=Paper=
{{Paper
|id=Vol-3306/paper7
|storemode=property
|title=A scalable pipeline for COVID-19: the case study of Germany, Czechia and Poland.
|pdfUrl=https://ceur-ws.org/Vol-3306/paper7.pdf
|volume=Vol-3306
|authors=Wildan Abdussalam,Adam Mertel,Kai Fan,Weronika Schlechte-Wełnicz,Lennart Schüler,Justin M. Calabrese
|dblpUrl=https://dblp.org/rec/conf/vldb/AbdussalamMFSSC22
}}
==A scalable pipeline for COVID-19: the case study of Germany, Czechia and Poland.==
https://ceur-ws.org/Vol-3306/paper7.pdf
A scalable pipeline for COVID-19: the case study of
Germany, Czechia and Poland.
Wildan Abdussalam1,* , Adam Mertel1 , Kai Fan1 , Lennart Schüler1,2 ,
Weronika Schlechte-Wełnicz1 and Justin M. Calabrese1,3,4
1
Center for Advanced Systems Understanding, Helmholtz-Zentrum Dresden-Rossendorf, Untermarkt 20, 02826 Görlitz, Germany
2
Department of Computational Hydrosystems, Helmholtz Centre for Environmental Research (UFZ), Permoserstraße 15, 04318 Leipzig, Germany
3
Department of Ecological Modelling, Helmholtz Centre for Environmental Research (UFZ),Permoserstraße 15, 04318 Leipzig, Germany
4
Department of Biology, University of Maryland, College Park MD, Maryland, USA
Abstract
Throughout the coronavirus disease 2019 (COVID-19) pandemic, decision makers have relied on forecasting models to
determine and implement non-pharmaceutical interventions (NPI). In building the forecasting models, continuously updated
datasets from various stakeholders including developers, analysts, and testers are required to provide precise predictions.
Here we report the design of a scalable pipeline which serves as a data synchronization to support inter-country top-down
spatiotemporal observations and forecasting models of COVID-19, named the where2test, for Germany, Czechia and Poland.
We have built an operational data store (ODS) using PostgreSQL to continuously consolidate datasets from multiple data
sources, perform collaborative work, facilitate high performance data analysis, and trace changes. The ODS has been built not
only to store the COVID-19 data from Germany, Czechia, and Poland but also other areas. Employing the dimensional fact
model, a schema of metadata is capable of synchronizing the various structures of data from those regions, and is scalable
to the entire world. Next, the ODS is populated using batch Extract, Transfer, and Load (ETL) jobs. The SQL queries are
subsequently created to reduce the need for pre-processing data for users. The data can then support not only forecasting
using a version-controlled Arima-Holt model and other analyses to support decision making, but also risk calculator and
optimisation apps [1, 2]. The data synchronization runs at a daily interval, which is displayed at https://www.where2test.de.
1. Introduction realise the data surveillance and outbreak response man-
agement, which have been implemented in fighting other
In building forecasting models of COVID-19, many re- endemic diseases [4, 5, 6, 7].
