• Previous Article
    Modeling dynamic changes in type 1 diabetes progression: Quantifying $\beta$-cell variation after the appearance of islet-specific autoimmune responses
  • MBE Home
  • This Issue
  • Next Article
    Modeling TB and HIV co-infections
2009, 6(4): 779-813. doi: 10.3934/mbe.2009.6.779

HIV/AIDS epidemic in India and predicting the impact of the national response: Mathematical modeling and analysis

1. 

Mathematical Institute, Centre for Mathematical Biology, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, United Kingdom

2. 

Department of Medicine, Christian Medical College, Vellore, India

3. 

Member, National AIDS Control Programmme Planning Team, Currently with Global AIDS Program, US Centers for Disease Control and Prevention, American Embassy New Delhi, India

4. 

Centre for Mathematical Biology, Mathematical Institute, University of Oxford, OX1 3LB Oxford

Received  June 2007 Revised  April 2009 Published  September 2009

After two phases of AIDS control activities in India, the third phase of the National AIDS Control Programme (NACP III) was launched in July 2007. Our focus here is to predict the number of people living with HIV/AIDS (PLHA) in India so that the results can assist the NACP III planning team to determine appropriate targets to be activated during the project period (2007-2012). We have constructed a dynamical model that captures the mixing patterns between susceptibles and infectives in both low-risk and high-risk groups in the population. Our aim is to project the HIV estimates by taking into account general interventions for susceptibles and additional interventions, such as targeted interventions among high risk groups, provision of anti-retroviral therapy, and behavior change among HIV-positive individuals. Continuing the current level of interventions in NACP II, the model estimates there will be 5.06 million PLHA by the end of 2011. If 50 percent of the targets in NACP III are achieved by the end of the above period then about 0.8 million new infections will be averted in that year. The current status of the epidemic appears to be less severe compared to the trend observed in the late 1990s. The projections based on the second phase and the third phase of the NACP indicate prevention programmes which are directed towards the general and high-risk populations, and HIV-positive individuals will determine the decline or stabilization of the epidemic. Model based results are derived separately for the revised HIV estimates released in 2007. According to revised projections there will be 2.08 million PLHA by 2012 if 50 percent of the targets in NACP III are reached. We perform a Monte Carlo procedure for sensitivity analysis of parameters and model validation. We also predict a positive role of implementation of anti-retroviral therapy treatment of 90 percent of the eligible people in the country. We present methods for obtaining disease progression parameters using convolution approaches. We also extend our models to age-structured populations.
Citation: Arni S. R. Srinivasa Rao, Kurien Thomas, Kurapati Sudhakar, Philip K. Maini. HIV/AIDS epidemic in India and predicting the impact of the national response: Mathematical modeling and analysis. Mathematical Biosciences & Engineering, 2009, 6 (4) : 779-813. doi: 10.3934/mbe.2009.6.779
[1]

Manuel Delgado, Cristian Morales-Rodrigo, Antonio Suárez. Anti-angiogenic therapy based on the binding to receptors. Discrete & Continuous Dynamical Systems - A, 2012, 32 (11) : 3871-3894. doi: 10.3934/dcds.2012.32.3871

[2]

Jianquan Li, Xiaoqin Wang, Xiaolin Lin. Impact of behavioral change on the epidemic characteristics of an epidemic model without vital dynamics. Mathematical Biosciences & Engineering, 2018, 15 (6) : 1425-1434. doi: 10.3934/mbe.2018065

[3]

Baba Issa Camara, Houda Mokrani, Evans K. Afenya. Mathematical modeling of glioma therapy using oncolytic viruses. Mathematical Biosciences & Engineering, 2013, 10 (3) : 565-578. doi: 10.3934/mbe.2013.10.565

[4]

Reihaneh Mostolizadeh, Zahra Afsharnezhad, Anna Marciniak-Czochra. Mathematical model of Chimeric Anti-gene Receptor (CAR) T cell therapy with presence of cytokine. Numerical Algebra, Control & Optimization, 2018, 8 (1) : 63-80. doi: 10.3934/naco.2018004

[5]

Harsh Vardhan Jain, Avner Friedman. Modeling prostate cancer response to continuous versus intermittent androgen ablation therapy. Discrete & Continuous Dynamical Systems - B, 2013, 18 (4) : 945-967. doi: 10.3934/dcdsb.2013.18.945

