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Sustainable Cities and Society 68 (2021) 102784
Available online 19 February 2021
2210-6707/© 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Exploring spatiotemporal effects of the driving factors on COVID-19
incidences in the contiguous United States
Arabinda Maiti
a
, Qi Zhang
b
,
c
, Srikanta Sannigrahi
d
,
*, Suvamoy Pramanik
e
,
Suman Chakraborti
e
, Artemi Cerda
f
, Francesco Pilla
d
a
Geography and Environment Management, Vidyasagar University, West Bengal, India
b
Department of Earth and Environment, Boston University, Boston, MA, 02215, USA
c
Frederick S. Pardee Center for the Study of the Longer-Range Future, Frederick S. Pardee School of Global Studies, Boston University, Boston, MA, 02215, USA
d
School of Architecture, Planning and Environmental Policy, University College Dublin Richview, Clonskeagh, Dublin, D14 E099, Ireland
e
Center for the Study of Regional Development, Jawaharlal Nehru University, New Delhi, Delhi, 110067, India
f
Soil Erosion and Degradation Research Group, Department of Geography, Valencia University, Blasco Ib`
a˜
nez, 28, 46010, Valencia, Spain
ARTICLE INFO
Keywords:
COVID-19
Spatial regression
Temporal change
Confounding factors
Migration
Income
ABSTRACT
Since December 2019, the world has witnessed the stringent effect of an unprecedented global pandemic,
coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-
CoV-2). As of January 29,2021, there have been 100,819,363 conrmed cases and 2,176,159 deaths reported.
Among the countries affected severely by COVID-19, the United States tops the list. Research has been conducted
to discuss the causal associations between explanatory factors and COVID-19 transmission in the contiguous
United States. However, most of these studies focus more on spatial associations of the estimated parameters, yet
exploring the time-varying dimension in spatial econometric modeling appears to be utmost essential. This
research adopts various relevant approaches to explore the potential effects of driving factors on COVID-19
counts in the contiguous United States. A total of three global spatial regression models and two local spatial
regression models, the latter including geographically weighted regression (GWR) and multiscale GWR (MGWR),
are performed at the county scale to take into account the scale effects. For COVID-19 cases, ethnicity, crime, and
income factors are found to be the strongest covariates and explain most of the variance of the modeling esti-
mation. For COVID-19 deaths, migration (domestic and international) and income factors play a critical role in
explaining spatial differences of COVID-19 deaths across counties. Such associations also exhibit temporal var-
iations from March to July, as supported by better performance of MGWR than GWR. Both global and local
associations among the parameters vary highly over space and change across time. Therefore, time dimension
should be paid more attention to in the spatial epidemiological analysis. Among the two local spatial regression
models, MGWR performs more accurately, as it has slightly higher Adj. R
2
values (for cases, R
2
=0.961; for
deaths, R
2
=0.962), compared to GWR’s Adj. R
2
values (for cases, R
2
=0.954; for deaths, R
2
=0.954). To inform
policy-makers at the nation and state levels, understanding the place-based characteristics of the explanatory
forces and related spatial patterns of the driving factors is of paramount importance. Since it is not the rst time
humans are facing public health emergency, the ndings of the present research on COVID-19 therefore can be
used as a reference for policy designing and effective decision making.
1. Introduction
The coronavirus disease 2019 (COVID-19), caused by the severe
acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and rst re-
ported in December 2019 in Wuhan city of China, has soon become a
new public health concern across the world (Ge et al., 2020; Jin et al.,
2020; Rumpler, Venkataraman, & G¨
oransson, 2020; Sun & Zhai, 2020).
The virus poses serious potential threats to the medical protection sys-
tem all over the world (European Centre for Disease Prevention &
Control, 2020; World Health Organization, 2020). As of January 29,
* Corresponding author at: School of Architecture, Planning, and Environmental Policy, University College Dublin, Beleld, Dublin 4, Ireland.
E-mail addresses: arabinda@mail.vidyasagar.ac.in (A. Maiti), qz@bu.edu (Q. Zhang), srikanta.sannigrahi@ucd.ie (S. Sannigrahi), suvamo60_ssf@jnu.ac.in
(S. Pramanik), suman87_ssf@jnu.ac.in (S. Chakraborti), artemio.cerda@uv.es (A. Cerda), francesco.pilla@ucd.ie (F. Pilla).
Contents lists available at ScienceDirect
Sustainable Cities and Society
journal homepage: www.elsevier.com/locate/scs
https://doi.org/10.1016/j.scs.2021.102784
Received 18 November 2020; Received in revised form 13 February 2021; Accepted 15 February 2021
Sustainable Cities and Society 68 (2021) 102784
2
2021, there have been 100,819,363 conrmed cases and 2,176,159
deaths reported (World Health Organization, 2020). Geography that
includes both spatial locations and characteristics of the spatial de-
terminants has played a key role in the early outbreak and transmitting
the virus across the scale (Andersen, Nielsen, Simone, Lewiss, & Jagsi,
2020; Sannigrahi, Pilla, Basu, & Basu, 2020, b). The spatial variability
and clustered concentration of both COVID-19 mortality and morbidity
in many countries have demonstrated a strong spatial dependency of the
confounding factors (Desmet & Wacziarg, 2020; Ren et al., 2020; Zhang
& Schwartz, 2020). Although several timely efforts (e.g., Luo, Yan, &
McClure, 2020) have analyzed spatial heterogeneous patterns and un-
even distributions of COVID-19 casualties, few studies have utilized the
spatial time-varying dimension in spatial econometric modeling for
analyzing geographic disparities in COVID-19 casualties in the United
States (Sun, Matthews, Yang, & Hu, 2020). The present research,
therefore, has made an effort to examine how spatial analysis can help
with identifying the hotspots and vulnerable locations as well as
exploring the spatial dependency of confounding factors that explain the
overall casualties caused by COVID-19.
Spatial regression models can be useful for quantifying the risk of
disease progression in the communities (Desmet & Wacziarg, 2020;
Ehlert, 2020; Xiong, Wang, Chen, & Zhu, 2020). Previous spatial
epidemiological research noted a strong spatial time-varying effect of
the confounding factors on virus outbreaks (Auchincloss, Gebreab, Mair,
& Diez Roux, 2012; Chakraborti et al., 2020; Fitzpatrick, Harris, &
Drawve, 2020; Kirby, Delmelle, & Eberth, 2017; Sannigrahi, Pilla, Basu,
Basu et al., 2020). Of them, a few studies have focused on the spatially
heterogeneous characteristics of the COVID-19 transmission (Bashir
et al., 2020; Conticini, Frediani, & Caro, 2020; Sarwar, Waheed, Sarwar,
& Khan, 2020; Xiong et al., 2020; Yao et al., 2020). The disproportionate
burden of COVID-19 could be due to place-based characteristics that
include cluster concentration and spatial aggregation of infected popu-
lation and the proximity of social interaction (Sannigrahi, Pilla, Basu,
Basu et al., 2020; Sun et al., 2020). Therefore, both characteristics of the
spatial confounding factors and spatial interconnection between the
places should be carefully considered while inspecting the factors that
exacerbate the spread of disease and identifying communities vulner-
able to the infection (Mansour, Al Kindi, Al-Said, Al-Said, & Atkinson,
2021; Zhu et al., 2018). Hence, developing spatial models and under-
standing the confounding effects of the variables is critical to reveal the
spatial variation of virus transmission at any spatial or administrative
scale (Ren et al., 2020; Zhang & Schwartz, 2020).
