A review on the transmission of COVID-19 based on cough/sneeze/breath flows

Impact of infectious density-induced additional screening and treatment saturation on COVID-19: Modeling and cost-effective optimal control

This study introduces a novel SI2HR model, where "I2" denotes two infectious classes representing asymptomatic and symptomatic infections, aiming to investigate and analyze the cost-effective optimal control measures for managing COVID-19. The model incorporates a novel concept of infectious density-induced additional screening (IDIAS) and accounts for treatment saturation. Furthermore, the model considers the possibility of reinfection and the loss of immunity in individuals who have previously recovered. To validate and calibrate the proposed model, real data from November-December 2022 in Hong Kong are utilized. The estimated parameters obtained from this calibration process are valuable for prediction purposes and facilitate further numerical simulations. An analysis of the model reveals that delays in screening, treatment, and quarantine contribute to an increase in the basic reproduction number R0, indicating a tendency towards endemicity. In particular, from the elasticity of R0, we deduce that normalized sensitivity indices of baseline screening rate (θ), quarantine rates (γ, αs), and treatment rate (α) are negative, which shows that delaying any of these may cause huge surge in R0, ultimately increases the disease burden. Further, by the contour plots, we note the two-parameter behavior of the infectives (both symptomatic and asymptomatic). Expanding upon the model analysis, an optimal control problem (OCP) is formulated, incorporating three control measures: precautionary interventions, boosted IDIAS, and boosted treatment. The Pontryagin's maximum principle and the forward-backward sweep method are employed to solve the OCP. The numerical simulations highlight that enhanced screening and treatment, coupled with preventive interventions, can effectively contribute to sustainable disease control. However, the cost-effectiveness analysis (CEA) conducted in this study suggests that boosting IDIAS alone is the most economically efficient and cost-effective approach compared to other strategies. The CEA results provide valuable insights into identifying specific strategies based on their cost-efficacy ranking, which can be implemented to maximize impact while minimizing costs. Overall, this research offers significant insights for policymakers and healthcare professionals, providing a framework to optimize control efforts for COVID-19 or similar epidemics in the future.

Prevalence and association of oral manifestations with disease severity in patients diagnosed with COVID‑19: A cross‑sectional study

Systemic disorders may exhibit early signs when conducting an oral examination. Since the onset of the COVID-19 pandemic, several studies have been published detailing the direct impact of the virus on the oral cavity. The present study aimed to determine whether indeed there are any significant disparities in oropharyngeal manifestations between individuals infected with severe acute respiratory syndrome coronavirus 2 and a control group, and whether the virus has the ability to invade and reproduce inside oral keratinocytes and fibroblasts, resulting in the development of oral ulcerations and superficial lesions. The present study provides an overview of the symptoms that occur at an early stage of the illness, and the most commonly affected regions of the oral cavity, including the tongue, lips, palate and oropharynx are examined. In the present retrospective study, 52 patients infected with COVID-19 were recruited between April, 2021 and October, 2022. In addition, 52 individuals who tested negative for the virus were recruited as the control group. The study was conducted through a thorough examination and questionnaire provided to all participants. The results revealed that among the cohort of patients from the COVID-19 group examined (n=52), a proportion (mean, 16.15) displayed oral manifestations. Specifically, 75% of the patients in the COVID-19 group described oral cavity pain, and 69% of these patients had changes in teeth color or dental caries. In summary, in relation to the control group, the prevalence of oropharyngeal symptoms was generally lower compared to the COVID-19 group, apart from oral cavity pain (30.8%), tonsillitis (17.3%), bleeding (34.6%), teeth color changes (36.5%), recurrence (15.4%) and abscesses (7.7%). Thus, on the whole, the patients without COVID-19 had fewer oral manifestations.

On the efficacy of facial masks to suppress the spreading of pathogen-carrying saliva particles during human respiratory events: Insights gained via high-fidelity numerical modeling

Respiratory fluid dynamics is integral to comprehending the transmission of infectious diseases and the effectiveness of interventions such as face masks and social distancing. In this research, we present our recent studies that investigate respiratory particle transport via high-fidelity large eddy simulation coupled with the Lagrangian particle tracking method. Based on our numerical simulation results for human respiratory events with and without face masks, we demonstrate that facial masks could significantly suppress particle spreading. The studied respiratory events include coughing and normal breathing through mouth and nose. Using the Lagrangian particle tracking simulation results, we elucidated the transport pathways of saliva particles during inhalation and exhalation of breathing cycles, contributing to our understanding of respiratory physiology and potential disease transmission routes. Our findings underscore the importance of respiratory fluid dynamics research in informing public health strategies to reduce the spread of respiratory infections. Combining advanced mathematical modeling techniques with experimental data will help future research on airborne disease transmission dynamics and the effectiveness of preventive measures such as face masks.

