The Very Model Of A Natural Disaster

Credit NHC 11am Sunday Track Map

 

# 6673

 

While it is impossible to know just how bad Hurricane-Hybrid Sandy will be as it approaches and crosses the eastern seaboard over the next 48 hours – or who, exactly, will be most affected – models continue to paint a sobering picture.

 

Tidal surge models from the National Hurricane Center suggest that large areas of the coastline could see storm tides 5-10 feet above normal. 

 

The following map is for a 5 ft surge:

 

Some models are suggesting that portions of New York Harbor could see tides exceeding 10 feet above normal, and the 11am advisory warns:

 

STORM SURGE...THE COMBINATION OF AN EXTREMELY DANGEROUS STORM SURGE AND THE TIDE WILL CAUSE NORMALLY DRY AREAS NEAR THE COAST TO BE FLOODED BY RISING WATERS. THE WATER COULD REACH THE FOLLOWING DEPTHS ABOVE GROUND IF THE PEAK SURGE OCCURS AT THE TIME OF HIGH TIDE...

NC NORTH OF SURF CITY INCLUDING PAMLICO/ALBEMARLE SOUNDS...4 TO 6 FT

 

SE VA AND DELMARVA INCLUDING LOWER CHESAPEAKE BAY...2 TO 4 FT

 

UPPER AND MIDDLE CHESAPEAKE BAY...1 TO 3 FT

 

LONG ISLAND SOUND...RARITAN BAY...AND NEW YORK HARBOR...6 TO 11 FT

 

ELSEWHERE FROM OCEAN CITY MD TO THE CT/RI BORDER...4 TO 8 FT

 

CT/RI BORDER TO THE SOUTH SHORE OF CAPE COD INCLUDING BUZZARDS
BAY AND NARRAGANSETT BAY...3 TO 6 FT

 

 

Based on the forecast of surge tides, heavy rains, and high winds the City of New York will suspend mass transit routes starting at 7pm tonight.

 

Gov: MTA will suspend all subway, bus and rail service as Hurricane Sandy advances

Last subway and rail trains will be at 7; last bus at 9pm

By Shane Dixon Kavanaugh / NEW YORK DAILY NEWS

 

The last time the New York Subway system was shut down due to flooding was for Hurricane Irene in 2011.  The NYC MTA carries more than 8 million passengers on a typical weekday, and shutting down the entire system will take 8 to 10 hours (cite WSJ).

 

From Johns Hopkins University, we get this `model’ of possible power outages due to Sandy, based on current track forecasts.  

 

 

MEDIA ADVISORY: Hurricane Sandy – 10 million could lose power

As many as 10 million in the mid-Atlantic will lose power in the coming week, according to a computer model developed by an engineer at The Johns Hopkins University.

Please note: A multicolored map of power outage predictions is available. Email acl@jhu.edu

A map of power outages as predicted by Guikema’s model based on the official National Hurricane Center track and intensity forecast from 18UTC (3 p.m. EDT) on Saturday, Oct. 27.

 

An engineer at The Johns Hopkins University predicts that 10 million people from northern Virginia through New Jersey and into southeastern Pennsylvania will be without power in the wake of Hurricane Sandy. Seth Guikema (pronounced Guy-keh-ma) and his team have developed a computer model built on outage data from 11 hurricanes to estimate the fraction of customers who will lose power, based on expected gust wind speed, expected duration of strong winds greater than 20 meters per second, and population density. They ran their model using the official National Hurricane Center track and intensity forecast from 18UTC (3 p.m. EDT) on Saturday, and emphasize that the number of power outages could change as the storm progresses and forecasts become more definitive. It is possible that 10 million people is a conservative estimate, Guikema said.

 

Guikema’s model may help power companies allocate resources by predicting how many people will be without power and where the most outages will take place, and it provides information that emergency managers can use to better prepare for storms. Guikema, an assistant professor in the Department of Geography and Environmental Engineering at the Johns Hopkins Whiting School of Engineering, says the goal is to restore power faster and save customers money. Guikema will be running the model throughout the weekend and into next week as Hurricane Sandy makes landfall.

 

The Governor of New Jersey warned yesterday that power could be out for some residents for `seven to ten days’.  And for many people, that means no ability to cook, or to heat their homes.

 

Municipal water supplies, or water quality, could be affected as well.   Hence the need to be prepared to go several days - at least - without city services or utilities.

