DURHAM, N.C. – Using data gleaned from the spread of COVID, researchers have created a mathematical model that can predict where pandemics or contagious disease outbreaks will most likely spread, in what patterns, and how quickly.
The model is the brainchild of environmental engineers and scientists at the New Jersey Institute of Technology (NJIT), Duke University, Princeton University and Rutgers University-New Brunswick.
It gives public health officials a new tool for anticipating and containing a disease’s spread without having to wait until they track down information about infected individuals’ movements or contacts.
The team published its peer-reviewed findings May 6 in the online edition of Proceedings of the National Academy of Sciences.
“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 lead 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, Theodore S. Coile Distinguished Professor of Hydrology and Micrometeorology at Duke University and a co-author.
The new kernel-modulated model builds upon the conventional “susceptible-infectious-removed” (SIR) model used by epidemiologists to track and project changes in disease status among populations who are susceptible to a disease, infected with it, or recovered from it (and thus “removed” from the general pool).
It expands on those capabilities by enabling public health officials to assess the impacts of long-range movement (supplied via a kernel), social distancing, and local or statewide mask mandates or stay-at-home orders. Those expansions allow the model to represent the time variations of the spatially intermittent nature of infections from scales spanning a county (about 10 kilometers) to the U.S. continent (about 2,600 kilometers).
Using a wealth of data collected by public health officials in all 50 U.S. states during the COVID pandemic, the researchers tested their new model and found that its calculations closely approximated the actual speed and pattern of COVID’s spread as recorded in each state and across states. It showed that COVID spread out across the country, in similar patterns, from large metro areas to rural communities. Urban hotspots located within broader regions where infections remained sporadic were common early on, but the spatial signature evolved over time. Eventually, most regions, and most communities, large or small, within those regions, were affected in proportion to their populations, albeit at different times and speeds.
The model confirmed that by the time most local and state governments put mask mandates and other control measures into place in late winter and early spring of 2020, it was already too late to slow the disease’s spread.
By then, “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 co-author Michel Boufadel, professor of environmental engineering and director of the Center for Natural Resources at NJIT.
Masking, social distancing and even closing down public spaces weren’t enough to slow its spread, he said, because different states and cities enacted different safety guidelines – or none at all – and there were still a lot of susceptible people in the general population pool, increasing the likelihood of superspreader events.
“States did not learn from each other and that made it difficult,” said Elie Bou-Zeid, a professor of civil and environmental engineering at Princeton University, who was also a co-author of the study.
Although the tests focused solely on COVID, the new model could be modified to map and predict the spread of other pandemics or contagious diseases, the researchers said.
With additional tweaks, it could also be used as a tool to help public health officials and policymakers to weigh the probable outcomes of different disease-control measures.
“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 takes one particular action, such as a mask mandate, versus a different action or no action at all,” said Katul.
Funding for the study came from a National Research Foundation RAPID research grant (#CBET 2028271). Other co-authors were Firas Gerges of NJIT and Hani Nassif of Rutgers University-New Brunswick.
CITATION: “A Kernel-modulated SIR Model for Covid-19 Contagious Spread from County to Continent,” Xiaolong Geng, Gabriel G. Katul, Firas Gerges, Elie Bou-Zeid, Hani Nassif and Michel Boufadel; Proceedings of the National Academy of Sciences, May 6, 2021. DOI: https://doi.org/10.1073/pnas.2023321118