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audreyhardmeyerbloh · 9 months ago

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Blog #4: The Sexualization and Objectification of Women and its Effects on its Viewers (9/23)

Next, I'll be talking about the sexualization &objectification of women in the media, the message it's sending, and its effect on its viewers. More times than not, I've seen advertisements, billboards, TV shows, films, magazines, and commercials objectifying sexualizing women for their own profit and the male gaze. Remember, a conventionally attractive naked body will bring in profit, money, and power, reinforcing this negative cycle of objectification. The majority of film makers or corporations that create movies are controlled by men. [Don't even get me started on the sexualization of women in video games, treating them as nothing but a sex trophy, seen as something to be won]. This leaves so much space and control for men to depict female characters and use women as they please. AKA allowance for sexism to run rampant! The sexualization of women online can translate to real-world violence and suppressive norms. I also think that the forceful sexualization of women in digital media and films takes away from women's choice to show their bodies, which can be self-empowering and showcase their own agency to use their bodies how they want to use them.

According to graph by the Annenberg School of Journalism, “over the past 10 years, top films have shown more than 40 percent of young women in “sexy clothes and 35 percent with nudity” (Smith et al., 2017). While women are already underrepresented in many careers, leadership roles, and specifically films, they are also objectified and used to evoke sexual desire in consumers. Many female characters and films don't get to show their personality, have an opinion, or have control, or power. Instead, they are given roles as a sexual “sidekick”, to keep people watching the films. As portrayed in Disney, many female characters exist primarily to serve the male protagonist storyline, such as Jasmine and Aladdin, Esmeralda in The Hunchback of Notre Dame, and Meg from Hercules. For example, in The Wolf of Wall Street, Margot Robbie is used as a sex pawn and has no other big role in the movie other than to be sexy and objectified. In a movie about scamming and life on Wall Street, there is no objective reason as to why her character should be naked, it is only for the male gaze and to push the agenda that it's OK to objectify women. Giada Gavazzi says that “ her main contribution to the film is aesthetic: we see her fully naked in one scene, topless in another, and otherwise clad mostly in lingerie and tight dresses” (Gavazzi, 2022).

Women are hyper-sexualized in the media, and it is everywhere. There will be commercials or digital advertisements that are trying to sell things that have nothing to do with a woman's body, but they use a woman's body to sell the product. And in this case, women's bodies are being used as sellable commodities and objectified via sexualization of them. I've even seen a soap commercial that uses a naked woman in the commercial and for what? To use women as a distraction or an object – the woman is the object getting as well. Elizabeth Gonzalez, in a presentation, made a really good distinction. She explains that sexualization occurs in four components. To paraphrase, women are first sexualized when their value is determined by their physical or sexual appeal, not their personality. The second is when young girls are treated as sexual or sexualizing children. The third is when a woman is held to a standard that only equates their attractiveness to being sexy, naked, or hot, rather than being smart, kind, or intelligent. The last is when a woman is made into a tool for others' sexual desires, rather than treated as a human person who has agency and independence. All of this exponentially is occurring in the media, specifically, films, and examples are shown in many of the classic Disney films.

It happens in politics, rallies, and other important events. What I also want to mention is how the sexualization of women takes away from the fights that they may have to battle. For example, in Pocahontas, the young Indigenous woman is sexualized and romanticized in a way that takes away from the historical event that is happening in the film. Pocahontas and the rest of her tribe are fighting for their lives, their land, and their agency. At the same time, Disney is creating a character who is seen as sexy and in need of a husband, to gain happiness. Sexualization here is being used to mask the reality of a brutal and cruel situation, which is colonization and genocide. This classic message is that life can be fixed with a man and that a beautiful, sexy woman will not have any issues if they have a man who is sick and shown in a sexualized narrative. Pocahontas does not need to be shown in sexy clothing that is not at all representative of Indigenous tribes, etcetera. This type of sexualization, on a broader scale, serves as some type of distraction from the deeper issues and battles that women face, such as inequality, systematic violence, and dehumanization. By focusing on a woman's breasts, no structure, cleavage, or waist-to-hip ratio, it honestly dulls conversations about women's rights, agency, and independence that women still fight for. In this case, it also masks the reality of the situation in the movie Pocahontas; Pocahontas and the rest of her Indigenous tribe are fighting for the land that they own.

To bring the Disney corporation into this as well, there are far too many examples of the princesses being sexualized and objectified. Just to note, many of the Disney princesses are very young, I'm talking 11 to 14 years old --some of them are dressed up in very revealing shorts, busty shirts, showing cleavage, and being half naked. Why do young characters need so much revealing clothing, if the Disney corporation is not trying to send a sexualized message about women to the consumers? Unfortunately, Disney sexualizes adolescent and preteen princesses, because they have An audience of not only young children, but parents and adults. How do they get older people to also watch these classic Disney films? Well, they make some of the women look sexy, flirtatious, and stunningly beautiful, to attract the male gaze and male viewers. Although on one side these princesses are idols for young girls and boys, they're also sex symbols for adults and older individuals. Pocahontas tells the story of an 11 to 12-year-old girl, who is romantically involved with a man. Jessica Rabbit is shown as a very voluptuous sexy symbol. Esmeralda from the Hunchback of Notre Dame. Ariel from The Little Mermaid only wears a bra and shows 75% of her entire body, and she's a young girl. Hanes found that even “ The Geena Davis Institute on Gender and Media found recently that even animated female characters tend to wear sexualized attire: Disney's Jasmine, for instance, has a sultry off-the-shoulder look, while even Miss Piggy shows cleavage” (Hanes, 2011).

Once again the prominent sexualization of women in films or digital media helps to shape perceptions of how women are valued and seen. From Coca-Cola advertising campaigns to the Friends TV show and the classic Disney Princess films, women are often depicted as mere objects of sexual desire, which do not attain agency, control, or power, and don't even get me started brains or intellect. This type of representation and sexist stereotype that prioritizes conventional beauty, bodies, and physical appearance is extremely harmful. The agenda is pushed that a woman's worth is linked to her body or sex appeal, which can have effects on both girls and boys, who view films that portray these messages. For example, if young girls and women view movies where women are constantly sexualized or used for their bodies, this can lead women to think that they are only as good as their bodies. Or that they need to achieve the unattainable beauty standards that the male patriarchy portrays through Disney films, such as a thin waist, a button nose, voluptuous hair, fair skin, etcetera. This can tank self-esteem, and create body image issues, and mental health disturbances. On the other hand, for young boys, it teaches them that it's OK to only look at women as inferior objects or as simple bodies come up rather than recognize them as individuals with agency and independence.

Persistent viewing of sexualized images holds the potential to desensitize young audiences, which creates the normalization of objectifying women. Swagata Sen says that “men tend to internalize that message, and it influences their subconscious biases of how they view women… they legitimize violence, harassment, and anti-women views” (Sen, 2019). The more this happens, the more people believe this is how socialization and society should function. Men have created dominant views in the USA because they are seen as the ones whose decisions and views should be taken as "facts." This includes perpetuating the idea that women are just objects that can be used and paraded around for men and their happiness or that when we have to look sexy and hot to even gain an ounce of respect or attention from our male counterparts. Furthermore, because the devaluation of women has become ingrained in daily life via digital media (AND DISNEY MOVIES), the widespread adoption of such imagery and characters fosters social acceptance of sexism and sexualization.

