The implication of using a fixed form factor in areas under different rainfall and soil conditions for<i>Pinus kesiya</i>in Zambia

Phiri, Darius; Phiri, Eliud; Kasubika, Robby; Zulu, Donald; Lwali, Chisala

doi:10.2989/20702620.2015.1108614

Cited by 7 publications

(1 citation statement)

References 15 publications

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“…The country receives rainfall between October and April, which ranges from 800 mm (in the southern region) to 1500 (northern region) mm annually. The major vegetation type is the dry tropical forests called “Miombo” and Zambia is also rich in wildlife resources with one of the largest National Park in the world - the Kafue National Park [36] .…”

Section: Methodsmentioning

confidence: 99%

Spread of COVID-19 in Zambia: An assessment of environmental and socioeconomic factors using a classification tree approach

et al. 2021

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The global pandemic emergent from SARS-COV-2 (COVID-19) has continued to cause both health and socioeconomic challenges worldwide. However, there is limited information on the factors affecting the dynamics of COVID-19, especially in developing countries, including African countries. In this study, we have focused on understanding the association of COVID-19 cases with environmental and socioeconomic factors in Zambia - a sub-Saharan African country. We used Zambia's district-level COVID-19 data, covering 18 March 2020 (i.e., from first reported cases) to 17 July 2020. Geospatial approaches were used to organise, extract and establish the dataset, while a classification tree (CT) technique was employed to analyse the factors associated with the COVID-19 cases. The analyses were conducted in two stages: (1) the binary analysis of occurrences of COVID-19 (i.e., COVID-19 or No COVID-19), and (2) a risk level analysis which grouped the number of cases into four risk levels (high, moderate, low and very low). The results showed that the distribution of COVID-19 cases in Zambia was significantly influenced by the socioeconomic factors compared to environmental factors. More specifically, the binary model showed that distance to the airport, population density and distance to the town centres were the most combination influential factors, while the risk level analysis indicated that areas with high rates of human immunodeficient virus (HIV) infection had relatively high chances of having many COVID-19 cases compared to areas with low HIV rates. The districts that are far from major urban establishments and that experience higher temperatures have lower chances of having COVID-19 cases. This study makes two major contributions towards the understanding of COVID-19 dynamics: (1) the methodology presented here can be effectively applied in other areas to understand the association of environmental and socioeconomic factors with COVID-19 cases, and (2), the findings from this study present the empirical evidence of the relationship between COVID-19 cases and their associated environmental and socioeconomic factors. Further studies are needed to understand the relationship of this disease and the associated factors in different cultural settings, seasons and age groups, especially as the COVID-19 cases increase and spread in many countries.

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Section: Methodsmentioning

confidence: 99%