ABSTRACT
The pandemic of the novel coronavirus disease 2019 (COVID-19) is continuously causing hazards for the world. Effective detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can relieve the impact, but various toxic chemicals are also released into the environment. Fluorescence sensors offer a facile analytical strategy. During fluorescence sensing, biological samples such as tissues and body fluids have autofluorescence, giving false-positive/negative results because of the interferences. Fluorescence near-infrared (NIR) nanosensors can be designed from low-toxic materials with insignificant background signals. Although this research is still in its infancy, further developments in this field have the potential for sustainable detection of SARS-CoV-2. Herein, we summarize the reported NIR fluorescent nanosensors with the potential to detect SARS-CoV-2. The green synthesis of NIR fluorescent nanomaterials, environmentally compatible sensing strategies, and possible methods to reduce the testing frequencies are discussed. Further optimization strategies for developing NIR fluorescent nanosensors to facilitate greener diagnostics of SARS-CoV-2 for pandemic control are proposed.
ABSTRACT
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 1 June 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 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 searches and tweeting about COVID-19, indicating that earlier collective awareness on Twitter/Google correlates with a 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.
ABSTRACT
As the novel coronavirus disease 2019 (COVID-19) has become a Public Health Emergency of International Concern, it is greatly significant to accurately predict the disease's incoming trend. Herein, we performed a stage-rolling Susceptible-Exposed-Infectious-Recovered (SEIR) model to measure the evolution of the basic reproduction number of COVID-19, based on the number of confirmed infections announced by the National Health Commission of the People's Republic of China. We assumed that the infected number under the spreading of infectious diseases will generally experience two different stages. In the first stage, due to the public's ignorance of the severity and harmfulness of the disease, the infected population grows exponentially and the process of disease transmission can be considered as the classic SEIR model with a constant basic reproduction number. Consequently, with limited awareness of the epidemic situation, there is a lack of effective preventive measures to control disease transmission. In the second stage, various control measures and medical resources are introduced in succession by the government, as well as the public gradually takes effective preventions (e.g. keep social distance and wear masks) based on the knowledge of the disease transmission. Collectively, the infected population grows much slower than the first stage. We performed a stage-rolling SEIR model, in which the basic reproduction number changes every day. Based on this model, the number of daily basic reproduction is estimated from the daily new infection number. We found that the daily basic reproduction number is expected to decline continually until it is less than 1, which means the eradication of the disease. Leveraging the evolution of the basic reproduction number, we extrapolate the incoming daily basic reproduction number, based on which we further predict the incoming trend of COVID-19 spreading in terms of the daily infection number. Our predictive model estimates that at the end of the epidemic, the total number of infections in China is nearly 14000 except for Hubei Province, and 32000 except for Wuhan city. We also found that in most parts of China, the number of newly confirmed infections increases linearly rather than exponentially before the day of "Wuhan travel restrictions", implying that the prevention and containing the infected people from Wuhan at the eve of the Spring Festival has been effective from the beginning.
ABSTRACT
During an outbreak of respiratory diseases including atypical pneumonia in Wuhan, a previously unknown ß-coronavirus was detected in patients. The newly discovered coronavirus is similar to some ß-coronaviruses found in bats but different from previously known SARS-CoV and MERS-CoV. High sequence identities and similarities between 2019-nCoV and SARS-CoV were found. In this study, we searched the homologous templates of all nonstructural and structural proteins of 2019-nCoV. Among the nonstructural proteins, the leader protein (nsp1), the papain-like protease (nsp3), the nsp4, the 3C-like protease (nsp5), the nsp7, the nsp8, the nsp9, the nsp10, the RNA-directed RNA polymerase (nsp12), the helicase (nsp13), the guanine-N7 methyltransferase (nsp14), the uridylate-specific endoribonuclease (nsp15), the 2'-O-methyltransferase (nsp16), and the ORF7a protein could be built on the basis of homology templates. Among the structural proteins, the spike protein (S-protein), the envelope protein (E-protein), and the nucleocapsid protein (N-protein) can be constructed based on the crystal structures of the proteins from SARS-CoV. It is known that PL-Pro, 3CL-Pro, and RdRp are important targets for design antiviral drugs against 2019-nCoV. And S protein is a critical target candidate for inhibitor screening or vaccine design against 2019-nCoV because coronavirus replication is initiated by the binding of S protein to cell surface receptors. It is believed that these proteins should be useful for further structure-based virtual screening and related computer-aided drug development and vaccine design.