Human Brain Project researchers have applied methods and techniques developed for the study of complex brain dynamics to study the spread of COVID-19, revealing when new waves of infection began and when they peaked. Accurately predict the time point of the trough. This epidemic model was one of the first to introduce psychological and socioeconomic factors into the equation and can be used to predict the future spread of COVID-19 and other pandemic diseases.
Despite becoming a part of our lives, the COVID pandemic is far from over and many countries are preparing to step up their measures for the turn of the season. limit. A new study by HBP researchers shows that factors such as socioeconomic status, mobility, self-reported health status and ‘lockdown fatigue’ play a major role in determining disease prevalence, providing reliable predictions. It suggests that it should be taken into account to provide
The study was conducted by the Institut de Neurosciences des Systèmes in Marseille, France, by a group dedicated primarily to complex brain dynamics in the context of epilepsy, led by Viktor Jirsa. His Spase Petkoski, one of the study’s authors, explains: “Epilepsy is a dynamic system problem with processes interacting on different timescales, much like the spread of a disease. Using data recorded in Germany and Denmark during the first months of the COVID pandemic through April 2020, the researchers predicted evolution in the months that followed ( Initial results were published early in Medrxiv). Although these he was confined to two countries, the researchers’ predictions matched the actual transmission curves recorded in the months that followed.
Standard mathematical models of infectious disease typically use SIR labels (Susceptibility, Infectivity, Recovery) to segment populations and track people across groups as the spread of infection progresses. “Our model further compartmentalizes groups by socioeconomic status, self-reported well-being, how much they travel for work and interact with others, and how much they already have under COVID. We divide them into vulnerable groups and resilient groups based on how much time they spend on restrictive measures,” says Petkoski. For example, consider an office worker who earns a decent salary and can work remotely, and an essential worker who must take public transportation to commute. A standard model might simply see both as susceptible, but how much we are affected by economic conditions, work patterns, restrictions, and how much we travel depends on how we catch and spread COVID. can make a big difference. “According to our model, the impact of psychosocial causes on the spread of the disease is almost as influential as government interventions and seasonality. , means it is also a means of combating the spread of infection through non-pharmaceutical practices to contain the disease,” Petkoski adds. “Our model also allows us to simulate what happens after the introduction of new measures and track how these events affect the contagion curve over multiple timescales.”
In addition to socioeconomic factors, well-being and mobility as reliable predictors, research also suggests that fatigue can occur if restrictive measures are not relaxed regularly. “Although derived from brain studies, a model like the one we developed could be applicable to his COVID,” suggests Petkoski.
The results of this study were published in PLOS Digital Health.
Text by Roberto Inchingoro
reference: Integrating Psychosocial Variables and Social Diversity in Epidemic Models to Predict COVID-19 Transmission Dynamics, Viktor K. Jirsa, Spase Petkoski, Huifang Wang, Marmaduke Woodman, Jan Fousek, Cornelia Betsch, Lisa Felgendreff, Robert Bohm, Lau Lilleholt, Ingo Zettler, Sarah Faber, Kelly Shen, Anthony Randal Mcintosh, PLOS Digital Health, https://doi.org/10.1371/journal.pdig.0000098
