COVID-19 Cases In Nigeria Research Articles

Modified deterministic modeling of Covid-19 in Nigeria: a case of a closed system

Summary In this research a closed system of testing and vaccination is considered using modified deterministic modeling of Covid-19 cases in Nigeria. A disease infection flow transmission diagram was constructed for a model with nine population compartments, represented as SNSVETeQIAISILR, and the assumptions governing the model were presented for the study. A set of nonlinear deterministic differential equations was obtained and tested for positive invariance, positivity of the system solution, boundedness of solution of the equation system, equilibrium point of system stability, endemic equilibrium point, and existence of endemic global stability. The simulated results showed that the equilibrium stability point of the system exists at a basic reproduction number Ro of 0.0000295, and the model estimates show a positive contribution of population recruitment rate (Λ), transmission rate from infected (asymptomatic – β1, symptomatic – β2, undetected but exposed - ф) population, testing rate (βV), (σ), population exposure, exposed tested becoming infected (ρ), quarantine, and isolation to promoting the Covid-19 epidemic infection in Nigeria. Following the findings, the following are recommended: early closure of the country’s borders to check increasing recruitment rate, introduction of social distancing, wearing of nose & mouth masks, early commencement of free testing for the disease (Covid-19), introduction of movement restrictions (close-down/lock-down), compulsory Covid-19 vaccination for every vulnerable person in the population, effective government quarantine and isolation (treatment) centers, and immediate engagement of both medical and non-medical researchers to find lasting solutions.

Evaluation of the Performance of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs) Models: Application to Confirmed Cases of Covid-19 in Nigeria

In this study, the performance of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs) models was investigated and evaluated using daily confirmed cases of COVID-19 in Nigeria. The stationarity status of the data collected was established using Augmented Dickey Fuller unit root test. The residual normality test was also carried out with the residual plots indicating adequacy of the fitted ARIMA model. The results of neural networks were analyzed using back-propagation for multilayer feed-forward powered by sigmoid function. Utilizing backpropagation method based on three factors expressed in terms of the learning rate, the distance between the actual output and predicted output and the activation function, the network weights were generated The performance indices for ARIMA and ANNs models were evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE and the results revealed that the ARIMA model performed better than the ANN model considering the minimum prediction error and forecasting ability. The ARIMA (2, 1, 1) model appeared to be the best fitted model over the ANN model for the daily confirmed covid-19 cases considered.

Intervention Analysis of COVID-19 Vaccination in Nigeria: The Naive Solution Versus Interrupted Time Series.

In this paper, an intervention analysis approach was applied to daily cases of COVID-19 in Nigeria in order to evaluate the utilization and effect of the COVID-19 vaccine administered in the country. Data on the daily report of COVID-19 cases in Nigeria were collected and subjected to two models: the naïve solution and the interrupted time series (the intervention model). Based on the Alkaike Information Criterion (AIC), sigma2, and log likelihood values, the interrupted time series model outperformed the Naïve solution model. ARIMA (4, 1, 4) with exogenous variables was identified as the best model. It was observed that the intervention (vaccination) was not significant at the 5% level of significance in reducing the number of daily COVID-19 cases in Nigeria since the start of the vaccination on March 5, 2021, until March 28, 2022. Also, the ARIMA (4, 1, 4) forecastsindicated that there will be surge in the number of daily COVID-19 cases in Nigeria between January and April 2023. As a result, we recommend strict adherence to COVID-19 protocols as well as further vaccination and sensitization programs to educate people on the importance of vaccine uptake and avoid Corona virus spread in the year 2023 and beyond.

Modelling COVID-19 Fluctuation in Nigeria Using Fractional Integrated Garch Model

This study applied a fractionally integrated GARCH (FIGARCH) process in modelling daily cases of COVID-19 in Nigeria from 28 February 2020 to 23 March 2022. The time plot of the series showed the constant fluctuation in the study variable. The daily COVID-19 data was tested for stationarity using Augmented Dickey-Fuller (ADF), the series was not stationary. The Geweke and Porter-Hudak (GPH) method was used to estimate the long memory parameter d of the FIGARCH model. The daily series was stationary at a fractional differencing of order (d=0.97). The presence of long memory was also detected using the autocorrelation function. The fractionally integrated GARCH model was used to detect the period of high and low crisis. The crisis period was identified by volatility clustering and the leverage effect process. However, four models were estimated for FIGARCH models. The best model was selected based on the information criteria. Finally, the most adequate model for estimating the volatility of COVID-19 in Nigeria was the FIGARCH (1,0.97, 1) model.

