vox--2020-04-17--Why France has 4 times as many coronavirus deaths as Germany

France and Germany , Europe ’ s two most powerful countries , have been hit hard by the coronavirus , with each approaching 150,000 confirmed cases .
But as of April 17 , France is near 18,000 dead from the infection , while Germany ’ s death toll has passed 4,000 .
Which raises the question : How did two similarly sized countries , located right next to each other and with comparable levels of wealth and resources , end up with such starkly different outcomes ?
The answer has a lot to do with how their respective governments responded @ @ @ @ @ @ @ continent ’ s first confirmed cases of the novel coronavirus , but the French government failed for weeks to take decisive action to impose strict social distancing measures or promote large-scale testing .
Germany , on the other hand , immediately began aggressively testing and tracking people with symptoms .
Now , France is under lockdown and has just extended it until May 11 at least .
Meanwhile , Germany plans to reopen part of its economy next week .
The experiences of these two countries show that just having substantial national wealth @ @ @ @ @ @ @ t enough to keep citizens safe from the deadly coronavirus .
Saving lives is also about how quickly , thoroughly , and effectively the government responds to the brewing crisis .
Any delay , it seems , is very costly .
“ Countries that were slow to respond have , so far , paid the price , ” Thomas Bollyky , a global health expert at the Council on Foreign Relations think tank , told me last month .
If there ’ s a lesson for world governments , then , it ’ @ @ @ @ @ @ @ not France .
How Germany kept its coronavirus death rate so low It ’ s not surprising that Germany has the world ’ s fifth-largest coronavirus outbreak .
It ’ s in the middle of Europe and nearly borders Italy , which early on in the crisis became the continent ’ s epicenter .
If the disease was going to spread , Germany was always going to be a likely victim .
What wasn ’ t predetermined , though , was its low death rate .
That result came from a combination of @ @ @ @ @ @ @ action .
Marieke Degen , the deputy spokesperson of Germany ’ s Robert Koch Institute , told me that the country ’ s earliest coronavirus carriers were skiers returning home from Austria and Italy .
Health authorities say that older adults , especially those over 60 years old , are at risk of severe complications .
Most skiers , however , don ’ t fit that age demographic .
While some still got sick , then , the chance they would die from the disease was low .
That trend continues : The @ @ @ @ @ @ @ Germany is 49 years old , compared to about 62.5 years old in France .
It also helped that the vast majority of early cases were clustered in the western region of Heinsberg .
That just happens to be near top German hospitals in Bonn , Düsseldorf , Cologne , and other cities , which means those patients were able to access the best care .
But young carriers in the area , even if they were asymptomatic , could spread the disease around the country to more vulnerable people .
Why didn @ @ @ @ @ @ @ scale ?
“ The reason why we in Germany have so few deaths at the moment compared to the number of infected can be largely explained by the fact that we are doing an extremely large number of lab diagnoses , ” Christian Drosten , the chief virologist at the Charité hospital in Berlin , told the New York Times this month .
Germany has Europe ’ s best pharmaceutical industry , allowing it to respond quickly to disease outbreaks .
In the case of Covid-19 , German laboratories started accumulating testing kits @ @ @ @ @ @ @ more real in early 2020 .
These labs were well stocked ahead of Germany ’ s first confirmed coronavirus case in February .
The Robert Koch Institute ’ s Degen told me that early testing helped the country ’ s public health officials get a better understanding of where the outbreaks were and how far the disease had spread before things got out of control .
“ This is probably why we started to see cases very early , and many cases , and also mild ones , ” she said .
This @ @ @ @ @ @ @ confirmed cases is so high but the number of deaths so small : hundreds of thousands are getting tested each week , and the vast majority of them won ’ t have a life-threatening case .
Every subsequent test , then , makes the infection-to-death ratio smaller and smaller .
But that ’ s not all : Germany has also gone the extra mile to track those with the disease .
In the city of Heidelberg , for example , the New York Times reports that vehicles known locally as “ corona taxis @ @ @ @ @ @ @ those who have been sick for five to six days .
“ They take a blood test , looking for signs that a patient is about to go into a steep decline .
They might suggest hospitalization , even to a patient who has only mild symptoms ; the chances of surviving that decline are vastly improved by being in a hospital when it begins , ” the New York Times ’ s Katrin Bennhold wrote .
This not only helps authorities keep tabs on a known patient , but also enables them @ @ @ @ @ @ @ the disease ’ s progression , thereby reducing the chances of death .
“ Testing and tracking is the strategy that was successful in South Korea and we have tried to learn from that , ” Hendrik Streeck , who leads the University of Bonn ’ s virology institute , told the New York Times .
It appears Germany plans to keep up intense tracking for the foreseeable future .
“ Once ... we are down to , let ’ s say , a couple of hundred cases per day or even better @ @ @ @ @ @ @ we will try to follow up on every case and get in touch with everyone who has been in touch with those new cases , quarantine and test them , ” Karl Lauterbach , an epidemiologist at the University of Cologne , told CNBC on April 3 .
Of course , many experts I spoke to warned that the situation could still worsen in Germany .
Degen told me that “ the [ death ] rate has been steadily rising ” — it ’ s roughly at 3 percent now — “ and @ @ @ @ @ @ @ ” She added that it ’ s “ very important to stress that Germany is still at the beginning of the epidemic ” and that more and more elderly people in the country are getting sick .
That means Germany isn ’ t out of the woods yet .
But it ’ s in a better position than most because it had good fortune and the good sense to start testing early and often .
France , on the other hand , had none of that .
France , like Germany , is @ @ @ @ @ @ @ system .
It doesn ’ t have the pharmaceutical prowess of its neighbor , experts tell me , but still has good hospitals with thousands of ICU beds and well-trained physicians .
If you were to list the countries best prepared , at least in theory , to weather the coronavirus outbreak , France would surely be near the top .
And yet that ’ s not what happened .
That ’ s in large part because French President Emmanuel Macron and his team completely missed their chance to quash the disease early @ @ @ @ @ @ @ .
“ Were we prepared for this crisis ?
On the face of it , not enough .
But we coped , ” he said in a televised national address in which he announced an extension of the nation ’ s lockdown to May 11 .
To understand just how badly Macron ’ s government bungled the country ’ s coronavirus response , it helps to go back to the beginning .
On January 24 , France ’ s then-Health Minister Agnès Buzyn announced that two people in the country tested positive for @ @ @ @ @ @ @ cases in all of Europe .
They had just been to China , Buzyn said , adding , “ We will probably have other cases.
” But if Macron ’ s government felt a sense of urgency , it didn ’ t show it .
February came and went with little action .
Health officials advised citizens to wash their hands , keep a safe distance from others , cover their mouths when sneezing , and stay away from retirement homes .
And even as Macron held video conference calls on the virus @ @ @ @ @ @ @ how his country was coping , few concrete actions were taken to impose strict social distancing measures or promote large-scale testing .
In fact , in early March , the government still allowed gatherings of up to 1,000 people to proceed .
Macron , for his part , attended a theater performance on March 6 , partly to show that life could continue unperturbed .
He also visited a retirement home that same day , even as the number of coronavirus infections in the country was at least doubling .
To make matters @ @ @ @ @ @ @ a clear picture of the growing problem due to a lack of tests .
As Politico reported last week , the country doesn ’ t manufacture its own testing kits , but rather “ relies on China for their main components.
” With China paralyzed by its coronavirus outbreak at the time , France was unable to quickly get more tests .
That severely limited the country ’ s ability to do widespread testing early on , which public health experts say is critical to slowing an outbreak .
Macron , in effect @ @ @ @ @ @ @ .
Two events finally woke him from his slumber , experts say .
The first was Italy ’ s coronavirus situation .
In late February , Italy had just three confirmed cases ; by mid-March , that number had skyrocketed to around 15,000 .
That got Macron ’ s attention and caused him and his leadership team to worry that perhaps the disease was worse than China had let on .
The second was the discovery of some 2,500 coronavirus cases in the country that could all be traced back to a single @ @ @ @ @ @ @ in mid-February .
As Reuters reports , during the week of February 17 , hundreds of worshippers from around the world attended an annual celebration at the Christian Open Door evangelical megachurch in Mulhouse , a city in eastern France near the country ’ s border with Germany .
One of the congregants carried the disease .
The first case linked to the church was identified on February 29 .
Over the following weeks , experts traced some 2,500 infections back to the event .
