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Modeling methods for estimating HIV incidence: a mathematical review.

Xiaodan Sun1, Hiroshi Nishiura2, Yanni Xiao1

  • 1Department of Applied Mathematics, Xi'an Jiaotong University, No 28, Xianning West Road, Xi'an, Shaanxi, 710049, China.

Theoretical Biology & Medical Modelling
|January 23, 2020
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Accurately estimating HIV incidence is vital for public health but challenging. This review details various methods, their data needs, and limitations to guide future HIV epidemiological studies.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Estimating HIV incidence is critical for effective disease surveillance, intervention planning, and control measure evaluation.
  • Challenges in accurate HIV incidence estimation stem from the long, variable incubation period and the impact of antiretroviral therapies.

Purpose of the Study:

  • To review commonly used methods for estimating HIV incidence.
  • To outline the data requirements, advantages, disadvantages, and mathematical underpinnings of these methods.
  • To provide guidance for selecting appropriate incidence estimation strategies based on data availability and regional context.

Main Methods:

  • Review of established HIV incidence estimation methodologies.
  • Analysis of data requirements, mathematical models, and likelihood derivations.
  • Categorization of methods including back-calculation, CD4+ T-cell depletion, case reporting, cohort studies, prevalence data, and biomarker approaches.

Main Results:

  • Detailed examination of multiple HIV incidence estimation techniques.
  • Comparison of the strengths and weaknesses of each method.
  • Identification of factors influencing the choice of estimation method.

Conclusions:

  • The selection of an HIV incidence estimation method depends on specific epidemiological and laboratory data availability.
  • Understanding the mechanistic features of each method is key to successful incidence estimation.
  • This review offers a framework for optimizing HIV incidence estimation strategies globally.