In the Disney movies, the female characters were way more sexualized than the male characters. They were wearing clothing that was revealing of a sexualized body part or physical feature and treated as objects, in a way. One person even said, "Ariel’s cinched waist and round derriere reminded me of Kim K, which is a little disturbing to see…” (Dockterman & Stampler, 2014). according to Disney, she's a 16-year-old girl. She is one of the youngest daughters of King Triton but is portrayed in such a way as to make her look much older and more developed. She has a seashell bra that is very provocative, shows 80% of her body, has perfect hair and skin, and a tinier waist than the length of her eyes. To me, this is outright sexualizing a young teenage girl, which has even broader implications to women in general Sexualization does include making young childlike girls look more adult like and sexy. I think that it's very twisted and pushes a certain agenda to make women look a certain way, via the way they portrayed Ariel in The Little Mermaid. In this short clip (https://www.youtube.com/watch?v=LG6zL909y_o&t=99s ) much of the focus and shots are on her body. Even I am watching back the film, couldn't help but stare at how beautiful and perfect she looks. It makes me feel bad about myself! I'll never have the waste of her, or the voluptuous lips, hips, breasts, and etcetera. Disney showcases but a perfect and conventional woman looks like, which is aerial, which sexualizes women in a negative way. I don't know if I know one woman who naturally looks like that, and women who do look like that are sexualized. Women who are beautiful, get catcalled, sexually harassed, and objectified for their looks, which is instigated via digital media and Disney films.

We also see this in Jasmine from Aladdin. Jasmine is supposedly around 15 or 16 years old. She wears a tiny crop top, and flowy pants accentuate our tiny waist and big size. She has perfect eyeliner, huge eyes, perfect hair, and tiny wrists and feet. Again, she looks breathtaking and stunning but focuses on her body rather than what she wants for her life and decisions. She even says in the movie that she is not an object or a prize to be sold or won. Jasmine says in this small clip (https://www.youtube.com/watch?v=Q_WRPz-arFA ), “ …all of you standing around and deciding my future, I am not a prize to be won.” They outright objectify and sexualize Jasmine in the film, her father, the Sultan, Jafar, and other men who are begging for her on their hands and knees. Another point that I want to point out is the dominance of the sexualization of women of color in the media in general. Women of color are often sexualized and objectified and stereotyped as exotic, sexual, or erotic -- which stems from racist and gendered depictions. The intersection of race and gender can be seen here in Jasmine or Esmeralda. Jasmine is a woman from the Middle East, unsure of her actual ethnicity. Women from the Middle East are often sexualized and exoticized when portrayed in white contemporary films, more so than if a white character is playing the same role. Back to my point, many women in the Middle East do not regularly wear revealing clothing because it's a part of the culture or religion. There is no good reason that a young 16-year-old Princess named Jasmine should be sexualized in a way where she's wearing ‘suggestive’ clothing(or written into the storyline as such) because it's not representative of who she is supposed to be.

There's also a scene where Jafar imprisons Jasmine. Again, she is dressed in the sexiest attire, with, well, I'm sure, his hair, beautiful jewelry, a tiny waist, and revealing clothes. So far, then says very sexist and stereotypical verbiage associated with sexualization, “a beautiful desert bloom like yourself should be on the arm of the most powerful man in the world.” (https://www.youtube.com/watch?v=SW95aHWcwQM&t=34s ) She's then seen talking in a flirtatious and sexy tone and using provocative body language towards Jafar. Disney explicitly made her look like a sex object in this scene, but again, she's only 16 years old. I don't understand the intent behind this whatsoever. This also creates the narrative that women can only get what they want or get out of a troubling situation if they sexualize themselves for the male gaze. Many times, I've been in this scenario or have seen others in a scenario where they have to objectify themselves to a male to get what they need. This may mean, in a way, using your body, flaunting your hips and waist, and being flirtatious to get out of the uncomfortable situation, get what you need, get safety, or anything in between. Jasmine has to flaunt her body and pretend that she is sexually desired by Jafar to escape his imprisonment. This is such a bad thing to teach young women that they have to use their bodies to be paid attention to or even respected in a way.

Citations:

https://assets.uscannenberg.org/docs/the-future-is-female.pdf (Smith et al. article)

https://prezi.com/mhlnspaqxzma/the-sexualization-of-women-in-the-media/?fallback=1 (Elizabeth Gonzalez presentation)

https://thematthewrome.com/2022/03/15/male-gaze-and-female-objectification-in-contemporary-cinema/ (Gavazzi article)

https://www.csmonitor.com/USA/Society/2011/0924/Little-girls-or-little-women-The-Disney-princess-effect (Hanes article)

https://www.rightsofequality.com/objectification-and-exploitation-of-girls-and-women-by-the-mass-media-and-the-social-media/ (Sen Article)

https://time.com/3586569/sexist-little-mermaid/ (Dockterman & Stampler article)

https://www.youtube.com/watch?v=LG6zL909y_o&t=99s (ariel clip)

https://www.youtube.com/watch?v=Q_WRPz-arFA (jasmine clip)

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sanddusted-wisteria · 2 years ago

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A Builder, a Researcher, and a Rooftop, Ch. 5: C8H10N4O2

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Minor CW/TW: Brief mentions of vomit. It's not too long and nothing visual is described, but if that makes you uncomfortable, skip a couple paragraphs when Qi starts feeling queasy and pick back up when the builder grabs him a glass of water.

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Qi wasn’t on the roof.

Strange.

He would always be here by the time they arrived, no matter how early. And they were late tonight, busy mining more iron to supplement their endless need for steel bars. Maybe he was still working. He had been pretty excited to get his Express Tea Maker today. No doubt he’d be trying to push his work limits. And his caffeine limits. That thought propelled the builder back down to the door of the research center. Hopefully he wasn’t a dried-up, twitching mess on the floor…

They cracked open the door slowly, a little afraid of what they might find. Over by the workbench, Qi was hunched over his notes, writing faster than they’ve ever seen before. His foot was tapping a rapid, irregular rhythm. They could hear him muttering…something…under his breath, too indistinct for them to make out.

The builder carefully drew closer. “Qi…?”

Qi’s head instantly snapped towards them. They froze, suppressing a yelp. “Aha! Builder! You’ve returned, just as I hypothesized! I knew you couldn’t resist the allure of all this lovely science.”

“I…”

“I really must thank you again for the Express Tea Maker. Now even on my normal dosage of tea, I can work exponentially longer!”

“You still drank six cups of–”

“Hardly any side effects too! The greatest inconvenience would probably be the muscle tremors–” He held up his shaky hands. “–so no hazardous chemical experiments, I’m afraid. But still! There’s still so much to do! I also have the occasional headache, and I’ve had more bathroom visits than predicted, probably because I neglected to consider caffeine’s diuretic effects–”

“Hey Qi–”

“–Oh, and a mild wave of nausea every now and again. Nothing…unmanageable…” Qi’s manic energy petered out. He suddenly keeled over, face looking clammy and a hand clenched over his stomach. His knees shook, and he had to lean on the nearby cabinet for support. The builder reached out a concerned hand.

“E-excuse me–!” He shot past the builder and up the stairs with heavy, thumping steps. From somewhere upstairs, they could faintly hear retching, followed by a flush. They winced.

Following him upstairs, they knocked on the closed bathroom door. “You okay?”

“Don’t come in,” came a dull croak, followed by more gagging and coughing. Eeesh. He was probably dehydrated already, and here he was puking his guts out.

They went over to his tiny kitchen to pour him a glass of water. Shuffling through all the neglected dirty dishes, they finally found a cup that didn’t look as dirty as the rest of them. Just as they filled it up, Qi emerged groaning from the bathroom.

“Pardon the damper on our conversation. Anyways, you’re here, so I should give you the fair share of science that you came for,” he said, brushing past the builder and the water they held out to him.

“Hey, you should probably–”

Qi was already back at his workbench. “I know, I know! I need to show you this proof I’ve just finished writing. I was working on it when you were last here. The way it all falls into place…almost like art, if I do say so myself. Except it’s science. So it’s better.”

The builder left the water on his table and hustled after him down the steps, exasperated. Qi, meanwhile, was leafing through his journal. “Where is…what?”

