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Estimating the per-exposure effect of infectious disease interventions
Justin J O'Hagan1, Marc Lipsitch, Miguel A Hernán
1From the aDepartment of Epidemiology, Harvard School of Public Health, Boston, MA; bCenter for Communicable Disease Dynamics, Harvard School of Public Health, Boston, MA; and cDepartment of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, MA.
Understanding infectious disease intervention effects requires a clear per-exposure measure. This study formally defines this measure, improving disease burden estimations and aiding public health decision-making.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Public Health
Background:
- Infectious disease intervention effects vary by population exposure levels.
- Per-exposure effect measures are favored for simulations but often poorly defined.
- Implicit assumptions in calculating per-exposure effects can limit their utility.
Purpose of the Study:
- To formally define the per-exposure effect of infectious disease interventions.
- To identify conditions for the unbiased estimation of per-exposure effects.
- To enhance the understanding and application of transmission models in public health.
Main Methods:
- Building upon prior work by Halloran and Struchiner.
- Developing a formal mathematical definition of the per-exposure effect.
- Analyzing assumptions and conditions for unbiased estimation.
Main Results:
- A precise definition of the per-exposure effect is established.
- Conditions for unbiased estimation of the per-exposure effect are discussed.
- Improved parameterization of transmission models is recommended.
Conclusions:
- Formal definition clarifies the per-exposure effect for infectious disease interventions.
- Unbiased estimation requires careful attention to model parameterization.
- Accurate per-exposure effects enhance disease burden estimation and decision-making.
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