Mobile Human Ad Hoc Networks: A Communication Engineering Viewpoint on Interhuman Airborne Pathogen Transmission

Pathogens such as viruses and bacteria play a vital role in human life, since they cause infectious diseases which can lead to epidemics. Recent coronavirus disease 2019 epidemic has shown that taking effective prevention measures such as wearing masks are important to reduce the human deaths and side effects of the epidemic. It is therefore requisite to accurately model the spread of infectious diseases whose one of the most crucial routes of transmission is airborne transmission. The transmission models in the literature are proposed independently from each other, at different scales and by the researchers from various disciplines. Thus, there is a need to merge all these research attempts. To this end, we propose a communication engineering approach that melts different disciplines such as epidemiology, biology, medicine, and fluid dynamics in the same pot to model airborne pathogen transmission among humans. In this approach, we introduce the concept of mobile human ad hoc networks (MoHANETs). This concept exploits the similarity of airborne transmission-driven human groups with mobile ad hoc networks and uses molecular communication as the enabling paradigm. The aim of this article is to present a unified framework using communication engineering, and to highlight future research directions for modeling the spread of infectious diseases among humans through airborne pathogen transmission. In this article, we first review the airborne pathogen transmission mechanisms. Then, the MoHANET is given with a layered structure. In these layers, the infectious human emitting pathogen-laden droplets through air and the exposed human to these droplets are considered as the transmitter and receiver, respectively. Moreover, the experimental methods for the proposed approach are reviewed and discussed.Comment: 7 pages, 6 figures, submitted to IEEE Communications Magazine Feature Topic on Nano-Networking for Nano-, Micro-, and Macro-Scale Application

A Stochastic Biofilm Disruption Model based on Quorum Sensing Mimickers

Quorum sensing (QS) mimickers can be used as an effective tool to disrupt biofilms which consist of communicating bacteria and extracellular polymeric substances. In this paper, a stochastic biofilm disruption model based on the usage of QS mimickers is proposed. A chemical reaction network (CRN) involving four different states is employed to model the biological processes during the biofilm formation and its disruption via QS mimickers. In addition, a state-based stochastic simulation algorithm is proposed to simulate this CRN. The proposed model is validated by the in vitro experimental results of Pseudomonas aeruginosa biofilm and its disruption by rosmarinic acid as the QS mimicker. Our results show that there is an uncertainty in state transitions due to the effect of the randomness in the CRN. In addition to the QS activation threshold, the presented work demonstrates that there are underlying two more thresholds for the disruption of EPS and bacteria, which provides a realistic modeling for biofilm disruption with QS mimickers.Comment: Accepted for publication in IEEE Transactions on Molecular, Biological, and Multi-Scale Communication

Stochastic Modeling of Biofilm Formation with Bacterial Quorum Sensing

Bacteria generally live in complicated structures called biofilms, consisting of communicating bacterial colonies and extracellular polymeric substance (EPS). Since biofilms are related to detrimental effects such as infection or antibiotic resistance in different settings, it is essential to model their formation. In this paper, a stochastic model is proposed for biofilm formation, using bacterial quorum sensing (QS). In this model, the biological processes in the biofilm formation are modeled as a chemical reaction network which includes bacterial reproduction, productions of autoinducer and EPS, and their diffusion. The modified explicit tau-leap simulation algorithm is adapted based on the two-state QS mechanism. Our approach is validated by using the experimental results of $\textit{Pseudomonas putida}$ IsoF bacteria for autoinducer and bacteria concentration. It is also shown that the percentage of EPS in the biofilm increases significantly after the state change in QS, while it decreases before QS is activated. The presented work shows how the biofilm growth can be modeled realistically by using the QS mechanism in stochastic simulations of chemical reactions.Comment: Submitted to ICC 202

A Computational Approach for the Characterization of Airborne Pathogen Transmission in Turbulent Molecular Communication Channels

Airborne pathogen transmission mechanisms play a key role in the spread of infectious diseases such as COVID-19. In this work, we propose a computational fluid dynamics (CFD) approach to model and statistically characterize airborne pathogen transmission via pathogen-laden particles in turbulent channels from a molecular communication viewpoint. To this end, turbulent flows induced by coughing and the turbulent dispersion of droplets and aerosols are modeled by using the Reynolds-averaged Navier-Stokes equations coupled with the realizable $k-\epsilon$ model and the discrete random walk model, respectively. Via simulations realized by a CFD simulator, statistical data for the number of received particles are obtained. These data are post-processed to obtain the statistical characterization of the turbulent effect in the reception and to derive the probability of infection. Our results reveal that the turbulence has an irregular effect on the probability of infection, which shows itself by the multi-modal distribution as a weighted sum of normal and Weibull distributions. Furthermore, it is shown that the turbulent MC channel is characterized via multi-modal, i.e., sum of weighted normal distributions, or stable distributions, depending on the air velocity.Comment: Accepted to be published in IEEE Transactions on Molecular, Biological and Multi-Scale Communication

Pathways for the Valorization of Animal and Human Waste to Biofuels, Sustainable Materials and Value-Added Chemicals

Human and animal waste, including waste products originating from human or animal digestive systems such as urine, feces, and animal manure, have constituted a nuisance to the environment. Inappropriate disposal and poor sanitation of human and animal waste often cause negative impacts on human health through contamination of the terrestrial environment, soil, and water bodies. Therefore, it is necessary to convert these wastes into useful resources to mitigate their adverse environmental effect. The present study provides an overview and research progress of different thermochemical and biological conversion pathways for the transformation of human- and animal-derived waste into valuable resources. The physicochemical properties of human and animal waste are meticulously discussed as well as nutrient recovery strategies. In addition, a bibliometric analysis is provided to identify the trends in research and knowledge gaps. The results reveal that the U.S.A, China and England are the dominant countries in the research areas related to resource recovery from human or animal waste. In addition, researchers from the University of Illinois, the University of California Davis, the Chinese Academy of Science and Zhejiang University are front runners in research related to these areas. Future research should be centred on developing technologies for the on-site recovery of resources, exploring integrated resource recovery pathways, and exploring different safe waste processing methods