Covid-19 by State: The Impact of Urbanization on the Spread

A.  Urban Concentration and Covid-19 Cumulative Deaths as of May 3

The virus that causes Covid-19, like other such viruses, spreads person to person.  Thus one should expect that there will be a more rapid pace of spread in urban areas, where people are in closer day-to-day contact.  This is not an indication of what the ultimate spread might be, as catching an infectious disease is a one-time event and contacts with others still add up over time.  It is just that instead of encountering a certain number of people in one day, it might instead take several days or even weeks.  But greater person-to-person contact increases the likelihood that one will catch the disease earlier.

Thus one should expect that at this point in the middle of the spread of Covid-19, those states that are more highly urbanized will have seen a greater number of deaths from the disease (per unit of population) than states that are more rural.  And that is indeed what one finds, although with some interesting exceptions.

The chart above shows the number of deaths in each US state per million of population, plotted against the percentage share of the urban population in the state.  The share of the state’s population that is defined as residing in an “urban” area comes from the US Census Bureau, which applies a very specific (and uniform) definition of what it labels as urban.  The calculations are based on what the Census Bureau defines as “urbanized areas”.  Under this definition, the urban population is the total population in the state living in an area with a dense urban core, including in the surrounding (suburban) areas meeting certain population density requirements, and with a total population within that area of 50,000 or more.  (Note that the Census Bureau also has a broader concept of what it considers “urban” that includes communities down to a population of 2,500.  Statements on urban populations in states are often based on this broader definition.)

While this is the best one can do in defining what it means to be living in an urban area, note that it is still highly imperfect for the purposes here.  Urban areas differ greatly.  The day-to-day contact one would experience in New York is quite different from what would normally find in a city of 50,000.  Even comparing similarly large cities, it will be quite different between New York and, say, Los Angeles.  Still, it is of interest to see whether states with a higher share of their population living in urbanized areas, as defined by the Census Bureau, have at this point in the spread of Covid-19 experienced a higher fatality rate from the disease.

The chart indicates that in general they have.  The data on the number of deaths from Covid-19 comes from the data set maintained by the New York Times, with the figures as of May 3, 2020 (and downloaded in the afternoon of May 4).  The Census Bureau figures on state total populations and on those living within urbanized areas (of 50,000 or more) are all from the 2010 census.  While these are now ten years old and will be updated once the 2020 census is completed, for the purposes of this exercise they more than suffice.  The relative populations across states will not have changed all that much.

At this point in the pandemic, states with urban population shares of up to almost 60% have uniformly relatively low (as compared to other states) death rates from Covid-19 per million of population, with all at about 100 or less (Mississippi is at 102).  Half the states (25 of the 51 including Washington, DC, as a 51st) fall into this category, with their names on the chart crowded and overlapping.  For those interested, the figures for individual states can be found in a table at the bottom of this post.

The states with urban population shares of just below 60% (Indiana) up to 80% then show more variety.  The fatality rates are very low for some (e.g. Hawaii, at 12.5 per million with an urban share of 71.5%) and substantially higher for others (e.g. Louisiana, at 434 per million and an urban share of 61%).

But the most substantial variation is seen in those states with an urban share of 80% or more.  The fatality rate at this point in the pandemic is just 18 per million in Utah despite an urban share of 81%, while it is close to 1,000 per million in the state of New York with an urban share of 83%.  Several other states in this group also have relatively low fatality rates, including California, Arizona, Nevada, and Florida.  Thus while there is a clear association seen between a higher share of a state’s population living in an urbanized area and the deaths per million from Covid-19, that relationship is not fate.  There are important exceptions.

The broad range in cumulative death rates among the states with the higher urban population shares is a consequence of several factors.  While it is not surprising that a higher urban share appears to make a location more vulnerable to a rapid spread of the virus, it is also clear that it is not inevitable.  There are a number of exceptions.  California, while vulnerable, imposed state-wide lockdown orders relatively early, for example.  The Utah public health system has also functioned particularly well.  And the state totals may be consistent with some very limited evidence (but disputed, and far from certain) that the virus that causes Covid-19 might spread less in warmer and moderately humid environments.  This might in part explain the low rates seen, despite high urbanized shares, in Arizona, California, Florida, and Nevada, as well as in Texas and Hawaii.

At the other end, the areas around New York City (in the states of New York, New Jersey, and Connecticut) saw an early and rapid spread of the virus before many were aware of it.  Based on analysis of the genome, researchers have found that the virus found there had in most cases arrived from Europe rather than directly from China.  Furthermore, they found that it was introduced to the New York area from multiple independent sources (i.e. not from just one traveler) and that it may well have arrived already in January.  There has also been a recent report that the virus had already been introduced into Europe as early as late December.  A recent analysis of a sample of bodily fluids taken from a French man living in the Paris region, who went to a local hospital on December 27 with a case of suspected pneumonia, indicated that he in fact had the virus that causes Covid-19.  He had not traveled abroad.

Thus bad luck can also play a role.  A region with a high degree of urban concentration (such as New York), with frequent travelers to and from a region where the disease had spread but where this was not known at the time (Europe), would be particularly susceptible to a highly infectious viral disease such as Covid-19.