Previous studies have utilized environmental, socio-economic and
demographic factors to explain spatial variability of the COVID-19 in-
cidents and discover the underlying risk of the outbreaks across multiple
scales (Desmet & Wacziarg, 2020; Karaye & Horney, 2020; Qi et al.,
2020; Ren et al., 2020; Sannigrahi, Pilla, Basu, Basu, & Molter, 2020).
Among the explanatory factors, several have been found strongly linked
to the early transmission of the virus and the overall casualties caused by
COVID-19. These key factors include traveling distance (Fortaleza,
Guimar˜
aes, de Almeida, Pronunciate, & Ferreira, 2020), concentration
of particulate matter (Bola˜
no-Ortiz et al., 2020), ethnic composition
(Oztig & Askin, 2020; Thakar, 2020), income and socio-demographic
factors (Sannigrahi, Pilla, Basu, Basu et al., 2020, 2020b), migration
(Chen et al., 2020; Xiong et al., 2020), and air transport (Christidis &
Christodoulou, 2020). Considering the country-specic analysis, in
Wuhan (China) for instance, population density, the proportion of
construction land, aged population density, tertiary industrial output
per unit land, are found to be strongly associated with the COVID-19
counts and the overall COVID-19 casualties (You, Wu, & Guo, 2020).
In the United States, from the thirty-ve explanatory variables
covering various types of characteristics, four variables (i.e., income
inequality, median household income, the proportion of black females,
and the proportion of nurse practitioners) are found the key determining
factors in COVID-19 casualties (Mollalo, Vahedi, & Rivera, 2020). In
another analysis covering 2,814 United States counties and using
COVID-19 data up to May 1, 2020, researchers found strong positive
correlations between the socioeconomic factors such as proportions of
elderly and COVID-19 incidence and mortality rate (Zhang & Schwartz,
2020). Considering the February 19 and June 14, 2020 COVID-19 data
in Iran, several infrastructure and climate factors (distance from bus
stations and the minimum temperature of the coldest month) were
found strongly associated with COVID-19 incidences and exhibited high
variable importance in the analysis (Pourghasemi et al., 2020). The
cross-country comparison of virus spread and their interaction with
demographic, economic, and environmental parameters are limited.
Among them, Sannigrahi, Pilla, Basu, Basu, Molter et al. (2020) focused
on the European region, and carried out the spatial models to under-
stand the spatially heterogeneous properties among the factors in
different European countries; this study found that income and
socio-demographic variables have the highest impact on COVID-19 fa-
talities in Germany, Austria, Slovenia, etc. A similar association was
found in Germany from another study (Ehlert, 2020). In cross-country
analysis, several confounding factors, such as out-of-pocket expendi-
ture, could signicantly explain the global variation of COVID-19 ca-
sualties in 175 countries. Among these factors, the age composition and
out-of-pocket expenditure were found to be positively related to
COVID-19 counts (Iyanda et al., 2020). In another study with a
world-level analysis, Chakraborti et al. (2020) had identied few key
determinants including air pollution, migration, economy, and de-
mographic factor, which had strong positive correlations with
COVID-19.
Omitting the time variable in spatial models can lead to erroneous
estimates and misleading conclusions. Moreover, assuming the time-
independent and homogenous impact of the confounding factors on
response variables (COVID-19 cases and deaths in the present study)
may introduce ambiguity in parameter approximation and eventually
produce unconvincing results. Therefore, the present research makes an
effort to address the current research gap in spatial COVID-19 studies by
conceptualizing time-dependent spatial regression models using open
source data with information in the contiguous United States. The hy-
pothesis of this study is framed as “the spatial association between the
confounding factors and COVID-19 counts strongly depend on time; thus,
space entity alone cannot fully explain the associations and the spreading of
diseases in the contiguous United States”. The specic objectives of this
study are to explore the overall associations between the explanatory
factors and COVID-19 cases and deaths and examine local association
between the explanatory drivers and COVID-19 incidences. The present
study also develops dynamic spatial regression models for exploring the
time-dependent local spatial association as well as measuring the rela-
tive importance of variables with parsimonious regression models.
2. Materials and methods
2.1. Data collection and pre-processing
This research utilized the most updated aggregated county-level
datasets provided by Johns Hopkins University (Killeen et al., 2020).
These datasets contain 348 relevant variables covering multiple do-
mains, such as demography, education, economy, health care capacity,
crime statistics, public transit, climate, and housing information
(Table S1). Since the main aim of the present study is to establish a
modeling framework to examine the space- and time-dependent asso-
ciations between COVID-19 incidences and potential explanatory fac-
tors, all the relevant variables were pre-processed to connect the
observations to their corresponding county units through the unique
Federal Information Processing Standard (FIPS) code. Each FIPS code
contains ve digits, with the rst two digits referring to state informa-
tion and the last three digits describing county information. The Johns
Hopkins team retrieves information from various governmental and
institutional sources, including the United States Census Bureau, United
States Department of Agriculture (USDA) Economic Research Service,
A. Maiti et al.
Sustainable Cities and Society 68 (2021) 102784
3
the National Oceanic and Atmosphere Administration (NOAA), the As-
sociation of American Medical Colleges (AAMC), Henry J. Kaiser Family
Foundation (KFF), the Center for Neighborhood Technology (CNT), the
Bureau of Justice Statistics, and Department of Justice (DOJ) (Killeen
et al., 2020). The data also retrieved key information on the health care
system at the county scale that indicates how a county’s health care
system performed in handling COVID-19 counts.
The daily COVID-19 counts, including conrmed cases and deaths,
were obtained for the period of January 22 to July 26, 2020 from
USAFacts
1
. The daily counts of COVID-19 cases and deaths were con-
verted to cumulative sum for subsequent analysis and interpretation.
The USAFacts team aggregates the most updated COVID-19 counts from
various sources, including Centers for Disease Control and Prevention
and state-level and local-level public health agencies. However, for most
of the states, the USAFacts team gathers the daily county-level cumu-
lative COVID-19 counts (positive cases and deaths) based on published
tables, web dashboards, or PDF reports available on state public health
websites through scraping or manual entry. The actual numbers
(COVID-19 counts) reported in USAFacts sometimes may not exactly
match with the statistics from the state public health organization re-
ports. This can be due to the frequency in which the USAFacts are col-
lecting and updating data is different from that of local governmental
organizations. Additionally, there are a few states where up-to-date
county-scale data is either not available on the public health website
or data collection is not sufciently frequent. For example, the updated
COVID-19 counts in California and Texas are not available on the state
public health websites. For these states, the USAFacts team extracted the
latest available numbers from the county-specic public health
websites.
Daily air pollution data were collected from the OpenAQ data re-
pository system for extracting ve key air pollutants, including two
kinds of particulate matter (PM
2.5
, PM
10
), Sulfur Dioxide, Nitrogen Di-
oxide, and Carbon Monoxide. Daily concentrations of these atmospheric
pollutants were converted to the monthly average unit for the exami-
nation of their associations with COVID-19 casualties. Currently, the
OpenAQ platform consists of 686 million air quality measurements, 150
data sources, 13,000 locations, and 95 countries in their system, which is
able to collect hourly air pollution concentration estimates from
governmental and sensor sources. An R package, called “ropenaq: Ac-
cesses Air Quality Data from the Open Data Platform OpenAQ”, was utilized
to assess the large volume of data for the entire contiguous United States
from January 22 to July 27 of 2020. The location wise air pollution data
were further converted to raster surface using the “inverse distance
weighting” interpolation method. Finally, the mean air pollution con-
centration of each county was calculated using zonal statistics as a table
function in ArcGIS Pro v2.6.
