#  STOPPAGE: Spatio-temporal Data Driven Cloud-Fog-Edge Computing Framework
for Pandemic Monitoring and Management

Shreya Ghosh<EMAIL_ADDRESS>Anwesha Mukherjee
<EMAIL_ADDRESS>Soumya K Ghosh<EMAIL_ADDRESS>Rajkumar
Buyya<EMAIL_ADDRESS>Department of Computer Science and Engineering,
Indian Institute of Technology Kharagpur, India  Department of Computer
Science, Mahishadal Raj College, West Bengal, India  Cloud Computing and
Distributed Systems (CLOUDS) Laboratory, School of Computing and Information
Systems,
The University of Melbourne, Australia

###### Abstract

Several researches and evidence show the increasing likelihood of pandemics
(large-scale outbreaks of infectious disease) which has far reaching sequels
in all aspects of human lives ranging from rapid mortality rates to economic
and social disruption across the world. In the recent time, COVID-19
(Coronavirus Disease 2019) pandemic disrupted normal human lives, and
motivated by the urgent need of combating COVID-19, researchers have put
significant efforts in modelling and analysing the disease spread patterns for
effective preventive measures (in addition to developing pharmaceutical
solutions, like vaccine). In this regards, it is absolutely necessary to
develop an analytics framework by extracting and incorporating the knowledge
of heterogeneous data-sources to deliver insights in improving administrative
policy and enhance the preparedness to combat the pandemic. Specifically,
human mobility, travel history and other transport statistics have significant
impacts on the spread of any infectious disease. In this direction, this paper
proposes a spatio-temporal knowledge mining framework, named STOPPAGE to model
the impact of human mobility and other contextual information over large
geographic area in different temporal scales. The framework has two major
modules: (i) Spatio-temporal data and computing infrastructure using fog/edge
based architecture; and (ii) Spatio-temporal data analytics module to
efficiently extract knowledge from heterogeneous data sources. Typically, we
develop a Pandemic-knowledge graph to discover correlations among mobility
information and disease spread, a deep learning architecture to predict the
next hot-spot zones; and provide necessary support in home-health monitoring
utilizing Femtolet and fog/edge based solutions. The experimental evaluations
on real-life datasets related to COVID-19 in India illustrate the efficacy of
the proposed methods.

###### keywords:

Internet of Spatial Things (IoST) , Spatio-temporal data , Healthcare ,
Pandemic , COVID-19 , Deep learning , Knowledge graph.

††journal: Journal of Network and Computer Applications

##  1 Introduction

The significant growth of global travel, improved communication, urbanization,
and greater exploitation of the natural environment have escalated the
likelihood of outbreak of infectious diseases in a larger geographical scale.
These fatal infectious diseases pose real threats for public health and
government agencies require effective health measures in pandemic situations.
In this context, the global outbreak of infectious disease COVID-19
(Coronavirus Disease 2019), caused by SARS-Cov-2, has swept 200+ countries or
territories and contracted more than 160+ million people (as on the third week
of March 2021). This human-to-human disease transmission is highly contagious
and it is already observed that traditional infection-control or public-health
measures to combat COVID-19 are inadequate. In the exigency situation,
governments had already taken varied measures and policies such as travel-
restriction, lockdown111Lockdown: International/ National border closures,
restrictions of public activity (school and business closures), and overall
movement restriction whenever possible of several regions, self-quarantine to
control the rapid growth/ spread of the pandemic. However, still the threat of
second wave of this pandemic persists in several countries of the world.

The rapid development and emergence of Internet of Things (IoT) has
significantly improved all life spheres and industries by connecting billions
of devices and people around the world. IoT has brought useful solutions in
different fields such as agriculture, farming, healthcare, smart building,
smart city, personalized recommendation etc. [1], [2]. The IoT devices need to
send data to cloud servers frequently for processing and analysing the
accumulated data. However, this increases the delay, therefore affects the
Quality of Service (QoS). Here, edge and fog nodes extend the functionality of
the cloud computing by processing, analysing and storing the information near
the end-user. The combination of these emerging technologies facilitate
several time-critical applications, namely, continuous patient monitoring in
home, ambulance, and hospital, as well as assisting users in emergency
situations, like disaster [3].