They peeked over his shoulder to see pages full of random scribbles. Sometimes they would be interrupted by an attempt at a drawing, a semblance of a graph, or a vague suggestion of an equation.

“What is all this rubbish…?” Qi muttered. “Did someone vandalize my journal? No, no, that’s physically impossible…” He glanced over at the builder. “Apologies. Again. I’ll be able to show you the proof once I manage to find it…” He kept flipping through page upon page of nonsense, frown growing deeper with each turn.

“Y’know what, bud? Maybe we should just…put this down for tonight,” the builder said, gently but quickly swiping the journal out of Qi’s hands by its leather-bound covers, closing it with a satisfying clap.

“Huh? Wait, what about my proof?”

“Maybe some other time. It’s late and you need water.”

“Late?” Qi looked at the clock. “Ah, nighttime already. Right. I assume that’s the true reason you came?”

“Yeah, but–”

Qi stepped around them and headed for the door, a spring in his step. “Wonderful! I can show you this fascinating piece of space debris that should be passing overhead in a few minutes or so.”

The builder ran ahead in front of Qi, arms spread out to block him from the doorway. “Uh-uh, no. None of that. You need to get to bed.”

“But you came for–”

“Yeah, but now I’m here to make sure your heart doesn’t explode. And also that you don’t shrivel up by tomorrow morning.”

“But the–”

“The science can wait, Qi,” they said, putting their hands on his shoulders and turning him around. “Now off to bed you go. Come on.” They pushed him to go back towards the stairs, while he tried to resist, leaning back and planting his feet.

“Ugh, must we really do this?”

“Yes.”

Qi made several more noises of protest, but slowly started dragging his feet up the stairs. They eased up on their pushing as they went along, Qi finally starting to cooperate and move on his own.

They grabbed the water from where they left it as Qi stalked into his room. They found him at his desk, trying to read one of the many books strewn about its surface.

He frowned as his unfocused eyes drifted across the pages. “I think the withdrawal is starting to take hold.”

The builder handed him the cup. “About time. Drink up, otherwise you’ll be as dry as the sand tomorrow.”

“The way for that to happen is if you cremate my body, builder,” Qi said, rolling his eyes. He drained the cup in one swig and handed it back to the builder.

“Good. Now lie down.”

Qi frowned. “You know that caffeine prevents sleep, yes?”

“Yeah, but you’re gonna crash any second now. Don’t want you passing out and hitting your head on something,” they said as they headed back out to get more water.

Qi sighed, but he was out of arguments. Or any energy to argue. He slipped off his glasses and shoes and tucked himself under his covers. The builder returned with a full glass and pitcher, brushing old papers and books aside on the desk to make room for them. They turned off some of the lights in the room, leaving only the warm glow of the desk lamp on. Satisfied, they took a seat in the chair.

Qi turned over and squinted in their direction. “Are you…just going to stay there?”

“Long enough to make sure you don’t spring up and try to get back to work the second I leave, yeah.”

Qi turned to face the wall, burying himself deeper into his blankets. “Mm. I don’t think it will be too long.” His voice was softer, more sluggish. “Feel free to read something while you’re here, I suppose. Just don’t move my bookmarks, please.”

The builder turned to the random pile of books. Carefully going through the closest stack, they scanned the titles in the dim light. Fluid Mechanics, Differential Equations, Science Weekly, A Comprehensive Listing of Old World Spacecraft… All things they’d expect Qi to read.

But then they started getting stranger. A Detailed History of Concrete? Building a Facade: Scams and Schemes from the Commerce Guilds of the Alliance? Highwind Birdwatching Guide?? Yakmel Ranching for Dummies?? 1000 Things to Do with Sulfuric Acid???

And the strangest one of all, a small volume sitting innocently at the bottom of the pile where Qi probably hoped no one would see: Gazing in Pink I: There’s No Way Someone Like Him Would Like Me!...?!

The builder threw a hand over their mouth to stifle the laugh threatening to burst its way out. Oh, this was rich. They silently picked it up and flipped it open to a random page. The crisp stiffness of the spine and the fresh smell of paper were tell-tale signs that he’d never opened it.

“...He gave me that beautiful, warm smile again… The one that I never felt like I deserved. I was just a lowly optician with a struggling eye clinic. What did he see in me?! Always coming in for eye exams and glasses fittings… How can he afford it?! His insurance has rejected his last 10 claims now!!”

A snort managed to make its way past their hand. Terrified, they looked back at the lump of Qi in his blankets, only to see it slowly rising and falling. He was out cold. Lucky them. They took their hand off their mouth, taking a couple deep breaths to try and filter the giddiness out of their system. Very carefully, they put the peculiar little novel back down and restacked the books. They turned the desk lamp off and left Qi’s room, quietly shutting the door behind them.

Leaving the research center into the barren streets, the sheer absurdity of it all hit them again. They howled with laughter, unrestrained this time, picturing Qi reading that hot garbage like a stiff-nosed scholar, leaving thoughtful annotations in the margins. They cackled all the way home, only stopping after they flopped onto their bed. Hopefully Qi wasn’t having any sordid fantasies about whoever he got his glasses from…

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Builder,

I just left Fang’s clinic. I told him about our Express Tea Maker and the symptoms I was experiencing yesterday. Judging by the odd expression on Dr. Fang’s face and the unusual silence from the bird, I inferred it had to do with my caffeine intake. As much as it pains me to write it, I will no longer be using the Express Tea Maker. Regardless, thank you for your efforts, and for your considerate visit last night. Do remind me to show you my proof in the near future, once I have rewritten it to be readable this time.

P.S.: The literature I have in my collection is STRICTLY for research purposes. I just thought that you might like to know.

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#shady's fics #brr #C8H10N4O2: the chemical formula for caffeine

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sstonki · 3 years ago

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Exponential graph equation maker

Note that the first function is sin(a*x). You can also enter an exact value into the box at the top of the slider, followed by the GRAPH button or the Enter key.įor example, in the chart above, press 'reset'. When you enter your equations, you can refer to up to four variables that are controlled by sliders.Īnd you can adjust the value of each variable by moving the slider up or down. However, if you change the axis limits, this may no longer be true. The initial range of values on the x and y axes are in the same ratio, so a graph of y = x will be at 45°,Īnd circles would be round, not squashed into ellipses. The aspect ratio (ratio of width to height) of the graph window is exactly 4:3. GFE will check to ensure that the lower value is at the bottom of the y axis or the left of the x-axis. To change them, simply edit them in place and press GRAPH or the Enter key again. If you enter a value that is off the graph, the cursor will not show,īut the values of the functions for that x value will be displayed correctly.Īt each end of the x and y axis is a box containing the end values. You can enter the x value for the cursor manually into the text box in the upper left.Īfter entering a value press "Graph" or the enter key. It shows the values of each function where the cursor intersects that function. If you click on "show cursor", a thin vertical line appears. If left unchecked, each function is shaded in a different color. This allows you to more easily see where complex functions overlap, since the more overlap there is, the darker the shading. If this is checked, the shaded areas for all three functions are all the same light gray. When plotting inequalities, the "monochrome shading" checkbox can be used. The area of the graph where y is greater than the function value is shaded. The function will be plotted as a line as usual. GFE can be used to plot inequalities by changing the relational operator in the pull-down menu to the left of the function. It is best to always enter the correct expression yourself. It will add two extra closing parentheses so they balance and evaluate it as 2+(sin(x)) Note: This may not always produce the desired result. When you press GRAPH or enter, it will automatically add enough closing parentheses to balance them. You may have meant it as one over 2sin(x). Since there are no parentheses, it is executed from left to right so it sees it as one half of sin(x). For example if you enter 1/2sin(x) GFE inserts a multiply between the 2 and the sin. It will not work if the function is preceded by a variable name. For exampleģcos(2.1) will be automatically treated as if you entered 3*cos(2.1): three times the cosine of 2.1. If a function (such as sin() ) is preceded by a number, GFE assumes you want to multiply them. See PI definition for more.įor example you could enter sin(pi) or e^2.1 There are two constants you can refer to. Returns the smallest integer greater than or equal to x Returns the highest integer less than or equal to x Returns x rounded off to the nearest whole number Returns the absolute value of x (always positive or zero) The power to which you must raise e to get x.Į (approx 2.718) raised to the power of x. The power to which you must raise the 10 to get x. The trigonometry cotangent function, x in radians. The trigonometry cosecant function, x in radians. The trigonometry secant function, x in radians. The trigonometry tangent function, x in radians. The trigonometry cosine function, x in radians. The trigonometry sine function, x in radians. The function names are not case sensitive.Īll trigonometric functions operate in radians. GFE has the following built-in functions.