“ Worshippers at the church [ had ] @ @ @ @ @ @ @ virus home to the West African state of Burkina Faso , to the Mediterranean island of Corsica , to Guyana in Latin America , to Switzerland , to a French nuclear power plant , and into the workshops of one of Europe ’ s biggest automakers , ” Reuters reports .
By the time researchers understood the extent of the outbreak , they knew bigger problems lay ahead .
“ We realized that we had a time bomb in front of us , ” Michel Vernay , an epidemiologist with France ’ s @ @ @ @ @ @ @ in March .
Put it all together , and it becomes clear that in addition to its early luck with younger carriers , Germany ’ s ability to test early , track often , and treat patients thoroughly has kept its death toll down despite a large number of confirmed infections .
France , meanwhile , dawdled on implementing significant measures , especially testing , for weeks .
The contrast shows how vital swift , aggressive measures are for combating the disease .
The hope is that other nations learn that lesson as @ @ @ @ @ @ @ globe .
Every day at Vox , we aim to answer your most important questions and provide you , and our audience around the world , with information that has the power to save lives .
Our mission has never been more vital than it is in this moment : to empower you through understanding .
Vox ’ s work is reaching more people than ever , but our distinctive brand of explanatory journalism takes resources — particularly during a pandemic and an economic downturn .
Your financial contribution will not constitute a @ @ @ @ @ @ @ staff to continue to offer free articles , videos , and podcasts at the quality and volume that this moment requires .
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France and Germany, Europe’s two most powerful countries, have been hit hard by the coronavirus, with each approaching 150,000 confirmed cases. But as of April 17, France has near 18,000 dead from the infection, while Germany’s death toll has passed 4,000.
Which raises the question: How did two similarly sized countries, located right next to each other and with comparable levels of wealth and resources, end up with such starkly different outcomes?
The answer has a lot to do with how their respective governments responded to the crisis.
France had the continent’s first confirmed cases of the novel coronavirus, but the French government failed for weeks to take decisive action to impose strict social distancing measures or promote large-scale testing. Germany, on the other hand, immediately began aggressively testing and tracking people with symptoms.
Now, France is under lockdown and has just extended it until at least May 11. Meanwhile, Germany plans to reopen part of its economy next week.
The experiences of these two countries show that just having substantial national wealth and high quality health care systems isn’t enough to keep citizens safe from the deadly coronavirus. Saving lives is also about how quickly, thoroughly, and effectively the government responds to the brewing crisis. Any delay, it seems, is very costly.
“Countries that were slow to respond have, so far, paid the price,” Thomas Bollyky, a global health expert at the Council on Foreign Relations think tank, told me last month.
If there’s a lesson for world governments, then, it’s to be more like Germany — not France.
How Germany kept its coronavirus death rate so low
It’s not surprising that Germany has the world’s fifth-largest coronavirus outbreak. It’s in the middle of Europe and nearly borders Italy, which early on in the crisis became the continent’s epicenter. If the disease was going to spread, Germany was always going to be a likely victim.
What wasn’t predetermined, though, was its low death rate. That result came from a combination of luck and the government’s quick action.
Let’s start with the luck part.
Marieke Degen, the deputy spokesperson of Germany’s Robert Koch Institute, told me that the country’s earliest coronavirus carriers were skiers returning home from Austria and Italy. Health authorities say that older adults, especially those over 60, are at risk of severe complications. Most skiers, however, don’t fit that age demographic. While some still got sick, then, the chance they would die from the disease was low.
That trend continues: The average age of an infected person in Germany is 49 years old, compared to about 62.5 years old in France.
It also helped that the vast majority of early cases were clustered in the western region of Heinsberg. That just happens to be near top German hospitals in Bonn, Düsseldorf, Cologne, and other cities, which means those patients were able to access the best care.
But young carriers in the area, even if they were asymptomatic, could spread the disease around the country to more vulnerable people. Why didn’t that happen on a wide scale?
Two words: testing and tracking.
“The reason why we in Germany have so few deaths at the moment compared to the number of infected can be largely explained by the fact that we are doing an extremely large number of lab diagnoses,” Christian Drosten, the chief virologist at the Charité hospital in Berlin, told the New York Times this month.
Germany has Europe’s best pharmaceutical industry, allowing it to respond quickly to disease outbreaks. In the case of Covid-19, German laboratories started accumulating testing kits as signs of a global spread became more real in early 2020. These labs were well stocked ahead of Germany’s first confirmed coronavirus case in February.
The Robert Koch Institute’s Degen told me that early testing helped the country’s public health officials get a better understanding of where the outbreaks were and how far the disease had spread before things got out of control. “This is probably why we started to see cases very early, and many cases, and also mild ones,” she said.
This also helps explain why the number of confirmed cases is so high but the number of deaths so small: hundreds of thousands are getting tested each week, and the vast majority of them won’t have a life-threatening case. Every subsequent test, then, makes the infection-to-death ratio smaller and smaller.
But that’s not all: Germany has also gone the extra mile to track those with the disease.
In the city of Heidelberg, for example, the New York Times reports that vehicles known locally as “corona taxis” transport physicians to the homes of those who have been sick for five to six days.
“They take a blood test, looking for signs that a patient is about to go into a steep decline. They might suggest hospitalization, even to a patient who has only mild symptoms; the chances of surviving that decline are vastly improved by being in a hospital when it begins,” the New York Times’s Katrin Bennhold wrote.
This not only helps authorities keep tabs on a known patient, but also enables them to intervene at a critical point in the disease’s progression, thereby reducing the chances of death.
“Testing and tracking is the strategy that was successful in South Korea and we have tried to learn from that,” Hendrik Streeck, who leads the University of Bonn’s virology institute, told the New York Times.
It appears Germany plans to keep up intense tracking for the foreseeable future. “Once ... we are down to, let’s say, a couple of hundred cases per day or even better, less than a hundred cases, we will try to follow up on every case and get in touch with everyone who has been in touch with those new cases, quarantine and test them,” Karl Lauterbach, an epidemiologist at the University of Cologne, told CNBC on April 3.
Of course, many experts I spoke to warned that the situation could still worsen in Germany.
Degen told me that “the [death] rate has been steadily rising” — it’s at roughly 3 percent now — “and we expect it to further do so.” She added it’s “very important to stress that Germany is still at the beginning of the epidemic” and that more and more elderly people in the country are getting sick.
That means Germany isn’t out of the woods yet. But it’s in a better position than most because it had good fortune and the good sense to start testing early and often.
France, on the other hand, had none of that.
How France bungled its coronavirus response
France, like Germany, is a wealthy country with a great health care system. It doesn’t have the pharmaceutical prowess of its neighbor, experts tell me, but still has good hospitals with thousands of ICU beds and well-trained physicians.
If one were to list the countries best prepared, at least in theory, to weather the coronavirus outbreak, France would surely be near the top.
And yet that’s not what happened.
That’s in large part because French President Emmanuel Macron and his team completely missed their chance to quash the disease early on.
Macron even admitted as much. “Were we prepared for this crisis? On the face of it, not enough. But we coped,” he said in a televised national address in which he announced an extension of the nation’s lockdown to May 11.
To understand just how badly Macron’s government bungled the country’s coronavirus response, it helps to go back to the beginning.
On January 24, France’s then-Health Minister Agnès Buzyn announced that two people in the country tested positive for the coronavirus, becoming the first known cases in all of Europe. They had just been to China, Buzyn said, adding, “We will probably have other cases.”
But if Macron’s government felt a sense of urgency, it didn’t show it.
February came and went with little action. Health officials advised citizens to wash their hands, keep a safe distance from others, cover their mouths when sneezing, and stay away from retirement homes. And even as Macron held video conference calls on the virus and inspected hospitals and clinics to see how his country was coping, few concrete actions were taken to impose strict social distancing measures or promote large-scale testing.
In fact, in early March, the government still allowed gatherings of up to 1,000 people to proceed. Macron, for his part, attended a theater performance on March 6, partly to show that life could continue unperturbed. He also visited a retirement home that same day, even as the number of coronavirus infections in the country was at least doubling.
To make matters worse, France couldn’t get a clear picture of the growing problem due to a lack of tests. As Politico reported last week, the country doesn’t manufacture its own testing kits, but rather “relies on China for their main components.” With China paralyzed by its coronavirus outbreak at the time, France was unable to quickly get more tests. That severely limited the country’s ability to do widespread testing early on, which public health experts say is critical to slowing an outbreak.