[6]

Hem Joshi, Suzanne Lenhart, Kendra Albright, Kevin Gipson. Modeling the effect of information campaigns on the HIV epidemic in Uganda. Mathematical Biosciences & Engineering, 2008, 5 (4) : 757-770. doi: 10.3934/mbe.2008.5.757

[7]

Oscar Patterson-Lomba, Muntaser Safan, Sherry Towers, Jay Taylor. Modeling the role of healthcare access inequalities in epidemic outcomes. Mathematical Biosciences & Engineering, 2016, 13 (5) : 1011-1041. doi: 10.3934/mbe.2016028

[8]

Xi Huo. Modeling of contact tracing in epidemic populations structured by disease age. Discrete & Continuous Dynamical Systems - B, 2015, 20 (6) : 1685-1713. doi: 10.3934/dcdsb.2015.20.1685

[9]

P. Daniele, S. Giuffrè, S. Pia. Competitive financial equilibrium problems with policy interventions. Journal of Industrial & Management Optimization, 2005, 1 (1) : 39-52. doi: 10.3934/jimo.2005.1.39

[10]

Grace Gao, Sasank Maganti, Karen A. Monsen. Older adults, frailty, and the social and behavioral determinants of health. Big Data & Information Analytics, 2017, 2 (3&4) : 1-12. doi: 10.3934/bdia.2017012

[11]

Brandy Rapatski, Juan Tolosa. Modeling and analysis of the San Francisco City Clinic Cohort (SFCCC) HIV-epidemic including treatment. Mathematical Biosciences & Engineering, 2014, 11 (3) : 599-619. doi: 10.3934/mbe.2014.11.599

[12]

Zi Sang, Zhipeng Qiu, Xiefei Yan, Yun Zou. Assessing the effect of non-pharmaceutical interventions on containing an emerging disease. Mathematical Biosciences & Engineering, 2012, 9 (1) : 147-164. doi: 10.3934/mbe.2012.9.147

[13]

Cristian Morales-Rodrigo. A therapy inactivating the tumor angiogenic factors. Mathematical Biosciences & Engineering, 2013, 10 (1) : 185-198. doi: 10.3934/mbe.2013.10.185

[14]

Carlo Brugna, Giuseppe Toscani. Boltzmann-type models for price formation in the presence of behavioral aspects. Networks & Heterogeneous Media, 2015, 10 (3) : 543-557. doi: 10.3934/nhm.2015.10.543

[15]

Nicola Bellomo, Livio Gibelli, Nisrine Outada. On the interplay between behavioral dynamics and social interactions in human crowds. Kinetic & Related Models, 2019, 12 (2) : 397-409. doi: 10.3934/krm.2019017

[16]

Danthai Thongphiew, Vira Chankong, Fang-Fang Yin, Q. Jackie Wu. An on-line adaptive radiation therapy system for intensity modulated radiation therapy: An application of multi-objective optimization. Journal of Industrial & Management Optimization, 2008, 4 (3) : 453-475. doi: 10.3934/jimo.2008.4.453

[17]

Notice Ringa, Chris T. Bauch. Spatially-implicit modelling of disease-behaviour interactions in the context of non-pharmaceutical interventions. Mathematical Biosciences & Engineering, 2018, 15 (2) : 461-483. doi: 10.3934/mbe.2018021

[18]

Jianjun Paul Tian. Finite-time perturbations of dynamical systems and applications to tumor therapy. Discrete & Continuous Dynamical Systems - B, 2009, 12 (2) : 469-479. doi: 10.3934/dcdsb.2009.12.469

[19]

Urszula Ledzewicz, Helen Moore. Optimal control applied to a generalized Michaelis-Menten model of CML therapy. Discrete & Continuous Dynamical Systems - B, 2018, 23 (1) : 331-346. doi: 10.3934/dcdsb.2018022

[20]

Rachid Ouifki, Gareth Witten. A model of HIV-1 infection with HAART therapy and intracellular delays. Discrete & Continuous Dynamical Systems - B, 2007, 8 (1) : 229-240. doi: 10.3934/dcdsb.2007.8.229

2018 Impact Factor: 1.313

Metrics

  • PDF downloads (8)
  • HTML views (0)
  • Cited by (0)

[Back to Top]