Optimization of Patient Allocation During an Epidemic Dengue Fever Outbreak in Ciudad del Este, Paraguay

• Main Authors: CUEVAS BRUN, EDGAR HERNAN, 古文嘉
• Other Authors: TSAI, JUNG-FA
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/44m58w

碩士 === 國立臺北科技大學 === 管理學院外國學生專班 === 107 === Dengue fever is a mosquito-borne disease that has rapidly spread throughout the past century. Initially only seen in tropical and subtropical areas, today it can also be encountered across temperate regions. In the past few decades, several dengue fever epidemics have taken place in numerous countries, some of which have been declared as endemoepidemic areas due to the constant recurrence of the disease. Most preventive mechanisms to deal with the disease focus on the eradication of the vector mosquito and vaccination campaigns; however, once an outbreak takes place, counting with the appropriate infrastructure, resources and mechanisms of response is indispensable to face the consequent events. This study presents single and multiple objective linear programming models that aim to optimize the allocation of patients and additional resources during an epidemic dengue fever outbreak, minimizing the summation of the distances traveled by all patients, while also minimizing individual journeys. By these means, the cost implied in transportation could be reduced. The case study was set in Ciudad del Este, Paraguay, nation that became an endemic area in 2002. Data provided by a privately-owned health insurance cooperative was used to test the three models presented in the study. Moreover, the results were computed and analyzed based on the algorithms that displayed the capabilities of their features, and the ε-constraint method was applied to solve multiple objective problems. Allocation of patients, resource shortage, and allocation of additional resources are shown in the results to highlight the advantages generated by the models, providing intrinsic information to both analysts and decision makers. Suggestions about expansions and improvements of the models are also mentioned, taking into consideration multiple scenarios where the models could be tested.