2.2. Variable selection and dimensionality reduction
Dimensionality reduction and critical information extraction from
datasets are crucial for regression modeling and effective decision
analysis. This research employed a stepwise forward regression
approach as a tool to separate the key variables from sets of unorganized
variables. A total of nine groups (i.e., crime, demography, education,
employment, ethnicity, pollution, health, migration, and climate), which
were assumed to have both synergistic and trade-off associations with
COVID-19 counts, were formed. Subsequently, key variables were
extracted from each group based on Variable Ination Factor (VIF) and
model variability score, the latter of which is characterized by the co-
efcient of determination (R
2
) and adjusted coefcient of determination
(Adj. R
2
). For the category of crime, totally 16 variables were incorpo-
rated into the modeling; for the other categories, a total of 14
(demography), 29 (education), 6 (employment), 72 (ethnicity), 63 (health-
care), 5 (pollution), 7 (migration), and 4 (climate) variables were consid-
ered, respectively (see detail in Table S1). Multiple collinearity tests,
including VIF, R
2
change, correlation coefcient, probability and t-sta-
tistics, were executed to detect the models’ redundant variables. High
collinearity would be evident in the model if the VIF value was greater
than 10; therefore, all the ltered variables considered in the regression
modeling were scrutinized to eliminate the redundancy in model
parametrization. Followed by stepwise forward regression, the enter
stepwise regression method was performed to measure the VIF value of
each explanatory variable to ensure that the multicollinearity was
entirely eliminated. The nal parsimonious models that relied on fewer
parameters and at the same time explained the maximum model vari-
ances with less uncertainty were parameterized for each category
regarding both COVID-19 cases and deaths. These processes of variable
selection and dimensionality reduction part were conducted in SPSS
V26.
2.3. Spatial regression
2.3.1. Global spatial regression
Spatial regression models have been used extensively in the COVID-
19 research across multiple spatial scales (Guliyev, 2020; You et al.,
2020). Among all the available global spatial regression models, we used
Ordinary Least Square (OLS), Spatial Error Model (SEM), and Spatial Lag
Model (SLM) for measuring the global associations between the
explanatory factors and COVID-19 counts at the county scale. The OLS
model can be conceptualized as follows:
yi=β0+βxi+
ε
i(1)
Where y
i
is the COVID-19 case or death counts at county i, β0 is the
model intercept, β is the slope parameter; x
i
is the selected independent
variable(s) at county i;
ε
i is the error term at model estimates. The global
OLS assumes to have spatial stationarity across the scale, and therefore,
also hypothesizes that a model conceptualized for a particular area can
be applied effectively to other areas of interest (Fang, Liu, Li, Sun, &
Miao, 2015). According to Anselin and Arribas-Bel (2013), the global
OLS has fundamental assumptions: the observation in the feature space
does not vary with space and therefore should be independent in nature,
and the residual model errors should not be correlated (Oshan, Smith, &
Fotheringham, 2020).
The Spatial Lag Model (SLM) has an assumption of spatial de-
pendency between the explanatory and response variables in feature
space and conceptualizes the global regression by incorporating spatial
dependence attributes in the modeling process. The SLM also assumes to
have spatially lagged dependent variable in the model estimation, which
can be ensured by the spatial dependence test resulted from OLS. If the
determinant factors, tested by Moran’s I (error), Lagrange Multiplier
(lag) and Robust LM (lag), exhibited statistically signicant estimates at
a dened probability level, then one should reconsider the model se-
lection process and go for SLM as a replacement for OLS. The SLM can be
formulated as:
yi=β0+βxi+
ρ
Wiyi+
ε
i(2)
Where
ρ
is the spatial lag component; W
i
contains spatial weights
(spatial weights matrix in a row format). The spatial weight matrix was
generated using multiple approaches, including the contiguity based
methods (Queen contiguity and Rook contiguity) and the distance based
methods (Euclidean distance, Arc distance and Manhattan distance).
The contiguity-based weight was approximated using the rst order of
contiguity. The county unique identier number was utilized as a base
for weight calculation. Since the accuracy and performance of all the
global regression models strongly rely on spatial weights, we adopted
both contiguity and distance-based weights for comparing the results at
various parameter setups. The reduced version of the SLM can be
1
Link: https://usafacts.org/visualizations/coronavirus-covid-19-spread-map
/
A. Maiti et al.
Sustainable Cities and Society 68 (2021) 102784
4
expressed as:
Y=A−1Xβ +A−1
ε
(3)
Where A=I-
ρ
W; I refers to the conformable identity matrix; A
−1
is the
spatial multiplier effect or Leontief inverse (Anselin, 2002; Lambert,
Brown, & Florax, 2010). This inverted A matrix distinguishes this model
from other spatial regression models as it gets feed-back/-forward ef-
fects of shocks between the dened spatial location and eventually
makes the model sufciently exible to process spatial non-linearity
(Lambert et al., 2010).
The Spatial Error Model (SEM) is an extension of global models that
fundamentally stands on the assumption of spatial dependence in the
residual error of OLS (Chi & Zhu, 2008; Fang et al., 2015; Guliyev, 2020;
Song, Du, Feng, & Guo, 2014; Yang & Jin, 2010). The SEM posits that
spatial autocorrelation among regression residuals is thus evident. Two
standard spatial dependence tests, Lagrange Multiplier (error) and
Robust LM (error), were executed to ensure statistical signicance in
spatial dependency in error terms, specied as follows.
yit =xitβ+
μ
i+
ε
it (4)
ε
it =λW
ε
t+
ν
it (5)
Where λW
ε
t
is the spatial error term; λ denotes the autoregressive factor;
ν
it
refers to the random error term, which is normally conceptualized to
be independent and ideally distributed in feature space;
ε
it
refers to the
spatially uncorrelated error term (Guliyev, 2020). The SEM consists of
two error terms, W
ε
t
and
ε
it
. The spatial dependence test derived from
OLS suggested a statistically signicant spatial dependency among the
observations for SLM and SEM. To provide multiple perspective of
model estimations, this study considered all the three standard global
spatial regression models for modeling and subsequent interpretation.
Meanwhile, the spatial dependence test showed that both LM (lag and
error) and Robust LM (lag and error) exhibited the statistical signi-
cance estimates. Therefore, both SEM and SLM were utilized to assess
the synergies and tradeoffs between COVID-19 counts and associated
factors at the county scale. When estimating the global models, both
dependent and independent variables were converted to cumulative
sum units. Additionally, the global associations between the variables
were assessed for all the seven sub-components for capturing the indi-
vidual effect of each sub-component on COVID-19 counts over the
feature space.
2.3.2. Local regression
In many real-life cases, the general global assumptions and spatial
stationarity among the observations in feature space could be ineffective
and thus produce inelastic and biased estimates at the local scale. Since
the main objective of this research is to establish predictive spatial
models at the local scale, two most used local spatial regression models,
Geographically Weighted Regression (GWR) and Multiscale GWR
(MGWR), were employed for local spatial regression modeling and
result interpretation. The GWR model is developed following Toddler’s
rst law of geography, “everything has some relationship with others,
but near things are more related compared to distant things”. In GWR,
each observation in feature space can vary and hence be associated with
locally varying coefcients of the regression parameters. This addition
of local spatial context in GWR modeling favors exploring the spatial
dependency among the parameters. GWR can be dened as:
yi=βi0+∑
M
j=1
βijXij +
ε
i,i=1,2,…,N(6)
Where y
i
is the dependent variable (COVID-19 case or death counts) in
county i; β
i0
refers to the regression intercept; β
ij
refers to the indepen-
dent regression parameter; X
ij
is the value of the jth regression param-
eter;
ε
i
refers to the regression error.