Hmecare of confirmed COVID-19 cases in Nigeria: Current situation, challenges in implementation, and strategies for improvement

Introduction: Health facility-based management has been inadequate to control COVID-19. This study aimed to assess the current situation of home-based care (HBC) of COVID-19 positives in Nigeria and implementation challenges, and strategies for its improvement.

Modelling and forecasting new cases of Covid-19 in Nigeria: Comparison of regression, ARIMA and machine learning models

Covid-19 remains a global pandemic threatening hundreds of countries in the world. The impact of Covid-19 has been felt in almost every aspect of life and it has introduced globally, a new normal of livelihood. This global pandemic has triggered unparalleled global health and economic crisis. Therefore, modelling and forecasting the dynamics of this pandemic is very crucial as it will help in decision making and strategic planning. Nigeria as the most populous country in Africa and most populous black nation in the world has been adversely affected by Covid-19 pandemic. This study models and compares forecasting performance of regression, ARIMA and Machine Learning models in predicting new cases of Covid-19 in Nigeria. The study obtained data on daily new cases of Covid-19 in Nigeria between 27th February, 2020 and 30th November, 2021. Graphical analysis showed that Nigeria had witnessed three waves of Covid-19 with the first wave between 27th February, 2020 and 23rd October, 2020, the second wave between 24th October, 2020 and 20th June, 2021 and the third wave between 21st June, 2021 and 30th November, 2021.The second wave recorded the highest spikes in new cases compared to the first wave and third wave. Result reveals that in terms of forecasting performance, inverse regression model outperformed other regression models considered as it shows lowest RMSE of 0.4130 compared with other regression models. Also, the ARIMA (4, 1, 4) outperformed other ARIMA models as it reveals the highest R2 of 0.856 (85.6%), least RMSE (0.6364), AIC (-8.6024) and BIC (-8.5299). Result reveals that Fine tree which is one of the Machine Learning models is more reliable in forecasting new cases of Covid-19 in Nigeria compared to other models as Fine tree gave the highest R2 of 0.90 (90.0%) and least RMSE of 0.22165. Result of 15 days forecasting indicates that Covid-19 pandemic is not over yet in Nigeria as new cases of Covid-19 is projected to increase on 15/12/2021 with predicted new cases of 988 compared with that of 14/12/2021, where only 729 new cases was predicted. This therefore emphasizes the need to strengthen and maintain the existing Covid-19 preventive measures in Nigeria.

Hybrid Neural Network Prediction for Time Series Analysis of COVID-19 Cases in Nigeria

The lethal coronavirus illness (COVID-19) has evoked worldwide discussion. This contagious, sometimes fatal illness, is caused by the severe acute respiratory syndrome coronavirus 2. So far, COVID-19 has quickly spread to other countries, sickening millions across the globe. To predict the future occurrences of the disease, it is important to develop mathematical models with the fewest errors. In this study, classification and regression tree (CART) models and autoregressive integrated moving averages (ARIMAs) are employed to model and forecast the one-month confirmed COVID-19 cases in Nigeria, using the data on daily confirmed cases. To validate the predictions, these models were compared through data tests. The test results show that the CART regression model outperformed the ARIMA model in terms of accuracy, leading to a fast growth in the number of confirmed COVID-19 cases. The research findings help governments to make proper decisions on how the prepare for the outbreak. Besides, our analysis reveals the lack of quarantine wards in Nigeria, in addition to the insufficiency of medications, medical staff, lockdown decisions, volunteer training, and economic preparation.

It Is a Hoax: Attitude of South-eastern Nigerian Toward COVID-19 and Vaccine: The Need for Social Workers

ABSTRACT Different behavioral shifts have been observed among the country’s population against the COVID-19 pandemic since its emergence in February 2020 in Nigeria. At first, people were scared of the pandemic and this was reinforced by their response to the National Center for Disease Control measures. As days go by and with the increasing number of confirmed cases of COVID-19 in Nigeria. To reduce the high burden of the pandemic, efforts were made by scientists to produce the COVID-19 vaccine, and Nigeria received about 4 million doses of COVID-19 vaccine in 2021. Thus, it becomes important to investigate the attitude of Nigerians toward COVID-19 and vaccination against COVID-19. To conduct this study, data were sourced using six focus group discussions involving 30 males and females, respectively. Findings revealed that Nigerians have lackadaisical attitude toward COVID-19 and COVID-19 vaccine owing to insincerity on the part of the government, poverty, belief system, low-level of awareness among others. To change the negative attitude of Nigerians toward COVID-19 and the vaccine, social worker interventions are recommended.