Optimal control and cost-effectiveness analysis for the human melioidosis model

In this work, we formulated and investigated an optimal control problem of the melioidosis epidemic to explain the effectiveness of time-dependent control functions in controlling the spread of the epidemic. The basic reproduction number ( R 0 c ) with control measures is obtained, using the next-generation matrix approach and the impact of the controls on R 0 c is illustrated numerically. The optimal control problem is analyzed using Pontryagin's maximum principle to derive the optimality system. The optimality system is simulated using the forward-backward sweep method based on the fourth-order Runge-Kutta method in the MATLAB program to illustrate the impact of all the possible combinations of the control interventions on the transmission dynamics of the disease. The numerical results indicate that among strategies considered, strategy C is shown to be the most effective in reducing the number of infectious classes compared to both strategy A and strategy B. Furthermore, we carried out a cost-effectiveness analysis to determine the most cost-effective strategy and the result indicated that the strategy B (treatment control strategy) should be recommended to mitigate the spread and impact of the disease regarding the costs of the strategies.

Three dimensional analysis of the exhalation flow in the proximity of the mouth

The human exhalation flow is characterized in this work from the three-dimensional velocimetry results obtained by using the stereo particle image velocimetry (SPIV) measurement technique on the flow emitted from a realistic airway model. For this purpose, the transient exhalation flow through the mouth of a person performing two different breaths corresponding to two metabolic rates, standing relaxed (SR) and walking active (WA), is emulated and studied. To reproduce the flow realistically, a detailed three-dimensional model obtained from computed tomography measurements on real subjects is used. To cope with the variability of the experimental data, a subsequent analysis of the results is performed using the TR-PIV (time resolved particle image velocimetry) technique. Exhalation produces a transient jet that becomes a puff when flow emission ends. Three-dimensional vector fields of the jet velocity are obtained in five equally spaced transverse planes up to a distance of Image 1 from the mouth at equally spaced time instants Image 2 which will be referred to as phases (φ), from the beginning to the end of exhalation. The time evolution during exhalation of the jet area of influence, the velocity field and the jet air entrainment have been characterized for each of the jet cross sections. The importance of the use of realistic airway models for the study of this type of flow and the influence of the metabolic rate on its development are also analyzed. The results obtained contribute to the characterization of the human exhalation as a pathway of the transmission of pathogens such as SARS-CoV-2 virus.

The Ripple Effect of Climate Change: Assessing the Impacts on Water Quality and Hydrology in Addis Ababa City (Akaki Catchment)

This research aimed to evaluate the effects of climate change on the hydrology and water quality in the Akaki catchment, which provides water to Addis Ababa, Ethiopia. This was performed using the soil and water assessment tool (SWAT) model and an ensemble of four global climate models under two Shared Socioeconomic Pathways (SSP) emission scenarios from Coupled Model Intercomparison Project Phase 6 (CMIP6). The climate data were downscaled and bias-corrected using the CMhyd tool and calibrated and validated using the SWAT-CUP software package. Change points and patterns in annual rainfall and temperature were determined using the homogeneity test and Mann-Kendell trend test. Water quality data were obtained from Addis Ababa Water and Sewerage Authority (AAWSA), and more samples were taken and analyzed in accordance with APHA recommended procedures. The SWAT model output was then used to assess the impacts of climate change on hydrological components and water quality. Rainfall increased by 19.39 mm/year under SSP2-4.5 and 12.8 mm/year under SSP8.5. Maximum temperature increased by 0.03°C/yr for SSP2-4.5 and 0.04°C/yr for SSP5-8.5. Minimum temperature increased by 0.03°C/yr under SSP2-4.5 and 0.07°C/yr under SSP5-8.5. This warming will augment the evapotranspiration rate which in turn will have a negative impact on the freshwater availability. Streamflow will increase by 5% under SSP2-4.5 and 9.49% under SSP5-85 which may increase sporadic flooding events. Climate change is expected to contribute to the deterioration of water quality shown by 61%, 36%, 79%, 115%, and 70% increased ammonia, chlorophyll-a, nitrite, nitrate, and phosphorus loadings, respectively, from 2022. The increase in temperature results in increases in nutrient loading and a decrease in dissolved oxygen. Overall, this research demonstrated the vulnerability of the catchment to climate change. The findings of this research can offer vital knowledge to policymakers on possible strategies for the sustainable management of water.

