Individuals socio-demographic and economic characteristics crucially shape the spread of an epidemic by largely determining the exposure level to the virus and the severity of the disease for those who got infected. While the complex interplay between individual characteristics and epidemic dynamics is widely recognized, traditional mathematical models often overlook these factors. In this study, we examine two important aspects of human behavior relevant to epidemics: contact patterns and vaccination uptake. Using data collected during the Covid-19 pandemic in Hungary, we first identify the dimensions along which individuals exhibit the greatest variation in their contact patterns and vaccination attitudes. We find that generally privileged groups of the population have higher number of contact and a higher vaccination uptake with respect to disadvantaged groups. Subsequently, we propose a data-driven epidemiological model that incorporates these behavioral differences. Finally, we apply our model to analyze the fourth wave of Covid-19 in Hungary, providing valuable insights into real-world scenarios. By bridging the gap between individual characteristics and epidemic spread, our research contributes to a more comprehensive understanding of disease dynamics and informs effective public health strategies.

翻译：个体的社会经济特征通过很大程度上决定其病毒暴露水平及感染后的疾病严重程度，从而深刻影响流行病的传播。尽管个体特征与流行病动态之间复杂的相互作用已被广泛认识，但传统数学模型往往忽略这些因素。本研究考察与流行病相关的两个重要人类行为方面：接触模式和疫苗接种率。利用匈牙利新冠疫情时期收集的数据，我们首先识别出个体在接触模式和疫苗接种态度方面表现出最大差异的维度。研究发现，总体上，特权群体相较于弱势群体拥有更高的接触人数和疫苗接种率。随后，我们提出一个纳入这些行为差异的数据驱动流行病学模型。最后，应用该模型分析匈牙利第四波新冠疫情，为现实场景提供了有价值的见解。通过弥合个体特征与流行病传播之间的差距，本研究有助于更全面地理解疾病动态，并为制定有效的公共卫生策略提供依据。