From top to bottom: gridded human population estimates in data-poor situations

Area of Science:

• Epidemiology
• Geographic Information Systems (GIS)
• Demography

Background:

• Infectious disease risk assessment necessitates spatially explicit population data for humans, livestock, and wildlife.
• High-resolution human population data are increasingly vital for public and animal health planning.
• Traditional census data, while comprehensive, often lacks currency and granularity in resource-poor regions.

Approach:

• This review examines the limitations of census data for spatial disease modeling.
• It explores census-independent, bottom-up approaches for small-area population estimation.
• These methods integrate microcensus surveys with ancillary data for spatially disaggregated estimates.

Key Points:

• Accurate population distribution data is essential for understanding and mitigating disease spread.
• Census data challenges in resource-limited settings hinder effective public health interventions.
• Bottom-up modeling provides a viable alternative for generating high-resolution gridded population data.

Conclusions:

• High-resolution gridded population data are indispensable for accurate disease risk assessment.
• Census-independent methods offer a robust solution for population estimation where census data is deficient.
• Spatially explicit population data are critical for informed public health policy and disease control strategies.