### Abstract

Mathematical models are increasingly used in social and behavioral studies of HIV transmission; however, model structures must be chosen carefully to best answer the question at hand and conclusions must be interpreted cautiously. In Pearson et al. (2007), we presented a simple analytically tractable deterministic model to estimate the number of secondary HIV infections stemming from a population of HIV-positive Mozambicans and to evaluate how the estimate would change under different treatment and behavioral scenarios. In a subsequent application of the model with a different data set, we observed that the model produced an unduly conservative estimate of the number of new HIV-1 infections. In this brief report, our first aim is to describe a revision of the model to correct for this underestimation. Specifically, we recommend adjusting the population-level sexually transmitted infection (STI) parameters to be applicable to the individual-level model specification by accounting for the proportion of individuals uninfected with an STI. In applying the revised model to the original data, we noted an estimated 40 infections/1000 HIV-positive persons per year (versus the original 23 infections/1000 HIV-positive persons per year). In addition, the revised model estimated that highly active antiretroviral therapy (HAART) along with syphilis and herpes simplex virus type 2 (HSV-2) treatments combined could reduce HIV-1 transmission by 72% (versus 86% according to the original model). The second aim of this report is to discuss the advantages and disadvantages of mathematical models in the field and the implications of model interpretation. We caution that simple models should be used for heuristic purposes only. Since these models do not account for heterogeneity in the population and significantly simplify HIV transmission dynamics, they should be used to describe general characteristics of the epidemic and demonstrate the importance or sensitivity of parameters in the model.

Original language | English (US) |
---|---|

Pages (from-to) | 858-862 |

Number of pages | 5 |

Journal | AIDS Care - Psychological and Socio-Medical Aspects of AIDS/HIV |

Volume | 21 |

Issue number | 7 |

DOIs | |

State | Published - Jul 2009 |

### Fingerprint

### Keywords

- HIV
- Mathematical modeling
- Prevention

### ASJC Scopus subject areas

- Public Health, Environmental and Occupational Health
- Health(social science)
- Social Psychology

### Cite this

*AIDS Care - Psychological and Socio-Medical Aspects of AIDS/HIV*,

*21*(7), 858-862. https://doi.org/10.1080/09540120802626204

**Discussion and revision of the mathematical modeling tool described in the previously published article Modeling HIV Transmission risk among Mozambicans prior to their initiating highly active antiretroviral therapy.** / Cassels, Susan; Pearson, Cynthia R.; Kurth, Ann E.; Martin, Diane P.; Simoni, Jane M.; Matediana, Eduardo; Gloyd, Stephen.

Research output: Contribution to journal › Article

*AIDS Care - Psychological and Socio-Medical Aspects of AIDS/HIV*, vol. 21, no. 7, pp. 858-862. https://doi.org/10.1080/09540120802626204

}

TY - JOUR

T1 - Discussion and revision of the mathematical modeling tool described in the previously published article Modeling HIV Transmission risk among Mozambicans prior to their initiating highly active antiretroviral therapy

AU - Cassels, Susan

AU - Pearson, Cynthia R.

AU - Kurth, Ann E.

AU - Martin, Diane P.

AU - Simoni, Jane M.

AU - Matediana, Eduardo

AU - Gloyd, Stephen

PY - 2009/7

Y1 - 2009/7

N2 - Mathematical models are increasingly used in social and behavioral studies of HIV transmission; however, model structures must be chosen carefully to best answer the question at hand and conclusions must be interpreted cautiously. In Pearson et al. (2007), we presented a simple analytically tractable deterministic model to estimate the number of secondary HIV infections stemming from a population of HIV-positive Mozambicans and to evaluate how the estimate would change under different treatment and behavioral scenarios. In a subsequent application of the model with a different data set, we observed that the model produced an unduly conservative estimate of the number of new HIV-1 infections. In this brief report, our first aim is to describe a revision of the model to correct for this underestimation. Specifically, we recommend adjusting the population-level sexually transmitted infection (STI) parameters to be applicable to the individual-level model specification by accounting for the proportion of individuals uninfected with an STI. In applying the revised model to the original data, we noted an estimated 40 infections/1000 HIV-positive persons per year (versus the original 23 infections/1000 HIV-positive persons per year). In addition, the revised model estimated that highly active antiretroviral therapy (HAART) along with syphilis and herpes simplex virus type 2 (HSV-2) treatments combined could reduce HIV-1 transmission by 72% (versus 86% according to the original model). The second aim of this report is to discuss the advantages and disadvantages of mathematical models in the field and the implications of model interpretation. We caution that simple models should be used for heuristic purposes only. Since these models do not account for heterogeneity in the population and significantly simplify HIV transmission dynamics, they should be used to describe general characteristics of the epidemic and demonstrate the importance or sensitivity of parameters in the model.

AB - Mathematical models are increasingly used in social and behavioral studies of HIV transmission; however, model structures must be chosen carefully to best answer the question at hand and conclusions must be interpreted cautiously. In Pearson et al. (2007), we presented a simple analytically tractable deterministic model to estimate the number of secondary HIV infections stemming from a population of HIV-positive Mozambicans and to evaluate how the estimate would change under different treatment and behavioral scenarios. In a subsequent application of the model with a different data set, we observed that the model produced an unduly conservative estimate of the number of new HIV-1 infections. In this brief report, our first aim is to describe a revision of the model to correct for this underestimation. Specifically, we recommend adjusting the population-level sexually transmitted infection (STI) parameters to be applicable to the individual-level model specification by accounting for the proportion of individuals uninfected with an STI. In applying the revised model to the original data, we noted an estimated 40 infections/1000 HIV-positive persons per year (versus the original 23 infections/1000 HIV-positive persons per year). In addition, the revised model estimated that highly active antiretroviral therapy (HAART) along with syphilis and herpes simplex virus type 2 (HSV-2) treatments combined could reduce HIV-1 transmission by 72% (versus 86% according to the original model). The second aim of this report is to discuss the advantages and disadvantages of mathematical models in the field and the implications of model interpretation. We caution that simple models should be used for heuristic purposes only. Since these models do not account for heterogeneity in the population and significantly simplify HIV transmission dynamics, they should be used to describe general characteristics of the epidemic and demonstrate the importance or sensitivity of parameters in the model.

KW - HIV

KW - Mathematical modeling

KW - Prevention

UR - http://www.scopus.com/inward/record.url?scp=70450184324&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70450184324&partnerID=8YFLogxK

U2 - 10.1080/09540120802626204

DO - 10.1080/09540120802626204

M3 - Article

VL - 21

SP - 858

EP - 862

JO - AIDS Care - Psychological and Socio-Medical Aspects of AIDS/HIV

JF - AIDS Care - Psychological and Socio-Medical Aspects of AIDS/HIV

SN - 0954-0121

IS - 7

ER -