Personal protective measures and settings on the risk of SARS-COV-2 community transmission: a case-control study

Background

During the SARS-CoV-2 pandemic, nurses of primary health care has been an important role in Spain. Even so, the data obtained in the tracing have been scarcely used to investigate the possible mechanisms of transmission. Few studies focused on community transmission, evaluating the effectiveness of individual protective measures and exposure environment. The main aim of the study was to evaluate the association between individual protective measures and SARS-CoV-2 transmission in the community and to compare secondary attack rates in different exposure settings.

Methods

A case-control study from contact tracing of SARS-CoV-2 index patients. COVID-19 contact tracing was led by nurses at the COVID-19 Coordinating Centre in Majorca (Spain). During the systematic tracing, additional information for this study was collected from the index patient (social-demographic variables, symptoms, the number of close contacts). And also, the following variables from their close contacts: contact place, ventilation characteristics mask-wearing, type of mask, duration of contact, shortest distance, case-contact relationship, household members, and handwashing, the test result for SARS-CoV-2 diagnostic. Close contacts with a positive test for SARS-CoV-2 were classified as "cases" and those negative as "controls."

Results

A total of 1,778 close contacts from 463 index patients were identified. No significant differences were observed between the sexes but between age groups. Overall Secondary Attack Rate (SAR) was 24.0% (95% CI: 22.0-26.0%), 36.9% (95% CI: 33.2-40.6%) in closed spaces without ventilation and 50.7% (95% CI: 45.6-55.8%) in exposure time > 24 h. A total of 49.2% of infections occurred among household members. Multivariate logistic regression analysis showed that open-air setting (OR 0.43, 95% CI: 0.27-0.71), exposure for less than 1 h (OR 0.19, 95% CI: 0.11-0.32), and wearing a mask (OR 0.49, 95% CI: 0.28-0.85) had a protective effect transmission of SARS-CoV-2 in the community.

Conclusion

Ventilation of the space, mask-wearing and shorter exposure time were associated with a lower risk of transmission in the community. The data obtained allowed an assessment of community transmission mechanisms and could have helped to improve and streamline tracing by identifying close contacts at higher risk.

A review on hybridization of plasmonic and photonic crystal biosensors for effective cancer cell diagnosis

Cancer causes one in six deaths worldwide, and 1.6 million cancer patients face annual out-of-pocket medical expenditures. In response to these, portable, label-free, highly sensitive, specific, and responsive optical biosensors are under development. Therefore, in this review, the recent advances, advantages, performance analysis, and current challenges associated with the fabrication of plasmonic biosensors, photonic crystals, and the hybridization of both for cancer diagnosis are assessed. The primary focus is on the development of biosensors that combine different shapes, sizes, and optical properties of metallic and dielectric nanoparticles with various coupling techniques. The latter part discusses the challenges and prospects of developing effective biosensors for early cancer diagnosis using dielectric and metallic nanoparticles. These data will help the audience advance research and development of next-generation plasmonic biosensors for effective cancer diagnosis.

A bifurcation analysis and model of Covid-19 transmission dynamics with post-vaccination infection impact

SARS COV-2 (Covid-19) has imposed a monumental socio-economic burden worldwide, and its impact still lingers. We propose a deterministic model to describe the transmission dynamics of Covid-19, emphasizing the effects of vaccination on the prevailing epidemic. The proposed model incorporates current information on Covid-19, such as reinfection, waning of immunity derived from the vaccine, and infectiousness of the pre-symptomatic individuals into the disease dynamics. Moreover, the model analysis reveals that it exhibits the phenomenon of backward bifurcation, thus suggesting that driving the model reproduction number below unity may not suffice to drive the epidemic toward extinction. The model is fitted to real-life data to estimate values for some of the unknown parameters. In addition, the model epidemic threshold and equilibria are determined while the criteria for the stability of each equilibrium solution are established using the Metzler approach. A sensitivity analysis of the model is performed based on the Latin Hypercube Sampling (LHS) and Partial Rank Correlation Coefficients (PRCCs) approaches to illustrate the impact of the various model parameters and explore the dependency of control reproduction number on its constituents parameters, which invariably gives insight on what needs to be done to contain the pandemic effectively. The foregoing notwithstanding, the contour plots of the control reproduction number concerning some of the salient parameters indicate that increasing vaccination coverage and decreasing vaccine waning rate would remarkably reduce the value of the reproduction number below unity, thus facilitating the possible elimination of the disease from the population. Finally, the model is solved numerically and simulated for different scenarios of disease outbreaks with the findings discussed.

Clinical Characteristics of Hospitalized Patients With COVID-19: A Retrospective Cohort Study in Pakistan

BACKGROUND

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a global pandemic of severe upper respiratory tract infections known as COVID-19. This single-center study aimed to investigate the demographics, comorbidities, symptoms, and disease severity of COVID-19 patients in Pakistan.