#Exponential graph equation maker

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bountyofbeads · 5 years ago

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DON’T BELIEVE THE COVID-19 MODELS.... That’s not what they’re for.

By Zeynep Tufekci | Published April 2, 2020 | The Atlantic Magazine | Posted April 04, 2020 |

The Trump administration has just released the model for the trajectory of the COVID-19 pandemic in America. We can expect a lot of back-and-forth about whether its mortality estimates are too high or low. And its wide range of possible outcomes is certainly confusing: What’s the right number? The answer is both difficult and simple. Here’s the difficult part: There is no right answer. But here’s the simple part: Right answers are not what epidemiological models are for.

Epidemiologists routinely turn to models to predict the progression of an infectious disease. Fighting public suspicion of these models is as old as modern epidemiology, which traces its origins back to John Snow’s famous cholera maps in 1854. Those maps proved, for the first time, that London’s terrible affliction was spreading through crystal-clear fresh water that came out of pumps, not the city’s foul-smelling air. Many people didn’t believe Snow, because they lived in a world without a clear understanding of germ theory and only the most rudimentary microscopes.

In our time, however, the problem is sometimes that people believe epidemiologists, and then get mad when their models aren’t crystal balls. Take the United Kingdom’s drastic COVID-19 policy U-turn. A few weeks ago, the U.K. had almost no social-isolation measures in place, and according to some reports, the government planned to let the virus run its course through the population, with the exception of the elderly, who were to be kept indoors. The idea was to let enough people get sick and recover from the mild version of the disease, to create “herd immunity.”

Things changed swiftly after an epidemiological model from Imperial College London projected that without drastic interventions, more than half a million Britons would die from COVID-19. The report also projected more than 2 million deaths in the United States, again barring interventions. The stark numbers prompted British Prime Minister Boris Johnson, who himself has tested positive for COVID-19, to change course, shutting down public life and ordering the population to stay at home.

Here’s the tricky part: When an epidemiological model is believed and acted on, it can look like it was false. These models are not snapshots of the future. They always describe a range of possibilities—and those possibilities are highly sensitive to our actions. A few days after the U.K. changed its policies, Neil Ferguson, the scientist who led the Imperial College team, testified before Parliament that he expected deaths in the U.K. to top out at about 20,000. The drastically lower number caused shock waves: One former New York Times reporter described it as “a remarkable turn,” and the British tabloid the Daily Mail ran a story about how the scientist had a “patchy” record in modeling. The conservative site The Federalist even declared, “The Scientist Whose Doomsday Pandemic Model Predicted Armageddon Just Walked Back the Apocalyptic Predictions.”

But there was no turn, no walking back, not even a revision in the model. If you read the original paper, the model lays out a range of predictions—from tens of thousands to 500,000 dead—which all depend on how people react. That variety of potential outcomes coming from a single epidemiological model may seem extreme and even counterintuitive. But that’s an intrinsic part of how they operate, because epidemics are especially sensitive to initial inputs and timing, and because epidemics grow exponentially.

Modeling an exponential process necessarily produces a wide range of outcomes. In the case of COVID-19, that’s because the spread of the disease depends on exactly when you stop cases from doubling. Even a few days can make an enormous difference. In Italy, two similar regions, Lombardy and Veneto, took different approaches to the community spread of the epidemic. Both mandated social distancing, but only Veneto undertook massive contact tracing and testing early on. Despite starting from very similar points, Lombardy is now tragically overrun with the disease, having experienced roughly 7,000 deaths and counting, while Veneto has managed to mostly contain the epidemic to a few hundred fatalities. Similarly, South Korea and the United States had their first case diagnosed on the same day, but South Korea undertook massive tracing and testing, and the United States did not. Now South Korea has only 162 deaths, and an outbreak that seems to have leveled off, while the U.S. is approaching 4,000 deaths as the virus’s spread accelerates.

Exponential growth isn’t the only tricky part of epidemiological models. These models also need to use parameters to plug into the variables in the equations. But where should those parameters come from? Model-makers have to work with the data they have, yet a novel virus, such as the one that causes COVID-19, has a lot of unknowns.

For example, the Imperial College model uses numbers from Wuhan, China, along with some early data from Italy. This is a reasonable choice, as those are the pandemic’s largest epicenters. But many of these data are not yet settled, and many questions remain. What’s the attack rate—the number of people who get infected within an exposed group, like a household? Do people who recover have immunity? How widespread are asymptomatic cases, and how infectious are they? Are there super-spreaders—people who seemingly infect everyone they breathe near—as there were with SARS, and how prevalent are they? What are the false positive and false negative rates of our tests? And so on, and on and on.

To make models work, epidemiologists also have to estimate the impact of interventions like social isolation. But here, too, the limited data we have are imperfect, perhaps censored, perhaps inapplicable. For example, China underwent a period in which the government yanked infected patients and even their healthy close contacts from their homes, and sent them into special quarantine wards. That seems to have dramatically cut down infections within a household and within the city. Relatively few infected people in the United States or the United Kingdom have been similarly quarantined. In general, the lockdown in China was much more severe. Planes are still taking off from New York, New Jersey, and everywhere else, even as we speak of “social isolation.” And more complications remain. We aren’t even sure we can trust China’s numbers. Italy’s health statistics are likely more trustworthy, but its culture of furbizia—or flouting the rules, part of the country’s charm as well as its dysfunction—increases the difficulty of knowing how applicable its outcomes are to our projections.

A model’s robustness depends on how often it gets tried out and tweaked based on data and its performance. For example, many models predicting presidential elections are based on data from presidential elections since 1972. That’s all the elections we have polling data for, but it’s only 12 elections, and prior to 2016, only two happened in the era of Facebook. So when Donald Trump, the candidate that was projected to be less likely to win the presidency in 2016, won anyway, did that mean that our models with TV-era parameters don’t work anymore? Or is it merely that a less likely but possible outcome happened? (If you’re flipping a coin, you’ll get four heads in a row about one every 16 tries, meaning that you can’t know if the coin is loaded just because something seemingly unusual happens). With this novel coronavirus, there are a lot of things we don’t know because we’ve never tested our models, and we have no way to do so.

So if epidemiological models don’t give us certainty—and asking them to do so would be a big mistake—what good are they? Epidemiology gives us something more important: agency to identify and calibrate our actions with the goal of shaping our future. We can do this by pruning catastrophic branches of a tree of possibilities that lies before us.

