No abstract

The rapid spread of contagious diseases poses a colossal threat to human existence. Presently, the emergence of coronavirus COVID-19 which has rightly been declared a global pandemic resulting in so many deaths, confusion as well as huge economic losses is a challenge. It has been suggested by the World Health Organization (WHO) in conjunction with different Government authorities of the world and non-governmental organizations, that efforts to curtail the COVID-19 pandemic should rely principally on measures such as social distancing, identification of infected persons, tracing of possible contacts as well as effective isolation of such person(s) for subsequent medical treatment. The aim of this study is to provide a framework for monitoring Movements of Pandemic Disease Patients and predicting their next geographical locations given the recent trend of infected COVID-19 patients absconding from isolation centres as evidenced in the Nigerian case. The methodology for this study, proposes a system architecture incorporating GPS (Global Positioning System) and Assisted-GPS technologies for monitoring the geographical movements of COVID-19 patients and recording of their movement Trajectory Datasets on the assumption that they are assigned with GPS-enabled devices such as smartphones. Accordingly, fifteen (15) participants (patients) were selected for this study based on the criteria of residency and business activity location. The ensuing participants movements generated 157, 218 Trajectory datasets during a period of 3 weeks. With this dataset, mining of the movement trace, Stay Points (hot spots), relationships, and the prediction of the next probable geographical location of a COVID-19 patient was realized by the application of Artificial Intelligence (AI) and Data Mining techniques such as supervised Machine Learning (ML) algorithms (i.e., Multiple Linear Regression (MLR), k-Nearest Neighbor (kNN), Decision Tree Regression (DTR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and eXtreme Gradient Boosting regression(XGBR) as well as density-based clustering methods (i.e., DBSCAN) for the computation of Stay Points (hot spots) of COVID-19 patient. The result of this study showed clearly that it is possible to determine the Stay Points (hot spots) of a COVID-19 patient. In addition, this study demonstrated the possibility of predicting the next probable geographical location of a COVID-19 patient. Correspondingly, Six Machine Learning models (i.e., MLR, kNN, DTR, RFR, GBR, and XGBR) were compared for efficiency, in determining the next probable location of a COVID-19 patient. The result showed that the DTR model performed better compared to other models (i.e., MLR, kNN, RFR, GBR, XGBR) based on four evaluation matrices (i.e., ACCURACY, MAE, MSE, and R 2 ) used. It is recommended that less developed Countries consider adopting this framework as a policy initiative for implementation at this burgeoning phase of COVID-19 infection and beyond. The same applies to the...

No abstract

In this paper, some closed form expressions for selected parameters for the probability density function (PDF) of the beta distribution are obtained. The closed form expressions are recovered from the solution of the ordinary differential equations (ODEs), obtained from the differentiation of the PDF of the distribution. The paper shows that the shape of the distributions also determines the nature of the resulting ODE which has shown how distributions related to the beta distribution can be traced via the solutions of the ODEs. Numerical methods are unnecessary because the closed form expressions are the same with the values obtained from the standard statistical software.

Background and aim:The successful isolation of the Covid-19 virus in Wuhan, China in December 2019 provided empirical/scientific proof of the existence of the Covid 19 virus and marked the beginning of a pandemic of great proportions. Although localized and moderate at inception, the Covid-19 pandemic has proceeded to overwhelm the health authorities in several countries of the world. Concerted efforts to monitor the spread of the virus had been undertaken since the advent of the Covid-19 pandemic. The attempt to flatten the curve of the Covid-19 pandemic initially relied on contact tracing of persons who made contacts with infected persons. Presently, owing to the dynamics of the pandemic, scientists appear more preoccupied with the higher task of geographical location prediction of pandemic disease patients. Accordingly, the aim of this study is to carry out a comparative analysis of six deep learning algorithms for predicting the geographical locations of Covid-19 patients from Big GPS trajectory datasets. Method: The methodology for this study, proposes to design and apply selected deep learning algorithms for predicting the location of infected Covid-19 patients under monitoring. Among the deep learning algorithms include Recurrent Neural networks (RNN), Long-Short Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU), Multilayer perceptron (MLP). The distinctive quality of the models is efficiency and effectiveness with regards to time saving, minimal use of computer resources, smart design, and development. Result: The result of this study showed that with application of the above mentioned deep learning models, it is possible to predict the location of infected Covid-19 patients. Thus, the predictive ability of these models is not in doubt. The GRU algorithm outperformed the other algorithms (i.e., RNN, LSTM, BiLSTM, CNN, and MLP) based on Key Performance Indicators (KPIs) metrics applied such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Logarithmic Error (RMSLE) using uniform geographical location data. The result further revealed that location prediction of Covid-19 patients is more optimally executed using the deep learning models. Based on the findings in this paper, it is recommended that the deep learning models be applied in other jurisdictions for location prediction problems. Conclusion: The result of this study recommends the deep learning models for location prediction problems of not only Covid-19 patients but all other pandemic disease situations around the globe.