In response to the ongoing COVID-19 outbreak, we extended the Global Epidemic and Mobility model (GLEAM) to incorporate the effects of travel restrictions, non-pharmaceutical interventions, age-structured contact patterns, and vaccination campaigns to study, project, and forecast the evolution of the COVID-19 pandemic. Read more..
We combine traditional epidemic modeling approaches with state-of-the-art machine learning and deep learning methods to improve forecasts, accelerate large-scale stochastic simulations, and reconstruct the early stages of an epidemic. Read more..
We introduce a multiscale modeling approach to study the diffusion and impact of SARS-CoV-2 at both global and local scale combining epidemic models that work at different geographical resolutions. Read more..
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. Read more..
We characterize collective physical distancing—mobility reductions, minimization of contacts, shortening of contact duration—in response to the COVID-19 pandemic by analyzing de-identified location data for a panel of over 5.5 million anonymized, opted-in U.S. devices. Read more..
Scientific discoveries do not occur in vacuum but rather by connecting existing pieces of knowledge in new and creative ways. Mapping the relation and structure of scientific knowledge is therefore central to our understanding of the dynamics of scientific production. In this project we introduce a new approach to generate scientific knowledge maps based on a machine learning approach. Read more..
In this project we use a data-driven global epidemic model to project the spatio-temporal spread of Zika virus in the Americas during the 2015-2016 epidemic. Read more..
Analyzing political preferences and projecting the results of the 2016 Italian constitutional referendum using Twitter, network science, and natural language processing. Read more..
Published in Banking Integration and Financial Crisis: Some Recent Developments, Bilbao, Fundacion BBVA, 2015
Recommended citation: Chinazzi, M.; Fagiolo, G. (2015). Banking Integration and Financial Crisis: Some Recent Developments, Bilbao, Fundacion BBVA, pp 115-161.
Published in Proceedings of the National Academy of Sciences, 114(22), E4334-E4343, 2017
Recommended citation: 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). Proceedings of the National Academy of Sciences, 114(22), E4334-E4343. https://www.pnas.org/content/114/22/E4334
Published in Nature Human Behaviour, 4(9), 964-971., 2020
Recommended citation: Aleta, A., Martin-Corral, D., Pastore y Piontti, A., Ajelli, M., Litvinova, M., Chinazzi, M., Dean, N.E., Halloran, M.E., Longini, I.M., Merler, S., Pentland, A., Vespignani, A., Moro, E., & Moreno, Y. (2020). Nature Human Behaviour, 4(9), 964-971 https://www.nature.com/articles/s41562-020-0931-9
Published in Journal of Medical Internet Research, 22(8), e20285., 2020
Recommended citation: Poirier, C., Liu, D., Clemente, L., Ding, X., Chinazzi, M., Davis, J.T., Vespignani, A., & Santillana, M. (2020). Journal of Medical Internet Research, 22(8), e20285 https://www.jmir.org/2020/8/e20285/
Recommended citation: Wu, D., Gao, L., Chinazzi, M., Xiong, X., Vespignani, A., Ma, Y., & Yu, R. (2021). KDD 21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. https://dl.acm.org/doi/10.1145/3447548.3467325
Recommended citation: 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. Nature. https://www.nature.com/articles/s41586-021-04130-w
Published in The Lancet Regional Health - Americas, 2022
Recommended citation: Du, Z., Wang, L., Bai, Y., Wang, X., Pandey, A., Fitzpatrick, M.C., Chinazzi, M., Pastore y Piontti, A., Hupert, N., Lachmann, M., Vespignani, A., Galvani, A.P., Cowling, B.J., & Meyers, L.A. (2022). The Lancet Regional Health - Americas, 8, 100182. https://www.sciencedirect.com/science/article/pii/S2667193X21001782
Recommended citation: Asher, J., Barker, C., Chen, G., Cummings, D., __Chinazzi, M.__, Daniel-Wayman, S., Fischer, M., Ferguson, N., Follman, D., Halloran, M.E., Johansson, M., Kugeler, K., Kwan, J., Lessler, J., Longini, I.M., Merler, S., Monaghan, A., Pastore y Piontti, A., Perkins, A., Prevots, D.R., Reiner, R., Rossi, L., Rodriguez-Barraquer, I., Siraj, A.S., Sun, K., Vespignani, A., Zhang, Q., & ZIKAVAT Collaboration. (2017). bioRxiv 187591 https://www.biorxiv.org/content/10.1101/187591v2
Recommended citation: Aleta, A., Martín-Corral, D., Bakker, M. A., Pastore y Piontti, A., Ajelli, M., Litvinova, M., Chinazzi, M., Dean, N.E., Halloran, M.E., Longini, I.M., Pentland, A., Vespignani, A., Moreno, Y., & Moro, E. (2020). medRxiv, 2020.12.15.20248273. https://www.medrxiv.org/content/10.1101/2020.12.15.20248273v1
Recommended citation: 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., ODea, E., Drake, J., Pagano, R., Walker, J., Slayton, R., Johansson, M., Biggerstaff, M., & Reich, N. (2021). medRxiv, 2021.02.03.21250974. https://www.medrxiv.org/content/10.1101/2021.02.03.21250974v1
Recommended citation: Cramer, E.Y., Huang, Y., Wang, Y., Ray, E.L., Cornell, M., Bracher, J., Brennen, A., Castero Rivadeneira, A.J., Gerding, A., House, K., Jayawardena, D., Kanji, A.H., Khandelwal, A., Le, K., Niemi, J., Stark, A., Shah, A., Wattanchit, N., Zorn, M.W., & Reich, N.G., on behalf of the US COVID-19 Forecast Hub Consortium (2021). medRxiv, 2021.11.04.21265886v1. https://www.medrxiv.org/content/10.1101/2021.11.04.21265886v1