We published the first HIV vaccine model (and coined the term imperfect vaccines) in 1993 and then published a further analysis of our model in Science in 1994. Our model was four ODEs. We then expanded our model to a five ODE model in subsequent publications, beginning with a letter to Science in 1995. Our most recent analysis of our five ODE model was published in the Lancet Infectious Diseases in 2004. The relevant publications are listed below:

• R.J. Smith and S.M. Blower. 2004. Could disease-modifying HIV vaccines cause population-level perversity? Lancet Infectious Diseases 4: 636-39. [Full Text] [Appendix]
• S.M. Blower, E.J. Schwartz and J. Mills. 2003. Forecasting the future of HIV epidemics: the impact of antiretroviral therapies and imperfect vaccines. AIDS Reviews 5 (2): 113-125. [Full Text]
• S.M. Blower, K. Koelle and J. Mills. 2002. Health policy modeling: epidemic control, HIV vaccines and risky behavior. Quantitative Evaluation of HIV Prevention Programs. Eds Kaplan and Brookmeyer. Yale University Press. Pages 260-289. [Full Text]
• A.R. McLean and S.M. Blower. 1995. Modeling HIV vaccination. Trends in Microbiology 3 (12): 458-463. [Full Text]
• S.M. Blower and A.R. McLean. 1995. AIDS: modeling epidemic control. Science 267 (5202): 1252-1253. [Full Text]
• S.M. Blower and A.R. McLean. 1994. Prophylactic vaccines, risk behavior change & the probability of eradicating HIV in San Francisco. Science 265: 1451-1454. [Full Text]
• A.R. McLean and S.M. Blower. 1993. Imperfect vaccines and herd immunity to HIV. Proceedings of the Royal Society of London, Series B, 253: 9-13. [Full Text]

Instructions

First, please note that these applets are viewed best at resolutions of 800 x 600 or higher. If you are having a hard time running these applets because they don't fit on the screen, try clicking the 'Open without Frames' button at the bottom of this page. Also some browsers, such as the internet explorer have full screen modes, which enable more of a page to be displayed (for IE 5.0 the F11 key switches to the full screen mode).

In (Applet 1.) we look at the effect of a vaccine over time. When the vaccine (represented by the blue line) is below the 'vaccine has no impact' line it is doing more harm then good. If the vaccine is above this line, it is having a positive effect. Vaccine effectiveness is calculated by looking at a fraction of the cumulative incidence for a vaccinated group over the cumulative incidence for an unvaccinated group. For more information on the model please click here.

To change a parameter in the model just select the parameter you want to change by using the drop box. Enter the new value for the parameter in the text box and click the 'Set' button.

We set up the the model so that:

1. The average time from infection to AIDS in an unvaccinated individual is 10 years (gu)
2. The average probability of transmission per partnership for an unvaccinated individual is 0.1 (bu).

1. take, which represents the fraction of vaccinated individuals in whom some level of protective immunological response is induced by the vaccine (take can range from 0.0 to 1.0). A value of 0.0 means that nobody who is vaccinated actually gains any protection from HIV. A value of 1.0 means that every individual who is vaccinated gains a degree of protection for a duration of time.

2. duration, which represents the average duration of vaccine induced immunity. Individuals lose immunity from HIV and become susceptible again at a rate w; 1/w is equal to the average duration in years.

3. degree, which represents the degree of vaccine-induced protection against HIV (degree can range from 0.0 to 1.0). A value of 0.0 means that a vaccine provides no additional protection from HIV given exposure to the virus. A value of 1.0 means that the vaccine gives complete protection from HIV given exposure.

4. coverage, which represents the fraction of individuals who are vaccinated (coverage can range from 0.0 to 1.0).

5. survival time, which represents the effect of the vaccine in increasing survival time in vaccinated infected individuals. If the survival time is 1.0 then the average survival time of vaccinated infected individuals is the same as the average survival time of unvaccinated infected individuals. If the survival time is 2.0 then the average survival time for vaccinated infected individuals is twice as long as the average survival time of unvaccinated infected individuals (1/gv = (survival time)*1/g).

6. delta, which represents the reduction in probability of transmission per partnership for a vaccinated individual in comparison with an unvaccinated individual. If alpha is 0.01 than vaccinated infected individuals are 100 fold less infectious than unvaccinated individuals. If alpha is 1.0 then vaccinated and unvaccinated infected individuals are equally infectious (bv = delta*b).

7. c, which represents the average number of new risky sex partners acquired per year. The baseline value of (c) is 2.0, therefore any value greater than 2.0 represents an increase in risky behavior.

To run the simulation click on the 'Graph' button. Each time you hit the 'Graph' button the simulation advances time by another 50 years.