tetano
Editor, Senior Moderator
PLoS One
. 2026 Apr 15;21(4):e0317648.
doi: 10.1371/journal.pone.0317648. eCollection 2026.
Spatial and temporal patterns of SARS-CoV-2 infection in uMgungundlovu, KwaZulu-Natal, South Africa
Radiya Gangat[SUP] 1 2 [/SUP], Veranyuy Ngah[SUP] 1 [/SUP], Rushambwa Tawonga[SUP] 3 [/SUP], Justine I Blanford[SUP] 4 [/SUP], Jabulani Ronnie Ncayiyana[SUP] 5 [/SUP], Peter Suwirakwenda Nyasulu[SUP] 1 6 [/SUP]
Affiliations
Background: Investigating the spatial distribution of SARS-CoV-2 at a local level and describing the pattern of disease occurrence can be used as the basis for efficient prevention and control measures. This research project aims to utilize geospatial analysis to understand the distribution patterns of SARS-CoV-2 infection and its relationship with certain co-existing factors in uMgungundlovu district, KwaZulu-Natal.
Methods: Spatial characteristics of SARS-CoV-2 were investigated over the first four waves of transmission using ESRI ArcGIS Pro v2.0, including Local Indicators of Spatial Association (LISA) with Moran's "I" as the measure of spatial autocorrelation; and Kernel Density Estimation (KDE). In implementing temporal analysis, time series analysis using the Python Seaborn library was used, with separate modelling carried out for each wave.
Results: Statistically significant SARS-CoV-2 incidence rates were noted across age groups with p-values = 0.0000. Statistically significant clustering was evident in wave 1 and wave 3 (Moran's I respectively: wave 1-0.096; wave 2-0.023; wave 3-0.039; wave 4-0.023). The KDE (Highest density of cases: wave 1: 25.0001-50.0, wave 2: 25.0001-50.0, wave 3: 100.001-150.0, wave 4: 50.0001-100.0). Temporal analysis showed more fluctuation at the beginning of each wave with less fluctuation in daily identified cases within the middle to end of each wave.
Conclusion: A Geospatial approach of analysing infectious disease transmission is proposed to guide control efforts (e.g., testing/tracing and vaccine rollout) for populations at higher vulnerability. Additionally, the nature and configuration of the social and built environment may be associated with increased infection. However, locally specific empirical research is required to assess other relevant factors associated with increased infection.
. 2026 Apr 15;21(4):e0317648.
doi: 10.1371/journal.pone.0317648. eCollection 2026.
Spatial and temporal patterns of SARS-CoV-2 infection in uMgungundlovu, KwaZulu-Natal, South Africa
Radiya Gangat[SUP] 1 2 [/SUP], Veranyuy Ngah[SUP] 1 [/SUP], Rushambwa Tawonga[SUP] 3 [/SUP], Justine I Blanford[SUP] 4 [/SUP], Jabulani Ronnie Ncayiyana[SUP] 5 [/SUP], Peter Suwirakwenda Nyasulu[SUP] 1 6 [/SUP]
Affiliations
- PMID: 41984906
- DOI: 10.1371/journal.pone.0317648
Background: Investigating the spatial distribution of SARS-CoV-2 at a local level and describing the pattern of disease occurrence can be used as the basis for efficient prevention and control measures. This research project aims to utilize geospatial analysis to understand the distribution patterns of SARS-CoV-2 infection and its relationship with certain co-existing factors in uMgungundlovu district, KwaZulu-Natal.
Methods: Spatial characteristics of SARS-CoV-2 were investigated over the first four waves of transmission using ESRI ArcGIS Pro v2.0, including Local Indicators of Spatial Association (LISA) with Moran's "I" as the measure of spatial autocorrelation; and Kernel Density Estimation (KDE). In implementing temporal analysis, time series analysis using the Python Seaborn library was used, with separate modelling carried out for each wave.
Results: Statistically significant SARS-CoV-2 incidence rates were noted across age groups with p-values = 0.0000. Statistically significant clustering was evident in wave 1 and wave 3 (Moran's I respectively: wave 1-0.096; wave 2-0.023; wave 3-0.039; wave 4-0.023). The KDE (Highest density of cases: wave 1: 25.0001-50.0, wave 2: 25.0001-50.0, wave 3: 100.001-150.0, wave 4: 50.0001-100.0). Temporal analysis showed more fluctuation at the beginning of each wave with less fluctuation in daily identified cases within the middle to end of each wave.
Conclusion: A Geospatial approach of analysing infectious disease transmission is proposed to guide control efforts (e.g., testing/tracing and vaccine rollout) for populations at higher vulnerability. Additionally, the nature and configuration of the social and built environment may be associated with increased infection. However, locally specific empirical research is required to assess other relevant factors associated with increased infection.