scholarly journals HIV/AIDS Model with Early Detection and Treatment

2012 ◽  
Vol 2012 ◽  
pp. 1-14
Author(s):  
Augustine S. Mbitila ◽  
Jean M. Tchuenche
Keyword(s):  
Early Detection ◽  
Disease Transmission ◽  
Deterministic Model ◽  
Positive Impact ◽  
Disease Status ◽  
Critical Threshold ◽  
Model Parameters ◽  
Case Detection ◽  
The Impact ◽  
Hiv Aids

A classical epidemiological framework is used to qualitatively assess the impact of early detection and treatment on the dynamics of HIV/AIDS. Within this theoretical framework, two classes of infected populations: those infected but unaware of their serological status and those who are aware of their disease status, are considered. In this context, we formulate and analyze a deterministic model for the transmission dynamics of HIV/AIDS and assess the potential population-level impact of early detection in curtailing the epidemic. A critical threshold parameter for which case detection will have a positive impact is derived. Model parameters sensitivity analysis indicates that the number of partners is the most sensitive (in increasing the average number of secondary transmission) parameter. However, the case detection coverage is the main drivers in reducing the initial disease transmission. Numerical simulations of the model are provided to support the analytical results. Early detection and treatment alone are insufficient to eliminate the disease, and other control strategies are to be explored.

scholarly journals Evolution of disease transmission during the COVID-19 pandemic: patterns and determinants

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie Zhu ◽  
Blanca Gallego
Keyword(s):  
Public Health ◽  
Disease Transmission ◽  
Clustering Algorithm ◽  
Health Interventions ◽  
Transmission Model ◽  
Model Parameters ◽  
Public Health Interventions ◽  
Forecasting Accuracy ◽  
Time Lagged ◽  
The Impact

AbstractEpidemic models are being used by governments to inform public health strategies to reduce the spread of SARS-CoV-2. They simulate potential scenarios by manipulating model parameters that control processes of disease transmission and recovery. However, the validity of these parameters is challenged by the uncertainty of the impact of public health interventions on disease transmission, and the forecasting accuracy of these models is rarely investigated during an outbreak. We fitted a stochastic transmission model on reported cases, recoveries and deaths associated with SARS-CoV-2 infection across 101 countries. The dynamics of disease transmission was represented in terms of the daily effective reproduction number ($$R_t$$ R t ). The relationship between public health interventions and $$R_t$$ R t was explored, firstly using a hierarchical clustering algorithm on initial $$R_t$$ R t patterns, and secondly computing the time-lagged cross correlation among the daily number of policies implemented, $$R_t$$ R t , and daily incidence counts in subsequent months. The impact of updating $$R_t$$ R t every time a prediction is made on the forecasting accuracy of the model was investigated. We identified 5 groups of countries with distinct transmission patterns during the first 6 months of the pandemic. Early adoption of social distancing measures and a shorter gap between interventions were associated with a reduction on the duration of outbreaks. The lagged correlation analysis revealed that increased policy volume was associated with lower future $$R_t$$ R t (75 days lag), while a lower $$R_t$$ R t was associated with lower future policy volume (102 days lag). Lastly, the outbreak prediction accuracy of the model using dynamically updated $$R_t$$ R t produced an average AUROC of 0.72 (0.708, 0.723) compared to 0.56 (0.555, 0.568) when $$R_t$$ R t was kept constant. Monitoring the evolution of $$R_t$$ R t during an epidemic is an important complementary piece of information to reported daily counts, recoveries and deaths, since it provides an early signal of the efficacy of containment measures. Using updated $$R_t$$ R t values produces significantly better predictions of future outbreaks. Our results found variation in the effect of early public health interventions on the evolution of $$R_t$$ R t over time and across countries, which could not be explained solely by the timing and number of the adopted interventions.

