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A Novel Predictor for Micro-Scale COVID-19 Risk Modeling: An Empirical Study from a Spatiotemporal Perspective

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Risk assessments for COVID-19 are the basis for formulating prevention and control strategies, especially at the micro scale. In a previous risk assessment model, various “densities” were regarded as the decisive driving factors of COVID-19 in the spatial dimension (population density, facility density, trajectory density, etc.). However, this conclusion ignored the fact that the “densities” were actually an abstract reflection of the “contact” frequency, which is a more essential determinant of epidemic transmission and lacked any means of corresponding quantitative correction. In this study, based on the facility density (FD), which has often been used in traditional research, a novel micro-scale COVID-19 risk predictor, facility attractiveness (FA, which has a better ability to reflect “contact” frequency), was proposed for improving the gravity model in combination with the differences in regional population density and mobility levels of an age-hierarchical population. An empirical analysis based on spatiotemporal modeling was carried out using geographically and temporally weighted regression (GTWR) in the Qingdao metropolitan area during the first wave of the pandemic. The spatiotemporally nonstationary relationships between facility density (attractiveness) and micro-risk of COVID-19 were revealed in the modeling results. The new predictors showed that residential areas and health-care facilities had more reasonable impacts than traditional “densities”. Compared with the model constructed using FDs (0.5159), the global prediction ability (adjusted R2) of the FA model (0.5694) was increased by 10.4%. The improvement in the local-scale prediction ability was more significant, especially in high-risk areas (rate: 107.2%) and densely populated areas (rate in Shinan District: 64.4%; rate in Shibei District: 57.8%) during the outset period. It was proven that the optimized predictors were more suitable for use in spatiotemporal infection risk modeling in the initial stage of regional epidemics than traditional predictors. These findings can provide methodological references and model-optimized ideas for future micro-scale spatiotemporal infection modeling.
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International Journal of
Environmental Research
and Public Health
Article
A Novel Predictor for Micro-Scale COVID-19 Risk Modeling:
An Empirical Study from a Spatiotemporal Perspective
Sui Zhang , Minghao Wang, Zhao Yang and Baolei Zhang *


Citation: Zhang, S.; Wang, M.; Yang,
Z.; Zhang, B. A Novel Predictor for
Micro-Scale COVID-19 Risk
Modeling: An Empirical Study from a
Spatiotemporal Perspective. Int. J.
Environ. Res. Public Health 2021,18,
13294. https://doi.org/10.3390/
ijerph182413294
Academic Editor: Abolfazl Mollalo
Received: 26 October 2021
Accepted: 13 December 2021
Published: 16 December 2021
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iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
College of Geography and Environment, Shandong Normal University, Jinan 250014, China;
201814010401@stu.sdnu.edu.cn (S.Z.); 201814010418@stu.sdnu.edu.cn (M.W.);
201814010402@stu.sdnu.edu.cn (Z.Y.)
*Correspondence: blzhangsd01@sdnu.edu.cn
Abstract:
Risk assessments for COVID-19 are the basis for formulating prevention and control
strategies, especially at the micro scale. In a previous risk assessment model, various “densities”
were regarded as the decisive driving factors of COVID-19 in the spatial dimension (population
density, facility density, trajectory density, etc.). However, this conclusion ignored the fact that the
“densities” were actually an abstract reflection of the “contact” frequency, which is a more essential
determinant of epidemic transmission and lacked any means of corresponding quantitative correction.
In this study, based on the facility density (FD), which has often been used in traditional research, a
novel micro-scale COVID-19 risk predictor, facility attractiveness (FA, which has a better ability to
reflect “contact” frequency), was proposed for improving the gravity model in combination with
the differences in regional population density and mobility levels of an age-hierarchical population.
An empirical analysis based on spatiotemporal modeling was carried out using geographically and
temporally weighted regression (GTWR) in the Qingdao metropolitan area during the first wave of the
pandemic. The spatiotemporally nonstationary relationships between facility density (attractiveness)
and micro-risk of COVID-19 were revealed in the modeling results. The new predictors showed that
residential areas and health-care facilities had more reasonable impacts than traditional “densities”.
Compared with the model constructed using FDs (0.5159), the global prediction ability (adjusted
R
2
) of the FA model (0.5694) was increased by 10.4%. The improvement in the local-scale prediction
ability was more significant, especially in high-risk areas (rate: 107.2%) and densely populated
areas (rate in Shinan District: 64.4%; rate in Shibei District: 57.8%) during the outset period. It was
proven that the optimized predictors were more suitable for use in spatiotemporal infection risk
modeling in the initial stage of regional epidemics than traditional predictors. These findings can
provide methodological references and model-optimized ideas for future micro-scale spatiotemporal
infection modeling.
Keywords:
COVID-19; gravity model; geographically and temporally weighted regression (GTWR);
spatiotemporal risk modeling; Qingdao
1. Introduction
The COVID-19 pandemic is the most serious global public health event that has
occurred in the 21st century thus far and has become a hot topic in many different disci-
plines [
1
]. The global pandemic has severely damaged the global economy, society, finance,
and even the ecosystem and environment [
2
5
], highlighting the importance of recognizing
the “risks” of regional epidemics [
6
,
7
]. The level of risk not only shows the current situation
of epidemic infection and the probability of new cases occurring in a region, but, more
essentially, determines what level of prevention and control measures should be taken in
this region to reduce the risk of a pandemic as far as possible [
8
,
9
]. At the same time, the
rapid transmission of COVID-19 and its complexity mean that the time and space in which
both human–human and human–place interactions take place are particularly important
Int. J. Environ. Res. Public Health 2021,18, 13294. https://doi.org/10.3390/ijerph182413294 https://www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2021,18, 13294 2 of 16
in the study of the spatial epidemiology of COVID-19 [
10
12
]. Therefore, it is self-evident
that the quantitative analysis and micro-modeling of COVID-19 from the spatiotemporal
perspective are necessary for epidemic risk measurement, especially in the initial stage
before the complete outbreak of a regional epidemic [13,14].
