Sustainable development plays a vital role in information and communication technology. In times of pandemics such as COVID-19, vulnerable people need help to survive. This help includes the distribution of relief packages and materials by the government with the primary objective of lessening the economic and psychological effects on the citizens affected by disasters such as the COVID-19 pandemic. However, there has not been an efficient way to monitor public funds’ accountability and transparency, especially in developing countries such as Nigeria. The understanding of public emotions by the government on distributed palliatives is important as it would indicate the reach and impact of the distribution exercise. Although several studies on English emotion classification have been conducted, these studies are not portable to a wider inclusive Nigerian case. This is because Informal Nigerian English (Pidgin), which Nigerians widely speak, has quite a different vocabulary from Standard English, thus limiting the applicability of the emotion classification of Standard English machine learning models. An Informal Nigerian English (Pidgin English) emotions dataset is constructed, pre-processed, and annotated. The dataset is then used to classify five emotion classes (anger, sadness, joy, fear, and disgust) on the COVID-19 palliatives and relief aid distribution in Nigeria using standard machine learning (ML) algorithms. Six ML algorithms are used in this study, and a comparative analysis of their performance is conducted. The algorithms are Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), Random Forest (RF), Logistics Regression (LR), K-Nearest Neighbor (KNN), and Decision Tree (DT). The conducted experiments reveal that Support Vector Machine outperforms the remaining classifiers with the highest accuracy of 88%. The “disgust” emotion class surpassed other emotion classes, i.e., sadness, joy, fear, and anger, with the highest number of counts from the classification conducted on the constructed dataset. Additionally, the conducted correlation analysis shows a significant relationship between the emotion classes of “Joy” and “Fear”, which implies that the public is excited about the palliatives’ distribution but afraid of inequality and transparency in the distribution process due to reasons such as corruption. Conclusively, the results from this experiment clearly show that the public emotions on COVID-19 support and relief aid packages’ distribution in Nigeria were not satisfactory, considering that the negative emotions from the public outnumbered the public happiness.

The global pandemic of the novel Coronavirus in 2019, known by the World Health Organisations (WHO) as Covid-19, has put various governments in a vulnerable situation around the world. For virtually every nation in the world, the effects of the Covid-19 pandemic, previously experienced by the people of China alone, has now become a matter of great concern. This research highlights its impact on the global economy, in addition to the immediate health consequences associated with the Covid-19 pandemic. The study further discussed the use of Text Analytics and Sentiment Analysis in Natural Language Processing (NLP) based on Twitter text to analyse public sentiment and derive insights regarding Covid-19 vaccines in the healthcare domain. Two machine learning algorithms were employed: Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) to classify and evaluate the results. Various pre-processing techniques were adopted to help in detecting the public sentiment based on the three sentiment polarity classes: positive, negative, and neutral. The result of the sentiment class distribution reveals that 31% of the public sentiment regarding Covid-19 vaccines is positive, 22% is negative while the remaining 47% were classified as neutral sentiment. The experimented machine learning algorithms reveals that SVM produced 88% accuracy which surpasses KNN with 78% accuracy.

The severe acute respiratory syndrome coronavirus 2 (SARSCoV2) outbreak has developed a major public health concern, particularly in northern hemisphere countries. The novel COVID-19 pandemic that broke out in 2019 presented obstacles to the scientific community and healthcare providers. The disparities across Africa highlight the value of assessment that go further than a continent- wide case number, which can distort the true situation on the ground. We wanted to look at epidemiology. In this review using data on confirmed cases and deaths, we attempt to assess the COVID-19 situation in Nigeria and Niger. We also contrasted the patterns of disease transmission and death. The main objective of this research is to address: How do the two countries relate in terms of COVID-19 distribution and mortality. The World Health Organization database, which updates data on the global number of confirmed cases and deaths for the Covid-19 pandemic on a regular basis, was used to get the reported numbers of cases and deaths between the two countries. A Descriptive statistics were used to analyze the average daily reported Covid-19 cases and death and annual cumulative cases and death between the two countries under study. Bivariate correlation analysis was conducted to assess the measure of direction and strength of association that exits between daily new cases and daily new death and also assess the same in terms of annual cumulative cases and death from 1 st April, 2020 to 31st March 2021. In Nigeria, the COVID19 spread pattern was similar to that of Niger. The daily deaths and cases distribution in Niger resembled those of Nigeria, so on daily basis in Niger they record 13.65 covid19 cases on average while Nigeria on average in daily basis they record 445.62 new cases of Covid-19. Then for the new death in Niger the average death on daily basis stand to be 0.50 compare to Nigeria on average which has 5.63 reported new death on daily basis. So for the bivariate correlation re