Classification of Public Health Centres in Accra through a Web-Based Portal Integrated with Geographical Information System (GIS).

A health system is described as a logically organized collection of resources, agents, and institutions that offer healthcare to a specific population based on the finance, regulation, and delivery of health services. Many health centres have been established in Accra, the capital city of Ghana, due to the importance of good health. People in other developed nations can seek adequate healthcare, since information about relevant health centres is readily available. However, there is a paucity of information about the services provided by existing health institutions in Ghana, particularly in Accra. The majority of patients commute to either Korle-Bu Teaching Hospital or Greater Accra Regional Hospital, putting a considerable medical strain on these facilities. In this study, we use a Geographic Information System (GIS) to establish a database for all of Accra's health centres and categorize them according to the services they provide. This research tackled the previously mentioned problem by proposing and developing a web-based map called Geohealth for the classification of public health centres in Accra using GIS to assist users in accessing information and locating health centres. We utilized a mixed-method approach consisting of quantitative as well as Build Computer Science Research Methods. Results of our study show that the majority of the participants and stakeholders in our research are eager to embrace Geohealth. Furthermore, in comparison with existing techniques such as Google Maps, our proposed approach, Geohealth, takes less time to obtain information and locate public health centres in Accra, Ghana.

SARPPIC: Exploiting COVID-19 Contact Tracing Recommendation through Social Awareness.

Globally, the current coronavirus disease 2019 (COVID-19) pandemic is resulting in high fatality rates. Consequently, the prevention of further transmission is very vital. Until vaccines are widely available, the only available infection prevention methods include the following: contact tracing, case isolation and quarantine, social (physical) distancing, and hygiene measures (washing of hands with soap and water and using alcohol-based hand sanitizers). Contact tracing, which is key in preventing the spread of COVID-19, refers to the process of finding unreported people who maybe infected by using a verified case to trace back possible infections of contacts. Consequently, the wide and fast spread of COVID-19 requires computational approaches which utilize innovative algorithms that build a memory of proximity contacts of cases that are positive. In this paper, a recommender algorithm called socially aware recommendation of people probably infected with COVID-19 (SARPPIC) is proposed. SARPPIC initially utilizes betweenness centrality in a social network to measure the number of target contact points (nodes/users) who have come into contact with an infected contact point (COVID-19 patient). Then, using contact durations and contact frequencies, tie strengths of the same contact points above are also computed. Finally, the above algorithmic computations are hybridized through profile integration to generate results for effective contact tracing recommendations of possible COVID-19-infected patients who will require testing in a healthcare facility. Benchmarking experimental results in the paper demonstrate that, using two interconnected relevant real-world datasets, SARPPIC outperforms other relevant methods in terms of suitable evaluation metrics such as precision, recall, and F-measure.