Although GWR models have been embraced as a solution for global
spatial stationarity in regression estimates, the same has been suffered in
cases when a constant and straightforward bandwidth is not able to
detect the spatial non-stationarity at varying bandwidths across the
feature space. To address this problem, Fotheringham, Yang, and Kang
(2017) and Oshan, Wolf et al. (2019) proposed a multiscale and multi
bandwidth GWR, which allows exploring the local relationships among
the varying factors across spatial scales by computing shifting band-
width based on the distributions of observation. MGWR can be dened
as:
yi=∑
M
j=1
βbwjXij +
ε
i,i=1,2,…,N(7)
Where βbwj refers to the differential bandwidth at feature space. The rest
is the same as discussed in GWR.
2.4. Variable importance
Machine Learning models have been used extensively in measuring
feature importance in multi-parameter models. This research utilized a
supervised machine-learning algorithm, Random Forest, for spotting the
key explanatory factors in the models. Random Forest models (Breiman,
2001), fundamentally based on bootstrap aggregating of decision trees,
can minimize the unexplained variance of models and thus improve
prediction accuracy (Altmann, Tolos¸i, Sander, & Lengauer, 2010).
Random Forest models have been utilized for many domain-specic
studies, such as gene expression-based cancer classication (Okun &
Priisalu, 2007), biology of ageing (Fabris, Doherty, Palmer, De Mag-
alhaes, & Freitas, 2018), remote sensing land cover mapping (Ma et al.,
2017; Zhang, Yang, Wang, Zhan, & Bian, 2020; Zhang, Wang et al.,
2020), screening underlying lead compounds (Cao et al., 2011), Struc-
ture damage detection (Zhou, Zhou, Zhou, Yang, & Luo, 2014). In this
study, we measured the variable importance based on the overall ca-
pacity of the variables to explain the total model variances. Relative
Importance and normalize importance scores were also computed for
each variable to verify the predictive accuracy of the models and the
individual contribution of each variable to the overall model
performances.
2.5. Experimental design
In this study, we structured the entire analysis into a few sequential
and logical steps (Fig. 1). The global and local spatial regression analysis
has been carried out through four separate models:
2.5.1. Model 1: global regression model considering static dependent and
independent variables
Model 1 was conceptualized for conducting global regression anal-
ysis between COVID-19 counts and the explanatory factors. The daily
COVID-19 observations from January 22 to July 26 were converted to
cumulative sum for changing the nature of the data from dynamic to
static. Only the nal ltered variables for cases and deaths were
considered in Model 1. Group-wise assessment was not considered in
Model 1. The nal selected variables, 6 for cases and 6 for deaths, had
exhibited acceptable VIF scores. This suggests that the multicollinearity
problems in the model appeared not evident for all the multi-parameters
regression models. All the global models, including OLS, SEM and SLM,
were conducted using the GeoDa and GeoDa Space software. The rst
order Queen and Rook contiguity was applied for spatial weight esti-
mation. The distance-based approach was utilized for generating the
spatial weights of the observations. Specically, the Euclidian distance
method was adopted for distance-based spatial weight calculation.
A. Maiti et al.
Sustainable Cities and Society 68 (2021) 102784
5
2.5.2. Model 2: local regression model using static dependent and
independent variables
Model 2 was developed by incorporating both static independent and
static dependent variables into the modeling process. Local GWR and
MGWR modeling was undertaken to explore the local correlation and
association between the explanatory and response variables. Both GWR
and MGWR were performed with the MGWR software package (Oshan,
Li, Kang, Wolf, & Fotheringham, 2019). For Model 2, only the nal
ltered variables (6 for cases and 6 for deaths) were taken as indepen-
dent variables. Using these variables, seven parameters local regression
models were developed for COVID-19 counts, with the cumulative sum
values accounted.
2.5.3. Model 3: group-wise local regression model using static dependent
and independent variables
Model 3 was conceptualized after incorporating group-wise (crime,
demography, education, employment, ethnicity, health, and migration)
variables into the modeling process. Using the stepwise forward and
enter regression method, the ltered variables with VIF smaller than 4
for each group was identied. Among the seven major groups, a total of
two variables (county population agency report crimes and ARSON),
one variable (female age 85+), two variables (less than a high school
diploma 2014–2018 and bachelor’s degree or higher 2014–2018), three
variables (unemployed 2018, median household income 2018, Median
household income percent of state total 2018), two variables (HBAC_-
MALE and NH_FEMALE), two variables (Geriatric Medicine and Pre-
ventive Medicine), and three variables (Population estimate 2018,
domestic migration 2018, and R international migration 2018), were
selected for crime, demography, education, employment, ethnicity,
health, and migration, respectively, for developing the local regression
models regarding COVID-19 cases. Similarly, for COVID-19 deaths,
totally two variables for crime (Robbery, Motor vehicle thefts), one
variable for demography (female age85+), one variable for education
(bachelor’s degree or higher 2014−18), three variables for employment
(unemployed 2018, median household income 2018, median household
income percent of state total 2018), two variables for ethnicity (HBA
Female, BA Female), one variable for health (endocrinology diabetes
and metabolism specialists (2019)), and four variables for migration
(Pop estimate 2018, domestic migration 2018, R international migration
2018, and R domestic migration 2018), were considered.
2.5.4. Model 4: dynamic local regression model using dynamic dependent
and static independent variables
In Model 4, the monthly COVID-19 cases and deaths were chosen to
be the dependent variables, while the annually averaged static group
variables were considered to be the independent variables. The monthly
sum values of COVID-19 cases and deaths were derived for March, April,
May, Jun, and July. A total of ten (ve for cases and ve for deaths)
multi-parameter local spatial regression models were developed for
exploring the dynamic associations between the response and the
explanatory factors. The nal ltered variables (six for cases, including
ARSON, median household income 2018, median household income
percent of the state total 2018, HBA male, domestic migration 2018, R
international migration 2018; six for deaths, including median house-
hold income 2018, median household income percent of state total
2018, HBA Female, domestic migration 2018, R international migration
2018, and R domestic migration 2018) were incorporated for the dy-
namic local regression modeling.
The adaptive bi-square spatial kernel weighted method was
employed for approximating the kernel bandwidth for GWR and MGWR
models. The default golden bandwidth search approach was chosen for
computing uniform (GWR) and locally varying (MGWR) bandwidths.
Among the different optimization criteria, AICc, AIC, BIC, and CV, the
AICc approach was considered for selecting the optimal bandwidth over
feature space. Local correlation diagnostics, including condition number
(CN), local spatial VIF, local variance decomposition proportions (VDP),
were computed for evaluating the local collinearity among the obser-
vations and parameters. Bandwidth condence intervals were also
measured at different levels of probability to ensure reliable spatially
varying bandwidths, derived from MGWR.
3. Results
3.1. Spatial patterns of COVID-19 cases and deaths in the contiguous
United States
Spatial distributions and patterns of COVID-19 cases and deaths per
10,000 people in the contiguous United States is shown in Fig. 2. Mul-
tiple spatial clusters of simultaneously high numbers of cases and deaths
are formed, which exhibit an unequal and heterogeneous distribution of
COVID-19 counts across the counties. Among the clusters, four main
clusters can be identied throughout the entire study period. The rst
cluster is formed over the North-Eastern coastal region, covering Mas-
sachusetts, Washington D.C., Maryland, Connecticut, Pennsylvania, and
New Jersey, as well as part of New York (New York City in particular).
The second cluster is observed in the South-Eastern region, which covers
states of Mississippi, Alabama, Georgia, South Carolina, North Carolina,
and Florida. The third cluster is detected in the Great Lakes region –
Michigan, Wisconsin and Illinois, centered at Chicago of Illinois, one of
the largest cities in the country. The last is located in the South-Western
Fig. 1. Flowchart of the research methods and data analysis procedures in detail.
