Simple epidemiological Agent Based Models (ABMs).
These are intended to help create "toy models" of disease transmission, initially for the purpose of showing the value of spatial analysis to provide hints about characteristics of disease transmission in low-income and/or unstable regions.
Very little explicitly spatial disease modelling (or even information about actual epidemic diseases) exists for low-income and unstable countries. The art and science of making inferences about diseases from the observed spatial data is quite advanced in high-income regions (even to the point of useful forecasting for certain pathologies such as influenza), lags badly in low-income unstable regions (such as sub-Saharan Africa, where even accurate base mapping data is often sparse and/or unreliable). Given recent advances in mapping, in particular the expanding coverage of OpenStreetMap and freely viewable high-resolution aerial imagery, it is probable that spatially explicit disease data will be increasingly available to public health practicioners in poor areas. In order to be ready for this, we propose creating "toy models" to demonstrate the types of inferences that can be drawn from spatially explicit data.
The toy model will create maps and animations showing epidemic disease simulations. These will be visibly different based on the transmission characteristics. Whether transmitted by airborne droplets, contact or bodily fluids, insect vectors which may or may not have specific habitats and seasonality, watercourses (fecal-oral route), animal vectors (for example rats and fleas), environmental reservoirs, or any other mode, each type of disease tends to create a certain spatial pattern. This project aims to provide a tool for people to acquire and/or sharpen their skills in drawing inferences from these patterns.
Just as John Snow was able to identify the water pump that was a key source of infection in the London cholera epidemic of 1854, we hope to be able to identify modern disease loci in low income-countries, 160 years after John Snow's achievement.
More to come