A framework for monitoring movements of pandemic disease patients based on GPS trajectory datasets

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...

Deep Learning Algorithms for predicting the geographical locations of Pandemic Disease Patients from Global Positioning System (GPS) Trajectory Datasets

“…To this end, Machine Learning (ML) approach was considered by researchers as a solution to this challenge. Authors such as [1], [15][16][17][18][19][20][21][22], [7] have studied and applied ML models in different ways for predicting geographical locations of moving objects. ML models are more efficient and effective in terms of accuracy, time saving, minimal use of computer resources, and so on.…”

“…ML models are more efficient and effective in terms of accuracy, time saving, minimal use of computer resources, and so on. Authors like Ugwoke et al [1], in their paper applied traditional machine learning models such as MLR, kNN, DTR, RFR, GBR, and XGBR to demonstrate the possibility of predicting the next probable geographical location of a COVID-19 patient. Their result showed that the DTR model performed optimally compared with the rest of the models (i.e., MLR, kNN, RFR, GBR, XGBR) based on the following evaluation matrices (i.e, ACCURACY, MAE, MSE, and R 2 ) used.…”

“…The data sample used in this study are Trajectory datasets (i.e., Location datasets) adopted from our previous work [1,2]. These datasets were generated using a sample of 33 participants drawn from Oshodi-Isolo, Local Government Area of Lagos State, Nigeria.…”

“…A major challenge that has confronted humanity throughout history is the spread of pandemic diseases [1]. This is because, Pandemic Diseases are threats to human health and global stability, and mostly spread across the world via human movements from one geographical location to another.…”

“…Some of these polices include but not limited to social gathering restrictions, lockdown orders, contact tracing, quarantine measures, setting-up sites for testing and/ or vaccination, and so on. Pandemic diseases such as Zika, AIDS, Hong Kong Flu, Influenza, Ebola, tuberculosis, Swine Flu, Spanish Flu, SARS, H7N9, and so on have been spread by human movements [1].…”