Social and cultural forces shape almost every aspect of infectious disease transmission in
human populations, as well as our ability to measure, understand, and respond to …

The Covid-19 pandemic has galvanized scientists to apply machine learning methods to
help combat the crisis. Despite the significant amount of research there exists no …

Modeling complex physical dynamics is a fundamental task in science and engineering.
Traditional physics-based models are first-principled, explainable, and sample-efficient …

Academic researchers, government agencies, industry groups, and individuals have
produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage …

Our ability to forecast epidemics far into the future is constrained by the many complexities of
disease systems. Realistic longer-term projections may, however, be possible under well …

Forecasts and alternative scenarios of COVID-19 mortality have been critical inputs for
pandemic response efforts, and decision-makers need information about predictive …

Scientists seek to understand the causal processes that generate sustainability problems
and determine effective solutions. Yet, causal inquiry in nature–society systems is hampered …

Disease modelling has had considerable policy impact during the ongoing COVID-19
pandemic, and it is increasingly acknowledged that combining multiple models can improve …

Networks allow us to describe a wide range of interaction phenomena that occur in complex
systems arising in such diverse fields of knowledge as neuroscience, engineering, ecology …

Background Since the US reported its first COVID-19 case on January 21, 2020, the science
community has been applying various techniques to forecast incident cases and deaths. To …