COVID-19: Tracking the Pandemic with A Simple Curve Approximation Tool (SCAT)
Courtney, J.
Show abstract
In the current COVID-19 pandemic, much focus is put on flattening the curve. This epidemiological curve refers to the cases versus time graph, which shows the rise of a disease to its peak before descending. The aim in a pandemic is to flatten this curve by reducing the peak and spreading out the timeline. However, the models used to predict this curve are often not clearly outlined, no model parameters are given, and models are not tested against real data. This lack of detail makes it difficult to recreate the curve. What is much needed is a simple tool for approximating the curve to allow ideas to be tested and comparisons made. This work presents a Simple Curve Approximation Tool (SCAT) which can be used by anyone. This tool allows the user to approximate and draw the curve and allows testing of assumptions, trajectories and the wildly varying figures reported in the media. The mathematics behind SCAT is clearly outlined here but understanding of this is not required. SCAT is provided online as a downloadable MS Excel workbook with some sample cases shown. Throughout this work, the parameters used are specified so that all results can be easily reproduced. Although not intended as a prediction tool, SCAT has achieved less than 0.5 % error in short-term forward prediction. It also shows a very significant improvement on the pandemic exponential approximations found throughout media reporting. As a comparison tool, it highlights obvious differences between COVID-19 and other diseases, such as influenza, and between countries at different stages of the pandemic (China, Italy and the UK are used here for demonstration purposes). SCAT allows for quick approximation of the curve and creates meaningful comparisons and understandable visualisations for COVID-19 and other diseases.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- On the use of growth models for forecasting epidemic outbreaks with application to COVID-19 data 95%
- How an election can be safely planned and conducted during a pandemic: Decision support based on a discrete event model 94%
- Characterizing Two Outbreak Waves of COVID-19 in Spain Using Phenomenological Epidemic Modelling 94%
Similar papers in this journal
- Real-time Estimation of Disease Activity in Emerging Outbreaks using Internet Search Information 94%
- EMULSION: transparent and flexible multiscale stochastic models in human, animal and plant epidemiology 93%
- An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City 93%
Similar papers in this journal
- Measuring the impact of nonpharmaceutical interventions on the SARS-CoV-2 pandemic at a city level: An agent-based computational modeling study of the City of Natal 94%
- Lessons from a pandemic 92%
- Quantifying the impact of social activities on SARS-CoV-2 transmission using Google mobility reports 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.