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Article ; Online: A multiscale modeling framework for Scenario Modeling: Characterizing the heterogeneity of the COVID-19 epidemic in the US.

Chinazzi, Matteo / Davis, Jessica T / Y Piontti, Ana Pastore / Mu, Kunpeng / Gozzi, Nicolò / Ajelli, Marco / Perra, Nicola / Vespignani, Alessandro

Epidemics

2024 Volume 47, Page(s) 100757

Abstract: The Scenario Modeling Hub (SMH) initiative provides projections of potential epidemic scenarios in the United States (US) by using a multi-model approach. Our contribution to the SMH is generated by a multiscale model that combines the global epidemic ... ...

Abstract	The Scenario Modeling Hub (SMH) initiative provides projections of potential epidemic scenarios in the United States (US) by using a multi-model approach. Our contribution to the SMH is generated by a multiscale model that combines the global epidemic metapopulation modeling approach (GLEAM) with a local epidemic and mobility model of the US (LEAM-US), first introduced here. The LEAM-US model consists of 3142 subpopulations each representing a single county across the 50 US states and the District of Columbia, enabling us to project state and national trajectories of COVID-19 cases, hospitalizations, and deaths under different epidemic scenarios. The model is age-structured, and multi-strain. It integrates data on vaccine administration, human mobility, and non-pharmaceutical interventions. The model contributed to all 17 rounds of the SMH, and allows for the mechanistic characterization of the spatio-temporal heterogeneities observed during the COVID-19 pandemic. Here we describe the mathematical and computational structure of our model, and present the results concerning the emergence of the SARS-CoV-2 Alpha variant (lineage designation B.1.1.7) as a case study. Our findings show considerable spatial and temporal heterogeneity in the introduction and diffusion of the Alpha variant, both at the level of individual states and combined statistical areas, as it competes against the ancestral lineage. We discuss the key factors driving the time required for the Alpha variant to rise to dominance within a population, and quantify the impact that the emergence of the Alpha variant had on the effective reproduction number at the state level. Overall, we show that our multiscale modeling approach is able to capture the complexity and heterogeneity of the COVID-19 pandemic response in the US.
Language	English
Publishing date	2024-03-05
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2467993-8
ISSN	1878-0067 ; 1755-4365
ISSN (online)	1878-0067
ISSN	1755-4365
DOI	10.1016/j.epidem.2024.100757
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Disentangled Multi-Fidelity Deep Bayesian Active Learning

Wu, Dongxia / Niu, Ruijia / Chinazzi, Matteo / Ma, Yian / Yu, Rose

2023

Abstract: To balance quality and cost, various domain areas of science and engineering run simulations at multiple levels of sophistication. Multi-fidelity active learning aims to learn a direct mapping from input parameters to simulation outputs at the highest ... ...

Abstract	To balance quality and cost, various domain areas of science and engineering run simulations at multiple levels of sophistication. Multi-fidelity active learning aims to learn a direct mapping from input parameters to simulation outputs at the highest fidelity by actively acquiring data from multiple fidelity levels. However, existing approaches based on Gaussian processes are hardly scalable to high-dimensional data. Deep learning-based methods often impose a hierarchical structure in hidden representations, which only supports passing information from low-fidelity to high-fidelity. These approaches can lead to the undesirable propagation of errors from low-fidelity representations to high-fidelity ones. We propose a novel framework called Disentangled Multi-fidelity Deep Bayesian Active Learning (D-MFDAL), which learns the surrogate models conditioned on the distribution of functions at multiple fidelities. On benchmark tasks of learning deep surrogates of partial differential equations including heat equation, Poisson's equation and fluid simulations, our approach significantly outperforms state-of-the-art in prediction accuracy and sample efficiency.
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2023-05-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Ensemble

Bay, Clara / St-Onge, Guillaume / Davis, Jessica T / Chinazzi, Matteo / Howerton, Emily / Lessler, Justin / Runge, Michael C / Shea, Katriona / Truelove, Shaun / Viboud, Cecile / Vespignani, Alessandro

Epidemics

2024 Volume 46, Page(s) 100748

Abstract: Throughout the COVID-19 pandemic, scenario modeling played a crucial role in shaping the decision-making process of public health policies. Unlike forecasts, scenario projections rely on specific assumptions about the future that consider different ... ...