searchers employ the training datasets provided by each To date, the data management have been applied in
country’s representative institutions, e.g., Robert Koch controlling the outbreak of COVID-19 [8, 9, 10, 11, 12,
Institute in Germany. The publicly accessible COVID- 13, 14, 15, 16, 17, 18, 19, 20, 21, 22]. Most of them pro-
19 data provided in raw textual format, such as CSV, vide maps and the prevalent data in the following re-
JSON, and XML are downloaded and analysed by the gional level: (i) National level, e.g., COVID-19 data of
researchers employing either statistical or machine learn- World wide [10], Europe [11, 12, 13], and Latin Amer-
ing approaches. However, the data are unwell struc- ica [14]; (ii) State and county levels, e.g., the COVID-19
tured and require heavy pre-processing as well as in- data warehouse for Italy [15], COVID-19 dashboard for
gestion activities for further analysis. This method is UK [17], the COVID-19 dashboard for Maryland [18],
inherently inefficient due to identical and manual paral- and for Germany [19].; (iii) County level, e.g., Dresden,
lel pre-processing of the RKI data (using e.g. python or Germany [20]. More completed version is provided by
R scripts) performed by each researcher. This reduces the John Hopkins University [21], which serves the dash-
the efficiency of each and everyone’s work as all have to board and the prevalent data for each regional level in
spend hours and days in pre-processing data before com- the USA as well as for most of countries around the
ing to modeling and forecasting. Advanced computing world. Likewise, the similar method in the presence
infrastructures and novel software pipelines are crucial of semi-automatic validation strategy was conducted to
tools to synchronize the data structures which originate check the data quality of daily updated numbers with
from various sources and to extremely reduce heavy pre- governmental/official data sources [22]. However, most
processing [3]. They serve as essential prerequisites to of dashboards and data warehouses have not provided
the features to let the users perform an inter-country
Proc. of the First International Workshop on Data Ecosystems (DEco’22), top-down spatiotemporal observation, i.e., observing the
September 5, 2022, Sydney, Australia inter-country prevalence and simultaneously being able
*
Corresponding author to observe to the microscopic level (nation → state →
$ w.abdussalam@hzdr.de (W. Abdussalam); j.calabrese@hzdr.de
county → municipality). The features could provide in-
(J. M. Calabrese)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License sights, for example, to study COVID-19 border dynam-
Attribution 4.0 International (CC BY 4.0).
CEUR
CEUR Workshop Proceedings (CEUR-WS.org)
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
ics which have been so far attracted considerable atten-
64
tions [23, 24, 25, 26]. Moreover, they are lack of fore-
casting features, which play a key role in predicting the
future prevalence as well as determining non pharma-
ceutical interventions (NPI). A tremendous number of
forecasting models, e.g., agent-base [27], machine learn-
ing [28, 29], combination model [30, 31], compartment
model [32, 33, 34, 35, 36], time series [37, 38, 39, 40, 41, 42,
43] have employed government datasets to provide essen-
tial inputs for public decisions. However, most of datasets
that were used in those studies are limited to the specific
time window which are likely to produce different results
when the datasets are updated. Establishing a system of
daily-updated-datasets assisted forecasts, therefore, is an
alternative to improve their consistency and precision.
In this paper, we address the aforementioned issues
by proposing the design of a scalable pipeline which
allow us to perform the top-down spatiotemporal obser-
vation among Germany, Czechia, and Poland as well as
to perform daily forecasts. The method of the pipeline
which consists of extraction of various data sources and
the ODS is described in subsec 2.1. More specifically,
we will describe the dimensional fact database model
and a daily migration process which underline the data
synchronization between various data sources and our Figure 1: (a) A workflow of data pipeline Hospitals, retire-
database server. We employ the dimensional fact model ment houses, and schools of Germany, Czechia and Poland
due to more flexibility and versatility in building spa- update the data of COVID-19 cases, vaccines and tests to the
tiotemporal aggregation functions than the nanocubes representative government institutions. A daily automatic ETL
model [44, 45]. Next, in subsec 2.2 we will describe the step is performed to synchronize the data sources and central
time-series forecasting models which are supported by database of CASUS. A daily and weekly automatic forecast
the presence of the ODS. Furthermore, the automatic employing, e.g. Arima-Holt model, is applied to provide rapid
system of daily forecasts owing to the presence of the predictions. The predictions and the actual data are shown
in the where2test website; (b) The scalable dimensional fact
pipeline will be laid out in this sub section. In Sec. 3, we
model. Datavalues and datavalue types represent measures,
will describe facilities that have been established due to
while region types and timeperiod types represent spatial and
the presence of the ODS. In order to demonstrate the temporal dimensions, respectively.
inter-country top-down spatiotemporal observations, an
analysis will begin from the macroscopic scale in which
the study of the virus spread across the national borders
is described in subsec 3.1. Herein we consider the border 2. Methods
among Germany, Czechia and Poland as a study case.
