Correction: Biological control of the Asian chestnut gall wasp in Portugal: Insights from a mathematical model.

[This corrects the article DOI: 10.1371/journal.pone.0254193.].

Biological control of the Asian chestnut gall wasp in Portugal: Insights from a mathematical model.

In recent years, the Asian gall wasp Dryocosmus kuriphilus has invaded chestnut trees and significantly affected the Portuguese chestnut production. Studies in other countries, such as Japan or Italy, have shown that the parasitoid Torymus sinensis can successfully achieve biological control of D. kuriphilus. Mathematical models help us to understand the dynamics of the interaction between the pest D. kuriphilus and its parasitoid T. sinensis and, consequently, they can help to implement measures that enhance crop pest management. In this work, the evolution of the density of D. kuriphilus and T. sinensis across time and space is studied through the numerical solution of models that include parameters based on observations made in Portugal. Simultaneous releases of the parasitoid are simulated at various locations and at different times. The results indicate that, in the case of a small and homogeneous orchard, biological control can be effective, but, in the case of extensive domains, the pest control is much more difficult to achieve. In order for biological control to be efficient, it is necessary to implement, in each chestnut-producing region, a collective strategy based on the annual monitoring of infestation levels.

Computational simulation of the COVID-19 epidemic with the SEIR stochastic model.

A small number of individuals infected within a community can lead to the rapid spread of the disease throughout that community, leading to an epidemic outbreak. This is even more true for highly contagious diseases such as COVID-19, known to be caused by the new coronavirus SARS-CoV-2. Mathematical models of epidemics allow estimating several impacts on the population and, therefore, are of great use for the definition of public health policies. Some of these measures include the isolation of the infected (also known as quarantine), and the vaccination of the susceptible. In a possible scenario in which a vaccine is available, but with limited access, it is necessary to quantify the levels of vaccination to be applied, taking into account the continued application of preventive measures. This work concerns the simulation of the spread of the COVID-19 disease in a community by applying the Monte Carlo method to a Susceptible-Exposed-Infective-Recovered (SEIR) stochastic epidemic model. To handle the computational effort involved, a simple parallelization approach was adopted and deployed in a small HPC cluster. The developed computational method allows to realistically simulate the spread of COVID-19 in a medium-sized community and to study the effect of preventive measures such as quarantine and vaccination. The results show that an effective combination of vaccination with quarantine can prevent the appearance of major epidemic outbreaks, even if the critical vaccination coverage is not reached.