MODELING THE IMPACT OF VOLUNTARY TESTING AND TREATMENT ON TUBERCULOSIS TRANSMISSION DYNAMICS

2012 ◽  
Vol 05 (04) ◽  
pp. 1250029 ◽  
Author(s):  
S. MUSHAYABASA ◽  
C. P. BHUNU
Keyword(s):  
Deterministic Model ◽  
Reproduction Number ◽  
Positive Impact ◽  
Transmission Dynamics ◽  
Effective Control ◽  
Reproductive Number ◽  
Voluntary Testing ◽  
Manifold Theory ◽  
The Impact ◽  
Disease Free

A deterministic model for evaluating the impact of voluntary testing and treatment on the transmission dynamics of tuberculosis is formulated and analyzed. The epidemiological threshold, known as the reproduction number is derived and qualitatively used to investigate the existence and stability of the associated equilibrium of the model system. The disease-free equilibrium is shown to be locally-asymptotically stable when the reproductive number is less than unity, and unstable if this threshold parameter exceeds unity. It is shown, using the Centre Manifold theory, that the model undergoes the phenomenon of backward bifurcation where the stable disease-free equilibrium co-exists with a stable endemic equilibrium when the associated reproduction number is less than unity. The analysis of the reproduction number suggests that voluntary tuberculosis testing and treatment may lead to effective control of tuberculosis. Furthermore, numerical simulations support the fact that an increase voluntary tuberculosis testing and treatment have a positive impact in controlling the spread of tuberculosis in the community.

scholarly journals Nonlinear Analysis of the Dynamics of Criminality and Victimisation: A Mathematical Model with Case Generation and Forwarding

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Chikodili Helen Ugwuishiwu ◽  
D. S. Sarki ◽  
G. C. E. Mbah
Keyword(s):  
Mathematical Model ◽  
Numerical Simulations ◽  
Nonlinear Analysis ◽  
Deterministic Model ◽  
Positive Impact ◽  
Threshold Value ◽  
Dynamical Analysis ◽  
Viable Option ◽  
Effective Implementation ◽  
The Impact

In this paper, a system of deterministic model is presented for the dynamical analysis of the interactional consequence of criminals and criminality on victimisation under two distinguishable forms of rehabilitation—the behavioural reformation of criminals and the emotional psychotherapy of victims. A threshold value, R0=maxRK,RV, responsible for the persistence of crime/criminality and victimisation, is obtained and, using it, stability analyses on the model performed. The impact of an effective implementation of the two forms of rehabilitation was found to be substantial on crime and criminality, while an ineffective implementation of same was observed to have a detrimental consequence. The prevention of repeat victimisation was seen to present a more viable option for containing crime than the noncriminalisation of victims. Further, the removal of criminals, either through quitting or death, among others, was also found to have a huge positive impact. Numerical simulations were performed for a variety of mixing criminal scenarios to verify the analytical results obtained.

Input and Design Optimization Under Uncertainty to Minimize the Impact Velocity of an Electrostatically Actuated MEMS Switch

2008 ◽  
Vol 130 (2) ◽  
Author(s):  
M. S. Allen ◽  
J. E. Massad ◽  
R. V. Field ◽  
C. W. Dyck
Keyword(s):  
Impact Velocity ◽  
Deterministic Model ◽  
Electrostatic Force ◽  
Optimization Under Uncertainty ◽  
Model Parameters ◽  
Rf Switch ◽  
Voltage Waveform ◽  
Rigid Barrier ◽  
Mechanical Impact ◽  
The Impact

