Grappling With the Numbers
Connie Zhang and Quinn Frankovsky
The epidemiological data that we see on our news stations and on our social media platforms greatly impacts our assessment of the virus. We create our own judgments about when we will return to normal society, and this judgment is different from place to place. Differences in local incidence and mortality rates create different perceptions of the virus, as exemplified between our current locations of King County in Washington (Connie) and Denver County in Colorado (Quinn). Media outlets and governing bodies use data that better advances their agenda, which is evident in the widespread use of the University of Washington’s Institute for Health Metrics and Evaluation (IHME) overly-optimistic model. It is ultimately one’s own prerogative to trust or distrust the data they see, but the undermining of realistic data could cause a significant increase of cases and deaths in the coming months.
Epidemiological data enumerates the trajectory of the current pandemic and allows us to make estimations regarding its future. This will inform our public health officials and political leaders about the effectiveness of our current containment strategies and when to change these strategies. For example, the stay-at-home orders we are under right now are aimed to “flatten the curve.” The above visualization from University Michigan Medical School demonstrates two scenarios, the dark blue one when we don’t take steps to slow the spread, and the yellow one when we do take steps to slow the spread. While the dark blue curve happens over a much shorter period of time, it overwhelms the hospital capacity, making sick people who need medical treatment unable to receive it. Moreover, if COVID-19 patients overwhelm the medical system, it increases the risks of infection for medical personnel. If a significant proportion of our medical staff are sick there will be fewer people to take care of the patients.
It is difficult to determine “when things will get better” because the meaning of the phrase is different to different people. Many people would consider that things are “better” if fewer people are getting sick everyday. I think this varies greatly by our geographical locations. For example, let’s compare the epidemiological data between where Quinn and Connie are living right now. Connie lives in King County, Washington, where the first US COVID-19 cases emerged. Quinn lives in Denver County, Colorado. While we observe a similar shaped curve for both graphs, the scale for King county is larger, from 0 to 6000 compared to Denver county’s 0 to 2000. Moreover, the number of positive test results per day also seems to decrease significantly in Denver County compared to King County. From the graphs that demonstrate cumulative counts of COVID-19 on this page and the previous page, we see that while it seems like both counties are recording a diminished number of positive cases, it will take King County longer to recover than Denver County. From an epidemiological point of view, social distancing can be eased when the rate of infection has dropped to 1 per 1 million people if only if wide-spread testing, contact tracing, isolation, and limitations on mass gathering are present as well. This rate of infection is what epidemiologists from IHME deemed to be manageable with containment strategies in place.
While data regarding overall positive cases and deaths regarding COVID-19 provides the bigger picture for our ongoing pandemic, we want to draw some attention to race/ethnicity data since there are some discussions regarding how racism has made racial minorities especially vulnerable during this pandemic. As we look at all the data, it is important to remember that there are many people who might have had COVID-19 but were never tested or treated, so we have to be cautious when drawing conclusions from the data.
From the data below (left King County, right Denver County), we observe that Black and Hispanic/Latino people makeup more of the percent of cases than they do in the percent of overall population. We suspect the social vulnerability index plays a role in this inequality, and further investigation is definitely needed.
To the right are graphs on the percent hospitalized by race/ethnicity in King County (I couldn’t find one for Denver County). The graph above is the percent of cases, and the graph below is the percent of hospitalization. We observe that while white makes up 50.2% of cases, they make up 60.0% of hospitalizations. For other races, their percent hospitalization is lower than their percent of cases. I think having a percentage of hospitalization that is lower than the percent of cases is normal, since not all cases necessitate hospitalization. I know that there are some outbreaks in eldercare centers with primarily white residents. It is also important to note that there are some missing cases, especially for the percent of cases. I think having access to this missing data is very important. Moreover, COVID-19 has very different effects to people with different ages, so having these data age-adjusted will aid in further analysis.
There has been an ongoing debate regarding whether we should reopen the economy, and epidemiological data can be used to support both sides of the argument. Below is the Johns Hopkins map for the number of confirmed COVID-19 cases per 100,000 by county. Let’s use Minnesota as an example since it recently started partially reopening its economy. We can see that Minnesota definitely has lower cases per population compared to some other states, and many of the counties in Minnesota are barely affected. This can be used to support reopening the economy. However, COVID-19 spread from person-to-person, and reopening the economy can cause diffusion of disease to surrounding areas. This map demonstrates a lot of clusters of cases, possibly due to people travelling to neighboring counties and states. Reopening the economy would run the risk of spreading this disease to areas that are not yet impacted. Another important thing to point out is that travelling connects people from all over the world, allowing quick diffusion of the disease. Therefore, we cannot confidently say that we are safe from COVID-19 until every country in this world has found solutions and ways to protect themselves from this virus.
