Quantifying The Significance of Distance to Temporal Dynamics of Covid-19 Cases in Nigeria Using a Geographic Information System
Abstract
The coronavirus disease 2019 (COVID-19) is caused by a new strain of coronavirus that spreads primarily by close contact. Although Nigeria adopted lockdown measures, no defined strategies were used in setting the distance threshold for these lockdowns. Hence, understanding the drivers of COVID-19 is pivotal to an informed decision for containment measures in the absence of vaccines. Spatial and temporal analyses are crucial drivers to apprehending the pattern of diseases over space and time. Thus, this study aimed to quantify the significance of distance to the temporal dynamics of COVID-19 cases in Nigeria using the Geographic Information System. Incremental spatial autocorrelation was used to analyze datasets of each month in ArcGIS. March, April, May, and June exhibited patterns with no significant peaks, while July and August exhibited patterns with two statistically significant peaks. The first and second peaks of July were 301,338.39 and 365,947.83 meters, respectively, while August was 301,338.39 and 336,128.09 meters, respectively. Therefore, a significant difference in the clustering of COVID-19 over distances between July and August was established. This indicated that progression in the spread of the virus increased the virus's spatial coverage while the distance of risk of exposure decreased. This study's findings could be utilized to establish maximum movement restriction areas to contain the spread of COVID-19.
Keywords: Distance; Incremental spatial autocorrelation; Covid-19; Disease; Nigeria
Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember
This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
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Blank, L., Cohen, Y., Borenstein, M., Shulhani, R., Lofthouse, M., Sofer, M., & Shtienberg, D. (2016). Variables associated with severity of bacterial canker and wilt caused by clavibacter michiganensis subsp. Michiganensis in tomato greenhouses. Phytopathology, 106(3), 254–261. https://doi.org/10.1094/PHYTO-07-15-0159-R.
CSR-in-Action. (2020). The COVID-19 disbelief amongst the nigerian citizenry. Retrieved from https://www.csr-in-action.org/the-covid-19-disbelief-amongst-the-nigerian citizenry/.
Dhama, K., Khan, S., Tiwari, R., Sircar, S., Bhat, S., Malik, Y. S., ... & Rodriguez-Morales, A. J. (2020). Coronavirus disease 2019–COVID-19. Clinical microbiology reviews, 33(4). https://doi.org/10.1128/CMR.00028-20.
Gayawan, E., Awe, O., Oseni, B., Uzochukwu, I., Adekunle, A., Samuel, G., … Adegboye, O. (2020). The spatio-temporal epidemic dynamics of COVID-19 outbreak in Africa. Epidemiology and Infection. https://doi.org/10.1101/2020.04.21.20074435.
GIS Laboratory Geography Department. (2020). Map of Nigeria. Kano : Bayero University.
Kiang, M., Chin, E., Huynh, B., Chapman, L., Rodríguez-Barraquer, I., Greenhouse, B., … Lo, N. (2020). Routine asymptomatic testing strategies for airline travel during the COVID-19 pandemic: a simulation analysis. The Lancet Infectious Diseases. https://doi.org/10.1101/2020.12.08.20246132.
Kim, S., & Castro, M. C. (2020). Spatiotemporal pattern of COVID-19 and government response in South Korea (as of May 31, 2020). International Journal of Infectious Diseases, 98(C), 328–333. https://doi.org/10.1016/j.ijid.2020.07.004.
Kuwait Government Online. (2020) COVID-19 Updates, State of Kuwait. Retrieved from https://e.gov.kw/sites/KGOArabic/Pages/HomePage.aspx.
Marziano, V., Pugliese, A., Merler, S., & Ajelli, M. (2017). Detecting a Surprisingly Low Transmission Distance in the Early Phase of the 2009 Influenza Pandemic. Scientific Reports, 7(1), 1–9. https://doi.org/10.1038/s41598-017-12415-2.
McKee, M., & Stuckler, D. (2020). If the world fails to protect the economy, COVID-19 will damage health not just now but also in the future. Nature Medicine, 26(5), 640–642. https://doi.org/10.1038/s41591-020-0863-y.
Memish, Z. A., Cotten, M., Meyer, B., Watson, S. J., Alsahafi, A. J., Al Rabeeah, A. A., … Drosten, C. (2014). Human Infection with MERS coronavirus after exposure to infected camels, Saudi Arabia, 2013. Emerging Infectious Diseases, 20(6), 1012–1015. https://doi.org/10.3201/eid2006.140402.