The dynamic response of a radio-frequency (RF) microelectromechanical system to a time-varying electrostatic force is optimized to enhance robustness to variations in material properties and geometry. The device functions as an electrical switch, where an applied voltage is used to close a circuit. The objective is to minimize the severity of the mechanical impact that occurs each time the switch closes because severe impacts have been found to significantly decrease the life of these switches. Previous works have demonstrated that a classical vibro-impact model, a single-degree-of-freedom oscillator subject to mechanical impact with a single rigid barrier, captures the relevant physics adequately. Certain model parameters are described as random variables to represent the significant unit-to-unit variability observed during fabrication and testing of a collection of nominally identical switches; these models for unit-to-unit variability are calibrated to available experimental data. Our objective is to design the shape and duration of the voltage waveform so that impact kinetic energy at switch closure is minimized for the collection of nominally identical switches, subject to design constraints. A voltage waveform designed using a deterministic model for the RF switch is found to perform poorly on the ensemble. An alternative waveform is generated using the proposed optimization procedure with a probabilistic model and is found to decrease the maximum impact velocity by a factor of 2 relative to the waveform designed deterministically. The methodology is also applied to evaluate a design change that reduces the impact velocity further and to predict the effect of fabrication process improvements.

Analyzing conversion rates in online hotel booking

2016 ◽  
Vol 28 (2) ◽  
pp. 286-304 ◽  
Author(s):  
Asunur Cezar ◽  
Hulisi Ögüt
Keyword(s):  
Conversion Rate ◽  
Positive Impact ◽  
High Rank ◽  
Low Rank ◽  
Likelihood Method ◽  
Model Parameters ◽  
Successful Implementation ◽  
Content Type ◽  
Conversion Rates ◽  
The Impact

Purpose – The aim of this paper is to examine the impact of three main technologies on converting browsers into customers: impact of review rating (location rating and service rating), recommendation and search listings. Design/methodology/approach – This paper estimates conversion rate model parameters using a quasi-likelihood method with the Bernoulli log-likelihood function and parametric regression model based on the beta distribution. Findings – The results show that a high rank in search listings, a high number of recommendations and location rating have a significant and positive impact on conversion rates. However, service rating and star rating do not have a significant effect on conversion rate. Furthermore, room price and hotel size are negatively associated with conversion rate. It was also found that a high rank in search listings, a high number of recommendations and location rating increase online hotel bookings. Furthermore, it was found that a high number of recommendations increase the conversion rate of hotels with low ranks. Practical implications – The findings show that hotels’ location ratings are more important than both star and service ratings for the conversion of visitors into customers. Thus, hotels that are located in convenient locations can charge higher prices. The results may also help entrepreneurs who are planning to open new hotels to forecast the conversion rates and demand for specific locations. It was found that a high number of recommendations help to increase the conversion rate of hotels with low ranks. This result suggests that a high numbers of recommendations mitigate the adverse effect of a low rank in search listings on the conversion rate. Originality/value – This paper contributes to the understanding of the drivers of conversion rates in online channels for the successful implementation of hotel marketing.

Comparing Virtual and Face-to-Face Team Collaboration: Insights From an Agent-Based Simulation

2021 ◽  
Author(s):  
Harshika Singh ◽  
Gaetano Cascini ◽  
Christopher McComb
Keyword(s):  
Empirical Study ◽  
Positive Impact ◽  
Virtual Team ◽  
Model Parameters ◽  
Virtual Collaboration ◽  
Team Collaboration ◽  
Face To Face ◽  
Task Cohesion ◽  
Virtual Teaming ◽  
The Impact

Abstract The ongoing COVID-19 pandemic has accelerated the acceptance of virtual team collaboration as a replacement for face-to-face collaboration. Unlike face-to-face collaboration, virtual collaboration has different factors like technology mediation influencing communication that affects a team’s processes. However, there is a lack of rigorous research that assesses the impact of virtual teaming on the engineering design process. Therefore, the current study investigates the effect of virtual team collaboration on design outcomes by means of the MILANO (Model of Influence, Learning, and Norms in Organizations) framework. To tailor MILANO for virtual collaboration, this paper first presents an empirical study of human design teams, that shows how the model parameters for face-to-face collaboration (like self-efficacy, perceived influencers, perceived degree of influence, trust and familiarity) must be modified. The empirical study also shows the positive impact of effective communication on conflict resolution, task cohesion and the model parameters listed above. The simulation results for both virtual and face-to-face collaboration show how design outcomes differ with collaboration mode.

