Network Systems Science and Advanced Computing | Population Health Informatics

Detection and Characterization of Critical Under-Immunized Hotspots

Sponsor

National Institutes of Health (NIH)

Emergence of undervaccinated geographical clusters for diseases like measles has become a national concern. A number of measles outbreaks have occurred in recent months, despite high MMR coverage in the United States (95%). Such undervaccinated clusters can act as reservoirs of infection that can transmit the disease to a wider population, magnifying their importance far beyond what their absolute numbers might indicate. The existence and growth of such undervaccinated clusters is often known to public health agencies and health provider networks, but they typically do not have enough resources to target people in each such cluster, to attempt to improve the vaccination rate. Preliminary results show that not all undervaccinated clusters are “equal” in terms of their potential for causing a big outbreak (referred to as its “criticality”), and the rate of undervaccination in a cluster does not necessarily correlate with its criticality. This project uses novel methods to identify these critical clusters.

It brings together a systems science approach, combines agent-based stochastic epidemic models, and techniques from machine learning, high performance computing, data mining, and spatial statistics, along with novel public and private datasets on immunization and incidence, to develop a novel methodology for identifying critical clusters, through the following tasks:

  1. Identify spatial clusters with significantly low immunization rates;
  2. Develop an agent based model for the spread of measles that incorporates detailed immunization data, and is calibrated using a novel source of incidence data;
  3. Develop methods to find and characterize critical spatial clusters, with respect to different metrics, which capture both epidemic and economic burden, and rank order underimmunized clusters based on their criticality; and
  4. Use the methodology to evaluate interventions in terms of their effect on criticality.

A highly interdisciplinary team involving two universities, a health care delivery organization and a state department of Health, will work together to develop this methodology. Characterization of such clusters will enable public health departments and policy makers in targeted surveillance of their regions and a more efficient allocation of resources.

Project Overview

Goals

  1. Identify spatial clusters with significantly low immunization rates
  2. Develop an agent-based model for the spread of measles that incorporates detailed immunization data, and is calibrated using a novel source of incidence data
  3. Develop methods to find and characterize critical spatial clusters, with respect to different metrics, which capture both epidemic and economic burden, and order underimmunized clusters based on their criticality
  4. Use the methodology to evaluate interventions in terms of their effect on criticality
Findings

One fundamental task in network analysis is detecting “hotspots” or “anomalies” in the network; that is, detecting subgraphs where there is significantly more activity than one would expect given historical data or some baseline process. Scan statistics is one popular approach used for anomalous subgraph detection. This methodology involves maximizing a score function over all connected subgraphs, which is a challenging computational problem. A number of heuristics have been proposed for these problems, but they do not provide any quality guarantees. Here, we provide a framework for designing algorithms for optimizing a large class of scan statistics for networks, subject to connectivity constraints. Our algorithms run in time that scales linearly on the size of the graph and depends on a parameter we call the “effective solution size,” while providing rigorous approximation guarantees. In contrast, most prior methods have super-linear running times in terms of graph size. Extensive empirical evidence demonstrates the effectiveness and efficiency of our proposed algorithms in comparison with state-of-the-art methods. Our approach improves on the performance relative to all prior methods, giving up to over 25% increase in the score. Further, our algorithms scale to networks with up to a million nodes, which is 1--2 orders of magnitude larger than all prior applications.

Emergence of under-immunized clusters is a growing concern for public health agencies because they can act as reservoirs of infection and increase the risk of infection into the wider population. We use realistic population models for Minnesota and Washington state, and combine this with school level immunization data to estimate vaccine coverage at the level of census block groups. A scan statistic method defined on networks is used for finding significant clusters of under-immunized block groups, without any restrictions on shape. Further we provide the demographic characteristics of these clusters. We find significant under-vaccinated clusters in MN and WA. These are very irregular in shape, in contrast to the circular disks reported in prior work, which rely on the SatScan approach. Some of the clusters found by our method are not contained in those computed using SatScan, a state-of-the-art software tool used in similar studies in other states. Higher resolution clusters computed using our network based approach and population models provide new insights on the structure and characteristics of such clusters and enable targeted interventions (Cadena et al. BMC Medical Informatics and Decision-Making).

Figures

Four graphs of under-immunized cluster data in Minnesota and Washington State
Figure 1: Top two significant clusters in MN (top right and top left) are shown. Each dot on the map is a block group. The same clusters are shown as block group polygons on the bottom right and left, with each marker corresponding to a block group. First cluster in Minnesota covers the city of St. Paul (top left and bottom left) and the second cluster covers the rural block groups west of Minneapolis (top right and bottom right)
Team

Professor

Professor of Public Health Sciences, School of Medicine

Professor

Professor of Computer Science, School of Engineering and Applied Science

Professor

Professor of Public Health Sciences, School of Medicine

Research Associate Professor

Other Team Members

Travis Porco | Professor of Global Health | University of California, San Francisco

Nicola Klein | Senior Research Scientist | Kaiser Permanente

Publications
Network Systems Science and Advanced Computing
AAMAS '20: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, pp. 1786-1788: (2020) Cadena J, Marathe A, Vullikanti A.
Network Systems Science and Advanced Computing
BMC Medical Informatics and Decision Making, 19:28.: (2019) Cadena J, Falcone D, Marathe A, Vullikanti A.
Network Systems Science and Advanced Computing
ACM Transactions on Knowledge Discovery from Data (TKDD), 13(2), pp.1-33.: (2019) Cadena J, Chen F, Vullikanti A.
Network Systems Science and Advanced Computing
Proceedings of the 2017 SIAM International Conference on Data Mining, June. pp. 624-632: (2017) Cadena J, Chen F, Vullikanti A.