Adherence to COVID-19 Preventive Behaviours: the Implication of Life Orientation and Sociodemographic Factors among Residents in Nigeria

There is a sporadic increase in the number of newly infected cases of COVID-19 in Nigeria and this conveys clearly, poor adherence to the preventive guidelines by WHO and NCDC. This study examined the roles of life orientation and socio-demographic factors on adherence to COVID-19 preventive behaviours among residents in Nigeria. Using an ex post facto design, 798 residents in Nigeria (M = 303, F = 485) responded to an online survey measuring the socio-demographics, Life Orientation Test-Revised (LOT-R) and validated adherence to COVID-19 preventive behaviours. Two research hypotheses were formulated and tested using t-test of independent samples and ANCOVA. Results revealed that life orientation had a significant influence on adherence to preventive behaviour during the COVID-19 outbreak, [t (796) = 14.72; p = 14.25, SD = 1.71) than respondents with pessimistic life orientation (= 10.28, SD = 3.20). Furthermore, respondents’ age [F (1, 753) = 04.69, p = .01]; sex, F (1, 753) = 06.07, p = .01]; education, [F (5, 753) = 11.70, p = .072) and marital status, [F (3, 753) = 3.84, p = .015] independently influenced adherence to COVID-19 preventive behaviours. The study concluded that life orientation and socio-demographic factors have serious implications for research efforts and policymaking in ensuring that people adhere to COVID-19 preventive behaviours in Nigeria.

A Time Series Model on the Occurrence of COVID-19 Pandemic in Nigeria

Coronaviruses belong to a large family of viruses which affect the hepatic, gastrointestinal, neurological and respiratory systems. The increase in the daily number of COVID-19 confirmed and deaths cases from different countries of the world has brought social, economic and political activities to a standstill, affecting individuals, government, public and private sectors. In this study, autoregressive integrated moving average (ARIMA) time series model for modeling and forecasting daily confirmed, recovered, and deaths cases of COVID-19 in Nigeria was used with data on daily cases of confirmed, recovered and deaths due to COVID-19 in Nigeria from 27/02/2020-31/07/2020 obtained from Nigeria Centre for Disease Control (NCDC) website. The data from 27/02/2020-16/07/2020 were used for model building while 15 observations from 17/07/2020-31/07/2020 were used for training and forecast evaluations. Time plots and Dickey-Fuller Generalized Least Squares unit root test were used to investigate the stationarity properties of the data. Schwarz Information Criterion (SIC) in conjunction with log likelihood were used to search for optimal ARIMA models while Mean Absolute Percentage Error (MAPE) was used for forecast evaluation. Results showed that all the study variables were differenced stationary and hence integrated of order one, I (1). ARIMA (2,1,4), ARIMA (2,1,2) and ARIMA (2,1,3) models were selected as the best candidates for modeling and forecasting the confirmed, recovered and deaths cases of COVID-19 in Nigeria respectively. The study found an approximate COVID-19 life cycle of 12 days among the infected population. The 15 days’ forecasts from ARIMA (2,1,4) and ARIMA (2,1,2) models showed increases in the daily number of confirmed and recovered cases of COVID-19 in Nigeria. The forecasts from ARIMA (2,1,3) model however showed fluctuating trend with decline in the number of deaths cases due to the disease. The result of the study further showed that improving on the present approach to treatment will further decrease the number of casualties due to COVID-19 in Nigeria.

Landscape Political Ecology: Rural-Urban Pattern of COVID-19 in Nigeria

AbstractThe socio-ecological and political properties of rural and urban landscapes have been argued to produce the differentials in rural-urban health. However, the mechanism of the COVID-19 pattern in this socio-political-ecological perspective has not been understood in Africa. The study used spatial techniques to explore the pattern of urbanization-COVID-19 nexus in Nigeria. It has been argued that three elements (demographic dynamics, infrastructure or governance) typify the socio-political-ecological landscape of urban places. They shape the spread of infectious diseases. We explored the extent to which these factors predict the COVID-19 pattern in Nigeria. The study used data from Nigeria’s Centre for Diseases Control and the National Bureau of Statistics. The results indicate that more urban states in Nigeria tend to have higher COVID-19 cases than rural states. The COVID-19 pattern is best predicted by population dynamics more than other elements. The result indicates demographic attributes are more critical to surges in COVID-19 cases in Nigeria. Places with higher populations and densities will tend to have more spread of the virus than places with lesser populations and densities. Therefore, in a future outbreak, places of high densities should be given more attention to prevent further spread.

