The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address these questions. Covasim includes country-specific demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, long-term care facilities, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing and protective equipment; pharmaceutical interventions, including vaccination; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. Implemented in pure Python, Covasim has been designed with equal emphasis on performance, ease of use, and flexibility: realistic and highly customized scenarios can be run on a standard laptop in under a minute. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine epidemic dynamics and inform policy decisions in more than a dozen countries in Africa, Asia-Pacific, Europe, and North America.

The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim , an open-source model developed to help address these questions. Covasim includes demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing, hygiene measures, and protective equipment; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine disease dynamics and policy options in Africa, Europe, Oceania, and North America.

We calculated carbon emissions associated with air travel of 4,834 participants at the 2019 annual conference of the American Society of Tropical Medicine and Hygiene (ASTMH). Together, participants traveled a total of 27.7 million miles or 44.6 million kilometers. This equates to 58 return trips to the moon. Estimated carbon dioxide equivalent (CO 2 e) emissions were 8,646 metric tons or the total weekly carbon footprint of approximately 9,366 average American households. These emissions contribute to climate change and thus may exacerbate many of the global diseases that conference attendees seek to combat. Options to reduce conference travel-associated emissions include 1) alternating in-person and online conferences, 2) offering a hybrid in-person/online conference, and 3) decentralizing the conference with multiple conference venues. Decentralized ASTMH conferences may allow for up to 64% reduction in travel distance and 58% reduction in CO 2 e emissions. Given the urgency of the climate crisis and the clear association between global warming and global health, ways to reduce carbon emissions should be considered.