Posts by Collection

current_projects

COVID-19 Modeling and Forecasting


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..

Deep Learning + Epidemic Modeling


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..

Multiscale Epidemic Modeling


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..

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. Read more..

past_projects

Mapping the physics research space


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..

Referendati


Analyzing political preferences and projecting the results of the 2016 Italian constitutional referendum using Twitter, network science, and natural language processing. Read more..

portfolio

publications

Systemic Risk, Contagion, and Financial Networks: A Survey

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.

Spread of Zika virus in the Americas

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

Modeling of future COVID-19 cases, hospitalizations, and deaths, by vaccination rates and nonpharmaceutical intervention scenarios—United States, April–September 2021

Published in Morbidity and Mortality Weekly Report, 70(19), 719., 2021

Recommended citation: 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). Morbidity and Mortality Weekly Report, 70(19), 719. https://www.cdc.gov/mmwr/volumes/70/wr/mm7019e3.htm?s_cid=mm7019e3_w

Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: Four complementary approaches

Published in PLOS Computational Biology, 17(6), e1008994, 2021

Recommended citation: 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). PLOS Computational Biology, 17(6), e1008994. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008994&rev=2

talks

teaching

workingpapers

Preliminary results of models to predict areas in the Americas with increased likelihood of Zika virus transmission in 2017

Published:

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

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

Published:

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

Cryptic transmission of SARS-CoV-2 and the first COVID-19 wave in Europe and the United States

Published:

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 in Europe and the United States. medRxiv 2021.03.24.21254199 https://www.medrxiv.org/content/10.1101/2021.03.24.21254199v1