Epidemiological models have “tails”—the extreme ends of the probability spectrum. They’re called tails because, visually, they are the parts of the graph that taper into the distance. Think of those tails as branches in a decision tree. In most scenarios, we end up somewhere in the middle of the tree—the big bulge of highly probable outcomes—but there are a few branches on the far right and the far left that represent fairly optimistic and fairly pessimistic, but less likely, outcomes. An optimistic tail projection for the COVID-19 pandemic is that a lot of people might have already been infected and recovered, and are now immune, meaning we are putting ourselves through a too-intense quarantine. Some people have floated that as a likely scenario, and they are not crazy: This is indeed a possibility, especially given that our testing isn’t widespread enough to know. The other tail includes the catastrophic possibilities, like tens of millions of people dying, as in the 1918 flu or HIV/AIDS pandemic.

The most important function of epidemiological models is as a simulation, a way to see our potential futures ahead of time, and how that interacts with the choices we make today. With COVID-19 models, we have one simple, urgent goal: to ignore all the optimistic branches and that thick trunk in the middle representing the most likely outcomes. Instead, we need to focus on the branches representing the worst outcomes, and prune them with all our might. Social isolation reduces transmission, and slows the spread of the disease. In doing so, it chops off branches that represent some of the worst futures. Contact tracing catches people before they infect others, pruning more branches that represent unchecked catastrophes.

At the beginning of a pandemic, we have the disadvantage of higher uncertainty, but the advantage of being early: The costs of our actions are lower because the disease is less widespread. As we prune the tree of the terrible, unthinkable branches, we are not just choosing a path; we are shaping the underlying parameters themselves, because the parameters themselves are not fixed. If our hospitals are not overrun, we will have fewer deaths and thus a lower fatality rate. That’s why we shouldn’t get bogged down in litigating a model’s numbers. Instead we should focus on the parameters we can change, and change them.

Every time the White House releases a COVID-19 model, we will be tempted to drown ourselves in endless discussions about the error bars, the clarity around the parameters, the wide range of outcomes, and the applicability of the underlying data. And the media might be tempted to cover those discussions, as this fits their horse-race, he-said-she-said scripts. Let’s not. We should instead look at the calamitous branches of our decision tree and chop them all off, and then chop them off again.

Sometimes, when we succeed in chopping off the end of the pessimistic tail, it looks like we overreacted. A near miss can make a model look false. But that’s not always what happened. It just means we won. And that’s why we model.

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We want to hear what you think about this article. Submit a letter to the editor or write to [email protected].

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ZEYNEP TUFEKCI is an associate professor at the University of North Carolina, and a faculty associate at the Harvard Berkman Klein Center for Internet and Society. She studies the interaction between digital technology, artificial intelligence, and society.

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The Curve Is Not Flat Enough

Hospitals are poised to face the kind of life-and-death decisions that industrialized countries typically encounter only in times of war and natural disaster.

By JAMES HAMBLIN | Published March 28, 2020 | The Atlantic Magazine | Posted April 04, 2020 |

wo weeks ago, a man came to an emergency room in New York with pain in the lower-right quadrant of his abdomen. A CT scan showed inflammation around a fingerlike projection at the base of his colon. Combined with a fever, this was a classic case of appendicitis. Surgeons took the man to the operating room and removed his appendix.

The next day, recovering upstairs, the man still had a fever. Doctors ordered a test for the coronavirus. A day later, his results came back positive.

Under usual circumstances, a person with a dangerous, infectious respiratory disease such as COVID-19 requires special precautions in a hospital. Everyone who enters the patient’s room—even to ask how they’re doing or to pick up a lunch tray—is required to don a fresh gown, gloves, and a mask. If the worker must get in close contact with the patient, the mask has to be an N95 respirator, and a face shield is required to guard the eyes. Without exception, every piece of this gear must be discarded in a biohazard dispenser upon leaving the room. An errant mask or glove or gown, coated in virus, can become lethal.

After the man with appendicitis (a patient of one of the doctors I spoke with for this story) tested positive, the hospital implemented such precautions. And staff members who’d cared for him went into two weeks of isolation.

Today, if every hospital employee who had a close encounter with a COVID-19 patient disappeared for two weeks, the medical workforce would quickly become depleted. A safe alternative would be to minimize potential exposures by testing everyone who stepped foot in the hospital: The virus has an average incubation period of five days, which means people can spread it in the absence of symptoms. The U.S. does not have that testing capacity. The next best thing might be to require some form of mask and other personal protective equipment (PPE) for all staff, and possibly even patients, presuming that anyone could be a disease transmitter. The U.S. does not have enough medical supplies to do this either.

Last week, the Illinois Department of Public Health sent a notice to clinics that only those people “hospitalized with severe acute lower respiratory illness” could be tested for the coronavirus. California and New York, similarly, have restricted testing to health-care workers and patients who plainly seem to have the disease. The lack of widespread screening means the coronavirus may well be present in countless hospital wards without anyone realizing it. Accordingly, many emergency-room workers are now behaving as if they’re already infected and separating from their families. One ER physician told me he has been sleeping in the guest bedroom for weeks. Other doctors have sent their families off to stay at second homes.

The majority of workers who keep America’s hospitals running don’t have the salary to afford extra bedrooms, much less extra properties. For technicians, respiratory therapists, first responders, cleaning staff, and many others, doing their job is an act of moral complexity. Without adequate PPE, they’re putting their own health at risk every time they report for duty, as well as that of their families. They also may have other urgent reasons for staying home: being sick themselves, for one, or caring for children who are out of school or for family members who have fallen ill. Not working, for the minority who could financially manage this, isn’t an easy choice either, given that it means increasing the burden on colleagues and putting them at greater risk of getting infected. And this isn’t even to mention the obligation workers at all levels of the hospital hierarchy feel to their patients.

With the United States now leading the world in COVID-19 diagnoses, the demands on the medical system are increasing with each passing day. Nowhere is that more evident than in New York City, the current epicenter of the crisis, where major academic hospitals are being forced to radically restructure how they deliver care. In talking with dozens of hospital workers over the past few weeks (most of whom asked not to be named for fear of professional repercussions), I heard that dermatologists are staffing emergency departments and cardiologists are taking ICU shifts. Medical students at NYU are being invited to graduate early so they can enlist as interns and begin practicing medicine immediately. Governor Andrew Cuomo has asked retired doctors to return to service as the city’s convention center is turned into a field hospital. On Thursday, Avril Benoit, the executive director of Doctors Without Borders—the group known for deploying teams to war zones and other medical deserts—told me she was working on plans to deploy resources to New York City.

[ Fred Milgrim: A New York doctor’s warning]

During World War II, Ford and General Motors rallied to the cause by building tanks and manufacturing ammunition instead of cars. These companies have now begun making ventilators, the devices that push air into the lungs of people who can’t breathe on their own. But without more widespread testing and basic protective equipment, the problem will be less the number of ventilators, and more the number of health-care workers available to operate them. The United States has entered its coronavirus rationing era, and the kind of medical care many people are used to isn’t going to be available all the time. The ubiquitous curve is being flattened by shutdowns and social distancing, but it is not flat enough. Those who might end up in a hospital, which is to say all of us, can do at least one thing to help relieve pressure on the medical system and its overtaxed, dwindling workforce.

On a gray monday in October 2018, a group of biomedical scientists convened in Saranac Lake, New York, to conduct a war game. The enemy was “Disease X,” a hypothetical doomsday pathogen. The scientists weren’t working for the government, but, like that of many experts who have gathered to offer guidance to bureaucrats and politicians, their goal was to take an inventory of existing U.S. capabilities, assess “gaps,” and suggest measures to “improve our position,” according to meeting records shared by Stephen Thomas, the chief of the infectious-disease division at SUNY Upstate Medical University.

One team was told to be risk-averse, modeling the steps the U.S. would take to be optimally prepared to save as many lives as possible. The other was risk-tolerant, modeling what the country would do if it chose to save money and roll the dice, hoping that things wouldn’t get too bad. A risk-averse approach would involve roughly doubling the country’s 170,000 mechanical ventilators, bulking up its strategic national stockpile of masks and medications, and expanding the ability to immediately scale up testing and vaccine development. It would also shore up supply chains of all sorts and create protocols to boost personnel in times of emergency.