In the recent times, there is a growing need of analysing spatio-temporal
datasets for extracting meaningful information, and providing location-aware
services, such as trip-planning, weather forecasting and even health-
management. Notably, one of the major aspects in epidemiological analysis is
retrieving the correlations among the people and disease spread in spatial and
temporal dimensions to effectively adapt the countermeasures. For instance, it
was found in a spatio-temporal data analytics [4] that the source of Cholera
outbreak (in London, 1854) was contaminated through bore wells. The finding
was immensely helpful to combat the spread of Cholera. To this end, Internet
of Spatial Things (IoST) combines IoT with spatial context [5], where location
information of the objects plays an important role. To fight against pandemic,
spatio-temporal information and health data need to be integrated and analysed
to predict the spread of the disease as well as assisting users about the risk
of infected. Here, we propose the term Internet of Spatio-Health Things
(IoSHT) as an integration of IoST with IoHT (Internet of Health Things), where
all the health-related information is combined with the location data. This
work mainly focuses on how novel spatio-temporal data analytics method is
beneficial in deciding strategical administrative planning to enhance the
preparedness to curb the pandemic. Undoubtedly, the entire world is suffering
enormous amount of challenges from the pandemic situation caused by COVID-19:

  * 1.

(Q1) How to share information about available medical facilities and disease
spread patterns in varied locations?

  * 2.

(Q2) What is the most effective measure to analyse available data sources and
predict next probable hotspots to further enhance the preparation for
pathological disease outbreak?

To resolve $Q1$, an efficient data infrastructure is required, which can
store, manage and share authentic information amongst stakeholders. Further,
novel spatio-temporal data analytics module is a must for understanding the
impact of human movement data in the disease spread and summarizing the
information in this situation ($Q2$). To this end, we propose a Pandemic-
knowledge graph (PKG) to capture the interdependence and connections among
several entities and users’ movement log, and subsequently identifying the
hotspot/containment zones. The knowledge graph is represented as a multi-
relational graph consisting of entities as nodes and relations as edges, and
support several Artificial Intelligence (AI) related applications. However, it
is difficult to represent mobility and other contextual information utilizing
the conventional knowledge-graph that changes in temporal scale. On the other
side, deep learning is the most feasible solution to extract the correlations
among several factors such as population density, mobility information and
other data-sources.

Table 1: Comparison of significant existing works and STOPPAGE framework in
the context of COVID-19.
A: Efficient COVID-19 related knowledge retrieval and management; B: Movement
data analysis to find out the impact; C: Deep learning module to analyse
pandemic data; D: Cloud-fog-edge enabled framework deployment for faster
response.

