# Refining Predictive Models for Neglected and Emerging Infectious Diseases

> **NIH NIH R35** · UNIVERSITY OF GEORGIA · 2024 · $377,500

## Abstract

PROJECT SUMMARY
Predictive models play an essential role in disease prevention and control. Recent advances in scientific
research have allowed more thorough and in-depth data collection from epidemiological studies (e.g., GPS
data, climate data, wearable device data). However, due to the many variables collected and the relatively
short time frame for epidemiological data collection during some of the epidemics, missing information is
unavoidable, and subsequent updates of the database may be necessary. How to incorporate data with partial
information, i.e., with missingness, and predictors measured dynamically over time, into existing models to
perform more accurate and efficient predictions remains a challenge. Recently, the PI and his team have
developed predictive models for various purposes among several neglected and emerging infectious diseases,
including schistosomiasis, COVID-19, and human seasonal influenza. While conducting these studies, we
identified several practical issues prohibiting a broader implementation of the proposed models, such as
missing data and a lack of adaptive mechanisms based on dynamic inflows of predictors. Existing models
adopting the complete data analysis approach will significantly reduce the statistical power and cause potential
bias. Moreover, predictive models applied in epidemiological infectious disease studies often rely on historical
data collected up to a time point without taking into consideration of future data inputs. Meanwhile, the
development in statistical and machine learning methods laid the foundation for new dynamic predictive
models based on trajectory data, with recent progress in functional concurrent regression and incremental
learning. However, these methodological advances have been poorly integrated into field applications. Even in
recent COVID-19 research where advanced dynamic models have been developed, balancing the data flow
and prediction window has not been well studied. In addition, existing models often require a large amount of
variable collection, so a practical two-stage approach allowing limited data collection early on can be more
time- and cost-effective. In this MIRA proposal, we aim at refining predictive models for several neglected and
emerging infectious diseases. Specifically, three coherent projects with distinct research activities will be
pursued, which include: 1) refining hotspot prediction models for schistosomiasis interventions; 2) development
and validation of prognostic risk models for COVID-19 in the US, with methods development on missing data
handling and functional regression for dynamic prediction; 3) development and validation of a vaccine benefits
score for human seasonal influenza. The refined models are expected to be accompanied by new and more
general predictive algorithms involving missing data processing and dynamic prediction mechanisms to
enhance model performance and adaptability. The methodological development from this proposal will a...

## Key facts

- **NIH application ID:** 10890028
- **Project number:** 5R35GM146612-03
- **Recipient organization:** UNIVERSITY OF GEORGIA
- **Principal Investigator:** Ye Shen
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $377,500
- **Award type:** 5
- **Project period:** 2022-09-21 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10890028

## Citation

> US National Institutes of Health, RePORTER application 10890028, Refining Predictive Models for Neglected and Emerging Infectious Diseases (5R35GM146612-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10890028. Licensed CC0.

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