GLEAM Project

The Global Epidemic and Mobility project, GLEAM, combines real-world data on populations and human mobility with elaborate stochastic models of disease transmission to deliver analytic and forecasting power to address the challenges faced in developing intervention strategies that minimize the impact of potentially devastating epidemics.

GLEAM is based on a multidisciplinary approach that combines mathematical modeling and computational science with real-world data and sophisticated user interface design.

Modeling

We use elaborate stochastic infectious disease models to supports a wide range of epidemiological studies, covering different types of infections and intervention strategies.

Real-world data

We use real-world data on population and mobility networks and integrate those in structured spatial epidemic models to generate data driven simulations of the worldwide spread of infectious diseases.

Computational thinking

The computer is our laboratory. GLEAM runs on high performance computers to create in-silico experiments that would be hardly feasible in real systems and to guide our understanding of typical non-linear behavior and tipping points of epidemic phenomena.

Tools development

We provide a suite of computational tools to help modeling the spread of a disease, understanding observed epidemic patterns, studying the effectiveness of different intervention strategies. The tools are available to researchers, health-care professionals and policy makers.

For more information about this project and access to the software please visit: https://www.gleamproject.org/.


Papers and Research Reports:

  • Wu, D., Gao, L., Xiong, X., Chinazzi, M., Vespignani, A., Ma, Y.A., & Yu, R. Quantifying Uncertainty in Deep Spatiotemporal Forecasting (2021). ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021.
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  • Lu, F. S., Nguyen, A. T., Link, N. B., Molina, M., Davis, J.T., Chinazzi, M., Xiong, X., Vespignani, A., Lipsitch, M., & Santillana, M. (2021). Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: four complementary approaches. PLOS Computational Biology, 17(6), e1008994.
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  • Borchering, R. K., Viboud, C., Howerton, E., Smith, C. P., Truelove, S., Runge, M. C., Reich, N.G., Contamin, L., Levander, J., Salerno, J., van Panhuis, W., Kinsey, M., Tallaksen, K., Obrecht, R.F., Asher, L., Costello, C., Kelbaugh, M., Wilson, S., Shin, L., Gallagher, M.E., Mullany, L.C., Rainwater-Lovett, K., Lemaitre, J.C., Dent, J., Grantz, K.H., Kaminsky, J., Lauer, S.A., Lee, E.C., Meredith, H.R., Perez-Saez, J., Keegan, L.T., Karlen, D., Chinazzi, M., Davis, J.T., Mu, K., Xiong, X., Pastore y Piontti, A., Vespignani, V., Srivastava, A., Porebski, P., Venkatramanan, S., Adiga, A., Lewis, B., Klahn, B., Outten, J., Schlitt, J., Corbett, P., Telionis, P.A., Wang, L., Peddireddy, A.S., Hurt, B., Chen, J., Vullikanti, A., Marathe, M., Healy, J.M., Slayton, R.B., Biggerstaff, M., Johansson, M.A., Shea, K., & Lessler, J. (2021). Modeling of future COVID-19 cases, hospitalizations, and deaths, by vaccination rates and nonpharmaceutical intervention scenarios—United States, April–September 2021. Morbidity and Mortality Weekly Report, 70(19), 719.
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  • Kogan, N.E., Clemente, L., Liautaud, P., Kaashoek, J., Link, N.B., Nguyen, A.T., Lu, F.S., Huybers P., Resch B., Havas C., Petutschnig A., Davis J.T., Chinazzi, M., Mustafa, B., Hanage, W.P., Vespignani, A., & Santillana, M. (2021). An early warning approach to monitor COVID-19 activity with multiple digital traces in near real-time. Science Advances, 7(10), eabd6989.
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  • Poirier, C., Liu, D., Clemente, L., Ding, X., Chinazzi, M., Davis, J.T., Vespignani, A., & Santillana, M. (2020). Real-time forecasting of the COVID-19 outbreak in Chinese provinces: machine learning approach using novel digital data and estimates from mechanistic models. Journal of Medical Internet Research, 22(8), e20285.
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  • Chinazzi, M., Davis, J.T., Ajelli, M., Gioannini, C., Litvinova, M., Merler, S., Pastore y Piontti, A., Mu, K., Rossi, L., Sun, K., Viboud, C., Xiong, X., Yu, H., Halloran, M.E., Longini, I.M., & Vespignani, A. (2020). The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science, 368(6489), 395-400.
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  • Chinazzi, M., Pastore y Piontti, A., Davis, J.T., Mu, K., Gozzi, N., Perra, N., Ajelli, M., & Vespignani, A. (2021). The path to dominance: geographical heterogeneity in the establishment of the alpha variant in the US. Working paper.
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  • Wu, D., Chinazzi, M., Vespignani, A., Ma, Y. A., & Yu, R. (2021). Accelerating Stochastic Simulation with Interactive Neural Processes. arXiv preprint arXiv:2106.02770.
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  • Davis, J. T., Chinazzi, M., Perra, N., Mu, K., y Piontti, A. P., Ajelli, M., Dean, N.E., Gioannini, C., Litvinova, M., Merler, S., Rossi, L., Sun, K., Xiong, X., Halloran, M.E., Longini, I.M., Viboud, C., & Vespignani, A. (2021). Cryptic transmission of SARS-CoV-2 and the first COVID-19 wave in Europe and the United States. medRxiv 2021.03.24.21254199.
