First, thanks for reading. And second, share often and share liberally. About 150+ days ago SARS-CoV-2 and the debilitating disease it causes COVID-19 were scientific unknowns to the American public. Of course, that is not the case today. And since March 2020 interest in the virus and the disease it causes has dominated scientific inquiry.
This post is comprised of questions (Qs) created by the author that challenge students to apply the knowledge acquired in chemistry class to COVID-19. The questions encourage students to think across scientific disciplines, to think 'outside the box', and/or 'connect the dots'. For an instructor it is a 'just in time' opportunity to teach students that dividing up the physical, natural world and its associated phenomena into chemistry, biology, physics, and so on is nothing more than a human construct. The reality is that this nice and tidy categorization of scientific inquiry does not accurately describe the overlapping, messy, fuzzy, undefined world of observable events. Is it chemistry? physics? biology? Does it really matter? Understanding how SARS-CoV-2 works inside and outside the human body is every bit about chemistry as it is about biology as it is about physiology. It is all inter-related. What differs is how each scientific discipline approaches the problem and how it interprets the data.
In summary, the Qs below are based on a mix of conceptual (= non-mathematical) and quantitative (= involves some mathematics and/or graph analysis) types. Though I have not had the chance to beta test this in class, nevertheless I surmise that the Think-Pair-Share (TPS) engagement instructional strategy (involving individual-to-individual or group-to-group) would work just fine. For some Qs students are asked to defend their conclusion(s). Like their instructors students draw a conclusion(s) based on a reason or reasons. Given that, students should have practice in defending (perhaps in the wrong) in front of others what they conclude.
For some of the Qs, you will find a brief commentary in bold below the Q. A horizontal line separates the Qs and commentary (if applicable). Q7 and 8 refer to data found in the permanently archived primary scientific literature and may serve as a catalyst to discuss how a scientific paper is organized. Let's get started.
Q1: The persistence of stability of SARS-CoV-2, the infectious virus that causes the disease COVID-19, on surfaces may or may not depend on a variety of environmental and/or physical factors. Below is a table with four factors. For each factor speculate whether the persistence of SARS-CoV-2 that resides on a surface depends on that factor. If there is a dependency, then speculate whether the persistence of the virus and the factor are directly or indirectly related. Be prepared to defend your conclusions in Q2.
Commentary: Q1 does not ask if the relationship is linear or non-linear. Data are needed to determine that. Q1 is an 'open ended' type. Its purpose is to get students thinking about and discussing how factors such as those listed above could influence how long a virus stays around (= persists).
Below are the answers to Q1. Answers based on the ACS webinar, How this coronavirus is (and isn't) different from other viruses (https://www.acs.org/content/acs/en/acs-webinars/popular-chemistry/coronaviruses.html). ACS = American Chemical Society
Q2: Find a classmate and compare/contrast your answers with your classmate's answers. Find another classmate and compare/contrast. Find a third classmate and compare/contrast. Complete the table below after comparing your answers with each classmate. As a class discuss reasons for disagreements.
Directions to complete table: Your classmate's answer will either agree or disagree with yours. If you both have the same answer, then place an A (= Agree) in the empty cell. If you have a different answer than your classmate, then place a D (= Disagree) in the empty cell. For example, if you and classmate #1 have the same conclusion (D, I, or NR) for temperature, then place an A in the empty cell.
Remember, you had a reason or reasons for your answer. Defend it. But realize that perhaps your answer may not be supported by the professional scientific evidence in spite of your belief or insistence that you are 'right'. Personal opinion does not equate to fact.
Q3: Persistence of a virus such as SARS-CoV-2 on a surface is measured in half-lives, a unit of time. Provide a rationale for why half-life is used.
Commentary: Viruses do not all die* at the same time. At any given moment in time some (= a population) are 'dead', some are not. A comparison in chemistry is radionuclides. At any given moment some nuclei are decaying while some are not. Eventually all will decay.
