via New Jersey Institute of Technology
Early on in the COVID-19 pandemic, health officials seized on contact tracing as the most effective way to anticipate the virus’s migration from the initial, densely populated hot spots and try to curb its spread. Months later, infections were nonetheless recorded in similar patterns in nearly every region of the country, both urban and rural.
A team of environmental engineers, alerted by the unusual wealth of data published regularly by county health agencies throughout the pandemic, began researching new methods to describe what was happening on the ground in a way that does not require obtaining information on individuals’ movements or contacts. Funding for their effort came through a National Research Foundation RAPID research grant (CBET 2028271).
In a paper published May 6 in the Proceedings of the National Academy of Science, they presented their results: a model that predicts where the disease will spread from an outbreak, in what patterns and how quickly.
“Our model should be helpful to policymakers because it predicts disease spread without getting into granular details, such as personal travel information, which can be tricky to obtain from a privacy standpoint and difficult to gather in terms of resources,” explained Xiaolong Geng, a research assistant professor of environmental engineering at NJIT who built the model and is one of the paper’s authors.
“We did not think a high level of intrusion would work in the United States so we sought an alternative way to map the spread,” noted Gabriel Katul, the Theodore S. Coile Distinguished Professor of Hydrology and Micrometeorology at Duke University and a co-author.
Their numerical scheme mapped the classic SIR epidemic model (computations based on a division of the population into groups of susceptible, infectious and recovered people) onto the population agglomeration template. Their calculations closely approximated the multiphase COVID-19 epidemics recorded in each U.S. state.
“Ultimately, we’d like to come up with a predictive model that would let us determine what the likely outcome would be if a state took a (particular) action,” Katul added.
By plotting on a map all of the data published weekly by county health agencies, the researchers also discovered that the disease spread out across the country in similar patterns, from the largest cities to the tiniest hamlets.
“High infection ‘hotspots’ interspersed within regions where infections remained sporadic were ubiquitous early in the outbreak, but the spatial signature of the infection evolved to affect most regions equally, albeit with distinct temporal patterns,” the authors wrote.
“We wondered whether the spread – in space and time – was predetermined, or if there were spatial variabiliy related to local policy,” said Elie Bou-Zeid, a professor of Civil and Environmental Engineering at Princeton University and a co-author. “We found that the distribution tended to be proportional to the population.”
When the alarm went out in March of last year, it was already too late, the authors noted.
“The virus could not be isolated. While the superhighways of contagion – air flights – were curtailed, the disease spread at the local level from city to city,” said Michel Boufadel, professor of environmental engineering, director of the Center for Natural Resources at NJIT, and the corresponding author of the study. “Using the standard precautions of masking and distancing is not enough if there are a lot of people out there. There will still be superspreader events, resulting from 5-10 people gathering.”
Their model, he said, allows them to separately examine the two key driving mechanisms of the pandemic: individuals taking preventive measures such as social distancing and wearing masks and local and state policies on shutting down or reopening public spaces.
“In this case, states did not learn much from each other and that made it difficult to have a different trend,” Bou-Zeid noted.
How do environmental engineers become pandemic experts?
“There are a lot of similarities between the spikes in the number cases and the bursts of energy found in turbulent mixing,” said Boufadel.
Original Article: Researchers Develop Mathematical Model Predicting Disease Spread Patterns
More from: New Jersey Institute of Technology | Duke University | Princeton University
The Latest Updates from Bing News & Google News
Go deeper with Bing News on:
Predicting disease spread patterns
- Good sleep patterns cut heart disease risk, study finds
Persistently favorable sleep patterns may reduce the risk of cardiovascular disease, even in individuals with higher genetic susceptibility.
- Mosquito-Borne Diseases To See 'Increasingly Frequent Outbreaks' Worldwide
"We must anticipate outbreaks and move to intervene early to prevent diseases from happening in the first place," researchers warn.
- Over half of world's population 'could be at risk of mosquito-borne diseases', scientists warn
The global number of mosquito-borne dengue cases alone has increased eight-fold in the last two decades, rising from 500,000 in 2000 to more than five million recorded instances in 2019 ...
- Expert sounds alarm as mosquito-borne diseases become a global phenomenon in a warmer, more populated world
Previously, dengue (spread by mosquitoes that bite during ... how the climate and disease transmission are linked to predict mosquito-borne disease outbreaks in 12 countries. "By analyzing weather ...
- Expert sounds alarm as mosquito-borne diseases becoming a global phenomenon in a warmer more populated world
Previously, dengue (spread by mosquitos ... transmission are linked to predict mosquito-borne disease outbreaks in 12 countries. "By analysing weather patterns, finding mosquito breeding sites ...
Go deeper with Google Headlines on:
Predicting disease spread patterns
[google_news title=”” keyword=”predicting disease spread patterns” num_posts=”5″ blurb_length=”0″ show_thumb=”left”]
Go deeper with Bing News on:
Predicting disease spread
- Healthfit Archives April 2024
New EPA rule says 200 US chemical plants must reduce toxic emissions that are likely to cause cancer ...
- The 2024 Avian Flu: Origins, Impact, And The Global Response
Avian influenza, commonly known as Avian Flu or bird flu, is a type of influenza virus that primarily affects birds, but as recent reports have shown, can also infect humans and other animals ...
- Texans should prepare for hotter temperatures, greater risk of fire and flooding
Weather conditions across the Lone Star State are getting more extreme and more dangerous by the year, according to a new report from Texas A&M University professor and State Climatologist John ...
- How 'Black Swan Events' Change Our Lives — For Good And Bad
The course of your life followed a meandering trail because of a series of unexpected and highly improbable surprises. That’s a fact. A “Black Swan event” is a rare, unpredictable, random occurrence, ...
- Avian flu is in Texas. Will H5N1 cause a pandemic? Here's everything you need to know
In April, Texas Department of State Health Services issued a health alert for avian flu, specifically H5N1, after a Texas case in a human was confirmed at the end of March. That human was in direct ...
Go deeper with Google Headlines on:
Predicting disease spread
[google_news title=”” keyword=”predicting disease spread” num_posts=”5″ blurb_length=”0″ show_thumb=”left”]