2 research outputs found
Fuzzy Multi-Agent Simulation of COVID-19 Pandemic Spreading
In this paper, we present a new approach for Covid-19 Pandemic spreading
simulation based on fuzzy multi agents. The agent parameters consider
distribution of the population according to age, and the index of
socio-economic fragility. Medical knowledge affirms that the COVID-19 main risk
factors are age and obesity. The worst medical situation is caused by the
combination of these two risk factors which in almost99% of cases finish in
ICU. The appearance of virus variants is another aspect parameter by our
simulation through a simplified modeling of the contagiousness. Using real data
from people from West Indies (Guadeloupe, F.W.I.), we modeled the infection
rate of the risk population, if neither vaccination nor barrier gestures are
respected. The results show that hospital capacities are exceeded, and the
number of deaths exceeds 2% of the infected population, which is close to the
reality
Misinformation making a disease outbreak worse: Outcomes compared for influenza, monkeypox and norovirus
Health misinformation can exacerbate infectious disease outbreaks. Especially pernicious advice could be classified as “fake news”: manufactured with no respect for accuracy and often integrated with emotive or conspiracy-framed narratives. We built an agent-based model that simulated separate but linked circulating contagious disease and sharing of health advice (classified as useful or harmful). Such advice has potential to influence human risk-taking behavior and therefore the risk of acquiring infection, especially as people are more likely in observed social networks to share bad advice. We test strategies proposed in the recent literature for countering misinformation. Reducing harmful advice from 50% to 40% of circulating information, or making at least 20% of the population unable to share or believe harmful advice, mitigated the influence of bad advice in the disease outbreak outcomes. How feasible it is to try to make people “immune” to misinformation or control spread of harmful advice should be explored