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Merging machine learning and mechanistic models to improve prediction and inference in emerging epidemics

Award Number: R01GM140564
ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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PROJECT SUMMARY When an outbreak of an established or emerging infectious disease occurs we ask a standard set of questions that are critical to a lifesaving public health response: Where will future incidence occur? How many cases will there be? And where can we most effectively intervene? The proposed research is motivated by real world instances where answering these questions was critical to making practical public health decisions, and current methods came up short: from deciding if and where to build additional Ebola Treatment Units in the 2014-15 West African Ebola epidemic, to identifying priority districts where oral cholera vaccine should be used in the 2016-17 cholera outbreak in Yemen, to picking locations where sufficient cases might occur to selecting and prioritizing interventions to slow the spread of COVID-19 worldwide. Forecasts informing such decisions are typically generated either using an epidemic model that relies on knowledge of the disease transmission mechanism and epidemic theory or using a statistical model to project the expected number of cases based on the relationship between covariates and observed counts. However, both approaches are subject to limitations, particularly early in an epidemic when few cases are observed. This project is based on the overarching scientific premise that inferences that combine the strengths of mechanistic epidemic models and statistical covariate models will substantially outperform either approach alone in forecasting and making decisions to confront emerging infectious disease threats. Specifically, this project aims to (1) Develop a framework to forecast incidence in ongoing outbreaks that merges mechanistic and machine learning approaches; (2) Validate the framework using retrospective data and apply the framework to inform decision making in emerging epidemics; (3) Integrate this inferential forecasting framework into causal decision theory to optimize critical actions in the public health response to emerging epidemics; and (4) Develop accessible and extensible tools for forecasting and decision analysis in infectious disease epidemics. We will validate these approaches using rigorous simulation studies and by applying the proposed approaches to retrospective data from important recent epidemics (e.g., Ebola, Cholera and COVID-19, as mentioned above). We will prospectively apply our approach to inform the response to emerging disease threats that occur during the project period, including the ongoing COVID-19 pandemic. To ensure that the tools developed are useful, efficient, and user friendly, we will work with international humanitarian organizations responding to epidemics. Successful completion of these aims will provide a flexible and validated framework for forecasting and decision making during ongoing epidemics, while allowing for innovation in mechanistic and statistical approaches. In doing so it will provide tools to optimize responses and reduce morbidity and mortality during public health crises.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2024 ( Subtotal = $413,057 )
2024	2024	UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL	104 AIRPORT DR STE 2200	CHAPEL HILL	NC	27599	ORANGE	USA	Biomedical Research and Research Training	000	5	12/20/2023	NON-COMPETING CONTINUATION	$413,057
														Subtotal = $413,057

Issue Date FY: 2023 ( Subtotal = $458,952 )
2023	2023	UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL	104 AIRPORT DR STE 2200	CHAPEL HILL	NC	27599	ORANGE	USA	Biomedical Research and Research Training	002	4	12/28/2022	NON-COMPETING CONTINUATION	$413,057
2023	2023	UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL	104 AIRPORT DR STE 2200	CHAPEL HILL	NC	27599	ORANGE	USA	Biomedical Research and Research Training	003	4	2/15/2023	NON-COMPETING CONTINUATION	$45,895
2023	2021	UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL	104 AIRPORT DR STE 2200	CHAPEL HILL	NC	27599	ORANGE	USA	Biomedical Research and Research Training	001	2	11/25/2022	CHANGE OF GRANTEE / TRAINING INSTITUTION / AWARDING INSTITUTION	$30,492
2023	2021	THE JOHNS HOPKINS UNIVERSITY	3400 N CHARLES ST	BALTIMORE	MD	21218	BALTIMORE CITY	USA	Biomedical Research and Research Training	000	1	11/25/2022	NEW	-$30,492
														Subtotal = $458,952

Issue Date FY: 2022 ( Subtotal = $479,359 )
2022	2022	UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL	104 AIRPORT DR STE 2200	CHAPEL HILL	NC	27599	ORANGE	USA	Biomedical Research and Research Training	002	3	9/23/2022	NON-COMPETING CONTINUATION	$458,952
2022	2021	UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL	104 AIRPORT DR STE 2200	CHAPEL HILL	NC	27599	ORANGE	USA	Biomedical Research and Research Training	001	2	8/19/2022	CHANGE OF GRANTEE / TRAINING INSTITUTION / AWARDING INSTITUTION	$357,821
2022	2021	JOHNS HOPKINS UNIVERSITY, THE	3400 N CHARLES ST	BALTIMORE	MD	21218	BALTIMORE CITY	USA	Biomedical Research and Research Training	000	1	8/18/2022	NEW	-$337,414
														Subtotal = $479,359

Issue Date FY: 2021 ( Subtotal = $429,701 )
2021	2021	JOHNS HOPKINS UNIVERSITY, THE	3400 N CHARLES ST	BALTIMORE	MD	21218	BALTIMORE CITY	USA	Biomedical Research and Research Training	000	1	1/26/2021	NEW	$429,701
														Subtotal = $429,701

Grand Total All Awards = $1,781,069

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Merging machine learning and mechanistic models to improve prediction and inference in emerging epidemics

Award Number: R01GM140564

ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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