publications
publications by categories in reversed chronological order.
2024
- IFAAMASValue-based Resource Matching with Fairness Criteria: Application to Agricultural Water TradingAbhijin Adiga, Yohai Trabelsi, Tanvir Ferdousi, and 8 more authorsProceedings of the 2024 International Conference on Autonomous Agents and Multiagent Systems, 2024
Optimal allocation of agricultural water in the event of droughts is an important global problem. In addressing this problem, many aspects, including the welfare of farmers, the economy, and the environment, must be considered. Under this backdrop, our work focuses on several resource-matching problems accounting for agents with multi-crop portfolios, geographic constraints, and fairness. First, we address a matching problem where the goal is to maximize a welfare function in two-sided markets where buyers’ requirements and sellers’ supplies are represented by value functions that assign prices (or costs) to specified volumes of water. For the setting where the value functions satisfy certain monotonicity properties, we present an efficient algorithm that maximizes a social welfare function. When there are minimum water requirement constraints, we present a randomized algorithm which ensures that the constraints are satisfied in expectation. For a single seller–multiple buyers setting with fairness constraints, we design an efficient algorithm that maximizes the minimum level of satisfaction of any buyer. We also present computational complexity results that highlight the limits on the generalizability of our results. We evaluate the algorithms developed in our work with experiments on both real-world and synthetic data sets with respect to drought severity, value functions, and seniority of agents.
2023
- UVA BIEvaluating the Impact of Bailout Strategies on Financial NetworksHanyang Li and Tanvir FerdousiComputing for Global Challenges (C4GC) Symposium, 2023
Interbank lending can be considered an interconnected banking network comprising interbank liabilities and assets. In such networks, one bank’s failure to pay its liabilities can lead to a cascading effect, triggering a system-wide shockwave, as seen in the infamous 2008 financial crisis and the recent Silicon Valley Bank collapse. The Eisenberg-Noe model provides a framework to simulate cascading failures as financial contagions. We study and compare the effectiveness of graph centrality-based intervention strategies in mitigating financial contagions.
- medRxivA Graph Based Deep Learning Framework for Predicting Spatio-Temporal Vaccine HesitancySifat Afroj Moon, Rituparna Datta, Tanvir Ferdousi, and 4 more authorsmedRxiv, 2023
Predicting vaccine hesitancy at a fine spatial level assists local policymakers in taking timely action. Vaccine hesitancy is a heterogeneous phenomenon that has a spatial and temporal aspect. This paper proposes a deep learning framework that combines graph neural networks (GNNs) with sequence module to forecast vaccine hesitancy at a higher spatial resolution. This integrated framework only uses population demographic data with historical vaccine hesitancy data. The GNN learns the spatial cross-regional demographic signals, and the sequence module catches the temporal dynamics by leveraging historical data. We formulate the problem on a weighted graph, where nodes are zip codes and edges are generated using three distinct mechanisms: 1) adjacent graph - if two zip codes have a shared boundary, they will form an edge between them; 2) distance-based graph - every pair of zip codes are connected with an edge having a weight that is a function of centroid distances, and 3) mobility graph - edges represent the number of contacts between any two zip codes, where the contacts are derived from an activity-based social contact network. Our framework effectively predicts the spatio-temporal dynamics of vaccine hesitancy at the zip-code level when the mobility network is used to formulate the graph. Experiments on the real-world vaccine hesitancy data from the All-Payer Claims Database (APCD) show that our framework can outperform a range of baselines.
- ACM/SIGSIMA Machine Learning Framework to Explain Complex Geospatial Simulations: A Climate Change Case StudyTanvir Ferdousi, Mingliang Liu, Kirti Rajagopalan, and 7 more authorsWinter Simulation Conference, 2023
The explainability of large and complex simulation models is an open problem. We present a framework to analyze such models by processing multidimensional data through a pipeline of target variable computation, clustering, supervised classification, and feature importance analysis. As a use case, the well-known large-scale hydrology and crop systems simulator VIC-CropSyst is utilized to evaluate how climate change may affect water availability in Washington, United States. We study how snowmelt varies with climate variables (temperature, precipitation) to identify different response characteristics. Based on these characteristics, spatial units are clustered into six distinct classes. A random forest classifier is used with Shapley values to rank static soil and land parameters that help detect each class. The results also include an analysis of risk across different classes to identify areas vulnerable to climate change. This paper demonstrates the usefulness of the proposed framework in providing explainability for large and complex simulations.
