Department of Demography, University of California, Berkeley, Berkeley, California, United States of America.
PLoS Comput Biol. 2022 Dec 2;18(12):e1010742. doi: 10.1371/journal.pcbi.1010742. eCollection 2022 Dec.
Population contact patterns fundamentally determine the spread of directly transmitted airborne pathogens such as SARS-CoV-2 and influenza. Reliable quantitative estimates of contact patterns are therefore critical to modeling and reducing the spread of directly transmitted infectious diseases and to assessing the effectiveness of interventions intended to limit risky contacts. While many countries have used surveys and contact diaries to collect national-level contact data, local-level estimates of age-specific contact patterns remain rare. Yet, these local-level data are critical since disease dynamics and public health policy typically vary by geography. To overcome this challenge, we introduce a flexible model that can estimate age-specific contact patterns at the subnational level by combining national-level interpersonal contact data with other locality-specific data sources using multilevel regression with poststratification (MRP). We estimate daily contact matrices for all 50 US states and Washington DC from April 2020 to May 2021 using national contact data from the US. Our results reveal important state-level heterogeneities in levels and trends of contacts across the US over the course of the COVID-19 pandemic, with implications for the spread of respiratory diseases.
人口接触模式从根本上决定了直接传播的空气传播病原体（如 SARS-CoV-2 和流感）的传播。因此，可靠的接触模式定量估计对于建模和减少直接传播传染病的传播以及评估旨在限制高风险接触的干预措施的有效性至关重要。虽然许多国家已经使用调查和接触日记来收集国家级别的接触数据，但特定年龄的局部接触模式的估计仍然很少。然而，这些地方层面的数据至关重要，因为疾病动态和公共卫生政策通常因地理位置而异。为了克服这一挑战，我们引入了一种灵活的模型，该模型可以通过使用多层回归后分层（MRP）将国家级别的人际接触数据与其他特定于位置的数据源相结合，来估算次国家级别的特定年龄的接触模式。我们使用来自美国的全国性接触数据，从 2020 年 4 月到 2021 年 5 月，为美国的 50 个州和华盛顿特区估算了每日接触矩阵。我们的结果揭示了美国在 COVID-19 大流行期间接触水平和趋势的重要州级异质性，这对呼吸道疾病的传播有影响。
