Publications – Center for Communicable Disease Dynamics

Predicting dengue incidence leveraging internet-based data sources. A case study in 20 cities in Brazil.

Author(s): Koplewitz G, Lu F, Clemente L, Buckee C, Santillana MJournal: PLoS Negl Trop DisPMID: 35073316 January 2022

The dengue virus affects millions of people every year worldwide, causing large epidemic outbreaks that disrupt people's lives and severely…

A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles.

Author(s): McGough SF, Clemente L, Kutz JN, Santillana MJournal: J R Soc InterfacePMID: 34129785 June 2021

Transmission of dengue fever depends on a complex interplay of human, climate and mosquito dynamics, which often change in time…

An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time.

Author(s): Kogan NE, Clemente L, Liautaud P, Kaashoek J, Link NB, Nguyen AT, Lu FS, Huybers P, Resch B, Havas C, Petutschnig A, Davis J, Chinazzi M, Mustafa B, Hanage WP, Vespignani A, Santillana MJournal: Sci AdvPMID: 33674304 March 2021

Given still-high levels of coronavirus disease 2019 (COVID-19) susceptibility and inconsistent transmission-containing strategies, outbreaks have continued to emerge across the…

Real-time forecasting of the COVID-19 outbreak in Chinese provinces: Machine learning approach using novel digital data and estimates from mechanistic models.

Author(s): Poirier C, Liu D, Clemente L, Ding X, Chinazzi M, Vespignani A, Santillana MJournal: J Med Internet ResPMID: 32730217 July 2020

The inherent difficulty to identify and monitor emerging outbreaks caused by novel pathogens can lead to their rapid spread; and…

A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models.

Author(s): Liu D, Clemente L, Poirier C, Ding X, Chinazzi M, Vespignani A, Santillana MJournal: ArXivPMID: 32550248 April 2020

We present a timely and novel methodology that combines disease estimates from mechanistic models with digital traces, via interpretable machine-learning…