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Modern Geomatics Technologies and Applications

Results obtained by this research can give us an insight of the situation of the disease incidence across Iran. Moreover, by
identifying the factors that have significant influences on the higher COVID-19 prevalence, it can be helpful for authorities to
make practical decisions.

2. Materials and methods

2.1 Data collection
In order to estimate the incidence rates of COVID-19 for each province in Iran, the estimated number of total infected and
total death cases of the disease and the population data of Iran were obtained until October 21, 2020. Data of the disease were
collected from Iran’s Minister of Health [13] and population data was derived from Statistical Center of Iran [14]. Until October
21, 2020, there were a total number of 536,181 infected cases and 29,403 associated deaths detected in Iran, which made this
country one of the most infected ones among the world. The incidence rate reveals the percentage of the number of people who
get COVID-19 in a given time period [15]. The calculated values of incidence rates inserted into ArcGIS 10.8 software for further
spatial analyses.
Besides, a set of 10 demographic, environmental and socioeconomic determinants were compiled at the province-level as
potential risk factors. Further, all these variables were attached to the boundary shapefile of Iran in ArcMap. Names and
descriptions of all variables are provided in Table 1.

Table 1 Explanatory variables used in this study
Category Variable Name Description
Demographic Urban population Refers to people living in urban areas
Population 60+ % of total population 60 years of age or older
Population density all residents per sq. km of land area
Literacy % the population that can read and write
Physicians specialist medical practitioners
Hospital beds Beds available in public rehabilitation centers
Environmental Average temperature Normal temperature for a year period
Average precipitation Normal precipitation for a year period
Socioeconomic GDP the sum of gross value added by all resident
producers in the economy
Inflation Considerable rise in the general price level of
goods over a period of time

2.2 Spatial analysis of COVID-19 distribution
2.2.1 Spatial autocorrelation: In spatial modeling, Global Moran’s I could be an essential technique which can calculate the
spatial distribution pattern of the data. Moran’s autocorrelation coefficient was used to measure the correlation among
neighbouring observations and the levels of spatial clustering among neighbouring districts [16]. Given a set of features and an
associated attribute, this tool evaluates whether the pattern expressed is clustered, dispersed, or random. The value of Global
Moran’s I is within a range of -1.0 to +1.0. A positive Moran's I value indicates tendency toward clustering while a negative
Moran's I value indicates tendency toward dispersion. In order to be able to use hot spot analysis, the distribution of incidence
rates should be clustered. In this research, according to Equation (1) and Equation (2), Global Moran’s I calculated the spatial
distribution of COVID-19 in Iran’s provinces based on incidence rates.

∑ =1 ∑
,
=1
= (1)
0 ∑ 2
=1

= ∑ ∑ (2)
,
0
=1 =1

2