Based on the empirical evidence provided by previous studies, people’s mobility and
contact are considered as decisive factors in the transmission of COVID-19 [
11
,
15
]. Theoret-
ically, the compact development of cities will lead to closer contact between people and
more frequent interactions occurring among residents, which also makes these high-density
areas potential hot spots for the rapid spread of emerging epidemics [
16
,
17
]. Therefore,
“densities” are given priority in most cases when micro-modeling the spread and risk of
infectious diseases [
18
,
19
]. “Densities” can be divided into three categories: population
density, trajectory density, and facility density [
11
,
20
,
21
]. Population density has always
been regarded as the determinant of epidemic exposure and transmission risk, and many
studies have explored the complex relationship between population density and epidemic
spread in various spatiotemporal contexts [
22
,
23
]. However, because most population
density data are in the form of statistical data, corresponding high-resolution geospatial
data usually need to undergo complex spatial correction processing, and it is difficult to
carry out microscopic epidemiological modeling directly [
24
,
25
]. This has caused urban big
data, which are more accurate than population data, to generally receive more attention in
recent studies [
26
,
27
]. Density analyses based on trajectory data (smartphone data, traffic
trajectory) have been implemented in many studies, providing unique advantages for
the analysis of population dynamic characteristics and the initial modeling of epidemic
diseases [
11
,
28
]. However, due to the high cost of accessing the trajectory data, some
researchers use points of interest (POI) instead to combine and model specific or multiple
facility densities. Different from urban roads, another spatial element of cities that often
leads to people being more mobile is the closeness of different facilities. This causes more
people to gather in one location and thus plays a pivotal role in the transmission of highly
contagious epidemics. Complementing population density, which is commonly used in the
classical theory of infectious disease ecology and transmission, the use of various facility
densities can better simulate the people’s contact frequency. Due to these characteristics,
modeling studies that predict epidemic risk from the perspective of urban planning based
on facility density, the mapping of urban functions in gathering places, and calculating the
probability of people gathering and coming into contact in airtight places have attracted
great attention [
21
,
29
,
30
]. Although several empirical studies have highlighted the sig-
nificance of the local epidemic source of COVID-19 as a driving mechanism in the initial
stage of the pandemic, various density indicators play a dominant role in the modeling of
micro-scale spatiotemporal epidemiology [13,31].
However, the use of “densities” is obviously not perfect for the micro-scale spatiotem-
poral modeling of COVID-19 risk. Firstly, a considerable number of studies have found that
“densities” have limited effects in the initial stage of regional epidemics [
31
]. Secondly, no
matter what the simple population density is, trajectory density or facility density cannot
reflect the real mechanism of human interaction. Although they can simulate the static and
dynamic density of the population (population density, track density) and the possibility
of people gathering in one space (facility density), respectively, such one-sided modeling
usually leads to biased estimations of densely populated areas with low gathering possibil-
ities and sparsely populated areas with high possibilities of people gathering [
16
,
17
,
32
].
In addition, differences in the age structure of the population and resulting differences in
travel modes and capacities are usually not considered in the micro-modeling of COVID-
19 risk [
13
,
33
]. To sum up, although traditional “densities” models have been widely
used in previous studies and achieved excellent results, compared with non-compound
“densities” the deeper determinants of the intracity risks of epidemics are undoubtedly
more multi-levelled. When complex population structures and the spatial heterogeneity
of different types of facility density are considered, the result of comprehensive modeling
is not so much related to “densities” in the traditional meaning, but more related to “con-
Int. J. Environ. Res. Public Health 2021,18, 13294 3 of 16
tact”, which is similar to the word “interaction” (which is often mentioned in the field of
geographical analysis) [
34
,
35
]. Therefore, in this study we try to simulate the frequency of
human–human and human–place interactions occurring between different kinds of people
and gathering places based on low-cost and accessible data. Taking population distribution,
age structure, and gathering probability into account, a quantitative predictor, facility
attractiveness (FA), which can be widely applied instead of the predictor of traditional
facility density (FD), was used. We then built a spatiotemporal risk model in a small
area where there was an outbreak of COVID-19 and evaluated its prediction efficiency in
different application scenarios.
The main purposes of this study include: (1) improving the gravity model to build
facility attractiveness (FA); (2) modeling and visualizing the varying spatiotemporal impact
of the selected driving factors on the risk of COVID-19 in the Qingdao metropolitan area
using geographically and temporally weighted regression (GTWR); (3) comparing and
discussing the advantages and disadvantages of the traditional FD model and the novel FA
model in different application scenarios.
2. Data and Methodology
2.1. Empirical Area
Qingdao is located in the east of Shandong Province, China, facing Japan and South
Korea across the sea. It is the economic center of Shandong Province and an important
international port city in China. Based on its geographical location and important business
status, Qingdao is considered to be one of the regions with the highest risk of the occurrence
of a local epidemic in China. In this study, the Qingdao metropolitan area was selected as
our empirical area (120
52
0
~120
28
0
E, 36
204
0
~36
24
0
N). This area is about 249.3 km
2
and
represents only 2.21% of Qingdao, but more than 42% of its population, making it a densely
populated area within Qingdao and an area with a high COVID-19 infection risk (Figure 1).
To better understand the spatiotemporal risk of COVID-19 at the micro scale, we referred
to the idea of Ling (2020) regarding community grid management and considered the
advantages of the use of a hexagonal grid for dealing with adjacency problems, modified
area unit problems (MAUPs), and high-precision spatiotemporal modeling [
31
,
36
]. In our
study, a hexagonal grid with a side length of 250 m was used to divide the study area into
blocks, and 1684 independent hexagons were selected for further analysis [37,38].
Figure 1. The geographical location of the empirical area.
Int. J. Environ. Res. Public Health 2021,18, 13294 4 of 16
2.2. Data Sources and Measures
The data used in this study include: COVID-19 case data, POI data, OpenStreetMap
road network data, and WorldPop population age structure data. Among these, the case
data include information regarding the age, gender, address, and date of symptom onset
of all cases confirmed in the empirical area from January to May, 2020, from the Qingdao
Municipal Health Commission (wsjkw.qingdao.gov.cn, accessed on 1 June 2020). The
POI data were sourced from Amap (www.amap.com, accessed on 6 June 2020). In this
study, catering places, residential areas, shopping places, public service facilities, health-
care facilities, education places, and entertainment places were initially highlighted as
places where people gather. However, due to the strict lockdown measures implemented
in China since January 2020, the possibility of people gathering in most education and
entertainment venues in Qingdao has become negligible (Figure 2). Therefore, this study
excluded these variables, and the remaining five types of places that experience the greatest
amount of human activity and that represent the greatest possibility of disease transmission
within the context of the pandemic were selected as independent variables (catering places,
residential areas, shopping places, public service facilities, and health-care facilities). The
100 m-resolution raster data for people of various ages were sourced from WorldPop (www.
worldpop.org, accessed on 1 March 2021); the road network data came from OpenStreetMap
(www.openstreetmap.org, accessed on 1 June 2020).
Figure 2. Development and stage definition of COVID-19 in the empirical area from 14 January to 23 February.