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region including southern California, Arizona, New Mexico (north-
western part), and Colorado is also notably among the areas with high
numbers (Fig. 2).
3.2. Association between explanatory factors and COVID-19 cases and
deaths
3.2.1. Model 1: static global regression analysis
Three global regression models, OLS, SLM and SEM, reveal the global
and spatial non-stationary associations between the explanatory factors
and the numbers of COVID-19 cases and deaths (Table 1).
For COVID-19 cases, the coefcient of determination (R
2
) statistics,
which denote the overall model strength and robustness, are measured
as 0.78, 0.80, and 0.80 for OLS, SLM and SEM, respectively. The spatial
dependence diagnostics criteria for the OLS model, namely LM Lag and
LM error, are found statistically signicant, thus indicating the
requirement of more appropriate and relevant global models, such as
SLM and SEM (Table S2). The AIC value, which denotes the overall
model accuracy and parsimonious character of the models, is shown to
be the lowest (most relevant) for SLM, followed by OLS and SEM. This
suggests that the SLM model can be a more relevant global regression
model with a better explanation of the model variability. Regarding the
Fig. 2. Bivariate choropleth map demonstrates the county wise distribution (per 10,000 population) of COVID-19 cases and deaths from January 22 to July 26, 2020.
Table 1
Global regression estimates derived from OLS, SLM, and SEM.
Variable
Cases
Ordinary Least Square Spatial Lag Spatial Error
Coefcient t-Statistic Probability Coefcient z-Statistic Probability Coefcient z-Statistic Probability
Case — — — 0.34 23.34 0.00 — — —
CONSTANT −120958 −8.49 0.00 −54546.7 −4.1 0.00 −103165 −6.02 0.00
ARSON 825.14 14.88 0.00 1073.06 21.2 0.00 1291.49 23.27 0.00
MHHInc 2.27559 5.36 0.00 −0.04 −0.1 0.92 2.32 3.40 0.00
MHHIncPer 224.50 0.78 0.43 633.62 2.43 0.02 20.76 0.05 0.96
HBACM 62.27 52.79 0.00 46.78 39.3 0.00 44.04 35.04 0.00
DomMig −22.42 −20.13 0.00 −21.33 −20.94 0.00 −22.37 −20.73 0.00
RIntMig 274.51 0.17 0.86 −392.91 −0.27 0.78 −87.65 −0.06 0.95
Lambda — — —— — — — 0.55 26.04 0.00
R
2
0.76 0.80 0.80
Adj. R
2
0.76 — —
F 1611.37 — —
P 0.00 — —
AIC 83,825 83325.90 83458.30
SIC 83867.30 83374.30 83500.60
Deaths
Death — — — 0.73 56.08 0.00 — — —
CONSTANT −9136.95 −5.65 0.00 332.33 0.29 0.76 1988.94 1.02 0.30
MHHInc 0.29 6.27 0.00 −0.05 −1.48 0.13 −0.13 −1.69 0.09
MHHIncPer −48.05 −1.50 0.13 28.54 1.28 0.19 88.23 1.83 0.06
DomMig −3.91 −37.32 0.00 −2.82 −37.14 0.00 −2.73 −36.2 0.00
RIntMig 1106.60 6.47 0.00 510.21 4.29 0.00 250.68 2.00 0.04
RDomMig 123.34 3.78 0.00 142.51 6.28 0.00 81.82 3.02 0.00
LAMBDA — — — —— — — 0.81 63.64 0.00
R
2
0.36 0.69 0.69
Adj. R
2
0.36 — —
F 349.83 — —
P 0.00 — —
AIC 70200.90 68324.50 68429.20
SIC 70237.10 68366.80 68465.50
Notes: MHHInc – Median household income, MHHIncPer – Median household income percent, DomMig – Domestic Migration, RIntMig – Rate of International
Migration, HBACM – Not Hispanic, Black or African American alone or in combination male population, RDomMig – Rate of Domestic Migration, ARSON – Arson.
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correlations of the explanatory variables
2
, ARSON, MHHInc,
MHHIncPer, and HBACM have positive correlations with the number of
COVID-19 cases. Among these four covariates, HBACM is found to have
the most statistically signicant relationship with the number of cases,
given that its t-/z-statistic is the highest among the three models (52.79,
39.3, and 35.04 for OLS, SLM, and SEM, respectively). The next with
substantial signicant coefcients is ARSON, with its t-/z-statistics being
14.88, 21.2, and 23.27 in OLS, SLM, and SEM, respectively. MHHInc and
MHHIncPer are found to have smaller signicance values. The former is
statistically signicant (at the 5% level) only in SLM, while the latter is
statistically signicant (5% level) in OLS and SEM. Meanwhile, DomMig
is found negatively (statistically signicantly) correlated with COVID-19
cases in all three models. Last, RIntMig shows statistically insignicant
Table 2
Group wise GWR and MGWR estimates computed from COVID-19 cases and deaths.
Factors
Cases
R
2
Adj. R
2
Adj. alpha
(95 %)
Adj. critical t value
(95 %)
AIC AICc BIC
GWR MGWR GWR MGWR GWR GWR GWR MGWR GWR MGWR GWR MGWR
Crime 0.96 0.953 0.954 0.95 0 3.577 −333.214 −272.611 −197.511 −241.079 2240.615 1014.878
Demography 0.935 0.93 0.927 0.925 0 3.612 989.264 973.424 1065.608 1002.018 2954.972 2201.44
Education 0.961 0.958 0.955 0.953 0 3.572 −395.974 −388.113 −265.737 −316.194 2129.215 1522.801
Employment 0.96 0.963 0.953 0.955 0 3.54 −220.113 −362.583 −33.805 −158.134 2758.259 2744.779
Ethnicity 0.953 0.952 0.946 0.946 0 3.572 136.66 81.321 266.864 171.804 2661.55 2211.106
Health 0.412 0.439 0.332 0.398 0 3.542 7915.288 7450.479 8017.685 7482.009 10172.44 8737.93
PopMig 0.964 0.962 0.957 0.957 0 3.552 −469.484 −574.647 −263.696 −462.762 2647.13 1778.057
All Variables 0.964 0.969 0.954 0.961 0.001 3.473 −133.397 −737.655 238.838 −434.883 3930.356 2971.075
Deaths
Crime 0.936 0.941 0.927 0.934 0 3.566 1077.624 701.324 1202.174 782.08 3550.934 2719.931
Demography 0.892 0.887 0.879 0.879 0 3.612 2555.903 2460.028 2632.247 2490.847 4521.611 3733.372
Education 0.779 0.781 0.762 0.77 0 3.515 4577.562 4401.507 4612.928 4417.641 5938.388 5331.019
Employment 0.948 0.953 0.938 0.944 0 3.54 646.627 334.34 832.936 538.789 3624.999 3441.702
Ethnicity 0.925 0.926 0.914 0.918 0 3.546 1542.145 1303.461 1647.646 1361.208 3831.098 3025.062
Health 0.936 0.939 0.929 0.932 0 3.607 909.58 755.337 982.54 828.208 2833.558 2678.189
PopMig 0.98 0.98 0.975 0.977 0 3.549 −2090.17 −2484.22 −1759.22 −2371.52 1768.129 −123.531
All Variables 0.964 0.97 0.954 0.962 0.001 3.472 −138.548 −731.431 230.621 −358.146 3910.451 3337.357
Fig. 3. Local associations between the confounding factors and COVID-19 incidences derived from GWR and MGWR. Model strength and spatial interactions of the
parameters were demonstrated by local R
2
, intercept, and residual.