In subsec 3.2, we explore more microscopic level by ap- 2.1. Data Pipeline
plying a daily-updated-datasets assisted forecast for the Fig. 1a shows a workflow of the data pipeline. The hos-
prevalence in the state of Saxony, Germany. Last but pitals, retirement houses and schools register the daily
not least, in subsec 3.3, most microscopic level that we number of the COVID-19 cases and vaccines to the rep-
will demonstrate is a superspreading event at a slaugh- resentative government. In order to consolidate these
ter house in Gütersloh, Lower Saxony, Germany. As the data, the relational database is built based on dimen-
COVID-19 situation begins to enter an endemic phase, sional fact model [46]. Having established the relational
a study of superspreading event will provide essential database, the daily automatic extract, transfer and load
information to trace the COVID-19 transmission after a (ETL) step is performed to migrate and integrate the data
mass event. sources to the PostgreSQL database of CASUS HZDR
(see Suplementary materials 7.1). Next, we create SQL
inquiries-based views to be analysed by our researchers
using the forecast and machine learning methods. The
tested and completed analysis methods are set in the
master stage and the other tested methods are set in the
65
develop stage. Only the forecasting method in the master 2.2. Forecasts
stage is integrated in the automatic pipeline.
We employ auto regression integrated moving average
The dimensional fact model is shown in Fig. 1b. The
(ARIMA) and Holt’s linear trend models to forecast the
model consists of three main concepts: (i) Facts, that
infected, test, and hospitalised data of COVID-19 for
refer to a subject of study (e.g., the study of infected,
Saxony (Germany), Czechia, and Poland. The ARIMA
dead, recovered, hospitalised, test and vaccinated cases
model has been successfully employed in predicting other
due to COVID-19); (ii) Measures, that refer to the quan-
endemic diseases [47, 48, 49, 50]. The model features
titative data of the concept (i). The measured data are
suitable prediction based on time analysis series which
stored in the table of datavalues. The tables of datavalues
is capable of providing short horizon forecast for most
contain the number of infected, dead, recovered, hospi-
COVID-19 cases around the world [38, 39, 40, 41, 42, 43].
talised, test, and vaccinated cases due to COVID-19 in
To make the model consistent and avoid overfitting, the
a given time and place. To date, the schema consists of
order parameter of the ARIMA model is fixed instead of
three datavalues, i.e., datavalues of Germany, Czechia
using the auto ARIMA model. The ARIMA is improved
and Poland; (iii) Dimensions, that refer to temporal and
by employing the Holt’s linear trend model [51]. The
spatial attributes. As the measured data are provided in a
Holt’s model uses the exponential smoothing method to
given time and place, the table of time period types and
compute the weighted average of the past observation
regions is necessary. The former stores the type of time
data [52]. The forecasts from the Holt’s linear model have
period which consists of day and week data type; and the
a trend, so the damped parameter is turned on to avoid
latter stores the necessary information of regions which
this trend [53, 54, 52]. A self-defined mix function is used
consist of the name, abbreviation, ID of regions, ID of
to compute the probability parameter m to combine the
region type, geometry and population. The table of re-
forecasts from two models and minimize the error. The
gions depends on the table of region types. The regions
Box-Cox transformation is used to normalize the input
are categorised based on their sizes. The order of as-
data [55, 52].
