# Statistical Methods for Improving Real-Time Public Health Surveillance and Integrated Outbreak Detection

> **NIH NIH F31** · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · 2022 · $38,696

## Abstract

Project Summary/Abstract
 The COVID-19 pandemic has accentuated the need for strong monitoring and surveillance systems.
To conduct early detection and response to emerging infectious diseases, there must be robust analytical
tools that examine historical and current data in order to identify potential aberrations in key health
indicators. This is especially needed when reliable testing and reporting data is lacking. Instead, key
associated indicators, namely mortality and related symptoms to a disease of interest, can be tracked and
analyzed. Two problems exist: (1) reporting delays lead to undercounts in current health indicators data,
and (2) prior anomalies such as spikes in mortality due to past outbreaks distort historical or baseline data.
Thus, the goal is to develop methods to conduct ongoing, rolling surveillance and outbreak detection in the
context of these two issues.
 Two large datasets resulting from collaborations are available: (1) state-level mortality data from the
Centers for Disease Control and Prevention (CDC) and Departments of Public Health (DPH) in Puerto
Rico, Massachusetts, and California from January 2017-December 2021, and (2) Partners in Health (PIH)
routinely collected health management information systems (HMIS) data on COVID-19-associated
indicators, specifically acute respiratory infections (ARI) from 900 health facilities in 8 countries from
January 2016-current. Through the first aim of the proposed research plan, the first dataset will be
analyzed to develop methods for imputing undercounts in current data. In doing so, various
methodological gaps in existing research will be addressed, including accounting for seasonality in
reporting lag patterns and providing measures of uncertainty around estimates. Through the second aim of
the proposed research plan, the second dataset set, along with a simulated version, will be analyzed to
develop methods for rolling outbreak detection by simultaneously addressing two gaps: accounting for
prior data aberrations and optimizing key statistical properties including bias, variance, and appropriate
model fit.
 Both goals are complementary and equally important in infectious disease surveillance. While the
specific datasets and indicators as described above will be analyzed, the developed methods will be
broadly applicable to monitoring of any key health indicators. As COVID-19-related challenges persist and
new threats emerge, statistically rigorous tools for early detection remain of paramount importance. Just
as important is dissemination of these tools in accessible, easily usable open-source software packages, a
key aspect of the proposed research plan.

## Key facts

- **NIH application ID:** 10535624
- **Project number:** 1F31AI172187-01
- **Recipient organization:** HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH
- **Principal Investigator:** Anuraag Gopaluni
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $38,696
- **Award type:** 1
- **Project period:** 2022-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10535624

## Citation

> US National Institutes of Health, RePORTER application 10535624, Statistical Methods for Improving Real-Time Public Health Surveillance and Integrated Outbreak Detection (1F31AI172187-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10535624. Licensed CC0.

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