America rolled the dice. For just one example, the federal government has invested only about $500 million annually in the strategic stockpile, maintaining about 12 million N95 masks and 16,600 ventilators. This is enough to equip an area hit by a localized disease outbreak, natural disaster, or terrorist attack. But it is nowhere near what could be necessary in a Disease X pandemic.

In January of this year, some Chinese scientists warned that a Disease X had arrived, based on genetic sequencing they’d performed. This novel coronavirus, SARS-CoV-2, was almost identical to others that had been found in bats and was capable of hijacking an enzyme in human cells to cause acute respiratory failure.

When I first spoke with Thomas in February, before New York had a single confirmed case, he told me his chief concern: “ICU beds will be limited, and that will mean rationing of expertise in the intensive-care setting. That’s a whole different type of medicine than most of us are used to practicing.” Thomas had spent 20 years in the Army developing “medical countermeasures” against infectious diseases, and, like other military experts who plan for disaster scenarios, he sounded coolheaded in talking about the looming catastrophe. He remained so when he told me on March 16 that his hospital had gotten its first case. At 10 p.m. that day, he emailed and said it had gotten its second. By March 20 he had seven. On Tuesday afternoon he wrote, “We are doing ok. Running out of PPE and trying to build a reliable supply chain.”

When we spoke by phone late Tuesday night, as he was driving home from the hospital, he sounded tired. I asked him to think back to the Disease X war game. The coronavirus “is much worse than what I had envisioned,” he said. “You never think the planets are going to align. You get used to the near misses. I’m taken aback by the scope, the speed, and how relentless it is. It’s amazing.”

Many doctors are nonetheless being asked to operate as usual. Last week an internal-medicine physician with whom I trained in residency told me she’d been chastised by the head of her department for wearing a surgical mask at work. She feels unsafe without one, given the lack of certainty about who has the virus—not to mention the worry that she herself could be an asymptomatic carrier.

Across the world, people are implored to avoid contact with anyone outside a small circle of family members or cohabitants. In clinics and hospitals, doctors aren’t doing their job if they are unwilling to get within inches of people, many of whom are in high-risk groups, and often do so without any protection. “This week we got an order that no masks are allowed for routine care and just walking around inside the hospital,” John Mandrola, a cardiologist in Kentucky, told me. He said his initial reaction was opposition, but he has now accepted that shortages demand rationing.

In fact, taking the standard precautions—using fresh masks and gowns—has become impossible in hospitals in the hardest-hit areas, even when treating people with florid cases of COVID-19. One New York doctor told me she keeps her mask in a brown paper bag until it is time to put it on again, though other doctors at her hospital leave theirs lying out on a countertop. Another physician has been taking his mask home and “sterilizing” it in his oven at night.

This reuse of equipment is a form of rationing, though it may not usually be considered as such. It began weeks ago, when the U.S. surgeon general urged people not to buy face masks. It continued last week when the New York Department of Health implored residents to “only seek health care if you are very sick.” It continues in New York with the cancellation of “elective surgeries,” which now include even cancer treatments that can reasonably be postponed. Many if not most sick people are not getting tested, and not everyone will be treated by the doctor they might expect. Deciding who gets to see the chief of infectious diseases and who is relegated to the retired ophthalmologist will involve rationing via triage.

At a small hospital in Sleepy Hollow, New York, James Lindsey works overnight as the sole doctor in the ER, a setup that is standard in all but the biggest medical centers. Lindsey told me that though he hasn’t yet felt unable to manage on his own, he has had to intubate more patients than usual. That involves inserting a tube into a person’s trachea, in order to force air into their lungs (via a ventilator). When a person can’t breathe on their own, intubation is the default action taken by all doctors and paramedics in the U.S., as is attempting to restart the heart with electric shocks, in between rounds of chest compression that often break ribs. In a typical ER, this process involves a team of people. The question on the minds of Lindsey and others is: What happens if or when there are more patients who need to be kept alive than there are equipment or personnel to help them?

[ Read: America’s hospitals have never experienced anything like this]

Already, ventilators in New York City are in short supply. “Everything is chaotic, and the staff is stretched really thin,” one physician wrote to me yesterday. She has had to pronounce two people dead who have been utterly alone, owing to the rule against visitors that hospitals have established for COVID-19 patients. “It’s really eerie and sad to have no family or visitors around to grieve their deaths,” she told me.

New York’s major medical centers are poised to face the kind of life-and-death decision making that industrialized countries typically experience only in times of war and natural disaster. And unlike with a hurricane, when the sudden force of nature makes obvious that not everyone can be saved, the drawn-out advance of the coronavirus will make these decisions more difficult to accept. We have failed to shore up protections for health-care workers. We have set ourselves up to experience the same shortages of vital care that have already happened in Italy. The rationing is already here.

“The assumptions in a pandemic scenario are that personal and community good can be expected to fall out of alignment,” Thomas told me in one of his emails.“Difficult decisions will need to be made.” Deciding how to allocate limited resources is a nightmare scenario for any physician, a violation of the oath to do no harm. As Thomas put it, “Doctors should not be put in the position of dispensing of justice.”

In an attempt to lift some of the burden from individual providers, Thomas’s hospital and others around the country are convening emergency meetings to develop guidelines for rationing, according to who is least likely to benefit from treatment. The goal is to make the guidelines objective, accurate, and easy to use, as well as to minimize the waste of resources. The instructions could be as strict as age limits for intensive care, or withholding care from people who have the lowest chance of survival, such as those suffering from heart failure or emphysema. On Thursday, The Washington Post reported that Northwestern University’s medical center, in Chicago, was considering putting every patient with COVID-19 on “do not resuscitate” (DNR) status. This would mean that if their heart stops, no “code blue” would be called; instead, a time of death would be noted.

As of Friday afternoon, Thomas’s county was up to 110 confirmed cases. “Winter is coming,” as he put it. But Thomas maintains hope that a blanket DNR policy will not be necessary. “Assess, make decisions, reassess, make another decision. Repeat” is how he described the coronavirus-treatment playbook to me. “We can do this … as long as we have PPE and vents.”

[ Kerry Kennedy Meltzer: I’m treating too many young people for the coronavirus]

Although explicit, widespread rationing by health-care providers is unprecedented in the modern history of the United States, it is constantly happening around the world. “Our doctors face moral dilemmas and impossible choices every day,” said Doctors Without Borders’ Avril Benoit. “Even while COVID-19 is requiring reallocation of resources, we still have women who need emergency C-sections and children with malnutrition. We are converting trauma and burn clinics to care for the disease. You do the best you can with what you have. And many of our locations will not be able to do more than isolate people and provide palliative care.”

Patients, too, make rationing decisions. Every time we weigh whether or not to go to the doctor or to take medication, we’re balancing costs and benefits. Many people—an estimated third of U.S. adults—also make decisions about what they want should they become very ill. In the form of advance directives, they give instructions about when medical professionals should extend their lives with so-called extraordinary measures, and when they shouldn’t.

The directives can be elaborate or spare, but generally land on a spectrum between prioritizing comfort and prolonging life, should the two become mutually exclusive. The most common designations are “full code” and “DNR,” but directives can also get very specific. The options are not binary, care or none. A person who voluntarily designates as “DNR” wouldn’t be abandoned—he or she would still get IV fluids, oxygen, and medication, especially for pain.

After determining advance directives, you should share them with family members or friends who might be communicating with medical professionals on your behalf. Have nuanced conversations with people close to you about what you do or don’t want in various dire scenarios. This eases the burden on them.

