We have seen the news and read the articles on testing. These include the amount of testing, the type of testing, the testing results, debates on how much testing is needed, and who should be tested. I have been in testing for about a decade and the information I see and hear is occasionally frustrating in its characterization, and many times presented without the context that could provide clarity.
Testing is an information collection activity. While that may be a simple explanation, it grows complex as you begin to explore what is tested, how testing is performed, and what test results mean.
The most important question in testing is: what do you want to learn? There are many answers because there are many people asking the same question. Some things to learn are:
- About the percentage of the population infected
- If a person is infected
- If some actions help reduce infection
Learning about the percentage of the population infected is important because it informs us of the spread of the infection. Learning about this frequently is more valuable because it informs us on the pace of the spread. That is, it can answer a secondary question of how fast the infection travels through the population.
Learning if I am infected is important because it informs me that my future actions could be harmful to others and I may become ill. Learning about this frequently is more valuable because it informs us that our actions may be helping to reduce the pace of the spread. Alternatively, it may inform me that my present actions are preventing infection in me.
Learning if my actions reduce infection, say in the case of drugs administered to address the infection, is important because it informs me that my actions are correct. Learning about this frequently is more valuable because we can inform others who may benefit from the same actions.
Once you determine what you want to learn, you ask what should be tested. Since the infection lives in bodily fluids (such as mucous or blood), the answer seems clear. There are challenges to collecting fluids because fluids can become contaminated and render the test invalid. Collection methods must be sterile and, very importantly, those who collect the fluids must be protected.
Once the fluid is collected, the test occurs. But how is the fluid tested? We hear some tests attempt to detect the infection, and some tests attempt to detect antibodies. My guess is the detection technique between the two is different. Each test provides different information and answers different questions. One test helps you learn if you might be infected; the other helps you learn if you might have been infected.
Note that I said “might be” and “might have”. An important aspect of testing is accuracy. An accurate testing result depends on an uncontaminated fluid sample, collected in a sterile manner, and evaluated by a highly accurate method. If a test for infection resulted in no infection and that result was wrong, it is called a “false positive.” Manufacturers of these tests must demonstrate the ability to produce accurate results and this ability requires time.
Lastly, the infection progression inside the body seems to show symptoms after many days. Since the growth rate appears slow, a test may show a negative result one day (not infected), and a positive result the next (infection present). This demonstrates test sensitivity. That is, there must be some threshold of infection present in the collected fluid for the test to render a positive result. A better sensitivity may detect infection sooner.
I have also read and heard information about models. In Information Technology, we are familiar with models and their uses. Recently, I have heard how wrong some models are. This conclusion seems uninformed and amateurish.
Weather forecasters use models to predict weather. The models are based on information collected over a long period of time. This history of information helps build an understanding of weather patterns. Presently, models predict local weather patterns with pretty good accuracy. As we all know, the weather forecaster occasionally makes an inaccurate prediction.
Models for infections are built in a similar manner. The primary difference between weather and a new infection is the amount of information available. With very little information about an infection, the prediction accuracy varies greatly. As more information is collected, the infection model is updated and accuracy may improve but it improves very slowly.
The model presents a number representing a prediction for infections. The number is not, and was never meant to be, exact. It is a guess, an estimate. The model may be inaccurate but I would not consider it wrong. The inaccuracy helps identify things to consider. These things can improve the accuracy for the next model.