Seasonal variations in Nigeria: understanding COVID-19 transmission dynamics and immune responses

Ayanshina, Oluwamuyiwa; Afolabi, Lukman O.; Adeshakin, Adeleye O.; Alli-Balogun, Ganiyu O.; Yan, Dehong; Essien-Baidoo, Samuel; Wan, Xiaochun

doi:10.29392/001c.14600

Cited by 6 publications

(5 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study also focused on the governmental and nongovernmental engagement and the partnership, providing good results regarding the fight of COVID-19. Moreover, the authors in [54] examined the measures taken in Nigeria to fight the spread of COVID-19. The study indicated that vitamin D improves the immunity in COVID-19 patients.…”

Section: Discussion and Interpretationsmentioning

confidence: 99%

A Simulation Model for Forecasting COVID-19 Pandemic Spread: Analytical Results Based on the Current Saudi COVID-19 Data

et al. 2021

View full text Add to dashboard Cite

The coronavirus pandemic (COVID-19) spreads worldwide during the first half of 2020. As is the case for all countries, the Kingdom of Saudi Arabia (KSA), where the number of reported cases reached more than 392 K in the first week of April 2021, was heavily affected by this pandemic. In this study, we introduce a new simulation model to examine the pandemic evolution in two major cities in KSA, namely, Riyadh (the capital city) and Jeddah (the second-largest city). Consequently, this study estimates and predicts the number of cases infected with COVID-19 in the upcoming months. The major advantage of this model is that it is based on real data for KSA, which makes it more realistic. Furthermore, this paper examines the parameters used to understand better and more accurately predict the shape of the infection curve, particularly in KSA. The obtained results show the importance of several parameters in reducing the pandemic spread: the infection rate, the social distance, and the walking distance of individuals. Through this work, we try to raise the awareness of the public and officials about the seriousness of future pandemic waves. In addition, we analyze the current data of the infected cases in KSA using a novel Gaussian curve fitting method. The results show that the expected pandemic curve is flattening, which is recorded in real data of infection. We also propose a new method to predict the new cases. The experimental results on KSA’s updated cases reveal that the proposed method outperforms some current prediction techniques, and therefore, it is more efficient in fighting possible future pandemics.

show abstract

Section: Discussion and Interpretationsmentioning

confidence: 99%

A Simulation Model for Forecasting COVID-19 Pandemic Spread: Analytical Results Based on the Current Saudi COVID-19 Data

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Because of many unknowns, a partial understanding of COVID-19 transmissibility has fueled the level of concern among the public health community, as most modeling suggests that the severity of the illness is more like influenza than SARS [4,11]. Similar to this is the fold of research affirming semblance of certain flu-like symptomatic features that COVID-19 shares with influenza [12], suggesting in this light the potentiality of mitigating factors on influenza being equally appropriate for quelling COVID-19. The development efforts to curtail the spread from hotspot to hotspot fervently proved abortive.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of the Implication of Periods Before and During Vaccination of COVID-19 Infection in Some Regional Leading African Countries

Owolabi

Ayinde

Adejumo

et al. 2022

J. Nig. Soc. Phys. Sci.

View full text Add to dashboard Cite

There has been a high expectation about the efficacy of coronavirus disease 2019 (COVID-19) vaccines. This research investigates and compares the efficiency of COVID-19 vaccines in five (5) African countries and evaluates the risk or preventive factors inherent in COVID-19 spread. Five different COVID-19 leading African countries in their respective regions (Nigeria, Ethiopia, South Africa, Morocco, and Cameroon) were considered in this study. Population sampling proportional to size concept was used to draw data for two periods (before and during COVID-19 vaccination). A sequential analysis approach was adopted, focusing on the estimates of some epidemiological metrics for the two distinct periods. Nigeria (a wet region) has the lowest risk of COVID-19 incidence during vaccination. The risk of being reported COVID-19 positive in South Africa (a high semi-arid region) is approximately 137 times the number in Nigeria. This study suggests that while vaccination has successfully reduced the case fatality rate in most countries considered except Ethiopia, infection and incidence rates increase during vaccination in all countries except Nigeria. Methods other than vaccination like wearing a face mask, washing hands, and avoiding large gatherings should be intensified to curtail incidence and infection rates.

show abstract

“…According to [8], the transmissions of most other human coronaviruses that result in mild respiratory symptoms, such as OC43 (HCoV-OC43) and HCoV-HKU1 spread rapidly based on seasonal variations. The seasonality of such viruses has been modelled to determine long-term simulation of the transmission of SARS-CoV-2 [9]. The geospatial modelling technique is an essential tool in examining the spatial distribution of most infectious diseases [10].…”

Section: Introductionmentioning

confidence: 99%

Geospatial Coronavirus Vulnerability Regression Modelling for Malawi Based on Cumulative Spatial Data from April 2020 to May 2021

Chinkaka¹,

Davis²,

Chiwanda³

et al. 2023

JGIS

View full text Add to dashboard Cite

In the past two to three years, the world has been heavily affected by the infectious coronavirus disease and Malawi has not been spared due to its interconnection with neighboring countries. There is no management tool to identify and model the vulnerabilities of Malawi's districts in prioritizing health services as far as coronavirus prevalence and other infectious diseases are concerned. The aim of this study was to model coronavirus vulnerability in all districts in Malawi using Geographic Information System (GIS) to monitor the disease's cumulative prevalence over the severely affected period between 2020 and 2021. To achieve this, four parameters associated with coronavirus prevalence, including population density, percentage of older people, temperature, and humidity, were prepared in a GIS environment and used in the modelling process. A multiscale geographically weighted regression (MGWR) model was used to model and determine the vulnerability of coronavirus in Malawi. In the MGWR modelling, the Fixed Spatial Kernel was used following a Gaussian distribution model type. The Results indicated that population density and older people (age greater than 60 years) have a more significant impact on coronavirus prevalence in Malawi. The modelling further shows that Malawi, between April 2020 and May 2021, Lilongwe, Blantyre and Thyolo were more vulnerable to coronavirus than other districts. This research has shown that spatial variability of Covid-19 cases using MGWR has the potential of providing useful insights to policymakers for targeted interventions that could otherwise not be possible to detect using non-geovisualization techniques.

show abstract

Seasonal variations in Nigeria: understanding COVID-19 transmission dynamics and immune responses

Cited by 6 publications

References 71 publications

A Simulation Model for Forecasting COVID-19 Pandemic Spread: Analytical Results Based on the Current Saudi COVID-19 Data

A Simulation Model for Forecasting COVID-19 Pandemic Spread: Analytical Results Based on the Current Saudi COVID-19 Data

Comparative Analysis of the Implication of Periods Before and During Vaccination of COVID-19 Infection in Some Regional Leading African Countries

Geospatial Coronavirus Vulnerability Regression Modelling for Malawi Based on Cumulative Spatial Data from April 2020 to May 2021

Contact Info

Product

Resources

About