Modelling Mutation in Equine Infectious Anemia Virus Infection Suggests a Path to Viral Clearance with Repeated Vaccination.

Equine infectious anemia virus (EIAV) is a lentivirus similar to HIV that infects horses. Clinical and experimental studies demonstrating immune control of EIAV infection hold promise for efforts to produce an HIV vaccine. Antibody infusions have been shown to block both wild-type and mutant virus infection, but the mutant sometimes escapes. Using these data, we develop a mathematical model that describes the interactions between antibodies and both wild-type and mutant virus populations, in the context of continual virus mutation. The aim of this work is to determine whether repeated vaccinations through antibody infusions can reduce both the wild-type and mutant strains of the virus below one viral particle, and if so, to examine the vaccination period and number of infusions that ensure eradication. The antibody infusions are modelled using impulsive differential equations, a technique that offers insight into repeated vaccination by approximating the time-to-peak by an instantaneous change. We use impulsive theory to determine the maximal vaccination intervals that would be required to reduce the wild-type and mutant virus levels below one particle per horse. We show that seven boosts of the antibody vaccine are sufficient to eradicate both the wild-type and the mutant strains. In the case of a mutant virus infection that is given infusions of antibodies targeting wild-type virus (i.e., simulation of a heterologous infection), seven infusions were likewise sufficient to eradicate infection, based upon the data set. However, if the period between infusions was sufficiently increased, both the wild-type and mutant virus would eventually persist in the form of a periodic orbit. These results suggest a route forward to design antibody-based vaccine strategies to control viruses subject to mutant escape.

Predicting COVID-19 using past pandemics as a guide: how reliable were mathematical models then, and how reliable will they be now?

During the earliest stages of a pandemic, mathematical models are a tool that can be imple-mented quickly. However, such models are based on meagre data and limited biological understanding. We evaluate the accuracy of various models from recent pandemics (SARS, MERS and the 2009 H1N1 outbreak) as a guide to whether we can trust the early model predictions for COVID-19. We show that early models can have good predictive power for a disease's first wave, but they are less predictive of the possibility of a second wave or its strength. The models with the highest accuracy tended to include stochasticity, and models developed for a particular geographic region are often applicable in other regions. It follows that mathematical models developed early in a pandemic can be useful for long-term predictions, at least during the first wave, and they should include stochastic variations, to represent unknown characteristics inherent in the earliest stages of all pandemics.