood. Once an infectious disease outbreak takes hold in a population, the emphasis shifts from containment (keeping the epidemic cycle from igniting) to mitigation (reducing negative impacts). As we have seen in Italy and elsewhere, the rate of spread largely determines our ability to handle the workload of caring for the sick and protecting those who are most vulnerable. We have only a finite number of doctors, hospital beds and breathing machines available, and if the outbreak moves too fast, we will exceed capacity and deaths and suffering will rise. To keep this from happening, we enact community mitigation strategies (called control measures in the graph below) to slow the pace of the outbreak. This means two crucial things: 1) While we hope we can reduce the total number of infections, this may not be possible and is not the point of flattening the curve; 2) the objective is to slow the epidemic so that we can stay below the breaking point where we run out of capacity to treat the sick. This implies that the outbreak will actually go on longer than it might without these control measures. The epidemic will end when the numbers of people who remain susceptible to infection dips below a threshold and the transmission cycle slows or stops. That’s how epidemics have worked throughout history. When containment is not possible (as is the case for COVID-19 in many countries), and a vaccine is not yet available, slowing the pace of transmission is the number one goal. That requires everyone to pitch in and understand that what we all do together (or don’t do) is not just to protect ourselves, but to reduce the risk of death and suffering for our grandparents, and those who are otherwise vulnerable.


Herd immunity
I taught epidemiology for 30+ years to some very smart people. Explaining herd immunity has been the Holy grail. It is a challenging idea to convey. The main reason is that herd immunity is a property of an entire population; it is not a characteristic of one person. Understanding it requires thinking about a population as a complex system. Herd immunity refers to the indirect protection offered to those who are susceptible to an infectious disease because a high proportion of the population is immune. It means that stopping or preventing an epidemic doesn’t require everyone to be immune. That is very important because lots of people can’t be vaccinated or have heightened risk of death if they get infected. To grasp this idea, consider that all persons in a population fall into one of three groups: A) those who are infected, B) those who are immune (due to vaccination or previous infection), and C) those who are susceptible. The chain of transmission remains alive when group A is mixing with group C. As group B grows, it creates a safety buffer between A and C that lowers the chance the pathogen can continue spreading. There is a critical threshold of persons who must be immune before the virus can not reach those who remain susceptible. Herd immunity happens when the right balance of these three groups is achieved so the disease can’t get from A to C. For example, small pox was not eliminated in 1977 because everyone was vaccinated or immune. It was eradicated because herd immunity was achieved, which stopped the chain of transmission. The critical herd immunity threshold (HIT) varies according to the R0 or contagiousness of the disease. For measles, one of the most contagious diseases, close to 95% must be immune for herd immunity to protect the susceptible group. Measles outbreaks around the U.S. have occurred because enough people have refused to vaccinate that herd immunity was lost. We don’t know what it will be for SARS-CoV-2 but current estimates are between 30% and 75%. Until we have a test for antibodies, that threshold value will remain unknown. Herd immunity reminds us that we are all in this together and that what we all do helps protect the most vulnerable.
Incubation period
Epidemiologists use the term incubation period to describe the length of time between infection and the onset of symptoms. When a person is infected with a disease, it takes time for the body to recognize the presence of the pathogen and to launch an immune response to fight it. It also takes time for the virus itself to multiply (or propagate) inside a new host. The symptoms that arise from viral infections (like fever, runny nose and fatigue) are actually signs of the body’s defensive reaction to the illness, not the pathogen itself. The reason the incubation period is so important is because it can help diagnose the disease, but more importantly, it helps us understand and predict how fast the disease will spread in a population. Currently, we believe the incubation period for COVID-19 is between two and 14 days. That’s a very wide range. The best studies show average incubation periods of between three and six days. The main thing to keep in mind is that we believe this coronavirus can be transmitted during the incubation period, before symptoms appear. For this reason, taking the temperature of travelers is not a foolproof control measure. Knowing the incubation period is also vitally important for determining how long people who have been exposed to the disease through contact or by travelling should remain isolated from others (see Quarantine).
Isolation (aka self-isolation)
An infection control measure used in times of infectious disease outbreak to contain or slow the epidemic cycle. Isolation refers to identifying and separating persons who have been diagnosed with the disease in question, or who are a presumptive case. Isolation is one of several social distancing measures. The concept applies at the community level (staying home and away from people) and the household level (staying separate and away from people who reside together).