Macron, in effect, seemed to be sleepwalking toward disaster. Two events finally woke him from his slumber, experts say.
The first was Italy’s coronavirus situation. In late February, Italy had just three confirmed cases; by mid-March, that number had skyrocketed to around 15,000. That got Macron’s attention and caused him and his leadership team to worry that perhaps the disease was worse than China had let on.
The second was the discovery of some 2,500 coronavirus cases in the country that could all be traced back to a single week-long religious gathering that had taken place in mid-February.
As Reuters reports, during the week of February 17, hundreds of worshippers from around the world attended an annual celebration at the Christian Open Door evangelical megachurch in Mulhouse, a city in eastern France near the country’s border with Germany. One of the congregants carried the disease.
The first case linked to the church was identified on February 29. Over the following weeks, experts traced some 2,500 infections back to the event. “Worshippers at the church [had] unwittingly taken the disease caused by the virus home to the West African state of Burkina Faso, to the Mediterranean island of Corsica, to Guyana in Latin America, to Switzerland, to a French nuclear power plant, and into the workshops of one of Europe’s biggest automakers,” Reuters reports.
By the time researchers understood the extent of the outbreak, they knew bigger problems lay ahead. “We realized that we had a time bomb in front of us,” Michel Vernay, an epidemiologist with France’s national public health agency, told Reuters in March.
Put it all together, and it becomes clear that in addition to its early luck with younger carriers, Germany’s ability to test early, track often, and treat patients thoroughly has kept its death toll down despite a large number of confirmed infections. France, meanwhile, dawdled on implementing significant measures, especially testing, for weeks.
The contrast shows how vital swift, aggressive measures are for combating the disease. The hope is that other nations learn that lesson as the virus continues to sweep across the globe.


vox--2020-04-18--Why it’s so hard to see into the future of Covid-19

Share All sharing options for : Why it ’ s so hard to see into the future of Covid-19 One of the greatest challenges of the coronavirus pandemic is that all levels of policy makers need to make decisions with imperfect information .
Scientists still don ’ t know everything about how this virus is transmitted , and due to the lack of widespread testing , they also don ’ t know , exactly , how prevalent it is .
They don ’ t know if the virus will show a strong seasonal effect , and decrease during the summer @ @ @ @ @ @ @ this will all end .
One way they are trying to answer these questions is through modeling .
Specifically , infectious disease models are tools — based on mathematical formulations — that try to game out what ’ s possible in the future .
These models are varied , often confusing to interpret , and are not crystal balls , especially because the ideal data isn ’ t yet available .
But they are a large part of what government leaders use to make decisions , influencing how resources are allocated to health @ @ @ @ @ @ @ are issued to the public .
In this piece , I ’ m going to try to explain the utility of coronavirus models and how to think about them when you see them reported in the news .
I ’ ll also explain a big idea to make these models work better in the future .
But before that , it ’ s key to stress what we don ’ t need them for .
We don ’ t need them to know that we ’ re in a very , very dangerous @ @ @ @ @ @ @ important is not the details of the model , it ’ s that this is a virus that can crush health care , ” says Bill Hanage , an epidemiologist who studies infectious diseases at Harvard .
“ That ’ s not a model result , that ’ s an observation .
We know it because of Wuhan , we know it because of Italy , because of Spain , we know it because , now , of New York.
“ In New York state , thousands have died , and hospitals are @ @ @ @ @ @ @ struggling with equipment shortages .
Covid-19 is “ a freight train , ” as Hanage calls it , and it has rammed into not just New York but several other parts of the United States .
But the models also suggest that the country is nearing , or has reached , the peak in daily deaths .
In the next phase of the outbreak , the models , and decisions made with them , will be critical : Things might seem like they are getting better , and they may be in terms @ @ @ @ @ @ @ this freight train will just be paused .
Without the right controls in place , it could start up again .
The models are not perfect : Some are even failing in their predictions ( more on that below ) .
More scientific oversight and analysis is needed to figure out which models are the most useful to use in decision making .
Still , modeling plays a very important role , and it can help the public know that they , are , in fact , doing the right thing by staying @ @ @ @ @ @ @ is not to precisely predict the future , it ’ s to influence the future , and choose a good course of action .
Modeling an outbreak is an immense challenge Leaders have tough choices to make in the weeks and months ahead , as the outbreak plays out differently in states .
Models can help predict rates of new infections , and estimate when the strain on the hospital system could peak .
In early April , Washington , DC , Mayor Muriel Bowser said that modeling projects a surge in DC @ @ @ @ @ @ @ Like all models , we hope this one will be proved wrong , ” she told MSNBC .
But she ’ s preparing for it anyway .
“ We are preparing for many people to come through our hospitals.
” A pandemic represents the mashup of some of the most complicated and messy things things we have ever tried to study : human behavior , virology , and the human immune system .
Forecasting disease outbreaks is an immense challenge , and even the smartest minds here don ’ t have the answers @ @ @ @ @ @ @ to end .
Models incorporate many different types of data into their projections .
There are a head-spinning number of potential inputs .
( And some models don ’ t use these inputs at all , but just rely on projecting data from earlier in the outbreak . )
A model can input the biology of the virus : How does it spread , how quickly does it infect , how quickly does it lead to symptoms , how quickly does it replicate to a level where it can jump from person to @ @ @ @ @ @ @ of these variables are still not completely known . )
“ What ’ s very important is not the details of the model , it ’ s that this is a virus that can crush health care ” It can account for human biology : How does the immune system mount a response to this virus , how many people will become immune after exposure , and for how long ?
Also , how many people can get infected with the virus , and have the ability to spread it , but never @ @ @ @ @ @ @ of this isn ’ t perfectly understood either . )
It also should , ideally , reflect how human society works : How many people do we come into contact with each day , and how does this vary in different communities , rural and urban ?
Models need to account for that ; in a big country like the US , outbreaks are going to be regional , with varying intensities and responses .
It needs to be realistic about the capacities of health care systems : How many beds are available @ @ @ @ @ @ @ they fill up , how many doctors and nurses are there to serve them , how many ventilators are there , and how many patients will need them , and when ?
Then , there ’ s chaos : How do people react to the news that tens of thousands are dying from a virus that probably started with a bat , and how might that influence the model ?
The question of “ how will an outbreak progress ” is clearly immense .
In a common modeling approach called SIR ( SIR @ @ @ @ @ @ @ ) , scientists are trying to figure how many people are susceptible to a disease , how many of them will become infected , at what rate and where .
But then , as more people recover from the disease , and become immune , that decreases the number of those who are susceptible .
To sum up : This stuff is complicated !
That we can get any insight into the future , considering the variables , is a miracle .
Yet scientists are trying , and their efforts are valuable .
@ @ @ @ @ @ @ types of models being used to try to plot out the course of this pandemic : statistical models and mechanistic models .
Statistical models try to predict the future by projecting current trends The Institute for Health Metrics and Evaluation ( IHME ) has the most commonly cited models — and it includes separate projections for every state .
Dr. Deborah Birx , the White House coronavirus response coordinator , has referenced it .
Hanage explains this model is what ’ s known as a statistical model .
The IHME , based at @ @ @ @ @ @ @ data of how Covid-19 outbreaks have progressed around the world .
It takes that data and then tries to project what the epidemic curve will look like as new outbreaks form in new areas based on what social distancing actions are being taken .
The goal is to predict the time of peak hospital strain in an area , and the number of deaths .
To use Hanage ’ s metaphor : It ’ s looking at how fast and hard the freight train has hit on other stops of its journey , @ @ @ @ @ @ @ and hard when it gets to new stops .
This model makes some assumptions , namely , that the conditions for the previous freight train collisions will be similar in the future .
Earlier in the outbreak , the model was mainly fed from data in China , which imposed extreme social distancing measures .
And so it assumes some high level of social distancing will continue into the future .
“ That makes this a best-case scenario model , ” Carl Bergstrom , a computational biologist at the University of Washington , @ @ @ @ @ @ @ of March .
It ’ s now also drawing from current social distancing actions in the US .
The IHME model assumes this behavior will continue .
And its creators are transparent about this limitation .
The projection , the IHME explains on its FAQ page , “ only goes until the beginning of August and does not predict how many deaths there may be if social distancing is not fully implemented and maintained through the end of May , or if there is a resurgence after early August.
” The hardest thing @ @ @ @ @ @ @ not the virus , but human behavior .
Its performance has been inconsistent .