scholarly journals Buddhists Care: Examining the Impact of Religious Elements on Reducing Discriminatory Attitudes toward People Living with HIV/AIDS

Religions ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 409 ◽  
Author(s):  
Song ◽  
Qin
Keyword(s):  
Positive Impact ◽  
Underlying Mechanism ◽  
People Living With Hiv ◽  
Infected People ◽  
Relevant Evidence ◽  
Positive Attitudes ◽  
Immunodeficiency Syndrome ◽  
The Impact ◽  
Perceived Religiosity ◽  
Hiv Aids

Faith-based programs have been long regarded as influential social approaches to form positive attitudes to human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) within the last few decades. However, recent scholars argue that religions serve a double role in supporting HIV-infected people. Moreover, relevant evidence is mainly collected from studies among participants of the Western religious traditions, such as Christianity. This study applies the theory of the attitude formation model to examine Buddhist factors impacting discriminatory attitudes towards HIV/AIDS and the causal path to positive behavior intention. To investigate its underlying mechanism, Buddhist elements, as an important antecedent, were introduced in the advertisement against HIV/AIDS-related discrimination to influence people’s attitudinal reaction. Results show that Buddhist advertising could significantly increase perceived religiosity and compassion. Then, both perceived religiosity and compassion jointly increase anti-prejudical attitudes towards HIV-infected people and have a positive impact on interaction intention at the end.

scholarly journals The Impact of SARS-CoV-2 Variant to COVID-19 Epidemic in Brazil

2020 ◽  
Author(s):  
S. Celaschi
Keyword(s):  
Cohort Study ◽  
Disease Transmission ◽  
Population Based ◽  
Published Data ◽  
Model Parameters ◽  
Global Dynamics ◽  
Adopted Model ◽  
Data Points ◽  
The Impact ◽  
First Time

AbstractThe impact of SARS-CoV-2 dominant global lineages to COVID-19 epidemics is for the first time modeled by an adaptation of the deterministic SEIR Model. Such a strategy may be applied worldwide to predict forecasts of the outbreak in any infected country. The objective of this study is to forecast the outcome of the epidemic in Brazil as a first cohort study case. The basic modeling design takes under consideration two of SARS-CoV-2 dominant strains, and a time-varying reproduction number to forecast the disease transmission behavior. The study is set as a country population-based analysis. Brazilian official published data from February 25 to August 30 2020 was employed to adjust a couple of epidemiological parameters in this cohort study. The population-based sample in this study, 4.2 Million Brazilians during the study period, is the number of confirmed cases on exposed individuals. Model parameters were estimated by minimizing the mean squared quadratic errors. The main outcomes of the study follows: The percentage values of non-symptomatic and symptomatic COVID-19 hosts were estimated to be respectively (54 ± 9) % and (46 ± 9) %. By the end of 2020, the number of confirmed cases in Brazil, within 95% CI, is predicted to reach 6 Million (5-7), and fatalities would account for 180.103 (160–200).103. Estimated forecast obtained preserving or releasing the NPIs during the last quarter of 2020, are included. Data points for extra three weeks were added after the model was complete, granting confidence on the outcomes. In 2020 the total number of exposures individuals is estimated to reach 13 ± 1 Million, 6.2% of the Brazilian population. Regarding the original SARS-CoV-2 form and its variant, the only model assumption is their distinct incubation rates. The variant SARS-CoV-2 form, as predicted by the SEIR adopted model, reaches a maximum of 96% of exposed individuals as previously reported for South America. By the end of 2020, a fraction in the range of 15–35 percentages of susceptible Brazilian individuals is to be depleted. Sufficient depletion of susceptibility (by NPIs or not) has to be achieved to weaken the global dynamics spread.

scholarly journals Age-Structured Model for COVID-19: Effectiveness of Social Distancing and Contact Reduction