Public discourse and social media are filled with conjectures about how the SARS-CoV-2 virus might have, or might in the future mutate into something more dangerous. I am not a virologist, but I listen to what they say. Mutation is one of those scary words that has gone, well, viral! The reality is that all viruses mutate randomly all the time. That is because the replication process for an RNA virus is very prone to error. As a result, the SARS-CoV-2 virus, like all viruses is constantly mutating. There is no surprise in that. In fact, within one infected person, there will be countless different “versions” of the virus in circulation, like documents spewing from an old broken Xerox machine. All viruses mutate; those mutations rarely make any meaningful difference in how the virus works or how it spreads. The vast majority of mutations are just genetic noise; they don’t take hold and change the behavior of the pathogen because they don’t produce a meaningful survival advantage. Big changes do occur in viruses, but not generally on the time-scale of a specific outbreak. We do know that one such big change occurred when this coronavirus ‘spilled over’ from it’s natural host to people (possibly though an as yet unknown intermediate host). In science fiction, viruses (like superheroes) mutate suddenly and acquire extraordinary capabilities. In real life, meaningful mutations that matter are rare, unlikely and impossible to predict. That doesn’t mean that a qualitative change in the disease due to a mutation is impossible. But, we now have more tools than ever in human history to monitor this pathogen in real time, to watch for mutations that change the infectivity, transmissibility or lethality of the disease. That usually doesn’t happen. Nature is more complex. Yes, the virus is always mutating. That’s all part of what experts described in a comment in Nature Microbiology as the “…humdrum aspect of life for an RNA virus”. Most mutations don’t matter.
Basically, a pandemic is a global epidemic.  Like all epidemics, a pandemic implies an outbreak of infectious disease in excess of what is normal and expected for a particular time and place.  Key things to remember are that a) an epidemic must cross multiple national boundaries and must occur on multiple continents; b) a pandemic refers to the spread of the disease, not it’s severity; c) the declaration of a pandemic implies that an epidemic can no longer be contained to 1 or more countries or a single regions but has become a challenge for the planet.  Once you try to define it more specifically, things get fuzzy and controversial fast.  The World Health Organization had, until recently, a very specific definition.  But the term has lost favor in many circles; in the case of COVID-19, there was fear that even using the word would cause panic. The WHO declared COVID-19 a pandemic on March 11, 2020.
Personal protective equipment (PPE)
Personal protective equipment (PPE) refers to a wide array of tools used by medical personal to protect against exposure to dangerous substances and to prevent transmission of infectious agents. Hazards addressed by PPE include physical, electrical, chemicals, heat, biohazards, and airborne particulate matter. In an outbreak, the main categories of PPE protect the user’s airway (masks and respirators), skin (gloves, caps, gowns), and eyes (goggles and shields). The requirements for PPE depend on the pathogen and how it is transmitted. Transmission via respiratory droplets vs. aerosolized particles require different PPE. To protect health workers against COVID-19, gloves, gowns, eye-protection and a respirator have been recommended. A respirator is a specialized facemask rated by NIOSH to filter particles. For example, an N95 respirator is a special mask made of dense fabric and a nose piece that fits tightly over the nose and mouth, which filters 95% of particles of 0.3 microns or larger. While they look similar, a regular medical mask is not technically a respirator and offers far less protection compared to a properly fitted N95 respirator. For additional information, see the CDC page on PPE.
Preclinical case
A person who has been infected with a virus but has not yet shown any signs or symptoms of illness is considered a preclinical case. These are cases in the period of incubation. It is widely believed that preclinical cases are infectious (meaning they are capable of passing the disease to others, but the details remain uncertain. Persons who have been exposed to someone who is infected should be considered presumptive preclinical cases during a period of quarantine. Because preclinical cases are a infectious, the COVID-19 epidemic spreads faster (and has a higher R0).
Presumptive case
A major challenge in any outbreak investigation is how to define, identify and count cases. In situations where a gold-standard test is available, this can be easy. In COVID-19, a major problem is that while we have a test, we can’t test enough people fast enough to know what we are dealing with. So, we are forced to assume that any and all persons with a cold or flu-like symptom profile is a presumptive case, meaning that until they can be tested, we act as though they might have COVID-19. In this disease it is especially challenging because we currently have a very vague case definition based solely on clinical symptoms (e.g., fever, dry cough and shortness of breath). As an epidemiologist, I am very unhappy with this situation because it is neither sensitive or specific. Since COVID-19 is known to be spread by people with mild symptoms or no symptoms at all, any occurrence of anything that could be COVID-19, should be assumed to be COVID-19.
Sensitivity (Sn) & Specificity (Sp)
Sensitivity and specificity are the two most important features of any test or case definition. Sensitivity is the probability that a given test/case definition correctly identifies all TRUE POSITIVES (people who really have the disease in question). Specificity is the probability that a given test/case definition correctly identifies TRUE NEGATIVES (people who really don’t have the disease in question). Ideally, we would like to find a test that does both things 100% of the time. Unfortunately, in practice, it requires a balancing act because the two goals compete against each other. For example, lets say we defined a COVID-19 case as anyone who reported having any 1 of the top 10 symptoms found to be associated with the disease. That might mean we had 100% sensitivity at least for symptomatic cases. But, the specificity would be poor because lots of TRUE NEGATIVES (people with some other illness that looks similar to COVID-19) would be mis-identified (as false positives). Or, we could define a COVID-19 case as those who report having all the common symptoms, and a positive PCR test result. Setting the diagnostic bar that high might yield 100% specificity, at the expense of misidentifying a huge fraction of TRUE POSITIVES simply because they hadn’t been tested, or were missing a single symptom. The trick is to find the delicate balancing point that yields both sensitivity and specificity at close to 90% as possible. If either falls below 80%, our ability to track, trace and treat the outbreak will be compromised.