*Links to are viruses alive or not question/debate:
https://www.newscientist.com/question/are-viruses-alive/
https://www.livescience.com/58018-are-viruses-alive.html
https://www.scientificamerican.com/article/are-viruses-alive-2004/
https://www.nationalgeographic.com/science/2008/08/news-virus-infecting-definition-life/
https://www.nature.com/scitable/topicpage/the-origins-of-viruses-14398218/
Q4: On the empty axes below draw what you predict is the relationship between the following x-y variables: x-axis: % Relative Humidity (RH) of Air, and y-axis: % Infectivity of Virus.
What is:
- % Relative Humidity (RH) = (partial pressure H2O(v) present/partial pressure H2O(v) at equilibrium) x 102; or the amount of water vapor in the air at any given time at a given temperature is usually less than that required to saturate the air; or (actual H2O(v) density/saturation H2O(v) density) x 102
- % Infectivity = (units or numbers of virus that remain infectious/total units of infectious virus) x 102
Commentary: It is unlikely that students will draw the relationship as the data suggest, a backwards (or mirror) S curve. Why? Because such a curve is uncommon in describing the chemical phenomena that is taught in general chemistry. But an S curve type is common in ecology. Students in chemistry just do not see such a curve very often and as such will understandably rely on known data relationships (curves on graphs) to predict relationships they have not seen before. Basically, use known relationships to predict the relationship between unknown factors. I too would do the same. What one knows and is comfortable with influences what one will predict. An S curve is seen in second semester general chemistry in acid-base titrations, specifically when an acid is titrated with a base. A backwards S curve is produced for a base titrated with an acid. See Q7 below for what the % Infectivity vs %RH (at constant temperature) curve looks like based on experimental data from the researchers Noti et al.
And what about % relative humidity (RH)? It is not a topic out right taught in general chemistry but it is taught in meteorology or environmental science. Indeed %RH is not a chemistry course topic but gas laws are. And RH is nothing more than application of gas laws, phase transitions, and P-T phase diagrams, all three being a part of first semester general chemistry.
Q5: Compare/contrast your graphical prediction in Q4 with three other classmates. Discuss reasons for the difference(s). As done in Q2, complete the table below. Place an A (= Agree) or D (= Disagree) in the empty cell. As a class discuss reasons for disagreements.
Sometimes numerical data are not arranged in graphical format. Rather the numerical data are formatted in a chart or table. Below are hypothetical experimental data. x-axis: % Relative Humidity (RH) and y-axis: % Infectivity. See Q4: for definitions of %RH and % Infectivity. IMPORTANT: Do not plot the data on an x-y axes graph. Instead, eyeball the numerical data to figure out what the data trends, i.e. slope(s), look like. Apply your 'eyeball' interpretation to Q6 below.
Something to think about: As you know a slope (= data curvature) exists between and among numerical data. Sometimes the slope is positive (very steep or gradual) and then changes abruptly or gradually to negative. And vice versa. Sometimes the x-y values are directly related, sometimes indirectly related, linearly and non-linearly, large and or small slopes.
Q6: The choices below relate to the slope between the numerical data embedded in the table above. Which choice, if any, best matches the slope direction, in general, amongst the x-y numerical data sets? + = positive - = negative
Commentary: I like to give students numerical data and by having them qualitatively eyeball the numbers sketch out the graphical relationship. This Q is similar in approach. Answer = D. Graphing the data gives a backwards (or mirror) S curve similar to the one in Q7.
Q7: As a member of a university research team you have been tasked with writing the Results section for a paper to be submitted to and hopefully published in a prominent peer-reviewed scientific journal. The Results section you are to write is based on the multiple experimental data plotted on the graph found below. Below the graph is an overview of the methodology in how the experiments were carried out, how the samples were collected, and what data were collected.