Florida may be a surprising case.  It is a state with a relatively high share (87%) of its population residing in urbanized areas (as defined by the Census Bureau measure).  But its cumulative death rate (as of May 3) is also relatively low.  Florida has been criticized for not shutting down the spring break holidays of mid-March when numerous college students from around the country fly to Florida for parties and more.  But while the impact on cases leading to deaths in Florida itself appears to have been limited, outbreaks of the virus in other parts of the US have been traced to the spring break vacationers in Florida then returning to their homes across the US.

B.  Urban Concentration and the Recent Daily Path of Covid-19 Deaths

The picture outlined above is a static one, as it focused on the rate of fatalities from the disease at a particular point in time (May 3).  It is also of interest to review what the path has been in daily deaths from the disease, particularly over the past several weeks.  The social distancing measures that the states imposed in mid to late-March (with a good deal of variation in both when they were imposed and how strong the measures were) would be expected to have an impact on reducing the pace of the spread, with a lag of a few weeks.  They would then hopefully reduce the number of deaths from the disease a further week or so later.

In this, it is clear that the social distancing measures did succeed in flattening and then bringing down the curve, but with an important difference between the more highly urbanized states and the less urbanized ones:

The fatality rate for the US as a whole has come down since reaching a peak of about 2,000 deaths per day in mid-April (using 7-day moving averages to smooth out day-to-day fluctuations, where the dates shown are for the end of each 7-day period).  The number of deaths then fell to just below 1,800 by May 4, a reduction of 10%.  Based in part on this, the Trump administration is now encouraging states to lift their social distancing measures so that economic activity would, they hope, then recover.

But while the number of fatalities from this disease have begun to fall in the US as a whole, this has been entirely in the more urbanized states.  Between the 7-day periods ending on April 17 and on May 4, the number of fatalities in the highly urbanized states fell by 25%.  During that same period, they rose by 15% in the less urbanized states.

While the daily number of deaths remains at this point higher in the more urbanized states than in the less urbanized ones, this might soon change:

The daily number of new confirmed cases of Covid-19 is now higher in the less urbanized states.  While the measurement of confirmed cases has been suspect (it depends on how broadly one is testing), it is better now than it was in March and even early April, when testing supplies were still limited and constrained the availability of testing.  And the chart suggests that with the number of new confirmed cases now higher in the less urbanized states than in the more urbanized ones, and still heading upwards, the number of deaths from the disease in the less urbanized states may soon be higher in absolute number.

C.  What is the Plan? 

The Trump administration, and especially Trump himself, are now encouraging states to lift their social distancing measures.  The stated aim is for the economy then to recover.  However, with all the disruption that has resulted from the failure of the Trump administration to take this pandemic seriously early on, it is far from clear that this will suffice.  The economy has been severely affected, where an astounding 30 million Americans (18% of the labor force) have already applied for unemployment insurance as of the week of April 25.  Such a sharp and rapid collapse is unprecedented.  It did not happen even in the Great Depression.

The Trump administration has argued that with the daily number of deaths from Covid-19 now falling in the US, the time has come to reopen businesses.  And a number of governors, primarily Republicans in the more rural states, have started to do this, arguing that with their more rural spaces there is no longer a need for such social distancing.  But as seen in the charts above, while the accumulated number of deaths per million from Covid-19 has often (but not always) been less in the less urbanized states, the absolute number of deaths in these states has continued to grow over the last several weeks even while they have gone down significantly in the more urbanized states.  And the number of deaths each day may indeed soon be higher in the less urbanized states than in the more urbanized ones.

But what is the plan to address this?  From all I can see, there is no plan.  The Trump administration has not set out any coherent plan to safely reopen the economy.  Rather, it has simply called for the lifting of social distancing measures while hoping for the best.

Could there be a plan?  Certainly.  As public health experts have called for from the start, and as the developed market economies of East Asia and the Pacific have demonstrated is possible, management of a pandemic requires wide testing of those who appear they may have the disease, isolation if the test proves that they do, tracing the contacts of all those found to have the disease, and then testing and quarantining for about two weeks those contacts who might have been exposed to the virus.

This can be most easily done early in the course of a pandemic, when the number of cases is relatively small.  However, in January (and still through February) Trump insisted that all was fine and under control, and little was done.  Now, with over 27,000 new confirmed cases each day (as of the week ending May 4), this will be far more difficult.  The social distancing measures were implemented to stabilize the situation and then bring this number down to more manageable levels.  But while they succeeded in bringing the total number down from its peak (the daily number of new cases had been over 31,000), it is still far too high.

In addition to bringing down the daily number of new cases to more manageable levels, the social distancing measures were also put in place to give the government time to develop the capacity then to carry out the standard public health measures of testing, isolating, contact tracing, and quarantining.  But while some states appear to be building up that capacity to the extent they can, the evidence for others is scant, and for few, if any, does the capacity appear to be anywhere close to adequate.

And what is certainly missing is any leadership at the top – from Trump and his administration.  States have rather been left largely on their own, with some assistance perhaps at the working levels but without a clear nationally-led program to build the necessary capacity.

The economy of course certainly needs to be reopened, with the social distancing measures loosened and eventually lifted.  The issue is not whether this should be done but instead under what conditions.  Rather than lead a national effort to bring down the number of daily new cases through a coherent and consistent program of social distancing measures (which may well differ between urban and rural areas, but not based on political boundaries), and using the time thus gained to ramp up the public health capacity that is required, the Trump administration has floundered, with a response that has been limited, ineffective, and rudderless.

