Since China locked down Wuhan on January 23, social distance measures put in place by regional and national governments around the world flattened the curve of confirmed COVID-19 cases and spared 7.9 million lives, according to a first-of-its-kind analysis by mathematicians and statisticians in Saudi Arabia.
The research team from King Abdullah University of Science and Technology (KAUST), working in collaboration with the Covid Compass initiative, applied new algorithms to data gathered from shared data based in the United States, China, and Europe. Most of the deaths – 7.2 million – would have occurred in China, the source of the pandemic, said the researchers.
The decisions by American state governments to close schools, cancel public events, and confine people in their homes starting in mid-March spared an estimated 65,000 lives, said the research team. Without those directives, the U.S. toll today would be close to 100,000 deaths by mid-April, four times more than occurred.
COVID-19 emptied America’s streets. Midday downtown Traverse City, Michigan on April 13, 2020. Photo © John Roberts Williams
KAUST researchers also found that over the last month social distance orders dramatically reduced the stress on hospitals in five American states where accurate data were available – California, Missouri, Ohio, Pennsylvania, and Washington. With the intervention measures in place, starting with Washington Gov. Jay Inslee’s order on March 11, the five states needed 21,576 hospital beds to treat COVID-19 patients. If those states had refrained from issuing social distancing orders, the number of hospital beds needed to meet demand would have totaled over 241,000.
The KAUST researchers reached their findings by evaluating data from open and shared scientific data bases in China, Spain, the United States and other nations. They applied algorithms developed at KAUST over the last month, incorporating results of analysis by other research teams around the world, to reach new estimates of the impact of the COVID-19 epidemic.
At this writing, the novel coronavirus has produced more than 2.1 million confirmed infections and over 140,000 deaths globally. The KAUST research team’s findings make clearer than any previous projections how significant social distancing has been to reduce mortality and curb the pressure on health care systems in the United States and around the world. They provide essential guidance to authorities in the U.S. and other nations who are preparing to lift confinement and revive commerce that was purposefully stalled over the last month.
“Our model estimates indicate that confinement policies can reduce the peak number of hospitalizations,” said Dr. Hernando Ombao, a statistics professor at KAUST, who conducted the analysis on hospital beds. “We could avoid further overwhelming the healthcare system, which is already stretched out to its maximum capacity. This will clearly lead to a significant reduction in medical cost and potential fatalities.”
Dr. David I. Ketcheson, associate professor of applied mathematics and computational science, analyzed the data on social distancing. He added: “We must be cautious. Flattening the curve greatly reduces the spread of the virus in the short term. But it also leads to a more prolonged epidemic, with a similar number of people possibly infected by the end. Unless we quickly develop smart long-term strategies and more effective treatment for severe COVID-19 cases, we may have only postponed many of these deaths.”
Along with the findings of lower mortality in China and the United States, Dr. Ketcheson’s findings on social distancing concluded that, by mid-April, purposeful confinement spared 124,000 lives in South Korea, 155,000 in Italy, 73,000 in Spain, 71,000 in France, 30,000 in Germany, and 30,000 in the United Kingdom.
He cautioned that the numbers of lives spared are estimates based on recorded numbers of deaths, which are not completely accurate. For instance, he said, in the last half of March almost one third of COVID-19 deaths in Spain were attributed to other causes. New York state also indicated similar inconsistencies in its official record of COVID-19 deaths.
Paula Moraga, an assistant professor in statistics at the University of Bath in the United Kingdom, and a member of the research team, applied another custom-designed algorithm to data bases in Spain and the United States to more accurately identify risk factors that lead to COVID-19 mortality. She found that as of today 67 percent of admissions to Spanish intensive care units, and 95 percent of deaths occurred among patients aged over 60. The risk of serious disease and death was much higher for men who encompassed 59 percent of ICU admissions and 61 percent of the deaths.
In the United States, according to data from the Centers For Disease Control and Prevention, 78 percent of ICU admissions and 71 percent non-ICU hospitalizations occurred among persons with one or more underlying health condition. Those include, but are not limited to diabetes mellitus, chronic lung disease, and cardiovascular disease.
Dr. Moraga based her findings on data from the Spanish Ministry of Health, Consumer Affairs and Social Welfare and from the Centers for Disease Control and Prevention.
Her findings will help the United States and other nations develop more targeted policies and practices for resuming normal business activity. “It is crucial that governments develop mitigation policies that protect the groups at higher risk, which will in turn relieve the demand for health care resources for the other groups,” said Dr. Moraga. “It helps prevent health systems around the world from becoming overwhelmed.”
The map above is colored by the average of our death forecasts based on three different intervention scenarios. The darker the red highlights areas of the world that are hot spots compared to other areas. The chart above shows global daily statistics on current COVID-19 statistics:
Click on the map above to generate the current and project COVID-19 statistics for that area as shown in the chart below the map. Highlight each statistic to see individual data series. Hit Reset to go back to the global view.
Dr. Ketcheson’s findings were based on data on the number of deaths each day by country and state compiled by Johns Hopkins University and the New York Times, along with demographic data from the United Nations. His findings on the infection fatality ratio, the mean infectious period, and the reproduction number are informed by many scientific articles, reports, and data sets. They include analyses of the Diamond Cruise patients, data from the first outbreak in Wuhan, the spread of the virus in Italy and Spain, and excess mortality data from Spain, Italy and New York.
The three researchers are among the 26 scientists at KAUST, led by Professor Carlos Duarte, an internationally prominent scientist and advisor to the G-20, who are collaborating on Covid Compass. An international team of researchers, data researchers, website designers, marketers, and journalists from three continents, Covid Compass is a data driven project to answer pressing pandemic-related questions about personal safety, the risk to families, and the urgency of the threat to regions and countries.
Covid Compass aggregates relevant, fact-checked, reliable data from over 20 shared scientific data bases around the world. It applies algorithms specifically designed by KAUST research scientists to 1) precisely quantify the behavior of the novel coronavirus, and 2) accurately project its consequences to individual health, regional safety, and national economies.
The KAUST data modelers have directed their focus to other crucial pandemic-related trends. Their work will help answer basic questions people have about the behavior and spread of the novel corona virus. Those questions include:
Covid Compass builds on the operating strategy of Circle of Blue, the U.S.-based nonprofit news group that deploys frontline reporting and data analysis on global environmental trends to contextualize findings from big data and on-the-ground reporting. In addition to the KAUST team, Vector Center is providing its Perception Reality Engine™ to model social actions, strategy and implementation for the initiative that is driven by Étage, a strategy and content company in San Francisco. O Inc., an AI and robotics company is providing programming. Data sourcing, validation and analytics, as well as economic impact is led by Moores Data of Boston.
Public data presented directly or used in models is collected from different sources and made available here, including research methodology notes: https://github.com/kaustcovid19/data/tree/master/en and https://github.com/ketch/covid_forecasting/blob/master/paper/covid_forecast_model.pdf
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