Epidemic Curve of COVID-19

00:01 So let's look at an example of COVID-19 epidemic curve in the early stages.

00:07 And this is taken from a Canadian province just as an example.

00:12 It shows that it's climbing to a peak and starts to diminish again, as you would expect from a propagated epidemic.

00:19 It's going to start again for a second wave after that.

00:23 We can look to this curve to learn about the ways in which changes in public health are manifesting in the data and how the effects of interventions might be noticeable in the Epi curve.

00:38 For example, sometime in early March, the lab testing criteria changed we're identifying and defining what is a case.

00:47 And so in this case it lowered the threshold for identifying a case.

00:51 As a result, the numbers increased a little bit.

00:55 If you didn't know that happened you'd think oh what happened on that one day? Why was that one day an outlier that so many people got sick? And it wasn't a case of a lot more people getting sick, it was a case of defining what a case is a bit differently.

01:12 The next day, we changed the policies in the community.

01:17 Gatherings were limited to 50 or fewer, and that caused a diminishing of transmission.

01:25 and so while the cases were still increasing, they're increasing a bit more slowly several days later.

01:32 So again we can use epidemic curves to see how various interventions in a community might be leading to changes in transmission rates.

01:44 So epidemic curves are lagging indicators.

01:50 They're a lagging indicator of an epidemic because you only know about an illness when you test for an illness.

01:58 You typically only test for an illness when there's a reason to test, if someone has symptoms and when they present to be tested.

02:05 So it's possible for someone to become infected, not show symptoms for several days and then will delay even longer until the symptoms are bad enough to go seek out care and then get tested and then we wait even longer for the test results to come back.

02:20 And maybe a bit longer for those results to be coded by an epidemiologist into the Epi curve.

02:25 So it's always late.

02:28 Curve is always a lagging indicator because of all these delays.

02:33 Keep in mind too that for some diseases, there's always going to be a background number of cases that are always there.

02:41 They're always percolating in the community but never enough to really be a crisis of any kind.

02:46 An outbreak is when we have more cases starting to mount in a short time period.

02:52 So if background cases are always there, it's difficult to say exactly which case is the index case, which case is the one that started the outbreak.

03:01 Of course, that's not true for all kinds of diseases but for some.

03:06 For some cases, the date of illness onset is not known because that takes a while for public health investigators to actually figure that out.

03:15 As a result, some of these data points will be wrong.

03:19 And it can be difficult to determine when a curve starts to go down because of the reporting delay.

03:26 We don't know when the epidemic has really plateaued.

03:30 More cases start mounting, test results come back So it's not uncommon to go back and revise the numbers we have from a few days ago This is very common in COVID-19 for example, so it's dangerous look at the daily case counts and assume that's the way it's always going to be.

03:47 It's safer to go back a couple days in the past and see, "ah that's what the real number is because we have more test results back now".

03:55 As a result we never really know when the curve has plateaued until after the plateau has been observed.

04:04 You never know the full shape of the curve until the outbreak is over.

04:10 Epi curves are powerful tools but they are not complete tools and it's really important to remember that they are incomplete data and only are suggestive of what's going on.

04:21 They are not complete.

04:23 Here's an example, erratical example of a COVID-19 epidemic curve showing infections and deaths.

04:32 Now a typical Epi curve looks just at infections but nothing prevents you from plotting deaths if you want.

04:38 Deaths are a lagging indicator to infection because it takes time for someone to become ill then gets sick enough to be hospitalized and then unfortunately to die.

04:50 So in the case of COVID-19 is about 21 and 28 days between infection and dying and maybe some days between that death being no test encoded in the data.

05:02 So deaths are a lagging indicator.

05:05 So it's not unusual then to see the Epi curve coming down as it is in this case, the curve is coming down fairly early in this epidemic but the deaths are still mounting.

05:18 Mounting deaths in this case should not be taken as a case of the outbreak is out of control, In fact, in this case the outbreak is under control but because deaths are lagging, it gives a false impression of what's going on.

05:33 So epidemics are difficult to keep track of for many reasons and one of them is this, nature of time delays and lagging indicators.