Search Results (7)

Background: Human Papillomavirus (HPV) is the primary cause of cervical cancer. Single-dose HPV vaccination can effectively prevent high-risk HPV infection that causes cervical cancer and accelerate progress toward achieving cervical cancer elimination goals. We modelled the potential impact of adopting single-dose HPV vaccination strategies on health and economic outcomes in Kenya, where a two-dose schedule is the current standard. Methods: Using a validated compartmental transmission model of HPV and HIV in Kenya, we evaluated the costs from the payer’s perspective to vaccinate girls by age 10 with either one or two doses and increasing coverage levels (0%, 70%, 77%, 90%). Additionally, we modelled single-dose strategies supplemented with either catch-up vaccination of adolescent girls and young women or vaccination for all by age 10, funded with the first five-years of cost savings of switching from a two- to one-dose schedule. Costs and outcomes were discounted at 3% annually, and incremental cost-effectiveness ratios (ICERs) were calculated per disability-adjusted-life-year (DALY) averted. Results: All one-dose and the two-dose 90% coverage strategies were on the efficiency frontier, dominating the remaining two-dose strategies. The two-dose 90% coverage strategy had a substantially higher ICER (US$6508.80/DALY averted) than the one-dose 90% coverage (US$197.44/DALY averted). Transitioning from a two- to one-dose schedule could result in US$21.4 Million saved over the first five years, which could potentially fund 2.75 million supplemental HPV vaccinations. With this re-investment, all two-dose HPV vaccination scenarios would be dominated. The greatest DALYs were averted with the single-dose HPV vaccination schedule at 90% coverage supplemented with catch-up for 11–24-year-old girls, which had an ICER of US$78.73/DALYs averted. Conclusions: Considering the logistical and cost burdens of a two-dose schedule, a one-dose schedule for girls by age 10 would generate savings that could be leveraged for catch-up vaccination for older girls and accelerate cervical cancer elimination in Kenya. Full article

Bamboo is an important agroforestry and forest plant managed and utilized by rural communities in some countries in the Asia Pacific region, which can generate various benefits to meet social and environmental needs. In rural areas of China, as a large number of forest land management rights have been allocated to small-scale farmers, the willingness of small-scale farmers to reinvest in bamboo forest management has become a key factor for bamboo forest ecosystems to be able to sustainably supply quality ecosystem services. Therefore, it is necessary to answer the question of how to enhance small-scale farmers’ willingness to reinvest in bamboo forest management in the current policy and market context. Combining the prospect theory, the mindsponge theory, the theory of planned behavior (TPB), and the technology acceptance model (TAM), this study constructs theoretical models of perceived property rights security, perceived bamboo forest certification, government support, group decision making, risk perception, perceived value, geographic conditions, and resource endowment affecting willingness to reinvest in bamboo forest management. Based on 1090 questionnaires from a field study in Fujian, China, in 2021, structural equation modeling (SEM) was used to test the theoretical model. The results show that, under the current policy and market environment, government support is the key to enhance small-scale farmers’ willingness to reinvest in bamboo forest management, and their perception of ecological certification also has a facilitating effect on small-scale farmers’ willingness to reinvest in bamboo forest management, in which risk perception plays a significant mediating role. The government can enhance small-scale farmers’ willingness to reinvest in bamboo forest management by maintaining stable land property rights policies, increasing the publicity and promotion of bamboo forest certification, and enhancing information exchange among farmers. Full article

It has been suggested that implicit motor learning via dual-task or analogy training during gait rehabilitation may yield better outcomes in older adults by reducing the propensity for the conscious processing of movements (movement-specific reinvestment). The current study investigated the immediate effects of single-task, dual-task, and analogy training on reinvestment propensity and fall-related rehabilitation outcomes among older adults at risk of falling. Seventy-one older adults were randomly allocated to the single-task (ST), dual-task (DT), or analogy (AG) training conditions and received 12 training sessions. We assessed the reinvestment propensity, functional gait and balance, functional mobility, balance ability, single-task and dual-task walking abilities, and fear of falling at baseline (before training) and immediately after training. Our findings revealed a lack of training effect on reinvestment propensity for all groups. However, all groups displayed significant improvements in functional gait and balance (p < 0.001), functional mobility (p = 0.02), and balance ability (p = 0.01) after training. AG appeared to be superior to DT and ST, as it was the only condition that resulted in significant improvements in both single-task and dual-task walking abilities (p < 0.001). Implementing movement analogies could be a feasible and useful gait rehabilitation strategy for fall prevention and wellbeing promotion among older adults. Full article

