Inferring HIV incidence trends and transmission dynamics with a spatio-temporal HIV epidemic model

TL;DR

This paper introduces a novel spatio-temporal HIV epidemic model that explicitly simulates spatial dynamics to better estimate incidence and transmission trends using integrated population data.

Contribution

It presents a new epidemic model that captures spatial interactions and integrates diverse data sources, improving inference of HIV incidence and treatment rates.

Findings

Model successfully infers spatially varying HIV incidence.

Data fusion enhances accuracy of epidemic parameter estimates.

Explicit spatial modeling improves understanding of transmission dynamics.

Abstract

Reliable estimation of spatio-temporal trends in population-level HIV incidence is becoming an increasingly critical component of HIV prevention policy-making. However, direct measurement is nearly impossible. Current, widely used models infer incidence from survey and surveillance seroprevalence data, but they require unrealistic assumptions about spatial independence across spatial units. In this study, we present an epidemic model of HIV that explicitly simulates the spatial dynamics of HIV over many small, interacting areal units. By integrating all available population-level data, we are able to infer not only spatio-temporally varying incidence, but also ART initiation rates and patient counts. Our study illustrates the feasibility of applying compartmental models to larger inferential problems than those to which they are typically applied, as well as the value of data fusion approaches to infectious disease modeling.