Estimating actual SARS-CoV-2 infections from secondary data

Area of Science:

• Epidemiology
• Public Health
• Infectious Disease Modeling

Background:

• Accurate infection data is vital for effective pandemic management, including control measures and vaccination.
• Underestimation of SARS-CoV-2 infections is significant due to numerous undocumented cases, complicating public health responses.

Purpose of the Study:

• To demonstrate and compare three distinct methods for estimating true infection dynamics.
• To assess the reliability of test positivity, infection fatality, and wastewater monitoring data for tracking infections.

Main Methods:

• Utilized Austrian national data from April 2020 to December 2022.
• Employed Approximate Bayesian Computation-rejection sampling and Genetic Algorithms for parameter estimation.
• Validated the estimation methods using a case study in Vienna.

Main Results:

• All three methods (test positivity, infection fatality, wastewater monitoring) produced comparable results for estimating true infection numbers.
• The findings suggest that these diverse data sources contain similar underlying information on infection trends.
• No single method was found to be universally superior, with effectiveness varying by specific case study.

Conclusions:

• The study validates the utility of test positivity, infection fatality, and wastewater monitoring as complementary tools for estimating infection dynamics.
• These methods offer valuable insights for pandemic management, supporting informed decision-making for public health interventions.
• The comparable outcomes underscore the robustness of using multiple data streams to understand disease spread.