 

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The data underlying the chart at the top of this post:

Urban population %

Deaths per million

Vermont

17.4%

84.7

Wyoming

24.5%

12.4

Maine

26.2%

42.9

Montana

26.5%

16.2

Mississippi

27.6%

102.1

South Dakota

29.9%

25.8

West Virginia

33.2%

27.0

Arkansas

39.5%

26.1

North Dakota

40.0%

37.2

Kentucky

41.0%

58.8

Iowa

41.7%

60.4

Alaska

44.5%

9.9

Oklahoma

45.8%

63.4

New Hampshire

47.3%

65.3

Alabama

48.7%

60.7

Kansas

50.2%

49.8

Idaho

50.5%

40.8

New Mexico

53.8%

73.3

Nebraska

53.8%

42.7

Tennessee

54.4%

34.7

North Carolina

54.9%

45.7

South Carolina

55.8%

59.5

Wisconsin

55.8%

59.6

Missouri

56.6%

63.1

Minnesota

58.0%

79.0

Indiana

59.2%

174.6

Louisiana

61.3%

434.3

Oregon

62.5%

28.5

Ohio

65.3%

90.0

Georgia

65.4%

120.6

Michigan

66.4%

409.7

Delaware

68.7%

197.1

Virginia

69.8%

82.5

Pennsylvania

70.7%

223.8

Hawaii

71.5%

12.5

Washington

75.0%

124.9

Texas

75.4%

35.4

Colorado

76.9%

167.0

Illinois

80.0%

205.1

Arizona

80.1%

56.6

Utah

81.2%

18.1

New York

82.7%

990.2

Maryland

83.5%

204.7

Connecticut

84.8%

681.6

Nevada

86.5%

97.0

Florida

87.4%

73.3

California

89.7%

60.0

Massachusetts

90.3%

611.5

Rhode Island

90.5%

304.0

New Jersey

92.2%

895.3

District of Columbia

100.0%

417.1

 

Trump’s Incompetent Management of the Covid-19 Pandemic: The Consequences for Health

Trump and his administration have utterly mismanaged the Covid-19 pandemic.  The direct result of this ineptitude has been tens of thousands more Americans dying (already) than would have been the case had the US managed the pandemic as well as any of the developed countries of the Asia and Pacific region.  The difference is not small.  This is also not a calculation comparing what the US did to what theoretically might have been possible.  Rather, it is a comparison to what these seven countries actually achieved, facing the same virus as the US.

The chart above shows the cumulative number of deaths from Covid-19 for the US and for seven Asian/Pacific countries between March 1 and April 25, with the numbers all scaled, for comparability, to what they would have been at the US population level.  The data comes from that assembled on an on-going basis by Johns Hopkins University, where a description is available here, and the country data itself available here.  The country population figures to work out the rates per million are those reported by the UN.

US deaths from the virus totaled 53,755 as of April 25.  The figures for each of the seven Asian/Pacific countries are, at this scale, all scrunched up at the bottom of the chart and are almost indistinguishable.  Each and every one has done far better than the US.

Focusing on the figures for April 25 only, one has:

The highest number of deaths among the seven Asian/Pacific countries (when scaled to the US population) would have been South Korea at 1,562.  That is 97% less than the US had.  The levels at all the other countries would have been even lower.  The least would have been Taiwan, with just 83 deaths from the virus (99.8% less than the US), despite far closer links to China.  Not re-scaled for population, the number of deaths so far in South Korea have totaled 242.  In Taiwan, there have been only 6.

US fatalities from Covid-19 have also just passed another significant milestone.  As of April 25 they now exceed the total number of US deaths in combat during World War I:

The figures for combat deaths are all from the Department of Veteran Affairs.  The total number of deaths from Covid-19 surpassed the number of combat deaths during the entire period of the Vietnam War on April 22, and passed the number of combat deaths during the Korean War on April 16.  And Covid-19 deaths of course continue to rise.  Over the most recent week, the daily increase averaged 2,156.

Yet Trump continues to assert that he and his administration have done a superb job of managing this crisis.  If there are any issues, he has at various times asserted the blame is with China, the Chinese President Xi Jinping (whom he, at other times, lauded), the WHO, Obama, Hillary Clinton, Democratic mayors, Democratic governors, Democrats in Congress and the Senate, Democrats more generally, the news media, government civil servants (the “deep state”), and others.

But not himself.  What can he be faulted for?  David Frum (a longtime self-described “conservative Republican” who served as a speechwriter for George W. Bush, but who is now very much a critic of Trump) puts it well in an April 7 article in The Atlantic.  I will quote it at length:

“That the pandemic occurred is not Trump’s fault. The utter unpreparedness of the United States for a pandemic is Trump’s fault. The loss of stockpiled respirators to breakage because the federal government let maintenance contracts lapse in 2018 is Trump’s fault. The failure to store sufficient protective medical gear in the national arsenal is Trump’s fault. That states are bidding against other states for equipment, paying many multiples of the precrisis price for ventilators, is Trump’s fault. Air travelers summoned home and forced to stand for hours in dense airport crowds alongside infected people? That was Trump’s fault too. Ten weeks of insisting that the coronavirus is a harmless flu that would miraculously go away on its own? Trump’s fault again. The refusal of red-state governors to act promptly, the failure to close Florida and Gulf Coast beaches until late March? That fault is more widely shared, but again, responsibility rests with Trump: He could have stopped it, and he did not.

