SBIR Phase I Proposal
SBIR Phase I Proposal
Project Title: Predictive Ecological Modeling for Zoonotic Risk and Space Force Operational Safety
Company / Principal Investigator: P. Barnacle, M.D., Ph.D. / Manley Engineering Corp
Duration: 6–12 months
Funding Requested: $200,000
1. Objective / Goal
The goal of Phase I is to develop a computational lattice-based predator-prey model that predicts zoonotic disease risk and informs Space Force operational planning. The system will integrate:
Stochastic wildlife behaviors using chaotic quantization (May, 1976; Hastings & Powell, 1991)
Population stabilization with thermal dynamic annealing
Environmental variability using fractal cyclic forcing (Levin, 1992; Tilman & Kareiva, 1997)
Human intervention impacts, including hunting, domestication, and habitat modification (Ripple et al., 2014; Ostfeld & Keesing, 2012)
The output will be a proof-of-concept simulation platform with actionable guidance for personnel deployment, field exercises, and risk mitigation.
2. Background / Significance
Zoonotic diseases account for a majority of emerging infectious diseases and pose significant risks to deployed personnel (Karesh et al., 2012; Plowright et al., 2011). The U.S. Space Force increasingly operates in remote terrestrial environments for:
Ground-based satellite tracking stations
Planetary analog simulations
Training and testing exercises
In these areas, wildlife such as lions and buffalo can create direct exposure risks or indirectly affect operations by influencing terrain usage. Existing ecological or epidemiological models fail to integrate complex predator-prey dynamics, stochastic behaviors, environmental variability, and human interventions (Levin, 1992; Hastings & Powell, 1991).
This project fills that gap by providing a predictive tool that:
Anticipates high-risk wildlife-human interfaces
Supports force health protection (Woolley & Glick, 2017; Defense Science Board, 2019)
Enhances operational readiness
Provides a dual-use framework for both military and public health applications
3. Innovation
The project is innovative in five key ways:
Chaotic Quantization of Behavior: Models stochastic predator and prey behaviors affecting contact networks (May, 1976; Hastings & Powell, 1991).
Thermal Dynamic Annealing: Stabilizes populations while preserving emergent dynamics for long-term simulation.
Fractal Environmental Forcing: Models realistic spatial and seasonal resource variability to predict movement and interaction patterns (Levin, 1992; Tilman & Kareiva, 1997).
Human Intervention Modeling: Simulates effects of hunting, domestication, and habitat modification (Ripple et al., 2014; Ostfeld & Keesing, 2012), providing operational insights for Space Force missions.
Operational Decision Support: Outputs actionable guidance for deployment, field placement, and risk mitigation in remote operational environments.
4. Phase I Work Plan
Task 1 – Model Development:
Build the lattice-based predator-prey simulation.
Implement stochastic behaviors (chaotic quantization), thermal annealing, and fractal environmental forcing.
Include human interventions: hunting, domestication, and habitat modification.
Task 2 – Operational Integration:
Adapt outputs for Space Force scenarios, including field station placement, training exercises, and movement planning.
Generate node-level maps of high-risk zones for zoonotic exposure.
Task 3 – Prototype Visualization / Dashboard:
Develop a user-friendly interface showing wildlife movements, intervention impacts, and zoonotic risk levels.
Enable scenario simulation for operational planning.
Task 4 – Proof-of-Concept Testing:
Run multiple simulations with different initial conditions and human interventions.
Validate model predictions for population dynamics, migration corridors, and high-risk zones.
Gather preliminary feedback from potential DoD/Space Force users.
5. Expected Outcomes
A validated prototype simulation platform integrating ecological dynamics and operational factors.
Identification of high-risk wildlife-human interface zones for Space Force personnel.
Initial dashboard interface for operational planning and risk mitigation.
Technical report summarizing results, limitations, and Phase II recommendations.
6. Phase II Potential
Expand model to additional wildlife species and pathogens of operational concern.
Incorporate real-time environmental and telemetry data.
Develop a full-scale operational dashboard for continuous use by Space Force and DoD.
Potential commercialization for other branches of the military or allied defense agencies.
7. Commercialization / Dual-Use Potential
Defense Applications: Operational planning, force health protection, training safety, and field deployment management (Woolley & Glick, 2017; Defense Science Board, 2019).
Civilian Applications: Wildlife disease risk prediction, zoonotic outbreak preparedness, and conservation management (Karesh et al., 2012; Plowright et al., 2011).
8. Key Personnel & Facilities
PI: [Your Name], expertise in computational modeling, ecology, and disease dynamics.
Team: Programmer/simulation specialist, wildlife ecologist, human health analyst.
Facilities: Access to computational servers, GIS tools, and visualization software.
9. References
Lotka, A. J. (1925). Elements of Physical Biology. Williams & Wilkins.
Volterra, V. (1926). Fluctuations in the abundance of a species considered mathematically. Nature, 118, 558–560.
Levin, S. A. (1992). The Problem of Pattern and Scale in Ecology: The Robert H. MacArthur Award Lecture. Ecology, 73(6), 1943–1967.
Tilman, D., & Kareiva, P. (1997). Spatial Ecology: The Role of Space in Population Dynamics and Interspecific Interactions. Princeton University Press.
Hastings, A., & Powell, T. (1991). Chaos in a Three-Species Food Chain. Ecology, 72(3), 896–903.
May, R. M. (1976). Simple mathematical models with very complicated dynamics. Nature, 261, 459–467.
Karesh, W. B., Dobson, A., Lloyd-Smith, J. O., et al. (2012). Ecology of zoonoses: Natural and unnatural histories. The Lancet, 380(9857), 1936–1945.
Plowright, R. K., Foley, P., Field, H. E., et al. (2011). Urban habituation, ecological connectivity and pathogen spillover. Philosophical Transactions of the Royal Society B, 366, 1899–1911.
Ripple, W. J., Estes, J. A., Beschta, R. L., et al. (2014). Status and ecological effects of the world’s largest carnivores. Science, 343(6167), 1241484.
Ostfeld, R. S., & Keesing, F. (2012). Effects of host diversity on infectious disease. Annual Review of Ecology, Evolution, and Systematics, 43, 157–182.
Woolley, J. R., & Glick, P. (2017). Force health protection in remote environments: Lessons from wildlife and ecological management. Military Medicine, 182(11–12), e1947–e1955.
Defense Science Board. (2019). Enhancing Force Readiness in Emerging Biological Threat Environments. Office of the Under Secretary of Defense for Acquisition & Sustainment.
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