A new logistic growth model applied to COVID-19 fatality data

TL;DR

This paper introduces a new logistic growth model tailored for power-law epidemic growth, demonstrating accurate predictions of COVID-19 fatalities and peak characteristics across multiple countries, especially during effective containment phases.

Contribution

The paper presents a novel phenomenological logistic model specifically designed for power-law COVID-19 growth, validated with data from four countries and used for forecasting future fatalities.

Findings

Accurately predicts peak heights and timing for COVID-19 fatalities.

Works well even with less stringent containment measures.

Successfully forecasts the third wave in South Africa.

Abstract

Background: Recent work showed that the temporal growth of the novel coronavirus disease (COVID-19) follows a sub-exponential power-law scaling whenever effective control interventions are in place. Taking this into consideration, we present a new phenomenological logistic model that is well-suited for such power-law epidemic growth. Methods: We empirically develop the logistic growth model using simple scaling arguments, known boundary conditions and a comparison with available data from four countries, Belgium, China, Denmark and Germany, where (arguably) effective containment measures were put in place during the first wave of the pandemic. A non-linear least-squares minimization algorithm is used to map the parameter space and make optimal predictions. Results: Unlike other logistic growth models, our presented model is shown to consistently make accurate predictions of peak heights, peak locations and cumulative saturation values for incomplete epidemic growth curves. We further show that the power-law growth model also works reasonably well when containment and lock down strategies are not as stringent as they were during the first wave of infections in 2020. On the basis of this agreement, the model was used to forecast COVID-19 fatalities for the third wave in South Africa, which is currently in progress. Conclusions: We anticipate that our presented model will be useful for a similar forecasting of COVID-19 induced infections/deaths in other regions as well as other cases of infectious disease outbreaks, particularly when power-law scaling is observed.