Statistical data influences our perception of the ongoing pandemic. Therefore, we tend to think similarly to the multimedia platforms we got our news and data from. Statistical data is readily available to the public, as it is consistently displayed on CNN or NBC’s daily news. The University of Washington’s Institute for Health Metrics and Evaluation (IHME) has published a predictive model of the virus that many institutions, media outlets, and government bodies use to create their narratives of the virus. There are several reasons why this model was and is so widely used: it is optimistic, it projects lower deaths than other models, it is clear and precise, and it has narrow confidence intervals to reduce uncertainty. It is thus apparent why its use was and is so frequent; it injects optimism into the audience. This is a noble practice in theory; it reduces the amount of worry by providing an overly optimistic outlook of the virus that the general public can accept. However, when the model is proved wrong (it already has been several times), this only causes anger and confusion in the public. There are many reasons why the IHME model didn’t correctly predict the course of the epidemic in the United States. The model only uses death rates and not all-cause mortality rates from coronavirus complications. This underestimates the mortality rate of COVID-19. While most epidemiological models use a susceptible, exposed, infected, and recovered classification strategy, as we have mentioned in class, the IHME model fits a curve from data in China and Italy to predict the diesease’s trajectory. Both of these qualities misrepresent the conditions of the virus in the United States.
The lack of uniform testing policy prevents critical data from being derived about transmission and origins. Because of this, experts do not have the necessary data to produce a realistic predictive model, because they do not have reliable transmission data. Institutions, media outlets, and governing bodies could represent the data a lot better if they had such adequate transmission reports. It is to be anticipated that not every model will be entirely accurate. However, there is fault in using a model, which has been proven wrong several times, as a leading source of guidance during all of this uncertainty. Providing an overly optimistic prediction calms the public down in the short term, but delays the inevitable. People will grow frustrated and confused as what they see on the news and what comes from their government continues to change and our condition in isolation prolonged. A recent report from the New York Times suggests a sharp rise in new cases per day, about 200,000, because some states are reopening as of May 5th. In the face of these new models that predict such an increase in deaths, many continue to decide against trusting the data and protest for the re-opening of their local economies. Colorado has been host to several quarantine protests, with an infamous counter-protest by two healthcare workers. This highlights a very dangerous ideology within some American citizens; choosing not to trust the data presented to them, for whatever reason. The blatant disregard of credible data is going to cause many preventable cases of the virus.
I have noticed that many of my friends and family, as well as myself, have stopped watching the news, as it causes too much strain on their wellbeing. The harrowing death counts and stories of great individuals that have lost the fight to the virus causes joy for no one. I noted in my journal that I haven’t watched a news segment since CNN interviewed Maura Lewinger on April 4th, the wife of Joe Lewinger who fell victim to the virus on March 28th. Lewinger’s family had to say goodbye to Joe via Facetime, their only avenue to communicate with Joe. If it weren’t for the kindness of the nurses present, the family would not have had the chance to see Joe for the last time. The interview shook me to my core; Joe was described as an upstanding citizen and role model for The Mary Louis Academy community, for which he was the assistant principal of Student Life and Athletic director for the collegiate prep school in Queens, NY. After this interview, I have avoided watching the news because I cannot bear the horrible circumstances this virus puts families in. I collect my data online, mostly through the Apple News app. I trust the predictive models that I see on the platform, partly because of the epidemiological themes I learned in this class, and partly because they come from credible sources. The distrust of credible data will cause many avoidable cases and deaths of the virus.
Works Cited
“COVID-19 Model FAQs.” Institute for Health Metrics and Evaluation, May 6, 2020. http://www.healthdata.org/covid/faqs.
“COVID-19 United States Cases by County.” Johns Hopkins Coronavirus Resource Center. Accessed May 6, 2020. https://coronavirus.jhu.edu/us-map.
Daily COVID-19 outbreak summary. (2020, May 1). Retrieved May 2, 2020, from https://www.kingcounty.gov/depts/health/covid-19/data/daily-summary.aspx
Data Dashboard. (2020, May 1). Retrieved May 2, 2020, from https://www.kingcounty.gov/depts/health/covid-19/data/race-ethnicity.aspx
The New York Times. (2020, May 4). Coronavirus Live Updates: As States Move to Reopen, Trump Administration Privately Predicts Deaths Will Rise. Retrieved May 5, 2020, from https://www.nytimes.com/2020/05/04/us/coronavirus-live-updates.html
Person. (2020, May 1). Denver COVID-19 Data Summary. Retrieved May 5, 2020, from https://storymaps.arcgis.com/stories/50dbb5e7dfb6495292b71b7d8df56d0a
Piper, K. (2020, May 2). This coronavirus model keeps being wrong. Why are we still listening to it? Retrieved May 3, 2020, from https://www.vox.com/future-perfect/2020/5/2/21241261/coronavirus-modeling-us-deaths-ihme-pandemic
Vera, A. (2020, April 4). A New York woman played her husband their wedding song on FaceTime as he passed away from coronavirus. Retrieved May 5, 2020, from https://www.cnn.com/2020/04/03/us/wife-facetime-husband-coronavirus-death/index.html
Zelinger, M. (2020, April 21). The story behind viral photos taken during Denver's stay-home order protest. Retrieved May 5, 2020, from https://www.9news.com/article/news/local/next/covid-coronavirus-stay-home-protest-denver-scrubs-woman-colorado/73-64196216-688d-45ec-8e17-3bec73ecefa5