COVID-19 Literature Situation Report February 5, 2020

• DeSalazar, et al. use air travel volume estimates out of Wuhan to identify countries that may have undetected cases. Their primary findings indicate the Indonesia likely has undetected cases; and that Cambodia and Thailand may have an undercount of cases. Improved public health surveillance is recommended for these countries. 

DeSalazar, et al. (Feb 5, 2020). Using predicted imports of 2019-nCoV cases to determine locations that may not be identifying all imported cases. Pre-Print downloaded on 5 Feb, 2020 from, https://www.medrxiv.org/content/10.1101/2020.02.04.20020495v1  

• Boldog, et al. model the risk for 2019-nCoV outbreaks outside China by developing local reproduction numbers (Rloc) that incorporate factors affecting epidemiology in China and new locations, and their connectivity. 
• Countries should balance controlling spread after introduction with preventing introduction. Countries with a low ability to control spread should focus on travel restrictions and traveler screening; whereas those better able, should focus on internal control activities. (Those in between should balance both.)

Boldog P, et al. (Feb 5, 2020). Risk assessment of novel coronavirus 2019-nCoV outbreaks outside China. Pre-Print downloaded on 5 Feb, 2020 from, https://www.medrxiv.org/content/10.1101/2020.02.04.20020503v1 

• Randhawa, et al. use a machine-learning approach to confirm classification of 2019-nCoV as a Betacoronavirus, subgenus Sarbecovirus, of bat origin

Randhawa, et al. (Feb 4, 2020). Machine learning-based analysis of genomes suggests associations between Wuhan 2019-nCoV and bat Betacoronaviruses. Pre-Print downloaded on 5 Feb, 2020 from, https://www.biorxiv.org/content/10.1101/2020.02.03.932350v1 

• A phylogenetic analysis including 2019-nCoV, SARS-CoV and bat CoVs found evidence of the evolutionary link between bat and bat/SARS-CoV-like viruses and 2019-nCoV, with bat/Yunnan/RaTG13/2013 as a potential most-recent ancestor. Additional epitope identification is also presented.

Ramaiah A and Arumugaswami V (Jan 30, 2020). Genomic variance of the 2019-nCoV coronavirus. Pre-Print downloaded on 5 Feb, 2020 from, https://www.biorxiv.org/content/10.1101/2020.01.29.925867v2 

• Ceraolo and Giorgi construct an expansive phylogenetic tree of representative coronaviridae. They confirm a >99% match among sequenced 2019-nCov isolates; and a 96.2% match between 2019-nCoV and the most closely-related bat coronavirus. While 2019-nCoV sequences were fairly homogenous, at least two hyper-variable genomic areas were identified (ORF 1ab, silent; ORF8, serine/leucine variation).
• They also assessed protein homology, finding again considerable similarity between 2019-nCoV and one of the bat CoVs. Envelope, membrane, and nucleocapsid proteins were generally highly conserved. 

Ceraolo C and Giorgi FM (Feb 4, 2020). Genomic variance of the 2019-nCoV coronavirus. Pre-Print downloaded on 5 Feb, 2020 from, https://www.biorxiv.org/content/10.1101/2020.02.02.931162v2

• Using previously described similarities between 2019-nCoV and SARS-CoV, Ahmed, et al. used SARS-CoV-derived experimentally-determined B- and T-cell epitope data to find epitopes in the S and N structural proteins of 2019-nCoV that were identical between the two viruses. 
• Epitopes are parts of the virus “seen” by the immune system. By finding comparable sites across the two viruses, the researchers were hoping to leverage what is known about SARS-CoV immunogenicity to identify potential vaccine antigen candidates 
• They found several sites where SARS-CoV epitopes mapped to 2019-nCoV proteins. The areas of the genes for the 2019-nCoV epitopes appeared stable (i.e., no reported mutations to date), making them good vaccine antigen candidates. 
• Also, based on population coverage criteria for the T-cell epitopes (i.e., considering MHC allele distribution in populations to assess the percentage of individuals within a population likely to elicit an immune response to at least one T cell epitope from the set), use of these epitopes as a vaccine target could provide population coverage of 84% (China) or 94% (global) coverage.

Ahmed SF, et al. (Feb 4, 2020). Preliminary identification of potential vaccine targets for 2019-nCoV based on SARS-CoV immunological studies. Pre-Print downloaded on 5 Feb, 2020 from, https://www.biorxiv.org/content/10.1101/2020.02.03.933226v1 

• Liver function abnormalities have been observed in some 2019-nCoV patients. Chai, et al. assess potential pathways for this damage given the presumed role ACE2 expression in 2019-nCoV infection. 
• They found liver abnormalities of 2019-nCoV patients could involve cholangiocyte dysfunction more than hepatocyte damage. Drug induced and systemic inflammatory response may also have a role. 

Chai X, et al. (Feb 4, 2020). Specific ACE2 Expression in Cholangiocytes May Cause Liver Damage After 2019-nCoV Infection. Pre-Print downloaded on 5 Feb, 2020 from, https://www.biorxiv.org/content/10.1101/2020.02.03.931766v1 

• Using BenevolentAI, knowledge of coronavirus receptors, and assumptions about vulnerable cell types, Richardson, et al. assess AP2-associated protein kinase 1 (AAK1) inhibitors for potential 2019-nCoV therapeutic use. They identify baricitinib as a candidate for trials based on its plasma concentration at therapeutic doses.

Richardson, et al. (Feb 3, 2020). Baricitinib as potential treatment for 2019-nCoV acute respiratory disease. The Lancet. https://doi.org/10.1016/S0140-6736(20)30304-4

• Gao, et al. apply a generative network complex (GNC) machine intelligence approach to identify candidate protease inhibitors for treating 2019-nCoV, including an assessment of two HIV protease inhibitors. 
• The GNC identifies 3-dimensional (3-D) drug candidates based on elements that include structure generation and property prediction algorithms. Molecule generation includes assessment of properties like binding affinity for known antigenic sites, solubility, similarity to know protease inhibitors, etc. Information on sequence identity and structural similarity between 2019-nCoV and SARS-CoV proteases was the basis of identifying candidates for an initial protease inhibitor dataset for the training set.
• 15 anti-2019-nCov molecules were generated through the GNC process. A related assessment of the HIV protease inhibitors lopinavir (Aluvia) and ritonavir (Norvir) found them to have some potential (e.g., fit with target protease), but generally scored lower than other protease inhibitors identified.

Gao, et al. (Feb 4, 2020). Machine intelligence design of 2019-nCoV drugs. Pre-Print downloaded on 5 Feb, 2020 from, https://www.biorxiv.org/content/10.1101/2020.01.30.927889v1