Oil hits three-month high as trade and Brexit fog lift

Global stocks and sterling gained amid relief at the UK election result and progress in resolving the US-China trade war. The 18-month dispute and uncertainty around Brexit have weighed heavily on the world economy. (AFP)
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Updated 14 December 2019

Oil hits three-month high as trade and Brexit fog lift

  • Investor hopes on the rise after US-China progress and UK poll result ‘remove layer of uncertainty for global economy’

LONDON: Oil rose on Friday to its highest price in nearly three months as progress in resolving the US-China trade dispute and Britain’s general election result appeared to lift two clouds that have been dampening investor appetite for risk.

US sources said on Thursday that Washington has set its terms for a trade deal with Beijing, offering to suspend some tariffs on goods and cut others in exchange for Chinese purchases of more American farm goods.

Brent crude, the global benchmark, climbed to the highest since Sept. 23. It was up 45 cents at $64.65 in mid-afternoon trade in London as West Texas Intermediate crude gained 21 cents to $59.39.

The 18-month trade war has been a dampener for oil prices, while uncertainty around Brexit has also weighed. Britain’s ruling Conservative Party won a large majority in Thursday’s general election, giving it the power to take the country out of the EU.

“An eventful past 24 hours has removed a layer of uncertainty for the global economy,” said Stephen Brennock of oil broker PVM.

“Yet it remains to be seen whether the return of the feelgood factor is enough to set oil prices on a definitive northerly trajectory.”

A drop in the US dollar against the backdrop of a strong pound helped boost commodities. 

“Risk appetite among financial investors is now likely to remain high thanks to the deal between the US and China and the forthcoming end to the Brexit cliffhanger,” said Eugen Weinberg, an analyst at Commerzbank.

“This will also benefit the oil price,” he added.

Brent has rallied by almost 21 percent in 2019, supported by efforts by the Organization of the Petroleum Exporting Countries and allies including Russia to cut production.

The alliance, known as OPEC+, agreed last week to lower supply by a further 500,000 barrels per day as of Jan. 1. They have been limiting supply since 2017, helping to clear a glut that built up in 2014-2016.

OPEC’s own research indicates that the oil market in 2020 may see a small supply deficit, although the International Energy Agency sees global inventories rising despite the further step by OPEC+. 

Global stocks and sterling also gained on Friday as the double dose of relief around US-China trade and the UK election undercut safe-haven sovereign bonds and the Japanese yen, and led markets to scale back expectations of more interest rates cuts around the world.

“Global investors have been given two of the biggest gifts on their Christmas list and should be appreciative for a while at least,” said Sean Callow, a senior forex analyst at Westpac.

“Global equity indices such as MSCI World should set more record highs and sterling could push above $1.36.”

The pound reached its highest since mid-2018 as exit polls and then UK election results wiped out any chance of a victory by the left-wing Labour opposition or a hung parliament, which had been a worry for investors.

Prime Minster Boris Johnson won a commanding majority in Britain’s Parliament, giving him the power to deliver Brexit, though trade talks with the EU are set to drag on for months yet.


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.