Alessandro Vespignani

Distinguished University Professor

Northeastern University

United States

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About

Alessandro Vespignani is a Distinguished University Professor at Northeastern University in the United States. His research focuses on the dynamics of infectious diseases, particularly in relation to COVID-19, as evidenced by his recent articles on behavioral epidemic models and real-time estimates of virus variants. He has also explored pandemic monitoring through wastewater surveillance and the impacts of mobility on disease spread, contributing significantly to the field of epidemiology.

Articles (15)

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2025

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2024

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2024

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2022

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2022

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2020

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2020

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2020

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2020

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2020

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2020

Collaborators (10)

Mauricio Santillana

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Alessandro Vespignani

Research Interests

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Articles (15)

Comparative evaluation of behavioral epidemic models using COVID-19 data

Pandemic monitoring with global aircraft-based wastewater surveillance networks

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

The decline of the 2022 Italian mpox epidemic: Role of behavior changes and control strategies

Real-time estimates of the emergence and dynamics of SARS-CoV-2 variants of concern: A modeling approach

Anatomy of the first six months of COVID-19 vaccination campaign in Italy

Comparing sources of mobility for modelling the epidemic spread of Zika virus in Colombia

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

Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil

Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China

Correction: Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models

Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models

Phase transitions in information spreading on structured populations

Detecting critical slowing down in highdimensional epidemiological systems

Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study

Collaborators (10)

Mauricio Santillana

Jessica Davis

Michele Tizzoni

Guillaume St-Onge

Nicola Perra

Antoine Allard

Xiyu Ding

Qian Zhang

Maria Litvinova

Matteo Chinazzi

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