cending sizes starts from municipality, county, state and
Our model provides a weekly forecast at first. In order
nation. For Germany, the order of region type starts from
to improve the daily variation and provide more real-
Gemeinde, Kreise and Bundesland. Similar to Germany,
time forecasts, we have built a daily forecast model. As
Poland consist of Gmina, Powiat, and Wojewodztwo. Dif-
the daily data have a clear weekly variation, the sea-
ferent from Germany and Poland, Czechia consist of 4
sonal parameters are added to the model; and seasonal
level, Obec, Orp, Okres and Kraj. The spatial and tempo-
ARIMA (SARIMA) and Holt-Winters’ seasonal model are
ral attributes are connected by means of hierarchies to
employed for the daily forecasts [56, 51, 57]. Similar to
represent a -to-one relationship between them. The table
the ARIMA model, the seasonal ARIMA model uses the
of mapping_types contains the hierarchical type of the
fixed order and seasonal parameters. After comparing
spatial attributes, e.g., for Germany (Gemeinde to Kreise,
the errors from multiple methods, the additive method is
Kreise to Bundesland), for Czechia (Obec to Orp, Orp to
selected for the Holt-Winters’ seasonal model. The mix
Okres and Okres to Kraj), and Poland (Gmina to Powiat
function is also used for the daily forecasts to combine
and Powiat to Wojewodztwo). Next, a many-to-one re-
the forecasts from two models and improve the forecast-
lationship between those spatial hierarchies are stored
ing accuracy. For study cases of (S)Arima-Holt model,
in the table of mapping_regions. Moreover, the table of
in Sec. 3.2, we will provide the number of infections for
timeperiod_types consists of the hierarchical type of the
Saxony, Germany. In addition to (S)ARIMA-Holt model,
temporal attributes.
we employ outlier detection to identify and quantify Su-
Aggregation functions are applicable on the measures
perspreading events. As suggested in [35], we identify
along the temporal and spatial dimensions. For the for-
and quantify superspreading events by using time se-
mer dimension, the weekly data are cumulative 7–day
ries analysis based outlier detection methods. The rate
data. For example, a 7–day case reported on 13.03.2022 is
of newly infected is modeled by an appropriate model,
an accumulation of the daily case for 07-13.03.2022. More-
which could be something as simple as a rolling average
over, for the latter dimension, county data are cumulative-
to more elaborate ones as SIR-based models. The residues
municipality data. Not only accumulating the data from
of the reported cases is used to identify outliers. At the
the municipality to a county level, in the presence of
same time, the residues can be used to quantify the size
mapping regions table, it is possible to accumulate the
of a superspreading event.
data from the county to the state level as well as the state
to the nation level. This allows us to scale the pipeline
to other areas provided that the data of municipality are 3. Results
available from the sources.
The presence of the pipeline has allowed us to provide
following facilities: (i) The released data hub for dead and
66
infected cases of all counties and states in Germany [58],
which allows a collaboration between CASUS research
staffs and other external collaborators. The post-processing
data serve as the clean data of daily infected and dead
cases for county and state levels. In addition, we have
also pre-processed the vaccination and hospitalization
data for the county and municipal levels; (ii) The daily
updated value of background risk for optimisation [1]
and risk calculator apps [2], which defines the chance of
an average person who lives in the focal area, and car-
ries out daily activities, will be infected over a one week
period; (iii) Blog posts which update current COVID-19
Figure 2: Difference in the pair-wise correlations for regions
situations in Germany. An interesting example of the within a 100 kilometer radius inside and outside the country.
posts would be the relation between the vaccination rate The red color represents the regions with the strongest dif-
and the 7-day incidence in all states of Germany [59]; ference, indicating the spread of the virus across the national
(iv) Forecast- and model-based analysis. We explore the borders.
study cases mentioned in Sec. 1, and begin by investi-
gating of the virus spread across the national borders of
Germany, Czechia, and Poland. wise correlations for each region considering the regions
in the radius of 100 kilometers, (i) within the same coun-
3.1. Analysis of the virus spread across try, (ii) outside this country. The difference of these val-
the national borders ues can be seen in Fig. 2. The bigger difference represents
regions where the incidence correlates much better than
COVID-19 spread among people. Therefore, human mo- the regions within the same country, indicating a strong
bility is one of the most important factors defining the national border effect on the virus spread.
