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Modern Geomatics Technologies and Applications

GIS-based spatial analysis and hotspot detection of COVID-19 outbreak

Nima Kianfar , Aliasghar Azma , Mohammad Saadi Mesgari 3
2
1*

1 Department of GIS, Faculty of Geodesy and Geomatics, K. N. Toosi University of Technology, Tehran, Iran

2 College of Architecture and Civil engineering, Beijing University of Technology, Beijing, China

3 Department of GIS, Faculty of Geodesy and Geomatics, K. N. Toosi University of Technology, Tehran, Iran

* nimakianfar@email.kntu.ac.ir

Abstract: Coronavirus disease (COVID-19), caused by acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has
become a global crisis due to its severe epidemiological characteristics, which was reported in Wuhan, China, in
December 2019. In this study, we calculated the COVID-19 cumulative incidence rate (CIR) and cumulative fatality rate
(CFR) for each country throughout the world from the first day of the outbreak by January 9, 2021. We analysed the
spatial patterns of COVID-19 using Global Moran’s I analysis. Further, Hot Spot analysis (Getis-Ord Gi*) was
implemented to investigate high-risk and low-risk clusters of COVID-19 globally. Yemen, Mexico, and Ecuador had the
highest CFR rates among others. In terms of CIR, the highest values belonged to Andorra, Gibraltar (United Kingdom),
and Montenegro at the time of the study. Spatial distribution of CIR values for all countries showed an intense clustered
pattern. Results of Hot Spot analysis revealed that almost all parts of Europe, especially eastern, western, and southern
countries were intensely infected and high-risk areas in terms of COVID-19 spread. United States was the other mostly
infected country demonstrating the confidence level of 99%. Besides, countries located in Africa presented the lowest
CIR levels, which made them cold spot areas. In conclusion, Applying spatial analysis could be beneficial due to its
applicability in illustrating the high-risk locations of disease and providing useful information for principals to
implement specific interventions in regions with different levels of risks.

1. Introduction
First COVID-19 infected cases were reported in Wuhan, China in December 2019 [1]. This disease, which is caused by acute
respiratory syndrome coronavirus 2 (SARS-Cov-2) [2], puts the elderly population at greater risks [3, 4]. Although the first
infected case was in Wuhan, China, which was the epicentre of the outbreak, the virus has changed the transmission pattern
several times since then [5]. After a short period, the United States and some countries of Europe had been reporting a high
number of COVID-19 cases with a rapid increase in both confirmed cases and mortalities [6]. To find out the main causes of
the outbreak and take preventive measures, some studies been conducted with the development of the COVID-19 [7-11]. For
example, in China, GUAN, W.-J., NI et al. analysed the spatial relationship between the COVID-19 infected cases and air
quality index (AQI), using a Poisson regression model. Their examination of the data extended from January 29, 2020, to
February 19, 2020, showed that there was a significant statistical association between infected cases and AQI in several cities.
[12]. Mollalo, A., B. Vahedi, and K.M. Rivera also applied five global and local regression models to examine the
geographical distribution of COVID-19 incidence rates across the continental United States, considering a set of
environmental, socioeconomic, demographic, behavioural, and topographic variables. The results illustrated that four risk
factors, including the household income, the percentage of nurse practitioners, the percentage of black females, and income
inequality were directly associated with the COVID-19 prevalence. They also concluded that the multiscale GWR model could
describe almost 68% of the total incidence rates of the COVID-19 disease [13]. By implementing two regression methods,
namely OLS and GWR, Karaye, I.M. and J.A. Horney predicted the potential association between social vulnerability and the
COVID-19 confirmed cases in the United States. The results revealed that there was a negative correlation between the number
of infected cases and transportation, housing, disability, and household composition data [14]. Moreover, Li, H., Ding, et al.
applied Global Moran’s I, Local Moran’s I and Getis-Ord Gi* analyses on COVID-19 confirmed cases in China. They
suggested that there was not any significant statistical relationship between space and disease incidence until March 5, 2020
[15]. As can be seen, a lot of research has been done on different dimensions of the disease to understand the spatial patterns of

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