2
The explanatory variables have different units with different value ranges,
hence their coefcients are not comparable; the associated t-statistics (OLS) and
z-statistics (SLM and SEM) instead can be compared in terms of the signicance
level of the associations.
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associations with the cases, although the associations differ across the
models.
Moving onto the number of COVID-19 deaths, the R
2
values are 0.36,
0.69, and 0.69 for OLS, SLM, and SEM, respectively. The AIC value is
found to be the lowest in SLM, compared to those in OLS and SEM,
indicating that the SLM model performs better under the given modeling
framework. To interpret the explanatory variables, DomMig, RIntMig,
and RDomMig are signicantly associated with the number of deaths for
all three models, and their associating directions are consistent. Spe-
cically, RIntMig and RDomMig covariates are positively correlated
with deaths, while DomMig (the one with the highest signicance level
measured by t-/z-statistics) negatively. As for MHHInc and MHHIncPer,
however, the correlations between MHHInc and deaths are observed
statistically signicant in OLS and SEM, but the correlating directions
are inconsistent between the two models of OLS and SEM; MHHIncPer is
found to be signicantly associated with deaths in only SEM and their
relating direction is positive.
3.2.2. Model 2: static local regression analysis
The (M)GWR-derived local spatial heterogeneity of the determinant
factors for COVID-19 cases and deaths are statistically and spatially
displayed in Table 2 and Fig. 3, respectively. These numbers and gures
collectively demonstrate the spatial variability of the local model at the
county scale in the contiguous United States. Local R
2
estimates for both
local regression models, MGWR and GWR, show high degrees of spatial
agreement. The counties, for which the highest R
2
(i.e., R
2
>0.90) values
are derived, form spatially clustered patterns across the country. The
high values of local R
2
are concentrated over the Wisconsin-Indiana-
Michigan region, as well as several parts of states of Texas, California,
Mississippi and Arkansas. The lowest R
2
scores are found in the Northern
and North-Western states (Montana, Washington, Oregon, Wyoming),
Southern states (New Mexico) and North-East coast region (North
Carolina and Georgia). For COVID-19 deaths, the spatial patterns of
high, moderate and low R
2
values appear similar to those of the COVID-
19 cases. Among the two local spatial regression models, MGWR per-
forms more accurately, as it has slightly higher Adj. R
2
values (for cases,
R
2
=0.961; for deaths, R
2
=0.962), compared to GWR’s Adj. R
2
values
(for cases, R
2
=0.954; for deaths, R
2
=0.954). Also, AICc values of the
MGWR model (for cases, AICc = − 434.883; for deaths,
AICc = − 358.146) are found much lower than those of GWR (for cases,
AICc =238.888; for deaths, AICc =230.621), as shown in Table 2 and
Fig. 3.
3.2.3. Model 3: group-wise static local regression analysis
The spatial associations between different groups (crime, de-
mographic, education, employment, ethnicity, health and migration)
and COVID-19 cases and deaths are depicted in Figs. 4 and 5. Among the
seven groups, six groups viz. demography, crime, education, ethnicity,
employment, and population migration show strong similarities in terms of
their spatial patterns of local R
2
. The highest local R
2
values
(R
2
=>0.90) are found in the Southern and South-Western states,
mainly Texas, Arizona, California, Utah; in the Eastern United States, or
the Wisconsin-Michigan-Indiana-Illinois region; in the tri-state area of
Mississippi-Arkansas-Alabama. In contrary, the health factor exhibits a
different association with the COVID-19 numbers. High local associa-
tions between the health factor and the COVID-19 cases are found in the
Colorado-Utah and New Hampshire areas. For all groups, low spatial
associations are found in states of Montana, North Dakota, Idaho, Ore-
gon. Based on the R
2
and AICc values, the population migration factor is
found to be the most critical component with the highest local estimates
(R
2
=0.96, AICc = − 462.76), followed by education and crime. A similar
spatial association is detected between the explanatory factors and
COVID-19 deaths across the counties. High local associations are found
over the South, South-Western United States (states of Texas, New
Fig. 4. Local effects of the driving factors (Demography, Crime, Education, Ethnicity, Employment, PopMig, and health) on COVID-19 cases at county scale derived
from GWR and MGWR.
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Mexico, Arizona, and California) and the East Central states (Wisconsin,
Michigan, Indiana, and Illinois). The Population and migration factors
explains the maximum model variability (R
2
=0.98 and
AICc = − 2371.52), followed by an order of employment, crime, health,
ethnicity, demography, and education (Table 2).
3.2.4. Model 4: dynamic local regression analysis
Spatial and temporal associations between the nal six selected
factors and COVID-19 counts are presented in Figs. 6 and 7, and Table 3.
Totally ten (ve for cases and ve for deaths) local regression models
reveal local associations between the explanatory factors and COVID-19
counts in each of the ve months, namely March, April, May, June, and
July. High spatial associations between the explanatory variables and
the response variables are found in states of Texas, New Mexico, Mis-
sissippi, Tennessee, Kentucky, Indiana, Illinois, Wisconsin and Michigan
(R
2
>=0.90). In April and May, high spatial associations are found in
Florida and California. In June and July, Arizona, Nevada, Oregon,
Idaho states exhibit high spatial associations, characterised by large
local R
2
values. On the contrary, low spatial associations are observed in
Washington, Oregon, Idaho, Montana, North Dakota, and South Dakota.
For COVID-19 deaths, the local association follows a similar pattern as
observed for the cases. In March, a high spatial association is seen in the
Wisconsin and Illinois states. In the later months, high spatial associa-
tions are shifted to multiple locations, such as Texas, California, Utah,
Idaho, Wyoming region, Arkansas, Mississippi, Tennessee. On the con-
trary, low spatial associations are found in the northern (i.e., Montana
and North Dakota) and eastern states (i.e., Florida, Georgia, and South
Carolina). All the dynamic models demonstrate the superiority of
MGWR, as it is found to be a well-suited model for the local regression
analysis throughout the study (Table 3, Figs. 6 and 7).
3.3. Variable importance
The levels of Relative Importance of the selected variables (nal
ltered variables, six for cases and six for deaths) measured by the
Random Forest machine-learning model are presented in Fig. 8. For
COVID-19 cases, among the variables, the highest level of Relative
Importance is found for HBACM (44.31 %), followed by DomMig (15.56
%), ARSON (12.38 %), RIntMig (10.53 %), MHHIncPer (5.22 %), and
MHHInc (3.7 %), respectively (Fig. 8a). For COVID-19 deaths, the HBAF
explains the maximum variances, and therefore, the highest RI score
appears in HBAF (26.56 %), followed by DomMig (13.23 %), RDomMig
(8.07 %), MHHInc (6.84 %), RIntMig (5.88 %), and MHHIncPer (0.76
%), respectively (Fig. 8b).
4. Discussion
It has been nearly one year since the outbreak of COVID-19 started in
Wuhan (China) and spread across the globe. The situation yet remains
globally elusive as many countries have witnessed the re-emergence of
COVID-19 incidents. Among all the countries, the United States is facing
the most critical challenge in attening the curve with urgent needs for
more effective and appropriate control measurements. To inform the
policy-makers at both national and state levels, understanding the
Fig. 5. Local effects of the driving factors (Demography, Crime, Education, Ethnicity, Employment, PopMig, and health) on COVID-19 deaths at county scale derived
from GWR and MGWR.
