  The declaration of COVID-19 as a pandemic has largely amplified the spread of
related information on social media, such as Twitter, Facebook, and
WeChat.Unlike the previous studies which focused on how to detect the
misinformation or fake news related toCOVID-19, we investigate how the disease
and information co-evolve in the population. We focus onCOVID-19and its
information during the period when the disease was widely spread in China,
i.e., from January 25th to March 24th, 2020. We first explore how the disease
and information co-evolve via the spatial analysis of the two spreading
processes. We visualize the geo-location of both disease and information at the
province level and find that disease is more geo-localized compared to
information. We find a high correlation between the disease and information
data, and also people care about the spread only when it comes to their
neighborhood. Regard to the content of the information, we find that positive
messages are more negatively correlated with the disease compared to negative
and neutral messages. Additionally, we introduce machine learning algorithms,
i.e., linear regression and random forest, to further predict the number of
infected using different disease spatial related and information-related
characteristics. We obtain that the disease spatial related characteristics of
nearby cities can help to improve the prediction accuracy. Meanwhile,
information-related characteristics can also help to improve the prediction
performance, but with a delay, i.e., the improvement comes from using, for
instance, the number of messages 10 days ago, for disease prediction. The
methodology proposed in this paper may shed light on new clues of emerging
infections