The lying about the coronavirus by hosts on Fox News and conservative talk radio is Trump’s fault: They did it to protect him. The false hope of instant cures and nonexistent vaccines is Trump’s fault, because he told those lies to cover up his failure to act in time. The severity of the economic crisis is Trump’s fault; things would have been less bad if he had acted faster instead of sending out his chief economic adviser and his son Eric to assure Americans that the first stock-market dips were buying opportunities. The firing of a Navy captain for speaking truthfully about the virus’s threat to his crew? Trump’s fault. The fact that so many key government jobs were either empty or filled by mediocrities? Trump’s fault. The insertion of Trump’s arrogant and incompetent son-in-law as commander in chief of the national medical supply chain? Trump’s fault.

For three years, Trump has blathered and bluffed and bullied his way through an office for which he is utterly inadequate. But sooner or later, every president must face a supreme test, a test that cannot be evaded by blather and bluff and bullying. That test has overwhelmed Trump.”

Since April 7 one could add much more to this list, most recently Trump’s comments at his April 23 press briefing on the possible curative effects of injecting disinfectants into the body.  Health professionals around the country scrambled to warn the public not to do this – it could be deadly – and Trump is responsible for this confusion.  Or the ousting in the last week of Dr. Rick Bright, a highly respected medical researcher with a career on vaccine development, who as the head of BARDA (the Biomedical Advanced Research and Development Authority) had a lead role in the urgent development of a vaccine for Covid-19.  The ousting of such an official in the middle of a pandemic is astonishing for any reason.  Dr. Bright, in a statement issued through his lawyers, said it was because he had resisted administration pressure to promote the anti-malarial drugs chloroquine and hydroxychloroquine that Trump had repeatedly touted, even though there was no good evidence of their efficacy.  Indeed, a recent study found that hydroxychloroquine significantly increased the number of deaths of Covid-19 patients compared to those given the usual care.

Trump, however, is unwilling to take responsibility for these repeated failures.  In a March 13 press briefing he famously said “I don’t take responsibility at all” (where this was with specific reference to the lack of adequate testing in the US for a critical two months).  Rather, it was someone else’s fault.

Leadership requires taking responsibility.  Successful presidents do.

Trump’s Mismanagement of the Covid-19 Crisis: South Korea Shows What Would Have Been Possible

Source:  David Leonhardt, Newsletter of April 13, 2020, The New York Times

I normally only include charts I have developed myself in this blog, but the chart above, from David Leonhardt of the New York Times, is particularly striking.  It comes from his newsletter of April 13, and shows the daily number of deaths (on a seven-day moving average) per 10 million people, from February 19 to now in the US and in South Korea.

It shows what the US could have achieved had the Trump administration managed this crisis as well as South Korea has.  And one cannot argue that South Korea is a rich country with resources that the US does not have – GDP per capita in the US is double that of South Korea.  Nor is it because of travel bans.  Trump repeatedly asserts that the crisis would have been far greater in the US had he not had the singular wisdom to impose a ban on travel (by non-US citizens) from China on February 2 (and from Europe and other countries later).  But the only travel ban South Korea has imposed has been travel from Hubei Province in China.  And South Korea has far more contact with China, from both business and personal travel and trade in goods, than the US has.  Yet despite this, the deaths from Covid-19 have been far fewer in South Korea than in the US even after scaling for population.

And it is not only South Korea that has demonstrated competence in the management of the Covid-19 virus.  Death rates in other countries of East Asia, all similarly heavily exposed to China, have been even lower than that of South Korea.  In terms of the cumulative number of deaths from Covid-19 since the crisis began (as of April 13), there have been 4 deaths per million of population in South Korea, but just 2 per million in Singapore, 1 per million in Japan, 0.5 per million in Hong Kong, and 0.3 per million in Taiwan.  For the US, in contrast, the total is 71 per million.  (Reminder:  The chart above tracks deaths per day, not the cumulative total, and shows the figures per 10 million of population.)

This also shows that Trump’s repeated assertion that the deaths suffered in the US were inevitable – that nothing more could have been done – is simply nonsense.  Sadly, it is deadly nonsense.  South Korea shows what could have been done.  Travel bans were not important.  Rather, it was the basic public health measures of large-scale testing, identifying those with the virus or who may have been exposed to the virus, quarantining or isolating those exposed (including self-isolating, along with self-monitoring and regular reporting), and then treating in hospitals those who developed severe symptoms.

None of this is new to public health professionals.  And the US has excellent public health professionals.  What was different in the US was Trump, who refused to listen to them and indeed treated many of those in government as enemies to be attacked (as those with expertise were seen as members of the “deep state”).

The US had prepared plans on what to do should an infectious disease such as Covid-19 threaten.  There was, for example, a major effort to develop such plans in 2006/2007, towards the end of the Bush administration.  The work included running exercises similar to war-games of various scenarios (“table-top” exercises), to see how officials would respond and what the likely outcomes then would be.  These plans were further developed during Obama’s two terms in office.  But the Trump administration then ignored this previous preparation, and indeed took pride in dismantling important elements of it.

