Manuscript Topics

Computational models have been important tools for analyzing different types of infectious disease surveillance data, which normally includes case-based data (e.g., demographic details, exposures, symptom onset), laboratory data (e.g., virology, serology, genome), behavioral data (e.g., contact patterns, mobility) and environmental data. The model outputs can provide key insights into the transmission dynamics, and inform the decision-making process regarding outbreak control. This special issue aims to collect studies regarding computational and modeling approaches tailored for various disease surveillance data to address topics including but not limited to the following areas:

• (New) methods for estimating the epidemiological characteristics (e.g., latent period, infectious period) of a pathogen
• Methods for modeling the viral load and serologic dynamics.
• Modeling the interplay between human behavior (e.g., precautious, socially active) and transmission dynamics.
• Methods for understanding the impact of individual heterogeneity in transmission on the infectious disease dynamics, and
• Identifying outbreaks and tracking new variants using wastewater data.

Individual-based, agent-based, or machine learning models are also welcomed. We encourage authors to incorporate real-world datasets with their theoretical frameworks.

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