Pandemic hospital site selection: a GIS-based MCDM approach employing Pythagorean fuzzy sets.

COVID-19 poses many challenges for hospitals around the world. Each country attempts to solve the problems in its hospitals using different methods. In Turkey, two pandemic hospitals were built in Istanbul, the most crowded province. In addition, some hospitals were designated as pandemic hospitals. This study focuses on the methods used for site selection for a pandemic hospital in Atakum, a district of Samsun City, Turkey. As a solution to the problem, initially, spatial analysis was performed using GIS to produce maps based on seven criteria obtained from the insight of an expert team. Analytic hierarchy process (AHP) augmented by interval-valued Pythagorean fuzzy numbers (PFNs) was then used to determine weights for the criteria. Distance to transportation network was the most important criterion influencing the selection process and the least significant one was the distance to fire stations. Based on the criteria weights, and five rules specified by the expert team, 13 suitable locations for a pandemic hospital were determined using GIS. The technique for order preference by similarity to ideal solution (TOPSIS) method was used to determine the final ranking of 13 alternative locations (A1-A13). A10 was identified as the most appropriate site and A11 as the least appropriate site for a pandemic hospital. Finally, sensitivity analysis was performed to investigate how changes in weight values of the criteria affect the ranking of the alternatives.

Site selection for waste vegetable oil and waste battery collection boxes: a GIS-based hybrid hesitant fuzzy decision-making approach.

The amount of municipal solid waste (MSW) has been increasing rapidly in the urban centres of developing countries during the last few decades; however, municipal solid waste management (MSWM) remains inadequate. One of the largest aspects of cost of the MSWM system is the collection of waste. This paper describes a methodology that combines geographic information systems (GIS), hesitant fuzzy linguistic term set (HFLTS), and the full multiplicative form of multi-objective optimization by ratio analysis (MULTIMOORA), to determine suitable locations for waste collection boxes (named AYPIKUT), which have been designed specifically for collection of domestic waste vegetable oil and waste batteries. It takes as case study, Atakum, a district of Samsun city, Turkey. As a solution to the problem, first, a total of 88 items have been identified for consideration by seven criteria elicited from the insights of experts, and spatial analyses were performed. Multi-criteria HFLTS was then used to determine weights of the criteria. Population density was the most significant criterion affecting the selection process, and proximity to housing complexes with more than 150 dwellings was the least important. According to the weights of the seven criteria, and three rules determined by the experts, 15 AYPIKUT locations were identified using GIS. As a final step, the alternative locations (A1-A15) were ranked with the MULTIMOORA method. A5 was the most suitable site, and A6 was the least suitable site for an AYPIKUT. The results indicated the ability of the proposed model to select the suitable locations for waste collection box.

A geographic information system-based analysis of ambulance station coverage area in Samsun, Turkey.

INTRODUCTION: The location of ambulance stations are of great importance, as location is a determining factor of whether ambulances are able to respond to emergency calls within the critical period. The aim of the present study was to determine whether the ambulance stations in the provincial centre of Samsun, Turkey, were able to cover their entire operational area within 10 mins of receiving an emergency call. METHODS: This study was based on emergency calls received by the emergency medical services of the study area. Detailed address data from the calls was used to produce thematic maps using the geographic information system (GIS). Buffer analysis was used to determine the adequacy of the stations' locations in relation to the time taken to respond to the emergency calls. RESULTS: In the study area, there were a total of 11,506 emergency ambulance calls made in 2009, which revealed a call density of 0.7 calls per ha and 23.8 calls per 1,000 population. A total of 75.8% of the calls were made due to medical reasons, while 11.6% were related to traffic accidents. The GIS-based investigation revealed that the 10-min coverage areas for the four ambulance stations in the provincial centre of Samsun served 76.9% of the area and 97.9% of its population. Of the 10,380 calls for which detailed address data were available, 99.2% were within the stations' 10-min coverage areas. CONCLUSION: According to the buffer analysis, the ambulance stations in the provincial centre of Samsun are able to reach 97.9% of the population within the critical 10-min response time. This study demonstrates that GIS is an indispensable tool for processing and analysing spatial data, which can in turn aid decision-making in the field of geographical epidemiology and public health.

Epidemiologic features and risk factors of Crimean-Congo hemorrhagic fever in Samsun province, Turkey.

BACKGROUND: Crimean-Congo hemorrhagic fever (CCHF) is a tick-borne infectious disease that has a considerable mortality risk and is a challenge for the population of endemic rural areas and health care workers. This study investigated the epidemiologic features and main risk factors of CCHF in Samsun Province, Turkey, using CCHF cases diagnosed from 2007 to 2011 recorded by the Samsun Provincial Health Directorate. METHODS: In the study area, 126 cases were evaluated statistically and spatially. Minitab 16 software was used for statistical analysis of the data, and ArcGIS 9.3 software was used for spatial analysis. RESULTS: Among those who received a diagnosis of CCHF, 69 (54.7%) were male, 57 (45.3%) were female, 114 (90.5%) were discharged, and 12 (9.5%) died. A total of 112 of the 126 (88.9%) cases occurred at an altitude higher than 600 m. In addition, 84.1% of cases were reported during May through July, which are the busiest months for those working in the agriculture and animal husbandry sectors. CONCLUSIONS: CCHF causes severe disease and has a high mortality rate (about 10% in Turkey). Early diagnosis of CCHF can be improved by periodic education of people at high risk, ie, men and women working in agriculture and animal husbandry in rural areas and those working in health care.