COVID-19 Urban Resurgence Simulator
Towards the end of April this year, we launched the COVID-19 Urban Resurgence Simulator. Looking at the current epidemiologic developments as we get into the fall season, the pertenance of modeling the SARS-CoV2 developments become even more clear. Continuous efforts in defining and refining parametres in the simulator are ongoing. This all in an effort to provide the necessary modeling capacity and insights in the evolution of the virus for particular urban evironments.
The original post (April 30th) below:
A team of collaborators based in the UK and Quebec developed a COVID-19 simulator (covid.muutaa.org) for an urban environment .This simulator was designed as an educational tool and to assist public health authorities who would like to predict resurgence risks depending on various parameters such as initial population, initial number of people infected, probability that contact between a contagious person and a healthy person results in the latter being infected and the proportion of asymptomatic people in the population. It also allows for a deeper understanding of the impact of COVID-19 on the healthcare system and the complexity in the management of the disease.
Running these simulations could help with determining end of lock down policies in different urban settings. Various testing and lockdown strategies can be simulated and the results such as the number of people in various stages of illness such as severe, critical, death or recovered can be seen and broken down by various age groups. Resurgence of the disease can also be seen based on various settings of the start and end dates of lockdown.
The simulator allows setting ‘Ease-In’ and ‘Ease-Out’ for Lockdown. This means that the lockdown is progressively implemented over the ‘Ease-in’ period and similarly, opening of the lockdown is progressively implemented over the ‘Ease-Out’ period. The contact rate goes down and up linearly over the ‘Ease-in’ and ‘Ease-Out’ periods.
The underlying data has been obtained from various literature sources including the following:
· Neil M Ferguson et al, ‘Impact of non-pharmaceutical interventions (NPIs) to reduce COVID- 19 mortality and healthcare demand’, Imperial College COVID-19 Response Team
For more information or to simulate a particular scenario please contact Professor Amar Ramudhin (firstname.lastname@example.org).