An agent-based model about the effects of fake news on a norovirus outbreak

Abstract

An agent-based model about the effects of fake news on a norovirus outbreak Abstract Background.-Concern about health misinformation is longstanding, especially on the Internet. Methods.-Using agent-based models, we considered the effects of such misinformation on a norovirus outbreak, and some methods for countering the possible impacts of 'good' and 'bad' health advice. The work explicitly models spread of physical disease and information (both online and offline) as two separate but interacting processes. The models have multiple stochastic elements; repeat model runs were made to identify parameter values that most consistently produced the desired target baseline scenario. Next, parameters were found that most consistently led to a scenario when outbreak severity was clearly made worse by circulating poor quality disease prevention advice. Strategies to counter 'fake' health news were tested. Results.-Reducing bad advice to 30% of total information or making at least 30% of people fully resistant to believing in and sharing bad health advice were effective thresholds to counteract the negative impacts of bad advice during a norovirus outbreak. Conclusion.-How feasible it is to achieve these targets within communication networks (online and offline) should be explored.

Misinformation making a disease outbreak worse: outcomes compared for influenza, monkeypox, and norovirus

“…Model assumptions and design features, such as the rate of information injections, are described in greater detail elsewhere. 2 The underlying Netlogo model code is available from the authors upon request. At least 100 iterations were run for the most likely candidate thresholds or parameters to verify their reliability.…”

“…The cascades of resulting information (number of times the information passed through unique sharers) were monitored and the model performance in this regard is documented elsewhere. 2 The frequency of relevant information sharing and the likelihood of sharing advice were both determined empirically so that the resulting information cascades would conform with information sharing patterns reported recently on Twitter for true and false stories. 27 Distinguishing true from false (good and bad advice in our model) was important, because false stories were observed to be four times more likely to be shared in the Twitter study.…”

“…Previously we constructed an agent-based model (ABM) 1,2 about what could happen during a norovirus outbreak exacerbated by circulating misinformation (which we call “bad advice”). Bad advice is relevant when it changes human behavior to be riskier (that is, a higher risk of getting disease).…”

Assessing the risks of “infodemics” in response to COVID-19 epidemics

“…1). In fact, the dangerousness of infodemics can compare and sum up to a large extent to that of the epidemics itself 29 .…”

Quantifying the effects of fake news on behaviour: Evidence from a study of COVID-19 misinformation.

“…O'Connor Tasnim et al, 2020;Van Bavel et al, 2020;Wright, 2020;Yoder, 2020). Computer modelling suggests that public response to disease outbreak might be negatively affected by exposure to misleading health news (Brainard et al, 2020), but no empirical evidence of this has been presented. We are aware of only one study that has attempted to trace the behavioural effects of fake 5 news in any context: Cantarella, Fraccaroli, & Volpe (2020) compared Italian municipalities with varying exposure to fake news prior to the 2018 general election, but found no effects on voting patterns.…”