Recently , a group of statisticians from Australia and the US took a careful look at modeling forecasts of the IHME .
In a preprint paper ( that ’ s not yet been peer-reviewed ) , the statisticians found that the model — and specifically how its predictions on the number of daily Covid-19 deaths matched with reality — wasn ’ t making very good guesses when it came to individual states .
“ It does well predicting what @ @ @ @ @ @ @ States , ” Sally Cripps , a statistician at the University of Sydney , who co-authored the analysis of the IHME model , says .
It also seems to predict the outbreak in New York well .
“ What it doesn ’ t do well , is predict individuals states.
” The model , she says , is “ underestimating its uncertainty.
” The actual reported number of deaths in a state fell outside the model ’ s prediction between 43 and 73 percent of the time ( depending on the date the @ @ @ @ @ @ @ to say : It isn ’ t very precise .
The model was often more accurate predicting outcomes three days ahead than it was predicting events one day ahead , she says .
Also , the analysis found some of the model ’ s predictions were underestimates , some were over .
There was no clear pattern .
Cripps says she fears this lack of precision “ undermines the public ’ s confidence or trust in science , ” and that she wouldn ’ t depend on the model for big policy decisions @ @ @ @ @ @ @ ’ t the only thing the model is trying to predict .
It tries to gauge out when the pandemic will peak in a given area , and can help hospitals plan for that extra strain .
Estimates of those peaks were not assessed in this paper — just the prediction of daily deaths were — and will have to be gauged after the peaks are met .
The model could get the number of deaths wrong , but the timing of the peaks , right , for example .
Also confusing matters @ @ @ @ @ @ @ times , as its creators have input new data from new outbreaks , new social distancing measures , and new resources ( like ventilators ) that have become available .
This has actually led the models to decrease their death toll projection for the US a few times , recently from 81,766 to 60,415 , or about 25 percent .
This doesn ’ t necessarily mean the model has been wrong .
It means collective actions have been working .
Also keep in mind : The IHME death toll projections come with a @ @ @ @ @ @ @ model , deaths per day are expected to peak soon in the US .
As of April 10 , it predicted the peak being two days away , and the error — the shaded area , was roughly 4,000 deaths per day .
“ I think it ’ s key not to get fixated on the exact numbers , ” Dominique Heinke , an epidemiologist in Massachusetts , says .
“ You can look at a range of models and say , we can expect it to be at least this bad.
” @ @ @ @ @ @ @ freight train is coming , and in many places , is already here .
Why do these statistical models change so often ?
Well , it reflects the complexity of the problem these models are trying to solve .
For example , weather forecasters use atmospheric models to predict the weather , and as they gather more data on temperature , humidity , and barometric pressure , their forecasts become more accurate and , thus , often change .
“ Unlike the weather , which we ’ re all accustomed to understanding and @ @ @ @ @ @ @ , unlike the weather [ here ] we actually influence the outcome , ” says Caitlin Rivers , a professor at the Johns Hopkins Center for Health Security .
“ So people see the numbers , and they are motivated then to be more aware , stay home , and using good hygiene and doing all the things that really change that outcome.
” The models change , because our actions change .
The models could change for the worse if local governments declare premature victories and decrease social distancing measures too early @ @ @ @ @ @ @ the model , we can tell how the virus is circulating in our own communities : in some places , cases and deaths are still going up , in some places they are starting to come down , ” says Ali Mokdad , a professor at IHME and chief strategy officer for population health at the University of Washington .
“ We can also use the model to ask what businesses we should open first as we recover : The key issue as we go into recovery mode is to do it in @ @ @ @ @ @ @ a second wave of infections that will hurt us even more in terms of mortality and the economy.
” Unless testing can be scaled up , some social distancing measures may have to be kept in place until there is a vaccine available , which can take a year or more .
What happens in the scenario when social distancing measures are relaxed , but then put in place again if cases spike again ?
“ I ’ m not sure we can model that , ” Hanage says .
Mechanistic models try @ @ @ @ @ @ @ under different scenarios The other type of model decision makers are using is a mechanistic model .
These models are designed to help policymakers understand the impacts specific policies and actions may have on a disease ’ s course .
These models also make a lot of assumptions , and often present very wide-ranging scenarios .
A good example of a mechanistic model comes from the Imperial College of London .
In the middle of March , it provided a scary wake-up call to the UK government to take more action .
Their @ @ @ @ @ @ @ Great Britain and in the US if the countries did nothing .
It took what it knew about the transmissibility of the virus and put it into a model designed for the flu — a caveat right off the bat , as Covid-19 is not the flu .
( Transmissibility here is often called the R0 , or R-naught ; it ’ s the average number of new cases expected to be spawned by each case of an illness .
Note : The value of the R0 is still just an estimation ) @ @ @ @ @ @ @ done , the model ’ s authors found , there could be 510,000 deaths in Great Britain , and 2.2 million in the US .
And that was “ not accounting for the potential negative effects of health systems being overwhelmed on mortality , ” the authors report .
That made headlines .
But their model didn ’ t just report the worst-case scenario .
It tried to gauge the impact of various social distancing policies , and tried to make estimates for many different R0 figures .
The estimates ranged , for @ @ @ @ @ @ @ assuming a low R0 of 2 , and the most aggressive social distancing , and 550,000 deaths assuming an R0 of 2.6 and no social distancing measures .
If you ’ re a leader of a country , looking at that spread , you know what you need to do : implement social distancing measures .
That ’ s what the UK did .
Later , when one of the model ’ s authors told the UK government in testimony that the deaths in Great Britain would probably number around 20,000 , he @ @ @ @ @ @ @ some critics complained .
Instead , he was reflecting that range of possibilities presented in the model .
Again , the point of these models is not precise prediction , it ’ s to influence action .
That ’ s helpful .
But again , as with the statistical models , these mechanistic models can ’ t game out every possible future .
Recently , Columbia University put out a model ( with a handy interactive map ) that tries to predict which US counties will have their health care systems overwhelmed , @ @ @ @ @ @ @ when .
The model also attempts to help hospitals by showing how different coping strategies in hospitals ( i.e .
converting operating room beds to Covid-19 care beds , for one example , and modifying ventilators for use in multiple patients for another ) could mitigate the problem , and help save lives .
The prediction is grim for the crush on hospital systems , which is expected to soon move from the northeast United States , to Southern states , as the outbreak starts to impact more and more rural areas .
@ @ @ @ @ @ @ in some cities taper off , outbreaks in other areas may just be getting started . )
It ’ s a complicated model .
It ’ s trying to predict hospital bed demand , ICU bed demand , and ventilator demand , Jeff Shaman , an infectious disease modeler at Columbia , explains .
“ It ’ s a mathematical description of transmission at county scale , where the counties are linked by movement between them based on ... travel patterns , and understanding that those have waned over time because of this @ @ @ @ @ @ @ account for a lot , but it can ’ t account for everything .
Something it can ’ t account for : the possibility that health care workers get sick and have to leave work , leaving these hospital systems more strained .
“ We ’ re in the process right now on establishing a national database on staffing levels , ” Charles Branas , chair of epidemiology at Columbia , says .
“ It ’ s been challenging to build this airplane while it is flying , quite frankly.
” That doesn @ @ @ @ @ @ @ .
It can still help guide decision-making .
You can look at the map and see which counties are still overwhelmed in their best-case scenarios .
“ Those could potentially be first-choice counties for supplementary resources , ” Branas says .
I asked the Columbia researchers how they ’ d like the public to think of their model .
“ These are not forecasts , they are projections , we ’ re dealing with a very , very uncertain environment , ” Shaman stressed .
“ The degree to which people are social @ @ @ @ @ @ @ is difficult to pin down what ’ s going on .
We ’ re making multiple projections because we don ’ t know what people will do .
We do it because we would like some window into the future .
So we can assess : Are we on a really bad trajectory no matter what we do ?
Or are we on a good trajectory no matter what we do ?
Or is it incumbent upon us to make certain decisions so we can more certainly move to a better outcome ?
@ @ @ @ @ @ @ is better disease forecasting There ’ s a lot that ’ s still unknown about the coronavirus , and the pandemic .
“ There will be people writing papers 100 years from now about what actually happened , there will be people making discoveries about the relative rates of increase in San Francisco vs. New York , ” Hanage says .
Rivers , the Johns Hopkins epidemiologist , hopes , in the future , we ’ ll get better at this .
