End-point RT-PCR based on a conservation landscape for SARS-COV-2 detection.

End-point RT-PCR is a suitable alternative diagnostic technique since it is cheaper than RT-qPCR tests and can be implemented on a massive scale in low- and middle-income countries. In this work, a bioinformatic approach to guide the design of PCR primers was developed, and an alternative diagnostic test based on end-point PCR was designed. End-point PCR primers were designed through conservation analysis based on kmer frequency in SARS-CoV-2 and human respiratory pathogen genomes. Highly conserved regions were identified for primer design, and the resulting PCR primers were used to amplify 871 nasopharyngeal human samples with a previous RT-qPCR based SARS-CoV-2 diagnosis. The diagnostic test showed high accuracy in identifying SARS-CoV-2-positive samples including B.1.1.7, P.1, B.1.427/B.1.429 and B.1.617.2/ AY samples with a detection limit of 7.2 viral copies/µL. In addition, this test could discern SARS-CoV-2 infection from other viral infections with COVID-19-like symptomatology. The designed end-point PCR diagnostic test to detect SARS-CoV-2 is a suitable alternative to RT-qPCR. Since the proposed bioinformatic approach can be easily applied in thousands of viral genomes and over highly divergent strains, it can be used as a PCR design tool as new SARS-CoV-2 variants emerge. Therefore, this end-point PCR test could be employed in epidemiological surveillance to detect new SARS-CoV-2 variants as they emerge and propagate.

Diffusion processes in multilayer transportation networks: The flight of the Coronavirus

At the end of December of 2019, a new type of coronavirus, SARS-CoV-2, responsible of the disease now called COVID-19, started spreading in Wuhan, China and later throughout the world. Due to the global emergency state, the official pandemic declaration by the World Health Organization, as well as the need to investigate more about the danger Mexico is in, we worked on analyzing the risk of the COVID-19 importation to Mexico through the Air Transportation Network with a multilayer network approach. Based on the data obtained from the public data bases of OpenFlights, we created a multiplex network in which nodes represented airports, flights represented links, and airlines represented layers. We then simulated the propagation of the coronavirus using an unbiased random walk model with probability p = 1 of infection once the random walker steps in a certain airport. We found the COVID-19 spread behavior the first month is anomalous (subdiffusion) and later behaves as a normal diffusion. We also found the risk of importing the virus to Mexico increases linearly over time and after approximately one year, there is almost a 90% probability of being infected. However, it is important to mention this high risk is due to contagions by people from other countries (not China) which have already confirmed cases of coronavirus. We concluded the risk of importing the COVID-19 to Mexico is almost ineludible over time unless effective medical interventions are imposed. © 2020 Sociedad Mexicana de Fisica.