2022
- IEEEA Web-Based System for Contagion Simulations on Networked PopulationsTanvir Ferdousi, Aparna Kishore, Lucas Machi, and 3 more authors2022 IEEE 18th International Conference on e-Science (e-Science), 2022
Motivated by a wide range of applications, research on agent-based models of contagion propagation over networks has attracted a lot of attention in the literature. Many of the available software systems for simulating such agent-based models require users to download software, build the executable, and set up execution environments. Further, running the resulting executable may require access to high performance computing clusters. Our work describes an open access software system (NetSimS) that works under the “Modeling and Simulation as a Service” (MSaaS) paradigm. It enables users to run simulations by selecting models and parameter values, initial conditions, and networks through a web interface. The system supports a variety of models and networks with millions of nodes and edges. In addition to the simulator, the system includes components that enable users to choose initial conditions for simulations in a variety of ways, to analyze the data generated through simulations, and to produce plots from the data. We describe the components of NetSimS and carry out a performance evaluation of the system. We also discuss two case studies carried out on large networks using the system. NetSimS is a major component within net.science, a cyberinfrastructure for network science.
2021
- IEEEA Windowed Correlation-Based Feature Selection Method to Improve Time Series Prediction of Dengue Fever CasesTanvir Ferdousi, Lee W Cohnstaedt, and Caterina M ScoglioIEEE Access, 2021
The performance of data-driven models depends on training samples. For accurately predicting dengue fever cases, historical incidence data are inadequate in many locations. This work aims to enhance temporally limited dengue case data by methodological addition of epidemically relevant case data from nearby locations as predictors (features). A novel framework is presented for windowing incidence data and computing time-shifted correlation-based metrics to quantify feature relevance. The framework ranks incidence data of adjacent locations around a target by combining metrics based on correlation, spatial distance, and local prevalence. Recurrent neural network models achieve up to 33.6% accuracy improvement on average using the proposed method. These models achieve mean absolute error (MAE) values as low as 0.128 on [0, 1] normalized incidence data for a municipality with the highest dengue prevalence in Brazil’s Espirito Santo. When predicting aggregate cases over geographical ecoregions, the models improve by 16.5%, using only 6.5% of ranked incidence data. This paper also presents two correlation window allocation methods: fixed-size and outbreak detection. Both perform comparably well, although the outbreak detection method uses less data for computations. The proposed framework is generalized, and it can be used to improve time-series predictions of many spatiotemporal datasets.
2020
- IEEEA Permissioned Distributed Ledger for the US Beef Cattle Supply ChainTanvir Ferdousi, Don Gruenbacher, and Caterina M ScoglioIEEE Access, 2020
Distributed ledgers using blockchain have gained traction in the supply chain industry due to their unique features of immutability and transparency. They have given people the abilities to solve business problems which were impossible using traditional systems. The US beef cattle industry lacks adequate traceability as most of the farm owners consider such data confidential; possibly harming their businesses if exposed. This article attempts to solve this problem by proposing a smart contract-based supply chain framework using a permissioned blockchain network. This system supports anonymity for the users to protect identities and lets every user store their data locally, while ensuring that the changes are recorded in the chain with cryptographic proofs (hashes). The proposed framework also has methods for the users to perform business transactions and transfer animal-related data to new owners as required. In addition to that, smart contracts have been added to conduct anonymous surveys for data aggregation. The technical contribution of this article is in the system design on how users, data, and communications are handled to maintain data ownership and user privacy while ensuring immutability and confidentiality at different levels of data aggregation. This article also contains an evaluation of the system using integration tests where the outcomes meet the expected design requirements. The framework can be applied to the US beef cattle industry as well as other supply chains with minimal modifications.
