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Compelling evidence continues to build to support the idea that SARS-CoV-2 Neutralizing Antibody (NAb) levels in an individual can serve as an important indicator of the strength of protective immunity against infection. It is not well understood why NAb levels in some individuals remain high over time, while in others levels decline rapidly. In this work, we present a two-state mathematical model of within-host NAb dynamics in response to vaccination. By fitting only four host-specific parameters, the model is able to capture individual-specific NAb levels over time as measured by the AditxtScore™ for NAbs. The model can serve as a foundation for predicting NAb levels in the long-term, understanding connections between NAb levels, protective immunity, and breakthrough infections, and potentially guiding decisions about whether and when a booster vaccination may be warranted. [Display omitted] • New ODE model predicts within-host strength of antibody response to mRNA vaccine. • ODE model tracks subject-specific persistence of SARS-CoV-2 NAbs over time. • Subject-specific predictive envelopes generated via MCMC application to ODE system. • Time series data from twice-mRNA-vaccinated Covid19-naive human subjects captured. • Model reflects range of subject-specific variation in protective immunity. [ABSTRACT FROM AUTHOR]

• We measure infection of human (A549-ACE2) and animal (Vero-E6) cells by SARS-CoV-2. • We use mathematical modelling to estimate SARS-CoV-2 infection parameters. • SARS-CoV-2 infection rate of A549-ACE2 cells is lower than that of Vero E6 cells. • Latent phase of infected A549-ACE2 cells is longer than that of Vero E6 cells. • We conclude that Vero-E6 cells are not suitable for long-term drug screening assays. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV -2), a causative agent of COVID-19 disease, poses a significant threat to public health. Since its outbreak in December 2019, Wuhan, China, extensive collection of diverse data from cell culture and animal infections as well as population level data from an ongoing pandemic, has been vital in assessing strategies to battle its spread. Mathematical modelling plays a key role in quantifying determinants that drive virus infection dynamics, especially those relevant for epidemiological investigations and predictions as well as for proposing efficient mitigation strategies. We utilized a simple mathematical model to describe and explain experimental results on viral replication cycle kinetics during SARS-CoV-2 infection of animal and human derived cell lines, green monkey kidney cells, Vero-E6, and human lung epithelium cells, A549-ACE2, respectively. We conducted cell infections using two distinct initial viral concentrations and quantified viral loads over time. We then fitted the model to our experimental data and quantified the viral parameters. We showed that such cellular tropism generates significant differences in the infection rates and incubation times of SARS-CoV-2, that is, the times to the first release of newly synthesised viral progeny by SARS-CoV-2-infected cells. Specifically, the rate at which A549-ACE2 cells were infected by SARS-CoV-2 was 15 times lower than that in the case of Vero-E6 cell infection and the duration of latent phase of A549-ACE2 cells was 1.6 times longer than that of Vero-E6 cells. On the other hand, we found no statistically significant differences in other viral parameters, such as viral production rate or infected cell death rate. Since in vitro infection assays represent the first stage in the development of antiviral treatments against SARS-CoV-2, discrepancies in the viral parameter values across different cell hosts have to be identified and quantified to better target vaccine and antiviral research. [ABSTRACT FROM AUTHOR]

[Display omitted] • We modify a model of zoonosis emergence to predict coronavirus intermediate hosts. • This technique requires only host species ecological and epidemiological information. • We accurately predict the intermediate hosts for SARS and MERS. • We find mink, pangolins, and ferrets as potential intermediate hosts for SARS-CoV-2. Intermediate host species provide a crucial link in the emergence of zoonotic infectious diseases, serving as a population where an emerging pathogen can mutate to become human-transmissible. Identifying such species is thus a key component of predicting and possibly mitigating future epidemics. Despite this importance, intermediate host species have not been investigated in much detail, and have generally only been identified by testing for the presence of pathogens in multiple candidate species. In this paper, we present a mathematical model able to identify likely intermediate host species for emerging zoonoses based on ecological data for the candidates and epidemiological data for the pathogen. Since coronaviruses frequently emerge through intermediate host species and, at the time of writing, pose an urgent pandemic threat, we apply the model to the three emerging coronaviruses of the twenty-first century, accurately predicting palm civets as intermediate hosts for SARS-CoV-1 and dromedary camels as intermediate hosts for MERS. Further, we suggest mink, pangolins, and ferrets as intermediate host species for SARS-CoV-2. With the capacity to evaluate intermediate host likelihood among different species, researchers can focus testing for possible infection sources and interventions more effectively. [ABSTRACT FROM AUTHOR]