# SSCIMA: Integrating Analysis of Socio-economic Sub-population Dynamics to Improve Spatial Models of Infectious Disease

> **NIH NIH U54** · NORTHERN ARIZONA UNIVERSITY · 2023 · $352,251

## Abstract

PROJECT SUMMARY
The striking disparities in disease dynamics and health outcomes between various socio-demographic groups
observed during the SARS-CoV-2 pandemic have highlighted the critical need for improved modeling
approaches that help us understand and predict such disparities. Most modeling studies currently produce
insights at spatial scales that are too coarse (e.g., counties, states) in scale, and are usually insensitive to local
socio-demographic variations in disease dynamics; this limits their practical value for informing public health
planning in local communities. What is needed is a set of standardized methods to efficiently create fine-
grained models capable of exposing and capturing key spatial features and socio-demographic factors that
impact transmission dynamics and disease outcomes, and that can reveal how different socio-demographic
sub-groups may experience disparate disease outcomes. Here we propose a novel SSCIMA (Social-Spatial
Clustering, Interconnection, and Movement Analysis) modeling approach to efficiently expose linkages
between local mobility, socio-demographic composition, and evolving disease surveillance and to optimize the
construction of meta-population disease models that can make more accurate disease forecasts at the scales
of census blocks (i.e., local neighborhoods). We will use SARS-CoV-2 and simulated data sets to drive the
design and rigorous testing of generalized methods and software that will be useful for future pandemic
preparedness. In Aim 1, we develop statistical methods that ingest disease data, mobility patterns, and socio-
demographic statistics at the scale of census blocks and use these data to determine the features that most
strongly explain patterns of local and regional transmission dynamics as well as disease outcomes (e.g.,
hospitalization rates). In Aim 2, we develop efficient methods leveraging the linkages revealed under Aim 1 to
fit meta-population models to sparse data at the scale of census blocks, integrating mobility data, and socio-
demographic features to yield high-fidelity meta-population models structured directly based on evolving
observed patterns of disease dynamics. Analyses driven by simulated and real data will reveal the potential for
SSCIMA-driven configuration of meta-population models to improve local forecast accuracy; and we will also
produce freely available software and a cloud-based modeling portal to allow exploration and testing of our
method and tools. In Aim 3, we will focus on dissemination and education, developing a new educational
module for deployment within SHERC’s existing outreach infrastructure, as well as a half-day training
workshop for the modeling community to learn about, engage with, and provide feedback on our technique and
tools. We expect the SCCIMA approach to enable more rapid, spatially refined, and equity-focused modeling
efforts that will better equip us for future epidemic events.

## Key facts

- **NIH application ID:** 10707497
- **Project number:** 5U54MD012388-07
- **Recipient organization:** NORTHERN ARIZONA UNIVERSITY
- **Principal Investigator:** Joseph Mihaljevic
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $352,251
- **Award type:** 5
- **Project period:** 2017-09-20 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10707497

## Citation

> US National Institutes of Health, RePORTER application 10707497, SSCIMA: Integrating Analysis of Socio-economic Sub-population Dynamics to Improve Spatial Models of Infectious Disease (5U54MD012388-07). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10707497. Licensed CC0.

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