# Prevalence predictor

Predicting prevalence of an outcome from point level data

# Rationale

Cross-sectional surveys are widely used to gather data on health outcomes among a population. These include prevalence of infection, prevalence of symptoms and coverage of vaccines and other interventions. Surveys are, however, expensive and as such are typically only conducted on a small fraction of the population. This leaves gaps in our understanding of these outcomes across the wider population. To fill in the gaps requires spatial modeling which is often not available to health programs. Even when available to programs, it is often expensive and time consuming.

Example of using prevalence predictor to create predictive maps of access to water in Zimbabwe

# Our approach

We have developed algorithms to automate the generation of predictive maps from survey data, simplifying access to these approaches.

# Implementations

This algorithm has been used to map hotspots of lymphatic filariasis (LF) in Samoa and will be used in a similar way as part of upcoming LF mapping exercises in Mali and Tanzania. This algorithm is also being used to help map vaccine coverage as well as mapping COVID-19 symptoms across the USA and risk of severe COVID-19 in Zimbabwe.

Think this sounds useful?

You can reach us at hello@locational.io to ask any questions, request additions or changes, or arrange a demo. We are actively developing these algorithms and would like to hear from you.