The following is a perspective piece based on media coverage of the COVID 19 (CV19) pandemic. It is not meant to diminish the efforts of those working to treat the afflicted nor cast a shadow on those who succumbed to the disease. Ultimately this paper looks to briefly lay out how the media, through its use of selective information, is portraying a specific storyline in the interest of propagating and benefiting from the fear of the disease. In other words, through selective reporting the media looks to first generate a level of fear, then report on said outcomes of the fear, to further perpetuate the cycle. From this induced fear, it can be suggested that it resulted in at least a portion of the reaction by political leaders, and in some cases resulted in overly drastic measures. Finally, to keep the paper of adequate length, only a small fraction of information was used in presenting the case – a far greater volume of similar reporting exists from numerous media outlets.
On April 19, 2020, Jacksonville Florida Mayor Lenny Curry tweeted a response on his decision to reopen local beaches: “We need methodical steps to open our cities. Shutting down cities indefinitely is not an option. National media has responsibility here. I’m gonna [sic] lead a step by step way forward. Governors and mayors will do the same. National media please think responsibly before u [sic] publish…Citizens make decisions based on what is reported by media. This worldwide pandemic has given certain cable news outlets and international media an issue to drive ratings. Local journalists/media will share the facts. Tune into and read local news.” These tweets captured a growing frustration displayed by many political leaders and citizens, and lends the questions: has the competition for ratings (and the related advertising dollars) between the various media sources resulted in the exchange of factual reporting for sensationalized propaganda, and is this reporting having a negative effect on the response to the CV19 outbreak?
Like any entertainment program broadcast on television, the various 24-hour news channels are not exempt from desiring higher ratings, and the associated advertising dollars. That being said, the CV19 pandemic has provided a ratings bonanza for these various media outlets. From the point that the World Health Organization (WHO) first reported a mysterious pneumonia at the end of 2019, the media coverage of the disease has continuously increased, with a corresponding ratings bump. Taking a snap-shot from the week of March 16, 2020, from the previous year in 2019, Fox News saw an increase of 89%, MSNBC was up 56%, and CNN up a whopping 193%. These rating numbers demonstrate a specific response by the population to the CV19 outbreak, and this response is potentially best explained by Lynn Bufka, associate executive director for research and policy at the American Psychological Association. “It’s a new, unknown illness, we don’t know how severe it’s going to be, and we don’t know how concerned to be…part of what drives that fear is a lack of information.” From this psychological analysis, combined with the ratings, it can be surmised that at the start of the pandemic a significant portion of the population had some level of a fear response to the disease [note: we are always most fearful of the things we don’t understand, can’t explain, and we can’t see…the roots of horror movies]. Thus, it would seem to be prudent for the media to provide information in a format to help quell the fear, but unfortunately this would potentially be counterproductive to the media providers. By being able to draw an individual in through fear to watch, then keep the person “tuned-in” with the promise that the solution to their fear will be delivered by the media source, then said media provider is able to keep the viewer and corresponding ratings. From this line of logic, to keep viewers tuning in repeatedly, new story lines need to be generated that contained a “fear” angle. Fortunately (for the media providers), there was a continual volume of new developments from various other countries at the onset of the pandemic – many of these stories contained sensational images of medical facilities in turmoil, empty cities, overwhelmed medical workers, masses of people wearing facemasks, body bags, and mass burials. Numerically defining this volume, on Jan. 7, 2020, CNN produced a report entitled “A mysterious virus is making China and the rest of Asia nervous,” and that there had been 59 cases of an unknown virus in the Wuhan province of China. Since that first report, CNN alone produced 113 different reports for the month of January into the beginning of February. Thus with the established volume of reporting and corresponding ratings to the reports, we only now need to examine if the reporting was meant to calm the viewer or garner fear.