Abstract	Throughout the COVID-19 pandemic, scenario modeling played a crucial role in shaping the decision-making process of public health policies. Unlike forecasts, scenario projections rely on specific assumptions about the future that consider different plausible states-of-the-world that may or may not be realized and that depend on policy interventions, unpredictable changes in the epidemic outlook, etc. As a consequence, long-term scenario projections require different evaluation criteria than the ones used for traditional short-term epidemic forecasts. Here, we propose a novel ensemble procedure for assessing pandemic scenario projections using the results of the Scenario Modeling Hub (SMH) for COVID-19 in the United States (US). By defining a "scenario ensemble" for each model and the ensemble of models, termed "Ensemble
MeSH term(s)	Humans ; United States/epidemiology ; Pandemics ; Forecasting ; COVID-19/epidemiology ; Public Policy ; Communication
Language	English
Publishing date	2024-02-08
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2467993-8
ISSN	1878-0067 ; 1755-4365
ISSN (online)	1878-0067
ISSN	1755-4365
DOI	10.1016/j.epidem.2024.100748
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Article ; Online: Estimating the impact of COVID-19 vaccine inequities: a modeling study.

Gozzi, Nicolò / Chinazzi, Matteo / Dean, Natalie E / Longini, Ira M / Halloran, M Elizabeth / Perra, Nicola / Vespignani, Alessandro

Nature communications

2023 Volume 14, Issue 1, Page(s) 3272

Abstract: Access to COVID-19 vaccines on the global scale has been drastically hindered by structural socio-economic disparities. Here, we develop a data-driven, age-stratified epidemic model to evaluate the effects of COVID-19 vaccine inequities in twenty lower ... ...

Abstract	Access to COVID-19 vaccines on the global scale has been drastically hindered by structural socio-economic disparities. Here, we develop a data-driven, age-stratified epidemic model to evaluate the effects of COVID-19 vaccine inequities in twenty lower middle and low income countries (LMIC) selected from all WHO regions. We investigate and quantify the potential effects of higher or earlier doses availability. In doing so, we focus on the crucial initial months of vaccine distribution and administration, exploring counterfactual scenarios where we assume the same per capita daily vaccination rate reported in selected high income countries. We estimate that more than 50% of deaths (min-max range: [54-94%]) that occurred in the analyzed countries could have been averted. We further consider scenarios where LMIC had similarly early access to vaccine doses as high income countries. Even without increasing the number of doses, we estimate an important fraction of deaths (min-max range: [6-50%]) could have been averted. In the absence of the availability of high-income countries, the model suggests that additional non-pharmaceutical interventions inducing a considerable relative decrease of transmissibility (min-max range: [15-70%]) would have been required to offset the lack of vaccines. Overall, our results quantify the negative impacts of vaccine inequities and underscore the need for intensified global efforts devoted to provide faster access to vaccine programs in low and lower-middle-income countries.
MeSH term(s)	Humans ; COVID-19 Vaccines ; COVID-19/epidemiology ; COVID-19/prevention & control ; Vaccines ; Vaccination ; Income
Chemical Substances	COVID-19 Vaccines ; Vaccines
Language	English
Publishing date	2023-06-06
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-023-39098-w
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Article ; Online: Estimating the effect of social inequalities on the mitigation of COVID-19 across communities in Santiago de Chile.

Gozzi, Nicolò / Tizzoni, Michele / Chinazzi, Matteo / Ferres, Leo / Vespignani, Alessandro / Perra, Nicola

Nature communications

2021 Volume 12, Issue 1, Page(s) 2429

Abstract: We study the spatio-temporal spread of SARS-CoV-2 in Santiago de Chile using anonymized mobile phone data from 1.4 million users, 22% of the whole population in the area, characterizing the effects of non-pharmaceutical interventions (NPIs) on the ... ...