trend of spatiotemporal spreading of the virus. Under- In the next step [63], we quantified the mitigation effect
standing human mobility allows us to predict the spa- of the national border in more detail. We picked the state
tiotemporal character of spread, evaluate the government of Saxony in Germany and the neighboring regions in
steps restrictions, and provide effective non-pharmaceutical Czechia. For both countries, we collected and integrated
interventions. Primarily due to the heterogeneity of the the incidence data on the level of single municipalities.
sources and the interest scope of the particular research For each municipality, we constructed a local regression
groups and communities, most of the COVID-19 research model which estimated the effect of three parameters, (i)
stays within the boundaries of one country. While most border presence, (ii) municipality size, and (iii) temporal
human mobility happens in the extent of one country or distance from other municipalities, on the spread of the
region, notably in Europe, the national border’s mitigat- virus. Based on this model, we identified very small-
ing effect is generally diminishing. To study the impact scale areas susceptible to a more intensive inter-national
of the national border, several research papers [60, 61] ap- spread of the COVID-19.
plied various methodologies of geostatistics and geospa- The top-down approach we selected for the study on
tial modeling. More thorough quantification of the effect the national border effect is possible thanks to the scala-
of border presence and international mobility on the epi- bility of the implemented dimensional-fact model. This
demy requires a data storage integrating heterogeneous principle allows the ODS to comprise various adminis-
datasets across more countries. trative levels and combine various relevant topics within
The presented ODS infrastructure offers a possibility the perspective of spacetime.
to study the spatiotemporal character of the virus spread
on more levels, considering the effect of the national
border. First, for our case study comprising the coun- 3.2. Weekly and daily forecast of
tries of Germany, Poland, and Czechia, we explored the Arima-Holt and Sarima-Holt
correlation of new cases in the region, the distance and For the case study, we provide a short-time forecast of 7-
the border presence. We observed that the neighbour day incidence up to 4 horizons performed on 13-04-2022
regions tend to have similar incidence values in the ab- using Arima-Holt model for Saxony, Germany. We used
sence of barrier in the form of a national border among a training dataset of 13-04-2022 version which consists of
them. This step followed the research of McMahon et al. the historical weekly data of Saxony and its counties from
[62], which showed a strong spatial autocorrelation of 01-03-2020 to 10-04-2022. The weekly data are automated-
incidence values in the USA. daily-updated data which are aggregated on Sunday (see
Further, we calculated the average time-lagged pair-
67
a different day, a deviation from the actual data for the
following 4 horizons is likely to occur. Additional realisa-
tions of Arima-Holt forecast in Saxony and its counties,
therefore, were performed to improve statistics. The re-
alisations were performed every Wednesday from 05-01-
2022 to 18-05-2022 in which the version-control dataset
were employed as training and test datasets. An example
would be a realisation of the Forecast on 05-01-2022. We
Figure 3: 7-day incidence of infected cases Jan - 8 May 2022 used the weekly data version of 05-01-2022 as its training
for Saxony, Germany. The black dots denote the historical dataset and the weekly data version of the following 1st,
data, the blue line (—) denotes a line guidance for the historical 2nd, 3rd and 4th week as its test datasets. For each region,
data, and the green (—), orange (—), and red line (—) denotes we then recorded a deviation of the forecast result from
the result of forecast using the Arima-Holt model performed
the historical data and quantified it as mean absolute
on 10-04-2022, 11-04-2022, and 13-04-2022, respectively. The
grey area shows the lower and upper limits of the forecast for
percentage error (MAPE). As shown in Fig. 4, the weekly
13-04-2022. Arima-Holt provides relatively low MAPE for the first
and second horizon. For the third and fourth horizon,
however, the range of MAPE tends to be wider than the
first and second.