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  • Gozzi, N., Chinazzi, M., Davis, J. T., Mu, K., Pastore y Piontti, A., Ajelli, M., Perra, N., & Vespignani, A. (2021). Estimating the spreading and dominance of SARS-CoV-2 VOC 202012/01 (lineage B.1.1.7) across Europe. medRxiv 2021.02.22.21252235.
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  • Wu, D., Gao, L., Xiong, X., Chinazzi, M., Vespignani, A., Ma, Y., & Yu, R. (2021). DeepGLEAM: a hybrid mechanistic and deep learning model for COVID-19 forecasting. arXiv preprint arXiv:2102.06684.
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  • Cramer, E., Ray, E., Lopez, V., Bracher, J., Brennen, A., Rivadeneira, A., Gerding, A., Gneiting, T., House, K., Huang, Y., Jayawardena, D., Kanji, A., Khandelwal, A., Le, K., Mühlemann, A., Niemi, J., Shah, A., Stark, A., Wang, Y., Wattanachit, N., Zorn, M., Gu, Y., Jain, S., Bannur, N., Deva, A., Kulkarni, M., Merugu, S., Raval, A., Shingi, S., Tiwari, A., White, J., Woody, S., Dahan, M., Fox, S., Gaither, K., Lachmann, M., Meyers, L., Scott, J., Tec, M., Srivastava, A., George, G., Cegan, J., Dettwiller, I., England, W., Farthing, M., Hunter, R., Lafferty, B., Linkov, I., Mayo, M., Parno, M., Rowland, M., Trump, B., Corsetti, S., Baer, T., Eisenberg, M., Falb, K., Huang, Y., Martin, E., McCauley, E., Myers, R., Schwarz, T., Sheldon, D., Gibson, G., Yu, R., Gao, L., Ma, Y., Wu, D., Yan, X., Jin, X., Wang, Y.X., Chen, Y., Guo, L., Zhao, Y., Gu, Q., Chen, J., Wang, L., Xu, P., Zhang, W., Zou, D., Biegel, H., Lega, J., Snyder, T., Wilson, D., McConnell, S., Walraven, R., Shi, Y., Ban, X., Hong, Q.J., Kong, S., Turtle, J., Ben-Nun, M., Riley, P., Riley, S., Koyluoglu, U., DesRoches, D., Hamory, B., Kyriakides, C., Leis, H., Milliken, J., Moloney, M., Morgan, J., Ozcan, G., Schrader, C., Shakhnovich, E., Siegel, D., Spatz, R., Stiefeling, C., Wilkinson, B., Wong, A., Gao, Z., Bian, J., Cao, W., Ferres, J., Li, C., Liu, T.Y., Xie, X., Zhang, S., Zheng, S., Vespignani, A., Chinazzi, M., Davis, J.T., Mu, K., Pastore y Piontti, A., Xiong, X., Zheng, A., Baek, J., Farias, V., Georgescu, A., Levi, R., Sinha, D., Wilde, J., Penna, N., Celi, L., Sundar, S., Cavany, S., Espana, G., Moore, S., Oidtman, R., Perkins, A., Osthus, D., Castro, L., Fairchild, G., Michaud, I., Karlen, D., Lee, E., Dent, J., Grantz, K., Kaminsky, J., Kaminsky, K., Keegan, L., Lauer, S., Lemaitre, J., Lessler, J., Meredith, H., Perez-Saez, J., Shah, S., Smith, C., Truelove, S., Wills, J., Kinsey, M., Obrecht, R., Tallaksen, K., Burant, J., Wang, L., Gao, L., Gu, Z., Kim, M., Li, X., Wang, G., Wang, Y., Yu, S., Reiner, R., Barber, R., Gaikedu, E., Hay, S., Lim, S., Murray, C., Pigott, D., Prakash, B., Adhikari, B., Cui, J., Rodriguez, A., Tabassum, A., Xie, J., Keskinocak, P., Asplund, J., Baxter, A., Oruc, B., Serban, N., Arik, S., Dusenberry, M., Epshteyn, A., Kanal, E., Le, L., Li, C.L., Pfister, T., Sava, D., Sinha, R., Tsai, T., Yoder, N., Yoon, J., Zhang, L., Abbott, S., Bosse, N., Funk, S., Hellewel, J., Meakin, S., Munday, J., Sherratt, K., Zhou, M., Kalantari, R., Yamana, T., Pei, S., Shaman, J., Ayer, T., Adee, M., Chhatwal, J., Dalgic, O., Ladd, M., Linas, B., Mueller, P., Xiao, J., Li, M., Bertsimas, D., Lami, O., Soni, S., Bouardi, H., Wang, Y., Wang, Q., Xie, S., Zeng, D., Green, A., Bien, J., Hu, A., Jahja, M., Narasimhan, B., Rajanala, S., Rumack, A., Simon, N., Tibshirani, R., Tibshirani, R., Ventura, V., Wasserman, L., O’Dea, E., Drake, J., Pagano, R., Walker, J., Slayton, R., Johansson, M., Biggerstaff, M., & Reich, N. (2021). Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US. medRxiv 2021.02.03.21250974v2.
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  • Davis, J.T., Chinazzi, M., Perra, N., Mu, K., Pastore y Piontti, A., Ajelli, M., Dean, N.E., Gioannini, C., Litvinova, M., Merler, S., Rossi, L., Sun, K., Xiong, X., Halloran, M.E., Longini, I.M., Viboud, C., & Vespignani, A. (2020). Estimating the establishment of local transmission and the cryptic phase of the COVID-19 pandemic in the USA. medRxiv 2020.07.06.20140285.
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  • Chinazzi, M., Davis, J. T., Mu, K., Pastore y Piontti, A., Perra, N., Scarpino, S.V., & Vespignani, A. (2020). Preliminary estimates of the international spreading risk associated with the SARS-CoV-2 VUI 202012/01. Northeastern University, Laboratory for the Modeling of Biological and Socio-technical Systems research report, December 26th, 2020. Available online at https://www.mobs-lab.org/2019ncov.html.
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  • Ray, E. L., Wattanachit, N., Niemi, J., Kanji, A. H., House, K., Cramer, E. Y., Bracher, J., Zheng, A., Yamana, T.K., Xiong, X., Woody, S., Wang, Y., Wang, L., Walraven, R.L., Tomar, V., Sherratt, K., Sheldon, D., Reiner, R.C., Prakash, B.A., Osthus, D., Li, M.L., Lee, E.C., Koyluoglu, U., Keskinocak, P., Gu, Y., Gu, Q., George, G.E., España, G., Corsetti, S., Chhatwal, J., Cavany, S., Biegel, H., Ben-Nun, M., Walker, J., Slayton, R., Lopez, V., Biggerstaff, M., Johansson, M.A., Reich, N.G., & COVID-19 Forecast Hub Consortium. (2020). Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the us. medRxiv 2020.08.19.20177493.
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  • MOBS Laboratory. Estimating the onset of local transmission of the COVID-19 epidemic in African countries (Report V1.0). Northeastern University, Laboratory for the Modeling of Biological and Socio-technical Systems research report, March 17th, 2020. Available online at https://www.mobs-lab.org/2019ncov.html.