It eases the burden on medical providers as well. Too often, Lindsey said, a person is found unconscious by paramedics, then shocked back to life and brought to the hospital, or put on a ventilator, and only hours later a family member shows up with an advance directive that indicates that this was not what the patient wanted. “This was a tragic and challenging scenario pre-COVID, particularly if an individual’s directives weren’t followed during that period of resuscitation,” he said. But in the midst of this pandemic, the delay puts “all the providers in the chain of care” at unnecessary risk of exposure. And it takes a ventilator out of use for someone who might have wanted it.

As straightforward as it is to establish an advance directive and talk through what kind of care you want with your family, many of us avoid doing precisely that. Who wants to talk about the possibility of getting sick and dying? Thomas does. “I’m still a relatively young person, and my wife and I have that discussion relatively often,” he told me. “It should be had frequently, but especially now.”

Related Podcast

Listen to James Hamblin talk with bioethicist Arthur Caplan on an episode of Social Distance, The Atlantic’s podcast about living through a pandemic:

Subscribe to Social Distance on Apple Podcasts or Spotify (How to Listen)

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JAMES HAMBLIN, M.D., is a staff writer at The Atlantic. He is also a lecturer at Yale School of Public Health and author of the forthcoming book Clean.

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THE INTERMINABLE BODY COUNT

We may never know how many people the coronavirus kills: “It sounds like it could be totally obvious—just count body bags. It’s not obvious at all.”

By ELAINE GODFREY | Published April 1, 2020 | The Atlantic Magazine | Posted April 04, 2020 |

We rely on numbers to understand the size and scope of tragedy—to gauge what went wrong and put the damage in perspective. More Americans have now died from the coronavirus than were killed in the September 11 terrorist attacks, multiple news outlets announced yesterday.

But we likely won’t have an estimate of how many Americans have died as a result of the pandemic for a very long time—maybe months, maybe a year. We will almost certainly never know the exact number. “It sounds like it could be totally obvious—just count body bags,” John Mutter, an environmental-science professor at Columbia University who studies the role of natural disasters in human well-being, told me in an interview this week. “It’s not obvious at all.”

When Hurricane Maria flattened Puerto Rico in September 2017, the storm’s devastation was overwhelming. Yet the official death toll in December stood at 64 people—a number that almost no one believed, as my colleague Vann Newkirk II wrote at the time. Nearly a year after the storm, a team of researchers tried to develop their own estimate. They gathered months’ worth of mortality data from households across the island and published a study concluding that, in actuality, more than 4,600 deaths were potentially attributable to the hurricane—70 times the official number.

I talked with Mutter, who led an effort to expand the scope of the official death count after Hurricane Katrina, about the trauma the COVID-19 outbreak could cause—and why that trauma is so hard to quantify. Our conversation has been lightly edited and condensed for clarity.

Elaine Godfrey: So right now, we’re hearing that more than 3,000 people have died from the virus in the U.S. The government is estimating that, in the best-case scenario, the total will be somewhere from 100,000 to 200,000 deaths. How confident can we be about those numbers?

John Mutter: The difficulty with estimating fatalities from natural disasters depends on what sort of a disaster it is. Earthquakes are pretty clear, because the typical cause of death is blunt trauma. There’s this old adage that earthquakes don’t kill people, buildings kill people, and it’s pretty true. Your house falls down.

When you die of coronavirus, what do you actually die of? [In some cases] your lungs fill up with fluid, just like it’s pneumonia. So do you die of the virus, or do you die of pneumonia?

This happened a lot in Katrina and with [other] hurricanes: People who already had conditions—heart conditions, respiratory conditions—[their] deaths could result from the exacerbation of those existing conditions. How do you count somebody who died of a heart attack during a natural disaster? Do you call it a disaster death or a heart-attack death? There’s no rules. None. And particularly from country to country.

The [Centers for Disease Control and Prevention] here has rules. They’re very conservative; you’ll always get the minimum number from the CDC. And they’re very clear what their criteria are. What you’ll hear about disaster deaths from the CDC is only those that can be absolutely verified as being directly related to that disaster. Then they’ll list all the heart-attack victims. They might call them indirect, but they won’t call them directly related to the disaster. So it’s more of a subtle business than you might think.

In this case, because many people are using emergency rooms and hospitals, it’s crowding out people who would normally be at emergency services. People who have chronic heart conditions, nothing to do with coronavirus, are crowded out of the emergency rooms because there's no space for them. So the death rate will rise with people like that. Do you count them?

Godfrey: The CDC at this point probably wouldn’t count those people, right?

Mutter: They would want to see evidence that the person died because they contracted this virus.

Godfrey: The governor of Puerto Rico said that 64 people died in Hurricane Maria. The Harvard study that came out almost a year later estimated it was something like 4,600.

Mutter: When Trump visited [Puerto Rico], he said the number was 16. And Trump said, Oh, then you’re not having a real disaster. Think about Katrina, where there were hundreds—when in fact there were thousands. He tried to diminish it.

The Economist made an estimate [for Maria], and they thought it was more like 1,000. They were looking at death records [to compare deaths from September 2017 to September 2016]. Every day in September, you get a number that’s pretty consistent. And then you look at it for the day of Maria, and it’s hugely different. This is called all-cause mortality, because it doesn’t determine a particular cause. This is an anomaly above the baseline.

Harvard was interested in that, but they were also interested in the long run. This is always a question with disasters: When do you stop counting? The day after? The day after that? Many people, if they are displaced, particularly the elderly, will die days and weeks afterwards, for many reasons—[it’s] the trauma of displacement.

So Harvard kept tracking it. How long did above-average deaths keep occurring? That’s how they got a big number. And you just have to ask yourself, Is that fair? Is that reasonable? Two months afterwards, can you really seriously call it a disaster death? Well, in my opinion, yes. If you can say this death would not have happened were it not for the hurricane … even if it’s weeks or months later, I think it’s completely fair.

After Katrina, there was a lot of infant mortality. Poor women in Mississippi—who probably didn’t get very good health care anyway—when they were pregnant [during and after the storm] got no health care. So infant mortality rose for a while, and the reason was not [physically related to the hurricane itself]. It’s because people can’t get access to health care. It just goes on and on.

Here in New York, after Hurricane Sandy, one of the veterans’ hospitals had to close. People have done studies on the excess mortality associated with closing one hospital. It’s a cumulative thing. It wouldn’t have happened if it weren’t for Sandy. That’s the way to think about it.

Godfrey: So, thinking about the COVID-19 outbreak as a cumulative thing, what could increase the death count long after this pandemic is technically over?

Mutter: In Katrina, suicide rates increased in the weeks and months afterwards because unstable people were very distressed.

The indirect effect often results from confinement, such as we are asked to do now. In crowded settings, like refugee camps and the FEMA trailers that housed so many Katrina survivors, if couples are not getting on well, then that sort of confinement can cause friction, abuse, and even death.

Displacement [when people are moved for safety reasons or to receive better care] is hard on older people with medical issues who need regular doctor visits. They are separated from their normal care providers and will not necessarily remember what the medications are that they need. They get stressed and can fail just from that. And just being displaced and not knowing when return might be possible, if at all, can cause deep depression among the elderly.

Addicts separated from dealers can become suicidal and, if they are able to find the drugs they want after a long time, can overdose.

With COVID-19, people are going to have to be removed from hospitals into different care situations [because of a] lack of hospital services. Almost certainly that care will be [worse] than hospital care.

Godfrey: When is the earliest you think we could start having good estimates about the casualties of this crisis?

Mutter: As soon as the numbers start diminishing—[when] we hear that this week, the number of cases was less than the previous week, when you’re over the peak, then you might be able to get good numbers. But that’s [likely] well into the summer.

Godfrey: And even then, if you’re looking at indirect deaths …

Mutter: That’s right. You’re looking at this issue of excess deaths, all-cause mortality. How many more people will die this April than normally die in April? It takes a little while to figure it out.