2020 ◽  
Author(s):  
Mark Kimathi ◽  
Samuel Mwalili ◽  
Viona Ojiambo ◽  
Duncan Gathungu
Keyword(s):  
Deterministic Model ◽  
Age Groups ◽  
School Closure ◽  
Model Parameters ◽  
Suitable Model ◽  
Age Group ◽  
Social Distancing ◽  
First Case ◽  
Age Structured ◽  
The Impact

Abstract Background: Coronavirus disease 2019 is caused by severe acute respiratory syndrome coronavirus 2. The disease has spread to almost every country in the world. Kenya reported its first case on 13th of March 2020. From 16th March 2020, the country instituted various social distancing strategies to reduce the transmission and flatten the epidemic curve. These strategies include school closure, dusk-to-dawn curfew, and travel restriction across counties, especially Nairobi, Mombasa and Kwale. An age-structured compartmental model was developed to assess the impact of non-pharmaceutical interventions on severity of infections, hospital demands and deaths. Methods: The population is divided into four age-groups and for each age-group there are seven compartments, namely: susceptible , exposed, asymptomatic, mild, severe, critical, death and recovered. The contact matrices between the different ages are integrated into an age-structured deterministic model via the force of infection. This model is represented by ordinary differential equations and solved using Runge–Kutta methods, with suitable model parameters. Simulation results for the unmitigated and mitigated scenarios were depicted, for the different age-groups. Results: The 45% reduction in contacts for 60-days period resulted to between 11.5-13% reduction of infections severity and deaths, while for the 190-days period yielded between 18.8-22.7% reduction. The peak of infections in the 60-days mitigation was higher and happened about 2 months after the relaxation of mitigation as compared to that of the 190-days mitigation, which happened just a month after mitigation were relaxed. Low numbers of cases in children under 15 years was attributed to low susceptibility of persons in this age-group. High numbers of cases are reported in the 15-29 years and 30-59 years age bands since these individuals have wider interaction spheres, and they form a significant percentage of Kenya population. Conclusion: Two mitigation periods, considered in the study, resulted to reductions in severe and critical cases, attack rates, hospital and ICU bed demands, as well as deaths, with the 190-days period giving higher reductions. The study revealed the age-dependency of the key health outputs.

scholarly journals Transportation Optimization Models for Intermodal Networks with Fuzzy Node Capacity, Detour Factor, and Vehicle Utilization Constraints

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2109
Author(s):  
Chia-Nan Wang ◽  
Thanh-Tuan Dang ◽  
Tran Quynh Le ◽  
Panitan Kewcharoenwong
Keyword(s):  
Linear Programming ◽  
Objective Function ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Deterministic Model ◽  
Mixed Integer ◽  
Model Parameters ◽  
Total Cost ◽  
The Impact ◽  
Node Capacity

This paper develops a mathematical model for intermodal freight transportation. It focuses on determining the flow of goods, the number of vehicles, and the transferred volume of goods transported from origin points to destination points. The model of this article is to minimize the total cost, which consists of fixed costs, transportation costs, intermodal transfer costs, and CO2 emission costs. It presents a mixed integer linear programming (MILP) model that minimizes total costs, and a fuzzy mixed integer linear programming (FMILP) model that minimizes imprecise total costs under conditions of uncertain data. In the models, node capacity, detour, and vehicle utilization are incorporated to estimate the performance impact. Additionally, a computational experiment is carried out to evaluate the impact of each constraint and to analyze the characteristics of the models under different scenarios. Developed models are tested using real data from a case study in Southern Vietnam in order to demonstrate their effectiveness. The results indicate that, although the objective function (total cost) increased by 20%, the problem became more realistic to address when the model was utilized to solve the constraints of node capacity, detour, and vehicle utilization. In addition, on the basis of the FMILP model, fuzziness is considered in order to investigate the impact of uncertainty in important model parameters. The optimal robust solution shows that the total cost of the FMILP model is enhanced by 4% compared with the total cost of the deterministic model. Another key measurement related to the achievement of global sustainable development goals is considered, reducing the additional intermodal transfer cost and the cost of CO2 emissions in the objective function.

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