The methods employed by the ISE discipline offer powerful new insights to understand and improve public health emergency preparedness and response systems. The models can be used by public health practitioners not only to inform their planning decisions but also to provide a quantitative argument to support public health decision making and investment.
We study the problem of transmitting data from a set of sensors to a base-station where the data is to be gathered. Each sensor continuously generates data and has to transmit it through the network (via other sensor nodes) to the basestation. Considering the battery limitations of the sensors, our goal is to find an optimum location of the base-station and a corresponding data transmission scheme for routing the data from the sensors, such that the network is operating for the longest possible time. We focus mainly on tree networks for 2-level trees, with at most 2 hops from sensor to the base-station. For such networks we give efficient algorithms for forwarding data from sensors to the base-station and for locating the base-station optimally for maximizing network lifetime. Further, we show that determining a transmission protocol for trees with 3 or more levels is NP-hard. We demonstrate the effectiveness of our methods with experimental results on simulated data, comparing our 2-level tree algorithm with methods based on linear programming.
Over the last decade, chemotherapy treatments have dramatically shifted to outpatient services such that nearly 90% of all infusions are now administered outpatient. This shift has challenged oncology clinics to make chemotherapy treatment as widely available as possible while attempting to treat all patients within a fixed period of time. Historical data from a Veterans Affairs chemotherapy clinic in the United States and staff input informed a discrete event simulation model of the clinic. The case study examines the impact of altering the current schedule, where all patients arrive at 8:00 AM, to a schedule that assigns patients to two or three different appointment times based on the expected length of their chemotherapy infusion. The results identify multiple scheduling policies that could be easily implemented with the best solutions reducing both average patient waiting time and average nurse overtime requirements.
ARTICLE HISTORY
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