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JHEEMJoint HIV Epidemiology
and Economic Model

For public health and HIV program partners

We study how policy and funding decisions shape local HIV epidemics.

JHEEM, the Joint HIV Epidemiology and Economic Model, projects HIV trends in U.S. cities and states under different scenarios.

We work with public health partners to make this research useful for local decisions, and we're looking for new collaborators.

You don't need modeling expertise or local data to work with us.

Get in touch

Developed by researchers at the Johns Hopkins Schools of Public Health and Medicine. Funded by the National Institutes of Health.

New HIV infections each year

Illustration, not real data

Illustration: three possible futures for new HIV infectionsDots show past reported data that a model is fit to. From today, the projection splits into three futures: a funding cut, where new infections rise; no change, where they stay roughly level; and scaled-up prevention, where they fall. Shaded bands around each line widen over time to show the range of likely outcomes. Not real data.TodayPastFutureReported dataNo changeFunding cutScaled-upprevention
Like a weather forecast, our models use past data to explore possible futures. Shaded bands show a range of likely outcomes, not a prediction of exactly what will happen.

What we can help you answer

We study questions like these at the city and state level.

  • How many new HIV infections should we expect?

    Projections of new infections, viral suppression and the number of people living with HIV, under current trends or alternative scenarios.

  • What mix of testing, treatment and PrEP would it take to reach our goals?

    Comparisons of interventions and the scale needed to move local infections toward Ending the HIV Epidemic targets.

  • What happens if Ryan White or CDC funding is cut or interrupted?

    Estimates of additional infections if federal HIV programs end or pause, and how a shorter interruption compares with permanent loss.

  • Does a budget cut save money, or cost more later?

    Comparisons of program spending today with the future care costs it may prevent, to inform how resources are allocated.

  • How will the population of people living with HIV change over the next 15 years?

    Projections of the size and age of the population living with HIV, to support planning for aging-related care, workforce and services.

Explore our modeling toolsRead our published research

How a collaboration works

We already work with several health departments. A collaboration usually looks like this.

  1. You bring the question

    Tell us the decision you're facing: a budget change, a new program or a planning target.

  2. We tailor and run the model

    We set up scenarios that match your question and run them for your city or state.

  3. We share results you can use

    Plain-language summaries, presentations for your leadership or board, and interactive tools for your staff.

What partners contribute

Our models are built on publicly available data. Your local knowledge helps us put it in context.

  • The local questions and priorities that shape our analyses
  • Context on programs, populations and policy that data alone can't capture
  • Feedback on how results are framed and presented

Get in touch

If your organization has a question our models could help answer, or you'd simply like to learn more, we'd be glad to hear from you.

A few lines about your organization, your city or state, and what you're working on is enough to start.

Email us
  • Portrait of Parastu Kasaie, PhD

    Parastu Kasaie, PhD

    Associate Scientist

    Johns Hopkins Bloomberg School of Public Health

  • Portrait of Todd Fojo, MD, MHS

    Todd Fojo, MD, MHS

    Associate Professor

    Johns Hopkins School of Medicine

With a team of epidemiologists, physicians and modelers. Meet the team.

This research is supported by the National Institutes of Health (K08MH118094, K01AI138853, P30AI094189, R01MD018539). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.