METHODS

A retrospective descriptive study was conducted at the Pakistan Kidney and Liver Institute and Research Center from April 2020 to August 2020. A total of 430 PCR-positive COVID-19 patients were categorized into symptomatic and asymptomatic groups. The symptomatic group was further classified into severe and non-severe subgroups. Patients' demographics, comorbid conditions, presenting symptoms, laboratory parameters, and clinical outcomes were assessed in these two subgroups. Statistical tests were applied to determine significant differences.

RESULTS

A total of 430 patients with COVID-19 presented in our center, of whom 334 (78%) were symptomatic and included in the study. Severe disease was observed in 83 (24.8%) symptomatic patients, with a male predominance (75.9%) and higher mean age (61.7 ± 13.2). Travel to high-risk destinations (p < 0.002) and close contact with COVID-19 patients (p < 0.001) were significant risk factors. Major comorbid conditions included diabetes mellitus (30.5%) and hypertension (39.8%). Frequent symptoms included fever (71.8%), cough (68.8%), dyspnea (53.8%), and myalgias (35.9%). Higher C-reactive protein (median = 12.76 vs. 1.45, p = 0.001), ferritin (median = 996.70 vs. 628, p = 0.004), and D-dimers (median = 1121 vs. 439.50, p = 0.009) were noted in severe vs non-severe disease. Lymphopenia was more prevalent in severe vs. non-severe disease (83.1% vs. 14.3% p-value = 0.033). More deaths (28.9%) and ICU admissions (53%) with a prolonged hospital stay (median = 25 days, IQR = 16.0-31.0) were noted in the severe group.

CONCLUSION

This retrospective study provides insights into the clinical characteristics and outcomes of COVID-19 patients. Age, male gender, comorbidities, and specific symptoms were associated with disease severity. Inflammatory markers, including D-dimers, ferritin, and CRP, were elevated in severe cases. These findings contribute to a better understanding of COVID-19 and may aid in clinical management and decision-making for patients affected by the disease.

Real-time 4D tracking of airborne virus-laden droplets and aerosols

There is currently no real-time airborne virus tracking method, hindering the understanding of rapid virus changes and associated health impacts. Nano-digital in-line holographic microscopy (Nano-DIHM) is a lensless technology that can directly obtain the interference patterns of objects by recording the scattered light information originating from the objects. Here, we provide evidence for real-time physicochemical tracking of virus-laden droplets and aerosols in the air using desktop label-free Nano-DIHM. The virus interference patterns, as single and ensemble particles, were imaged by the Nano-DIHM with 32.5 ms resolution. The next-generation Stingray and Octopus software was used to automate object detection, characterization and classification from the recorded holograms. The detection system was demonstrated to detect active MS2 bacteriophages, inactivated SARS-CoV-2 and RNA fragments, and an MS2 mixture with metallic and organic compounds. This work demonstrates the feasibility of using Nano-DIHM to provide rapid virus detection to improve transmission management in real time.

Devendra Pal and coworkers report an imaging system using Nano-Digital in-line Holographic Microscopy (NanoDIHM) to detect airborne viruses in droplets and aerosols in real time. This system is able to detect various viruses in air, water and heterogeneous matrices within one minute, enabling real-time tracking of pollutant particles for efficient epidemic management.

Rapid antigen test as a tool for the identification of SARS-CoV-2 infection and its potential as a self-testing device

BACKGROUND

SARS-CoV-2 virus originated in Wuhan (China) in December (2019) and quickly spread worldwide. Antigen tests are rapid diagnostic tests (RDT) that produce results in 15-30 min and are an important tool for the scale-up of COVID-19 testing. COVID-19 diagnostic tests are authorized for self-testing at home in some countries, including Brazil. Widespread COVID-19 diagnostic testing is required to guide public health policies and control the speed of transmission and economic recovery.

METHODS

Patients with suspected COVID-19 were recruited at the Hospital da Baleia (Belo Horizonte, Brazil). The SARS-CoV-2 antigen-detecting rapid diagnostic tests were evaluated from June 2020 to June 2021 using saliva, nasal, and nasopharyngeal swab samples from 609 patients. Patient samples were simultaneously tested using a molecular assay (RT-qPCR). Sensitivity, specificity, accuracy, and positive and negative predictive values were determined using the statistical program, MedCalc, and GraphPad Prism 8.0.

RESULTS

The antigen-detecting rapid diagnostic tests displayed 98% specificity, 60% sensitivity, 96% positive predictive value, and moderate concordance with RT-qPCR. Substantial agreement was found between the two methods for patients tested < 7 days of symptom onset.