SI | Publication | System | Feature
---|---|---|---
| Reference |  | A | B | C | D
[I] | Wang et al. [6] | Comprehensive knowledge discovery framework to extract finegrained knowledge elements | ✓ | ✘ | ✘ | ✘
| from scientific literature, and a case study on Drug Repurposing Report Generation has been presented |  |  |  |
Esteva et al. [7] | Retriever-ranker semantic search engine to resolve complex queries to find scientific answers | ✓ | ✘ | ✘ | ✘
Fernandez et al. [8] | Cause-and-effect network generated from scientific literature and presented a knowledge graph. | ✓ | ✘ | ✘ | ✘
| The paper analyzes comorbidities, symptoms, and discovered over 300 candidate drugs for COVID-19 |  |  |  |
| Chen et al. [9] | Formalization and extraction of meaningful insights from the PubMed dataset and generation of | ✓ | ✘ | ✘ | ✘
|  | knowledge graph using two methods: co-occurrence frequency and cosine similarity |  |  |  |
| Reese et al. [10] | Present flexible framework to integrate biomedical data for producing knowledge graphs (KGs) | ✓ | ✘ | ✘ | ✘
[1] Most of the works deal with knowledge mining related to pharmaceutical
data, drug discovery or extracting information from scientific literature.
No existing works deal with information storage and knowledge extraction from
heterogeneous data sources, like travel-logs, aggregate movement
information, COVID-19 statistics in different spatio-temporal granularity and
find the correlations amongst them.
[II] | Kraemer et al. [11] | The spatial distribution of COVID-19 cases in China and the mobility traces are analyzed | ✘ | ✓ | ✘ | ✘
Pepe et al. [12] | The daily time-series of three different aggregated mobility metrics have been presented. The | ✘ | ✓ | ✘ | ✘
| authors use 170,000 smartphone users’ data to monitor the impact and assisting in making decisions. |  |  |  |
Huajun et al. [13] | Identification of suspected infected crowds based on the human movement trajectories. | ✘ | ✓ | ✘ | ✘
Samuel et al. [14] | Analyse human mobility data to find out disease spread for restrictions order to stay home. | ✘ | ✓ | ✘ | ✘
| Hamda et al. [15] | A mathematical modelling with the movement dynamics and disease outbreaks have been proposed. | ✘ | ✓ | ✘ | ✘
|  | The work helps in taking measures of social distancing and other policies in the 25 counties in the USA |  |  |  |
[III] | Rahman et al. [16] | Distributed deep learning framework for COVID-19 diagnosis in 5G network at the edge | ✘ | ✘ | ✓ | ✘
Kapoor et al. [17] | Forecasting approach using Graph Neural Networks and mobility data for COVID-19 case prediction. | ✘ | ✘ | ✓ | ✘
Luz et al. [18] | An efficient method of COVID-19 screening in chest X-rays in less time and computational cost. | ✘ | ✘ | ✓ | ✘
[II]-[III] To the best of our knowledge, there is no deep learning module
which analyzes the movement patterns and other contextual facts to understand
the impact of the disease spread and predicts hotspots efficiently.
[IV] | Tuli et al. [19] | Cloud computing based framework to predict growth of the epidemic and appropriate | ✘ | ✘ | ✘ | ✓
| strategies have been presented. The authors present a prediction framework. |  |  |  |
Wang et al. [20] | Anonymous and secure aggregation model in fog-based public cloud computing model | ✘ | ✘ | ✘ | ✓
Md et al. [21] | Utilize mobile and fog computing to trace and prevent COVID-19 community transmission | ✘ | ✘ | ✘ | ✓
| Adarsh et al. [22] | Multi-layered architecture to collect real-time information from drones and utilize in | ✘ | ✘ | ✘ | ✓
|  | disease monitoring, control, thermal imaging, social distancing, and statistics generation |  |  |  |
| STOPPAGE | Cloud-fog-edge enabled collaborative framework conducive to construct a novel time-dependent | ✓ | ✓ | ✓ | ✓
| (Proposed framework) | Pandemic-Knowledge graph (PKG) based on the spatio-temporal data. Presenting deep learning based |  |  |  |
|  | analytics module to find out hotspots by mining contextual data. |  |  |  |

Although, the full lockdown measure (complete restriction of the movements) is
one of the optimal solution to reduce the human-to-human transmission rate, on
the counter side, it has a drastic impact on the economy of the country. This
paper analyses the impact of the movement in two phases in the context of
India: (a) In the pre-lock down phase when the International travel was
allowed. It analyses the in-flow of the International flights in several
regions of the country, and the cases reported within a temporal buffer of the
visits. The approximate count of the travellers and air-crafts in the time
span of March $10$ to March $21$, 2020222The international flights were
suspended in India from March 22, 2020 onwards. and the reported
cases333https://api.covid19india.org/ have been analysed; (b) In the next
phase, we develop a disease spread module based on the factors such as
population density, changes of mobility patterns444Google mobility data:
https://www.google.com/covid19/mobility/, POI (point-of-interest) information
etc. Here, we have proposed a variant of co-occurrence pattern where the
mobility information has been augmented.

The major significance and contributions of STOPPAGE are as follows:

  * 1.

IoSHT: An end-to-end spatial data infrastructure framework named, IoSHT,
consisting cloud, fog, edge and IoT layers is presented which stores, manages
and analyses pandemic related data effectively and assists in taking effective
decisions. IoSHT is capable of collecting BAN (Body area network) information
and other contextual data such as environment-temperature, location data,
mobility information etc. Femtolet (a small cell base station with storage and
computation ability) [23] is used for faster reporting of patient information
of hospitals. The delay and power consumption of the user device for health
status monitoring have been determined and compared with the cloud only system
to show the efficacy of the proposed framework.

  * 2.

Pandemic-Knowledge Graph (PKG): We propose a novel time-dependent Pandemic-
knowledge graph (PKG) based on the active cases, POI, road and air-travel
connectivity index and users’ movement history. PKG captures correlations and
impacts among locations, temporal information, movement semantics and
infectious disease spread.

  * 3.

