Assessing the Predictive Power of Online Social Media to Analyze COVID-19 Outbreaks in the 50 U.S. States (preprint)

As the coronavirus disease 2019 (COVID-19) continues to rage worldwide, the United States has become the most affected country with more than 34.1 million total confirmed cases up to June 1, 2021. In this work, we investigate correlations between online social media and Internet search for the COVID-19 pandemic among 50 U.S. states. By collecting the state-level daily trends through both Twitter and Google Trends, we observe a high but state-different lag correlation with the number of daily confirmed cases. We further find that the predictive accuracy measured by the correlation coefficient is positively correlated to a state’s demographic, air traffic volume and GDP development. Most importantly, we show that a state’s early infection rate is negatively correlated with the lag to the previous peak in Internet search and tweeting about COVID-19, indicating that earlier collective awareness on Twitter/Google correlates with lower infection rate. Lastly, we demonstrate that correlations between online social media and search trends are sensitive to time, mainly due to the attention shifting of the public.

Identify Hidden Spreaders of Pandemic over Contact Tracing Networks (preprint)

The COVID-19 infection cases have surged globally, causing devastations to both the society and economy. A key factor contributing to the sustained spreading is the presence of a large number of asymptomatic or hidden spreaders, who mix among the susceptible population without being detected or quarantined. Here we propose an effective non-pharmacological intervention method of detecting the asymptomatic spreaders in contact-tracing networks, and validated it on the empirical COVID-19 spreading network in Singapore. We find that using pure physical spreading equations, the hidden spreaders of COVID-19 can be identified with remarkable accuracy. Specifically, based on the unique characteristics of COVID-19 spreading dynamics, we propose a computational framework capturing the transition probabilities among different infectious states in a network, and extend it to an efficient algorithm to identify asymptotic individuals. Our simulation results indicate that a screening method using our prediction outperforms machine learning algorithms, e.g. graph neural networks, that are designed as baselines in this work, as well as random screening of infection's closest contacts widely used by China in its early outbreak. Furthermore, our method provides high precision even with incomplete information of the contract-tracing networks. Our work can be of critical importance to the non-pharmacological interventions of COVID-19, especially with increasing adoptions of contact tracing measures using various new technologies. Beyond COVID-19, our framework can be useful for other epidemic diseases that also feature asymptomatic spreading

In Silico Identification of Nsp12 and Nsp13 as Potential Targets for Development of Broad-spectrum Antiviral Agents Against SARS-CoV, MERS-CoV, and 2019-nCoV (SARS-CoV-2) (preprint)

Background2019 novel coronavirus (2019-nCoV) is officially named severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), and is a positive-sense, single-stranded RNA coronavirus. The virus is the pathogen of coronavirus disease 2019 (COVID-19) and is infectious through human-to-human transmission. The fact that 2019-nCoV is very close to SARS-CoV has been proved by several evidences, but there are significant differences between MERS-CoV and them. Therefore, in this work, we used MERS-CoV as a probe to find the homology proteins with conserved sequences among these three known human highly pathogenic coronaviruses.MethodsThe primary protein sequences of three viruses translated from the complete genome were downloaded from National Center for Biotechnology Information (NCBI). The sequence alignments of ORF1ab proteins of three viruses were done by using Clustal Omega. The assessments of the feasibility of homology modeling were performed by using SWISS-MODEL.ResultsHere, by using computational biology, we propose that four nonstructural proteins nsp12, nsp13, nsp14, and nsp16 exhibit considerable homology among SARS-CoV, MERS-CoV, and 2019-nCoV. Among them, nsp12 and nsp13 amino acid sequences are more conserved. Considering the crucial role of these two proteins in the process of virus invasion and pathological response, we first proposed these two proteins as priority targets to design new or screen existing broad-spectrum antiviral drugs. The high consistency of primary sequence indicates the great similarity of three-dimensional structure and similar targets are likely to be inhibited by the same inhibitor. The inhibitors designed for these targets are likely to have broad-spectrum antiviral effect.ConclusionVery recently, some clinical trial reports preliminarily proved that Favipiravir and Remdesivir are effective for COVID-19. These clinical data provide some proof and basis for our conjecture in some degree. It is believed that the effective broad-spectrum antiviral drugs are not only helpful for the current epidemic situation, but also more beneficial for the future unpredictable epidemic situation.

More Active Internet-Search on Google and Twitter Posting for COVID-19 Corresponds with Lower Infection Rate in the 50 U.S. States (preprint)

As the novel coronavirus disease 2019 (COVID-19) continues to rage worldwide, the United States has become the most affected country with more than 2.5 million total confirmed cases up to now (June 2, 2020). In this work, we investigate the predictive power of online social media and Internet search for the COVID-19 pandemic among 50 U.S. states. By collecting the state-level daily trends through both Twitter and Google Trends, we observe a high but state-different lag correlation with the number of daily confirmed cases. We further find that the predictive accuracy measured by the correlation coefficient is positively correlated to a state’s demographic, air traffic volume and GDP development. Most importantly, we show that a state’s early infection rate is negatively correlated with the lag to the previous peak in Internet search and tweeting about COVID-19, indicating that the earlier the collective awareness on Twitter/Google in a state, the lower is the infection rate.