Taking a sample of various reports, in a Jan. 24, 2020, report from CNN’s chief medical officer Dr. Sanjay Gupta provided an analysis of the CV19, comparing it to two other viruses in the corona family: SARS and MERS. In this report he noted that 75% of the individuals who contacted MERS died and that SARS had a fatality rate of 10% (where the flu has a mortality rate of 0.1%), and that unlike these two variants, CV19 could be transmitted person-to-person. What he failed to mention was that there were only 8,096 cases of SARS and 2,494 cases of MERS reported worldwide at the time, also that numerous “common cold” viruses are contained within the corona family (of particular note as well the WHO reports that the actual fatality rate for MERS was 34%; a later CNN report revised and corrected that number). With an ever increasing volume of reports depicting chaos overseas, it was only a matter of time before similar stories were generated from within the U.S. For example, on Jan. 26, 2020, a story discussing how a medical supply store in Texas was running out of medical-grade facemasks, while a second story five days later captured how a student-led petition at Arizona State University was started to cancel classes due to the CV19 fear [note: at the time that the petition was started there had not been an official death accounted for within the U.S.; the first was reported the week of Feb. 29, 2020]. These two stories touch on a significant link for the media, they were able to relate actions from other countries to fear within the U.S. – a great opportunity as demonstrated by the above rating numbers. Unfortunately, there was a significant impact from this flavor of reporting, being the ensuing panic buying by the U.S. population (e.g. the hoarding of toilet paper, masks, meat, etc.). With a population in fear it behooves the responsible political leadership to provide the population gain a sense of calm – unfortunately, as the politicians looked to the subject matter experts for advice, it can be hypothesized that the information provided only spurred the fear (and thus provided the media fuel for their reporting cycle).
While briefing Congress at the beginning of March, Dr. Anthony Fauci, director of the National Institute of Allergy and Infectious Diseases (NIAID), stated that “the flu has a mortality rate of 0.1 percent. This [CV19] has a mortality rate of 10 times that.” Regrettably there is an issue with this statement; to determine a mortality rate we need to know the number of deaths per number infected (ideally), or estimated using the disease burden combined with a historical statistical analysis. The issue with Dr. Fauci’s statement starts with the nature of the virus and is then further impacted by inconsistent reporting to the Centers for Disease Control and Prevention (CDC). At this juncture we are all aware of how the virus can manifest in a person and present no obvious signs or symptoms, making it virtually impossible to project and define the baseline of the population affected. Similarly, there would be a portion of the population that has only mild symptoms and does not seek treatment, or were told to “stay home” by the medical facility since they do not meet the threshold for treatment. Next, the CDC guidance to hospitals, on whether a person should be tested for CV19, adds confusion to the reported statistics: “Clinicians should use their judgment to determine if a patient has signs and symptoms compatible with COVID-19 and whether the patient should be tested.” Questions can therefore be asked on if, especially with a stated test shortage, all patients displaying some symptoms are being tested (under reporting?). Additional questions have been raised on the federal guidance for reporting deaths, where as if CV19 contributed to a preexisting condition resulting in a death then the clinician is supposed to report the death as due to CV19 (over inflation?). Finally, if we use data collected from overseas, myriad other issues (which will be addressed later) are then injected into this calculation. Taking all of these considerations into account, at the point in the timeline of the disease, the “10x more fatal” comment only served to exacerbate the panic and to increase the media’s reporting “value.” Once this lethality statement was given, the next question by many is will I be affected and how much will this disease spread?
In an effort to help provide insight to this question, and more to the intent of helping prepare the populace and medical infrastructure for the pending impact, on March 26, 2020, researchers from the University of Washington’s Institute for Health Metrics and Evaluation (IHME) released their first computer aided statistical analysis (computer-based model) of the COVID-19. The IHME model, as well as other models, were rapidly deployed in the interest in providing projections for the response. However, in this rush to employ, there were several issues with the databases and quality assurance, resulting in varying and potentially errant results [note: see the “model vignette” for additional detail]. While on the surface this errant analysis appears as only an exercise in mathematical integrity, when the distorted and unverified projections are used to push a story line it leaves the realm of probability and statistics and enters the land of propaganda. It can be surmised, based on timelines, that it was the media’s repeated reporting on model projections that drove President Trump and Dr. Fauci to brief at the end of March 2020 that “based on the models, 100,000 Americans or more could die from the virus” (models being used by the media were predicting 100-240K Americans fatalities). A week later the director of the CDC attempted to downplay that projection, stating that the burden and death toll would be much lower; however, that view was only peripherally reported by the media versus a full retraction.
