Biomedical Modeling

Health Policy Modeling

Philosophy

UNAIDS has stated treatment targets for eliminating HIV by 2030: to diagnose 95% of HIV-infected individuals, to treat 95% of diagnosed individuals, and to achieve viral suppression in 95% of treated individuals. We focus on identifying strategies that will help reach these targets, especially the diagnosis target which is likely to be the most challenging.

Recently Published Project

Using a data-based mapping approach to reach the diagnosis treatment target

Here, we show how to reach the diagnosis target (which we refer to as the last milestone) by utilizing a data-based mapping approach to find undiagnosed people living with HIV infection (PLHIV). Our approach: (i) enables the identification of countrywide geographic variation in the level of diagnosis, (ii) identifies the geographic location of undiagnosed PLHIV, and (iii) estimates how many undiagnosed individuals live in these areas. We demonstrate the utility of our approach by applying it to Lesotho, a mountainous landlocked country in South Africa, which – by 2016/17 – had diagnosed 81% of PLHIV, provided 92% of these individuals with treatment, and achieved viral suppression in 88% of treated individuals.

The Figure shows (a) a continuous surface map (for Lesotho) of geographic variation in the level of diagnosis, and (b) a map showing the density of undiagnosed PLHIV per km2.

Recent Relevant Publications

How to reach the last milestone for HIV elimination in Africa: a data-based mapping approach

Okano et al., The Lancet Global Health (2023)

Use of mobile phone data in HIV epidemic control

Valdano et al., The Lancet HIV (2022)

The potential impact of country-level migration networks on HIV epidemics in sub-Saharan Africa: the case of Botswana

Okano et al., The Lancet HIV (2021)

Using mobile phone data to reveal risk flow networks underlying the HIV epidemic in Namibia

Valdano et al., Nature Communications (2021)

Travel-time to health-care facilities, mode of transportation, and HIV elimination in Malawi: a geospatial modelling analysis

Palk et al., The Lancet Global Health (2020)