CONCLUSIONS

Our findings support the use of Ag-RDT as a valuable and safe diagnostic method. Ag-RDT was also demonstrated to be an important triage tool for suspected COVID-19 patients in emergencies. Overall, Ag-RDT is an effective strategy for reducing the spread of SARS-CoV-2 and contributing to COVID-19 control.

A novel fractional order model of SARS-CoV-2 and Cholera disease with real data

This study presents a novel approach to investigating COVID-19 and Cholera disease. In this situation, a fractional-order model is created to investigate the COVID-19 and Cholera outbreaks in Congo. The existence, uniqueness, positivity, and boundedness of the solution are studied. The equilibrium points and their stability conditions are achieved. Subsequently, the basic reproduction number (the virus transmission coefficient) is calculated that simply refers to the number of people, to whom an infected person can make infected, as R 0 = 6 . 7442389 e - 10 by using the next generation matrix method. Next, the sensitivity analysis of the parameters is performed according to R 0 . To determine the values of the parameters in the model, the least squares curve fitting method is utilized. A total of 22 parameter values in the model are estimated by using real Cholera data from Congo. Finally, to find out the dynamic behavior of the system, numerical simulations are presented. The outcome of the study indicates that the severity of the Cholera epidemic cases will decrease with the decrease in cases of COVID-19, through the implementation and follow-up of safety measures that have been taken to reduce COVID-19 cases.

Infinite Shear Rate Viscosity Model of Cross Fluid Flow Containing Nanoparticles and Motile Gyrotactic Microorganisms Over 3-D Cylinder

Cross nanofluidic model yields extraordinary results and describes the behaviour of nanofluid at very high and very low shear rate. In this paper infinite shear rate viscosity model of cross nanofluid flow containing nanoparticles and motile gyrotactic microorganisms over three dimensional horizontal cylinder is taken. In this attempt simultaneous utilization of nanoparticles along with motile microorganisms attached mathematical model of cross fluid and three-dimensional geometry of cylinder has been carried out as an innovation. For the inspection of velocity profile of cross nanofluid inclined magnetic field is scrutinized. Temperature of Cross nanofluid and its concentration is also carried out with several facts. Mass flux and heat flux values for motile microorganisms and nanoparticles are calculated through statistical graphs. This attempt reveals that small variation of Brownian motion parameter gives lower concentration of nanoparticle about 80.21% and 78.44% reduction is found in concentration of motile microorganisms.

A noncontact modular infectious disease screening clinic aiming to achieve zero cross-contaminations

Screening clinics play a major role in preventing the transmission of infectious diseases. The main problem that should be addressed is the exposure to cross-infection between healthcare workers and individuals intended to be tested. In this study, a noncontact modular screening clinic (NCMSC) was developed that addresses the problems of existing screening clinics and the risks of cross-contamination during the infectious disease sampling process. The space and ventilation system of the NCMSC were designed to effectively remove viral aerosols to avoid cross-contamination. The spatial configurations that enabled noncontact specimen sampling and pressure differential control was achieved. Regarding the measurement method with the use of tracer gas, an experimental field test framework and procedure that can evaluate the cross-contamination between rooms were presented. It is the observation of pollutants (tracer gas) in two different modes (normal breathing and AGP from a patient) in a screening clinic with ventilation, compared to the room next door, where the HCW is located. Additionally, based on onsite experiments using SF₆ tracer gas that mimics the viral aerosol at an actual scale, it was verified that no cross-contamination occurred in the NCMSC; accordingly, it was possible to protect sufficiently the healthcare workers. It will be possible to use the outcomes of this study as basic data for the development of standards for the installation and operation of screening clinics for infectious diseases.

A memory effect model to predict COVID-19: analysis and simulation

On 19 September 2020, the Centers for Disease Control and Prevention (CDC) recommended that asymptomatic individuals, those who have close contact with infected person, be tested. Also, American society for biological clinical comments on testing of asymptomatic individuals. So, we proposed a new mathematical model for evaluating the population-level impact of contact rates (social-distancing) and the rate at which asymptomatic people are hospitalized (isolated) following testing due to close contact with documented infected people. The model is a deterministic system of nonlinear differential equations that is fitted and parameterized by least square curve fitting using COVID-19 pandemic data of Pakistan from 1 October 2020 to 30 April 2021. The fractional derivative is used to understand the biological process with crossover behavior and memory effect. The reproduction number and conditions for asymptotic stability are derived diligently. The most common non-integer Caputo derivative is used for deeper analysis and transmission dynamics of COVID-19 infection. The fractional-order Adams-Bashforth method is used for the solution of the model. In light of the dynamics of the COVID-19 outbreak in Pakistan, non-pharmaceutical interventions (NPIs) in terms of social distancing and isolation are being investigated. The reduction in the baseline value of contact rates and enhancement in hospitalization rate of symptomatic can lead the elimination of the pandemic.