Effects of misinformation on COVID-19 prevention and control in Nigeria

Objectives: To evaluate the impact of misinformation on COVID-19 prevention and control in Nigeria. Methods: This review adopted a documentary research method involving personal and official documents sourced from Google, PubMed, and Google Scholar databases from February 2020 to October 2020. Related information was extracted from newspapers, social media, journal articles and grey literatures. The searched areas were: COVID-19 outbreak in Nigeria, spread of misinformation on COVID-19 cases in Nigeria, updates of COVID-19 cases in Nigeria, and the effect of health misinformation on COVID-19 prevention and control. Results: Data from 31 reviewed literature shows that Social media poses a threat to public health by facilitating the widespread misinformation, especially during health crises. Social media platform (WhatsApp) was highly used in the spread of misinformation across the globe, thereby resulting in fear or tension which often kills faster than the disease itself. These experiences show that Nigeria is not just fighting against COVID-19, but also facing the battle of misinformation which can also be deadly. Conclusion: Misinformation is increasingly more sophisticated than ever and its potentials spread wider and faster in social media era resulting in fear or tension. Improved e-health literacy and dissemination of increased corrective information are highly recommended. Keywords: control; COVID-19; corona virus; misinformation; prevention.

Comparative Analysis and Forecasting on the Death Rate of COVID-19 Patients in Nigeria Using Random Forest and Multinomial Bayesian Epidemiological Models

The novel COVID-19 (SARS-COV-2) is a disease currently ravaging the world, bringing unprecedented health and economic challenges to several nations. There are presently close to 175,000 reported cases in Nigeria with fatalities numbering over 2,163 persons. The main objective of this paper is to compare the analysis and predictive accuracy between the Random Forest and the Multinomial Bayesian Epidemiological model for a cumulative number of deaths for COVID-19 cases in Nigeria by identifying the underlying factors which may propagate future occurrences. It is worthy to note that the Random Forest algorithm is an ensemble learning approach for classification, regression, and other tasks that works by training a large number of decision trees G(t) while the Multinomial Bayesian algorithm provides an excellent theoretical framework for analyzing experimental data and the highlight of its success relies on its ability to integrate prior knowledge about the parameters of interest as a distribution function p(Ck|d).

A Time Series Model to Forecast Covid-19 Infection rate in Nigeria Using Box-Jenkins Method

Coronavirus declared as a global pandemic by WHO has emerged as the most aggressive disease negatively affecting more than 90% countries of the world. Nigeria, one of the most populated countries in Africa is not an exception. This study focuses on analyzing the intrinsic patterns in the COVID-19 spread in Nigeria using the Box-Jenkins procedure. Data of daily confirmed cases of COVID-19 in Nigeria was retrieved from Nigeria Centre for Disease Control (NCDC) official website from February 27, 2020 to October 31, 2020 to identify the series components, estimate parameters, develop an appropriate stochastic predictive model and use the model to forecast future trend of the deadly virus. The Autoregressive Integrated Moving Average (ARIMA) of order (0,1,1) was identified as the most suitable model based on the analysis of the autocorrelation (ACF), partial autocorrelation functions (PACF) and Akaike Information Correction (AICc) value. R software version 4.0.3 was used to analyze the trend which moothen the series by using 8-point moving average to extract the irregular component as wellas differencing the series one step further to obtain a stationary series. We performed the Augmented Dickey-Fuller Unit root test, parameter estimation and Ljung-Box test to check the proposed model’s conformity to the stationary univariate process. A 85 – day (1st Oct., 2020 – 24th Jan., 2020)forecast shows a gradual decline in the successive number of confirmed cases of infection indicating the effectiveness of the intervention strategies employed by the Task Force to contain the virus. The concerned authorities can apply the forecasted trend to make further informed decisions on the measures to be put in place to reduce diffusion of the deadly virus into the country.

