Spatiotemporal tracking of COVID-19 using open-source gridded population rasters and mathematical modelling

We present a spatial SVEIRD (which stands for Susceptible, Vaccinated, Exposed, Infectious, Recovered and Dead compartments) epidemic model to capture the transmission dynamics of the spread of COVID-19 and provide insight that would support the Public Health officials towards informed, data-driven decision making.

We use the freely available population count data downloaded as a gridded raster map from WorldPop.org to assess the geographical spread COVID-19. Each grid cell has a population count, which is divided into disease compartments. Each grid cell can transmit disease to its neighbors, with probabilities that decline exponentially with the Euclidean distance.

Predicting the transmission dynamics of COVID-19 using mathematical models is challenging and comes with a lot of uncertainty. First, we run the spatial simulations under the worst-case scenario, in which there are no major public health interventions. Next, we account for mitigation efforts including strict mask wearing and social distancing mandates, targeted lockdowns, and widespread vaccine rollout to vaccinate priority groups. Predictions for disease prevalence with and without mitigation efforts are presented via time-series graphs for the epidemic compartments. All simulations are carried out using R programming language.

By the end of the short course participants will be able to:

• know how to build a compartment model of epidemiology (for ex: SIR, SEIR, SEIRD, SVEIRD etc.) to track the spatial spread of an infectious disease outbreak. • apply ideas to a realistic scenario involving tracking COVID-19 in a particular country. • gain basic understanding of spatiotemporal modelling using mathematical models.

Authors and Affiliations – Krishnamurthy, Ashok Associate Professor Department of Mathematics and Computing Mount Royal University 4825 Mount Royal Gate SW Calgary, AB, T3E 6K6 Canada

Requirements for the Attendees – A basic understanding from Calculus, Linear Algebra and Introductory Statistics may be beneficial.

Track – Open data

Topic – Data visualization: spatial analysis, manipulation and visualization

Level – 2 - Basic. General basic knowledge is required.

Language of the Presentation – English