Jiangzhuo Chen

TL;DR: EpiFast runs extremely fast for realistic simulations that involve large populations consisting of millions of individuals and their heterogeneous details, dynamic interactions between the disease propagation, the individual behaviors, and the exogenous interventions, as well as large number of replicated runs necessary for statistically sound estimates about the stochastic epidemic evolution.

TL;DR: This paper describes one such agent-based model framework developed for forecasting the 2014-2015 Ebola epidemic in Liberia, and subsequently used during the Ebola forecasting challenge, and concludes by highlighting how such a data-driven approach can be refined and adapted for future epidemics.

TL;DR: In this paper, the authors used a multiple-model approach comprising six models to assess the potential course of COVID-19 in the United States across four scenarios with different vaccination coverage rates and effectiveness estimates and strength and implementation of nonpharmaceutical interventions (NPIs) (such as physical distancing and masking) over a 6-month period (April-September 2021) using data available through March 27, 2021 (4).

TL;DR: This paper presents a detailed prospective analysis on the generation of robust quantitative predictions about temporal trends of flu activity, using several surrogate data sources for 15 Latin American countries, and presents a novel matrix factorization approach using neighborhood embedding to predict flu case counts.

TL;DR: This work proposes DEFSI (Deep Learning Based Epidemic Forecasting with Synthetic Information), an epidemic forecasting framework that integrates the strengths of artificial neural networks and causal methods that significantly outperforms the other methods for short-term ILI forecasting at the state level.