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

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 1.5×1.5-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.

以女性性工作者（female sex workers, FSW）、男男性行为者（men who have sex with men, MSM）及注射毒品使用者（people who inject drugs, PWID）为代表的特定亚群，其艾滋病（HIV/AIDS）感染率通常更高，且因污名化、歧视与刑事化政策难以直接开展普查绘图。对这类人群开展精细化空间绘图，有助于推进艾滋病防控的公平性改善与终结艾滋病流行的进程。2016至2017年，研究团队在马拉维28个行政区中的20个，于3290个场所开展PLACE调查，以估算女性性工作者的人群规模。该类场所数据属于仅存在数据集（presence-only data）：与同时知晓人群居住与非居住地点的存在-缺失数据集（presence-absence data）不同，仅可获取人群到访过的场所信息。本研究针对仅存在数据集构建贝叶斯模型（Bayesian model），并依托PLACE调查数据，以1.5×1.5平方千米的空间分辨率，对马拉维全境的女性性工作者人群规模及其不确定区间开展估算。该估算结果还可聚合至任意所需层级（城市/行政区/区域），用于在女性性工作者社区实施针对性的艾滋病预防与治疗项目——此类项目已被证实可有效降低艾滋病及其他性传播感染的发病率。