What is it about?

Since its outbreak in 2019, COVID-19 has spread to over 180 countries. The virus is a threat to public health as well as social stability in these countries. To protect against the virus, it is important to predict its spread. Numerical models are commonly used for this purpose. But this can be quite a challenging task. This is because such predictions require a deep knowledge of how the disease spreads. In this paper, the authors collected COVID-19 data from different countries. They showed that the predictions depend strongly on the cases reported. The last data point before reaching maximum daily infection rate was also important. The authors tested their ideas with models currently in use. They showed that poor data quality as well as inaccurate estimations of model variables affected the prediction accuracy. Finally, they offered guidelines on using such models to estimate the infection rates.

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Why is it important?

The COVID-19 pandemic is a global health crisis. Outbreaks of this kind will likely occur in the future. It is, thus, necessary to predict them accurately. These predictions are given by numerical models. These estimate how the disease spreads, the infection rates, and the number of infections. But, such estimates are inconsistent across models. This study highlights the main factors affecting the prediction accuracy. KEY TAKEAWAY: For any model, the estimations are only as good as the data used. The cases reported during the early stages of the pandemic are critical. The disease spread should be modeled at different stages of the pandemic. Taking input variables specific to each region into account can also improve estimations.

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This page is a summary of: Asymptotic estimates of SARS-CoV-2 infection counts and their sensitivity to stochastic perturbation, Chaos An Interdisciplinary Journal of Nonlinear Science, May 2020, American Institute of Physics, DOI: 10.1063/5.0008834.
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