SARS-CoV-2 Cases in uMgungundlovu District: Geospatial Analysis of Four Waves

A geospatial analysis of SARS-CoV-2 infection distribution across the first four waves of transmission in the uMgungundlovu district of KwaZulu-Natal, South Africa. The dataset, 17.5 KB in size, was created by Radiya Gangat and last updated on 2026-04-15. It includes results from spatial autocorrelation (Moran's I) and Kernel Density Estimation analyses.

Use Cases

• Identify statistically significant spatial clusters of COVID-19 cases based on Local Indicators of Spatial Association (LISA) results.
• Model the temporal progression of infection waves using time series analysis methods described.
• Analyze the relationship between infection density and co-existing social or built environment factors suggested in the research.
• Guide resource allocation for testing, tracing, or vaccination by identifying areas of higher case density from Kernel Density Estimation bands.

Strengths

• Includes specific statistical results for spatial autocorrelation across four waves (e.g., Moran's I for wave 1: -0.096).
• Provides Kernel Density Estimation bands with defined case density ranges for each wave.
• Dataset is openly licensed under CC-BY-4.0.
• Analysis is focused on a specific geographic area (uMgungundlovu district), allowing for localized insights.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small (17.5 KB), indicating it likely contains summary or processed results rather than raw case data.

Provenance

Source

Radiya Gangat via figshare.

Collection Method

Spatial analysis conducted using ESRI ArcGIS Pro v2.0; temporal analysis used Python Seaborn library.

Time Range

Covers the first four waves of SARS-CoV-2 transmission.

Freshness

Last updated 2026-04-15 17:44:30; freshness should be verified.

Geography

uMgungundlovu district, KwaZulu-Natal, South Africa.