Situation assessment and natural dynamics of COVID-19 pandemic in Nigeria, 31 May 2020

BackgroundThe coronavirus disease (COVID-19) remains a global public health issue due to its high transmission and case fatality rate. There is apprehension on how to curb the spread and mitigate the socio-economic impacts of the pandemic, but timely and reliable daily confirmed cases' estimates are pertinent to the pandemic's containment. This study therefore conducted a situation assessment and applied simple predictive models to explore COVID-19 progression in Nigeria as at 31 May 2020. MethodsData used for this study were extracted from the websites of the European Centre for Disease Control (World Bank data) and Nigeria Centre for Disease Control. Besides descriptive statistics, four predictive models were fitted to investigate the pandemic natural dynamics. ResultsThe case fatality rate of COVID-19 was 2.8%. A higher number of confirmed cases of COVID-19 was reported daily after the relaxation of lockdown than before and during lockdown. Of the 36 states in Nigeria, including the Federal Capital Territory, 35 have been affected with COVID-19. Most active cases were in Lagos (n = 4064; 59.2%), followed by Kano (n = 669; 9.2%). The percentage of COVID-19 recovery in Nigeria (29.5%) was lower compared to South Africa (50.3%), but higher compared to Kenya (24.1%). The cubic polynomial model had the best fit. The projected value for COVID-19 cumulative cases for 30 June 2020 in Nigeria was 27,993 (95% C.I: 27,001–28,986). ConclusionThe daily confirmed cases of COVID-19 are increasing in Nigeria. Increasing testing capacity for the disease may further reveal more confirmed cases. As observed in this study, the cubic polynomial model currently offers a better prediction of the future COVID-19 cases in Nigeria.

Food Insecurity Amid the COVID-19 Lockdowns in Nigeria: Do Impacts on Food Insecurity Persist After Lockdowns End?

ObjectivesIn response to a rapid rise in COVID-19 cases in Nigeria in March and April 2020, governments in some states imposed lockdowns. While lockdowns may be crucial for disease prevention and control, they also disrupt food systems and economic activity and may have devastating impacts on vulnerable households. This study uses longitudinal data to examine trends in household food insecurity in Nigeria just after lockdowns were imposed and at multiple points later in 2020, and assesses the impacts of lockdowns in spring 2020 on household food insecurity.MethodsThis study utilizes data from the Nigerian General Household Survey 2018/19 and the first seven rounds of the LSMS-ISA National Longitudinal Panel Survey on COVID-19 collected between April-November 2020. We assess trends in household food insecurity, as measured by the Food Insecurity Experience Scale (FIES), before and during the COVID-19 pandemic and use a difference-in-difference design to estimate the impact of COVID-19 lockdowns on household food insecurity.ResultsHousehold food insecurity in Nigeria increased significantly between Jan/Feb 2019 and the first months of the COVID-19 pandemic in April/May 2020. As the pandemic continued, food insecurity decreased between April/May and November of 2020 for all items in the FIES yet remained significantly higher than before the pandemic began. Difference-in-difference models, however, suggest that the lockdowns that were imposed in some Nigerian states did not significantly increase household food insecurity.ConclusionsWhen lockdowns were imposed in response to COVID-19 outbreaks in early 2020, many researchers and policymakers worried that households in low- and middle-income countries, many of which rely on informal work and/or daily wages for their livelihoods, would be vulnerable to food insecurity and hunger. Still, lockdowns are a key public health strategy for slowing the spread of infectious disease. As we continue to address COVID-19 and prepare for new emerging infectious diseases, we must weigh the risks and benefits strategies such as lockdowns. These results will help policymakers understand how measures to prevent and control COVID-19 influence livelihoods during a prolonged public health crisis.Funding SourcesThis study does not have any funding sources.

ARIMA Modelling and Forecasting of COVID-19 Daily Confirmed/Death Cases: A Case Study of Nigeria

Aims: The aim of this work is to develop suitable ARIMA models which can be sued to forecast daily confirmed/death cases of COVID-19 in Nigeria. This is subject to developing the model, checking them for suitability and carrying out eight months forecast, and making recommendations for the Nigerian Health sector.
 Study Design: The study used daily confirmed and death cases of COVID-19 in Nigeria.
 Methodology: This work covers times series data on the on the daily confirmed/death cases of COVID-19 in Nigeria, obtained from the Nigerian Centre for Disease Control (NDCD) from 21 March 2020 to 5 May 2020, covering a total of 51 data points. This work is geared towards developing a suitable Autoregressive Integrated Moving Average (ARIMA) models which can be used to forecast total daily confirmed/death cases of COVID-19 in Nigeria. Two adequate subset ARIMA (2, 2, 1) and AR (1) models for the confirmed/death cases, respectively, is fitted and discussed
 Results: A forecast of 239 days – from 6th May 2020 to 31 December 2020 was conducted using the fitted models and we observed that the COVID19 data has an upward trend and is best forecasted within a short period.
 Conclusion: Critical investigation into the rate of spread of COVID-19 pandemic has shown that, that the daily confirmed cases as well as death cases of the disease tends to follow an upward trend. This work aimed at developing a suitable ARIMA models which can be used to fit a most appropriate subsets to statistically forecast the actual number of confirmed cases as well as death cases of COVID-19 recorded in Nigeria for a period of 8 months.