The paper proposes a deep learning network which incorporates the spatio-
temporal data instances and finds out the probable next hotspot zones by
mining the inherent knowledge. To be specific, the deep learning architecture
is capable to incorporate the impacts of human mobility and other spatio-
temporal features of PKG, and finds out the hotspots effectively.

  * 4.

The experimentation have been carried out using the real-life datasets
available in India. The road-network, POI information and air-traffic datasets
have been collected from OpenStreet Map (OSM), Google Map Services and
crawling web-pages of Airports Authority of India (AAI). The information about
the testing facilities and hospitals have been collected from Indian Council
of Medical Research555ICMR:https://www.icmr.gov.in/. The changes of mobility
information have been analysed from Google Mobility Report. Finally, we have
also accumulated individuals’ mobility history from Google Map Timeline from
specific regions of India to evaluate the efficacy of STOPPAGE framework.

The rest of the paper is organized as follows. Section 2 summarizes the
related works in this domain. Then, the system architecture and the deep
learning module have been presented in section 3. Then we present a real-life
example of home-health monitoring system and usages of Femtolet to reduce the
reporting time in section 4 followed by the experimental evaluations in
section 5. Finally, we conclude the paper with research challenges and
opportunities in this domain in section 6.

##  2 Background and Related Works

To begin with, Internet of Spatio-Health Things is an emerging field of IoT,
where integration of IoHT and IoST is carried out to improve the health care
system. In IoHT the IoT devices are integrated with mobile technologies to
process and exchange data for monitoring health condition of individuals [24,
25]. While several IoT-enabled medical devices, BAN accumulate health data and
provide insights into symptoms and trends, the information-exchange,
communication and interoperability of IoT devices make healthcare services
more effective. Inside the health-care centers, IoHT can enable vital signs
monitoring of a patient [24]. However, the COVID-19 pandemic situation can not
be managed by only IoHT solutions, it also requires the monitoring of human-
to-human transmission of the disease. To this end, IoHT needs to be integrated
with IoST to monitor the overall spread of the disease and reduce the
transmission rate by early identification and taking preventive measures, such
as restricting human movement or close contact. Further, IoT devices
associated with real-time location tracking of medical equipment such as
oxygen pumps, wheelchairs, defibrillators make medical resource management
efficient in this pandemic. It is evident that location-information has a huge
significance to curb the pandemic. Specifically, mobility traces play a
pivotal role in several real-life applications, such as, location-prediction
[26, 27], outdoor navigation of visually impaired people [28] etc. It is quite
obvious mobility is also an important factor for spreading infectious
diseases. Internet of Spatial Things (IoST) is a new domain and only a very
few works (e.g. [5, 29]) have been carried out in this direction. In this
paper, we introduce Internet of Spatio-Health Things (IoSHT), where individual
health monitoring and assistance can be provided anytime anywhere, as well as
the information-sharing, pandemic/disease spread pattern are analysed to
enhance the preparedness.

Figure 1: STOPPAGE: Overall Workflow

The onset of COVID-19 has resulted large volume of research papers proposing
several methods to curb the pandemic. While there are numerous articles
dealing with the pharmaceutical methods, we emphasize more on analysis of
contextual information collected from mobile-phones and other sources, such as
movement patterns, cumulative count etc. to predict the future spread of the
disease. Table 1 shows four major features and the existing approaches in the
context of COVID-19. Data management and information retrieval is one of the
major step in combating the pandemic. A comprehensive insight on how IoMT
(Internet of Medical Things) systems can be beneficial in the context of
COVID-19 along with the architecture, tools and technologies are discussed in
[30]. Marcello et al. [31] states that large scale collection of data and
maintaining the privacy and public trust is one of prime step. There are
several works on summarizing the knowledge from drug discovery or other
pharmaceutical methods [8], [9]. Also, researchers [6] have put significant
efforts to summarize information from scientific literature by proposing
knowledge graph. There are works on aggregate movement dynamics and
understanding the impact in disease spread [11], [12]. Huajun et al. [13]
present a novel trajectory pruning method to find out suspected crowds in a
region. However, to the best of our knowledge, no existing works has
extensively studied the movement patterns and contextual information in a
region in the context of COVID-19 and effectively stores and manages
information for better decision making. There are also few works leveraging
cloud-fog paradigm for contact tracing, real-time drone based system and
analysing the growth of the disease [19], [21].

