Abstract
Epidemic forecasting provides an opportunity to predict geographic disease spread and counts when an outbreak occurs and plays a key role in preventing or controlling their adverse impact. However, conventional prediction models based on complex mathematical modelling rely on the estimation of model parameters, which yields unreliable and unsustainable results. Herein, we proposed a simple model for predicting the epidemic transmission dynamics based on nonlinear regression of the epidemic growth rate and iterative methods, which is applicable to the progression of the COVID-19 outbreak under the strict control measures of the Chinese government. Our model yields reliable and accurate results as confirmed by the available data: we predicted that the total number of infections in mainland China would be 91 253, and the maximum number of beds required for hospitalised patients would be 62 794. We inferred that the inflection point (when the growth rate turns from positive to negative) of the epidemic across China would be mid-February, and the end of the epidemic would be in late March. This model is expected to contribute to resource allocation and planning in the health sector while providing a theoretical basis for governments to respond to future global health crises or epidemics.
Highlights
In December 2019, the outbreak of COVID-19 that could cause severe respiratory symptoms and even deaths emerged in Wuhan, Hubei province, China [1, 2]
The daily growth rate of confirmed cases is obtained by using formula (1) and is plotted in Figure 1a, where the hollow markers are calculated from real data and the solid lines are the fitted curve
The growth rate T fluctuates during the early stage of the outbreak and shows a large value because of a lack of governmental health intervention as well as the small sample size has a greater impact on T
Summary
In December 2019, the outbreak of COVID-19 that could cause severe respiratory symptoms and even deaths emerged in Wuhan, Hubei province, China [1, 2]. This new coronavirus was confirmed to be able to transmit between humans on 20 January 2020, significantly increased the risk of international spread [3, 4]. To mitigate the spreading of the epidemic, the Chinese central government progressively implemented the highest-level metropolitan-wide quarantine control in Wuhan city and 31 provinces since 23–24 January 2020. As the Chinese government has implemented stringent highest-level health intervention, none of these models are suitable for predicting the development of epidemics under this circumstance. Finding an accurate and simple predictive dynamic model is the key for predicting the evolution of the epidemic
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