Section of Integrative Biology, The University of Texas at Austin, Austin, Texas, United States of America.
PLoS Comput Biol. 2011 Jan 27;7(1):e1001062. doi: 10.1371/journal.pcbi.1001062.
The effectiveness of seasonal influenza vaccination programs depends on individual-level compliance. Perceptions about risks associated with infection and vaccination can strongly influence vaccination decisions and thus the ultimate course of an epidemic. Here we investigate the interplay between contact patterns, influenza-related behavior, and disease dynamics by incorporating game theory into network models. When individuals make decisions based on past epidemics, we find that individuals with many contacts vaccinate, whereas individuals with few contacts do not. However, the threshold number of contacts above which to vaccinate is highly dependent on the overall network structure of the population and has the potential to oscillate more wildly than has been observed empirically. When we increase the number of prior seasons that individuals recall when making vaccination decisions, behavior and thus disease dynamics become less variable. For some networks, we also find that higher flu transmission rates may, counterintuitively, lead to lower (vaccine-mediated) disease prevalence. Our work demonstrates that rich and complex dynamics can result from the interaction between infectious diseases, human contact patterns, and behavior.
季节性流感疫苗接种计划的效果取决于个人层面的遵从性。对感染和接种疫苗相关风险的认知会强烈影响接种决策，从而影响疫情的最终发展。在这里，我们通过将博弈论纳入网络模型来研究接触模式、流感相关行为和疾病动态之间的相互作用。当个体基于过去的疫情做出决策时，我们发现接触者多的个体接种疫苗，而接触者少的个体不接种疫苗。然而，接种疫苗的接触者阈值数量高度依赖于人群的整体网络结构，并且有可能比实际观察到的更为剧烈地波动。当我们增加个体在做出接种决策时回忆的过去季节数量时，行为和疾病动态的变化就会减少。对于某些网络，我们还发现，流感传播率的升高可能会出人意料地导致（疫苗介导的）疾病流行率降低。我们的工作表明，传染病、人类接触模式和行为之间的相互作用会导致丰富而复杂的动态。
