Oct. 27 (UPI) -- Dividing large populations into smaller sub-populations that don't intermix can help contain COVID-19 outbreaks without wider lockdowns, according to the authors of an analysis published Tuesday by the journal Chaos.
The goal is to limit "random" interactions between people in affected communities and, as a result, curtail disease spread before it can expand across larger areas and populations, the researchers said.
The approach also effectively gives "more control to local authorities," allowing them to employ larger lockdowns only when necessary, they said.
Restrictions would be put in place when infection numbers cross a certain threshold in these smaller groups, based on cases per 100,000 inhabitants, according to the researchers.
Businesses, schools and other public spaces would remain open in other areas, provided infection numbers are still low and there is no crossover between people living in restricted and unrestricted areas, the researchers said.
"Our aim was to find a way to help create possibilities for relatively normal conditions for small businesses and day-to-day activities of people," analysis co-author Ramin Golestanian told UPI
"There is, of course, always a price to pay, but our idea was that the next best thing after global lockdown [would] be protection of sub-populations" by determining the optimal size for sub-populations, said Golestanian, director of the Max Planck Institute for Dynamics and Self-Organization in Germany.
New York state has taken a similar approach with its "micro-cluster" containment strategy, which implements COVID-19 containment measures in communities when case counts and disease spread rise above predetermined levels.
The strategy saw localized restrictions imposed in communities in Rockland, Orange and Broome counties, as well as in sections of Queens in New York City earlier this month, with some loosened in Queens in recent days after infection rates dropped.
The analysis in Chaos builds on earlier work conducted by Golestanian and his colleagues that modeled the approach using different thresholds for cases and disease spread, with data from Germany, Italy, England, New York state and Florida.
For the study, the researchers modeled a population of 8 million individuals with 500 people initially infected with COVID-19 and "mild" social distancing measures in place.
With these parameters, if the population is allowed to mix, the disease spreads exponentially with infections doubling every 12 days, with a peak infection rate of 5%, according to the researchers.
However, if the population is split into 100 subgroups of 80,000 people each, and restrictions are put in place in groups with higher infection rates, the peak percentage of infected individuals drops to 3%, the researchers said.
If the community is split up even further to 500 subgroups of 16,000 people each, and restrictions are put in place in groups with higher infection rates, the infection rate peaks at only 1% of the initial population, they said.
Subdividing the population -- and implementing restrictions as needed -- effectively breaks the "infection chains" in these communities and snuffs out the outbreak, the researchers said.
Even if outbreaks occur in the smaller communities, the peaks may come at different times, keeping overall case counts low and avoiding stressing the healthcare system, they said.
However, as "theoretical physicists," their model should only serve as the "basic starting point" for more detailed public health plans, Golestanian said.
"We'd like to propose more frequent lockdowns triggered at much smaller thresholds to take maximum advantage of the possibility of infection extinction ... to create an overall balance and reservoir of infrastructure capacity, such as hospital beds etc.," Golestanian told UPI.
"Our hope is that our model can be implemented at a number of different scales; from counties and districts, to countries -- basically anywhere that the infrastructure allows such a control," he said.