The word count for the Results section based on the graph below should be no less than 100 words. When done, compare/contrast what you wrote for the Results section to that of another student. See Q4: for definitions of %RH and % Infectivity.
Experiment Overview: An aqueous solution containing a known amount of influenza virus was aerosolized with a nebulizer and loaded into a cough simulator, which was located in a simulated constant volume examination room (also known as the aerosol exposure simulation chamber). The examination room was held at constant temperature. Examination room dimensions (depth x length x height, in meters): 3.16 x 3.16 x 2.27. The cough simulator 'coughed' aerosols containing aqueous influenza virus into the simulated examination room at a rate of 5 coughs/min for a total of one hour.
Bioaerosol samplers located throughout the examination room collected size-fractionated virus-containing aerosols (aerodynamic diameters: <1mcm, 1-4mcm, and >4mcm where mc = micro, m = meter). Aerosol samples were collected throughout the examination room at 15 minute intervals. The number of infectious influenza virus within a collected aerosol sample at each collection time interval was determined by the VPA (Virus Plaque Assay) method. 'Coughing' conditions were repeated in the simulated constant volume and temperature examination room at varying RHs. The total amount of virus captured by each aerosol sampler was approximately the same regardless of the sampler location within the room, 4.56 x 103 total virus/L of room air.
Commentary: Q7 probably is best done as a takehome assignment. And I suspect that an instructor may need to spend some time teaching students how a scientific paper is typically organized. Students may confuse Discussion with Results, or combine the two. First, have students type their Results. Collect the Results and remove student names. Distribute Results collection to the class and as a class discuss whether the paper turned in met the criteria for the Results section of a scientific paper.
What did the authors write? Below is some selected wording from the Results section from the original paper (Noti et al.; see paper citation below graph). Hand out copies to students so they can compare what they wrote to what the authors wrote.
RESULTS: To assess the effect of humidity on infectivity, influenza virus was coughed into a simulated examination room where the RH was adjusted from 7–73%. Total virus collected for 60 minutes retained 70.6–77.3% infectivity at relative humidity <23% but only 14.6–22.2% at relative humidity >43%. Analysis of the individual aerosol fractions showed a similar loss in infectivity among the fractions. The percentage of virus that retained infectivity relative to that prior to coughing was determined to be highest (70.6–77.2%) at 7–23% RH with a dramatic drop to the lowest (14.6%) at 43% RH. Increasing the RH to 57% resulted in a modest increase in the retention of infectivity (22.2%). A similar pattern of infectivity in response to humidity was observed among the three aerosol fractions when examined after 60 minutes of collection. Specifically, in each of the 3 fractions there was a significant decline in infectivity as humidity levels increased. However this percentage decrease in infectivity as a function of humidity occurs to similar extent across the 3 fractions as the 3 slopes are not significantly different from one another.
Q8: Let's analyze the graph. Determine if each italicized statement #1 and #2 below is overall TRUE or FALSE. For every FALSE statement, rewrite the statement to be TRUE.
#1 TRUE/FALSE:
To assess the effect of humidity on infectivity, influenza virus was coughed into a simulated constant volume and temperature examination room. The data suggest that for RH levels greater than approximately 23% the infectivity of influenza for the three categories- total aerosols and for aerosols with diameters <1mcm and >4mcm- is, in general, reduced compared to RH levels less than 23%. The percentage of virus for all three aerosol categories that retained infectivity is highest- between 69–82% (for the three categories collectively)- for RH levels <23%. Increased inactivation of the virus occurs rapidly between 23% to 42% (approximate range) of RH.
#2 TRUE/FALSE:
The RH within the examination room was adjusted to different humidity levels spanning from 6% to 73%. The influenza virus retains maximal infectivity at lower compared to higher %RH levels. The data show that aerosol particle size does not confer significantly* increased stability of influenza at RH levels <23%.
*: significant means >10% difference
Commentary: Both statements are TRUE. Hopefully, some students will answer FALSE as that in turn can lead to discussion.