Sec. 2.1). Although we update the data daily, for the
Therefore, we performed the Sarima-Holt model to
case of Germany, the current and previous-day data are
improve the performance of forecast for the third and
unavailable. In addition, the previous third day data are
fourth horizon. Owing to daily-updated data, the version-
still to be updated from the source. When the forecast was
control of daily data is employed as the seasonal pa-
performed on Sunday 10-04-2022, the number of infection
rameters. In addition to the daily data, the Sarima-Holt
on that day was less than the number of the same day
forecast was performed using the same version-control
for the following-day version. As a result, this produces
weekly data employed to the Arima-Holt model. For the
inaccurate forecast (see Fig. 3). As the day elapsed, more
daily data, we removed the current and two previous-
cases were automatically added and aggregated to the last
day data due to zero values for current and yesterday
Sunday data. Consequently, the performed forecast on
data, and inconsistent data for the previous third day. We
13-04-2022 provides higher exponent than the one with
then compared its performance in the presence and the
the dataset version of 10 and 11-04-2022. Moreover, the
absence of the Box-Cox transformation (BCT) used to
dataset of Wednesday consists of relatively-stable version.
normalize the input data. As shown in Fig. 4, the Sarima-
Therefore, the forecast is performed every Wednesday
Holt model in the absence of the BCT provides lower
due to the consistency of data source for the last Sunday.
MAPE than either the Arima-Holt or the Sarima-Holt in
the presence of the BCT for not only the first and second
horizons, but also the third and four horizons.
3.3. Superspreading events
Superspreding events play an important role in the dis-
persion dynamics of COVID-19 [64]. However, one of
the most commonly used epidemiological model types,
the compartment models, are not able to accuratly cap-
ture these events [35, 65]. We are currently working on a
Figure 4: Mean absolute percentage error of Arima-Holt solution to the problem by using outlier detection meth-
(weekly), Sarima-Holt in the presence of Box-cox transfor- ods on a county level. Many different methods exist and
mation (daily_originT), and Sarima-Holt in the absence of
they can produce more robust results, when more than
Box-cox transformation (daily_originF) for 1𝑡ℎ - 4𝑡ℎ horizon.
one timeseries is taken into account. A database as pre-
sented in this work is very advantageous, as it makes it
In order to check the four-horizon forecast, we com- very convenient to query the reported infections from all
pare it to the weekly-historical data updated on 11-05- neighboring counties and use this additional data to more
2022. The latter consists of relatively stable data from robustly identify outliers, which might be superspread-
17-04-2022 to 08-05-2022. As shown in Fig. 3, the weekly- ing events. The largest confirmed superspreading to date
historical data is surprisingly in quantitative agreement in Germany with 1766 infections happened in a meat pro-
with the four-horizon forecast. However, this agreement cessing facility in the North Rhine-Westphalian district
occurs occasionally. When the forecast is performed in of Gütersloh in June 2020. The facilities’ environmental
68
conditions combined with relatively close physical dis- The Sarima-Holt model is trained by the daily data, and
tance between workers were likely the main reason for the variation of the data could make the model more
efficient aerosol transmission [66]. We take this event as sensitive to the infection change compared to the Arima-
an example to show the result of a Z-score based outlier Holt model trained by the weekly data. However, the
detection method (Fig. 5). BCT reduces the variation of the daily data, and conse-
quently the daily forecasts perform worse than in the
absence of the BCT.
5. Conclusion
Our work has demonstrated the utility of the data pipeline
for top-down spatiotemporal analysis. We have first
shown the macroscopic analysis, in which the investi-
gation of the virus spread across the national border is
presented. At more microscopic level, we have demon-
Figure 5: The official reported COVID-19 daily incidence
strated data-driven approach due to the presence of the
per 100.000 inhabitants in the district of Gütersloh. A super-
spreading event in a meat processing plant in June 2020 is
pipeline which is applied to the prevalence of the county
successfully identified by an outlier detection method based region. The daily-updated data has improved the preci-
on the Z-score (the black dot). sion of the model for longer horizon. This data-driven
epidemic models provide more realistic forecast results
than either the parsimonious [34] or more number of
parameters with agent-based method [27] due to the us-
4. Discussions age of daily-updated data. This may contribute to public
health policy making, including contributing to public
Our analysis, implementing the pipeline in the presence health forecasting teams. Last but not least, exploring
of dimensional fact model has allowed us to daily mi- to lower level of region, we have demonstrated that the
grate the data efficiently due to the functions of spa- outlier model is applicable to capture the superspreading
tiotemporal aggregation. To provide the weekly data of event which occurred in 2020. These have shown that
counties, states, and nations, we only migrate the data of our work is capable of performing top-down analysis as
daily municipalities/counties (depends on the data avail- well as rapid and precise forecasts due to the presence of
ability of each nation) to the database server which are the pipeline.