Moran, P. A. (1948). The interpretation of statistical maps. Journal of the Royal Statistical Society: Series B (Methodological), 10(2), 243-251.
Murray, C. J. (2020). Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator-days and deaths by US state in the next 4 months. MedRxiv. https://doi.org/10.1101/2020.03.27.20043752.
National Bureau of Statistics (2011). Annual Abstract of Statistics. Abuja : National Bureau of Statistics.
Nigeria Centre for Disease Control (2020). COVID-19 Data. Retrieved from https://covid19.ncdc.gov.ng/.
Okunade, S. K., & Ogunnubi, O. (2019). The African union protocol on free movement: A Panacea to end border porosity? Journal of African Union Studies, 8(1), 73–91. https://doi.org/10.31920/2050-4306/2019/v8n1a4.
Prem, K., Liu, Y., Russell, T. W., Kucharski, A. J., Eggo, R. M., Davies, N., … Klepac, P. (2020). The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study. The Lancet. Public Health, 5(5), e261–e270. https://doi.org/10.1016/S2468-2667(20)30073-6.
Rapone, C. (2020); Migrant Workers and the covid 19 Pandemic. United Nations Food and Agriculture Organization. Retrieved from http://www.fao.org/documents/card/en/c/ca8559en/.
Rocklöv, J., & Sjödin, H. (2020). High population densities catalyze the spread of COVID-19. Journal of Travel Medicine. https://doi.org/10.1093/jtm/taaa038/5807719.
Salje, H., Cummings, D. A. T., & Lessler, J. (2016). Estimating infectious disease transmission distances using the overall distribution of cases. Epidemics, 17(C), 10–18. https://doi.org/10.1016/j.epidem.2016.10.001.
Scarpone, C., Brinkmann, S. T., Große, T., Sonnenwald, D., Fuchs, M., & Walker, B. B. (2020). A multimethod approach for county-scale geospatial analysis of emerging infectious diseases: A cross-sectional case study of COVID-19 incidence in Germany. International Journal of Health Geographics, 19(1), 1–17. https://doi.org/10.1186/s12942-020-00225-1.
Teachout, M., & Zipel, C., (2020). The economic impact of COVID-19 lockdowns in sub-Saharan Africa. Retrieved from https://www.theigc.org/wp-content/uploads/2020/05/Teachout-and-Zipfel-2020-policy-brief-.pdf.
Wang, Y., Liu, Y., Struthers, J., & Lian, M. (2021). Spatiotemporal Characteristics of the COVID-19 Epidemic in the United States. Clinical Infectious Diseases, 72(4), 643–651. https://doi.org/10.1093/cid/ciaa934.
Woodhill, J (2020). Responding to the impact of covid-19 on rural people and food systems. Retrieved from https://www.foresight4food.net/wp-content/uploads/2020/05/Impact-of-COVID-19-on-Rural-Poverty-and-Food-Systems-V2.pdf.
Worldometer (2020). Nigeria Population. Retrieved from https://www.worldometers.info/world-population/nigeria-population/.
Xie, Z., Qin, Y., Li, Y., Shen, W., Zheng, Z., & Liu, S. (2020). Spatial and temporal differentiation of COVID-19 epidemic spread in mainland China and its influencing factors. Science of the Total Environment, 744(C), 140929. https://doi.org/10.1016/j.scitotenv.2020.140929.
Yang, F., Pahlavan, A. A., Mendez, S., Abkarian, M., & Stone, H. A. (2020). Towards improved social distancing guidelines: Space and time dependence of virus transmission from speech-driven aerosol transport between two individuals. Physical Review Fluids, 5(12). https://doi.org/10.1103/PhysRevFluids.5.122501.
Published
2021-04-25
How to Cite
OBUEKWE, Ifeyinwa Sarah et al.
Quantifying The Significance of Distance to Temporal Dynamics of Covid-19 Cases in Nigeria Using a Geographic Information System.
Geosfera Indonesia, [S.l.], v. 6, n. 1, p. 40-54, apr. 2021.
ISSN 2614-8528.
Available at: <https://jurnal.unej.ac.id/index.php/GEOSI/article/view/21405>. Date accessed: 13 nov. 2024.
doi: https://doi.org/10.19184/geosi.v6i1.21405.
Section
Original Research Articles