Abstract	We study the spatio-temporal spread of SARS-CoV-2 in Santiago de Chile using anonymized mobile phone data from 1.4 million users, 22% of the whole population in the area, characterizing the effects of non-pharmaceutical interventions (NPIs) on the epidemic dynamics. We integrate these data into a mechanistic epidemic model calibrated on surveillance data. As of August 1, 2020, we estimate a detection rate of 102 cases per 1000 infections (90% CI: [95-112 per 1000]). We show that the introduction of a full lockdown on May 15, 2020, while causing a modest additional decrease in mobility and contacts with respect to previous NPIs, was decisive in bringing the epidemic under control, highlighting the importance of a timely governmental response to COVID-19 outbreaks. We find that the impact of NPIs on individuals' mobility correlates with the Human Development Index of comunas in the city. Indeed, more developed and wealthier areas became more isolated after government interventions and experienced a significantly lower burden of the pandemic. The heterogeneity of COVID-19 impact raises important issues in the implementation of NPIs and highlights the challenges that communities affected by systemic health and social inequalities face adapting their behaviors during an epidemic.
MeSH term(s)	Algorithms ; COVID-19/epidemiology ; COVID-19/prevention & control ; COVID-19/virology ; Chile/epidemiology ; Communicable Disease Control/methods ; Communicable Disease Control/statistics & numerical data ; Disease Transmission, Infectious/prevention & control ; Disease Transmission, Infectious/statistics & numerical data ; Humans ; Incidence ; Models, Theoretical ; Pandemics ; SARS-CoV-2/isolation & purification ; SARS-CoV-2/physiology ; Socioeconomic Factors ; Time Factors
Language	English
Publishing date	2021-04-23
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-021-22601-6
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Article ; Online: Estimating the impact of COVID-19 vaccine inequities

Nicolò Gozzi / Matteo Chinazzi / Natalie E. Dean / Ira M. Longini Jr / M. Elizabeth Halloran / Nicola Perra / Alessandro Vespignani

Nature Communications, Vol 14, Iss 1, Pp 1-

a modeling study

2023 Volume 10

Abstract: Abstract Access to COVID-19 vaccines on the global scale has been drastically hindered by structural socio-economic disparities. Here, we develop a data-driven, age-stratified epidemic model to evaluate the effects of COVID-19 vaccine inequities in ... ...

Abstract	Abstract Access to COVID-19 vaccines on the global scale has been drastically hindered by structural socio-economic disparities. Here, we develop a data-driven, age-stratified epidemic model to evaluate the effects of COVID-19 vaccine inequities in twenty lower middle and low income countries (LMIC) selected from all WHO regions. We investigate and quantify the potential effects of higher or earlier doses availability. In doing so, we focus on the crucial initial months of vaccine distribution and administration, exploring counterfactual scenarios where we assume the same per capita daily vaccination rate reported in selected high income countries. We estimate that more than 50% of deaths (min-max range: [54−94%]) that occurred in the analyzed countries could have been averted. We further consider scenarios where LMIC had similarly early access to vaccine doses as high income countries. Even without increasing the number of doses, we estimate an important fraction of deaths (min-max range: [6−50%]) could have been averted. In the absence of the availability of high-income countries, the model suggests that additional non-pharmaceutical interventions inducing a considerable relative decrease of transmissibility (min-max range: [15−70%]) would have been required to offset the lack of vaccines. Overall, our results quantify the negative impacts of vaccine inequities and underscore the need for intensified global efforts devoted to provide faster access to vaccine programs in low and lower-middle-income countries.
Keywords	Science ; Q
Subject code	306
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Characterizing collective physical distancing in the U.S. during the first nine months of the COVID-19 pandemic.

Klein, Brennan / LaRock, Timothy / McCabe, Stefan / Torres, Leo / Friedland, Lisa / Kos, Maciej / Privitera, Filippo / Lake, Brennan / Kraemer, Moritz U G / Brownstein, John S / Gonzalez, Richard / Lazer, David / Eliassi-Rad, Tina / Scarpino, Samuel V / Vespignani, Alessandro / Chinazzi, Matteo

PLOS digital health

2024 Volume 3, Issue 2, Page(s) e0000430

Abstract: The COVID-19 pandemic offers an unprecedented natural experiment providing insights into the emergence of collective behavioral changes of both exogenous (government mandated) and endogenous (spontaneous reaction to infection risks) origin. Here, we ... ...