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explanatory drivers and related confounding factors with spatial pat-
terns and is of paramount importance. Timely studies have done much
work of doing so (e.g., Beria & Lunkar, 2020; Hu, Roberts, Azevedo, &
Milner, 2020; Rahman et al., 2020). However, this may not uncover the
full picture since most of the factors change over time, namely being
time-variant variables. The present study contributes to forwarding the
knowledge of the outbreak by examining a set of factors over space and
across time. Specically, the most relevant variables are teased out from
a large group of potential factors for explaining the COVID-19 cases and
deaths at the county level, as well as for each month covering a
ve-month study period (Table 4).
Choosing the best models when taking into account spatial and
temporal features have always been a crucial point in spatial epidemi-
ological research. Previously, several methodological approaches have
evolved to capture the inuence of explanatory variables on the
response variables in the epidemiological study (Bashir et al., 2020).
Among these are Spearman’s, Pearson’s and Kendall’s Correlation Co-
efcient, Ordinary Least Square regression (M´
endez-Arriaga, 2020),
Poisson regression, Distributed Lag Nonlinear Model (Runkle et al.,
2020), cluster-based analysis (Andersen, Harden, Sugg, Runkle, &
Lundquist, 2021), spatial lag model, spatial error model (Sun et al.,
2020). These models are mainly global models in nature and therefore
have proven ineffective to capture the local or spatial patterns between
explanatory and response variables.
Based on the present research, notably, the overall regression models
reveal that population migration, as indicated by domestic migration
and the rate of international migration, is highly correlated with the
numbers of COVID-19 cases and deaths. The move of people across
continents internationally is accompanied with high risk of virus spread,
as the air traveling means by its nature increases the likelihood of
person-to-person COVID-19 transmissions (Zhang, Yang et al., 2020;
Zhang, Wang et al., 2020). Given this evidence, air ight restrictions
could be effective in undermining the virus spread, which is in line with
the conclusion of positive associations between travel restrictions and
COVID-19 spread from previous ndings (Christidis & Christodoulou,
2020), although this involves trade-offs between air-transporting public
health and social-economics risks (Cotfas, Delcea, Milne, & Salari,
2020). The other population moving variable, domestic migration, is
found to be negatively related to numbers of both cases and deaths,
which may be because that the redistribution of population from high
density areas (e.g., megacities) to low population density areas (e.g.
mountainous suburban regions) can diffuse the infected people while
decreasing the frequency of person-to-person contact. A study suggests
that residents from New York City, especially those in high wealth sta-
tus, tend to ee the city to lower physical exposure to COVID-19 (Coven
& Gupta, 2020). Apart from domestic migration and population ows
that have been recorded during the outbreak, the intra/inter city and
county transport connectivity plays a crucial role in spreading the dis-
ease especially at the early transmitting phase. Although this study in-
cludes both domestic and international migration into the assessment,
the explicit role of transport network in transmitting the virus spatially is
not focused. It should be noted that this relationship is based on the
overall regression model, lacking heterogeneity over time and space.
Socioeconomically, median household income at the county level is
shown to be positively related to COVID-19 spread, as it indicates that
the larger cities and higher population densities with more burden of
virus transmissions.
Interestingly, when viewing different time periods (monthly from
March to July) as revealed from the dynamic local regression analysis,
there exists high spatial heterogeneity in how the explanatory variables
are associated with COVID-19 cases and deaths. Such heterogeneity is
dynamic over time, which is also supported by the better performance of
MGWR than GWR (Figs. 6 and 7). In the early phase of the COVID-19
outbreak (mainly in March), associations between the potential factors
and the infected numbers in most regions have not been well manifested
except for the Chicago-centred Great Lake region and the Tennessee-
Arkansas-Mississippi region (Fig. 6c). However, since April, several
prominent hotspots of such correlations have been discovered including
the states of California and Florida as well as many regions in the middle
east part of the country (Fig. 6d, g, h, k). These regions identied as
hotspots have characteristics of high population densities and hence the
outbreak outcomes are more likely to be explained by the selected fac-
tors, particularly the migration-related variables of domestic migration
behaviours. This implication again demonstrates the importance of
controlling people mobility as effective measures to combat the virus
spread by the government in high populated states (Badr et al., 2020), as
those actions taken in other countries including China (Kraemer et al.,
2020). In terms of COVID-19 deaths, the spatial patterns of the modeling
outcomes also begin to exhibit high explanatory powers over large scales
after April and remain stable during April-July, covering most of the
Fig. 6. Time-varying effects of the confounding factors on COVID-19 cases based on GWR and MGWR.
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Fig. 7. Time-varying effects of the confounding factors on COVID-19 deaths based on GWR and MGWR.
Table 3
Month wise GWR and MGWR estimates for cases and deaths.
Months
Cases
R
2
Adj. R
2
Adj. alpha (95
%)
Adj. critical t value (95
%)
AIC AICc BIC
GWR MGWR GWR MGWR GWR GWR GWR MGWR GWR MGWR GWR MGWR
March 0.886 0.887 0.858 0.87 0.001 3.447 3290.311 2854.937 3588.543 2974.945 6971.588 5284.201
April 0.931 0.944 0.914 0.932 0.001 3.447 1719.785 943.653 2018.018 1169.966 5401.063 4195.497
May 0.953 0.962 0.941 0.953 0.001 3.447 541.379 −141.82 839.612 158.405 4222.657 3550.403
Jun 0.966 0.971 0.956 0.964 0.001 3.473 −332.63 −939.287 40.276 −631.086 3731.492 2796.307
July 0.974 0.976 0.966 0.97 0 3.49 −1077.61 −1533.93 −647.896 −1217.58 3246.505 2245.248
Death
March 0.855 0.912 0.844 0.897 0.002 3.161 3262.754 2180.349 3297.051 2343.071 4602.697 4977.473
April 0.957 0.965 0.945 0.957 0.001 3.472 371.277 −470.549 741.026 −224.624 4420.234 2905.75
May 0.959 0.969 0.95 0.961 0.001 3.42 −11.612 −677.245 229.337 −360.743 3333.698 3102.754
Jun 0.963 0.969 0.953 0.96 0.001 3.472 −120.738 −586.839 249.011 −212.922 3928.219 3482.117
July 0.962 0.966 0.951 0.957 0.001 3.472 37.51 −372.678 407.259 −6.848 4086.467 3657.341
Fig. 8. Relative inuence of the variables utilized for developing parsimonious regression models.
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contiguous United States (except for a few regions such as northern
California and northern New York). These results conrm that the
selected factors of migration and household economic status can be
useful for understanding the deaths caused by COVID-19 across counties
and states during the study period.
Although the United States is equipped with best healthcare facilities
in the world, the high-level response to the pandemic has been argued as
inadequate and leading to “surprisingly” resurgence of COVID-19 cases
in for example California
3
. Currently, despite the authorization of vac-
cines, the most effective measures to protect people from virus spread
and minimize exposure risk are keeping social distances, wearing masks,
and high frequency of washing hands (Badr et al., 2020). At the state
level, local governments have been sufciently vigilant to anticipate the
situations and have taken preventive and protective measures (e.g.
implementing anti-contagion policies) beyond federal guidance to
minimize the potential damage. These government-imposed contain-
ment policies include, for instance, large event bans, school closures,
and mandating social distances, which could reduce the growth of new
cases (Courtemanche, Garuccio, Le, Pinkston, & Yelowitz, 2020). State
travel restrictions as well as quarantine rules for out-of-state visitors
have been put into practices by many states such as Vermont
4
. Educa-
tional institutions transferred from in-person classes to online meetings,
or otherwise designed protocols specifying different categories of stu-
dents/staff/faculty members, regular testing, restricted public room
usages, etc.
