# Understanding the relationship between herd immunity and geographic scale to improve estimates of localized infectious disease outbreak risk

> **NIH NIH K01** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2020 · $135,974

## Abstract

ABSTRACT
The size and frequency of outbreaks of vaccine-preventable diseases in the US are increasing. For example,
although it was officially declared eliminated in 2000, there already have been as many measles cases in the
US in the first five months of 2019 (940) than any full calendar year since 1994. Given trends in vaccine hesitancy,
future outbreaks of measles and other vaccine-preventable diseases are all but certain to occur. Herd immunity
describes the phenomenon wherein individuals without immunity from an infection are indirectly protected from
that infection by immunized individuals within the population. It is an important concept for designing and
monitoring vaccination campaigns and understanding infectious disease transmission dynamics. Despite its
importance, a number of aspects of herd immunity remain under- or unexamined, thereby limiting its usefulness
in applied epidemiological or public health settings. This K01 Award proposal focuses on herd immunity and its
relationship with infectious disease outbreak risk at local geographic scales. My career goal is to become a
leading scholar in the spatial epidemiology of vaccination and vaccine-preventable diseases, specializing in
research that links together human behavior, policy, and disease transmission systems to understand the
evolving nature of disease outbreak risk. The training activities focus on expanding my current expertise in health
geography and spatial data analysis with specialized training in infectious disease epidemiology methods, agent-
based modeling, and social network analysis. The proposed research program supports an interdisciplinary
approach that integrates concepts and techniques from geography, epidemiology, data science and
computational modeling, and public health practice to examine the complex relationships among vaccination
coverage, herd immunity, geographic scale, spatial and social connectivity patterns, and disease transmission
dynamics. My research aims are: 1) Evaluate approaches to define herds using network-based community
detection algorithms, 2) Identify geographic scales at which the relationship between vaccination coverage and
the herd immunity effect is detectable, and 3) Develop improved estimates of local disease outbreak risk by
integrating potential chains of disease transmission with vaccination coverage data. My mentoring and advisory
team have specialized expertise across the training and research topics, as well as experience leading
interdisciplinary research teams. The outcomes of the research will be an innovative approach to define
epidemiologically-relevant herds in the population, new information regarding the ability to detect the herd
immunity effect across various geographic scales of analysis, and improved estimates of local infectious disease
outbreak risk. The research, training, and mentoring plans proposed in this K01 award will support the
development of a future R-level proposal to examine how the risk of loca...

## Key facts

- **NIH application ID:** 9952890
- **Project number:** 1K01AI151197-01
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Paul Delamater
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $135,974
- **Award type:** 1
- **Project period:** 2020-03-13 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9952890

## Citation

> US National Institutes of Health, RePORTER application 9952890, Understanding the relationship between herd immunity and geographic scale to improve estimates of localized infectious disease outbreak risk (1K01AI151197-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9952890. Licensed CC0.

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