A simple transmission dynamics model for predicting the evolution of COVID-19 under control measures in China

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.

Using the Angle Index to Assess COVID-19 Damage at the Early Stage and Throughout 2020 in Comparison of Mathematical Models

“…Although many mathematical models [2,[10][11][12][13][14][15][16][17][18][19] have been proposed to predict the number of COVID-19 cases, none of those models-except one using item response theory (IRT)…”

“…The non-linear regression and iterative methods are commonly used in the study of natural science [21,22]. The exponential growth (EXPO) has been proposed to construct the COVID-19 prediction model at an early outbreak stage [18] based on the daily growth rate (GR) of confirmed cases using Equation (2):…”

“…where casen and casen−1 are the daily number of confirmed cases on day n and n − 1, respectively. The GR can be modeled in Equation ( 3), where a is a constant that represents the growth rate at t = 0, β is an attenuation coefficient that indicates the efficiency of government isolation and quarantine, and t is the time representing the evolution of the epidemic [18].…”

Spatio-temporal characteristics and control strategies in the early period of COVID-19 spread: a case study of the mainland China

“…Taking mainland China as an example, since the first round of the epidemic disappeared, no largescale outbreak has been caused except for scattered cases in some regions. Therefore, some COVID-19 studies focused on the early period of the epidemic (Shang et al 2020;Tang et al 2020;Zhang et al 2020b). On the one hand, this period can fully reflect the epidemic's process from outbreak to containment (Xiong et al 2020;Chen et al 2021).…”

“…In fact, studying the spatial agglomeration effect of daily new cases and their temporal trends are more conducive to show the epidemic's whole process, i.e., from sporadic emergence to agglomeration and finally disappearance, which can benefit the public's understanding of the epidemic. Secondly, while using various methods to simulate the epidemic curve and calculate the inflection point was an important part of COVID-19 research, previous researches mainly did it from the perspective of the entire country or a specific region, lacking horizontal and spatial comparison (Liu et al 2020a;Shang et al 2020;Ye et al, 2020).…”