In order to construct effective and reliable panel data to meet the computational
requirements of geographically and temporally weighted regression (GTWR) and according
to the date of onset of these cases (Figure 2), the local epidemic was divided into five stages
(Stage 1: 14 January to 25 January; Stage 2: 26 January to 3 February; Stage 3: 4 February to
5 February; Stage 4: 6 February to 10 February; Stage 5: 11 February to 23 February). Stage
1 represents the initial stage of the local epidemic; before the Chinese New Year, the end of
this stage, many people in China were engaging in annual large-scale population migration,
the so-called (pre-) Spring Festival travel rush, which enabled COVID-19 to spread rapidly
across the country. Stage 2 represents the accelerate stage of the local epidemic, which was
also the strictest traffic blockade period; after this stage, the traffic blockade of the Chinese
mainland, except for Hubei Province, was gradually relaxed. Stage 3 represents the peak
period of the epidemic in the empirical area, which was also the turning point of the local
epidemic; Due to the traffic control caused by the pandemic and the drastic weakening
Int. J. Environ. Res. Public Health 2021,18, 13294 5 of 16
of people’s willingness to travel, the scale of post-Spring Festival travel rush has shrunk
greatly, which made its contribution to the national spread of the epidemic limited. Stage 4
represents the decelerate duration of the local epidemic, and it was at the end of this stage
that people in Chinese mainland began to resume work and production in stages. Stage 5
represents the last stage of the local epidemic and the local epidemic was finally controlled.
2.3. Methodology
2.3.1. Mapping the Micro-Scale Spatiotemporal COVID-19 Risk
Existing theories generally hold that risk decays proportionally with the distance from
the source [
39
], and that the distance decay function it depends on has various forms (such
as subsection, gravity, Gaussian, etc.), among which the Gaussian distance decay function
has been widely proven to have unique advantages in simulating human travel rules; it
is also applicable to the preliminary mapping of epidemic risk [
40
]. In this study, kernel
density estimation (KDE) based on the Gaussian kernel function was used to spatially
smooth the epidemic data [
41
,
42
]. Kernel density estimation produces a smooth and
continuous surface on which each position in the study area is assigned a density value,
regardless of any administrative boundary. Considering the space of the empirical area
and the spatial range of COVID-19 cases, the bandwidth of the kernel function was set to
2.5 km and the average density values in each grid were calculated as the epidemic risk
values (Figure 3).
Figure 3. The overall workflow diagram of this empirical modeling study.
2.3.2. Facility Attractiveness (FA) and Optimized Gravity Model
The gravity model is a mathematical model that is suitable for the study of human
activities; it is an extension of Newton’s law of gravity in the field of social sciences [
43
,
44
].
This model indicates that the force between two places is proportional to their “mass” and
inversely proportional to the square of the distance between them, and that the “mass” in
the model can be replaced by equivalent demographic indicators [
45
]. In this study, the
density of facilities and the number of people in an area were used as modeling indicators.
The formula is expressed as follows:
FAit =k(FDi t)
j
(POPj)d2
ij (1)
Int. J. Environ. Res. Public Health 2021,18, 13294 6 of 16
where FA
it
is the total attractiveness of facilities tin grid i;FD
it
is the number of facilities tin
grid i—that is, the facility density mentioned above; POP
j
is the population (100,000 people)
of grid j;dis the Euclidean distance between grid Iand grid j;kis a constant, which is
usually set to 1 in practical applications.
Previous literature has proven that the law of human travel within cities is more in
accordance with the Gaussian function than with the power function [
46
,
47
]. In addition,
the definition of “life circle” popularized by the modernized urban planning concept has
greatly reduced peopl’s daily travel ranges [
48
,
49
]; this is particularly evident in China
within the epidemiological context due to the strict community control and lockdown
measures imposed [
50
]. Moreover, different age groups have different travel abilities. Thus,
an age-hierarchical Gaussian optimized gravity model was constructed to make it more
suitable for use in empirical studies in the context of COVID-19 [51,52]:
FAit = (FDit)"
j
(POPj(2069))Wij(2069)+
j
(POPj(70+))Wij(70+)#(2)
Wij(2069)=
e1
2tij
3600 2
e1
2
1e1
2
,tij <3600
0, tij 3600
;Wij(70+) =
e1
2sij
1200 2
e1
2
1e1
2
,sij <1200
0, sij 1200
(3)
The steps used for the construction of the age-hierarchical Gaussian optimized gravity
model were as follows (Figure 3):
(1)
We carried out a topology inspection and correction on OSM road network data and
calculated the mileage s
ij
between place iand place j. On this basis, the hierarchy of
the OSM road network was considered as well as the actual situation of Qingdao,
the average travel speed of roads was set for all levels, and the travel time t
ij
was
calculated. These two data points were used to replace the role of d
ij
in the traditional
gravity model in this study (Table 1).
Table 1. Hierarchical road speed setting.
Trip Mode Road Classification Speed
(km/h)
Walk Footway, Living Street, Path, Pedestrian, Residential, Service, Steps 5
Drive Tertiary/Unclassified/Secondary/Primary/Trunk 10/20/30/40/50
(2)
During the pandemic in the first half of 2020, almost all public transportation was
suspended in most parts of China, which made self-driving travel the only realistic
and convenient way to travel long distances. Since Chinese laws prohibit citizens
under the age of 18 and over the age of 70 from obtaining a motor vehicle driving
license, the possible travel modes used by people of different age groups would
have been quite different in the pandemic era. In order to take into account the
heterogeneity of the mobility and travel range of the age-hierarchical population, the
total population was divided into three groups according to their ages: 0–19 years old
(adolescent group), 20–69 years old (adult group), and 70+ years old (elderly group).
According to the previous references and the research group’s visit to Qingdao
[5255]
:
a.
Due to campus closures and the strict community control measures imple-
mented during the first wave of the pandemic, most students (under 20 years
old) received their education online and lacked sufficient time or motivation
to travel [
53
]. Therefore, this group, which also had extremely limited travel
possibilities, was not included in the model used in this study.
b.
As most elderly people over 70 years old do not live with their children in
China, this group are likely to have a high travel frequency in order to carry
Int. J. Environ. Res. Public Health 2021,18, 13294 7 of 16
out necessary daily [
56
]. However, due to the limitations of transportation
modes and mobility levels, the range of activities of elderly people is generally
limited to less than 1200 m [
57
,
58
]. Therefore, in this model we set 1200 m as the
travel threshold for the 70+ age group in order to calculate the attractiveness of
various facilities to the elderly more reasonably.
c. Qingdao has become one of the cities in China with the longest average travel
times due to the separation of occupation areas and residential spaces [
59
]. The
20–69 age group is the most active group and has the largest travel range. Given
the diversity of their travel modes, setting our search threshold according to
mileage will lead to great deviations. Therefore, in this study referred to survey
results and adopted a travel time of 1 h (3600 s) as the travel threshold for the
20–69 age group.
d.
