# Novel data and approaches for dynamic modeling of human behavior and infectious disease ecology

> **NIH NIH R35** · UNIVERSITY OF CALIFORNIA BERKELEY · 2024 · $401,248

## Abstract

PROJECT SUMMARY
Demographic and social forces that influence human behavior fundamentally shape the epidemiology of
infectious diseases, but are often left out from mathematical models of disease transmission. Our recent work
has added to the growing body of evidence that patterns of how humans come into contact with one another,
and the adoption of behaviors to reduce transmission, are highly heterogeneous across individuals and groups
in a population; these behaviors are strongly influenced by how societies are structured and the socio-
economic, environmental, and political contexts in which epidemics occur. These same forces influence
patterns of exposure, susceptibility, and health outcomes. Human behavior also varies temporally and
spatially, leading to heterogeneities in transmission across space and time and the possibility of dynamical
feedback between the disease environment and the socio-demographic processes that influence human
behavior. Driven by the public health imperative to understand and address disparities in infection exposure
and risk, I propose to develop new models and methods for understanding how demographic and social
processes – from individual heterogeneities to large-scale patterns of population behavior – drive infectious
disease risk across scales. Specifically, my research group will develop a generalized framework for jointly
modeling human behavioral changes and disease dynamics, with bi-directional feedback between behavior
and the disease environment. We will also build and validate models for incorporating data on human mobility
and aggregation at the population level, while incorporating other extrinsic drivers of transmission such as
climate. We will parameterize these models by leveraging detailed human behavioral data from an ongoing
survey platform as well as novel sources of mobile phone and digital trace data, and validate model results by
fitting to epidemiological geo-located time series data. Our modeling approach will be flexible and dynamic,
allowing us to adapt it for specific pathogens and regions of interest. The project results will provide a better
understanding of the complex interactions between human behavior and the fundamental biological processes
underlying disease transmission, with the ultimate aim of guiding control efforts, designing equity-focused
interventions, and improving our ability to predict and prepare for outbreaks.
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## Key facts

- **NIH application ID:** 11019433
- **Project number:** 1R35GM156856-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Ayesha Sanchita Mahmud
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $401,248
- **Award type:** 1
- **Project period:** 2024-09-24 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11019433

## Citation

> US National Institutes of Health, RePORTER application 11019433, Novel data and approaches for dynamic modeling of human behavior and infectious disease ecology (1R35GM156856-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/11019433. Licensed CC0.

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