According to research on the COVID-19 pandemic in the Netherlands, localized pandemic control measures might have been equally as successful as lockdowns at the federal level. Based on a mathematical model they created, researchers from Utrecht University, Erasmus MC, and the University of Amsterdam draw this conclusion. The results could be utilized to establish public health policies that effectively balance control measures to minimize illness spread while minimizing their socio-economic impact because the model can be extended to various nations and diseases. The scientific publication eLife has just released the model study's findings.
Control of the first COVID-19 pandemic wave in the Netherlands, like in many other nations, was largely focused on the widespread implementation of measures including lockdowns, social isolation, and reduced mobility. While infections and the related health burden only temporarily plagued particular sections of the country, the associated societal burden affected the entire nation.
Impact on people
"We wanted to know if only targeting control measures to the most affected areas would make it possible to better balance the control of the pathogen with the impact on people's well-being and the economy," says Deb Panja, assistant professor of computer science at Utrecht University and vice director of the Centre for Complex Systems Studies.
Deb Panja
Mark Dekker, the paper's first author and a Ph.D. student with Panja at the time, and colleagues developed a model that includes data on how people move around, engage, and socialize in various localities to try to answer this question.
This made it possible for the interdisciplinary team to foresee whether or not subnational efforts would have resulted in worse or similar control of the epidemic across the entire nation.
Model
They constructed their model in stages. The researchers first divided the Dutch population into 380 municipalities and various demographic groupings. As "agents" were defined as groups of 100 persons, the model contained about 170,000 agents. Based on the monitoring of mobile phone signals from anonymized phones as well as mobility data from Google, the movements of the agents between towns were simulated at hourly resolution. Based on the agents' activities—at "home," "work," "school," or "other," the team then calculated the possibility of mixing between agents within a municipality.
Visualization of model simulation
Then, they brought in information concerning COVID-19 transmission: every agent was marked as 'vulnerable', 'exposed', 'infected', or 'recovered'. Depending on the frequency of infected cases and anticipated contact rates, "susceptible" agents could be moved to the "exposed" group. On top of it, there were the pandemic control measures—movement restrictions and school closings—which were similar to those put in place nationally during the pandemic's initial wave.
Model-based on Temporal Agent Networks
Targeted approach
The model's findings, for instance, indicate that 167 municipalities could have started the first COVID-19 wave without a lockdown and 12 could have continued to be open after five weeks with just a 3.4% increase in hospital admissions if interventions had been targeted at the subnational level.
Future applications
During the pandemic, live infection data was not accessible, but Panja cautions that this may change in the future for emerging diseases. Panja says, "Our analysis demonstrates that a more regionally tailored approach could considerably minimize the societal burden of lockdowns to prevent infectious diseases. Emerging methodologies and technologies such as sewage monitoring, and possible fast identification of disease biology."
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