MHD flow of nanofluid over moving slender needle with nanoparticles aggregation and viscous dissipation effects

The study of boundary layer flows over an irregularly shaped needle with small horizontal and vertical dimensions is popular among academics because it seems to have a lot of uses in fields as different as bioinformatics, medicine, engineering, and aerodynamics. With nanoparticle aggregation, magnetohydrodynamics, and viscous dissipation all playing a role in the flow and heat transmission of an axisymmetric T i O 2 - C 2 H 6 O 2 nanofluid via a moving thin needle, this article provides guidance on how to employ a boundary layer for this purpose. In this case, we utilized the similarity transformation to change the dimensional partial differential equation into the dimensionless ordinary differential equation. We utilize MATHEMATICA to include shooting using RK-IV methods after identifying the numerical issue. Several characteristics were measured, leading to the discovery of a broad variety of values for things like skin friction coefficients, Nusselt numbers, velocity profiles, and temperature distributions. Velocity profile decreases with increasing values of ϕ , M , e and increases against ε . Temperature profiles enhances with increasing values of ϕ , M , e , ε , and E c . The reduction in skin friction between a needle and a fluid can be observed when the values of M and ϕ are boosted. Furthermore, it was also noticed an increase in heat transfer on needle surface dramatically when ϕ , e , and M were raised, whereas E c displayed the opposite effect. The findings of the current study are compared with prior findings for a particular instance in order to confirm the findings. Excellent agreement between the two sets of results is found.

A new fractional mathematical model to study the impact of vaccination on COVID-19 outbreaks

This study proposes a new fractional mathematical model to study the impact of vaccination on COVID-19 outbreaks by categorizing infected people into non-vaccinated, first dose-vaccinated, and second dose-vaccinated groups and exploring the transmission dynamics of the disease outbreaks. We present a non-linear integer order mathematical model of COVID-19 dynamics and modify it by introducing Caputo fractional derivative operator. We start by proving the good state of the model and then calculating its reproduction number. The Caputo fractional-order model is discretized by applying a reliable numerical technique. The model is proven to be stable. The classical model is fitted to the corresponding cumulative number of daily reported cases during the vaccination regime in India between 01 August 2021 and 21 July 2022. We explore the sensitivities of the reproduction number with respect to the model parameters. It is shown that the effective transmission rate and the recovery rate of unvaccinated infected individuals are the most sensitive parameters that drive the transmission dynamics of the pandemic in the population. Numerical simulations are used to demonstrate the applicability of the proposed fractional mathematical model via the memory index at different values of 0 . 7 , 0 . 8 , 0 . 9 and 1. We discuss the epidemiological significance of the findings and provide perspectives on future health policy tendencies. For instance, efforts targeting a decrease in the transmission rate and an increase in the recovery rate of non-vaccinated infected individuals are required to ensure virus-free population. This can be achieved if the population strictly adhere to precautionary measures, and prompt and adequate treatment is provided for non-vaccinated infectious individuals. Also, given the ongoing community spread of COVID-19 in India and almost the pandemic-affected countries worldwide, the need to scale up the effort of mass vaccination policy cannot be overemphasized in order to reduce the number of unvaccinated infections with a view to halting the transmission dynamics of the disease in the population.

Composition-dependent photoconductivities in indium aluminium nitride nanorods grown by magnetron sputter epitaxy

Photoconduction (PC) properties were investigated for ternary indium aluminium nitride (In x Al1-x N) nanorods (NRs) with different indium compositions (x) from 0.35 to 0.68, as grown by direct-current reactive magnetron sputter epitaxy. Cross-sectional scanning transmission electron microscopy (STEM) reveals single-crystal quality of the vertically aligned In x Al1-x N NRs. Single-rod photodetector devices with good ohmic contacts were fabricated using the focused-ion-beam technique (FIB), where the In-rich In0.68Al0.32N NR exhibits an optimal photocurrent responsivity of 1400 A W-1 and photoconductive gain of 3300. A transition from a positive photoresponse to a negative photoresponse was observed, while increasing the In composition x from 0.35 to 0.57. The negative PC was further enhanced by increasing x to 0.68. A model based on the coexistence and competition of deep electron trap states and recombination centers was proposed to explain the interesting composition-dependent PC in these ternary III-nitride 1D nanostructures.

Real-World Evidence on the Effectiveness of Plexiglass Barriers in Reducing Aerosol Exposure

Reprinted with permission, Cleveland Clinic Foundation ©2022. All Rights Reserved.

