# Development of an Open-Source and Data-Driven Modeling Platform to Monitor and Forecast Disease Activity

> **NIH NIH R01** · BOSTON CHILDREN'S HOSPITAL · 2021 · $364,519

## Abstract

PROJECT SUMMARY
Reliable and real-time municipality-level predictive modeling and forecasts of infectious disease activity have
the potential to transform the way public health decision-makers design interventions such as information
campaigns, preemptive/reactive vaccinations, and vector control, in the presence of health threats across the
world. While the links between disease activity and factors such as: human mobility, climate and
environmental factors, socio-economic determinants, and social media activity have long been known in the
epidemic literature, few efforts have focused on the evident need of developing an open-source platform
capable of leveraging multiple data sources, factors, and disparate modeling methodologies, across a large
and heterogeneous nation to monitor and forecast disease transmission, over four geographic scales (nation,
state, city, and municipal). The overall goal of this project is to develop such a platform.
Our long-term goal is to investigate effective ways to incorporate the findings from multiple disparate studies
on disease dynamics around the globe with local and global factors such as weather conditions, socio-
economic status, satellite imagery and online human behavior, to develop an operational, robust, and real-
time data-driven disease forecasting platform.
The objective of this grant is to leverage the expertise of three complementary scientific research teams
and a wealth of information from a diverse array of data sources to build a modeling platform capable of
combining information to produce real-time short term disease forecasts at the local level. As part of this, we
will evaluate the predictive power of disparate data streams and modeling approaches to monitor and forecast
disease at multiple geographic scales--nation, state, city, and municipality--using Brazil as a test case.
Additionally, we will use machine learning and mechanistic models to understand disease dynamics at
multiple spatial scales, across a heterogeneous country such as Brazil.
Our specific aims will (1) Assess the utility of individual data streams and modeling techniques for disease
forecasting; (2) Fuse modeling techniques and data streams to improve accuracy and robustness at the four
spatial scales; (3) Characterize the basic computational infrastructure necessary to build an operational
disease forecasting platform; and (4) Validate our approach in a real-world setting.
This contribution is significant because It will advance our scientific knowledge on the accuracy and
limitations of disparate data streams and multiple modeling approaches when used to forecast disease
transmission. Our efforts will help produce operational and systematic disease forecasts at a local level (city-
and municipality-level). Moreover, we aim at building a new open-source computational platform for the
epidemiological community to use as a knowledge discovery tool. Finally, we aim at developing this platform
under the guidance of a Subje...

## Key facts

- **NIH application ID:** 10244988
- **Project number:** 5R01GM130668-04
- **Recipient organization:** BOSTON CHILDREN'S HOSPITAL
- **Principal Investigator:** Mauricio Santillana
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $364,519
- **Award type:** 5
- **Project period:** 2018-09-21 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10244988

## Citation

> US National Institutes of Health, RePORTER application 10244988, Development of an Open-Source and Data-Driven Modeling Platform to Monitor and Forecast Disease Activity (5R01GM130668-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10244988. Licensed CC0.

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