Dr. James Lawler, now an infectious disease doctor at the University of Nebraska but then serving in the Bush White House, participated in the 2006/2007 task force.  Over the weekend, the New York Times released a trove of over 80 pages of emails (obtained through a Freedom of Information Act request) of late-January to mid-March from Dr. Lawler and other experts, in and out of government, discussing how to address the crisis.  Particularly telling is a March 12 email from Dr. Lawler in which he said:

“We are making every misstep initially made in the table-tops at the outset of pandemic planning in 2006.  We had systematically addressed all of these and had a plan that would work – and has worked in Hong Kong/Singapore.  We have thrown 15 years of institutional learning out the window …”

Throwing those 15 years of institutional learning out the window has had deadly consequences.

The Rapid Growth in Deaths from Covid-19: The Role of Politics

Deaths from Covid-19 have been growing at an extremely rapid rate.  The chart above shows what those rates have been in the month of March, averaged over seven day periods to smooth out day-to-day fluctuations.  The figures are for the daily rate of growth over the seven day period ending on the date indicated.  The curves start in the first period when there were at least 10 cases, which was on March 3 for the US as a whole.  Hence the first growth rate shown is for the one week period of March 3 to 10.  As I will discuss below, the chart has not only the growth rates for the US as a whole but also for the set of states that Trump won in 2016 and for the set that Clinton won.  They show an obvious pattern.

The data come from the set assembled by The New York Times, based on a compilation of state and local reports.  The Times updates these figures daily, and has made them available through the GitHub site.  And it provides a summary report on these figures, with a map, at least daily.

I emphasize that the figures are of daily growth rates, even though they are calculated over one week periods.  And they are huge.  For the US as a whole, that rate was just over 28% a day for the seven day period ending March 30.  It is difficult to get one’s head around such a rapid rate of growth, but a few figures can be illustrative.  In the New York Times database, 3,066 Americans had died of Covid-19 as of March 30.  If the 28% rate of growth were maintained, then the entire population of the US (330 million) would be dead by May 16.  For many reasons, that will not happen.  The entire population would have been infected well before (if there was nothing to limit the spread) and it is fatal for perhaps 1% of those infected.  And the 99% infected who do not die develop an immunity, where once they recover they cannot spread the virus to others.  For this reason as well, 100% of those not previously exposed will not catch the virus.  Rather, it will be some lower share, as the spread becomes less and less likely as an increasing share of the population develops an immunity.  This is also the reason why mass vaccination programs are effective in stopping the spread of a virus (including to those not able to receive a vaccination, such as very young children or those with compromised immune systems).

So that 28% daily rate of growth has to come down, preferably by policy rather than by running out of people to infect.  And there has been a small reduction in the last two days (the seven day periods ending March 29 and March 30), with the rate falling modestly to 28% from a 30% rate that had ruled since the seven day period ending March 22.  But it has much farther to go to get to zero.

The recent modest dip might be an initial sign that the social distancing measures that began to be put in place around parts of the nation by March 16 are having a positive effect (and where many individuals, including myself, started social distancing some time before).  It is believed that it takes about 4 to 7 days after being infected before one shows any symptoms, and then, in those cases where the symptoms are severe and require hospitalization (about 20% of the total), another several days to two weeks before it becomes critical for those where it will prove fatal.  Hence one might be starting to see the impacts of the policies about now.

But the social distancing measures implemented varied widely across the US.  They were strict and early in some locales, and advisory only and relatively late in other locales.  Sadly, Trump injected a political element into this.  Trump belittled the seriousness of Covid-19 until well into March, even calling Covid-19 a “hoax” conjured up by the Democrats while insisting the virus soon would go away.  And even since mid-March Trump has been inconsistent, saying on some days that it needs to be taken seriously and on others that it was not a big deal.  Fox News and radio hosts of the extreme right such as Rush Limbaugh also belittled the seriousness of the virus.

It is therefore understandable that Trump supporters and those who follow such outlets for what they consider the news, have not shown as much of a willingness to implement the social distancing measures that are at this point the only way to reduce the spread of the virus.  And it shows in the death figures.  The red curve in the chart at the top of this post shows the daily growth rates of fatalities from this virus in those states that voted for Trump in the 2016 election.  While the spread of the virus in these states, many of which are relatively rural, started later than in the states that voted for Clinton, their fatalities from the virus have since grown at a substantially faster pace.

The pace of growth in the states that voted for Clinton has also been heavily influenced by the rapid spread of the virus in New York.  As of March 30, more than half (57%) of the fatalities in the Clinton states was due to the fatalities in New York alone.  And New York is a special case.  With its dense population in New York City, where a high proportion use a crowded subway system or buses to commute to work, with the work then often in tall office buildings requiring long rides in what are often crowded elevators, it should not be surprising that a virus that goes person to person could spread rapidly.

Excluding New York, the rate of increase in the other states that voted for Clinton (the curve in green in the chart above) is more modest.  The rates are also then even more substantially lower than those in the Trump-voting states.

But any of these growth rates are still incredibly high, and must be brought down to zero quickly.  That will require clear, sustained, and scientifically sound policy, from the top.  But Trump has not been providing this.

The Ineffectiveness of Travel Bans for Addressing the COVID-19 Pandemic

A)  Introduction

The US is sinking into what looks likely to be its biggest public health crisis in over a century (i.e. since the Spanish Flu pandemic of 1918/19).  But President Trump continues to insist that he is not to be blamed for its mismanagement.  Rather, he insists that he should be commended for instituting the travel ban on China in early February, that “everyone” was opposed to him doing it but he decided to impose anyway, and that it turned out to be a “great success”.

