Fitting dynamic measles models to subnational case notification data from Ethiopia: Methodological challenges and key...

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• Ongoing measles transmission persists due to undervaccinated populations, even with high national coverage.
• Assessing measles susceptibility gaps is crucial for effective vaccination programs and disease control.
• Geospatial methods are increasingly used to estimate subnational vaccination coverage in high-burden areas like Ethiopia.

Purpose of the Study:

• To develop and apply a dynamic transmission model to estimate measles incidence and susceptibility across time, age, and space.
• To identify geographical and age-related gaps in measles susceptibility in Ethiopia.
• To inform targeted subnational and local planning for measles control.

Main Methods:

• Developed a dynamic transmission model integrating geospatial vaccination coverage, supplemental immunization data, and reported cases.
• Utilized gridded population estimates, a synthetic contact matrix for age-group mixing, and a modified gravity model for spatial movement.
• Employed maximum likelihood estimation with block coordinate descent for model fitting to address data challenges and uncertainty.

Main Results:

• Identified substantial heterogeneity in reported measles cases and susceptibility across ages and administrative units in Ethiopia.
• The model successfully estimated measles incidence and susceptibility patterns over time and space.
• Sensitivity analyses explored variations in vaccine effectiveness and susceptibility distributions.

Conclusions:

• The developed modeling approach provides valuable insights into measles susceptibility at a subnational level.
• These estimates can guide tailored interventions to reduce preventable measles burden.
• Widespread application requires addressing computational and data challenges.