Like the US has the National Weather Service — a government @ @ @ @ @ @ @ test their predictive power — she hopes to see the creation of a National Infectious Disease Forecasting Center .
“ The reason that we have accurate weather forecast today , is because there was a federal agency responsible for weather forecast , ” she says .
We need to learn from the modeling approaches being used now , to make better models for the future .
The weather service does this for hurricanes : You can clearly see in the weather service data how hurricane forecast tracks ( i.e .
forecast models ) @ @ @ @ @ @ @ doesn ’ t see that as an accident .
The weather models have improved because there ’ s a centralized service to study and create them .
She says there needs to be some central agency collecting these models in an archive , so that researchers later on can figure out which ones worked the best , and why .
It could then incorporate that understanding to better forecast future outbreaks .
“ The point of these models is not to precisely predict the future , it ’ s to influence the future @ @ @ @ @ @ @ action ” Right now , there are a lot of models .
There are a lot of projections .
We ’ re not sure which ones will be most accurate , or useful .
“ Don ’ t end up being obsessed with a specific number , ” Hanage gives a final piece of advice .
“ Just end up recognizing the number is large .
That ’ s the best way to think about it.
” Hanage offers another potentially helpful metaphor : “ A very , very good physicist will be @ @ @ @ @ @ @ you walk out into the interstate and say exactly where your body parts might land , but the fact that another model puts the body parts in another place , doesn ’ t alter the central conclusion that you ’ re going to get run down by a car , ” he says .
For now , the biggest message from all of them is that social distancing measures are indeed saving lives .
The models predicted that weeks ago — and that prediction is coming true .
We can all feel good @ @ @ @ @ @ @ Every day at Vox , we aim to answer your most important questions and provide you , and our audience around the world , with information that has the power to save lives .
Our mission has never been more vital than it is in this moment : to empower you through understanding .
Vox ’ s work is reaching more people than ever , but our distinctive brand of explanatory journalism takes resources — particularly during a pandemic and an economic downturn .
Your financial contribution will not constitute a donation , @ @ @ @ @ @ @ continue to offer free articles , videos , and podcasts at the quality and volume that this moment requires .
Please consider making a contribution to Vox today .

Share All sharing options for: Why it’s so hard to see into the future of Covid-19
One of the greatest challenges of the coronavirus pandemic is that all levels of policymakers need to make decisions with imperfect information. Scientists still don’t know everything about how this virus is transmitted, and due to the lack of widespread testing, they also don’t know, exactly, how prevalent it is. They don’t know if the virus will show a strong seasonal effect, and decrease during the summer. They don’t know how this will all end.
One way they are trying to answer these questions is through modeling. Specifically, infectious disease models are tools — based on mathematical formulations — that try to game out what’s possible in the future. These models are varied, often confusing to interpret, and are not crystal balls, especially because the ideal data isn’t yet available. But they are a large part of what government leaders use to make decisions, influencing how resources are allocated to health care facilities and how social distancing orders are issued to the public.
In this piece, I’m going to try to explain the utility of coronavirus models and how to think about them when you see them reported in the news. I’ll also explain a big idea to make these models work better in the future.
But before that, it’s key to stress what we don’t need them for. We don’t need them to know that we’re in a very, very dangerous situation.
“What’s very important is not the details of the model, it’s that this is a virus that can crush health care,” says Bill Hanage, an epidemiologist who studies infectious diseases at Harvard. “That’s not a model result, that’s an observation. We know it because of Wuhan, we know it because of Italy, because of Spain, we know it because, now, of New York.“
In New York state, thousands have died, and hospitals are at, or exceeding, capacity and struggling with equipment shortages. Covid-19 is “a freight train,” as Hanage calls it, and it has rammed into not just New York but several other parts of the United States.
But the models also suggest that the country is nearing, or has reached, the peak in daily deaths. In the next phase of the outbreak, the models, and decisions made with them, will be critical: Things might seem like they are getting better, and they may be in terms of new infections and deaths. But this freight train will just be paused. Without the right controls in place, it could start up again.
The models are not perfect: Some are even failing in their predictions (more on that below). More scientific oversight and analysis is needed to figure out which models are the most useful to use in decision making.
Still, modeling plays a very important role, and it can help the public know that they, are, in fact, doing the right thing by staying home. The point of these models is not to precisely predict the future, it’s to influence the future, and choose a good course of action.
Modeling an outbreak is an immense challenge
Leaders have tough choices to make in the weeks and months ahead, as the outbreak plays out differently in states. Models can help predict rates of new infections, and estimate when the strain on the hospital system could peak.
In early April, Washington, DC, Mayor Muriel Bowser said that modeling projects a surge in DC area hospitals during the summer. “Like all models, we hope this one will be proved wrong,” she told MSNBC. But she’s preparing for it anyway. “We are preparing for many people to come through our hospitals.”
A pandemic represents the mashup of some of the most complicated and messy things things people have ever tried to study: human behavior, virology, and the human immune system. Forecasting disease outbreaks is an immense challenge, and even the smartest minds here don’t have the answers as to how this all is going to end.
The most common question I am asked is when this will all be over. No really, when will we be able to do xyz? There is no way of knowing. There are too many possible futures, and too few hints. 2/ — Caitlin Rivers, PhD (@cmyeaton) April 17, 2020
Models incorporate many different types of data into their projections. There are a head-spinning number of potential inputs. (And some models don’t use these inputs at all, but just rely on projecting data from earlier in the outbreak.)
A model can input the biology of the virus: How does it spread, how quickly does it infect, how quickly does it lead to symptoms, how quickly does it replicate to a level where it can jump from person to person? (Note: A lot of these variables are still not completely known.)
“What’s very important is not the details of the model, it’s that this is a virus that can crush health care”
It can account for human biology: How does the immune system mount a response to this virus, how many people will become immune after exposure, and for how long? Also, how many people can get infected with the virus, and have the ability to spread it, but never feel sick themselves? (A lot of this isn’t perfectly understood either.)
It also should, ideally, reflect how human society works: How many people do we come into contact with each day, and how does this vary in different communities, rural and urban? Models need to account for that; in a big country like the US, outbreaks are going to be regional, with varying intensities and responses.
It needs to be realistic about the capacities of health care systems: How many beds are available for Covid-19 patients, how quickly will they fill up, how many doctors and nurses are there to serve them, how many ventilators are there, and how many patients will need them, and when?
Then, there’s chaos: How do people react to the news that tens of thousands are dying from a virus that probably started with a bat, and how might that influence the model?
The question of “how will an outbreak progress” is clearly immense. In a common modeling approach called SIR (SIR stands for susceptible, infected, recovered), scientists are trying to figure how many people are susceptible to a disease, how many of them will become infected, at what rate and where. But then, as more people recover from the disease, and become immune, that decreases the number of those who are susceptible.
To sum up: This stuff is complicated! That we can get any insight into the future, considering the variables, is a miracle. Yet scientists are trying, and their efforts are valuable.
Hanage explains there are basically two main types of models being used to try to plot out the course of this pandemic: statistical models and mechanistic models.
Let’s start by explaining statistical models.
Statistical models try to predict the future by projecting current trends
The Institute for Health Metrics and Evaluation (IHME) has the most commonly cited models — and it includes separate projections for every state. Dr. Deborah Birx, the White House coronavirus response coordinator, has referenced it. Hanage explains this model is what’s known as a statistical model.
The IHME, based at the University of Washington, looks at data of how Covid-19 outbreaks have progressed around the world. It takes that data and then tries to project what the epidemic curve will look like as new outbreaks form in new areas based on what social distancing actions are being taken. The goal is to predict the time of peak hospital strain in an area, and the number of deaths.
To use Hanage’s metaphor: It’s looking at how fast and hard the freight train has hit on other stops of its journey, and predicting it will hit that fast and hard when it gets to new stops.
This model makes some assumptions, namely, that the conditions for the previous freight train collisions will be similar in the future.
Earlier in the outbreak, the model was mainly fed from data in China, which imposed extreme social distancing measures. And so it assumes some high level of social distancing will continue into the future. “That makes this a best-case scenario model,” Carl Bergstrom, a computational biologist at the University of Washington, assessed on Twitter, at the end of March. It’s now also drawing from current social distancing actions in the US.
The IHME model assumes this behavior will continue. And its creators are transparent about this limitation. The projection, the IHME explains on its FAQ page, “only goes until the beginning of August and does not predict how many deaths there may be if social distancing is not fully implemented and maintained through the end of May, or if there is a resurgence after early August.” The hardest thing to model in all of this is not the virus, but human behavior.