 

And while most people automatically worry about storm surge or high winds from hurricanes, between 1970 and 1999, the most lives have been claimed due to inland flooding. 

 

 

The NHC has released this five-day precipitation forecast, which should provide some idea of the extent of flooding that may occur.

 

 

 

While it is possible that Sandy won’t be as destructive as has been billed, storms like Camille, Andrew, and Katrina have shown us the folly of underestimating nature’s fury.

 

Today is the last day that people in the path of this storm will have to prepare. Those who are ordered to evacuate need to do so immediately.

 

 

To help track this storm, you may wish to revisit my blog from yesterday  Resources To Follow The Northeast Storm Online.

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MIT: Contagion Dynamics Of International Air Travel

 

 

 

# 6446

 

In 2009, about 6 weeks before news of the outbreak of H1N1 in Mexico was announced, I came across a fascinating video on Youtube which inspired a blog called How The Next Pandemic Will Arrive.

 

I wrote:

 

There is a lot we don't currently know about the next pandemic.  We don't know when it will arrive.  We don't know what virus will cause it.  And we don't know how bad it will be.

 

But there is one thing almost certain.

 

It will arrive in most countries by airplane.

 

 

 

Not exactly an earth shattering revelation, given that air travel is an obvious mode of viral spread. But my timing was excellent.

 

By the end of following month the new H1N1 virus was winging its way around the globe in large part due to spring break vacationers returning from Mexico.

 

While obviously a major factor, the dynamics of disease spread through airports is only partially understood.  

 

We’ve a new study, appearing in PloS One, that looks at the early spread of a pandemic virus through air travel, and through the use of Monte Carlo simulations, finds some airports contributing more to the spread of a pandemic than the number of travelers passing through it might suggest.

 

The study, conducted by researchers at MIT, is called:

 

A Metric of Influential Spreading during Contagion Dynamics through the Air Transportation Network

Christos Nicolaides, Luis Cueto-Felgueroso, Marta C. González, Ruben Juanes

Abstract

The spread of infectious diseases at the global scale is mediated by long-range human travel. Our ability to predict the impact of an outbreak on human health requires understanding the spatiotemporal signature of early-time spreading from a specific location.

 

Here, we show that network topology, geography, traffic structure and individual mobility patterns are all essential for accurate predictions of disease spreading. Specifically, we study contagion dynamics through the air transportation network by means of a stochastic agent-tracking model that accounts for the spatial distribution of airports, detailed air traffic and the correlated nature of mobility patterns and waiting-time distributions of individual agents.

 

From the simulation results and the empirical air-travel data, we formulate a metric of influential spreading––the geographic spreading centrality––which accounts for spatial organization and the hierarchical structure of the network traffic, and provides an accurate measure of the early-time spreading power of individual nodes.

 

I would invite those with a better grasp of statistical analysis than I to read the entire study, but for the rest of us, we have the following report from MIT News.

 

Monday, July 23

New model of disease contagion ranks U.S. airports in terms of their spreading influence

Airports in New York, Los Angeles and Honolulu are judged likeliest to play a significant role in the growth of a pandemic.

Denise Brehm, Civil and Environmental Engineering

World map shows flight routes from the 40 largest U.S. airports.

Image: Christos Nicolaides, Juanes Research Group

Public health crises of the past decade — such as the 2003 SARS outbreak, which spread to 37 countries and caused about 1,000 deaths, and the 2009 H1N1 flu pandemic that killed about 300,000 people worldwide — have heightened awareness that new viruses or bacteria could spread quickly across the globe, aided by air travel.

<SNIP>

 

Outsize role for Honolulu

For example, a simplified model using random diffusion might say that half the travelers at the Honolulu airport will go to San Francisco and half to Anchorage, Alaska, taking the disease and spreading it to travelers at those airports, who would randomly travel and continue the contagion.

 

In fact, while the Honolulu airport gets only 30 percent as much air traffic as New York's Kennedy International Airport, the new model predicts that it is nearly as influential in terms of contagion, because of where it fits in the air transportation network: Its location in the Pacific Ocean and its many connections to distant, large and well-connected hubs gives it a ranking of third in terms of contagion-spreading influence.