2019
- NATUREUnderstanding the survival of Zika virus in a vector interconnected sexual contact networkTanvir Ferdousi, Lee W Cohnstaedt, David Scott Mcvey, and 1 more authorScientific Reports, 2019
The recent outbreaks of the insect-vectored Zika virus have demonstrated its potential to be sexually transmitted, which complicates modeling and our understanding of disease dynamics. Autochthonous outbreaks in the US mainland may be a consequence of both modes of transmission, which affect the outbreak size, duration, and virus persistence. We propose a novel individual-based interconnected network model that incorporates both insect-vectored and sexual transmission of this pathogen. This model interconnects a homogeneous mosquito vector population with a heterogeneous human host contact network. The model incorporates the seasonal variation of mosquito abundance and characterizes host dynamics based on age group and gender in order to produce realistic projections. We use a sexual contact network which is generated on the basis of real world sexual behavior data. Our findings suggest that for a high relative transmissibility of asymptomatic hosts, Zika virus shows a high probability of sustaining in the human population for up to 3 months without the presence of mosquito vectors. Zika outbreaks are strongly affected by the large proportion of asymptomatic individuals and their relative transmissibility. The outbreak size is also affected by the time of the year when the pathogen is introduced. Although sexual transmission has a relatively low contribution in determining the epidemic size, it plays a role in sustaining the epidemic and creating potential endemic scenarios.
- NATUREEstimation of swine movement network at farm level in the US from the Census of Agriculture dataSifat A Moon, Tanvir Ferdousi, Adrian Self, and 1 more authorScientific Reports, 2019
Swine movement networks among farms/operations are an important source of information to understand and prevent the spread of diseases, nearly nonexistent in the United States. An understanding of the movement networks can help the policymakers in planning effective disease control measures. The objectives of this work are: (1) estimate swine movement probabilities at the county level from comprehensive anonymous inventory and sales data published by the United States Department of Agriculture - National Agriculture Statistics Service database, (2) develop a network based on those estimated probabilities, and (3) analyze that network using network science metrics. First, we use a probabilistic approach based on the maximum information entropy method to estimate the movement probabilities among different swine populations. Then, we create a swine movement network using the estimated probabilities for the counties of the central agricultural district of Iowa. The analysis of this network has found evidence of the small-world phenomenon. Our study suggests that the US swine industry may be vulnerable to infectious disease outbreaks because of the small-world structure of its movement network. Our system is easily adaptable to estimate movement networks for other sets of data, farm animal production systems, and geographic regions.
- PLOSGeneration of swine movement network and analysis of efficient mitigation strategies for African swine fever virusTanvir Ferdousi, Sifat Afroj Moon, Adrian Self, and 1 more authorPLoS ONE, 2019
Animal movement networks are essential in understanding and containing the spread of infectious diseases in farming industries. Due to its confidential nature, movement data for the US swine farming population is not readily available. Hence, we propose a method to generate such networks from limited data available in the public domain. As a potentially devastating candidate, we simulate the spread of African swine fever virus (ASFV) in our generated network and analyze how the network structure affects the disease spread. We find that high in-degree farm operations (i.e., markets) play critical roles in the disease spread. We also find that high in-degree based targeted isolation and hypothetical vaccinations are more effective for disease control compared to other centrality-based mitigation strategies. The generated networks can be made more robust by validation with more data whenever more movement data will be available.
2018
- AIMSQuantifying the impact of early-stage contact tracing on controlling Ebola diffusionNarges Montazeri Shahtori, Tanvir Ferdousi, Caterina Scoglio, and 1 more authorMathematical Biosciences & Engineering,, 2018
Recent experience of the Ebola outbreak in 2014 highlighted the importance of immediate response measure to impede transmission in the early stage. To this aim, efficient and effective allocation of limited resources is crucial. Among the standard interventions is the practice of following up with the recent physical contacts of the infected individuals – known as contact tracing. In an effort to understand the effects of contact tracing protocols objectively, we explicitly develop a model of Ebola transmission incorporating contact tracing. Our modeling framework is individual-based, patient-centric, stochastic and parameterizable to suit early-stage Ebola transmission. Notably, we propose an activity driven network approach to contact tracing, and estimate the basic reproductive ratio of the epidemic growth in different scenarios. Exhaustive simulation experiments suggest that early contact tracing paired with rapid hospitalization can effectively impede the epidemic growth. Resource allocation needs to be carefully planned to enable early detection of the contacts and rapid hospitalization of the infected people.