then aggregated to the higher spatiotemporal level. This
model provides more advantages than the nanocubes
model [44, 45]. For the nanocubes model, each spatial
6. Data sources
(municipality, county, state and nation) and temporal • COVID-19 data for Germany, Czechia and Poland.
(daily and weekly) data are required to be migrated to
the database server. Consequently, this leads to a longer – Robert Koch Institute
migration process than the one performed using the di- – Czech Ministry of Health
mensional fact model. Moreover, its spatiotemporal map- – Polish Ministry of Health
ping enables us to perform an efficient table join among – Age-based hospitalisation of state level for
national data which is confirmed by the application on Germany (https://github.com/KITmetricsl
the Subsec. 3.1. ab/hospitalization-nowcast-hub/blob/ma
The presence of daily-updated data due to the presence in/data-truth/COVID-19/).
of the pipeline has allowed us to develop the Sarima-Holt – Age-based and type-based doses of vaccine
model. The model shows more robust prediction for for county level (https://github.com/rober
longer horizon than the Arima-Holt one. More specifi- t-koch-institut/COVID-19-Impfungen_i
cally, the Sarima-Holt in the absence of the BCT outper- n_Deutschland/blob/master/Aktuell_De
forms the Arima-Holt model for the third and fourth hori- utschland_Landkreise_COVID-19-Impfu
zon. This performance is due to the seasonal-parameter ngen.csv).
contribution to the model. As a result, the forecast tends – COVID-19 infected, recovered, hospitalised
to better predict for the third and fourth horizon. In con- and dead cases of Dresden (http://daten.dr
tradiction, the Sarima-Holt in the presence of the BCT esden.de/duva2ckan/files/de-sn-dresden
provides lower performance than the absence one due to -corona_-_covid-19_-_fallzahlen_md1_d
less variation of the training data after BCT (see Fig. 7). resden_2020ff/content).
69
– COVID-19 infected, dead, and test cases 7. Supplementary information
of Czechia for Municipality level (https:
//onemocneni-aktualne.mzcr.cz/api/v2/c 7.1. Data workflow
ovid-19/).
We use https://www.talend.com/products/talend-open-
– Age-based and gender-based infected and
studio/ to perform data migration. The migration be-
dead cases for county level of Germany
tween the data sources and the PostgreSQL database of
(https://experience.arcgis.com/experience
CASUS HZDR has been performed as follows:
/478220a4c454480e823b17327b2bf1d4).
– COVID-19 cases for municipality level of
Saxony, Germany (https://www.coronavi
rus.sachsen.de/corona-statistics/rest/inf
ectionOverview.jsp).
– COVID-19 cases for county level of Saxony,
Germany (https://media.githubuserconten
t.com/media/robert-koch-institut/SARS
-CoV-2_Infektionen_in_Deutschland/ma
ster/Aktuell_Deutschland_SarsCov2_Infe
ktionen.csv) Figure 6: Data workflow of the ETL process (see texts for its
– COVID-19 infected, dead, and test cases description).
for county level of Poland (https://wojewo
dztwa-rcb-gis.hub.arcgis.com/pages/dane
-do-pobrania).
1. Data acquisition
– COVID-19 vaccine for county level of Poland The data are automatically downloaded from sources 6.