The ruin probability is used to determine the overall operating risk of an insurance company. Modeling risks through the characteristics of the historical data of an insurance business, such as premium income, dividends and reinvestments, can usually produce an integral differential equation that is satisfied by the ruin probability. However, the distribution function of the claim inter-arrival times is more complicated, which makes it difficult to find an analytical solution of the ruin probability. Therefore, based on the principles of artificial intelligence and machine learning, we propose a novel numerical method for solving the ruin probability equation. The initial asset $u$ is used as the input vector and the ruin probability as the only output. A trigonometric exponential function is proposed as the projection mapping in the hidden layer, then a block trigonometric exponential neural network (BTENN) model with a symmetrical structure is established. Trial solution is set to meet the initial value condition, simultaneously, connection weights are optimized by solving a linear system using the extreme learning machine (ELM) algorithm. Three numerical experiments were carried out by Python. The results show that the BTENN model can obtain the approximate solution of the ruin probability under the classical risk model and the Erlang(2) risk model at any time point. Comparing with existing methods such as Legendre neural networks (LNN) and trigonometric neural networks (TNN), the proposed BTENN model has a higher stability and lower deviation, which proves that it is feasible and superior to use a BTENN model to estimate the ruin probability. Full article

In this research, the concept of Duration with a new application in project management has been defined. The Duration of each project provides the project manager with a combined measure containing concepts of return, cost and time of the project. Further in this article, the changes in project return, based on different assumptions such as discount rate, have been examined. To examine the effect of the changes in these factors, the Monte Carlo simulation has been used. The relationship between these factors is nonlinear which reflects the great importance of investment on appropriate risk management systems. The data from a set of construction projects have been used in order to verify the results of this study. Similar relationships can be expected to exist in other industries as well. Full article

Game-theoretic models are a convenient tool to systematically analyze competitive situations. This makes them particularly handy in the field of security where a company or a critical infrastructure wants to defend against an attacker. When the optimal solution of the security game involves several pure strategies (i.e., the equilibrium is mixed), this may induce additional costs. Minimizing these costs can be done simultaneously with the original goal of minimizing the damage due to the attack. Existing models assume that the attacker instantly knows the action chosen by the defender (i.e., the pure strategy he is playing in the i-th round) but in real situations this may take some time. Such adversarial inertia can be exploited to gain security and save cost. To this end, we introduce the concept of information delay, which is defined as the time it takes an attacker to mount an attack. In this period it is assumed that the adversary has no information about the present state of the system, but only knows the last state before commencing the attack. Based on a Markov chain model we construct strategy policies that are cheaper in terms of maintenance (switching costs) when compared to classical approaches. The proposed approach yields slightly larger security risk but overall ensures a better performance. Furthermore, by reinvesting the saved costs in additional security measures it is possible to obtain even more security at the same overall cost. Full article

The concept of best-estimate, prescribed by regulators to value insurance liabilities for accounting and solvency purposes, has recently been discussed extensively in the industry and related academic literature. To differentiate hedgeable and non-hedgeable risks in a general case, recent literature defines best-estimates using orthogonal projections of a claim on the space of replicable payoffs. In this paper, we apply this concept of best-estimate to long-maturity claims in a market with reinvestment risk, since in this case the total liability cannot easily be separated into hedgeable and non-hedgeable parts. We assume that a limited number of short-maturity bonds are traded, and derive the best-estimate price of bonds with longer maturities, thus obtaining a best-estimate yield curve. We therefore use the multifactor Vasiˇcek model and derive within this framework closed-form expressions for the best-estimate prices of long-term bonds. Full article