None of this is true.

What was in fact done?  On January 31, the Trump administration announced that he would impose a ban on travelers from China entering the US, with this going into effect the evening of February 2.  It would not apply to returning US citizens. But there were other countries doing the same at that point, or even earlier (not many, but some).  Australia, for example, set a ban on travelers from China which went into effect on February 1, and New Zealand set a ban effective February 2.  Furthermore, numerous airlines were already suspending their flights from China.  American Airlines had implemented a suspension on all its flights to the US from China effective on January 31.  Delta and United Airlines had already announced that they would also be suspending their flights, and Delta did so on February 2 and United on February 5.  Air Canada had already suspended its flights on January 30, and numerous European airlines suspended theirs starting January 29 (Lufthansa, Swiss International, Austrian, British Airways), January 30 (KLM, Air France), and January 31 (SAS, Iberia).

And it is not correct for Trump to claim that “everyone” was opposed to such a travel ban.  I read the news closely, and I cannot recall any politician, nor any widely expressed public sentiment, arguing against the ban (although I acknowledge that there may well have been some – just not enough to be significant).  Infectious disease experts did say that such a ban would not do much good at that point, as the disease was certainly already in the US and would spread.  Keep in mind that any such disease starts with only one case, of a newly mutated virus that some animal carries (scientists believe it originated in bats, and then passed to some other animal species before jumping to some person).  It then expands person to person from that one case.  A travel ban, by itself, will not stop a spread if there are cases already here.

What a travel ban can do is buy some time.  It can postpone a major spread of the disease by a few weeks.  That can be of value if the ban is implemented very early and if those weeks are then spent to address aggressively the spread of the disease.  This includes rapid testing of all those individuals that may have been exposed to the virus, the isolation of all the cases thus found, and the quarantining of all those who may have been exposed but have not shown symptoms at that point.

But none of this was done in the US.  And as the experts noted, such travel bans will be harmful if they lull policymakers into a false sense of security, with an excuse then to delay taking urgent measures in the false belief that the country is now protected.  It is clear that Trump himself believed this, or at least acted (or rather did not act) consistent with such a belief.

If such a travel ban might buy time, how much time might that be?  This blog post will present some calculations of scenarios of what to expect.  I should stress that I am not an epidemiologist, and the scenarios discussed here are in no way a forecast of what specifically might have happened.  Epidemiologists are looking at that now, using far more sophisticated models (and with far greater knowledge than I have), but are still in an early stage as many of the characteristics of the disease are not yet known with any degree of certainty.

But what matters most is the basic mathematics of pandemics or epidemics (I will use the terms interchangeably here – a pandemic is simply an epidemic of greater range or coverage).  An infectious disease will expand at an exponential rate early on and is subject to a ceiling on those it can infect (no more than 100% of the population, and normally less).  And it is that basic mathematics of the process which shows why travel bans will be futile, and at best will simply delay by only a very short time the spread of a virus such as that which causes COVID-19.

The basic result is summarized in the chart at the top of this post and will be discussed in the next section below.  With plausible parameters, a complete and total travel ban applied to all travelers (including US citizens) might have delayed the spread of the disease by perhaps 2 1/2 weeks.  That is not much.

Far more effective would be policies to reduce the pace at which the disease spreads.  Such policies include “social distancing”, where activities involving crowds are canceled or avoided, and one encourages everyone to wash their hands frequently, stay away from others to the extent they can, and so on.  The chart shows (in the curve in orange) what that might achieve for a plausible parameter.  Its impact is far greater than that of a travel ban.

Slowing down the pace at which the virus spreads is also supremely important, as otherwise our health system could easily become swamped with an overwhelming number of cases requiring care all at one time.  As will be discussed and illustrated in section C below, a travel ban does not help with this at all.  But social distancing will, and quite remarkably so.  It could reduce the peak load on our health system (for the parameters examined here) by 75%.  That could literally mean that thousands of lives could be saved.

What was done during February, after the travel ban had been put into effect?  Sadly, not much.  There was no significant effort to identify and then isolate cases, and quarantine those exposed to those cases.  The development of a rapid COVID-19 specific test was also delayed as the initial version of the test turned out to be flawed.  While other nations around the world developed and quickly applied tests of their own, the US only tested (through other means) a small number of possible cases of individuals meeting highly restrictive criteria (such as recent travel in China).  And with only highly limited testing being done, the reported number of confirmed cases in the US was low.  But you can only confirm cases if you test, and if you do not test you will have no confirmations.

President Trump, and his administration, has yet to acknowledge its responsibility in this fiasco.  Trump has instead insisted that cases in the US are exceptionally low because, and only because, of the travel ban on China.  The numbers suggest otherwise.

B)  The Simple Mathematics of an Epidemic, and the Impact of a Travel Ban

One can model what an epidemic might look like (in terms of how fast it will spread) with some simple mathematics.  While this is far from the sophisticated models epidemiologists have for such processes, a simple model will suffice for an examination of the issue of what a travel ban might do.

The basic characteristic of an epidemic is that it will grow at an exponential rate to start with, but since it is subject to a ceiling (it cannot infect more than 100% of a population, and normally will tail off well before this point), the expansion will eventually have to level off.  A simple model with such characteristics is a logistic model, which was first proposed by a Belgian mathematician, Pierre Verhulst, in 1838.

