Use of Geographic Information System in disease prevention and control
A Geographic Information System (GIS) is a computer-aided database management system (DBMS) and mapping technology used to organize, store, and display spatial data (Aggarwal & Officer, 2017). GIS allows for the visual display of spatial information while providing an interface between the data and map. GIS makes it easy for information to be presented to key decision-makers in a way that is quick, efficient, and effective for the implementation of timely disease preventive measures. The use of GIS in dealing with public health issues can be traced back to John Snow, who used points to represent the location of cholera cases in the 1854 cholera outbreak in London (Dong, 2017). GIS has made it easy for public health officers to prevent and control diseases, and the goal of this paper is to explore how GIS has been used for this purpose.
During disease investigation, the field investigator often extracts location information that the laboratory reports provide (Boulos & Geraghty, 2020). In most cases, the residential address of the patient is collected at the time of diagnosis along with the specimen for laboratory testing and this is true for the confirmation of COVID-19 diagnosis. After the hospital or laboratory reports the diagnosis results, this geographic information must be availed to relevant parties (Aggarwal & Officer, 2017). GIS information related to cases reported revealed that COVID-19 emerged in Wuhan, Hubei Province, China, in December 2019, and rapidly spread to other parts of China as well all around the world. With location data, health practitioners can narrow down to individual cases and identify where they started.
The population at risk is often determined when determining whether a given health outcome is occurring at a greater than expected rate. This involves estimating a population within a specified geographic location and this requires GIS (Zhou et al., 2020). For the COVID-19 pandemic, it was identified that people of all ages can contract the disease. However, in any given population, the elderly and those with underlying health conditions, such as diabetes and heart disease, are at higher risk for severe illness (Dong, 2017). GIS has helped in mapping risk factors such as state of health systems, press freedom, urban density, government transparency, urban population, conflict, international exposure, and displacement (Africa Center for Strategic Studies, 2020). Being aware of these vulnerabilities helps inform governments on how best to protect their citizens. For example, many countries have banned international traveling to limit the risk of international exposure.
Figure 1( Retrieved from Africa Center for Strategic Studies, 2020)
GIS is used in the exploration of disease outbreaks (Boulos & Geraghty, 2020). Spatial analyses of disease baseline rates are essential in the early investigation of diseases by providing evidence of unusual disease rates across a given location over a given time. For the COVID-19, spatial analyses made it possible for the existence of the outbreak to be established in Wuhan, China. Spatial analyses are used to identify the changing rates of diseases across time. Static maps present temporal trends of the distribution of the disease. Micromaps display the rates of COVID-19 in different population groups (Sarwar et al., 2020).
GIS is essential in disease prevention and control, especially since it is used in uncovering risk factors of diseases (Boulos & Geraghty, 2020). GIS helps in uncovering common exposure routes. It also helps in exploring and defining social networks crucial in understanding the spread of disease. Location information was used by health practitioners in the generation of hypothesis on exposure and transmission factors for the virus. Visualization through GIS made it possible for COVID-19 health teams to determine the potential transmission factors as well as changing disease patterns early enough to come up with prevention measures to prevent further spread. Since the COVID-19 virus can be spread through respiratory droplets and contact routes, various governments advised on social distancing, banned public gatherings, and encouraged people to wear protective clothing e.g., masks (Gao et al., 2020).
When it comes to identifying and counting cases, location data must be collected and geocoded (Boulos & Geraghty, 2020). Global Positioning System (GPS) is used in the collection of latitude and longitude locations of particular households. For the COVID-19 epidemic, since the virus has the potential of spreading rapidly, the identification of infected persons rapidly and their essential isolation is essential for limiting its transmission. The use of GPS for locating households has made it possible for spatiotemporal analysis of transmission risk factors to be conducted (Chen et al., 2018). Spatial data has been used to represent abstract ideas including activity space. Activity spaces are inclusive of workplaces, religious centers, recreational areas, restaurants, residences, points of food purchases, etc. where persons of interest might have frequented or visited. During this COVID-19 pandemic, spatial data reflecting the points of interaction with affected individuals has been used to track down suspect cases and improve understanding of COVID-19 risk factors.
Maps and geostatistical data are used to guide decisions made regarding where and when to implement measures related to disease control, prevention, and surveillance (Boulos & Geraghty, 2020). At the same time, GIS methods are helpful in the evaluation of prevention and control efforts. Maps comprising of points that represent the location of every COVID-19 case and rates in a given area were drawn. The accumulation of cases in homes, villages, localities, or cities was mapped and these are compared to those of other areas. Visualization of COVID-19 disease rates by location makes it possible for health professionals to highlight locales where control measures might be more or less effective (Gao et al., 2020). This made it possible for COVID-19 hot zones to be identified hence the applicability of given preventative policies. An example is a lockdown implemented in Nairobi whereby movement in and out of the city is modulated since the city was identified as a COVID-19 hot zone.
Maps have been used in guiding situational awareness and communicating location-related information regarding the disease’s incidence, prevalence, and exposures related to spatial information (Boulos & Geraghty, 2020). During this COVID-19 outbreak, maps have helped in the visualization of incidence and shifts in the presence of the disease. Internet-based maps have been used to access data related to the status and location of major health infrastructure including isolation centers. The maps have also helped governments in determining where the medical resources should be best directed (Gao et al., 2020). Maps published online have helped provide the public with updated information regarding how the COVID-19 virus is changing.
References
Africa Center for Strategic Studies. (2020, April 3). Mapping Risk Factors for the spread of COVID-19 in Africa. Retrieved May 30, 2020, from https://africacenter.org/spotlight/mapping-risk-factors-spread-covid-19-africa/
Aggarwal, G., & Officer, A. M. (2017). GIS for control of communicable diseases. In Geospatial World Forum. Hyderabad India.
Boulos, M. N. K., & Geraghty, E. M. (2020). Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: how 21st century GIS technologies are supporting the global fight against outbreaks and epidemics.
Chen, X., Hu, H., Xu, X., Gong, J., Yan, Y., & Li, F. (2018). Probability sampling by connecting space with households using GIS/GPS technologies. Journal of Survey Statistics and Methodology, 6(2), 149-168.
Dong, W. (2017, December). Investigation on the Applications of GIS Methods in Epidemiologic Study. In 2017 7th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2017). Atlantis Press.
Gao, S., Rao, J., Kang, Y., Liang, Y., & Kruse, J. (2020). Mapping county-level mobility pattern changes in the United States in response to COVID-19. Available at SSRN 3570145.
Sarwar, S., Waheed, R., Sarwar, S., & Khan, A. (2020). COVID-19 challenges to Pakistan: Is GIS analysis useful to draw solutions?. Science of The Total Environment, 139089.
Zhou, C., Su, F., Pei, T., Zhang, A., Du, Y., Luo, B., … & Song, C. (2020). COVID-19: Challenges to GIS with big data. Geography and Sustainability.