Modeling the Marked Presence-only Data: A Case Study of Estimating the Female Sex Worker Size in Malawi

TL;DR

This paper develops a Bayesian modeling approach for presence-only data to estimate the size of female sex worker populations in Malawi at fine spatial resolution, aiding targeted HIV interventions.

Contribution

It introduces a novel Bayesian method tailored for presence-only data, enabling detailed spatial estimates of hidden populations like FSWs.

Findings

Estimated FSW population sizes at 1.5x1.5 km resolution across Malawi.

Provided uncertainty intervals for population estimates.

Demonstrated utility for targeted HIV prevention programs.

Abstract

Certain subpopulations like female sex workers (FSW), men who have sex with men (MSM), and people who inject drugs (PWID) often have higher prevalence of HIV/AIDS and are difficult to map directly due to stigma, discrimination, and criminalization. Fine-scale mapping of those populations contributes to the progress towards reducing the inequalities and ending the AIDS epidemic. In 2016 and 2017, the PLACE surveys were conducted at 3,290 venues in 20 out of the total 28 districts in Malawi to estimate the FSW sizes. These venues represent a presence-only data set where, instead of knowing both where people live and do not live (presence-absence data), only information about visited locations is available. In this study, we develop a Bayesian model for presence-only data and utilize the PLACE data to estimate the FSW size and uncertainty interval at a $1.5 \times 1.5$-km resolution for all of Malawi. The estimates can also be aggregated to any desirable level (city/district/region) for implementing targeted HIV prevention and treatment programs in FSW communities, which have been successful in lowering the incidence of HIV and other sexually transmitted infections.