(https://www.gov.pl/web/szczepimysie/ra They are subsequently stored on the repository
port-szczepien-przeciwko-covid-19). of where2test server. The downloaded data serve
– COVID-19 types in Sachsen (https://www. as data inputs of a migration process.
coronavirus.sachsen.de/infektionsfaelle-i 2. Dictionaries and data augmentation
n-sachsen-4151.html). To integrate and further augment data from het-
• Dictionaries of regions. erogeneous sources (various forms, schema, tem-
– Administrative areas in Germany (https: poral and spatial extent), we needed to prepare
//gdz.bkg.bund.de/index.php/default/digi a list of dictionaries. We formed a dictionary for
tale-geodaten/verwaltungsgebiete.html). each spatial level in every country to cover all
– Administrative areas in Poland (https://gi regions in our datasets. Here we included the
s-support.pl/baza-wiedzy-2/dane-do-pob unique region id, all alternative names, full names,
rania/granice-administracyjne/) geometries, and population numbers. This con-
cept can be further extended to other values such
– Administrative areas in Czechia (https://ge
as socioeconomical parameters, and information
oportal.cuzk.cz/(S(1nhx02lray0vkrhce1y2
about the region. This way we are able to main-
d53d))/Default.aspx?mode=TextMeta&te
tain the consistency in all datasets and enable
xt=dSady_RUIAN&side=dSady_RUIAN)
their integration process. The list of sources used
– Population numbers in Czech municipali- for building the dictionaries can be found in sec-
ties (https://www.czso.cz/csu/czso/pocet tion Data Sources 6.
-obyvatel-v-obcich-k-112021)
3. Data cleaning
– Postal codes in Germany (https://www.ge We migrate first timeperiod_types, region_types,
onames.org/postal-codes/postleitzahle datavalues_types, and mapping_types. While
n-deutschland.html) migrating the data to those tables, primary key
– Population numbers in Poland (https://st are automatically set by a transformator (The
at.gov.pl/obszary-tematyczne/ludnosc/lu script which migrates the data to the postgreSQL
dnosc/ludnosc-stan-i-struktura-ludnosc database.). Next, the primary key of those tables
i-oraz-ruch-naturalny-w-przekroju-teryt serves as the foreign key of other tables following
orialnym-stan-w-dniu-30-06-2021,6,30.h the table relation shown in Fig. 1b. An example
tml) would be a table of regions which contains intrin-
sic ID set by representative governments. In order
to differentiate ID among Germany, Czechia and
70
Poland, we add ’DE’, ’CZ’, ’PL’, respectively, fol-
lowed by the intrinsic ID. For the table of regions,
the primary key of region_types serves as its
foreign key. The intrinsic IDs are categorised
based on the ID of region types. A specific ex-
ample would be Dresden, whose the intrinsic ID
14162. After cleaning processes, the intrinsic ID
will be DE 14162 and categorised to the state level
of Kreise.
Having migrated the data to the aforementioned
tables, the table of mapping_regions is occu-
pied by the spatial-relation data. It contains the
foreign key of the mapping type ID. An example
would be a county Dresden. Dresden are mapped Figure 7: Time series of daily infected cases from
onto the state of Saxony and categorized to the Aug. 5, 2020 to Apr. 30, 2022 (a) before and (b) after
mapping type Kreis_To_Bundesland. Next, the Box-Cox transformation, respectively.
table of datavalues for nations is occupied by the
data input. The datavalues table consists of three
foreign keys which originate from the tables of Research (BMBF) and by the Saxon Ministry for Science,
timeperiod_types, regions, datavalues_types. Culture and Tourism (SMWK) with tax funds on the basis
In the presence of these foreign keys, a data merg- of the budget approved by the Saxon State Parliament.
ing process is feasible, which is described on the We thank to Jens Steiner for providing us virtual server
following item. of HZDR.
4. Data merging In addition to the aforementioned
three-foreign keys, date is set as the fourth at-
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