The Epidemiology Workbench: a Tool for Communities to Strategize in Response to COVID-19 and other Infectious Diseases

Abstract

COVID-19 poses a dramatic challenge to health, community life, and the economy of communities across the world. While the properties of the virus are similar from place to place, the impact has been dramatically different from place to place, due to such factors as population density, mobility, age distribution, etc. Thus, optimum testing and social distancing strategies may also be different from place to place. The Epidemiology Workbench provides access to an agent-based model in which demographic, geographic, and public health information a community together with a social distancing and testing strategy may be input, and a range of possible outcomes computed, to inform local authorities on coping strategies. The model is adaptable to other infectious diseases, and to other strains of coronavirus. The tool is illustrated by scenarios for the cities of Urbana and Champaign, Illinois, the home of the University of Illinois at Urbana-Champaign. Our calculations suggest that massive testing is the most effective strategy to combat the likely increase in local cases due to mass ingress of a student population carrying a higher viral load than that currently present in the community.

CppyABM: An open‐source agent‐based modeling library to integrate C++ and Python

“…For further discussion, refer to the original article. 44 It can be also seen that the stochasticity in the system causes large variations in the results (notice the shaded area in Figure 8). Therefore, the high number of replicas is essential to draw a valid trend in the results.…”

“…It can be seen that testing can significantly control the number of active cases as the more fraction of the population is tested, the lower the active cases. For further discussion, refer to the original article 44 . It can be also seen that the stochasticity in the system causes large variations in the results (notice the shaded area in Figure 8).…”

“…To demonstrate the application of CppyABM for a real-world problem, we reimplement the model published by Núñez-Corrales and Jakobsson 2020, 44 which is available on the GitHub repository. † † This study simulates the spread of infectious diseases such as COVID-19.…”

“…The complete definition of these terms can be found in the original paper. 44 The behavioral properties of agents are dependent on the epidemiological state as well as the given policies (introduced later in the text). For example, when an agent is in the state of Symptomatic, it is quarantined and is not allowed to move from its location until the recovery time is exhausted.…”

Bugs in Gyms - New Zealand attitudes to infection risk management in a gym setting: A pilot study

“…Poor hygiene practices, lack of awareness of infection risks, and greater population density, have contributed to the challenge of addressing the spread of infectious diseases, which people's ability to travel long distances in a short amount of time continue to spread. Shared community spaces, including gyms, have been identified as areas which potentially harbour infectious diseases (Núñez-Corrales & Jakobsson, 2020). Traditionally, cleanliness in gym facilities has been maintained to a high standard through education of patrons and staff.…”