Dengue is endemic in the Philippines and poses a substantial economic burden in the country. In this work, a compartmentalized model which includes healthcare-seeking class is developed. The reproduction number is determined to investigate critical parameters influencing transmission. Partial rank correlation coefficient (PRCC) technique is performed to address how the model output is affected by changes in a specific parameter disregarding the uncertainty over the rest of the parameters. Results show that mosquito biting rate, transmission probability from mosquito to human, respectively, from human to mosquito, and fraction of individuals who seek healthcare at the onset of the disease, posted high PRCC values. In order to obtain the values for the desired parameters, the reported dengue cases by morbidity week in the Philippines for the year 2014 and 2015 are used. The reliability of parameters is then verified via parametric bootstrap.

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We consider a Volevič system of linear partial differential equations with general singularity, for which we establish existence and uniqueness theorems that are analogues of the Cauchy-Kowalevsky and Holmgren Theorems. Our results are generalizations of those of Elschner [

One of the fundamental problems in mathematical finance is the pricing of derivative assets such as options. In practice, pricing an exotic option, whose value depends on the price evolution of an underlying risky asset, requires a model and then numerical simulations. Having no a priori model for the risky asset, but only the knowledge of its distribution at certain times, we instead look for a lower bound for the option price using the Monge-Kantorovich transportation theory. In this paper, we consider the Monge-Kantorovich problem that is restricted over the set of martingale measure. In order to solve such problem, we first look at sufficient conditions for the existence of an optimal martingale measure. Next, we focus our attention on problems with transports which are two-dimensional real martingale measures with uniform marginals. We then come up with some characterization of the optimizer, using measure-quantization approach.

<abstract><p>In this paper, we consider a market model where the risky asset is a jump diffusion whose drift, volatility and jump coefficients are influenced by market regimes and history of the asset itself. Since the trajectory of the risky asset is discontinuous, we modify the delay variable so that it remains defined in this discontinuous setting. Instead of the actual path history of the risky asset, we consider the continuous approximation of its trajectory. With this modification, the delay variable, which is a sliding average of past values of the risky asset, no longer breaks down. We then use the resulting stochastic process in formulating the state variable of a portfolio optimization problem. In this formulation, we obtain the dynamic programming principle and Hamilton Jacobi Bellman equation. We also provide a verification theorem to guarantee the optimal solution of the corresponding stochastic optimization problem. We solve the resulting finite time horizon control problem and show that close form solutions of the stochastic optimization problem exist for the cases of power and logarithmic utility functions. In particular, we show that the HJB equation for the power utility function is a first order linear partial differential equation while that of the logarithmic utility function is a linear ordinary differential equation.</p></abstract>