The key parameter, called the “basic reproduction number” (and often designated as R0) is the number of new people who will, on average, be infected by a person who has been infected.  If that number is 2.0, then (to start) two new people will be infected by each person that has been infected, and the number of people who are infected at any given time will double in each period (to start).  If that number is 1.0, then (again, to start) one person will on average be infected by each person that has been infected, and the number of people who are infected in any given period will be constant (and the number who have cumulatively been infected will grow linearly over time).  And if the number is less than 1.0, then the number of new cases of infection will decline in each period, eventually going to zero (with the cumulative total climbing week to week as long as there are any new cases, but at a diminishing rate and eventually leveling off).

The basic reproduction number depends both on the characteristics of the disease, and on the degree of interpersonal contact in the society.  For the disease itself, some are more easily transmissible than others.  Measles, for example, spreads extremely easily.  Ebola (fortunately, as it has a high fatality rate) spread only if you had direct contact with bodily fluids, and hence did not spread easily.

But the spread also depends on what society is doing.  When people are in close direct contact, for example in crowds at concerts or in church or in a crowded subway car, more will be infected than if people are well separated.  Hence policy matters, and we will examine below the impact of measures that would reduce the degree of such close contact.

A key question for the virus that causes COVID-19 is how transmissible it is.  A number of scholars have hurriedly examined this, mostly using data from the initial spread in Wuhan, China, but have come up with a fairly wide range of possible figures.  The parameter is inherently hard to measure as data on the total number of people coming down with the virus week to week are simply not available, with the published figures possibly underestimates.  But a careful study published in The Lancet on March 11 estimated a figure of 2.35 in Wuhan before travel restrictions were imposed, falling to 1.05 after the rather draconian travel and quarantine measures went into effect.  An early study by a group of Chinese researchers published in the New England Journal of Medicine on January 29 (and summarized in an editorial co-authored by Dr. Anthony Fauci and others in the New England Journal of Medicine on February 28) arrived at an estimate of 2.2.  An estimate in a study published on February 22 and based on the spread of the virus in the cruise ship Diamond Princess came to a figure of 2.28.  And a review published on February 13 that examined as many other published studies as they could find up to that point (a total of 12, some of which might not have been of high quality) found a median estimate of 2.79, a mean of 3.28, and a range of 1.4 to 6.49.

I used an R0 of 2.3 for the calculations here.  It might be a bit on the low side, and if it were higher then the impact of a full travel ban (the main issue I am examining here) would be even less.  I am erring on the conservative side.  I am also, for these scenarios, looking at what the impact would be if that number remains unchanged over time.  That is, the scenarios examine what the impact would be if nothing is done to reduce the R0 by social distancing measures, either from policy (i.e. school closures) or simply by individuals being more careful and avoiding crowds or places where they could pick up an infection.  I stress again that these are scenarios of what would happen under specific circumstances, not forecasts of what will happen.

Assumptions are required for several other factors as well.  For simplicity, I am taking a discrete form of the logistic model, with calculations of week to week changes.  It is assumed that there will be a one week incubation period of a person who has been infected, and that that person can then infect others in their second week of infection.  After that, they can no longer infect others.  These assumptions are broadly consistent with what appear to be some of the basic parameters of the disease (based on material from a good summary article published in The Lancet on March 9), where the authors state that the mean time it takes for a newly infected person to pass the disease on to others is estimated to be 4.4 to 7.5 days.  So roughly one week after someone catches the virus they, on average, pass it on to others.

To examine the impact of a travel ban, I included as part of the model that a certain number of infected people would arrive from abroad each week, and that they would then add to those who could infect others domestically in the next week.  That is, those who would (domestically) be infected each week depends on the number who had been infected domestically in the prior week plus those infected who had arrived from abroad in the prior week.  To start, in period zero, I assumed there were 100 cases already active in the country domestically, and that 100 cases arrived from abroad.  I also assumed that the cases arriving from abroad, if nothing were done, would increase exponentially week to week (reflecting that the number of cases abroad are also growing) until they reached 10,000 per week (given that there are only so many who fly back and forth, even in normal times), after which the number was kept at 10,000 per week.

Finally, I set the ceiling on the population that might be infected by the virus at roughly one-third of the US population.  This model is too simple to forecast what that ceiling might be, so I used estimates made by others of the share of the US population that might in the end be infected if nothing is done.  But this ceiling is primarily just a scaling variable.  The results would not be impacted much by a different ceiling, within a reasonable range.  What matters is that, for the scaling used here, one starts with 100, caps those coming from abroad at 10,000, and has an overall domestic ceiling of over 100 million.

The scenario then looked at what would happen with a complete and total ban on anyone coming from abroad.  This would be far more extreme than any actual travel ban would be, as it would exclude returning American citizens and not just foreigners, plus it would cover travel from all countries in the world.  This was far more comprehensive than simply a ban on non-citizen arrivals from China.  But the aim was to be as generous as possible in calculating what the impact of a travel ban would be.

The chart at the top of this post shows what that impact might be.  It would not be much.  Even under such an extreme ban on travel, the path of the epidemic would be delayed by only about 2 1/2 weeks.  With other values assumed for the basic reproduction number R0 within a reasonable range, that time delay might be as short as 2 weeks or as long as 3 1/2 weeks.  None of these are large.  A travel ban would, at best, buy some time, but not much time.

