Shorter, but more frequent lockdowns, could lead to fewer cases of COVID-19 than the current practice of long lockdowns, found York University researchers, whose modelling considers individual decisions around the personal cost of complying to social measures.
The researchers developed a novel model that reacts to realistic social dynamics, such as non-compliance of social distancing and isolation, or delayed compliance. They found that social fatigue and the cost of isolation, which could include lost wages or a psychological/social cost, can diminish the effectiveness of lockdowns and lead to worse health outcomes. This cost increases with each lockdown. Cases could increase unless shutdowns are optimized.
“Modelling the dynamics of intervention based entirely on disease progression assumes that people will immediately distance or relax at the beginning or end of a lockdown. The reality of how people react is less straight forward,” says lead researcher Assistant Professor Iain Moyles of the Faculty of Science’s Department of Mathematics and Statistics and the Canadian Centre for Disease Modeling (CCDM), which is hosted at York.
“It’s realistic to assume disease dynamics drive people into isolation, but an individual’s personal decision to relax their isolation often takes their cost of staying at home into account, and this is often missing in current disease models.”
While models generally factor in the larger economic influences, they often miss the smaller individual economic choices. The research team, including Faculty of Science Professor Jane Heffernan and Assistant Professor Jude Kong both of CCDM, used a model with separate dynamics for isolation and relaxation dependent on the progression of COVID-19 and the cost of relaxing measures.
It’s important to consider and include the isolation cost since repeated lockdowns would have diminishing returns as people’s tolerance, and the financial or psychological burdens of staying at home, become too overwhelming. Having shorter bursts provide less time for this cost to grow, say the researchers.
“Using a dynamic response model allows for more realistic policy strategies for disease mitigation and mortality prevention,” says Moyles. “Understanding how people will react to a change in policy regarding lockdowns or bans on social gatherings will inform how and when to enact social measures for maximum effectiveness. This is essential in gauging the impact that COVID-19 and mitigation strategies will have on infections and mortality.”
Improving this aspect of modelling, could ensure policies are put into place at the right time so people will react accordingly. It could also play an important role in limiting the impact on health care services, as well as delaying the outbreak peak time and reducing the outbreak duration.
The research was published today in the journal Royal Society Open Science.
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