Possible travel routes exceeding the travel threshold of the two age groups
mentioned above were not considered in this model.
(3)
We replaced the power function distance decay function in (1) with the Gaussian
distance decay function corresponding to the travel threshold of each age group.
(4)
We then summed the calculation results of the multi-age models.
FAs are integrated variables based on the optimized gravity model. The number of
independent variables does not increase when they are used in various models compared
with traditional FD predictors, meaning that FA predictors have a wide application range
and prospect, which enables them to be tested together with the same highly flexible FDs
in different models to compare their advantages and disadvantages. This study will also
make use of this advantage of FA predictors to carry out further empirical evaluations of
their application efficiency.
2.3.3. Geographically and Temporally Weighted Regression (GTWR)
In order to solve the problem of spatial heterogeneity and autocorrelation when
modeling spatial information, the local estimation method represented by geographically
weighted regression (GWR) is widely used in empirical research in various fields. The
advantage of this method is that it can provide local estimators for each predictor [
60
].
On this basis, geographically and temporally weighted regression (GTWR), which takes
spatiotemporal heterogeneity into account, was proposed [
61
63
]. Compared with the
traditional GWR, which can only model in phases when processing data with both spatial
and temporal dimensions, this often leads to biased and unsmooth results in time series,
while the GTWR framework integrating the temporal autocorrelation of data is more
advantageous in spatiotemporal epidemiological modeling [
64
,
65
]. It can be expressed as:
Yi=β0(ui,vi,ti)+
k
βk(ui,vi,ti)Xik +εi(4)
where (u
i
,v
i
,t
i
) is the spatiotemporal coordinate for observation iand
β
(u
i
,v
i
,t
i
) is the
coefficient of the kth independent variable X
ik
for observation i. GTWR integrates spatial
coordinates with temporal coordinates by the following formula, so as to “upgrade” the
traditional spatial distance to the spatiotemporal distance to build the weight matrix.
Therefore, GTWR is suitable for solving both the spatial and temporal nonstationarity
of data:
dST
ij 2=˘huiuj2+vivj2i+µtitj2(5)
where
dST
ij
is the spatiotemporal distance between observation iand j;t
i
and t
j
are the time
coordinates of stages iand jwhich defined following the epi curve.
λ
and
µ
are the weights
for harmonizing the differing units between space and time. The bandwidth of GTWR is
selected by the Akaike Information Criterion correction (AICc), which converges to the
Akaike Information Criterion (AIC) when the sample size is large enough [66].
Int. J. Environ. Res. Public Health 2021,18, 13294 8 of 16
The model is based on the hypothesis of normal distribution, all independent variables
contained in GTWR should pass the statistical significance test under the OLS model frame-
work and there is no serious multicollinearity problem (Table 2). The modeling process
is based on the add-in program “Geographically and Temporarily Weighted Regression
(GTWR)” for the ArcMap 10.7 software, and the Origin 2021 and ArcGIS Pro software are
used to chart and visualize the results in 3D.
Table 2. Statistical description of the variables of FD and FA models.
Variables Characteristic Mean Std. Dev. Min Max VIF
Dependent variable Covid risk Dynamic 0.06 0.13 0 1.37 -
Independent variables
Facility density (FD)
Catering Static 0.75 2.43 0 36 1.92
Residences Static 2.24 3.33 0 26 1.62
Shopping Static 1.55 5.05 0 80 1.97
Public services Static 0.88 1.76 0 13 2.03
Health-care Static 0.86 1.95 0 22 1.38
Facility attractiveness (FA)
Catering Static 0.62 2.05 0 30.77 1.94
Residences Static 1.83 2.81 0 22.18 1.67
Shopping Static 1.29 4.30 0 68.38 1.98
Public services Static 0.72 1.47 0 11.25 2.07
Health-care Static 0.71 1.62 0 18.78 1.39
3. Model Results
Table 3shows the diagnostic information of model estimation under the OLS, TWR,
GWR, and GTWR frameworks with the same independent variables to prove the effect of
spatiotemporal information on the improvement in model performance. All independent
variables passed the significance level test of the OLS model. Among the four models
considered in this paper, the GTWR model outperformed all other models in terms of all
diagnostic coefficients. Based on the outstanding model efficiency of the models under the
GTWR framework and the spatiotemporal three-dimensional attribute of the empirical
data, in this study we use the GTWR framework for micro-scale spatiotemporal COVID-19
risk modeling.
Table 3. Global diagnostic information for the estimation with FDs and FAs under various regression frameworks.
Diagnostic Information Facility Density (FD) Facility Attractiveness (FA)
OLS TWR GWR GTWR OLS TWR GWR GTWR
Adjusted R20.0827 0.1594 0.4036 0.5159 0.0804 0.1555 0.4078 0.5694
Residual sum of squares 124.84 114.35 81.13 65.86 125.15 114.88 80.56 58.57
AICc 11,553 12,258 15,080 16,813 11,531 12,219 15,144 17,690
Taking the FD and FA values of five types of gathering places as independent variables,
the risk value of COVID-19 in the Qingdao metropolitan area was modeled spatiotempo-
rally and spatiotemporally varying coefficients with local significance levels higher than
95% were visualized (Figure 4). Generally speaking, except for catering places and shop-
ping places, most of the significant GTWR coefficients of FD in various gathering places
were negative, demonstrating a negative impact on COVID-19 transmission risk. Resi-
dential areas and public service facilities had consistent negative effects in Shibei District,
Shinan District, and Licang District from the initial stage of the epidemic, and this effect
showed a decreasing trend from the city center to the central fringe. Variables that also
showed a strong negative influence in the central city were catering places and health-care
facilities. Although the influence of these two variables was not significant in the initial
Int. J. Environ. Res. Public Health 2021,18, 13294 9 of 16
stage, with the transmission of the epidemic they began to have significant effects in the
central city. However, shopping places were shown to increase the risk of transmission in
the city center during the research period, and this risk increased continuously. Similarly,
catering places at the junction of Shibei District and Licang District and public service
facilities and health-care facilities in Laoshan District also had remarkable positive effects.
Figure 4. Estimation results for the GTWR coefficients of FD (a) and FA (b) in various gathering places.
The result gained using the spatiotemporally varying coefficient of FAs was quite
different from the result gained from modeling using FDs as the core variable (Table 4).
