Preventing Infectious Disease in Dynamic Populations Under Uncertainty


  • Bryan Wilder University of Southern California
  • Sze-Chuan Suen University of Southern California
  • Milind Tambe University of Southern California



Infectious disease, submodularity, stochastic optimization


Treatable infectious diseases are a critical challenge for public health. Outreach campaigns can encourage undiagnosed patients to seek treatment but must be carefully targeted to make the most efficient use of limited resources. We present an algorithm to optimally allocate limited outreach resources among demographic groups in the population. The algorithm uses a novel multiagent model of disease spread which both captures the underlying population dynamics and is amenable to optimization. Our algorithm extends, with provable guarantees, to a stochastic setting where we have only a distribution over parameters such as the contact pattern between agents. We evaluate our algorithm on two instances where this distribution is inferred from real world data: tuberculosis in India and gonorrhea in the United States. Our algorithm produces a policy which is predicted to avert an average of least 8,000 person-years of tuberculosis and 20,000 person-years of gonorrhea annually compared to current policy.




How to Cite

Wilder, B., Suen, S.-C., & Tambe, M. (2018). Preventing Infectious Disease in Dynamic Populations Under Uncertainty. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).



Computational Sustainability and Artificial Intelligence