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Jack Heavey

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3 papers

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AAMAS Conference 2026 Conference Paper

Optimizing Pool Testing for Epidemic Surveillance

Jack Heavey
Abhijin Adiga
Anil Vullikanti

Testing is one of the key tools in public health surveillance. Often testing resources are limited, and pooled testing emerged as a viable strategy during the COVID outbreak, for early detection of the outbreak or clearing the most number of individuals (maximum “welfare”). Here, we study the problem of selecting pools for testing which maximizes welfare. However, this problem is a very challenging optimization problem because the infection status of individuals can be correlated. Prior work on choosing pools has ignored network correlations. We design an efficient approximation algorithm for this problem, using techniques from stochastic and combinatorial optimization: sample average approximation, linear programming and randomizedrounding. Wefurtherspeedupouralgorithmsusingtechniques for combinatorially solving the linear program. We evaluate our method on multiple networked datasets, including one derived from a hospital, and show significant benefit in modeling network correlations.

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AAAI Conference 2023 Conference Paper

Reconstructing an Epidemic Outbreak Using Steiner Connectivity

Ritwick Mishra
Jack Heavey
Gursharn Kaur
Abhijin Adiga
Anil Vullikanti

Only a subset of infections is actually observed in an outbreak, due to multiple reasons such as asymptomatic cases and under-reporting. Therefore, reconstructing an epidemic cascade given some observed cases is an important step in responding to such an outbreak. A maximum likelihood solution to this problem ( referred to as CascadeMLE ) can be shown to be a variation of the classical Steiner subgraph problem, which connects a subset of observed infections. In contrast to prior works on epidemic reconstruction, which consider the standard Steiner tree objective, we show that a solution to CascadeMLE, based on the actual MLE objective, has a very different structure. We design a logarithmic approximation algorithm for CascadeMLE, and evaluate it on multiple synthetic and social contact networks, including a contact network constructed for a hospital. Our algorithm has significantly better performance compared to a prior baseline.

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AAAI Conference 2022 Conference Paper

Provable Sensor Sets for Epidemic Detection over Networks with Minimum Delay

Jack Heavey
Jiaming Cui
Chen Chen
B. Aditya Prakash
Anil Vullikanti

The efficient detection of outbreaks and other cascading phenomena is a fundamental problem in a number of domains, including disease spread, social networks, and infrastructure networks. In such settings, monitoring and testing a small group of pre-selected nodes from the susceptible population (i. e. , a sensor set) is often the preferred testing regime. We study the problem of selecting a sensor set that minimizes the delay in detection—we refer to this as the MinDelSS problem. Prior methods for minimizing the detection time rely on greedy algorithms using submodularity. We show that this approach can sometimes lead to a worse approximation for minimizing the detection time than desired. We also show that MinDelSS is hard to approximate within an O(n1−1/γ )factor for any constant γ ≥ 2 for a graph with n nodes. This instead motivates seeking a bicriteria approximations. We present the algorithm ROUNDSENSOR, which gives a rigorous worst case O(log n)-factor for the detection time, while violating the budget by a factor of O(log2 n). Our algorithm is based on the sample average approximation technique from stochastic optimization, combined with linear programming and rounding. We evaluate our algorithm on several networks, including hospital contact networks, which validates its effectiveness in real settings.

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