It may be concluded that machine learning and movement data analytics may act
as major scientific tools for combating the spread of COVID-19 pandemic. In
this direction, our work explores and analyses the human movement related data
proposing novel knowledge graph (PKG) and deep learning architecture.

##  3 STOPPAGE: System Architecture

The proposed framework, STOPPAGE has two major modules, namely: spatio-
temporal data infrastructure (SDI) and a data analytics engine running over
the SDI. Figure 1 illustrates the overall workflow and modules of STOPPAGE.
The spatial data infrastructure consists of backbone IoT network and
geospatial resource management module. The spatio-temporal data analytics
engine analyses the COVID-19 data and mobility related information to find out
the probable hotspot zones by deploying deep learning architecture.
Furthermore, STOPPAGEproposes the use of Femtolet to report any health events
in minimal latency. Also, the health status monitoring is provisioned in the
framework by a common API-endpoint. Fig. 2 illustrates the overall block-
diagram of data analytics engine of STOPPAGE. It has three major components
(i) Pandemic knowledge graph (PKG) construction, (ii) deep learning based
movement analytics and (iii) faster reporting of COVID-19 events and data
sharing using cloud-fog-edge based paradigm. Fig. 3(a) represents the SDI
(Spatial Data Infrastructure) and Fig. 3(b) illustrates the deployment of
IoSHT in cloud-fog-edge structure.

Figure 2: STOPPAGE: Spatio-temporal Data Analytics Module Figure 3: (a) SDI
(Spatial Data Infrastructure) in the context of IoSHT, (b) Cloud-Fog-Edge
hierarchical structure

###  3.1 Spatial Data Infrastructure (SDI): An Enabler of IoSHT

Spatial data infrastructure (SDI)666The term Spatial Data Infrastructure (SDI)
was coined in 1993 by the U.S. National Research Council denotes a framework
consisting technologies, policies and orchestration method to create,
exchange, and utilize geospatial information and services among the community
[32]. The goals of this framework are:

  * 1.

ease of search and discovery of geospatial services and information

  * 2.

reduce data duplication of information among the national agencies
(government)

  * 3.

seamless data sharing technique

  * 4.

maintaining the data integrity and privacy

Further, SDI enables the communication between the repositories of cities,
states, countries and industries to share the data and services. In other
words, the difficulty in sharing and accessing different format/structure of
data is resolved by SDI. In the context of COVID-19, different stakeholders,
such as health-department, transportation authority or epidemic-control team
along with the end-users need to seamlessly share data and communicate to
effectively handle the situation. Without a proper underlying infrastructure
of data sharing and managing mechanism, any epidemic-control measure or policy
is difficult to implement. Therefore, in our proposed IoSHT framework, we have
adapted SDI as the backbone infrastructure. The SDI in the context of IoSHT is
represented in Fig. 3(a). To combat the disease spread and take
countermeasures, we need to analyse heterogeneous data-sources. The authentic
data about the country’s population, demography, health care centers etc. can
be found from the government departments. In the context of India, we collect
demography and population data from SOI and open-source platform777Survey of
India: http://www.surveyofindia.gov.in/,888https://www.diva-gis.org/gdata,
health-related information from MoHFW999Ministry of Health and Family Welfare
of India: https://www.mohfw.gov.in/, transport data from NHAI101010National
Highways Authority of India: https://nhai.gov.in/ and raster information
(satellite data) from ISRO111111Indian Space Research Organisation:
https://www.isro.gov.in/. All of these datasets of country’s population,
demography information of the people, health care center related information,
traffic data are useful to analyse the disease spread patterns. Further, the
crowdsourcing data from Google Map, OpenStreetMap (OSM), or Android
application are also beneficial to get the present status of the region. The
data and health service providers provide the metadata information to the
catalog. This catalog is maintained in each of the nearest fog devices, where
the data or the service is generated. User can search the catalog any time,
and extract the metadata of the information or service he/she requires. Once,
the user selects the required service/information, he/she can consume the
service. It may be noted, in the proposed STOPPAGE framework, the services
are: (i) contact the health care center for medical help; (ii) personalized
health-checkup by sending the health-parameters’ values collected from the IoT
devices/BAN; (iii) get the present disease spread information of any region;
(iv) find the risk of infection analysing his/her movement history. In our
model, the metadata information is retrieved in .xml or .json format. All of
these analytics are computed in the cloud servers, and the user gets the
result through the web-dashboard or android app.

