Examining the Influence of Imbalanced Social Contact Matrices in Epidemic Models.

Transmissible infections such as those caused by SARS-CoV-2 spread according to who contacts whom. Therefore, many epidemic models incorporate contact patterns through contact matrices. Contact matrices can be generated from social contact survey data. However, the resulting matrices are often imbalanced, such that the total number of contacts reported by group A with group B do not match those reported by group B with group A. We examine the theoretical influence of imbalanced contact matrices on the estimated basic reproduction number (R0). We then explore how imbalanced matrices may bias model-based epidemic projections using an illustrative simulation model of SARS-CoV-2 with two age groups (<15 and 15+). Models with imbalanced matrices underestimated the initial spread of SARS-CoV-2, had later time to peak incidence, and smaller peak incidence. Imbalanced matrices also influenced cumulative infections observed per age group, and the estimated impact of an age-specific vaccination strategy. Stratified transmission models that do not consider contact balancing may generate biased projections of epidemic trajectory and impact of targeted public health interventions. Therefore, modelling studies should implement and report methods used to balance contact matrices for stratified transmission models. © The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.

Citation

Mackenzie Hamilton, Jesse Knight, Sharmistha Mishra. Examining the Influence of Imbalanced Social Contact Matrices in Epidemic Models. American journal of epidemiology. 2023 Sep 15

PMID: 37715459

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