No abstract

Abstract. Reward-risk ratio optimization is an important mathematical approach in finance. We revisit the model by considering a situation where an investor does not have complete information on the distribution of the underlying uncertainty and consequently a robust action is taken to mitigate the risk arising from ambiguity of the true distribution. We consider a distributionally robust reward-risk ratio optimization model varied from ex ante Sharpe ratio where the ambiguity set is constructed through prior moment information and the return function is not necessarily linear. We transform the robust optimization problem into a nonlinear semi-infinite programming problem through standard Lagrange dualization and then use the well known entropic risk measure to construct an approximation of the semi-infinite constraints, we solve the latter by an implicit Dinkelbach method (IDM). Finally, we apply the proposed robust model and numerical scheme to a portfolio optimization problem and report some preliminary numerical test results. The proposed robust formulation and numerical schemes can be easily applied to stochastic fractional programming problems.

Objective: To describe an injecting network of PWID living in an isolated community on the Isle of Wight (UK) and the results of a agent-based simulation, testing the effect of Hepatitis C (HCV) treatment on transmission. Method: People who inject drugs (PWID) were identified via respondent driven sampling and recruited to a network and bio-behavioural survey. The injecting network they described formed the baseline population and potential transmission pathways in an agent-based simulation of HCV transmission and the effects of treatment over 12 months. Results: On average each PWID had 2.6 injecting partners (range 0-14) and 137 were connected into a single component. HCV in the network was associated with a higher proportion of positive injecting partners ( p = 0.003) and increasing age ( p = 0.011). The treatment of well-connected PWID led to significantly fewer new infections of HCV than treating at random (10 vs. 7, p < 0.001). In all scenarios less than one individual was re-infected. Conclusion: In our model the preferential treatment of well-connected PWID maximised treatment as prevention. In the real-world setting, targeting treatment to actively injecting PWID, with multiple injecting partners may therefore represent the most efficient elimination strategy for HCV.

No abstract

In this paper we address the clinic location selection problem for a fully integrated Sexual Health Service across Hampshire. The service provides outpatient services for Genito-Urinary Medicine, contraceptive and reproductive health, sexual health promotion and a sexual assault referral centre. We aim to assist the planning of sexual health service provision in Hampshire by conducting a location analysis using both current and predicted patient need. We identify the number of clinic locations required and their optimal geographic location that minimise patient travel time. To maximise the chances of uptake of results we validate the developed simple algorithm with an exact method as well as three well-known, but complex meta-heuristics. The analysis was conducted using car travel and public transport times. Two scenarios were considered: current clinic locations only; and anywhere within Hampshire. The results show that the clinic locations could be reduced from 28 to 20 and still keep 90% of all patient journeys by public transport (e.g. by bus or train) to a clinic within 30 minutes. The number of clinics could be further reduced to 8 if the travel time is based on car travel times within 15 minutes. Results from our simple solution method compared favourably to the exact solution as well as the complex meta-heuristics.