Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, Indiana, USA.
Shanghai Institute of Infectious Disease and Biosecurity, Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China.
Influenza Other Respir Viruses. 2024 May;18(5):e13301. doi: 10.1111/irv.13301.
Human contact patterns are a key determinant driving the spread of respiratory infectious diseases. However, the relationship between contact patterns and seasonality as well as their possible association with the seasonality of respiratory diseases is yet to be clarified.
We investigated the association between temperature and human contact patterns using data collected through a cross-sectional diary-based contact survey in Shanghai, China, between December 24, 2017, and May 30, 2018. We then developed a compartmental model of influenza transmission informed by the derived seasonal trends in the number of contacts and validated it against A(H1N1)pdm09 influenza data collected in Shanghai during the same period.
We identified a significant inverse relationship between the number of contacts and the seasonal temperature trend defined as a spline interpolation of temperature data (p = 0.003). We estimated an average of 16.4 (95% PrI: 15.1-17.5) contacts per day in December 2017 that increased to an average of 17.6 contacts (95% PrI: 16.5-19.3) in January 2018 and then declined to an average of 10.3 (95% PrI: 9.4-10.8) in May 2018. Estimates of influenza incidence obtained by the compartmental model comply with the observed epidemiological data. The reproduction number was estimated to increase from 1.24 (95% CI: 1.21-1.27) in December to a peak of 1.34 (95% CI: 1.31-1.37) in January. The estimated median infection attack rate at the end of the season was 27.4% (95% CI: 23.7-30.5%).
Our findings support a relationship between temperature and contact patterns, which can contribute to deepen the understanding of the relationship between social interactions and the epidemiology of respiratory infectious diseases.
人际接触模式是驱动呼吸道传染病传播的关键决定因素。然而，接触模式与季节性之间的关系以及它们与呼吸道疾病季节性之间的可能关联尚不清楚。
我们使用 2017 年 12 月 24 日至 2018 年 5 月 30 日在中国上海进行的基于横断面日记的接触调查中收集的数据，调查了温度与人际接触模式之间的关系。然后，我们根据接触人数的季节性趋势开发了一个流感传播的房室模型，并将其与同期在上海收集的 A(H1N1)pdm09 流感数据进行了验证。
我们发现，接触人数与季节性温度趋势之间存在显著的负相关关系，温度趋势定义为温度数据的样条插值（p=0.003）。我们估计 2017 年 12 月的平均接触人数为 16.4（95%可信区间：15.1-17.5），到 2018 年 1 月增加到平均 17.6 次接触（95%可信区间：16.5-19.3），然后在 2018 年 5 月下降到平均 10.3 次接触（95%可信区间：9.4-10.8）。房室模型估计的流感发病率与观察到的流行病学数据相符。繁殖数估计从 12 月的 1.24（95%可信区间：1.21-1.27）增加到 1 月的峰值 1.34（95%可信区间：1.31-1.37）。估计在季节结束时的中位感染攻击率为 27.4%（95%可信区间：23.7-30.5%）。
我们的发现支持温度与接触模式之间的关系，这有助于加深对社会互动与呼吸道传染病流行病学之间关系的理解。