The aerosols produced in the coughing simulator are- like those produced when we cough- spherical in shape and differ in volume. Re-read the Experiment Overview: in Q7. Answer the three questions 9-11 below. For each Q find a classmate with a different answer. Share your thinking with your classmate. Remember, your answers are different so the both of you cannot be right. Possibilities are: 1. You both are wrong. or 2. One of you is right, the other wrong.
Q9: What is the total amount of virus released into the examination room? Assume aerosol distribution is uniform, only one aerosol sampler is at work in the examination room, and air flow sampling is not duplicated. Report value in #.## x 10# format.
Q10: Presume the number of virus particles in an aerosol is constant regardless of the aerosolic volume. Which aerosol then has the greater concentration of virus- 1mcm or 4mcm?
Q11: How many times _?_ (choices: greater/lesser) is the concentration virus in an aerosol with an aerodynamic diameter of 4mcm versus 0.5mcm?
Commentary: Quantitative type questions involving basic calculations. See calculations below.
Q9: Vair in room = 3.16m x 3.16m x 2.27m = 22.67m3
Q10: Answer: 1mcm aerosol; Concentration = # virus particles/volume aerosol. Numerator stays the same as stated in question. But the denominator (volume) is different. The larger the diameter of the aerosolic sphere, the larger the volume of the aerosolic sphere. Hence, for sphere volume: 4mcm > 1mcm. Applying this to concentration, a larger numerical denominator (volume of sphere with 4mcm diameter) divided into the same numerator results in a smaller quotient (= concentration). Conversely, a smaller numerical denominator (volume of sphere with 1mcm diameter) divided into the same numerator results in a larger quotient (= concentration).
Q11: Answer: [virus]0.5mcm = 512 x [virus]4mcm; Vsphere = 4.176r3. The radius of a sphere with a diameter (d) of 4mcm is 8 times greater than the radius of a sphere with d = 0.5mcm (radius ratio: 2mcm/0.25mcm = 8). The volume of a sphere is related as a cube function to its radius. Consequently, the volume of a sphere with a radius 8 times greater than another sphere means that its volume is 83 times larger, which equates to 512. Since the numerator is the same for both aerosol sizes but the denominator differs by a factor of 512, this means that the concentration of the virus in the spheric aerosol with d = 0.5mcm is 512 times greater than the concentration of the virus in the spheric aerosol with d = 4mcm.
Perhaps of interest is primary literature related to this blog post. Enjoy and stay safe!
Lindsley W.G., Blachere F.M., Beezhold D.H., et al. (2016) Viable influenza A virus in airborne particles expelled during coughs versus exhalations. Influenza Other Respir Viruses 10(5):404-413; doi:10.1111/irv.12390. Available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4947941/
Kudo E., Song E., Yockey L.J., Rakib T., Wong P.W. Homer, R.J., and Iwasaki A. (2019) Low ambient humidity impairs barrier function, innate resistance against influenza infection. Proceedings of the National Academy of Sciences 116(22):10905-10910; doi.org/10.1073/pnas.1902840116. Available at https://www.pnas.org/content/116/22/10905
Noti J.D., Blachere F.M., McMillen C.M., Lindsley W.G., Kashon M.L., Slaughter D.R., and Beezhold, D.H. (2013) High humidity leads to loss of infectious influenza virus from simulated coughs. PLoS ONE 8(2): e57485; doi.org/10.1371/journal.pone.0057485. Available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3583861/
Yan J, Grantham M, Pantelic J, Bueno de Mesquita PJ, Albert B, Liu F, Ehrman S, Milton DK. (2018) Infectious virus in exhaled breath of symptomatic seasonal influenza cases from a college community. Proceedings of the National Academy of Sciences 115(5):1081-1086; DOI: 10.1073/pnas.1716561115. Available at https://www.pnas.org/content/115/5/1081
Cheerio, Scott Donnelly