Brainard J, Hunter P R, Hall I R
Norwich Medical School, Norwich, United Kingdom.
Norwich Medical School, Norwich, United Kingdom.
Rev Epidemiol Sante Publique. 2020 Apr;68(2):99-107. doi: 10.1016/j.respe.2019.12.001. Epub 2020 Feb 6.
Concern about health misinformation is longstanding, especially on the Internet.
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.
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.
How feasible it is to achieve these targets within communication networks (online and offline) should be explored.
对健康错误信息的担忧由来已久，尤其是在互联网上。
我们使用基于主体的模型，考虑了此类错误信息对诺如病毒爆发的影响，以及应对“好”与“坏”健康建议可能产生的影响的一些方法。这项工作明确将身体疾病和信息（线上和线下）的传播建模为两个相互独立但又相互作用的过程。这些模型具有多个随机因素；进行了多次模型运行，以确定最能持续产生所需目标基线情景的参数值。接下来，找到了在传播质量不佳的疾病预防建议时最能持续导致爆发严重程度明显恶化的情景的参数。测试了应对“虚假”健康新闻的策略。
将不良建议减少到总信息的30%，或使至少30%的人完全抵制相信和传播不良健康建议，是在诺如病毒爆发期间抵消不良建议负面影响的有效阈值。
应探索在通信网络（线上和线下）内实现这些目标的可行性。
