SARS-CoV-2 Cases in uMgungundlovu District, Wave 3 Analysis

A geospatial analysis of SARS-CoV-2 infection patterns in the uMgungundlovu district of KwaZulu-Natal, South Africa, across the first four waves of transmission. The dataset was created by Radiya Gangat using ESRI ArcGIS Pro and Python Seaborn for temporal analysis. It was last updated on April 15, 2026.

Use Cases

• Identify spatial clusters of COVID-19 incidence based on Local Indicators of Spatial Association (LISA) analysis
• Model temporal fluctuations in daily case counts using time series analysis per wave
• Analyze the relationship between infection density and social/built environment factors
• Guide targeted testing, tracing, and vaccine rollout efforts for vulnerable populations

Strengths

• Includes statistically significant incidence rates across age groups (p-values = 0.0000)
• Provides Moran's I spatial autocorrelation values for each of the first four waves
• Contains Kernel Density Estimation (KDE) results showing highest density ranges per wave

Limitations

• Row count is unknown, which may limit suitability assessment
• Column-level documentation is absent; field semantics must be inferred after download
• Data may reflect geographic bias inherent to the single district study area

Provenance

Source

Radiya Gangat

Collection Method

Analysis performed using ESRI ArcGIS Pro v2.0 for spatial analysis and Python Seaborn for temporal analysis.

Time Range

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

Freshness

Last updated 2026-04-15 17:44:29; freshness should be verified

Geography

uMgungundlovu district, KwaZulu-Natal, South Africa.