However, effort has been regarded as seemingly being put in vein
based on the possible rebounding trend of newly found cases
5
. Given the
critics based on the fact that the contiguous United States has the size of
conrmed cases far more than any other places, policy-makers have
been placed on a verge of taking critically adaptive and learning actions
by referring to successful examples. China, the world’s second largest
economy (after the United States), has put tremendous resources for
controlling virus spread (primarily through city lockdown), which was
reported as effective as potentially prevented hundreds of thousands of
cases outside Hubei province (World Health Organization, 2020).
Challenges such as those rooted in difference in political systems are
admittedly persistent when learning from the way in which China
respond to the virus crisis, yet quick actions as the Chinese government
has taken should be undoubtedly encouraged as the priority by other
countries (Kupferschmidt & Cohen, 2020). With more evidence accu-
mulated for testing the underlying forces of COVID-19 spread, it is ur-
gent to call for taking serious and sophisticated consideration by the
federal government of socioeconomics and demographics especially
population migration at the county or state level in addition to physical
protection at the individual level. Without taking these temporally and
spatially dynamic factors into account, the COVID-19 mitigation out-
comes and the future of public health of the country in response to the
pandemic would remain uncertain and risky.
The ndings in the present studies are generally in agreement with
previous investigations, meanwhile not only adding values to the
existing knowledge of COVID-19 spread in the United States but also
possessing international relevance for combating the crisis worldwide.
Consistent with what have been previously found, several (e.g., de-
mographic, economic) factors have played key role in determining the
casualties incurred by COVID-19 across countries. Bashir et al. (2020)
showed that minimum temperature and average temperature are greatly
related to the spread of COVID-19 spreading in New York city. Apart
from that, specic humidity are found positively related with COVID-19
in four cities – New Orleans, LA; Albany, GA; Chicago, IL; Seattle, WA
(Runkle et al., 2020). Different socio-economic factors such as median
household income equality are also found to be determining drivers of
COVID-19 related casualties (Mollalo et al., 2020). In addition, de-
mographic prole of the health care professional (over 55 years old
population) is found substantially correlated with the disease (Dowd
et al., 2020). Economic prole of the communities including unem-
ployed population and existence of socio-economic disparities, s also
found to be one of the key regulating factors of COVID-19 casualties in
the United States. The present study, however, did not nd any signif-
icant relationships between climate, air pollution and COVID-19 cases or
deaths (Fig. S2). This nding is in line with the observation of Mollalo
et al. (2020).
This research has explored local and global spatial associations be-
tween the explanatory factors and COVID-19 casualties at the county
scale in the contiguous United States. This study adopts many relevant
approaches and methods to allow multiple-perspective model estimates,
which can further be used as a reference for similar research interest and
policy design. Still, there exist unavoidable uncertainties and biases both
in parameter approximation and model design. Cumulated COVID-19
deaths and cases were used as a dependent variable in the spatial
models. Though, we consider the latest COVID-19 counts (COVID-19
Table 4
Changes in Local R
2
values in different months.
R
2
range Case
March April May June July
GWR MGWR GWR MGWR GWR MGWR GWR MGWR GWR MGWR
0 - 0.34 379 851 167 362 104 311 76 214 41 145
0.34 - 0.66 384 553 300 413 366 403 219 321 133 185
0.66 - 0.79 391 449 378 462 541 410 420 384 224 304
0.79 - 0.85 347 277 356 409 306 320 352 313 243 281
0.85 - 0.89 349 341 337 397 333 321 303 308 263 299
0.89 - 0.93 465 358 492 419 452 511 428 401 447 394
0.93 - 0.96 347 161 427 253 387 428 438 468 519 509
0.96–1.00 447 119 652 394 620 405 873 700 1239 992
Death
0 - 0.34 878 63 538 80 414 45 358 17 264 11
0.34 - 0.66 892 414 642 102 680 62 575 60 512 45
0.66 - 0.79 643 501 575 178 559 177 547 130 511 122
0.79 - 0.85 367 420 351 325 302 270 348 201 418 227
0.85 - 0.89 57 316 243 319 304 321 253 287 262 260
0.89 - 0.93 186 449 192 421 281 391 330 376 325 411
0.93 - 0.96 31 504 193 336 233 379 280 454 328 475
0.96–1.00 55 442 375 1348 336 1464 418 1584 489 1558
3
Website: https://www.latimes.com/opinion/story/2020-07-02/u-s-was-pe
rfectly-equipped-to-beat-coronavirus-federal-government-failed
4
Website: https://accd.vermont.gov/covid-19/restart/cross-state-travel
5
Websites: 1) https://www.cnn.com/videos/politics/2020/04/12/anthony-
fauci-polls-november-rebound-jake-tapper-sotu-vpx.cnn; 2) https://coronaviru
s.jhu.edu/testing/individual-states
A. Maiti et al.
Sustainable Cities and Society 68 (2021) 102784
13
datasets from January 22 to July 26, 2020, was collected) for the
modeling, there is high chance to have different estimates if the pro-
posed models are performed considering different time frame datasets.
To clearly understand this uncertainty, we compare our modeled esti-
mates with Mollalo et al. (2020) observations; this study has conducted
the analysis considering 90 days of aggregated COVID-19 data. While, in
the present research, we consider 348 variables and sort out few nal
uncorrelated variables for the explanation of COVID-19 cases and
deaths, respectively, after processing nearly 184 days of data (both
aggregated and daily COVID-19 counts were considered). The nal
ltered variables identied in our study has not matched perfectly with
others’ estimation. This can be due to the difference in time frame taken
between Mollalo et al. (2020) (90 days of COVID-19 data) and our study
(184 days of COVID-19 data). Moreover, in our study, we consider seven
groups of factors (crime, demography, education, ethnicity, employ-
ment, health, and population & migration) for the modeling and sub-
sequent interpretation. The causal effects of the other factors, such as the
lockdown date, the strictness of lockdown (partial or complete), re-
strictions on social gathering and human mobility, have not been
explored in the present research, which can be an issue for future
research.
5. Conclusion
The present research aims to explore the local and global associations
between explanatory factors and COVID-19 counts in the contiguous
United States with local and global spatial regression and machine-
learning models. To capture the time varying effects of the potential
factors on COVID-19 counts, several dynamic local parsimonious models
have been conceptualized. Among the confounding factors, crime, in-
come, and migration are found to be strongly associated with COVID-19
casualties, and hence explain the maximum model variances. Interest-
ingly, when viewing different time periods (monthly from March to
July) as revealed from the dynamic local regression analysis, there exists
high spatial heterogeneity in how the explanatory variables are associ-
ated with COVID-19 cases and deaths. Additionally, both global and
local associations among the parameters vary highly over space and
change across time. This spatial variability of the model estimates
exhibit the varying behavior of the explanatory factors and COVID-19
incidences at the county scale. Thus, the application of various models
can be effective to uncover the global and local spatial associations from
multiple perspectives. The ndings in the present studies are generally
in agreement with previous investigations, meanwhile not only adding
values to the existing knowledge of COVID-19 spread in the United
States but also possessing international relevance for combating the
crisis worldwide. To inform policy-makers at the nation and state levels,
understanding the explanatory forces and related confounding factors
with spatial patterns is of paramount importance. The present study can
be a reference for future spatial epidemiological research and informing
decision making in the case of crisis.
Declaration of Competing Interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Acknowledgement
The authors are grateful to three anonymous reviewers and handling
editor for making constructive comments that helps to improve the
quality of the manuscript. The authors also acknowledge Dr. Prasenjit
Acharya for the continuous help and support.
Appendix A. Supplementary data
Supplementary material related to this article can be found, in the
online version, at doi:https://doi.org/10.1016/j.scs.2021.102784.
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