The project focuses on two related topics: agricultural commodity flow and invasive species spread. With rapid population growth, shrinking farm acreage, and intensive agriculture, society has come to critically depend on long-distance flows of agricultural commodities. While this phenomenon has led to the availability of a variety of commodities around the year, it has also made our food systems more vulnerable to perturbations due to unpredictable and often extreme weather events, changes in policies, and political and social phenomena. Therefore, modeling them in all their complexity and identifying the environmental, economic, and social factors that drive them is critical to ensuring food security, biodiversity, health, and economic stability. Further, as our food systems become increasingly sophisticated, they are more susceptible to the threat of pests. For example, trade networks act as conduits, enabling their rapid dispersal through crops, livestock, packaging, propagating material, etc. There is a great need to account for such complexities and improve our capacity to respond to such threats.

The backdrop for this project is the threat of invasion from Tuta absoluta or the South American tomato leafminer. Indigenous to South America, T. absoluta was accidentally introduced into Spain in 2006, and since then has rapidly spread throughout Europe, Africa, Western and Central Asia, the Indian subcontinent, and parts of Central America. With tomato being a commercially important crop, this invasion has had significant global impact.

The overarching goals of this project are to develop (i) a framework to represent and analyze spatiotemporal flows of agricultural commodities in the United States by incorporating diverse open source datasets, statistical & machine learning techniques, network science, and computational models; and (ii) a multi-pathway modeling framework that couples the above commodity flow models with ecological and bioeconomic models to assess the spread and impact of non-indigenous invasive species. The project contributes novel network-based approaches for data integration, data analytics, and computational modeling. The project employs state-of-the-art statistical and machine-learning techniques for data integration and network construction. We develop methods for structural and dynamical analysis of these networks in a novel context of directed and time-varying networks.

In the context of invasive species, the developed tools will provide policymakers with guidance and support to identify vulnerabilities in the food system, inform monitoring efforts, and assess various intervention strategies. These analyses will be particularly valuable and timely in addressing the imminent threat of T. absoluta invasion.

Data exploration: Several datasets corresponding to commodity flow, crop production, economic impact, and climate have been explored. Some of the main datasets include Freight Analysis Framework (FAF), vegetable production from National Agricultural Statistics Service (NASS), and trade matrix from Food and Agricultural Organization (FAO).

Modeling and analysis of commodity flows: We have developed a general framework for constructing the spatiotemporal representation of production, flow, and consumption of agricultural commodities. These data representations are derived by fusing gridded, administrative-level, survey datasets on production, trade, and consumption. Further, they are periodic temporal networks reflecting seasonal variations in production and trade of the crop. We apply this approach to create networks of tomato flow for several regions. Using statistical methods and network analysis, we gain insights into spatiotemporal dynamics of production and trade. Our results suggest that agricultural systems are increasingly vulnerable to attacks through trade of commodities due to their vicinity to regions of high demand and seasonal variations in production and flows.

Multi-pathway models for invasive species spread and interventions: We developed a multi-scale intervention framework to optimize control of the spread process to delay or stifle the spread of the pest in the event of its introduction. Optimal control of epidemics is a challenging problem even for simple diffusion processes over static networks. We developed this algorithm for the multi-scale epidemiological process on a temporal network in the context of invasive species spread across a landscape. In this setting, we study the problem of group-scale interventions, where the objective is to find an optimal set of regions represented by groups of nodes to minimize the spread under budget constraints and intervention delays. We present an integer linear programming-based algorithm for finding effective group-scale interventions and prove rigorous bounds on its performance. Our results indicate that early intervention has the benefit of significant reduction in spread for low-budget and stable solutions under model uncertainty.