Modeling the positive testing rate of Covid-19 in South Africa using a semi-parametric smoother for binomial data
Bibliographic Data
| ID | 22088571 |
|---|---|
| Authors | Olajumoke Evangelina Owokotomo (Hasselt University, corresponding author), Samuel Manda (0000-0002-9672-3312, University of Pretoria), Jürgen Cleasen (Hasselt University), Adetayo Kasim (0000-0003-1907-0204, Durham University), Rudradev Sengupta (0000-0002-2656-1979, Janssen (Belgium)), Rahul Shome (0000-0002-1689-9220, Rice University), Soumya Subhra Paria (The Open University), Tarylee Reddy (0000-0002-9521-2692, South African Medical Research Council), Ziv Shkedy (0000-0002-6306-0823, Hasselt University) |
| Year | 2023 |
| Volume | 11 |
| Pages | 979230-979230 |
| Publication date | 2023-02-22 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2023.979230 |
| PMID | 36908419 |
| OpenAlex | W4321497420 |
| Language | EN |
| References cited | 26 |
Identification and isolation of COVID-19 infected persons plays a significant role in the control of COVID-19 pandemic. A country's COVID-19 positive testing rate is useful in understanding and monitoring the disease transmission and spread for the planning of intervention policy. Using publicly available data collected between March 5th, 2020 and May 31st, 2021, we proposed to estimate both the positive testing rate and its daily rate of change in South Africa with a flexible semi-parametric smoothing model for discrete data. There was a gradual increase in the positive testing rate up to a first peak rate in July, 2020, then a decrease before another peak around mid-December 2020 to mid-January 2021. The proposed semi-parametric smoothing model provides a data driven estimates for both the positive testing rate and its change. We provide an online R dashboard that can be used to estimate the positive rate in any country of interest based on publicly available data. We believe this is a useful tool for both researchers and policymakers for planning intervention and understanding the COVID-19 spread
Biology · Dashboard · Data science · Disease · Econometrics · Negative binomial distribution · Pandemic · Parametric statistics · Poisson distribution · Smoothing · Statistics · Computer Science · COVID-19 Clinical Research Studies · COVID-19 diagnosis using AI · COVID-19 epidemiological studies · Mathematics · Medicine
| Citation velocity | historical |
|---|---|
| Highly cited | No |