 

Kennedy Airport is ranked first by the model, followed by airports in Los Angeles, Honolulu, San Francisco, Newark, Chicago (O'Hare) and Washington (Dulles). Atlanta's Hartsfield-Jackson International Airport, which is first in number of flights, ranks eighth in contagion influence. Boston's Logan International Airport ranks 15th.

(Continue . . . )

 

 

Complicating matters - attempts to identify and quarantine air travelers with fevers, or other signs of illness - have proved notoriously difficult.

 

Last April, in EID Journal: Airport Screening For Pandemic Flu In New Zealand, we looked at a study that found that the screening methods used at New Zealand’s airport were inadequate to slow the entry of the 2009 pandemic flu into their country, detecting less than 6% of those infected.

 

Unlike some other countries in 2009, New Zealand did not employ thermal scanners, which look for arriving passengers or crew with elevated temperatures. 

(Thermal Imaging for SARS in 2003)

 

But even countries that employed thermal scanners and far more strict interdiction techniques during the summer of 2009 failed to keep the flu out.

 

Just as the pandemic was ramping up, in Vietnam Discovers Passengers Beating Thermal Scanners, we saw evidence of flyers taking fever-reducers to beat the airport scanners in order to get home.

 

In December of 2009, in Travel-Associated H1N1 Influenza in Singapore, I wrote about a NEJM Journal Watch of a new study that has been published, ahead of print, in the CDC’s  EID Journal  entitled:

 

Epidemiology of travel-associated pandemic (H1N1) 2009 infection in 116 patients, Singapore. Emerg Infect Dis 2010 Jan; [e-pub ahead of print]. Mukherjee P et al

Travel-Associated H1N1 Influenza in Singapore

Airport thermal scanners detected only 12% of travel-associated flu cases; many travelers boarded flights despite symptoms.

And finally, in June of 2010  CIDRAP carried this piece on a study of thermal scanners in New Zealand in 2008 (before the pandemic) presented at 2010’s ICEID.

 

Thermal scanners are poor flu predictors

Thermal scanners for screening travelers do moderately well at detecting fever, but do a poor job at flagging influenza, according to researchers from New Zealand who presented their findings today at the International Conference on Emerging Infectious Diseases (ICEID) in Atlanta.

 

 

As far as the transmission of the influenza virus aboard an airliner, in May of 2010 we saw a study in the BMJ that looked at that very topic (see BMJ: Flu Transmission Risks On Airplanes)

 

BMJ 2010;340:c2424

Research

Transmission of pandemic A/H1N1 2009 influenza on passenger aircraft: retrospective cohort study

 

Conclusions

 

A low but measurable risk of transmission of pandemic A/H1N1 exists during modern commercial air travel. This risk is concentrated close to infected passengers with symptoms. Follow-up and screening of exposed passengers is slow and difficult once they have left the airport.

 

Another study, conducted by researchers at UCLA and published in BMC Medicine in late 2009:

 

Calculating the potential for within-flight transmission of influenza A (H1N1)

Bradley G Wagner, Brian J Coburn and Sally Blower^*

Results

The risk of catching H1N1 will essentially be confined to passengers travelling in the same cabin as the source case. Not surprisingly, we find that the longer the flight the greater the number of infections that can be expected. We calculate that H1N1, even during long flights, poses a low to moderate within-flight transmission risk if the source case travels First Class.

 

(Continue . . .)

 

While it may prove impossible to halt the spread of a pandemic via airline passengers, knowing which airports are the most likely to contribute to the spread of a new virus could aid in attempts to slow its progress.

 

Which makes research like what we’ve seen out of MIT today of more than just academic interest.

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Estimating Fukushima’s Health Impact

 

Photo credit IAEA

 

# 6438

 

 

While the worst fears from last year’s radiation release from Japan's Fukushima Daiichi nuclear disaster have not been realized, the long-term health impacts remain largely unknown. 

 

Today, researchers at Stanford University have released a modeling study – published in the journal Energy and Environmental Science – that attempts to quantify the mortality and morbidity due to that nuclear release.

 

 

Worldwide health effects of the Fukushima Daiichi nuclear accident

John E. Ten Hoeve and Mark Z. Jacobson

Energy Environ. Sci., 2012, Advance Article

Abstract

A 3-D global atmospheric model evaluated against data is used to quantify worldwide health effects from the Fukushima nuclear accident.