Abstract	The COVID-19 pandemic offers an unprecedented natural experiment providing insights into the emergence of collective behavioral changes of both exogenous (government mandated) and endogenous (spontaneous reaction to infection risks) origin. Here, we characterize collective physical distancing-mobility reductions, minimization of contacts, shortening of contact duration-in response to the COVID-19 pandemic in the pre-vaccine era by analyzing de-identified, privacy-preserving location data for a panel of over 5.5 million anonymized, opted-in U.S. devices. We define five indicators of users' mobility and proximity to investigate how the emerging collective behavior deviates from typical pre-pandemic patterns during the first nine months of the COVID-19 pandemic. We analyze both the dramatic changes due to the government mandated mitigation policies and the more spontaneous societal adaptation into a new (physically distanced) normal in the fall 2020. Using the indicators here defined we show that: a) during the COVID-19 pandemic, collective physical distancing displayed different phases and was heterogeneous across geographies, b) metropolitan areas displayed stronger reductions in mobility and contacts than rural areas; c) stronger reductions in commuting patterns are observed in geographical areas with a higher share of teleworkable jobs; d) commuting volumes during and after the lockdown period negatively correlate with unemployment rates; and e) increases in contact indicators correlate with future values of new deaths at a lag consistent with epidemiological parameters and surveillance reporting delays. In conclusion, this study demonstrates that the framework and indicators here presented can be used to analyze large-scale social distancing phenomena, paving the way for their use in future pandemics to analyze and monitor the effects of pandemic mitigation plans at the national and international levels.
Language	English
Publishing date	2024-02-06
Publishing country	United States
Document type	Journal Article
ISSN	2767-3170
ISSN (online)	2767-3170
DOI	10.1371/journal.pdig.0000430
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Article ; Online: Anatomy of the first six months of COVID-19 vaccination campaign in Italy.

Gozzi, Nicolò / Chinazzi, Matteo / Davis, Jessica T / Mu, Kunpeng / Pastore Y Piontti, Ana / Ajelli, Marco / Perra, Nicola / Vespignani, Alessandro

PLoS computational biology

2022 Volume 18, Issue 5, Page(s) e1010146

Abstract: We analyze the effectiveness of the first six months of vaccination campaign against SARS-CoV-2 in Italy by using a computational epidemic model which takes into account demographic, mobility, vaccines data, as well as estimates of the introduction and ... ...

Abstract	We analyze the effectiveness of the first six months of vaccination campaign against SARS-CoV-2 in Italy by using a computational epidemic model which takes into account demographic, mobility, vaccines data, as well as estimates of the introduction and spreading of the more transmissible Alpha variant. We consider six sub-national regions and study the effect of vaccines in terms of number of averted deaths, infections, and reduction in the Infection Fatality Rate (IFR) with respect to counterfactual scenarios with the actual non-pharmaceuticals interventions but no vaccine administration. Furthermore, we compare the effectiveness in counterfactual scenarios with different vaccines allocation strategies and vaccination rates. Our results show that, as of 2021/07/05, vaccines averted 29, 350 (IQR: [16, 454-42, 826]) deaths and 4, 256, 332 (IQR: [1, 675, 564-6, 980, 070]) infections and a new pandemic wave in the country. During the same period, they achieved a -22.2% (IQR: [-31.4%; -13.9%]) IFR reduction. We show that a campaign that would have strictly prioritized age groups at higher risk of dying from COVID-19, besides frontline workers and the fragile population, would have implied additional benefits both in terms of avoided fatalities and reduction in the IFR. Strategies targeting the most active age groups would have prevented a higher number of infections but would have been associated with more deaths. Finally, we study the effects of different vaccination intake scenarios by rescaling the number of available doses in the time period under study to those administered in other countries of reference. The modeling framework can be applied to other countries to provide a mechanistic characterization of vaccination campaigns worldwide.
MeSH term(s)	COVID-19/epidemiology ; COVID-19/prevention & control ; COVID-19 Vaccines ; Humans ; Immunization Programs ; Italy/epidemiology ; SARS-CoV-2 ; Vaccination ; Vaccines
Chemical Substances	COVID-19 Vaccines ; Vaccines
Language	English
Publishing date	2022-05-25
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1010146
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Article: Estimating the impact of COVID-19 vaccine allocation inequities: a modeling study.