Almost all the five types of gathering places played a positive role in the spread of the
epidemic on a large scale (Figure 4). This promotion was most obvious for the variables
of health-care facilities and which most areas were affected, and the intensity of this
impact was strengthened with the escalation of risks. The spatiotemporally nonstationary
characteristics of catering places and residential areas were similar, showing that Shinan
District was the core area exhibiting a restraining effect and Licang District was the core
area exhibiting a promoting effect. However, the area affected for catering places was wider
than that affected for residential areas and also showed strong restraining characteristics
in certain areas of Laoshan District, while the promotion effect of residential areas was
more remarkable in Licang District. The coefficients of shopping places and public service
facilities showed opposite spatiotemporal characteristics. These coefficients played a
significant role in the central city and the edge of the city center, respectively, and the
intensity of this role usually reached its peak in the final stage of the epidemic, similar to
the modeling results of FDs to a certain extent.
Int. J. Environ. Res. Public Health 2021,18, 13294 10 of 16
Table 4. Estimation summaries for the GTWR coefficients of the FD and FA models.
Variables Facility Density (FD) Facility Attractiveness (FA)
Min LQ Med UQ Max Min LQ Med UQ Max
Catering 10.57 0.72 0.7 1.32 10.22 18.33 4.62 0.4 2.79 38.7
Residence 63.57 6.82 2.61 0.07 4.73 7.63 0.87 3.39 7.22 25.9
Shopping 3.21 0.48 0.11 0.76 9.72 4.77 0.02 1.71 3.79 11.15
Public service 16.6 3.9 1.44 0.13 3.37 11.23 2.33 1.1 5.86 29.24
Health-care 19.71 2.4 0.57 1.11 6.21 1.41 2.49 5.2 9.61 40.35
Constant 0 0.02 0.04 0.09 0.53 0 0.01 0.03 0.07 0.42
Note: All coefficients illustrated in Table 4except for the intercept term need to be multiplied by 103.
4. Discussion
4.1. Comparison of Model Performance between FD and FA
Although, theoretically, facility attractiveness, which integrates the heterogeneity of
the regional population distribution, age structure, and facility distribution, is obviously
a more suitable indicator for COVID-19 risk modeling than facility density, the theoret-
ical advantages may not be well quantified in empirical studies, especially in different
spatiotemporal contexts [
16
,
22
,
23
]. Therefore, we illustrate and discuss the prediction
efficiency of FD and FA models at the global and local scales (different risk levels and
different administrative regions), respectively, below:
4.1.1. Global Explanatory Ability of the Models
According to the model diagnostic information for the whole spatiotemporal model,
FA is a better predictor for COVID-19 risk modeling than FD (Table 3). An FA model can
explain 56.9% of the spatiotemporal risk changes in the empirical area, which is 5.35%
more than that explained by the traditional model, and the improvement rate exceeds 10%.
At the same time, the AICc value of the model based on the FA predictor is also greatly
reduced, which proves that the FA model is more reasonable.
The cross-sectional explanatory ability of the FA model for spatial COVID-19 risk is
also stronger than that of the FD model (Table 5). The predictive ability of the FD and FA
models showed an upward trend in almost all the five stages, and the adjusted R
2
of the
FA model reached more than 0.6 at its highest point.
Table 5. Phased modeling results of the adjusted R2with FDs and FAs.
Epidemic Stage Adjusted R2 Improvement Rate
Facility Density (FD) Facility Attractiveness (FA)
Stage 1 0.3847 0.4808 24.99%
Stage 2 0.4190 0.4747 13.28%
Stage 3 0.5039 0.5629 11.72%
Stage 4 0.4950 0.5669 14.51%
Stage 5 0.5532 0.6179 11.70%
In addition, in all the five stages, the R
2
improvement rate of the FA model compared
with the FD model was over 10% and was the most obvious (25.0%) in the initial stage
of the epidemic, which proved that the effect of “upgrading” FDs to FAs to increase the
modeling effectiveness at the initial stage of the epidemic with a limited sample size was
remarkable. The reason for this good model performance and the improvement may be
that, in the latent and initial stage of the epidemic, due to the extremely limited number of
infected persons, it was difficult for these few and scattered people to transmit the virus
through short-term contact with crowds in open spaces. This means that areas with a
higher facility attractiveness—that is, areas where people are more likely to gather—were
crucial places in the spread of the epidemic [
29
,
30
]. At this time, due to the lack of virus
hosts in the sparsely populated urban fringe areas, the facilities in these areas played a less
Int. J. Environ. Res. Public Health 2021,18, 13294 11 of 16
pivotal role in the transmission of the epidemic [
67
,
68
], meaning that the FA predictor had
the most obvious optimization effect compared with FD in the initial stage of the epidemic.
However, with the spread of virus, there is no such huge shortage of infected people in
sparsely populated areas [
69
]. The magnitude of the role played by facilities in different
areas in epidemic transmission tended to be similar, and the optimization effect of the FA
predictor declined. However, due to the exponential development of the epidemic [
70
],
FA predictors, which have a larger differentiation in space because of the overlap of the
facilities’ density and their effect on causing people to gather, still have advantages over
traditional facility densities.
4.1.2. Local Explanatory Ability of Grids with Different Risk Levels
It is necessary to test the prediction ability of the model in grids with different risk
levels. The model should be able to accurately identify high-risk grids and avoid overes-
timating the risk in low-risk areas to the greatest extent in order to prevent unnecessary
problems caused by epidemic prevention and control and ensure people’s freedom of travel
and the urban vitality in low-risk areas as much as possible. According to the risk value
obtained in our Gaussian kernel density analysis, grids were divided into four grades:
Level 1 contained grids with a risk value in the top first third among grids with a value
greater than 0, Level 2 contained grids with a risk value in the top two thirds among
grids with a value greater than 0, Level 3 contained grids with a risk value greater than
0, and Level 4 contained all grids in the empirical area (Figure 5). From the perspective
of their ability to predict the risk, both FDs and FAs were relatively stable in low-risk and
medium-low-risk areas, being stable between 30% and 60%. Compared with low-risk areas,
the stability of the prediction ability of the model in medium-high and high-risk areas,
especially in high-risk areas, declined. The temporal stability of the prediction effectiveness
was not as good as the performance for low-risk areas, and the risk prediction ability in
the initial stage of the epidemic was also limited. From the perspective of improving the
efficiency of the model by changing the predictor, the explanatory ability of FA compared
with that of the FD model was improved by 10–30% in most conditions, proving that
the improvement effect was remarkable. It is worth noting that the prediction ability of
the FA model was greatly improved (107.2%) in high-risk areas compared with the FD
model in the initial stage of the epidemic, which also shows the significance of population
density and age-hierarchical travel capacity in model optimization at the initial stage of
the epidemic.
