ABSTRACT
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 and its relationship with certain co-existing factors. Methods. Spatial characteristics of SARS-CoV-2 were investigated over the first four waves of transmission using ESRI ArcGISPro v2.0, including Local Indicators of Spatial Association (LISA) with Morans "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 incidences were noted across age groups with p-values consistently < 0.001. The central region of the district experienced a higher level of clusters indicated by the LISA (Morans I: Wave 1 - 0.22, Wave 2 - 0.2, Wave 3 - 0.11, Wave 4 - 0.13) and the KDE (Highest density of cases: wave 1: 25.1-50, wave 2: 101-150, wave 3: 101-150, wave 4: 50.1-100). Temporal analysis showed more fluctuation at the beginning of each wave with less fluctuation in 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 transmission. However, locally specific empirical research is required to assess other relevant factors associated with increased transmission.
Subject(s)
Communicable DiseasesABSTRACT
Background: The incidence and burden of endemic or potentially endemic infectious diseases (IDs) is highest in Sub-Saharan Africa (SSA) with a new disease to emerge every 8 months on average. The epidemic of preventable IDs becoming endemic to SSA serves as a stack reminder of the region’s systematic failings to health security. It is more important now than ever to evaluate SSA’s vulnerability to COVID-19, a global pandemic becoming endemic to the region after it perhaps gets eliminated from the other world regions.Method: The International Health Regulations (IHR [2005]) and Global Health Security Index (GHSI) scores for SSA before COVID-19 reached the region were obtained and evaluated to assess its health security preparedness. The number of cases, deaths and stringency measures of the first year of the virus in the region were used to ascertain the possibility of the virus becoming endemic to SSA. By this, COVID-19 confirmed cases and deaths of the region were obtained from the COVID-19 Data Repository by the Centre for Systems Science and Engineering (CSSE) at Johns Hopkins University. The stringency measures put in place by each country were obtained from the Oxford COVID-19 Government Response Tracker (OxCGRT). Data from the Global Infectious Diseases and Epidemiology Online Network (GIDEON) was then used to evaluate the intensity and distribution of endemic or potentially endemic IDs after COVID-19 reached SSA.Findings: Before the virus reached the region, 31% and 37% of the SSA countries moderately adhered to the IHR and GHSI score respectively. The overall performance of the region in its first year of responding and containing the virus was 56% compared to a global average of 60%. For the first year period of the virus, South Africa with the highest GHSI of 54·8% before the virus reached the region accounted for 55% of the total cases within the first year. Majority of the cases (48%) and deaths (55%) of the first year were recorded in the last three months when values of the stringency measures fell below 50%. For the region, the number of mortalities to cases ratio for the first wave (July 2020) was 1·47:98·53 compared to the second wave (February 2021) of 3·54:96·46. It was also observed that, an average of 222 IDs were endemic or potentially endemic to SSA after COVID-19 reached the region.Interpretation: It was revealed that there is a high possibility of devastating subsequent waves of COVID-19 and it becoming endemic to the region after assessing the stringency measures that were put in place, relative to the number of cases and deaths over the first year period.Funding: None to declare. Declaration of Interest: None to declare.