Its performance has been inconsistent. Recently, a group of statisticians from Australia and the US took a careful look at modeling forecasts of the IHME.
In a preprint paper (that’s not yet been peer-reviewed), the statisticians found that the model — and specifically, how its predictions on the number of daily Covid-19 deaths matched with reality — wasn’t making very good guesses when it came to individual states.
“It does well predicting what’s happening across the entire United States,” Sally Cripps, a statistician at the University of Sydney, who co-authored the analysis of the IHME model, says. It also seems to predict the outbreak in New York well. “What it doesn’t do well, is predict individual states.” The model, she says, is “underestimating its uncertainty.”
The actual reported number of deaths in a state fell outside the model’s prediction between 43 and 73 percent of the time (depending on the date the model was assessed).
Which is to say: It isn’t very precise. The model was often more accurate predicting outcomes three days ahead than it was predicting events one day ahead, she says. Also, the analysis found some of the model’s predictions were underestimates, while some were over. There was no clear pattern. Cripps says she fears this lack of precision “undermines the public’s confidence or trust in science,” and that she wouldn’t depend on the model for big policy decisions.
To be fair, deaths isn’t the only thing the model is trying to predict. It tries to forecast when the pandemic will peak in a given area, and can help hospitals plan for that extra strain. Estimates of those peaks were not assessed in this paper — just the prediction of daily deaths were — and will have to be gauged after the peaks are met. The model could get the number of deaths wrong, but the timing of the peaks right, for example.
(Yes, models are nuanced and confusing.)
Also confusing matters, the model has been updated several times, as its creators have input new data from new outbreaks, new social distancing measures, and new resources (like ventilators) that have become available. This has actually led the models to decrease their death toll projection for the US a few times, recently from 81,766 to 60,415, or about 25 percent. This doesn’t necessarily mean the model has been wrong. It means collective actions have been working.
Our estimates for total COVID-19 deaths have dropped from 81,766 to 60,415 in today’s update. Why? Because social distancing measures in the US are working. Some Americans stayed at home before the orders came. Encouraging news #COVID19 #socialdistancing »https://t.co/KIhdSrXK7z — Ali H. Mokdad (@AliHMokdad) April 8, 2020
Also keep in mind: The IHME death toll projections come with a huge range of error. In the model, deaths per day are expected to peak soon in the US. As of April 10, it predicted the peak being two days away, and the error — the shaded area, was roughly 4,000 deaths per day.
“I think it’s key not to get fixated on the exact numbers,” Dominique Heinke, an epidemiologist in Massachusetts, says. “You can look at a range of models and say, we can expect it to be at least this bad.” Again, we know this: The freight train is coming, and in many places, is already here.
Why do these statistical models change so often?
Well, it reflects the complexity of the problem these models are trying to solve. For example, weather forecasters use atmospheric models to predict the weather, and as they gather more data on temperature, humidity, and barometric pressure, their forecasts become more accurate and, thus, often change.
“Unlike the weather, which we’re all accustomed to understanding and incorporating forecast into whatever decision you make, unlike the weather [here] we actually influence the outcome,” says Caitlin Rivers, a professor at the Johns Hopkins Center for Health Security. “So people see the numbers, and they are motivated then to be more aware, stay home, and using good hygiene and doing all the things that really change that outcome.”
The models change, because our actions change. The models could change for the worse if local governments declare premature victories and decrease social distancing measures too early.
“By keeping an eye on the model, we can tell how the virus is circulating in our own communities: in some places, cases and deaths are still going up, in some places they are starting to come down,” says Ali Mokdad, a professor at IHME and chief strategy officer for population health at the University of Washington. “We can also use the model to ask what businesses we should open first as we recover: The key issue as we go into recovery mode is to do it in stages so we don’t have a second wave of infections that will hurt us even more in terms of mortality and the economy.”
Unless testing can be scaled up, some social distancing measures may have to be kept in place until there is a vaccine available, which can take a year or more. What happens in the scenario when social distancing measures are relaxed, but then put in place again if cases spike again? “I’m not sure we can model that,” Hanage says.
Mechanistic models try to game out how the outbreak looks under different scenarios
The other type of model decision makers are using is a mechanistic model. These models are designed to help policymakers understand the impacts specific policies and actions may have on a disease’s course. These models also make a lot of assumptions, and often present very wide-ranging scenarios.
A good example of a mechanistic model comes from the Imperial College of London.
In the middle of March, it provided a scary wake-up call to the UK government to take more action. Their model looked at what would happen in Great Britain and in the US if the countries did nothing. It took what it knew about the transmissibility of the virus and put it into a model designed for the flu — a caveat right off the bat, as Covid-19 is not the flu.
(Transmissibility here is often called the R0, or R-naught; it’s the average number of new cases expected to be spawned by each case of an illness. Note: The value of the R0 is still just an estimation).
In the scenario where nothing is done, the model’s authors found, there could be 510,000 deaths in Great Britain, and 2.2 million in the US. And that was “not accounting for the potential negative effects of health systems being overwhelmed on mortality,” the authors report.
That made headlines. But their model didn’t just report the worst-case scenario. It tried to gauge the impact of various social distancing policies, and tried to make estimates for many different R0 figures. The estimates ranged, for Great Britain, from just 5,600 deaths assuming a low R0 of 2, and the most aggressive social distancing, and 550,000 deaths assuming an R0 of 2.6 and no social distancing measures.
If you’re a leader of a country, looking at that spread, you know what you need to do: implement social distancing measures. That’s what the UK did. Later, when one of the model’s authors told the UK government in testimony that the deaths in Great Britain would probably number around 20,000, he was not revising the model, as some critics complained. Instead, he was reflecting that range of possibilities presented in the model.
Again, the point of these models is not precise prediction, it’s to influence action.
That’s helpful. But again, as with the statistical models, these mechanistic models can’t game out every possible future.
Recently, Columbia University put out a model (with a handy interactive map) that tries to predict which US counties will have their health care systems overwhelmed, under different social distancing scenarios, and when.
The model also attempts to help hospitals by showing how different coping strategies in hospitals (i.e. converting operating room beds to Covid-19 care beds, for one example, and modifying ventilators for use in multiple patients for another) could mitigate the problem, and help save lives.
The prediction is grim for the crush on hospital systems, which is expected to soon move from the northeast United States, to Southern states, as the outbreak starts to impact more and more rural areas. (Keep in mind: As outbreaks in some cities taper off, outbreaks in other areas may just be getting started.)
It’s a complicated model. It’s trying to predict hospital bed demand, ICU bed demand, and ventilator demand, Jeff Shaman, an infectious disease modeler at Columbia, explains. “It’s a mathematical description of transmission at county scale, where the counties are linked by movement between them based on ... travel patterns, and understanding that those have waned over time because of this ongoing Covid crisis.”
It tries to account for a lot, but it can’t account for everything. Something it can’t account for: the possibility that health care workers get sick and have to leave work, leaving these hospital systems more strained. “We’re in the process right now on establishing a national database on staffing levels,” Charles Branas, chair of epidemiology at Columbia, says. “It’s been challenging to build this airplane while it is flying, quite frankly.”
That doesn’t mean the model is useless. It can still help guide decision-making. You can look at the map and see which counties are still overwhelmed in their best-case scenarios. “Those could potentially be first-choice counties for supplementary resources,” Branas says.
I asked the Columbia researchers how they’d like the public to think of their model.
“These are not forecasts, they are projections, we’re dealing with a very, very uncertain environment,” Shaman stressed. “The degree to which people are social distancing ... is changing day-to-day. It is difficult to pin down what’s going on. We’re making multiple projections because we don’t know what people will do. We do it because we would like some window into the future. So we can assess: Are we on a really bad trajectory no matter what we do? Or are we on a good trajectory no matter what we do? Or is it incumbent upon us to make certain decisions so we can more certainly move to a better outcome?“
(Another mechanistic model to check out: The University of Pennsylvania has a tool for regional leaders to input their own observations, and see how an outbreak might impact their area’s hospitals.)
What we need in the future is better disease forecasting
There’s a lot that’s still unknown about the coronavirus, and the pandemic.
“There will be people writing papers 100 years from now about what actually happened, there will be people making discoveries about the relative rates of increase in San Francisco vs. New York,” Hanage says.
