Predictions of 2019-nCoV Transmission Ending via Comprehensive Methods

TL;DR

This paper introduces multi-model neural networks and model-free methods to predict the transmission and end of 2019-nCoV in China, demonstrating their effectiveness through numerical experiments and real-world data.

Contribution

It presents novel neural network and model-free approaches for epidemic prediction, improving upon traditional epidemiological models.

Findings

Transmission likely decelerated before February 18, 2020

Epidemic expected to end before April 2020

Proposed methods provided consistent, reasonable predictions

Abstract

Since the SARS outbreak in 2003, a lot of predictive epidemiological models have been proposed. At the end of 2019, a novel coronavirus, termed as 2019-nCoV, has broken out and is propagating in China and the world. Here we propose a multi-model ordinary differential equation set neural network (MMODEs-NN) and model-free methods to predict the interprovincial transmissions in mainland China, especially those from Hubei Province. Compared with the previously proposed epidemiological models, the proposed network can simulate the transportations with the ODEs activation method, while the model-free methods based on the sigmoid function, Gaussian function, and Poisson distribution are linear and fast to generate reasonable predictions. According to the numerical experiments and the realities, the special policies for controlling the disease are successful in some provinces, and the transmission of the epidemic, whose outbreak time is close to the beginning of China Spring Festival travel rush, is more likely to decelerate before February 18 and to end before April 2020. The proposed mathematical and artificial intelligence methods can give consistent and reasonable predictions of the 2019-nCoV ending. We anticipate our work to be a starting point for comprehensive prediction researches of the 2019-nCoV.