But that extra time was not used in any case.  A travel ban in the very early stages of an epidemic can play a role if it is early enough (and February 2 was not early enough), and with then a major effort mounted to test all possible cases for the virus, with those testing positive isolated and those who had come into contact with such cases (or possible cases) quarantined.  None of this was done.

More modestly, what could have been done would be immediately to have increased social distancing, so that the infection rate (the R0) would be reduced.  The chart at the top of this post shows (in the curve in orange) what the impact would be had such measures been undertaken instead of the travel ban, and were sufficient to reduce the R0 to 1.5 from the 2.3 assumed in the other scenarios.  That is, the curve shows the impact where, on average, each infected person then infects a further 1.5 people instead of 2.3 people.  And again, to be clear, the curve assumes no foreign travel restrictions were imposed.

The spread of the disease is then slowed significantly.  Furthermore, the total number infected rises just to 75 million, or one-third less than come down with the disease in the base scenarios (with or without a travel ban).  The lower total number infected following from a lower R0 is an outcome of the random processes assumed in the logistic function, where as you approach the ceiling on the number who might be infected (the population), there is an increased likelihood that one will encounter only people who have already been infected and hence are now immune.  When one encounters fewer people (an R0 of 1.5 rather than 2.3), the likelihood goes up that all of the people encountered will be immune, and hence the number who will be infected in those later periods falls below 1.0.  The further spread of the disease then dies out.  It is for this reason as well that the curves for the case where R0 equals 2.3 level off at the odd number of 113.5 million.  I assumed a potential population of 120 million, and the logistic curve will level off below this.

Another scenario examined was one where the total travel ban was not implemented in week zero but rather in week six.  This would be similar to a delayed travel ban, such as that Trump recently imposed for travelers from Europe.  In the simple model, by week six the number of infected travelers coming in from abroad has reached its assumed peak of 10,000 per week.  I assumed that this was instead brought to zero in week six and then remained at zero.  The impact was trivial.  A plot of the new curve sits basically on top of the old (no travel ban) curve.  I therefore did not include it here as it simply looks almost exactly the same as the curve with no travel ban imposed.

C)  Impact on Cases to be Treated

As many have stressed, what matters is not only the total number of people being infected but also the number of new cases of infection each week.  Since about 20% of those coming down with the disease will likely need hospital treatment (based on current estimates), the burden on our hospital system will depend on how rapidly the number of new cases increase.  There are only a limited number of hospital beds, a far more limited number of the ventilators (about 160,000) that many of those who come down with this respiratory illness will require, and an even more limited number of beds in intensive care units (only 46,500, with perhaps a similar number that could be added in a crisis).  Furthermore, the patients that will need these ventilators and ICU beds may need to use them for two or three weeks.  This is far longer than would be the typical use of such hospital facilities for other disease treatments where they are required.

Hence, as numerous news reports have flagged in recent days, we need to “flatten the curve”.  That is, there is a critical need to reduce the peak load on such hospital facilities, with the need instead spread out over time.  A travel ban does not do this:

The peak loads on our hospital facilities would be almost exactly the same, with or without a total travel ban.  The peak is just shifted by 2 1/2 weeks.  In contrast, policies that by social distancing and other such measures reduces the basic reproduction number to 1.5 would have quite a marked effect, reducing the peak load by almost 75%.  That could directly translate into possibly thousands of lives that might be saved.  A travel ban does not help.

D)  Conclusion

The US is facing a major public health crisis.  Yet the response has been terribly mismanaged by the Trump administration.  Direction starts at the top, but Trump has repeatedly asserted that there is no major problem and that the disease will soon go away.  Even as late as March 10 (less than one week from when I am writing this), Trump said in remarks to the press at the White House that “And it will go away.  Just stay clam.  It will go away.”  He also continued to assert in those remarks that the ban on travel from China that he put in place, which he insisted others would not have done, had “made a big difference”.

But as shown above, imposing a travel ban, and one far more sweeping than the one Trump imposed on non-American travelers from China, will not have a major effect on the path of an epidemic such as the one we are facing.  This follows from the mathematics of compound growth as a disease spreads person to person.  At best it will buy some time, but plausible estimates are that it would amount to only a few weeks at best.  And those extra few weeks will only help if one makes use of that time to aggressively attack the disease.  That was not done.

Furthermore, a travel ban will not change the basic pattern of the epidemic.  It will merely shift it.  The peak loads on a stretched hospital system will remain the same.  Far more effective would be an early and sustained effort to promote social distancing.  This will not only reduce the total number getting the infection, but will also spread the infections out significantly over time.  Even a relatively modest reduction in the pace at which the disease spreads will have a major impact on those peak loads.  And reducing those peak loads on the hospital system can make a major difference in the number of deaths, reducing them by quite possibly thousands.

Why, then, the travel bans?  Probably because it may lead some to believe you are being serious and decisive, even macho, with such a clear-cut (albeit ineffective) measure.  Plus this makes it look like foreigners are to blame.  All this is appealing to someone like Trump.  And as he has repeatedly done throughout his term in office, he discounts the evidence-based advice of scientists with expertise in a field.  He thinks he knows better.

Sadly, Trump is accepting no responsibility for this fiasco.  On March 13, when asked specifically whether he accepts any responsibility for the delay of more than a month in rolling out the extensive testing that is critical early in an epidemic to identify and quickly isolate those infected, Trump replied “No.  I don’t take responsibility at all.”