 

As you will see, the actual numbers are fairly low, but the range of possibilities is considerable. The press release from Stanford University provides us with the details.

 

 

Stanford researchers calculate global health impacts of the Fukushima nuclear disaster

Radiation from Japan's Fukushima Daiichi nuclear disaster may eventually cause anywhere from 15 to 1,300 deaths and from 24 to 2,500 cases of cancer, mostly in Japan, Stanford researchers have calculated.

 

The estimates have large uncertainty ranges, but contrast with previous claims that the radioactive release would likely cause no severe health effects.

 

The numbers are in addition to the roughly 600 deaths caused by the evacuation of the area surrounding the nuclear plant directly after the March 2011 earthquake, tsunami and meltdown.

 

Recent PhD graduate John Ten Hoeve and Stanford civil engineering Professor Mark Z. Jacobson, a senior fellow at the Precourt Institute for Energy and the Stanford Woods Institute for the Environment, are set to publish their findings Tuesday (July 17) in the journal Energy and Environmental Science. The research constitutes the first detailed analysis of the event's global health effects.

 

(Continue . . . )

 

As Professor Emeritus of Statistics at the University of Wisconsin George E. P. Box famously observed:

 

“All models are wrong, but some models are useful.”

 

While this study may not be the last word on the health impacts from Fukushima, and it only provides a range of effects, it at least provides a starting point. 

 

Interestingly, the authors modeled what a similar meltdown would look like if it occurred in the United States. 

 

From the press release:

 

To test the effects of varying weather patterns and geography on the reach of a nuclear incident, the two researchers also analyzed a hypothetical scenario: an identical meltdown at the Diablo Canyon Power Plant, near San Luis Obispo, Calif.

 

Despite California's population density being about one-fourth that of Japan's, the researchers found the magnitude of the projected health effects to be about 25 percent larger.

 

The model showed that rather than being whisked toward the ocean, as with Fukushima, a larger percentage of the Diablo Canyon radioactivity deposited over land, including population centers such as San Diego and Los Angeles.

 

Jacobson stressed, however, that none of the calculations expressed the full scope of a nuclear disaster.

 

"There's a lot more to the issue than what we examined, which were the cancer-related health effects," he said. "Fukushima was just such a large disaster in terms of soil and water contamination, displacement of lives, confidence in government oversight, cost and anguish."

 

In May we saw the WHO Report On Radiation Exposure From Fukushima Reactor Accident, which concluded that:

 

. . . the estimated effective doses outside Japan from the Fukushima Daiichi NPP accident are below (and often far below) dose levels regarded as being very small by the international radiological protection community. Low effective doses are also estimated in much of Japan.

 

Another report is expected from the World Health Organization later this summer that will attempt to quantify the short and long term health-risk due to exposure to radioactivity from Fukushima.

It will be interesting to compare those findings with the ones released today.

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MIT: Forecasting Flu Pandemics

 

 

# 5119

 

The old adage (attributed to George E. P. Box, Professor Emeritus of Statistics at the University of Wisconsin) is that:

 

“All models are wrong, but some models are useful.”

 

We use models to try to mathematically simulate real-life events.  Mathematical and computer models are used to analyze everything from city traffic flow, to your supermarket’s inventory control, to weather forecasting.

 

Models that attempt to simulate scenarios which are based on rare events – those without much historical data – are naturally more difficult to develop.

 

Epidemics and pandemics are fairly rare life-threatening events that require a massive, sometimes global response.

 

Knowing when and where to put resources, and what steps need to be taken in advance to prepare for an outbreak, are things that better models could conceivably tell us.

 

Today, an interesting article from MIT’s Technology Review, which looks at the future of pandemic modeling and prediction.

 

Included are quotes from a number of researchers, including Klaus Stohr, director of influenza vaccine franchises at Novartis - Martin Meltzer, a health economist with the CDC – and Cecile Viboud, an epidemiologist at the National Institutes of Health.

 

Although we are still a long way off from being able to predict when the next pandemic will arrive, or even which virus will be the cause, this is fascinating article and well worth reading in its entirety.

 

 

Forecasting Flu Pandemics Hinges on Insights into the Virus

Scientists have made strides in predicting how influenza outbreaks will spread, but now the pressure is on for a breakthrough way to model how deadly new strains form.

• Monday, December 6, 2010
• By Lauren Gravitz

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