Severe acute respiratory syndrome (SARS)
Severe acute respiratory syndrome (or SARS) is a viral respiratory disease caused by the SARS coronavirus. The first strain (SARS-CoV, or SARS-CoV-1) was identified in the early 2000s in China, having made the jump from horseshoe bats to humans. This strain became an epidemic affecting approximately 8,098 persons and killing 775 in 37 nations between Nov. 2002 and July 2003. Unlike the second strain (SARS-CoV-2), SARS-CoV never became a pandemic. The outbreak was declared contained on 5 July 2003. Fever was the only symptom common to all patients. The average incubation period was 4-6 day. Estimates of R0 have been between 2-4 initially, and 0.4 after epidemic control measures were put in place. The CFR was between 9-10%, considerably higher than COVID-19.
This is the official name (for now) of the virus that causes COVID-19. It is one of seven coronaviruses that are known to infect humans and to be capable of spreading from person to person. It is spread mainly through contact with respiratory droplets from coughs and sneezes. The virus does not spread primarily through the air, but rather through contamination of surfaces. Wearing surgical masks therefore does little to protect those who are not infected; it can actually make infection more likely because mask wearers touch their faces more often. We know that the virus can live on surfaces for up to three hours (shorter if the surface is dry and smooth). Fortunately, this virus is fairly fragile in the sense that it is easily killed by contact with disinfectants such as rubbing alcohol, hand sanitizer, soap, Lysol and even vodka (although each of these works differently depending on its alcohol content).
Secondary transmission
Secondary transmission is the transfer of an infectious disease from primary cases to persons in close proximity to them. It means different things in different diseases but in COVID-19, primary cases are generally those who are identified, tested and hospitalized because they are very sick. In primary cases, the source of the disease is often unknown; primary patients tend to be older and more vulnerable than the general population. We study secondary transmission because it gives us invaluable clues about transmission dynamics that are more typical. It is also easier and more practical to do contact tracing of those who live with, work with or take care of a primary case than to do it on everyone. Those who acquire the illness through secondary transmission are referred to as secondary cases. The secondary infection rate (SIR) is a measure of the frequency of new cases among the household contacts of a primary case in a defined period.
Serial interval
The serial interval is the time interval (in days) between when person A (the infector) develops symptoms, and when Person B (the infectee) develops symptoms as a result of secondary transmission. This number is very important because it is a vital clue to whether people can transmit an infection before they are sick. A study by Du and colleagues finds this interval is around 4 days in COVID-19. That is a short interval and supports the view that this coronavirus can be spread before symptoms are apparent.
Shelter in place (SIP)
Shelter in place refers to a strategy of epidemic control that involves remaining in your home as much as possible to avoid being exposed to a disease or to transmit the disease to others. It can be used either informally to mean hunkering down, or it can refer to an official order issued by local or federal officials in response to an emergency such as a mass shooting, chemical spill or natural disaster. The term implies the need for provisioning your household for a period of non-contact with the outside world, which requires having adequate supplies of food, medicine, cleaning supplies and other essentials in order to ride out the storm.
Social distancing (SD)
Epidemiologists use the term social distancing to refer to actions intended to slow the rate of disease transmission by keeping infected persons and susceptible persons from coming into contact with each other. Since we don’t know who is infected, social distancing measures can be aimed at everyone. SD is the most important community mitigation strategy we have to slow the spread of the disease (see Flattening the curve). Common measures include closing schools, shopping malls, theatres and other places where crowds gather, spacing people out in stores, restaurants and health care centers, telecommuting for work and school, restricting public social gatherings, self-quarantine, restricting access to public transportation etc. In a daily briefing, I opine that it may be time to get some distance from the term. In times of crisis, social solidarity and connection saves lives. What’s needed is physical distance, not, strictly speaking social distance. Until a better term emerges, it’s important to remember that social distancing must not be an excuse for selfishness and social division. For more information, see The Atlantic’s article on The Dos and Don’ts of ‘Social Distancing’.
Test Positivity Rate (TPR)
The test positivity rate (TPR) is another rate calculation that provides a critically important clue to the disease detective. Computationally, it is just the number of positive tests divided by the total number of tests (positive + negative) excluding pending or tests of unknown result. Ideally, it is calculated on a per-test basis, but can also be used on a per-person-tested basis. The TPR is important because it lets us track the maturation of a testing regime over the course of an outbreak. Initially, tests are in short supply; testing is limited to those who have returned from a hot spot, or those who have frank symptoms. The TPR tends to be high (e.g., >30%) because the only people tested are those who have a high likelihood of having been infected. Over time, as testing expands, the goal is to cast an increasingly wide testing net, by widening the range and scope of those who are eligible to be tested. As this happens, the TPR falls as the purpose of testing shifts from highly selective to population-focused. While the initial goal is to confirm illness in those who are hospitalized, the emphasis then shifts toward establishing the prevalence of the disease and also identifying sporadic cases that can feed into contact tracing to stop chains of transmission at the root. One way to know this is happening is that the TPR drops to somewhere between 1-5%. If everyone is tested at random, the TPR will equal the disease prevalence. If it remains over 10%, testing is most likely still being done selectively.


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