Godfrey: When would we be able to stop counting?

Mutter: It will differ from country to country. But when the new cases get to be very, very small—one new one a week—then you can go back and look at what happened. But until the numbers stabilize, it’s a moving target.

Godfrey: The Harvard study about Hurricane Maria came out about a year after the hurricane. Is that the timeline we could be looking at here?

Mutter: Yep. For sure. And you can only expect the numbers to go up, as in Maria.

Godfrey: Will we ever know how many people have really died of the coronavirus? Will we ever be able to confidently quantify it?

Mutter: There are sure to be many studies about this pandemic. Some will be accurate, some will be partisan, and the average reader will have a difficult time distinguishing. The CDC will certainly do follow-up studies to try to get numbers as right as possible.

We’ll be able to say [for example] that maybe it’s 2 million, plus or minus 100,000. You won’t get an exact number. You almost never do.

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We Need to Link Local Leaders Directly to One Another

A strategy that helped Americans defeat al-Qaeda could be the key to stopping the coronavirus.

By Chris Fussell, Former U.S. Navy SEAL & president of McChrystal Group | Published April 03, 2020 | The Atlantic Magazine | Posted April 04, 2020 |

As the United States fights the spread of the coronavirus, health-care workers are recycling personal protective equipment, governors are engaging in bidding wars for ventilators, and large sections of our health-care infrastructure are being overwhelmed. Despite the herculean efforts of personnel across the federal government, it’s clear that a top-down approach to fighting COVID-19 is insufficient, and will continue to create silos between our frontline leaders. This war is being fought by governors, mayors, and hospitals, and they need a network that links them directly to one another, and moves as fast as the virus they are working to defeat. Otherwise, even if they win their local battles, the nation could still lose its war on COVID-19.

I’ve seen this kind of challenge before. In 2008, 10 years into my career as a Navy SEAL, I visited a small, inconspicuous green tent on the outskirts of Baghdad. After years on the front line of the fight against al-Qaeda, I’d joined General Stanley McChrystal’s staff for a one-year tour as an aide-de-camp. Our visit that day was to something I’d heard about, but only vaguely understood—a “fusion cell.” Although Baghdad was still racked daily by horrendous violence, we knew we’d gained the upper hand on the al-Qaeda network, but it wasn’t until I stepped into this small tent that I understood why.

While McChrystal spent time with the various members of the fusion cell, I sat quietly in the back and watched. A small number of personnel from different agencies and military units were reading intelligence reports, shouting across the room, running between desks, and hopping on and off calls to various strike forces on the front line. I realized: This is how we’re moving faster than the al-Qaeda network.

I was late to the game, of course. The people in that fusion cell, and McChrystal’s senior leadership team, had appreciated the importance of this approach for years. They knew that the most easily exploited location on a traditional battlefield is where two lines meet. This can be a physical gap between units on the ground, or a gap in lines of authority between different agencies. When these gaps in communication are encountered, bureaucracy steps in to ensure deliberate, albeit slow, coordination. In Special Operations, we referred to these as “blinks”; moments when our eyes were closed, and the enemy network was safe to expand.

Our fusion-cell network was the answer. Under McChrystal’s leadership, we placed small teams of intelligence analysts from Special Operations, conventional military units, civilian intelligence, and law-enforcement agencies at key locations around the world, as close to key nodes in the al-Qaeda network as possible. They weren’t frontline operators, but they were only one step away from, and in direct communication with, those teams. Wherever al-Qaeda was around the world, McChrystal fought to place a fusion cell there as well.

These interagency teams were constantly scanning raw data from ongoing missions in the field, which they fused across their agencies. Each member of a fusion cell had the authority and responsibility to quickly connect with other fusion cells, in real time, without letting their home bureaucracy slow things down. The larger this global, interconnected fusion-cell network became, the more exponential its returns. While the visible fight was mostly centered on Iraq and Afghanistan, our network would grow to more than 70 discreet locations around the globe. If operators and helicopters were the muscle and skeleton of the fight against al-Qaeda, the fusion-cell network was its nervous system.

The fusion-cell network accomplished three major goals that no bureaucracy could keep pace with. First, it captured and shared raw intelligence from one location that could drive immediate action at another. Second, it gave a nonsiloed view of the fight so that crucial decisions about where to allocate resources—where to send operators, helicopters, surveillance drones—were made with one common operating picture. And third, it provided a real-time network through which best practices on one side of the fight could be shared with other units, immediately saving lives on the battlefield. To illustrate: In a single night, the information gleaned from a raid in downtown Baghdad could be sent directly to a team 200 miles away in Anbar, which would step off for a mission with additional resources that had been coordinated by frontline leaders, and crucial intelligence that had yet to reach higher headquarters.

The close-quarters fight against the COVID-19 pandemic will be waged—and the losses borne—by our doctors, nurses, and first responders. But those who have the privilege of leading these men and women—our mayors, governors, and medical experts—must be provided with a similar network methodology to tap into; we must ensure they’re not being forced to fight 50 state-level battles against COVID-19, but one unified war as a nation. A fusion-cell-network approach would ensure that intelligence sharing isn’t limited by state borders, bureaucratic rules, or the down-and-in structure of a hospital system, city, or state. We must ensure that they can establish real-time connectivity with one another, and not depend solely on traditional bureaucratic channels.

In short, mayors, governors, and the federal agencies assisting them should stand up fusion cells across the country. This is a light and fast solution. With two or three people in key locations, armed simply with smartphones and laptops, a network could quickly be put into place across our country. An existing entity, such as the U.S. Conference of Mayors, could quickly create a network of local leaders who are fighting this threat in a coordinated fashion.

The results of fusion cells would be quickly apparent. Raw, accurate emergency-room numbers from New York City wouldn’t need to go through layers of national bureaucracy and spreadsheet input before reaching other cities. A tactical improvement made in a Los Angeles emergency room would be shared immediately with doctors and nurses in Detroit and San Francisco. The network would provide mayors and governors with a more reliable single operational picture of this fight, so they can make informed decisions about resource allocation. Such a network wouldn’t be perfect—it never is—and would require trusting your team, but it could be crucial as the nation faces shortages and overload. We should not have governors or hospital systems in bidding wars for ventilators and personal protective equipment; instead, they need a network that allows them to make effective cross-border, cross-agency, and cross-party decisions.

After my time on McChrystal’s staff in Iraq, I was able to spend a year at graduate school, and my thesis team’s research focused on interagency fusion cells. This was relatively early in the special-operations community’s recognition of their importance, and our goal was to flesh out the key variables that were making some of them so successful. We found, surprisingly, that one key factor far outweighed more obvious and visible ones, such as technological infrastructure, geographic location, the number or seniority of personnel, or physical constructions. Success hinged, quite simply, on the human factor.

If the members of a fusion cell were experienced players who enjoyed high levels of trust in their home community, regardless of their seniority or positional authority, they were empowered to quickly push insights across bureaucratic firewalls and create action on the front lines. Without personnel like that, a fusion cell became just another repository for information from which those closest to the fight needed to pull insights. A network node intended to add speed and connectivity can quickly turn into another bureaucratic layer. Keeping the nodes fast, light, and staffed by seasoned people proved key to success.

This pandemic presents an incredible challenge for our nation, but we’ve learned previously how to defeat a problem like this. Agency and state bureaucracy will help us make sound and structured decisions, but it’s impossible to move key insights and raw intelligence through traditional means alone. The doctors, nurses, and first responders who are in this battle each day deserve every solution we can possibly offer. Minutes count. They need a network.

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CHRIS FUSSELL is a former U.S. Navy SEAL and the president of McChrystal Group. He is the co-author of Team of Teams: New Rules of Engagement for a Complex World.

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