4.1.3. Local Explanatory Ability of Grids in Different Administrative Regions
The optimized model should have the ability to predict the risk of COVID-19 in densely
populated areas and economic agglomeration areas in order to maximize socio-economic
cost savings. Therefore, the explanatory ability of the model in different administrative
regions was compared (Figure 5). The FA model had the strongest ability to predict the
risk in Laoshan District, with its ability generally being between 55% and 75%. However,
the optimized effect was the least obvious there, and most of the time the model perfor-
mance was still slightly inferior to that of the traditional model. The fitting efficiencies for
Shibei District and Licang District were similar, with both consistently being around 50%,
demonstrating a good prediction ability. In the second stage of the epidemic, the FD model
was shown to be slightly better than the FA model, but this phenomenon was gradually
reversed with the spread of the epidemic. Shinan District is in the old city of Qingdao and
also its center; it is a densely populated area and a place where many elderly people live.
Although the fitting efficiency of the GIS-based spatiotemporal model in this area is often
inferior to that in the other three districts, the effectiveness of the model greatly improved
over time from about 40% to about 70%. In addition, the FA model showed the highest
level of improvement in this area, which emphasizes the advantages of the optimized
method adopted by this research in modeling densely populated areas and aging areas,
which are some of the areas most susceptible to epidemics.
Int. J. Environ. Res. Public Health 2021,18, 13294 12 of 16
Figure 5.
Comparison of the effectiveness of the model prediction between FDs and FAs in two types of application scenarios:
(
a
) temporal trend of the effectiveness of the differentiation of the two models (the colored surface is the optimized model,
while the gray surface and side filling represent the traditional model); (b) improvement rate.
4.2. Limitation and Prospection
The estimation and comparison results obtained for the models’ performances (global
and local fitting effectiveness), as shown above, prove the usefulness of the novel predictor,
facility attractiveness (FA), in the spatiotemporal risk modeling of COVID-19 and its
superiority over the traditional predictor of “facility density”. This advantage is mainly
reflected in the initial stage of the epidemic, as the initial prediction of epidemic risk is
often regarded to be weak in previous studies based on the “densities” model [
71
,
72
].
Theoretically, the outbreak of a regional epidemic is often started by the entry of a virus
carrier in the beginning, and the impact of the effect of gathering on the micro-scale
COVID-19 spatiotemporal risk is not obvious at this stage. Models based on facility
attractiveness weighted by population density can make up for this deficiency of traditional
models
[7274]
. At the same time, because the limited travel capacity of the elderly means
that facilities beyond their travel range cannot be made more “attractive”, the lower FAs
value seen in these areas will also separate them from areas where there are more adults
who have a stronger travel ability and who are more likely to come into contact with viruses
from outside the area, thus reducing the cost of an indiscriminate lockdown [
55
,
58
]. In
addition, the FA model also has good prediction efficiency for high-risk areas and potential
high-risk areas (densely populated areas), which makes it possible to target high-risk areas
quickly and accurately at the initial stage of the epidemic. This is beneficial to hierarchical
prevention and control strategies for regional epidemics.
However, this model is just the beginning. Limited by the length of this study and the
limitation of our empirical area, when constructing the FA predictor, we selectively ignored
some other factors that may affect the attractiveness of facilities, such as the complex
relationship between the weak travel ability of the elderly and their high possibility of
Int. J. Environ. Res. Public Health 2021,18, 13294 13 of 16
infection [
33
,
55
]; the sanitation and disinfection of public facilities [
75
]; the effect of social
distancing and the impact of epidemic prevention measures taken at the individual level,
such as wearing masks [
76
,
77
]; the different behavior characteristics of human beings
from different social classes and with different living conditions within cities [
78
]; racial
differences, which, although almost nonexistent in this empirical area, are very important
in Europe and US [
46
]; factors such as temperature, precipitation and humidity, which some
previous studies claim should be considered more and which greatly affect human travel
habits within cities [
79
81
]. The construction of FA predictors is not only a formula, but
also a reference for thinking. Researchers should combine the characteristics of the study
area and modify this model to adapt it to specific local situations. In addition, due to the
lack of micro-scale data for the early stage of the epidemic in China’s most epidemic-prone
areas, the discussion of this study mainly focuses on the initial stage of the epidemic; the
sensitivity and universality of this model need to be supported by subsequent multi-scale
and multi-perspective empirical studies.
Different from traditional models, which require behavior data at the individual
level and complicated calculation processes [
73
,
74
], the availability of data and strong
predictability of multivariate geographic data in COVID-19 risk modeling highlight the
need for further spatiotemporal epidemiology research to be carried out, especially research
on spatiotemporal risk modeling based on GIS and using geographic methods and spatial
analysis techniques [
10
,
12
]. More attention should be paid to participation forms and the
improvement of the prediction ability of geographic and spatiotemporal analysis methods
in applications. This is not only the case for the COVID-19 pandemic but also for the
optimization of the regionalized emergency response ability next time human beings have
to face a global health crisis.
5. Conclusions
This study highlights the importance of considering the heterogeneity of population
mobility in order to gain a better understanding of the driving factors spatiotemporally
influencing epidemic diffusion in micro-scale areas. Considering this aspect in models will
allow us to capture the impact of gathering in various places (the exposure and probability
of people gathering) on epidemic transmission more accurately. Therefore, a predictor
calculated based on the optimized gravity model, facility attractiveness (FA), is proposed.
Geographically and temporally weighted regression is used to measure the effectiveness
of this predictor and the spatiotemporal nonstationarity of the influence for the various
facilities on epidemic diffusion. The modeling results show that the predictor is superior
to the traditional “densities” indicator, especially in areas considered to be high-risk and
densely populated during the initial stage of the epidemic. Considering that the novel
predictor can only be used with easily accessible data and a relatively simple operation
process, it can provide an optimal means for researchers in relevant fields to predict the
micro-scale risk of a pandemic.
Author Contributions:
Conceptualization, S.Z.; data curation, S.Z., M.W. and Z.Y.; methodology,
S.Z.; formal analysis, S.Z.; writing—original draft preparation, S.Z.; writing—review and editing, S.Z.
and B.Z.; visualization, S.Z.; supervision, B.Z.; funding acquisition, B.Z. All authors have read and
agreed to the published version of the manuscript.
