Novel estimates reveal subnational heterogeneities in disease-relevant contact patterns in the United States

Abstract

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.

Characterizing US contact patterns relevant to respiratory transmission from a pandemic to baseline: Analysis of a large cross-sectional survey

“…Nevertheless, contact patterns remain poorly understood and difficult to predict. Previous contact studies have focused on European nations and measured contact at coarse spatial and temporal scales (e.g., [1,21,27, 32]). These broad scopes leave open questions about how contact patterns vary subnationally, across seasons, and between demographic and social classifications.…”

“…There has been recent attention on contact patterns in the US; several studies have been produced in the last three years (with the exception of earlier studies [19, 20]) which shed light on variation in contact patterns across multiple dimensions. Breen et al [21] demonstrate that contact varies between states but are unable to characterize spatial heterogeneity at finer scales in the US, even though other public health-related behaviors have been demonstrated to vary at the county-level [22, 23]. Dorelien et al use time-use surveys to demonstrate that contact between urban and rural areas may not differ [24] despite the perception that urban inhabitants have more contacts.…”

Uncertain SEIAR system dynamics modeling for improved community health management of respiratory virus diseases: A COVID-19 case study

“…Song et al (2022) [ 15 ] emphasized non-pharmaceutical interventions' importance for both COVID-19 and influenza transmission. Breen et al (2022) [ 16 ] quantified disease-relevant contact patterns, while Fazio et al (2022) [ 17 ] assessed mobility restriction impacts through an agent-based approach. Ringa et al (2022) [ 18 ] highlighted close-contact rates' significance in disease dynamics.…”

“…According to equations (6) , (13) , (14) , is defined as Equations [ 15 , 16 ] Removed individuals: According to equations (9) , (10) , (13) , (14) , is defined as Equations [ 17 , 18 ] …”