Harvard T.H. Chan School of Public Health professor Marc Lipsitch told the Wall Street Journal this week that “it is likely we’ll see a global pandemic” of the coronavirus with up to 70 percent of people infected worldwide.
“I think it is likely we’ll see a global pandemic,” Lipsitch claimed, adding that “If a pandemic happens, 40% to 70% of people world-wide are likely to be infected in the coming year.”
What proportion of those will be symptomatic, I can’t give a good number,” he continued.
Others have also predicted that the coronavirus could infect between 60 and 80 percent of the planet.
Outside of China, coronavirus cases have been confirmed in the United States, United Kingdom, Canada, Germany, France, and Australia, among others.
Fifteen cases have been reported in the US, 15 in Australia, 14 in Germany, 11 in France, 9 in the UK, and 7 in Canada.
In an article for the New Scientist, Tuesday, reporters Michael Le Page and Debora MacKenzie wrote that “no one knows” whether the coronavirus “could infect 60 per cent of the world’s population and kill 1 in 100 of those infected – around 50 million people,” because “there are many things we still don’t know about the virus.”
Disease modelers gaze into computers to see future of Covid-19 - STAT
At least 550,000 cases. Maybe 4.4 million. Or something in between.
Like weather forecasters, researchers who use mathematical equations to project how bad a disease outbreak might become are used to uncertainties and incomplete data, and Covid-19, the disease caused by the new-to-humans coronavirus that began circulating in Wuhan, China, late last year, has those everywhere you look. That can make the mathematical models of outbreaks, with their wide range of forecasts, seem like guesswork gussied up with differential equations; the eightfold difference in projected Covid-19 cases in Wuhan, calculated by a team from the U.S. and Canada, isn’t unusual for the early weeks of an outbreak of a never-before-seen illness.
But infectious-disease models have been approximating reality better and better in recent years, thanks to a better understanding of everything from how germs behave to how much time people spend on buses.
“Year by year there have been improvements in forecasting models and the way they are combined to provide forecasts,” said physicist Alessandro Vespignani of Northeastern University, a leading infectious-disease modeler.
That’s not to say there’s not room for improvement. The key variables of most models are mostly the same ones epidemiologists have used for decades to predict the course of outbreaks. But with greater computer power now at their disposal, modelers are incorporating more fine-grained data to better reflect the reality of how people live their lives and interact in the modern world — from commuting to work to jetting around the world. These more detailed models can take weeks to spit out their conclusions, but they can better inform public health officials on the likely impact of disease-control measures.
Models are not intended to be scare machines, projecting worst-case possibilities. (Modelers prefer “project” to “predict,” to indicate that the outcomes they describe are predicated on numerous assumptions.) The idea is to calculate numerous what-ifs: What if schools and workplaces closed? What if public transit stopped? What if there were a 90% effective vaccine and half the population received it in a month?
“Our overarching goal is to minimize the spread and burden of infectious disease,” said Sara Del Valle, an applied mathematician and disease modeler at Los Alamos National Laboratory. By calculating the effects of countermeasures such as social isolation, travel bans, vaccination, and using face masks, modelers can “understand what’s going on and inform policymakers,” she said. For instance, although many face masks are too porous to keep viral particles out (or in), their message of possible contagion here! “keeps people away from you” and reduces disease spread, Del Valle said. “I’m a fan of face masks.”
To make models more realistic, he and his colleagues argue, they should abandon the simplistic assumption that everyone has the same likelihood of getting sick from Covid-19 after coming in contact with someone already infected. For SARS, for instance, that likelihood clearly varied.
“Bodies may react differently to an infection, which in turn can facilitate or inhibit the transmission of the pathogen to others,” Allard said. “The behavioral component is also very important. Can you afford to stay at home a few days or do you go to work even if you are sick? How many people do you meet every day? Do you live alone? Do you commute by car or public transportation?”
When people’s chances of becoming infected vary, an outbreak is more likely to be eventually contained (by tracing contacts and isolating cases); it might reach a cumulative 550,000 cases in Wuhan, Allard and his colleagues concluded. If everyone has the same chance, as with flu (absent vaccination), the probability of containment is significantly lower and could reach 4.4 million there. Or as the researchers warn, “the outbreak almost certainly cannot be contained and we must prepare for a pandemic ….”
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