###  3.2 Health status Monitoring and Reporting using Fog-Edge Solution

In the previous section, we have discussed the proposed system architecture of
STOPPAGE. For current health status prediction of people in a region, we have
to consider the reports of health care centres/hospitals. Here, Femtolet is
used in the health care centers for faster reporting of the number patients
affected, deceased and cured, because this information is also very important
to monitor the current status of the pandemic in a geographic region. Femtolet
is small cell base station which has storage and processing ability [23].

Figure 4: Smart health care framework based on Femtolet

In the proposed model, in each ward of the hospital Femtolets are deployed to
store and process the information of the patients currently admitted in that
ward. The number of Femtolets to be allocated depends on the coverage area,
storage capacity, data processing speed of the Femtolet, and the number of
patients currently admitted in that ward. The health status of a patient is
collected using BAN and sent to an edge device (for each patient BAN and an
edge device are used). The edge device (ED) accumulates the data and sends to
the Femtolet, connected to the edge device. The Femtolet processes the
incoming data and stores it under the respective patient registry (for each
patient a data registry is maintained). If any emergency situation arises, the
Femtolet sends signal to the connected alarm which after receiving the signal
starts ringing. The health personnel of that ward then takes required action.
The Femtolet periodically sends the patient health status to the private cloud
servers of the health centre, which maintains the information regarding the
number of patients cured, dead and affected. From the private cloud servers
the details of the number of patients affected, cured, and deceased in the
hospital with respect to the disease are reported to the public cloud servers
periodically. The overall architecture of Femtolet based health care model is
shown in Fig. 4. As the public cloud has the information regarding the number
of patients admitted, cured, and dead due to COVID-19 in each hospital, the
total number of admitted, cured and dead patients (considering all the
hospitals’ information) due to COVID-19 is determined. The people can get this
information if he/she wishes to know the same. However, the information of
each hospital will not be disclosed without authorization due to privacy
management. The cloud already has the information regarding hotspot zone (in
Section 3.4 hotspot identification is discussed). By considering both the
information, the people can be made aware of the current scenario.

###  3.3 Spatio-temporal Data Analytics Engine: Pandemic-Knowledge Graph (PKG)
Construction

This section describes the features of Pandemic-Knowledge graph (PKG) and how
it is constructed from the available dataset. This is a multi-layer network -
which captures the movement behaviour individuals as well as the overall
movement semantics of the ROI (Region-of-interest) and the statistics related
to COVID-19, such as growth pattern of active cases, changes of aggregated
mobility patterns, available medical facilities etc. The intuitions behind
constructing knowledge graph are: (i) Transforming the underlying relations of
mobility semantics in a machine-readable format to support information
retrieval and query-processing, (ii) The complex spatio-temporal mobility
dataset can be represented by graph structure effectively instead of other
storage, (iii) Updation of relations and facts can be easily incorporated in
this structure compared to SQL-based processing, therefore provides more
flexible schema, and finally (iv) Knowledge graph provides a higher level
abstraction of information, which may help to extract more complex and
previously unknown interrelations merging more than one relations of the
graph. Therefore, it facilitates building a semantically enriched knowledge-
base.

### Pandemic-Knowledge Graph (PKG)

The Pandemic-Knowledge graph is formulated as triplet of $<s,r,o>$, where
$s\subseteq\Omega$ and $o\subseteq\Omega$ are entities and $r\subseteq\varphi$
is the relation between two entities. The sets of entities and relations are
denoted by $\Omega$ ($\Omega_{1}\cup\Omega_{2}$) and $\varphi$ respectively.
Notably, in our Pandemic-Knowledge graph proposition, each triplet (or fact)
has a time-slot, when the fact is valid. Therefore the facts of $PKG$ takes
the form of $MF:<s,r,o,[t_{1},t_{2}],f>$ where the $[t_{1},t_{2}]$ entry
denotes the time-interval when the fact is true. $f$ is the feature value of
the relation. This $f$ is introduced to capture the semantic correlation with
the COVID data.

