SARS-CoV-2 Cases in uMgungundlovu District: Geospatial and Temporal Analysis of Wave 1

Geospatial analysis of SARS-CoV-2 incidence in the uMgungundlovu district of KwaZulu-Natal, South Africa, focusing on the first wave of transmission. The 17.5 KB dataset, authored by Radiya Gangat and last updated in 2026, includes results from Local Indicators of Spatial Association and Kernel Density Estimation. Statistically significant clustering was evident in wave 1, with a Moran’s I of -0.096.

Use Cases

• Identify spatial clusters of COVID-19 cases based on Local Indicators of Spatial Association (LISA) results.
• Model temporal fluctuations in daily case counts for the first wave using time series analysis.
• Assess the relationship between infection density and local factors using Kernel Density Estimation (KDE) outputs.
• Guide resource allocation for testing and tracing in high-vulnerability populations based on geospatial patterns.

Strengths

• Dataset is openly available under a CC-BY-4.0 license.
• Analysis includes specific statistical results, such as Moran’s I values for spatial autocorrelation across four waves.
• Research applies multiple geospatial techniques (LISA, KDE) and temporal analysis.

Limitations

• Dataset is very small at 17.5 KB, suggesting limited scope or aggregated summary data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for detailed modeling.

Provenance

Source

Radiya Gangat via figshare.

Collection Method

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

Time Range

Covers the first four waves of SARS-CoV-2 transmission; descriptive analysis focuses on wave 1.

Freshness

Last updated 2026-04-15 17:44:26; freshness should be verified.

Geography

uMgungundlovu district, KwaZulu-Natal, South Africa.