# New approaches to measuring and containing the spatial spread of human pathogens

> **NIH NIH R35** · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · 2020 · $398,323

## Abstract

Project Summary
In an increasingly crowded and connected world, infectious diseases can spread rapidly between regions, as highlighted by
increasingly frequent global pandemics including SARS, H1N1 influenza, Ebola virus, and now Zika virus. The spatial spread
of disease mediated by human mobility also impacts endemic pathogens like malaria, where control programs and elimination
strategies are undermined by travel to and from high transmission regions, drug resistant parasites are spread by human
mobility, and distinguishing local from imported cases is critical for planning interventions. Understanding the distribution and
dynamics of human populations underlies all aspects of infectious disease control, from the interpretation of surveillance data
to the allocation of resources. Until recently, however, there was a glaring lack of information about human mobility patterns
that spread diseases, particularly in low-income settings.
 New sources of data on human mobility and the spatial spread of diseases are increasingly available. In particular, data
from mobile phones provide passively collected, real-time information on the scale of millions of individuals, with operators
routinely collecting data on the cell towers associated with calls/texts that – when appropriately anonymized – can be modeled
to provide longitudinal maps of where people are and how they are moving. We have been developing approaches to these
models into epidemiological frameworks for understanding the spatial spread of infections, showing that these approaches
provide specific targets for malaria control, accurate predictions about the location and timing of dengue epidemics, and
insights into seasonal peaks of rubella, for example. Sequencing technology is also producing large volumes of geocoded
pathogen genomic data, which can be used to estimate gene flow between populations – a measure of the rate at which
infections are spreading. We have been analyzing malaria genetic data to adapt standard population genetic methods to
accommodate the complex lifecycle and high diversity of the malaria parasite, in order to estimate this internal measure of
migration.
 This proposal brings together these sources of information about the spatial spread of infectious diseases, focusing on
the spread of the malaria parasite in Southeast Asia, working with collaborators collecting parasite genomic data in the region,
mobile operators, and National Malaria Control Programs, to develop practical mathematical tools for integrating mobility
data and pathogen genomics into the risk mapping, drug resistance monitoring, and resource allocation protocols used by
control programs when planning for elimination. The project will lead to an analytical pipeline for generating mobility models
from mobile phone data that can also be applied to other infectious diseases, and in particular in response to emerging
epidemics. New tools are needed to understand the interaction between human population dy...

## Key facts

- **NIH application ID:** 9995510
- **Project number:** 5R35GM124715-04
- **Recipient organization:** HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH
- **Principal Investigator:** Caroline O'Flaherty Buckee
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $398,323
- **Award type:** 5
- **Project period:** 2017-08-15 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9995510

## Citation

> US National Institutes of Health, RePORTER application 9995510, New approaches to measuring and containing the spatial spread of human pathogens (5R35GM124715-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9995510. Licensed CC0.

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