The entities consist of: $U\subseteq\Omega_{1}$ and $P\subseteq\Omega_{2}$,
where $U$ and $P$ are the set of users and set of place-ids of a ROI. Each
entity $pl\subseteq P$ has attributes: POI-type, location (latitude and
longitude), enclosing area. Few $pl$ has additional features such as
opening/closing time, available facilities. Each entity $u\subseteq U$ has
unique user-id, age, gender, residence-area, health-profile and travel-
history. The derived attributes of POIs such as GPS footprint or edge segment
are extracted from the GPS log of the users in the ROI. The facts connectedBy
and boundingBox can be directly computed from the road-network and latitude
and longitude information of the POI. Based on the historical movement log the
relations $r\subseteq\varphi$ are extracted.

Apart from the mobility fact label, each edge in the graph has a feature value
associated, which depicts the probability of the occurrence of the edge. Few
such facts are represented as follows:

$visit:MF(u_{a},visit,p_{i},[t_{1},t_{2}],f_{x})=TRUE$ (1)

User $u_{a}$ visits place $p_{i}$ in the time-interval $[t_{1},t_{2}]$ with a
probability of being infected $f_{x}$. Here, the feature value of the fact is
computed by the graph embedding approach [33].

$\begin{split}group:MF(u_{a},group,U^{\prime}\setminus
u_{a},[t_{1},t_{2}],f_{x})=TRUE\\\
\exists(u_{1},\dots,u_{n})\exists(p_{1},\dots,p_{m})\forall_{j}^{n}\\\
[MF(u_{j},visit,p_{1},[t_{1},t_{1}^{{}^{\prime}}],f_{1})\wedge
MF(u_{j},visit,p_{2},[t_{2},t_{2}^{{}^{\prime}}],f_{2})\\\ \wedge\dots\wedge
MF(u_{j},visit,p_{m},[t_{m},t_{m}^{{}^{\prime}}],f_{m})]=TRUE,\;\;m\geq 3\\\
\end{split}$ (2)

The sequence of places $(p_{1},\dots,p_{m})$ are visited by a group of users
$(U^{\prime})$ in the same time-intervals. $f_{x}$ may have a value within
[0,1).

$\begin{split}flow:MF(P_{1},flow,P_{2},[t_{1},t_{2}],f_{x})=TRUE\\\
\exists(u_{1},\dots,u_{n})\forall_{j}^{n}[MF(u_{j},visit,p_{a},[t_{1},t_{1}^{{}^{\prime}}],f_{x})\\\
\wedge
MF(u_{j},visit,p_{b},[t_{2},t_{2}^{{}^{\prime}}],f_{x})=TRUE,\;\;n\geq\nu\\\
\end{split}$ (3)

The above mentioned movement flow (MF) is detected from $p_{a}$ to $p_{b}$ in
a specific time-slot, when a set of users ($u_{1},\dots,u_{n}$) visit the POI
$p_{a}$ followed by $p_{b}$ in the same time-interval. The count ($n$) of GPS
footprints satisfying the fact must be greater than a threshold value $\nu$.
In other words, if $\geq\nu$ people visit the sequence of POIs in same time-
interval, then it is considered that there is a mobility flow from $p_{a}$ to
$p_{b}$.

$hotspot:MF(boundingbox(\\{p\\}),infected,\\{u\\},[t_{1},-],co)=TRUE$

(4)

The bounding box of the set of regions (${p}$) is a hotspot with $co$ number
of infected people.

$connectivity:MF(p_{a},connect,p_{b},[t_{1},t_{2}],f_{p})=TRUE$ (5)

The place $p_{b}$ can be visited from place $p_{a}$ in the time-interval
$[t_{1},t_{2}]$ where $f_{p}$ denotes the number of available routes. It may
be noted that due to the lockdown measure, this fact changes frequently for
all pairs of places. The connectivity index ($CI$) of a place $p$ is computed
based on the connectivity and the in-flow and out-flow of traffic from a
particular place $p$. The high connectivity index between two place represents
higher mobility flow, and thus can be a potent feature for the hotspot
detection.

