Controlling infection in predator-prey systems with transmission dynamics

The effect of the fear factor on the dynamics of an eco-epidemiological system with standard incidence rate

In order to protect endangered prey, ecologists suggest introducing parasites into predators which have achieved the expected goal in practice. Then how to explain the inherent mechanism and validate the effectiveness of this approach theoretically? In response to this question, we propose an eco-epidemiological system with the standard incidence rate and the anti-predator behavior in this paper, where the predator population is infected by parasites. We show the existence and local stability of equilibria for the system, and verify the occurrence of Hopf bifurcation. Theoretical and numerical results suggest that the fear effect reduces the density of the predator population but has no effect on the density of prey population. In addition, the cost of fear may not only break the stability of the equilibrium of the system, but also induce the equilibrium to change from unstable to stable. Based on the theoretical analysis, we confirm that introducing parasites into the predator population is an effective method to protect endangered prey.

An optimal control model for Covid-19 spread with impacts of vaccination and facemask

A non-linear system of differential equations was used to explain the spread of the COVID-19 virus and a SEIQR model was developed and tested to provide insights into the spread of the pandemic. This article, which is related to the aforementioned work as well as other work covering variations of SIR models, Hermite Wavelets Transform, and also the Generalized Compartmental COVID-19 model, we develop a mathematical control model and apply it to represent optimal vaccination strategy against COVID-19 using Pontryagin's Maximum Principle and also factoring in the effect of facemasks on the spread of the virus. As background work, we analyze the mathematical epidemiology model with the facemask effect on both reproduction number and stability, we also analyze the difference between confirmed COVID-19 cases of the Quarantine class and anonymous cases of the Infectious class that is expected to recover. We also apply control theory to mine insights for effective virus spread prevention strategies. Our models are validated using Matlab mathematical model validation tools. Statistical tests against data from Jordan are used to validate our work including the modeling of the relation between the facemask effect and COVID-19 spread. Furthermore, the relation between control measure ξ, cost, and Infected cases is also studied.

Detection, Detrimental Effects, and Transmission Pathways of the Pathogenic Bacterium Acaricomes phytoseiuli in Commercial Predatory Mites

Arthropod pathogens and other microorganisms have been documented from mass production systems aimed at producing natural enemies for pest control. If losses due to pathogens are encountered in such systems, they could lead to uneconomical production of natural enemies and/or a loss of predator quality for effective field control of target pests. Here, we identify and describe the laboratory transmission of a bacterial pathogen, Acaricomes phytoseiuli, in a Chinese strain of the local predatory mite Neoseiulus californicus following contact with Phytoseiulus persimilis, a predatory mite imported for the control of small sap-sucking pests in greenhouses. Analysis of the 16S rRNA gene and whole-genome sequences of A. phytoseiuli isolated from the Chinese strain of N. californicus showed 99.6 and 99.78% similarity, respectively, to the pathogen isolated from a European population (DSM14247 strain). This is the first report of P. persimilis infected with A. phytoseiuli outside Europe and transmitting to a local predatory mite species. A. phytoseiuli severely damaged local N. californicus, leading to a dorso-ventrally flattened body and reduced prey consumption and reproduction as well as early death. Through fluorescence in situ hybridization, A. phytoseiuli was shown to accumulate in the digestive tract of mites and in the oviductal bulb of adult females. Infected males had no obvious symptoms, but they still were able to pass on the infection to healthy females through contact and mating. The pathogen was transmitted vertically to offspring by either infected parent through adherence to eggshells. A. phytoseiuli could also persist in other herbivorous arthropods from the same habitat, suggesting wider potential risks. Our study highlights pathogen risk to predators in local biocontrol industries through pathogen spread from imported material. IMPORTANCE Predatory mites are important natural enemies for biological control of pests, but mass rearing of the mites can be affected by pathogens. In particular, the mite pathogen Acaricomes phytoseiuli may pose a threat to predatory mite production, and we have now detected this pathogen in China. We explored the pathogen's transmission within species, its ability to transfer to a locally important predatory mite species, and symptoms following transfer. The detection of A. phytoseiuli and its ability to transfer to a local predator where it reduces performance highlight the importance of ongoing monitoring and hygiene in the predatory mite industry.

A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis

COVID-19 is a life threatening disease which has a enormous global impact. As the cause of the disease is a novel coronavirus whose gene information is unknown, drugs and vaccines are yet to be found. For the present situation, disease spread analysis and prediction with the help of mathematical and data driven model will be of great help to initiate prevention and control action, namely lockdown and qurantine. There are various mathematical and machine-learning models proposed for analyzing the spread and prediction. Each model has its own limitations and advantages for a particluar scenario. This article reviews the state-of-the art mathematical models for COVID-19, including compartment models, statistical models and machine learning models to provide more insight, so that an appropriate model can be well adopted for the disease spread analysis. Furthermore, accurate diagnose of COVID-19 is another essential process to identify the infected person and control further spreading. As the spreading is fast, there is a need for quick auotomated diagnosis mechanism to handle large population. Deep-learning and machine-learning based diagnostic mechanism will be more appropriate for this purpose. In this aspect, a comprehensive review on the deep learning models for the diagnosis of the disease is also provided in this article.

A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis

COVID-19 is a life threatening disease which has a enormous global impact. As the cause of the disease is a novel coronavirus whose gene information is unknown, drugs and vaccines are yet to be found. For the present situation, disease spread analysis and prediction with the help of mathematical and data driven model will be of great help to initiate prevention and control action, namely lockdown and qurantine. There are various mathematical and machine-learning models proposed for analyzing the spread and prediction. Each model has its own limitations and advantages for a particluar scenario. This article reviews the state-of-the art mathematical models for COVID-19, including compartment models, statistical models and machine learning models to provide more insight, so that an appropriate model can be well adopted for the disease spread analysis. Furthermore, accurate diagnose of COVID-19 is another essential process to identify the infected person and control further spreading. As the spreading is fast, there is a need for quick auotomated diagnosis mechanism to handle large population. Deep-learning and machine-learning based diagnostic mechanism will be more appropriate for this purpose. In this aspect, a comprehensive review on the deep learning models for the diagnosis of the disease is also provided in this article.

Modelling the dynamics of exchanged novel coronavirus (2019-nCov) between regions in terms of time and space

To date, many models have been proposed which estimate the transmission risk of COVID-19 in terms of time; however, its dependency on space dimensions has been ignored. In this research, by multiplying risk parameters in certain regions and bridging, we obtain a stable action, which means that the transmission risk worldwide could shrink to a constant. Thus, by increasing the risk parameters in one region, the risk parameters in other regions decrease. Then, by adding space dimensions to the parameters in transmission risk models, and using the wave equations of manifolds for the regions, we obtain the dynamics of the exchanged novel coronavirus (2019-nCov) between countries. We calculate the risk factors of COVID19 for different regions in this model, and observe that they are in good agreement with experimental data.