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Quantifying seasonal risk of Eastern Equine Encephalitis virus using stochastic branching process models

Award Number: R03AI196481
ORGANIZATION: NATIONAL INSTITUTE OF ALLERGY & INFECTIOUS DISEASES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 04/23/2026
PERIOD OF PERFORMANCE END DATE: 03/31/2028

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Quantifying seasonal risk of Eastern Equine Encephalitis virus using stochastic branching process models - Project Summary/ Abstract Eastern equine encephalitis virus (EEEV) is an alphavirus (family Togaviridae) transmitted among birds by mosquitoes, primarily Culiseta melanura. Transmission foci of EEEV exist throughout the eastern U.S. and occur in a mix of hardwood and cedar forested wetlands, which are the main habitats of Cs. melanura. Patterns of EEEV detections in Cs. melanura mosquitoes in the northeast US are typified by multi-year interepidemic periods of minimal detections punctuated by intense, single season epidemics. These dynamics make EEEV a difficult zoonotic pathogen to predict. To quantify seasonal probabilities of EEEV epidemics for any given surveillance season in the northeast, our proposed modeling framework combines data on EEEV dynamics from the Connecticut Agricultural Experiment Station (CAES; Connecticut, United States) with Continuous Time Markov Chain models (CTMCs) and Galton-Watson (GW) branching processes. CTMCs are capable tools for analyzing the stochastic nature of pathogen dynamics, while GW processes are capable of estimating probabilities of epidemic extinction based on observed environmental conditions. CTMCs are well suited to represent pathogen dynamics that exhibit small levels of infection and/or acute epidemics, and GW process models work well in combination with stochastic processes based on data that showcases overall low prevalence of disease. Together, the combination of CAES data with these modeling frameworks will examine three main hypotheses: EEEV epidemic probabilities are a function of vector population size (H1); Pathogen introduction drives EEEV epidemic probabilities (H2); Avian herd immunity drives EEEV epidemic probabilities (H3). For H1, we will explore vector population growth dynamics estimated from CAES data or approximated using periodic functions at different vector: host ratios. For H2, we will explore how pathogen introduction through long distance avian migration or passive dispersal from nearby transmission foci influences the timing and magnitude of EEEV epidemics. For H3, we will explore hybrid models that track exposure and immunity in hosts over multiple seasons, which will provide estimates of the interepidemic periodicity of EEEV. H2 and H3 build on models developed in H1, such that while all three hypotheses are likely interrelated and not mutually exclusive, we expect our results to identify the relative contribution of each factor to overall risk of seasonal EEEV epidemics. At the conclusion of our research, we expect to deliver a predictive framework that quantitatively measures seasonal risk of EEEV in the northeast. This would be a significant advancement over current EEEV risk assessments in Connecticut and beyond which are reactive (i.e., management decisions occur as information, such as positive mosquito pools and/or identified infected human cases, are received) and ill-equipped at keeping pace with rapidly developing epizootics as typified by EEEV.


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: 2026 ( Subtotal = $95,691 )
2026	2026	TEXAS TECH UNIVERSITY SYSTEM	2500 BROADWAY	LUBBOCK	TX	79409	LUBBOCK	USA	Allergy and Infectious Diseases Research	000	1	4/27/2026	NEW	$95,691
														Subtotal = $95,691

Grand Total All Awards = $95,691

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Quantifying seasonal risk of Eastern Equine Encephalitis virus using stochastic branching process models

Award Number: R03AI196481

ORGANIZATION: NATIONAL INSTITUTE OF ALLERGY & INFECTIOUS DISEASES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

PERIOD OF PERFORMANCE START DATE: 04/23/2026

PERIOD OF PERFORMANCE END DATE: 03/31/2028

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