Modeling the effect of information campaigns on the HIV epidemic in Uganda

  • Received: 01 December 2007 Accepted: 29 June 2018 Published: 01 October 2008
  • MSC : 34K34, 92D30

  • The increasing prevalence of HIV/AIDS in Africa over the past twenty-five years continues to erode the continent's health care and overall welfare. There have been various responses to the pandemic, led by Uganda, which has had the greatest success in combating the disease. Part of Uganda's success has been attributed to a formalized information, education, and communication (IEC) strategy, lowering estimated HIV/AIDS infection rates from 18.5% in 1995 to 4.1% in 2003. We formulate a model to investigate the effects of information and education campaigns on the HIV epidemic in Uganda. These campaigns affect people's behavior and can divide the susceptibles class into subclasses with different infectivity rates. Our model is a system of ordinary differential equations and we use data about the epidemics and the number of organizations involved in the campaigns to estimate the model parameters. We compare our model with three types of susceptibles to a standard SIR model.

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

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  • The increasing prevalence of HIV/AIDS in Africa over the past twenty-five years continues to erode the continent's health care and overall welfare. There have been various responses to the pandemic, led by Uganda, which has had the greatest success in combating the disease. Part of Uganda's success has been attributed to a formalized information, education, and communication (IEC) strategy, lowering estimated HIV/AIDS infection rates from 18.5% in 1995 to 4.1% in 2003. We formulate a model to investigate the effects of information and education campaigns on the HIV epidemic in Uganda. These campaigns affect people's behavior and can divide the susceptibles class into subclasses with different infectivity rates. Our model is a system of ordinary differential equations and we use data about the epidemics and the number of organizations involved in the campaigns to estimate the model parameters. We compare our model with three types of susceptibles to a standard SIR model.


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