Estimation in emerging epidemics: biases and remedies

Area of Science:

• Epidemiology
• Mathematical Biology
• Biostatistics

Background:

• Emerging infectious disease outbreaks require timely parameter estimation for prediction and control.
• Typical observational data is limited in scope and duration during the early outbreak phase.
• Key parameters include growth rate, R0, case fatality rate, and various time distributions.

Purpose of the Study:

• To investigate inference problems during the emerging phase of infectious disease outbreaks.
• To identify and analyze potential sources of bias in parameter estimation.
• To propose statistical modeling methods for bias reduction.

Main Methods:

• Analysis of inference problems in early outbreak dynamics.
• Focus on biases from backward contact tracing, serial intervals vs. generation times, multiple infectors, and censoring effects.
• Development and application of statistical modeling techniques.

Main Results:

• Identified significant biases in estimating generation time distribution and case fatality rate.
• Demonstrated how these biases propagate to estimates of R0 and growth rate.
• Proposed methods to mitigate identified biases.

Conclusions:

• Accurate parameter estimation in early outbreak phases is crucial but challenging due to inherent biases.
• Statistical modeling offers a viable approach to correct for biases in epidemiological data.
• Reducing bias improves the reliability of outbreak predictions and public health interventions.