The combination of a lethal disease and aired rampant spread, the story line immediately draws the individual to the conclusion that there is threat to survival. With the U.S. fringing on a projected catastrophe, political leaders needed to demonstrate to their constituents that they are addressing the threat in the hopes of offsetting the pending devastation and ensuring their political future. However, being dependent on an errant model for developing response policy resulted in misguided actions. Perhaps the most obvious example being initial model projections that showed the state of New York would require 140,000 beds and tens of thousands of ventilators, most within New York City, yet a week later Gov. Andrew Cuomo stated that they seem to be OK with only 18,000 beds on hand. Since the models used had not gone through a robust V&V process, we cannot scientifically say if the significantly lower burden was due to social distancing and/or other counter-measures. Yet even though the response to the request for assistance was not met by the projected impact, the media and local personalities continued to tout that in New York “more people will die in the tri-state area, and the country, than previously thought before the pandemic ebbs – and the fatalities won’t level off for a longer period of time, according to the Gates Foundation-backed IHME model.”
In examining the broader response to CV19, from February through March there were numerous reports by the media depicting medical countermeasures being implemented in impacted areas (e.g. China and Europe), resulting in incidents similar to the previous example of the store in Texas selling out of personal-protective equipment (PPE). Even though at the beginning of April 2020 the CDC and WHO recommended wearing a facemask only if you were sick or caring for someone who was sick, CNN’s Dr. Gupta counter-recommended that facemasks should be worn by everyone as it would prevent individuals who are asymptomatic from spreading the disease. Within a few days after this report, the CDC published an online guide on sewing your own facemask, ironically titled “Use of Cloth Face Coverings to Help Slow the Spread of COVID-19.” There is a scientific problem with the suggestion on wearing facemasks to protect against a virus. A study conducted at Manufacturing Development Center at the Wake Forest Institute for Regenerative Medicine explored how well masks performed in filtering 0.3 to 1.0 micron particles; the CV19 is 0.12micron) when it reported that “The best homemade masks achieved 79% filtration as compared to surgical masks (62% to 65%) and N95 masks (97%). But other homemade masks tested performed significantly worse, sometimes demonstrating as little as 1% filtration…The best-performing design was made of two layers of high-quality, heavyweight ‘quilter’s cotton’ with a thread count of 180 or more, and those with an especially tight weave and thicker thread such as batiks…The inferior performers were made of single-layer masks or double-layer designs of lower quality, lightweight cotton.” This study only looked at the fabric and did not take into account proper fitting of a mask. Considering most Americans have not been properly trained on how to fit a mask, nor would they tolerate a close-fitting one that could inhibit airflow, it can be safely assumed that many do not fit well. Numerous experts concur with this assessment that homemade masks provide no actual protection – at most only serve to remind people that there is a threat of a contagion (don’t touch your face), and at worst provides individuals a false sense of security.
Looking scientifically at the other predominant medical counter-measures being employed through governmental policy, social distancing and quarantines, we can see other issues. As eluded to above, we do not know the true number of infected individuals, and thus we can’t say how much the spread has been affected by the social distancing protocols. That said, by keeping everyone in a minimal form of isolation, it can be confidentially stated that those who are sick, but only displaying minimal symptoms, are forced to stay home thus limiting additional vectors. The downside with this sort of implementation, for which political leaders are now grappling, without an accurate understanding through a scientific study of the disease transport and an understanding of the baseline/affected areas by density, is that it becomes a “guessing game” as to when to open their specific locales. Therefore, while exclusionary practices based upon the best available scientific evidence may be scientifically and ethically sound for one population, those same practices may not be sound for all populations.
Looking beyond the attempt of political leaders to use the isolation measures to control the CV19 spread, there is a potential nefarious reason for why the media has used it to increase the fear level and thus maintain higher ratings. A study conducted by the CDC after the SARS outbreak summarized this aspect quite well: “Fear is further fueled when infection control techniques and restrictive practices such as quarantine and isolation are employed to protect the public’s health.” If the media was truly in favor of the isolation measures to increase the fear level, then we should begin seeing reporting on the need to maintain said measures now that political leaders are looking to ease the measures. As suggested evidence, we are now seeing counter-reporting with the narrative that with eased restrictions we will see renewed escalation of cases and that we will require additional quarantines for the next two years.