Gozzi, Nicolò / Chinazzi, Matteo / Dean, Natalie E / Longini, Ira M / Halloran, M Elizabeth / Perra, Nicola / Vespignani, Alessandro

medRxiv : the preprint server for health sciences

2022

Abstract: Access to COVID-19 vaccines on the global scale has been drastically impacted by structural socio-economic inequities. Here, we develop a data-driven, age-stratified epidemic model to evaluate the effects of COVID-19 vaccine inequities in twenty lower ... ...

Abstract	Access to COVID-19 vaccines on the global scale has been drastically impacted by structural socio-economic inequities. Here, we develop a data-driven, age-stratified epidemic model to evaluate the effects of COVID-19 vaccine inequities in twenty lower middle and low income countries (LMIC) sampled from all WHO regions. We focus on the first critical months of vaccine distribution and administration, exploring counterfactual scenarios where we assume the same per capita daily vaccination rate reported in selected high income countries. We estimate that, in this high vaccine availability scenario, more than 50% of deaths (min-max range: [56% - 99%]) that occurred in the analyzed countries could have been averted. We further consider a scenario where LMIC had similarly early access to vaccine doses as high income countries; even without increasing the number of doses, we estimate an important fraction of deaths (min-max range: [7% - 73%]) could have been averted. In the absence of equitable allocation, the model suggests that considerable additional non-pharmaceutical interventions would have been required to offset the lack of vaccines (min-max range: [15% - 75%]). Overall, our results quantify the negative impacts of vaccines inequities and call for amplified global efforts to provide better access to vaccine programs in low and lower middle income countries.
Language	English
Publishing date	2022-11-18
Publishing country	United States
Document type	Preprint
DOI	10.1101/2022.11.18.22282514
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Article ; Online: Forecasting hospital-level COVID-19 admissions using real-time mobility data.

Klein, Brennan / Zenteno, Ana C / Joseph, Daisha / Zahedi, Mohammadmehdi / Hu, Michael / Copenhaver, Martin S / Kraemer, Moritz U G / Chinazzi, Matteo / Klompas, Michael / Vespignani, Alessandro / Scarpino, Samuel V / Salmasian, Hojjat

Communications medicine

2023 Volume 3, Issue 1, Page(s) 25

Abstract: Background: For each of the COVID-19 pandemic waves, hospitals have had to plan for deploying surge capacity and resources to manage large but transient increases in COVID-19 admissions. While a lot of effort has gone into predicting regional trends in ... ...

Abstract	Background: For each of the COVID-19 pandemic waves, hospitals have had to plan for deploying surge capacity and resources to manage large but transient increases in COVID-19 admissions. While a lot of effort has gone into predicting regional trends in COVID-19 cases and hospitalizations, there are far fewer successful tools for creating accurate hospital-level forecasts. Methods: Large-scale, anonymized mobile phone data has been shown to correlate with regional case counts during the first two waves of the pandemic (spring 2020, and fall/winter 2021). Building off this success, we developed a multi-step, recursive forecasting model to predict individual hospital admissions; this model incorporates the following data: (i) hospital-level COVID-19 admissions, (ii) statewide test positivity data, and (iii) aggregate measures of large-scale human mobility, contact patterns, and commuting volume. Results: Incorporating large-scale, aggregate mobility data as exogenous variables in prediction models allows us to make hospital-specific COVID-19 admission forecasts 21 days ahead. We show this through highly accurate predictions of hospital admissions for five hospitals in Massachusetts during the first year of the COVID-19 pandemic. Conclusions: The high predictive capability of the model was achieved by combining anonymized, aggregated mobile device data about users' contact patterns, commuting volume, and mobility range with COVID hospitalizations and test-positivity data. Mobility-informed forecasting models can increase the lead-time of accurate predictions for individual hospitals, giving managers valuable time to strategize how best to allocate resources to manage forthcoming surges.
Language	English
Publishing date	2023-02-14
Publishing country	England
Document type	Journal Article
ISSN	2730-664X
ISSN (online)	2730-664X
DOI	10.1038/s43856-023-00253-5
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