# Detection and characterization of critical under-immunized hotspots

> **NIH NIH R01** · UNIVERSITY OF VIRGINIA · 2020 · $324,178

## Abstract

Detection and characterization of critical under-immunized hotspots
 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.
 However, there are no existing methods to estimate the potential risk of such clusters, and to identify the most “critical”
ones. Some of the key reasons are: (i) purely data-driven spatial statistics methods rely only on immunization coverage,
which does not give any indication of the risk of an outbreak; and (ii) current causal epidemic models need to be combined
with detailed incidence data, which has not been easily available.
 This proposal brings together a systems science approach, combining 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: (i) Identify spatial clusters with significantly low immunization rates, or strong anti-vaccine
sentiment; (ii) 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; (iii) 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; and (iv) 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.

## Key facts

- **NIH application ID:** 9887876
- **Project number:** 2R01GM109718-07
- **Recipient organization:** UNIVERSITY OF VIRGINIA
- **Principal Investigator:** Achla Marathe
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $324,178
- **Award type:** 2
- **Project period:** 2014-08-15 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9887876

## Citation

> US National Institutes of Health, RePORTER application 9887876, Detection and characterization of critical under-immunized hotspots (2R01GM109718-07). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9887876. Licensed CC0.

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