Funding:
This research was funded by the National Social Science Foundation of China (No. 18BJY086).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
Publicly available datasets were analyzed in this study. The COVID-19
case data can be found in the Qingdao Municipal Health Commission: [wsjkw.qingdao.gov.cn]; The
POI data can be found in Amap: [www.amap.com]; The raster data for people of various ages can be
found in WorldPop: [www.worldpop.org]; The road network data can be found in OpenStreetMap:
[www.openstreetmap.org].
Int. J. Environ. Res. Public Health 2021,18, 13294 14 of 16
Acknowledgments:
The authors wish to express their great appreciation for Shengli Zhu and Shixuan
Lyu (Shandong Normal University) for their suggestions concerning the original research idea. We
would also like to gratefully acknowledge the anonymous reviewers who helped to improve this
paper through their thorough review.
Conflicts of Interest: The authors declare no conflict of interest.
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... Among the 17 articles concerned with risk assessment in specific areas, five articles focused on a single province in China, such as Qingdao 17 and Hubei. 18 India followed China as the second most popular specific area to have been studied, but the frequency was just two. ...
... Personal risk was calculated by an equation consisting of COVID-19 transmission risk, public health risk, and socioeconomic risk. Regional risk focused on food productivity and supply chain network in a region [25] A framework was generated to dynamically assess the infection risk on board ships, based on a data-driven approach [28,41] Optimized gravity models and spatiotemporal risk modelling Geographically and temporally weighted regressions (GTWR) were used to build the models, and kernel density estimations (KDE) based on the Gaussian kernel function were used to spatially smooth the epidemic data [17] Multidimensional item response theory, confirmatory factor analyses, and structural equation modelling These techniques were used to construct and assess the quality of the proposed pandemic-risk-perception scale [33] (Continued) ST (seeding time) and DT (doubling time) Model A 2D plane was divided into four quadrants by using the mean ST and mean DT, with ST on the x-axis and DT on the y-axis to construct the model Sensitivity analyses were conducted to verify and validate the model [43] Conceivable mathematical model -Accelerated Phase ...
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Background The COVID-19 pandemic presents the possibility of future large-scale infectious disease outbreaks. In response, we conducted a systematic review of COVID-19 pandemic risk assessment to provide insights into countries’ pandemic surveillance and preparedness for potential pandemic events in the post-COVID-19 era. Objective We aim to systematically identify relevant articles and synthesize pandemic risk assessment findings to facilitate government officials and public health experts in crisis planning. Methods This study followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and included over 620,000 records from the World Health Organization COVID-19 Research Database. Articles related to pandemic risk assessment were identified based on a set of inclusion and exclusion criteria. Relevant articles were characterized based on study location, variable types, data-visualization techniques, research objectives, and methodologies. Findings were presented using tables and charts. Results Sixty-two articles satisfying both the inclusion and exclusion criteria were identified. Among the articles, 32.3% focused on local areas, while another 32.3% had a global coverage. Epidemic data were the most commonly used variables (74.2% of articles), with over half of them (51.6%) employing two or more variable types. The research objectives covered various aspects of the COVID-19 pandemic, with risk exposure assessment and identification of risk factors being the most common theme (35.5%). No dominant research methodology for risk assessment emerged from these articles. Conclusion Our synthesized findings support proactive planning and development of prevention and control measures in anticipation of future public health threats.
... neural networks, extreme gradient boosting and random forest regression models [77][78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93]). The feature importance analysis highlighted three interesting results: the geospatial autocorrelation between postal codes was informative for the model to predict the number of COVID-19 cases; demographic characteristics such as number of households in each postal code were not; that the feature importance differed per age group. ...
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Source and contact tracing (SCT) is a core public health measure that is used to contain the spread of infectious diseases. It aims to identify a source of infection, and to advise those who have been exposed to this source. Due to the rapid increases in incidence of COVID-19 in the Netherlands, the capacity to conduct a full SCT quickly became insufficient. Therefore, the public health services (PHS) might benefit from a restricted strategy targeted to geographical regions where (predicted) case-to-case transmission is high. In this study, we set out to develop a prediction model for the number of COVID-19 cases per postal code within the Netherlands using geographic and demographic features. The study population consists of individuals residing in one of the participating nine Dutch PHS regions who tested positive for SARS-CoV-2 between 1 June 2020 and 27 February 2021. Using a machine learning random forest regression model, we predicted the top 100 postal codes with the highest number of cases with an accuracy of 49% for the current week, 42% for next week, and 44% for two weeks from present. In addition, the age groups of 20-39 and 40-64 years had a higher prediction accuracy than groups outside these age ranges. The developed model provides a starting point for targeted preventive SCT efforts that incorporate geospatial and demographic characteristics of a neighbourhood. It should nonetheless be noted that during the early stages of the outbreak, the number of available datapoints needed to inform such models are likely insufficient. Given the accuracy and data requirements of the developed model, it is unlikely that this class of models can play a pivotal role in informing policy during the early phases of a future epidemic.
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The frequent occurrence of local COVID-19 today gives a strong necessity to better understand the effects of "source-case" distance and gathering places, which are often considered to be the key factors of the localized spatial clustering of an epidemic. In this study, the localized spatial clustering of COVID-19 cases, which originated in the Xinfadi market in Beijing from June to July 2020, was investigated by exploring the spatiotemporal characteristics of the clustering using descriptive statistics, point pattern analysis, and spatial autocorrelation calculation approaches. Spatial lag zero-inflated negative binomial regression model (SL-ZINB) and geographically weighted Poisson regression with spatial effects (GWPR-SL) were also introduced to explore the factors which influenced the clustering of COVID-19 cases at the micro spatial scale. It was found that the local epidemic can be significantly divided into two stages which are asymmetric in time. A significant spatial spillover effect of COVID-19 was identified in both global and local modeling estimation. The dominant role of the "source-case" distance effect, which was reflected in both global and local scales, was revealed. Relatively, the role of gathering places is not significant at the initial stage of the epidemic, but the upward trend of the significance of some places is obvious. The trend from "distance-driven" to "density-driven" of the localized spatial clustering of COVID-19 was predicted. The effectiveness of blocking the transformation trend will be a key issue for the global response to the local COVID-19.