Rivers, the Johns Hopkins epidemiologist, hopes, in the future, we’ll get better at this. Like the US has the National Weather Service — a government agency staffed to create weather models and test their predictive power — she hopes to see the creation of a National Infectious Disease Forecasting Center.
“The reason that we have accurate weather forecast today, is because there was a federal agency responsible for weather forecast,” she says.
We need to learn from the modeling approaches being used now, to make better models for the future. The weather service does this for hurricanes: You can clearly see in the weather service data how hurricane forecast tracks (i.e. forecast models) have greatly improved over time. Rivers doesn’t see that as an accident. The weather models have improved because there’s a centralized service to study and create them.
She says there needs to be some central agency collecting these models in an archive, so that researchers later on can figure out which ones worked the best, and why. It could then incorporate that understanding to better forecast future outbreaks.
“The point of these models is not to precisely predict the future, it’s to influence the future, and choose a good course of action”
Right now, there are a lot of models. There are a lot of projections. We’re not sure which ones will be most accurate, or useful. “Don’t end up being obsessed with a specific number,” Hanage gives a final piece of advice. “Just end up recognizing the number is large. That’s the best way to think about it.”
Hanage offers another potentially helpful metaphor: “A very, very good physicist will be able to model what will happen if you walk out into the interstate and say exactly where your body parts might land, but the fact that another model puts the body parts in another place, doesn’t alter the central conclusion that you’re going to get run down by a car,” he says.
For now, the biggest message from all of them is that social distancing measures are indeed saving lives. The models predicted that weeks ago — and that prediction is coming true. We can all feel good about our sacrifices because of that.


What day is it today?

Today is Saturday , April 18 , 2020 , at least in the continental United States .
It is already Sunday , April 19 , in some parts of the world .
To some degree , time is an illusion .
We are bound by its forward passage , being the third-dimensional creatures we are , but the perceptions we have placed upon time have been invented .
Humans invented the year .
Humans invented the month .
Humans invented the day , the hour , the minute , the second .
These distinctions are not arbitrary — it really @ @ @ @ @ @ @ Earth to orbit the sun — but their creation was not inevitable .
Our numerical system is likely base-10 because we have 10 fingers ; the way we understand time is almost entirely dependent on the fact that we evolved on this planet , circling this sun .
Time , at least as we understand it , is also a byproduct of capitalism .
In his seminal 1967 economic paper “ Work-Discipline and Industrial Capitalism , ” E. P. Thompson notes that in early human cultures , delineations of time were broad understandings @ @ @ @ @ @ @ went down , when the seasons changed .
But as industrialization became the norm , time became a mechanized system that no longer served those who ’ d invented it .
We increasingly served at the beck and call of time , for that is how those who possessed capital could best regulate those who performed the labor .
For we are now at a point where sociologists are discussing the “ problem ” of leisure .
And a part of the problem is : how did it come to be a problem @ @ @ @ @ @ @ convenience with industrial capitalism , was the agent which converted men to new valuations of time ; which taught children even in their infancy to improve each shining hour ; and which saturated men ’ s minds with the equation , time is money .
One recurrent form of revolt within Western industrial capitalism , whether bohemian or beatnik , has often taken the form of flouting the urgency of respectable timevalues .
And the interesting question arises : if Puritanism was a necessary part of the work-ethos which enabled the industrialized world @ @ @ @ @ @ @ of the past , will the Puritan valuation of time begin to decompose as the pressures of poverty relax ?
Is it decomposing already ?
Will men begin to lose that restless urgency , that desire to consume time purposively , which most people carry just as they carry a watch on their wrists ?
But it remains that on some base level , we will always understand time .
The sun comes up , the sun goes down .
Though we might be inside , where that process is more difficult to @ @ @ @ @ @ @ does not obscure the day and night cycle .
What is lost , as each day blends into the next , in vaguely similar ways , is the mechanistic definition of time , the capitalist definition of time , the industrial definition of time .
We do not know what day it is , but we do know when there is a new day .
To live inside is to lose distinction .
There is a numbing effect that occurs when each day is the same , when our schedules are subsumed by @ @ @ @ @ @ @ undifferentiated time .
And yet isn ’ t this how so many of the less privileged around the world , forced to work jobs involving menial labor , day after day after day , already live ?
Isn ’ t this why we invented the weekend ?
Has this quarantine simply shown to those of us with more economic privilege that our “ free time ” was always a mirage , which could disappear before our eyes , by giving us so much “ free ” time ?
I have a wall calendar @ @ @ @ @ @ @ I need to orient myself in time , I look at it .
But even the usefulness of my calendar has started to fray around the edges .
I began writing this story because I couldn ’ t quite believe it was a Thursday , even as I knew it had to be a Thursday .
But it might as well not be .
A cycle that bears the sun across our sky until it sets in the west is a reality ; “ Thursday ” is an invention .
Is it still @ @ @ @ @ @ @ .
I will update this article daily until we don ’ t need it anymore .
I hope there is a time when we know it is Thursday or Tuesday or June 17 without having to look at a calendar or ask each other or Google .
I hope there is a time when going outside to see the sun doesn ’ t feel like exposing yourself to toxic levels of radiation .
( I know I should be taking walks ; every time , I feel like I ’ m risking everything @ @ @ @ @ @ @ time in which all this “ free ” time no longer feels like a period we must exploit for maximum value .
I hope there is a time when I remember what it ’ s like to grab lunch with a friend , to watch dogs play in the surf , to breathe in the air and just be .
Until then , find a way to make time have meaning for you .
Read a long book .
Cook an elaborate meal .
Take up knitting .
Write an epic poem .
@ @ @ @ @ @ @ always meant to get to .
Sleep .
Remember that time doesn ’ t own you , and that you own time .
This will be over someday , a part of the past .
It will someday be a memory .
Hang on to that .
I ’ ll see you outside again .
For now , it is Saturday , April 18 , 2020 .
In some parts of the world , it is Sunday , April 19 , 2020 .
Are you a “ What day is it today ?
@ @ @ @ @ @ @ theme song .
It ’ s written by composer Laura Stratford , and it is wonderful .

Today is Wednesday, July 1, 2020, at least in the continental United States. It is already Thursday, July 2, 2020, in some parts of the world. Life isn’t resuming gracefully, but the herky jerky train wreck of America’s reopening certainly speaks to some desire to exit this quarantine period. And so “What Day Is It Today” will exit the stage, too. For now, at least.
To some degree, time is an illusion. We are bound by its forward passage, being the third-dimensional creatures we are, but the perceptions we have placed upon time have been invented. Humans invented the year. Humans invented the month. Humans invented the day, the hour, the minute, the second.
These distinctions are not arbitrary — it really does take about 365 days for the Earth to orbit the sun — but their creation was not inevitable. Our numerical system is likely base-10 because we have 10 fingers; the way we understand time is almost entirely dependent on the fact that we evolved on this planet, circling this sun.
Time, at least as we understand it, is also a byproduct of capitalism. In his seminal 1967 economic paper “Work-Discipline and Industrial Capitalism,” E. P. Thompson notes that in early human cultures, delineations of time were broad understandings of when the sun came up and went down, when the seasons changed. But as industrialization became the norm, time became a mechanized system that no longer served those who’d invented it. We increasingly served at the beck and call of time, for that is how those who possessed capital could best regulate those who performed the labor.
Thompson writes:
For we are now at a point where sociologists are discussing the “problem” of leisure. And a part of the problem is: how did it come to be a problem ? Puritanism, in its marriage of convenience with industrial capitalism, was the agent which converted men to new valuations of time; which taught children even in their infancy to improve each shining hour; and which saturated men’s minds with the equation, time is money. One recurrent form of revolt within Western industrial capitalism, whether bohemian or beatnik, has often taken the form of flouting the urgency of respectable timevalues. And the interesting question arises: if Puritanism was a necessary part of the work-ethos which enabled the industrialized world to break out of the poverty-stricken economies of the past, will the Puritan valuation of time begin to decompose as the pressures of poverty relax? Is it decomposing already? Will men begin to lose that restless urgency, that desire to consume time purposively, which most people carry just as they carry a watch on their wrists?
But it remains that on some base level, we will always understand time. The sun comes up, the sun goes down. Though we might be inside, where that process is more difficult to witness, the fact of our insideness does not obscure the day and night cycle. What is lost, as each day blends into the next, in vaguely similar ways, is the mechanistic definition of time, the capitalist definition of time, the industrial definition of time. We do not know what day it is, but we do know when there is a new day.