  • Chinazzi, M., Davis, J.T., Mu, K., Pastore y Piontti, A., Ajelli, M., Dean, N.E., Gioannini, C., Litvinova, M., Merler, S., Rossi, L., Sun, K., Viboud, C., Halloran, M.E., Longini, I.M., & Vespignani, A. (2020). Estimating the risk of sustained community transmission of COVID-19 outside Mainland China. Northeastern University, Laboratory for the Modeling of Biological and Socio-technical Systems research report, March 11th, 2020. Available online at https://www.mobs-lab.org/2019ncov.html.

  • Chinazzi, M., Davis, J. T., Gioannini, C., Litvinova, M., Pastore y Piontti, A., Rossi, L., Xiong, X., Halloran, M.E., Longini, I.M., & Vespignani, A. (2020). Preliminary assessment of the International Spreading Risk Associated with the 2019 novel Coronavirus (2019-nCoV) outbreak in Wuhan City. Northeastern University, Laboratory for the Modeling of Biological and Socio-technical Systems research report. 8 reports between January 17th and January 29th, 2020. Available online at https://www.mobs-lab.org/2019ncov.html.

  • Sun, K., Zhang, Q., Pastore y Piontti, A., Chinazzi, M., Mistry, D., Dean, N.E., Rojas, D.P., Merler, S., Poletti, P., Rossi, L., Halloran, M.E., Longini, I.M., & Vespignani, A. (2018). Quantifying the risk of local Zika virus transmission in the contiguous US during the 2015–2016 ZIKV epidemic. BMC Medicine, 16(1), 1-12.
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  • Zhang, Q., Sun, K., Chinazzi, M., Pastore y Piontti, A., Dean, N.E., Rojas, D.P., Merler, S., Mistry, D., Poletti, P., Rossi, L., Bray, M., Halloran, M.E., Longini, I.M., & Vespignani, A. (2017). Spread of Zika virus in the Americas. Proceedings of the National Academy of Sciences, 114(22), E4334-E4343.
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