# Spatio-temporal data integration methods for infectious disease surveillance

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA BERKELEY · 2021 · $716,058

## Abstract

Project Summary/Abstract
 Effective surveillance systems are essential to developing targeted, efficient public health responses to
infectious disease. In many domestic and global settings, multiple systems are being employed with varying
degrees of overlap and with each system designed to achieve different surveillance objectives. The objective of
this research project is to develop and distribute a spatio-temporal data integration toolset (“OPTI-SURVEIL”) for
analyzing data from multiple surveillance systems, reducing spatial uncertainty by linking data across systems,
space and time. With the support and guidance of the US CDC and China CDC (see letters of support), we will
develop practical, multi-system analytical tools that will: (1) provide improved estimates of disease burden across
space and time; (2) assess individual-level and system-level sensitivity and bias in case ascertainment; and (3)
identify redundancies and gaps in current surveillance strategies. Approach: In Aim 1, we will develop spatial-
temporal capture-recapture methods for performing data integration at the individual-level. The methods will
account for individual-level heterogeneity in case ascertainment and key system-level parameters (sensitivity,
bias, dependency). The methods will be extended to consider multiple diseases simultaneously, in order to
address the increasing interest in the burden of co-infections. In Aim 2, we will develop spatial-temporal Bayesian
hierarchical models for performing data integration using aggregated surveillance data that commonly arise from
public surveillance databases. The models will exploit spatial-temporal dependence in county-level case
numbers, account for spatial-temporal missing data due to system availability, and probabilistically incorporate
information on ascertainment sensitivity and bias at the individual-level and at the system-level. In Aim 3, we will
develop simulation-based methods to perform joint evaluation of multiple surveillance system designs. These
methods will be applied to optimize a system’s design to maximize case detection while considering resource
constraints, system sensitivity/bias, and the presence of other systems. We will apply OPTIM-SURVEIL to
existing data in China, and will focus on four infectious diseases of global importance: tuberculosis (TB), malaria,
schistosomiasis and hookworm. These diseases exhibit a diverse set of surveillance challenges, including
diagnostic accuracy, increasingly rare case counts as elimination is approached, variability in disease severity,
and challenges identifying key co-infections (e.g., TB and malaria). Expected Outcomes: The methods that we
will develop and distribute will provide timely and practical tools for analyzing data from multiple disease
surveillance systems. For the four target infections, we will answer specific questions about how information on
specific surveillance architectures and properties—such as alternative spatial configurations of sy...

## Key facts

- **NIH application ID:** 10098288
- **Project number:** 5R01AI125842-05
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Howard H Chang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $716,058
- **Award type:** 5
- **Project period:** 2017-02-01 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10098288

## Citation

> US National Institutes of Health, RePORTER application 10098288, Spatio-temporal data integration methods for infectious disease surveillance (5R01AI125842-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10098288. Licensed CC0.

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