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Application of Geospatial Technologies in the COVID-19 Fight of Ghana

ABSTRACT: The SARS-CoV-2 infections continue unabated in Ghana and globally. The identification of country dynamics of the virus, its spread, and country-specific interventions in tackling the menace including the application of geospatial technologies. This research sought to highlight the use of geospatial technologies in the fight against COVID-19 in Ghana with best practices from China where the infections originated from; present the trends in Ghana and model near future trends of the virus. It was evident that just as other places, Ghana has employed geospatial technologies and continues to ply unchartered paths in solutions. The trend in Ghana is in line with a population concentration and tends to record higher figures in the southern parts. It is modeled that through incessant mobility patterns, infections will spread through to the middle parts and then the northern parts. The research, therefore, recommends the use of infrared scanners to augment testing practices and enhanced tracing of infected persons as well as the use of drones for the distribution of essential services.

SUBMITTER: Sarfo A

PROVIDER: S-EPMC7334632 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Project description:BackgroundGhana did not meet the Millennium Development Goal 4 of reducing child mortality by two-thirds and may not meet SDG (2030). There is a need to direct scarce resources to mitigate the impact of the most important risk factors influencing high neonatal deaths. This study applied both spatial and non-spatial regression models to explore the differential impact of environmental, maternal, and child associated risk factors on neonatal deaths in Ghana.MethodsThe study relied on data from the Ghana Demographic and Health Surveys (GDHS) and the Ghana Maternal Health Survey (GMHS) conducted between 1998 and 2017 among 49,908 women of reproductive age and 31,367 children under five (GDHS-1998 = 3298, GDHS-2003 = 3844, GDHS-2008 = 2992, GDHS-2014 = 5884, GMHS-2017 = 15,349). Spatial Autoregressive Models that account for spatial autocorrelation in the data at the cluster-level and non-spatial statistical models with appropriate sampling weight adjustment were used to study factors associated with neonatal deaths, and a p-value less than 0.05 was considered statistically significant.ResultsPopulation density, multiple births, smaller household sizes, high parity, and low birth weight significantly increased the risk of neonatal deaths over the years. Among mothers who had multiple births, the risk of having neonatal deaths was approximately four times as high as the risk of neonatal deaths among mothers who had only single birth [aRR = 3.42, 95% CI: 1.63-7.17, p < 0.05]. Neonates who were perceived by their mothers to be small were at a higher risk of neonatal death compared to very large neonates [aRR = 2.08, 95% CI: 1.19-3.63, p < 0.05]. A unit increase in the number of children born to a woman of reproductive age was associated with a 49% increased risk in neonatal deaths [aRR = 1.49, 95% CI: 1.30-1.69, p < 0.05].ConclusionNeonatal mortality in Ghana remains relatively high, and the factors that predisposed children to neonatal death were birth size that were perceived to be small, low birth weight, higher parity, and multiple births. Improving pregnant women's nutritional patterns and providing special support to women who have multiple deliveries will reduce neonatal mortality in Ghana.

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Application of Geospatial Technologies in the COVID-19 Fight of Ghana

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