Coronavirus disease 2019 (COVID-19) is a novel human respiratory disease caused by the SARS-CoV-2 virus. Asymptomatic carriers of the virus display no clinical symptoms but are known to be contagious. Recent evidence reveals that this sub-population, as well as persons with mild disease, are a major contributor in the propagation of COVID-19. The asymptomatic sub-population frequently escapes detection by public health surveillance systems. Because of this, the currently accepted estimates of the basic reproduction number (R 0 ) of the disease are inaccurate. It is unlikely that a pathogen can blanket the planet in three months with an R 0 in the 1 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

Asymptomatic individuals in the context of malarial disease refers to subjects who carry a parasite load but do not show clinical symptoms. A correct understanding of the influence of asymptomatic individuals on transmission dynamics will provide a comprehensive description of the complex interplay between the definitive host (female Anopheles mosquito), intermediate host (human) and agent (Plasmodium parasite). The goal of this article is to conduct a rigorous mathematical analysis of a new compartmentalized malaria model accounting for asymptomatic human hosts for the purpose of calculating the basic reproductive number (R 0 ), and determining the bifurcations that might occur at the onset of disease free equilibrium. A point of departure of this model from others appearing in literature is that the asymptomatic compartment is decomposed into two mutually disjoint sub-compartments by making use of the naturally acquired immunity (NAI) of the population under consideration. After deriving the model, a qualitative analysis is carried out to classify the stability of the equilibria of the system. Our results show that the dynamical system is locally asymptotically stable provided that R 0 < 1. However this stability is not global, owning to the occurrence of a sub-critical bifurcation in which additional non-trivial sub-threshold equilibrium solutions appear in response to a specified parameter being perturbed. To ensure that the model does not undergo a backward bifurcation, we demand that an auxiliary parameter denoted Λ < 1 in addition to the threshold constraint R 0 < 1. The authors hope that this qualitative analysis will fill in the gaps of what is currently known about asymptomatic malaria and aid in designing strategies that assist the further development of malaria control and eradication efforts.

Projects in the life sciences continue to increase in complexity as they scale to answer deeper and more diverse questions. They employ technologies that generate increasingly large 'omic' datasets and research teams regularly include experts ranging from animal care technicians, veterinarians, human health clinicians, geneticists, immunologists, and biochemists to computer scientists, mathematical modelers, and data scientists, often located at different institutions. Providing the cyberinfrastructure support framework (IT, data management, communication, documentation, and aspects of project management related to these areas) for these projects requires a diverse set of technical tools and soft skills. These skills must be able to meet both the broad needs of data generators and consumers within the project and the needs of the larger scientific community. Here we describe recommendations for cyberinfrastructure support teams responsible for systems biology research programs. Recommendations are based on lessons learned while establishing and leading a complex, transdisciplinary, host-pathogen malaria systems biology consortium involving many institutions, a variety of disciplines, animal infectious disease models, and clinical studies. While some technical suggestions are included, the primary foci are situational and sociological challenges and tips for handling them.

It is shown that the solution of the Cauchy problem for the BBM-KP equation converges to the solution of the Cauchy problem for the BBM equation in a suitable function space whenever the initial data for both equations are close as the transverse variable y → ±∞.

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