Evidence map›Paper›PMID 33073068›Full record

ArticleInfectious Disease Modelling2020

COVID-19 prevalence estimation: Four most affected African countries.

Adewale F Lukman, Rauf I Rauf, Oluwakemi Abiodun, Olajumoke Oludoun, Kayode Ayinde, Roseline O Ogundokun

Abstract read
In one paragraph

Article in Infectious Disease Modelling, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

  1. Article
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  7. Review
  8. Socioeconomic-Related Inequalities in COVID-19 Vulnerability in South Africa.International journal of environmental research and public health · 2022
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Prediction of Incidence Trend of Influenza-Like Illness in Wuhan Based on ARIMA Model.Computational and mathematical methods in medicine · 2022
    Article
  14. Article
  15. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Adewale F LukmanDepartment of Mathematics and Computer Science, Landmark University, Omu-Aran, Kwara State, Nigeria.
Rauf I RaufDepartment of Statistics, University of Abuja, Abuja, Nigeria.
Oluwakemi AbiodunDepartment of Mathematics and Computer Science, Landmark University, Omu-Aran, Kwara State, Nigeria.
Olajumoke OludounDepartment of Mathematics and Computer Science, Landmark University, Omu-Aran, Kwara State, Nigeria.
Kayode AyindeDepartment of Statistics, Federal University of Technology, Akure, Nigeria.
Roseline O OgundokunDepartment of Mathematics and Computer Science, Landmark University, Omu-Aran, Kwara State, Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The world at large has been confronted with several disease outbreak which has posed and still posing a serious menace to public health globally. Recently, COVID-19 a new kind of coronavirus emerge from Wuhan city in China and was declared a pandemic by the World Health Organization. There has been a reported case of about 8622985 with global death of 457,355 as of 15.05 GMT, June 19, 2020. South-Africa, Egypt, Nigeria and Ghana are the most affected African countries with this outbreak. Thus, there is a need to monitor and predict COVID-19 prevalence in this region for effective control and management. Different statistical tools and time series model such as the linear regression model and autoregressive integrated moving average (ARIMA) models have been applied for disease prevalence/incidence prediction in different diseases outbreak. However, in this study, we adopted the ARIMA model to forecast the trend of COVID-19 prevalence in the aforementioned African countries. The datasets examined in this analysis spanned from February 21, 2020, to June 16, 2020, and was extracted from the World Health Organization website. ARIMA models with minimum Akaike information criterion correction (AICc) and statistically significant parameters were selected as the best models. Accordingly, the ARIMA (0,2,3), ARIMA (0,1,1), ARIMA (3,1,0) and ARIMA (0,1,2) models were chosen as the best models for SA, Nigeria, and Ghana and Egypt, respectively. Forecasting was made based on the best models. It is noteworthy to claim that the ARIMA models are appropriate for predicting the prevalence of COVID-19. We noticed a form of exponential growth in the trend of this virus in Africa in the days to come. Thus, the government and health authorities should pay attention to the pattern of COVID-19 in Africa. Necessary plans and precautions should be put in place to curb this pandemic in Africa.

Indexed as

ARIMA modelsCOVID-19Exponential growthForecastingPrevalenceStatistical toolsTime series model

Identifiers

PMID33073068
PMCPMC7550075

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.