Since the advent of the 24-hour news channels and the related competition amongst providers, the parent companies of the various media sources have sought advantages to bring in ratings – for which human psychology has provided an exceptional avenue being fear and the need for information to help quench that emotion. We have seen that the initial reporting of the CV19 outbreak by the media was successfully able to induce an initial level of fear in a seed of the populace, and then capitalize on and simultaneously cultivate the fear by increasing the volume and sensationalism of the storylines. With this ever-growing level of reporting on the apprehensive populace, political leaders had to demonstrate to their constituents that they were dealing with the pending disaster. Unfortunately, many of their statements only exacerbated the fear cycle, and the derived counter-measures were not based on science, but rather could be considered in the realm of “security theater.” The media has a distinct responsibility to provide factual reporting, but in the case of CV19 they have elected to continue with dramatized storylines at the expense of the affected – the citizens of the United States. Finally, for perspective, on average 647,000 Americans die each year from heart disease, an estimated 795,000 Americans suffer a stroke each year of which approximately 140,000 are fatal, and approximately 34.2 million Americans are diabetic with more than 270,000 succumbing to the disease each year. Yet despite these numbers, as well as the associated financial burden of ~$580 billion dollars per year attributed directly to the diseases, there has been virtually no media coverage.
Since the deployment of the IHME model, a number of other models have been used by numerous entities, to include segments of the government (state and federal) as well as the media. While many have produced different projections of the burden, duration, and mortality rate, they all share a common flaw being the data set and mathematical assumptions. To first provide a small background on the types of models being used: most fall into one of two different types of modes, the first is a statistical analysis using knowns, baseline, noise, uncertainty, etc. (like the IHME model) and the other is the traditional method called SIER[*] that looks at how many people are susceptible, how many become exposed, how many of those become infected, and how many recover and therefore have immunity (the model traditionally used for flu projections). There is a “pseudo” third type being a conglomerate, where results are statistically merged from a number of different models, but these only combine projections and do not generate outcomes from raw data.
Considering just CV19, there is a common issue amongst the various models, that being an elevated standard deviation amongst the individual data points, resulting in increased error in the output. This deviation is caused by data being collected from vastly different pools and then mixed in the calculations: namely urban, suburban, rural, and international sets. Each of these sets have different infection rate reporting, mortality reporting, social interaction tendencies, demographics, hospital care capability and infrastructure, populations health susceptibility, disease transport mechanism, to name a few. To explain from a different angle, ideally the calculations should use values collected from common environment pools for projections towards a similar environment, i.e. using data collected in New York City to project an impact in Chicago and data from Dayton, Ohio, for projections in Sioux City, Iowa – obviously due to population density, social aspects, health measure, etc. Wuhan, China, data is not the same as New York City. Taking this data mismanagement into account, numerous experts have called out the IHME model specifically for the errors being generated and the shifting projections, yet other models have demonstrated similar problems in generating accurate forecasts. Unfortunately, the problem with the models is not limited to just the databases; rather, to demonstrate that the generated theoretical projections are in fact accurate, the model results need to be scrutinized in a systematic approach against real events in a process of verification and validation (V&V). As this V&V process is involved in time and effort, and looking at the relative short timeline of the outbreak, it is obvious that at most only an abbreviated V&V has been undertaken. These two aspects inject enough scientific error and related doubt into the models, resulting in much of what we are seeing in radically different and shifting projections. Even though there has not been a sufficient V&V of the models, this did not preclude the media from operating under the guise that the models are accurate. To establish validity in reporting of projections, at the start of the outbreak they pointed to the conditions in Italy: “Northern Italy has one of the best public health systems in the Western world. Its doctors and medical professionals are well-trained. They felt prepared when the coronavirus began to spread through their prosperous, well-educated region. And they still could do nothing to prevent what happened…More than 2,500 people have died in about four weeks in Italy. With over 31,500 confirmed cases…in the first four weeks” Applying these timeframe-based numbers, proportionally to the U.S., would mean that 13,500 individuals would have succumbed to the disease. Yet in the first four weeks of the U.S. outbreak there were only 553 deaths. The problem with these stories is that Italy ranks 51 out of 195 countries for the response and mitigation of the spread of an epidemic and 54 out of 195 countries for sufficient and robust health system to treat the sick and protect health workers – a distinct difference in capability to respond and treat an outbreak versus the U.S., which ranks number 2 and number 1 out of 195 countries, respectably.
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