Multiscale Epidemic Modeling


We introduce a multi-scale modeling approach to study the diffusion and impact of SARS-CoV-2 at both global and local scale. Specifically, we combine distinct epidemic models that work at different geographical resolutions: the Global Epidemic and Mobility model (GLEAM) and a country-specific Local Epidemic and Mobility model for the United States (LEAM-US). Both models are multi-strain, stochastic, spatial, and age-structured metapopulation models that integrate time varying human mobility data, high resolution socio-demographic data, location-specific policy intervention timelines, and information on vaccination campaigns. The proposed approach aims at striking a balance between the data and computational requirements needed for modeling the circulation of a virus at a global scale while achieving high spatio-temporal resolution of the disease dynamic within a specific target country.

This modeling framework is currently used to characterize the effects of introductions of globally circulating SARS-CoV-2 variants of concern in the United States, both at the state and metropolitan level, and the results are used in the projections of the COVID-19 epidemic trajectory as contributed to the Scenario Modeling Hub initiative.

At the following online dashboard we report the results of this modeling approach. The projections are intended to bound plausible outbreak trajectories and should not be considered as forecasts of the most likely outcome. Considerable uncertainty is inherent when modeling the trajectory of COVID-19 over long timeframes because of deviations that may or may not be captured by the different scenarios (e.g., vaccine hesitancy, change in the pace of NPIs relaxation, etc.).


Papers and Research Reports:

  • 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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  • 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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  • 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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  • Gozzi, N., Tizzoni, M., Chinazzi, M., Ferres, L., Vespignani, A., & Perra, N. (2021). Estimating the effect of social inequalities on the mitigation of COVID-19 across communities in Santiago de Chile. Nature communications, 12(1), 1-9.
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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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