Guiding Documents:
Publications:
Preprints:
Lofgren, E.T., Myers, K., & Fefferman, N.H. (2026). When is Enough Enough? A Proposed Termination Point for the Number of Replicates in Computational Simulations.
Biswas, P., Pedrielli, G., & Candan, K.S. (2026). Causality by Abstraction: Symbolic Rule Learning in Multivariate Timeseries with Large Language Models. CoRR abs/2602.17829.
Karami, H., Anton, J.R., Jang, G., Candan, K.S., & Chowell, G. (2026). Adaptive COVID-19 Trajectory Forecasting Using Reinforcement-Learning Ensembles. International Journal of Forecasting.
Ryan, S.J., Lippi, C., & Meredith, J. (2026). Pandemic prediction in the space age: Use of earth observation system (EOS) data.
Lavelle, T.E., Sánchez, C.A., Andrijevic, M., Becker, D.J., Gibb, R., Gonsalves, G.S., O'Donoghue, Z., Pachauri, S., Pereira, L.M., Poisot, T., Ryan, S.J., Seifert, S.N., Whittaker, C., & Carlson, C.J. (2026). Pandemic risk in the Shared Socioeconomic Pathways.
Bleichrodt, A., Bourouiba, L., Chowell, G., Lofgren, E.T., Michael Reed, J., Ryan, S.J., Fefferman, N.H. (2024). Assembling ensembling: An adventure in approaches across disciplines. arXiv
2026
Mostafa, F., Sharma, K,. & Khan, H. (2026). Development and validation of dementia diagnosis in adults through machine learning frameworks: a cross-sectional study using clinical and imaging data. BMC Medical Informatics and Decision Making.
Hasenjager, M.J., Bailey, M.M., & Fefferman, N.H. (2026). Group composition influences diffusion dynamics via impacts on behavioural production. Animal Behaviour, 233(C), 123482.
Malinzi, J. & Dubey, P. (2026). Editorial: Integrative mathematical models for disease, volume II. Frontiers in Applied Mathematics and Statistics, 12.
Mandal, P., Gorantla, A., Candan, K.S., & Sapino, M.L. (2026). Causal Search for Skylines (CSS): Causally-Informed Selective Data De-Correlation. Proceedings of the ACM on Management of Data, 4(3), 1–27.
Anton, J.R., Sapino, M.L., & Candan, K.S. (2026). Gradient Guided Parameter Space Sampling for Knowledge Discovery With Limited Budgets. IEEE Access, 14, 53958–53977.
Silk, M.J. & Fefferman, N.H. (2026). Multilayer contagions in animal groups. Animal Behaviour, 235(C), 123525.
Sisk, A., Silk, M.J., Williams, N.D., & Fefferman, N.H. (2026). A typology of rules for knowledge exchange in higher-order interactions. PLOS Complex Systems, 3(1), e0000080.
2025
Bohon, S.A. & Hodges, S. (2025). Digging into Indigenous History. Contexts, 24(3), 34–39.
Flory, S.L., Ryan, S.J., Taneja, Y., Munoz, M., Lippi, C., Allan, B. (2025). Impacts of plant invasions on tick-borne disease risk. BioScience, biaf153
McAlister, J.S., Brunner, J.L., Galvin, D.J., & Fefferman, N.H. (2025). A game theoretic treatment of contagion in trade networks. PLOS Computational Biology, 21(12), e1013845.
Fefferman, N.H., Blum, M.J., Bourouiba, L., Gibson, N.L., He, Q., Miller, D.L., Papeș, M., Pasquale, D.K., Verheyen, C., Ryan, S.J. (2025). Identifying outbreak risk factors through case-controls comparisons. Communications Medicine, 5(1).
Nuritdinov, F., Woo, J., Schmidt, M.J., Odjourian, N.M., Cristaldo, M., Dougher, M., Antilus-Sainte, R., Heldt, T., Rhee, K., Bourouiba, L., & Gengenbacher, M. (2025). Experimental system enables studies of Mycobacterium tuberculosis during aerogenic transmission. mBio, 16(10).
Shen, N. & Bourouiba, L. (2025). Assessing bias in susceptible–infected–recovered estimation from aggregated epidemic data. Royal Society Open Science, 12(7).
2024
Reed, J.M., Candan, K.S., Miller, D.L., Bromberg, Y., Schreiner, C., Fefferman, N.H., Blum, M.J., He, Q., McAlister, J.S., Szabo-Rogers, H., & Lofgren, E. (2024). An interdisciplinary perspective of the built-environment microbiome. FEMS Microbiology Ecology, 101(1).
Conference Abstracts:
2026
2025
Poster Presentations:
2025
Presentations:
2026
2025
Podcast/Media:
Public Exhibit:
Software/Tools:
Tutorials:
2026
2025
Workshops:
2026
2025
For all work that results from your APPEX-related effort, please include the following acknowledgement: "This work was supported by NSF grant DBI 2412115 as part of the US NSF Center for Analysis and Prediction of Pandemic Expansion (APPEX)." Similarly, the NSF grant and APPEX Center should be acknowledged in all reports and interviews covering work related to your efforts as part of APPEX.