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Human mobility impacts many aspects of a city, from its spatial structure1,2,3 to its response to an epidemic4,5,6,7. It is also ultimately key to social interactions⁸, innovation9,10 and productivity¹¹. However, our quantitative understanding of the aggregate movements of individuals remains incomplete. Existing models—such as the gravity law12,13 or the radiation model¹⁴—concentrate on the purely spatial dependence of mobility flows and do not capture the varying frequencies of recurrent visits to the same locations. Here we reveal a simple and robust scaling law that captures the temporal and spatial spectrum of population movement on the basis of large-scale mobility data from diverse cities around the globe. According to this law, the number of visitors to any location decreases as the inverse square of the product of their visiting frequency and travel distance. We further show that the spatio-temporal flows to different locations give rise to prominent spatial clusters with an area distribution that follows Zipf’s law¹⁵. Finally, we build an individual mobility model based on exploration and preferential return to provide a mechanistic explanation for the discovered scaling law and the emerging spatial structure. Our findings corroborate long-standing conjectures in human geography (such as central place theory¹⁶ and Weber’s theory of emergent optimality¹⁰) and allow for predictions of recurrent flows, providing a basis for applications in urban planning, traffic engineering and the mitigation of epidemic diseases.
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Background The urban built environment (BE) has been globally acknowledged as one of the main factors that affects the spread of infectious disease. However, the effect of the street network on coronavirus disease 2019 (COVID-19) incidence has been insufficiently studied. Severe acute respiratory syndrome coronavirus 2, which causes COVID-19, is far more transmissible than previous respiratory viruses, such as severe acute respiratory syndrome coronavirus, which highlights the role of the spatial configuration of street network in COVID-19 spread, as it is where humans have contact with each other, especially in high-density areas. To fill this research gap, this study utilized space syntax theory and investigated the effect of the urban BE on the spatial diffusion of COVID-19 cases in Hong Kong. Method This study collected a comprehensive dataset including a total of 3815 confirmed cases and corresponding locations from January 18 to October 5, 2020. Based on the space syntax theory, six space syntax measures were selected as quantitative indicators for the urban BE. A linear regression model and Geographically Weighted Regression model were then applied to explore the underlying relationships between COVID-19 cases and the urban BE. In addition, we have further improved the performance of GWR model considering the spatial heterogeneity and scale effects by adopting an adaptive bandwidth. Result Our results indicated a strong correlation between the geographical distribution of COVID-19 cases and the urban BE. Areas with higher integration (a measure of the cognitive complexity required for a pedestrians to reach a street) and betweenness centrality values (a measure of spatial network accessibility) tend to have more confirmed cases. Further, the Geographically Weighted Regression model with adaptive bandwidth achieved the best performance in predicting the spread of COVID-19 cases. Conclusion In this study, we revealed a strong positive relationship between the spatial configuration of street network and the spread of COVID-19 cases. The topology, network accessibility, and centrality of an urban area were proven to be effective for use in predicting the spread of COVID-19. The findings of this study also shed light on the underlying mechanism of the spread of COVID-19, which shows significant spatial variation and scale effects. This study contributed to current literature investigating the spread of COVID-19 cases in a local scale from the space syntax perspective, which may be beneficial for epidemic and pandemic prevention.
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The ongoing Coronavirus Disease 2019 (COVID‐19) has posed a serious threat to human public health and global economy. Population mobility is an important factor that drives the spread of COVID‐19. This study aimed to quantitatively evaluate the impact of population flow on the spread of COVID‐19 from a spatiotemporal perspective. To this end, a case study was carried out in Hubei Province, which was once the most affected area of COVID‐19 outbreak in Mainland China. The geographically and temporally weighted regression (GTWR) model was applied to model the spatiotemporal association between COVID‐19 epidemic and population mobility. Two patterns of population flows, including the population inflow from Wuhan and intra‐city population movement, were considered to construct explanatory variables. Results indicate that the GTWR model can reveal the spatial–temporal‐varying relationships between COVID‐19 and population mobility. Moreover, the association between COVID‐19 case counts and population movements presented three stages of temporal variation characteristics due to the virus incubation period and implementation of strict lockdown measures. In the spatial dimension, evident geographical disparities were observed across Hubei Province. These findings can provide policymakers useful knowledge about the impact of population movement on the spatio‐temporal transmission of COVID‐19. Thus, targeted interventions, if necessary in certain time periods, can be implemented to restrict population flow in cities with high transmission risk.
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The coronavirus disease (COVID‐19) has spread rapidly to multiple countries including Indonesia. Mapping its spatiotemporal pattern and forecasting (small area) outbreaks are crucial for containment and mitigation strategies. Hence, we introduce a parsimonious space–time model of new infections that yields accurate forecasts but only requires information regarding the number of incidences and population size per geographical unit and time period. Model parsimony is important because of limited knowledge regarding the causes of COVID‐19 and the need for rapid action to control outbreaks. We outline the basics of Bayesian estimation, forecasting, and mapping, in particular for the identification of hotspots. The methodology is applied to county level data of West Java Province, Indonesia. This article is protected by copyright. All rights reserved.
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Since its emergence in late 2019, the COVID-19 pandemic has attracted the attention of researchers in various fields, including urban planning and design. However, the spreading patterns of the disease in cities are still not clear. Historically, preventing and controlling pandemics in cities has always been challenging due to various factors such as higher population density, higher mobility of people, and higher contact frequency. To shed more light on the spread patterns of the pandemic, in this study we analyze 43,000 confirmed COVID-19 cases at the neighborhood level in Tehran, the capital of Iran. To examine spatio-temporal patterns and place-based factors contributing to the spread of the pandemic, we used exploratory spatial data analysis and spatial regression. We developed a geo-referenced database composed of 12 quantitative place-based variables related to physical attributes, land use and public transportation facilities, and demographic status. We also used the geographically weighted regression model for the local examination of spatial non-stationarity. According to the results, population density (R2 = 0.88) and distribution of neighborhood centers (R2 = 0.59), drugstores (R2 = 0.64), and chain stores (R2 = 0.59) are the main factors contributing to the spread of the disease. Additionally, density of public transportation facilities showed a varying degree of contribution. Overall, our findings suggest that demographic composition and major neighborhood-level physical attributes are important factors explaining high rates of infection and mortality. Results contribute to gaining a better understanding of the role of place-based attributes that may contribute to the spread of the pandemic and can inform actions aimed at achieving Sustainable Development Goals, particularly Goals 3 and 11.
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The outbreak of the unprecedented Coronavirus Disease 2019 (COVID-19) pandemic calls for innovative risk assessment and mapping approaches to prompt public messaging. Most of the existing approaches aim to present population risks associated with geographic areas (e.g., county), thus providing limited values to guide individuals to take proactive measures against COVID-19. To better facilitate the general public to make informed decisions on daily activity plans, we propose an activity-based spatiotemporal risk mapping approach to capture and represent exposure risk at a personal level. This approach leverages the classical space-time representations to capture personal activity space and measures exposure risk in such activity space. This approach further implements geovisualization designs to communicate measured exposure information. To illustrate the usability of the approach, we have conducted a case study in Denver, Colorado with COVID-19 data from October 2020 and four representative travel profiles.