BACKGROUND

Barriers are commonly installed in workplace situations where physical distancing cannot be maintained to reduce the risk for transmission of respiratory viruses. Although some types of barriers have been shown to reduce exposure to aerosols in laboratory-based testing, limited information is available on the efficacy of barriers in real-world settings.

METHODS

In an acute care hospital, we tested the effectiveness of in-use plexiglass barriers in reducing exposure of staff to aerosolized particles. A nebulizer was used to release 5% NaCl aerosol 1 meter from staff members with and without the barrier positioned between the point of aerosol release and the hospital staff. Particle counts on the staff side of the barrier were measured using a 6-channel particle counter. A condensed moisture (fog) generating device was used to visualize the airflow patterns.

RESULTS

Of 13 in-use barriers tested, 6 (46%) significantly reduced aerosol particle counts detected behind the barrier, 6 (46%) reduced particle counts to a modest, non-significant degree, and 1 (8%) significantly increased particle counts behind the barrier. Condensed moisture fog accumulated in the area where staff were seated behind the barrier that increased particle exposure, but not behind the other barriers. After repositioning the ineffective barrier, the condensed moisture fog no longer accumulated behind the barrier and aerosol exposure was reduced.

CONCLUSION

In real-world settings, plexiglass barriers vary widely in effectiveness in reducing staff exposure to aerosols, and some barriers may increase risk for exposure if not positioned correctly. Devices that visualize airflow patterns may be useful as simple tools to assess barriers.

Four-scroll attractor on the dynamics of a novel Hopfield neural network based on bi-neurons without bias current

The dynamics of a neural network under several factors (bias current and electromagnetic induction effect) are recently used to simulate activities of the brain under different excitation. In this paper, we introduce a novel Hopfield neural network (HNN) based on two neurons with a memristive synaptic weight connected between neuron one and two based of flux controlled memristor recently proposed by Hua M. et al., in 2022. Using analysis tools, we proved that this model can develop rich dynamical characteristics such as various number of equilibrium points when the parameters are varied, four-scroll attractors, transient chaos, multistability of more than three different attractors and intermittency chaos phenomenon are reported. Moreover, when increasing a synaptic weight, the model shows bursting oscillations phenomenon. To obtain the normal state of the brain, the control of multistability to a strange monostable state is carry out. Finally, microcontroller implementation of the model is considered to verify the numerical analysis.

Weather Conditions and COVID-19 Cases: Insights from the GCC Countries

The prediction of new COVID-19 cases is crucial for decision makers in many countries. Researchers are continually proposing new models to forecast the future tendencies of this pandemic, among which long short-term memory (LSTM) artificial neural networks have exhibited relative superiority compared to other forecasting techniques. Moreover, the correlation between the spread of COVID-19 and exogenous factors, specifically weather features, has been explored to improve forecasting models. However, contradictory results have been reported regarding the incorporation of weather features into COVID-19 forecasting models. Therefore, this study compares uni-variate with bi- and multi-variate LSTM forecasting models for predicting COVID-19 cases, among which the latter models consider weather features. LSTM models were used to forecast COVID-19 cases in the six Gulf Cooperation Council countries. The root mean square error (RMSE) and coefficient of determination (R2) were employed to measure the accuracy of the LSTM forecasting models. Despite similar weather conditions, the weather features that exhibited the strongest correlation with COVID-19 cases differed among the six countries. Moreover, according to the statistical comparisons that were conducted, the improvements gained by including weather features were insignificant in terms of the RMSE values and marginally significant in terms of the R2 values. Consequently, it is concluded that the uni-variate LSTM models were as good as the best bi- and multi-variate LSTM models; therefore, weather features need not be included. Furthermore, we could not identify a single weather feature that can consistently improve the forecasting accuracy.

Application of piecewise fractional differential equation to COVID-19 infection dynamics

We proposed a new mathematical model to study the COVID-19 infection in piecewise fractional differential equations. The model was initially designed using the classical differential equations and later we extend it to the fractional case. We consider the infected cases generated at health care and formulate the model first in integer order. We extend the model into Caputo fractional differential equation and study its background mathematical results. We show that the fractional model is locally asymptotically stable whenR 0 < 1 at the disease-free case. ForR 0 ≤ 1 , we show the global asymptotical stability of the model. We consider the infected cases in Saudi Arabia and determine the parameters of the model. We show that for the real cases, the basic reproduction isR 0 ≈ 1 . 7372 . We further extend the Caputo model into piecewise stochastic fractional differential equations and discuss the procedure for its numerical simulation. Numerical simulations for the Caputo case and piecewise models are shown in detail.

