Collaborative Workshop on Big Data Analysis of COVID-19
A FREE collaborative Workshop on Big Data Analysis of COVID-19 will be hosted by SEPIMOD, CSIR and the Department of Statistics on the 30-31st May 2022. The aim of the workshop is bring together national and international researchers who have done extensive work on COVID-19 data. For more information, click here. The programme of the two days can be found here.
If you would like to attend online (for FREE), then please apply here: Online registration.
HURRY! The deadline for online registration is Saturday 28 May 2022! For enquires, contact Prof Inger Fabris-Rotelli at [email protected].
STATOMET COVID-19 Initiatives
STATOMET is coordinating multiple COVID-19 research projects within the Department of Statistics. These projects are using data currently available in the public domain, generally at the provincial level.
This is of particular relevance with the Level 4 lockdown relaxation in South Africa specifically: the relaxation, and the corresponding response to a flare-up of positive cases, needs an early detection mechanism that is spatially explicit, with ancillary data on medical care facilities, place of treatment and relief distribution points. Combined, these projects below have the potential to provide much of the infrastructure for a spatially explicit early warning programme. The infrastructure has the potential to direct relief efforts as well, assuring a better distribution of limited relief.
In addition to the above, we have also embarked on developing a longer-term data scraping system creating a COVID-19 small area data repository (I Fabris-Rotelli and other contributors). This project will develop the architecture to be used as more data become available.
The following evidence-based research & policy projects are currently included in this initiative:
- Heat map retrieval of COVID-19 spread in South Africa since lockdown using spatial analysis (submitted)
M Arashi, A Bekker, M Salehi, S Millard, B Erasmus, M Golpaygani, T Cronj
Provincial, interprovincial, relationships between provinces, autocorrelations. Could be used in coordinating efforts related to hospital infrastructure and resource distribution.
- A probabilistic COVID-19 screening model
A de Waal
Developing an efficient screening model (mass testing critical). Currently expert based using limited data.
- Modelling the COVID-19 14-day infection rate
S Millard, F Kanfer
Provincial model. Could assist in “measuring” the impact of interventions on the spread of the virus, also linking this to latent behavioural patterns, eg. social interaction.
- A synergetic R-Shiny portal to track COVID-19 demographic information (https://mahdisalehi.shinyapps.io/Covid19Dashboard/)
M Salehi, M Arashi, A Bekker, J Ferreira, D Chen and other contributors
The purpose of this dashboard is to share the useful information using an online interactive dashboard visualizing and follow confirmed cases/deaths and testing of COVID-19 in real time. This dashboard also include the statistical modelling results from the projects mentioned above. This dashboard will assist in the coordination between government and the private sector regarding various socio economic initiatives.
Accepted: Salehi et. al. 2021. Frontiers in Public Health, 8(623624), https://doi.org/10.3389/fpubh.2020.623624
- Reconstructing and Forecasting the COVID-19 Epidemic in the United States using a 5-Parameter Logistic Growth Model (The case of USA)
D Chen, X Chen, Jenny K. Chen (accepted)
We used the Center for Disease Control and Prevention data documenting the daily new and cumulative cases of confirmed COVID-19 in the U.S. from January 22 to April 6, 2020, and reconstructed the epidemic using a 5-parameter logistic growth model. We fitted the model to data from a 2-week window (i.e., from March 21 to April 4, approximately one incubation period) during which large-scale testing was conducted. With parameters obtained from the modelling, we reconstructed and predicted the growth of the epidemic and evaluated the extent and potential effects of under-detection.
- Impact of Lockdown in Slowing the South Africa COVID-19 Epidemic-An Interrupted Time-Series Analysis
D Chen, X Chen, S Millard, F Kanfer
Epidemic time-series data from March 3 to April 22 are compiled and an interrupted time-series analysis is used to analyse the impact of the government implemented lockdown.
- Nowhere to hide: the significant impact of coronavirus disease 2019 (COVID-19) measures on elite and semi-elite South African athletes (accepted)
L Pillay, DC Janse van Rensburg, A Jansen van Rensburg, DA Ramagole, L Holtzhausena, HP Dijkstrac, T Cronje
The objectives of this research are to describe the perceptions of South African elite and semi-elite athletes on return to sport(RTS); maintenance of physical conditioning and other activities; sleep; nutrition; mental health; health-care access; and knowledge of coronavirus disease 2019 (COVID-19).
- An exploratory view on the association between observed weather patterns and COVID-19 Infection rates
H Masoumi-Karakani, F Kanfer, S Millard
An exploratory study into the relationship between weather patterns and COVID-19 infection rates.
- Pitting the Gumbel and logistics growth models against one another to model COVID-19 spread
K Yoo, M Arashi, A Bekker
Journal of Health Science and Development, Vol 3(2)
More resources may be found at:
- DSFSI dashboard (University of Pretoria) https://datastudio.google.com/u/0/reporting/1b60bdc7-bec7-44c9-ba29-be0e043d8534/page/hrUIB
- Wits dashboard https://www.covid19sa.org/
- UCT dashboard http://corona-stats.co.za/national.php
- Department of Mathematics and Applied Mathematics COVID-19 response page
For more information, contact Sollie Millard at [email protected].
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