Quantifying The Significance of Distance to Temporal Dynamics of Covid-19 Cases in Nigeria Using a Geographic Information System

The coronavirus disease 2019 (COVID-19) is caused by a new strain of coronavirus that spreads primarily by close contact. Although Nigeria adopted lockdown measures, no defined strategies were used in setting the distance threshold for these lockdowns. Hence, understanding the drivers of COVID-19 is pivotal to an informed decision for containment measures in the absence of vaccines. Spatial and temporal analyses are crucial drivers to apprehending the pattern of diseases over space and time. Thus, this study aimed to quantify the significance of distance to the temporal dynamics of COVID-19 cases in Nigeria using the Geographic Information System. Incremental spatial autocorrelation was used to analyze datasets of each month in ArcGIS. March, April, May, and June exhibited patterns with no significant peaks, while July and August exhibited patterns with two statistically significant peaks. The first and second peaks of July were 301,338.39 and 365,947.83 meters, respectively, while August was 301,338.39 and 336,128.09 meters, respectively. Therefore, a significant difference in the clustering of COVID-19 over distances between July and August was established. This indicated that progression in the spread of the virus increased the virus's spatial coverage while the distance of risk of exposure decreased. This study's findings could be utilized to establish maximum movement restriction areas to contain the spread of COVID-19.
 Keywords: Distance; Incremental spatial autocorrelation; Covid-19; Disease; Nigeria
 Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember
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Low First Wave COVID-19 cases and health seeking behaviors across the six geopolitical zones of Nigeria

This study was carried out across the six geopolitical zones to assess knowledge and health seeking behaviors of Nigerians during the first wave of COVID-19 pandemic lockdown in order to understand the seemingly low cases of COVID-19 in Nigeria. Structured and pretested short questionnaires were employed to obtain information electronically and physically across the six geopolitical zones on socio-demographic characteristics of participants; the knowledge of participants on COVID-19 disease; availability and accessibility to testing and isolation centers in the communities, attitudes and health seeking behaviors. Atotal of 1023 respondents; 705 (68.9%) males and 318 (31.1%) females across the six geopolitical zones in Nigeria participated in this study with 477(46.6%) within the 26-45 years age category, and 6(0.6%) above 66 years. Atotal of 985(96.3%) had heard about COVID-19 at the time of survey while a total of 859(84.0%) of the respondents were aware of the presence of COVID-19 laboratory testing centers. Across the geopolitical zones, majority of the respondents 487(47.6%) claimed there are no routine testing for COVID-19 at their community level, while 303 (29.6%) affirmed otherwise and 216(21.1%) did not know if there were routine testing going on or not. On health seeking behaviors across the country, majority of the respondents 558(54.5%) affirmed they would visit the hospital as the first point of contact if the opportunitypresented itself, 244(23.9%) claimed they would call the NCDC toll line, 2(0.2%) said they would use prayers, while 1(0.1%) respectively claimed they would use home management strategies through visiting chemist stores and checking the internet for solutions. Findings from this study confirm that there is very high awareness of the COVID-19 disease across the country, and limited number of testing centers at the community level. The implications include the possibility of unreported COVID19 cases in the cities as well as in the communities.
 Keywords: COVID-19, knowledge, health seeking behaviors, community level

Seasonal change and COVID-19: Will the harmattan season lead to an increase in COVID-19 cases in Nigeria?

The harmattan season, which is a period characterized by low temperature, dry air and increased air pollution leads to widespread airborne disease and exacerbation of pre-existing conditions, should be recognized as a period of potential risk of high COVID-19 infection rates. This period also coincides with the Christmas season which comes with so many festivities and can become a COVID-19 super-spreader. With many Nigerians now abandoning the non-pharmaceutical protection measures against COVID-19, the harmattan season and the forthcoming spike in social gatherings might usher in the second wave of the virus which can potentially be more catastrophic. There is need for the Nigerian government to start planning and instituting new protection measures and guidelines for safe Christmas celebration while also educating and encouraging the populace to adopt the protection measures recommended by experts.