Lassa fever susceptibility modelling and mapping in Nigeria using geospatial technique
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Date
2020
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Publisher
Universiti Teknologi Malaysia
Abstract
The increase in the frequency of Lassa Hemorrhagic Fever (LHF) outbreak in Nigeria is of great concern to medical geography and the society. This study investigated Lassa fever susceptibility modelling mapping in Nigeria using the geospatial technique. The study was conducted over Edo State because it has presented more cases of LHF occurrence than any other states in Nigeria recently. The three research objectives of the study were to assess the land use change pattern in Edo State of Nigeria, to investigate the spatiotemporal trend of Lassa fever occurrences in the study area, and to develop the LHF susceptibility model and mapping from geospatial data of the study area. In order to fulfill the first objective, Landsat imageries of 2000, 2005, 2010 and 2015 were collected and processed using ERDAS Imagine software. The imageries were classified into seven land use classes using the Maximum Likelihood Classifier method, while the level of land use conversion were studied using the land use transition as generated using Idrissi Selva software. From the study, it was revealed that forestland and mixed land had a transition decrease of 62.86% and 16.80% respectively from 2000 to 2015, while built-up area and vegetation had a transition increase of 150.22% and 20.66% respectively from 2000 to 2015. On the other hand, water body and rock outcrop showed less significant land use change. Next, the second objective was achieved by analysing monthly incidences of Lassa Fever from 2011 to 2016 over Edo State. The temporal dynamics of the disease was examined by analysing the annual trend using the spatial and attribute data of Lassa fever occurrence. It was found that the LHF occurred in about 60 towns in Edo State from 2011 to 2016. The finding from the study indicated random occurrence and reoccurrence of Lassa fever within the States. The monthly trend of LHF occurrence reveals that the highest occurrences each year fall between November and March, which are the dry season months in Nigeria and it showed that Ekpoma town in Edo State is the hotspot of LHF disease in the State. To realize the final objective, the susceptibility modelling was done by first deriving the modelling factors from the acquired datasets. Multi-criteria evaluation technique was utilised. Eleven factor elements were derived, ranked and weighted; then the LHF susceptibility model was obtained using ArcGIS model builder. All the weighted criteria were combined in overlay analysis to generate the susceptibility mapping. From the results, random index (RI) of 1.52 was obtained from the assigned weight on each of the eleven factors considered, which yields a Consistency Ratio (CR) of zero indicating that CR < 0.10 suggests a reasonable level of consistency in the pair-wise comparison. Therefore, the weight assigned to each criterion was considered reasonable. All the eleven criteria showed positive relationship with the LHF occurrence; the top ranked criteria for determining LHF susceptibility are solar radiation, housing quality, travel time to health centre, land use and proximity of disposal sites to built-up areas with weight of 2.66, 1.84, 1.35 and 1.10 respectively. The susceptibility map indicated that the study area are moderately (11%), more (18.5%) and the most susceptible (70.4%) to LHF respectively. The susceptibility map was overlaid on the LHF spatial distribution map for validation yielding 0.8 correlation. Hence, the findings from this study proofs beneficial for in-depth decision making on prevention and control of LHF disease in Nigeria.
Description
Thesis (PhD (Geoinformatics))
Keywords
Lassa fever -- Nigeria, Spatial systems -- Data processing