WEEKLY ENERGY RECAP: Trapped between two viruses, oil market faces double trouble

People wearing a protective facemasks walk on an overpass in Lujiazui financial district in Pudong in Shanghai on February 8, 2020. The new coronavirus that emerged in a Chinese market at the end of last year has killed more than 700 people and spread around the world. (AFP)
Short Url
Updated 09 February 2020

WEEKLY ENERGY RECAP: Trapped between two viruses, oil market faces double trouble

  • The deadly coronavirus outbreak in China has hurt short-term oil market demand

Oil prices continued to fall for a fifth week in a row and reached their lowest level in a year.

Brent crude dropped to $54.47 per barrel while WTI retreated to $50.32.

The global oil market now finds itself trapped between two viruses.

The deadly coronavirus outbreak in China has hurt short-term oil market demand and at the same time cyber-attack attempts on Saudi Aramco have also increased.

One threatens supply, the other demand.  However, the downward trend in oil prices is still ultimately about macroeconomics.

Despite such headwinds, the global oil market remains largely in balance as a result of the fourth year of efforts by OPEC+ to ensure it is so — even with a surge of oil supplies from unconventional resources.

Expectations of slowing growth in the global economy in the second half of 2020 should result in slightly lower crude oil demand. 

OPEC efforts to curb production still need to be unanimously extended, especially after Chinese oil demand dropped by about 3 million barrels a day, which is about a fifth of total Chinese refining capacity. Demand could fall further as storage capacity gradually fills, causing delays in discharging cargoes and leaving refiners, already under pressure from weak margins, facing hefty demurrage charges to compensate shipowners for delays.

The situation has also discounted crude oil barrels in the spot physical market.

For instance, the spot price premiums for Russian ESPO crude oil cargoes, the only barrels that arrive in China through pipeline, as the most popular crude grade for the independent Shandong “teapot” refineries have hit their lowest in five months.

The biggest impact was felt in China’s eastern Shandong province, which accounts for almost 20 percent of total Chinese crude oil imports. Here refining utilisation rates have fallen by half.

Such shrinking demand and weakening refining capacity from China has created fallout in the shipping industry as charterers are forced to pay penalty fees to ship owners for delays in unloading cargoes. This has caused tanker rates to tumble.


Man vs. machine in bid to beat virus

Updated 20 February 2020

Man vs. machine in bid to beat virus

  • Human and artificial intelligence are racing ahead to detect and control outbreaks of infectious disease

BOSTON: Did an artificial-intelligence system beat human doctors in warning the world of a severe coronavirus outbreak in China?

In a narrow sense, yes. But what the humans lacked in sheer speed, they more than made up in finesse.

Early warnings of disease outbreaks can help people and governments to save lives. In the final days of 2019, an AI system in Boston sent out the first global alert about a new viral outbreak in China. But it took human intelligence to recognize the significance of the outbreak and then awaken response from the public health community.

What’s more, the mere mortals produced a similar alert only a half-hour behind the AI systems.

For now, AI-powered disease-alert systems can still resemble car alarms — easily triggered and sometimes ignored. A network of medical experts and sleuths must still do the hard work of sifting through rumors to piece together the fuller picture. It is difficult to say what future AI systems, powered by ever larger datasets on outbreaks, may be able to accomplish.

The first public alert outside China about the novel coronavirus came on Dec. 30 from the automated HealthMap system at Boston Children’s Hospital. At 11:12 p.m. local time, HealthMap sent an alert about unidentified pneumonia cases in the Chinese city of Wuhan. The system, which scans online news and social media reports, ranked the alert’s seriousness as only 3 out of 5. It took days for HealthMap researchers to recognize its importance.

Four hours before the HealthMap notice, New York epidemiologist Marjorie Pollack had already started working on her own public alert, spurred by a growing sense of dread after reading a personal email she received that evening.

“This is being passed around the internet here,” wrote her contact, who linked to a post on the Chinese social media forum Pincong. The post discussed a Wuhan health agency notice and read in part: “Unexplained pneumonia???”

Pollack, deputy editor of the volunteer-led Program for Monitoring Emerging Diseases, known as ProMed, quickly mobilized a team to look into it. ProMed’s more detailed report went out about 30 minutes after the terse HealthMap alert.

Early warning systems that scansocial media, online news articles and government reports for signs of infectious disease outbreaks help inform global agencies such as the World Health Organization — giving international experts a head start when local bureaucratic hurdles and language barriers might otherwise get in the way.

Some systems, including ProMed, rely on human expertise. Others are partly or completely automated.

“These tools can help hold feet to the fire for government agencies,” said John Brownstein, who runs the HealthMap system as chief innovation officer at Boston Children’s Hospital. “It forces people to be more open.”

The last 48 hours of 2019 were a critical time for understanding the new virus and its significance. Earlier on Dec. 30, Wuhan Central Hospital doctor Li Wenliang warned his former classmates about the virus in a social media group — a move that led local authorities to summon him for questioning several hours later.

Li, who died Feb. 7 after contracting the virus, told The New York Times that it would have been better if officials had disclosed information about the epidemic earlier. “There should be more openness and transparency,” he said.

ProMed reports are often incorporated into other outbreak warning systems. including those run by the World Health Organization, the Canadian government and the Toronto startup BlueDot. WHO also pools data from HealthMap and other sources.

Computer systems that scan online reports for information about disease outbreaks rely on natural language processing, the same branch of artificial intelligence that helps answer questions posed to a search engine or digital voice assistant.

But the algorithms can only be as effective as the data they are scouring, said Nita Madhav, CEO of San Francisco-based disease monitoring firm Metabiota, which first
notified its clients about the outbreak in early January.

Madhav said that inconsistency in how different agencies report medical data can stymie algorithms. The text-scanning programs extract keywords from online text, but may fumble when organizations variously report new virus cases, cumulative virus cases, or new cases in a given time interval. The potential for confusion means there is almost always still a person involved in reviewing the data.

“There’s still a bit of human in the loop,” Madhav said.

Andrew Beam, a Harvard University epidemiologist, said that scanning online reports for key words can help reveal trends, but the accuracy depends on the quality of the data. He also notes that these techniques are not so novel.

“There is an art to intelligently scraping web sites,” Beam said. “But it’s also Google’s core technology since the 1990s.”

Google itself started its own Flu Trends service to detect outbreaks in 2008 by looking for patterns in search queries about flu symptoms. Experts criticized it for overestimating flu prevalence. Google shut down the website in 2015 and handed its technology to nonprofit organizations such as HealthMap to use Google data to build their own models.

Google is now working with Brownstein’s team on a similar web-based approach for tracking the geographical spread of the tick-borne Lyme disease.

Scientists are also using big data to model possible routes of early disease transmission.