Application of fractional optimal control theory for the mitigating of novel coronavirus in Algeria

In this paper, we investigate the dynamics of novel coronavirus infection (COVID-19) using a fractional mathematical model in Caputo sense. Based on the spread of COVID-19 virus observed in Algeria, we formulate the model by dividing the infected population into two sub-classes namely the reported and unreported infective individuals. The existence and uniqueness of the model solution are given by using the well-known Picard-Lindelöf approach. The basic reproduction numberR 0 is obtained and its value is estimated from the actual cases reported in Algeria. The model equilibriums and their stability analysis are analyzed. The impact of various constant control parameters is depicted for integer and fractional values of α . Further, we perform the sensitivity analysis showing the most sensitive parameters of the model versusR 0 to predict the incidence of the infection in the population. Further, based on the sensitivity analysis, the Caputo model with constant controls is extended to time-dependent variable controls in order obtain a fractional optimal control problem. The associated four time-dependent control variables are considered for the prevention, treatment, testing and vaccination. The fractional optimality condition for the control COVID-19 transmission model is presented. The existence of the Caputo optimal control model is studied and necessary condition for optimality in the Caputo case is derived from Pontryagin's Maximum Principle. Finally, the effectiveness of the proposed control strategies are demonstrated through numerical simulations. The graphical results revealed that the implantation of time-dependent controls significantly reduces the number of infective cases and are useful in mitigating the infection.

Propagation of H1N1 virus through saliva movement in oesophagus: a mathematical model

H1N1 (Swine flu) is caused by the influenza A virus which belongs to the Orthomyxoviridae family. Influenza A is very harmful to the elderly, and people with chronic respiratory disease and cardiovascular disease. Therefore, it is essential to analyse the behaviour of virus transmission through the saliva movement in oesophagus. A mathematical paradigm is developed to study the saliva movement under the applications of transverse magnetic field. Jeffrey fluid model is considered for saliva to show the viscoelastic nature. The flow nature is considered creeping and assumptions of long wavelength and low Reynolds number are adopted for analytical solutions. The Basset-Boussinesq-Oseen equation is employed to understand the propagation of H1N1 virus through saliva under the effect of applicable forces such as gravity, virtual mass, basset force, and drag forces. The suitable data for saliva, oesophagus and H1N1 virus are taken from the existing literature for simulation of the results using MATLAB software. From the graphical results, it is observed that the susceptibility to viral infections is less because the magnetic field reduces the motion of the virus particle. Further, the chances of infections in males are more as compared to females and children due to variation in viscosity of saliva. Such findings provide an understanding of the mechanics of the virus floating through the saliva (viscoelastic fluids) in the oesophagus.

A nonlinear memductance induced intermittent and anti-phase synchronization

We introduce a model to mimic the dynamics of oscillators that are coupled by mean-field nonlinear memductance. Notably, nonlinear memductance produces dynamic nonlinearity, which causes the direction of coupling to change over time. Depending on the parameters, such a dynamic coupling drives the trajectory of oscillators to a synchronization or anti-synchronization manifold. Specifically, depending on the forcing frequency and coupling strength, we find anti-phase and intermittent synchronization. With the increase in coupling magnitude, one can observe a transition from intermittent synchronization to complete synchronization through anti-phase synchronization. The results are validated through numerical simulations. The hypothesis has a huge impact on the study of neuronal networks.

Fractional order modelling of omicron SARS-CoV-2 variant containing heart attack effect using real data from the United Kingdom

In this study, a new approach to COVID-19 pandemic is presented. In this context, a fractional order pandemic model is developed to examine the spread of COVID-19 with and without Omicron variant and its relationship with heart attack using real data from the United Kingdom. In the model, heart attack is adopted by considering its relationship with the quarantine strategy. Then, the existence, uniqueness, positivity and boundedness of the solution are studied. The equilibrium points and their stability conditions are achieved. Subsequently, we calculate the basic reproduction number (the virus transmission coefficient) that simply refers to the number of people, to whom an infected person can make infected, asR 0 = 3.6456 by using the next generation matrix method. Next, we consider the sensitivity analysis of the parameters according to R 0 . In order to determine the values of the parameters in the model, the least squares curve fitting method, which is one of the leading methods in parameter estimation, is benefited. A total of 21 parameter values in the model are estimated by using real Omicron data from the United Kingdom. Moreover, in order to highlight the advantages of using fractional differential equations, applications related to memory trace and hereditary properties are given. Finally, the numerical simulations are presented to examine the dynamic behavior of the system. As a result of numerical simulations, an increase in the number of people who have heart attacks is observed when Omicron cases were first seen. In the future, it is estimated that the risk of heart attack will decrease as the cases of Omicron decrease.