To live inside is to lose distinction. There is a numbing effect that occurs when each day is the same, when our schedules are subsumed by what feels like an endless gulf of undifferentiated time. And yet isn’t this how so many of the less privileged around the world, forced to work jobs involving menial labor, day after day after day, already live? Isn’t this why we invented the weekend? Has this quarantine simply shown to those of us with more economic privilege that our “free time” was always a mirage, which could disappear before our eyes, by giving us so much “free” time?
I have a wall calendar hanging above my desk, and when I need to orient myself in time, I look at it. But even the usefulness of my calendar has started to fray around the edges. I began writing this story because I couldn’t quite believe it was a Thursday, even as I knew it had to be a Thursday. But it might as well not be. A cycle that bears the sun across our sky until it sets in the west is a reality; “Thursday” is an invention. Is it still necessary? I don’t know.
What’s important to remember is that time doesn’t own you. You own time. This will be over someday, a part of the past. It will someday be a memory. Hang on to that. I’ll see you outside again.
This story was originally published Friday, March 27, 2020.
Are you a “What day is it today?” superfan? Check out our official theme song. It’s written by composer Laura Stratford, and it is wonderful.


vox--2020-04-18--This viral Angela Merkel clip explains the risks of loosening social distancing too fast

When you have a huge hole where your nation ’ s leadership should be , it is wise to borrow the best of other people ’ s leaders .
They can ’ t make America ’ s big decisions , but they can fill in some of the gaps .
In the Covid-19 pandemic , we can take comfort in their competence and use their wisdom to guide us about what we each should do .
Right now , the US and many other nations are considering easing social distancing and other restrictions if and when their new coronavirus cases @ @ @ @ @ @ @ fall .
And German Prime Minister Angela Merkel ( who happens to have scientific chops ) has an important lesson that we should all listen to .
On Wednesday , she laid out important logic about the coronavirus pandemic that hasn ’ t been communicated clearly enough here in the US .
In simple and clear terms , she explains why Germany doesn ’ t have much “ wiggle room ” in its hospital capacity .
Because of this , any lifting of its lockdown , like allowing some shops to open next @ @ @ @ @ @ @ ice.
” Merkel ’ s explanation , which went viral , is centered on the metric called R0 , or basic reproduction number .
It represents the number of people a sick person will infect on average in a group that ’ s susceptible to the disease ( meaning they don ’ t already have immunity ) .
She says that if Germany ’ s R0 were to shift from a flat rate of 1.0 to 1.1 , the nation ’ s hospitals will be crushed by October , without sufficient resources to @ @ @ @ @ @ @ Covid-19 patients .
If the R0 goes up to 1.2 , that overload hits in July .
And so on .
Covid ’ s current global average R0 is 2-2.5 , but Germany has done a good enough job of managing its outbreak to get its reported estimated R0 down to 0.7 as of April 17 .
That ’ s low enough for Merkel to sanction “ a tentative easing of restriction.
” Germany is not out of the woods , however .
Marieke Degen , the deputy spokesperson of Germany ’ s @ @ @ @ @ @ @ s Alex Ward that it ’ s “ very important to stress that Germany is still at the beginning of the epidemic ” and that more and more elderly people in the country are getting sick .
America , for many reasons , has even less wiggle room than Germany .
Germany has eight hospital beds per capita compared to America ’ s 2.7 beds .
In ICU beds , Germany has 8.3 per capita while America has 6.6 .
It is also testing for coronavirus at twice our rate ( 21 vs @ @ @ @ @ @ @ Without robust testing , you can ’ t keep good tabs on R0 or the related Rt — and you can end up flying blind , risking health system overload and avoidable deaths .
Covid-19 spreads in an exponential way , and it ’ s worth emphasizing exponential growth ’ s dynamics .
Tiny shifts in risk grow very quickly , leading to deadly results as this useful tweet thread shows : The small choices we each make about risky behaviors are like playing Russian Roulette , but with a machine gun .
@ @ @ @ @ @ @ ’ re not in a high-risk group ( like older adults ) , and the case fatality rate is around 1 percent , then the threat isn ’ t so great .
Surely we can loosen restrictions ?
But that ’ s like being locked in a room with 100 people , where your collective behavior determines how many bullets are live in the machine gun that ’ s about to strafe all of you .
You might not die , but others surely will .
Covid-19 is already the leading cause of @ @ @ @ @ @ @ York state , Louisiana , and Washington , DC .
Do you want to add ammunition to its arsenal ?
Every day at Vox , we aim to answer your most important questions and provide you , and our audience around the world , with information that has the power to save lives .
Our mission has never been more vital than it is in this moment : to empower you through understanding .
Vox ’ s work is reaching more people than ever , but our distinctive brand of explanatory journalism takes @ @ @ @ @ @ @ an economic downturn .
Your financial contribution will not constitute a donation , but it will enable our staff to continue to offer free articles , videos , and podcasts at the quality and volume that this moment requires .
Please consider making a contribution to Vox today .

When you have a huge hole where your nation’s leadership should be, it is wise to borrow the best of other people’s leaders. They can’t make America’s big decisions, but they can fill in some of the gaps.
In the Covid-19 pandemic, we can take comfort in their competence and use their wisdom to guide us about what we each should do.
Right now, the United States and many other nations are considering easing social distancing and other restrictions if and when their new coronavirus cases and hospitalizations become flat or start to fall. And German Chancellor Angela Merkel (who happens to have scientific chops) has an important lesson that we should all listen to.
On Wednesday, she laid out important logic about the coronavirus pandemic that hasn’t been communicated clearly enough here in the US. In simple and clear terms, she explains why Germany doesn’t have much “wiggle room” in its hospital capacity. Because of this, any lifting of its lockdown, like allowing some shops to open next week, will remain “on thin ice.”
Merkel’s explanation, which went viral, is centered on the metric called R0, or basic reproduction number. It represents the number of people a sick person will infect on average in a group that’s susceptible to the disease (meaning they don’t already have immunity).
She says that if Germany’s R0 were to shift from a flat rate of 1.0 to 1.1, the nation’s hospitals would be crushed by October, without sufficient resources to care for all of the severely ill Covid-19 patients. If the R0 goes up to 1.2, that overload hits in July. And so on.
Related Why France has 4 times as many coronavirus deaths as Germany
Covid’s current global average R0 is 2-2.5, but Germany has done a good enough job of managing its outbreak to get its reported estimated R0 down to 0.7 as of April 17. That’s low enough for Merkel to sanction “a tentative easing of restriction.”
Germany is not out of the woods, however. Marieke Degen, the deputy spokesperson of Germany’s Robert Koch Institute, told Vox’s Alex Ward that it’s “very important to stress that Germany is still at the beginning of the epidemic” and that more and more elderly people in the country are getting sick.
America, for many reasons, has even less wiggle room than Germany. Germany has eight hospital beds per capita compared to America’s 2.7 beds. In ICU beds, Germany has 8.3 per capita while America has 6.6.
It is also testing for coronavirus at twice the US rate (21 vs 9.8 tests per 1,000 people). Without robust testing, you can’t keep good tabs on R0 or the related Rt — and you can end up flying blind, risking health system overload and avoidable deaths.
Here's Instagram co-founder @kevin's new virus site that takes his recent models and updates them live in real time, in collaboration with @mikeyk — https://t.co/zpi35TIEdw pic.twitter.com/2qHZLDaL5n — Emily Chang (@emilychangtv) April 17, 2020
Covid-19 spreads in an exponential way, and it’s worth emphasizing exponential growth’s dynamics. Tiny shifts in risk grow very quickly, leading to deadly results, as this useful tweet thread shows:
Exponentials still have the capacity to shock me.
If mass wearing of masks make just 0.01 shift in the spread per day, from say 1.22x to 1.21x, there would now be ~20% fewer cases (and ~20% fewer deaths) in the UK since March 12.
I had to double check that multiple times. — Do
Syme (@dsymetweets) April 5, 2020
The small choices we each make about risky behaviors are like playing Russian roulette, but with a machine gun. You may have thought that if you’re not in a high-risk group (like older adults), and the case fatality rate is around 1 percent, then the threat isn’t so great. Surely we can loosen restrictions?
But that’s like being locked in a room with 100 people, where your collective behavior determines how many bullets are live in the machine gun that’s about to strafe all of you. You might not die, but others surely will.
Covid-19 is already the leading cause of death in many areas, including New York state, Louisiana, and Washington, DC. Do you want to add ammunition to its arsenal?