Data Collection
Being a crucial part of data analytics applications, data collection is the process of gathering all data needed in order to answer questions, analyze and predict future trends, actions and scenarios. The data collection stage concerns the identification of the necessary sources from where spatial (and non-spatial) data related to COVID-19 will be retrieved.
The Datasets created contain all the available information relative to COVID19 in Cyprus (eg. number of daily confirmed cases, number of tests, numbers of patients hospitalized and cases in intensive care units, etc.). Additionally, the governmental decrees and response measures are also collected. Data are obtained from the Cyprus National Open Data Portal, the Cyprus Press and Information Office (PIO), the European Centre for Disease Prevention and Control, the Cyprus Official Government Gazette and from the daily announcements of the Cyprus Ministry of Health.
Local Survey
The questionnaire, administered via Google Form, consisted of 31 questions divided into three main sections. It remained open for a duration of 3 months (from November 20, 2023, to February 20, 2024), during which information was gathered anonymously and voluntarily from residents in Cyprus who were >= 18 years old. In the first part, information regarding demographic (gender, age, district of residence and employment) and key mobility characteristics (frequency, purpose, duration, distance, and means of transportation) is collected. The second section accumulates critical information regarding the mobility timeline of the individuals, indicating location and activity every two hours during a typical day. The third and final section, seeks participants’ opinions on essential measures to limit the spread of the recent COVID19 pandemic. By analyzing the collected data (responses) from the questionnaire, the mobility profiles of Cypriot residents are created for each age group.
The study received approval from the Cyprus National Bioethics Committee (EEBK-ΕΠ2023.01.273) and a total of 787 valid and complete responses were collected. Through this comprehensive survey, the research documents travel patterns, commuting habits, and transportation preferences across various age groups and districts of Cyprus. Key findings include insights into travel distances, frequencies, duration and means of transportation, and timelines of mobility activities on typical workdays, Saturdays, and Sundays. Additionally, the study explores the participants’ reported response to and acceptance of public health measures aimed at addressing the COVID-19 pandemic.
Agent-Based Modelling (ABM)
Agent-based models (ABM) are computer simulations used to study the interactions between individuals, things, places, and time. They combine elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming.
An ABM is a stochastic computational model for simulating the actions and interactions of autonomous agents (individuals -often people in epidemiology- or entities such as organizations or groups) in order to understand the behavior of a system and what governs its outcomes.
The model contains three main components: 1) the agents, 2) the environment and 3) the interactions.
The agents are programmed to behave and interact with other agents in a spatial environment or a social network. As a result, these interactions produce emergent effects that may differ from effects of individual agents.
Analysis
Deep understanding and analysis require advanced satellite remote sensing and spatial analysis techniques in Epidemiology. Advanced techniques and methods are to be used with the help of specific software. A variety of results are expected, including models of spatio-temporal human behavior, cartographic products and GIS applications (like digital maps, animated time-series maps and dashboards).
The doctoral research aims to contribute to the better understanding of the spatial behavior, extent and intensity of the COVID-19 pandemic in Cyprus, while considering and investigating the effects of local population connectivity, human mobility and transport. Through the geographical study of human activity dynamic characteristics, this research will model the effects of spatio-temporal behavior of individuals during the transmission and spread of the infectious coronavirus (SARS-CoV-2) disease. To this end, extensive and in-depth collection and analysis of local high-quality spatial, mobility and population data are required. The primary research focus is on human mobility, while Cyprus is the geographical area of study.
COVID-19 Data Analytics
Using COVID-19 data to fight & contain the pandemic with advanced analytics is critical to protect public health.
- Maps provide an intuitive & efficient way to visualize key data points relating to COVID-19 spread.
- Spatial analysis can be used to determine areas at risk from overcrowding.
- Using spatial data on human mobility, governments can see where measures are or aren’t working using dashboards and monitor critical POIs (hospitals, supermarkets, clinics) to understand spatial patterns and decide for response measures.