Next, we define cascading pattern ($P_{ca}$) and co-occurrence pattern
($P_{co}$) extracted from PKG. These patterns can be analysed from the travel
history of the persons who are detected COVID-19 positive. Cascading patterns
represent events whose instances are located together and occur serially. For
instance, analysing the PKG data of a hotspot may reveal that a group of users
visit a POI on a day and spends a particular time-duration within a distance
threshold. Also, air-travel of a group of users from a region with high number
of active cases and new hotspot emergence in the destination region within a
time-span is another example of cascading pattern. Also, $P_{ca}$ covers
patterns such as: higher number of medical facilities in a city-region, more
cases of COVID-19 patient transfer and emergence of hotspot. On the other
side, co-occurrence patterns reflects patterns occurring frequently within a
spatial range and a temporal span. This patterns comprise properties from
different contexts. For example, given three different contexts, namely,
population density, aggregate movement pattern and COVID-19 active/ new cases
(say, within a spatial buffer of 2km and time-span of 7days):
$P_{co}:\\{population-
density>\delta;aggregate\;movement\;pattern>\gamma;Hotspot\\_prob=HIGH\\}$.
Also, $P_{co}$ covers patterns such as: festival/ gathering events in a region
and emergence of new hotspot; or more amount of location-based service
requests (ridesharing services/ food delivery services) and hotspot detection
in the same region within a temporal bound. Algorithm 1 shows the steps of
extracting cascading and co-occurrence patterns from PKG, which in turn helps
in extracting the knowledge of disease spread, and finding the possible
hotspot zones in future. The algorithm needs a threshold value called pattern
participation index ($PI$) to prune the irrelevant patterns.

$\begin{split}PI=\min[Pr(P_{ca}|MF)\;or\;Pr(P_{co}|MF)]\\\
=\min[\frac{facts\;or\;events\;participating\;in\;P_{ca}\;or\;P_{co}}{Total\;no.\;of\;facts\;or\;events\;in\;PKG}]\end{split}$
(6)

Here, $MF$ represents the mobility facts and events present in PKG. It may be
noted that the conditional probability of a pattern; given the possible
similar type of events/ facts is measured by $PI$. It is also an useful
measure for predicting the near future occurrence of a pattern in the spatio-
temporal proximity of an observed instance of a participating event-type. We
set two threshold values namely $PI_{1}$ and $PI_{2}$ for pruning the
irrelevant patterns from PKG. The neighbor spatio-temporal or ST relation
represents the time and spatial constraint on finding the co-occurrence
patterns. In brief, the algorithm first extracts the plausible candidates from
PKG, followed by pruning technique to eliminate irrelevant patterns.

Algorithm 1 Extracting Cascading and Co-occurrence patterns from PKG

Input: Pandemic Knowledge Graph ($PKG$), neighbor ST relation ($NR[]$), set of
event/ fact types ($MF$), pattern participation index thresholds
($PI_{1},PI_{2}$)

Output: Set of $P_{co}$ and $P_{ca}$ patterns

1:$P_{ca}[\;]\leftarrow NULL\;and\;P_{co}[\;]\leftarrow NULL$ $\triangleright$
Create NULL arrays for storing patterns

2:for $i=1$ to $size$ do $\triangleright$ size: maximum length of the patterns

3: filter$(P_{ca}[i],PI_{1})$ $\triangleright$ Remove candidates by analysing
threshold value

4: Genpat(i) $\leftarrow P_{ca}\;of\;length\;(i-1)$ $\triangleright$ Extract
new patterns of i length from patterns with length (i-1)

5: $temp\leftarrow ST-Join(P_{ca}[i],NR)$ $\triangleright$ Perform spatio-
temporal join with instances from direct neighbor relation

6: if $checkThreshold(temp)\geq PI_{1}$ then

7: $P_{ca}[i]\leftarrow Append(temp)$ $\triangleright$ Append the new pattern
if it satisfy the threshold condition

8: end if

9:end for

10:for $t=1\;to(n-1)$ do

11: $te\leftarrow GenCand(PKG(MF,NR))$ $\triangleright$ Generate candidates
from PKG using possible facts/ events and neighbor relation

12: $P_{co}[t]\leftarrow sweepLine(te,P_{2})$$\triangleright$ Sweep-line based
method to extract events satisfying ST-property and threshold

13:end for

Construction of the Pandemic-Knowledge graph consists of following steps: (i)
In the first step, the POIs and users along with the attributes are extracted
and stored. The information are discovered by geo-tagging step and segmenting
the trajectory of individuals at different time-scales. (ii) Next, the links
between the entities (or facts) at different time-instances are discovered by
knowledge graph embedding approach. The relations such as visit, group, flow
etc. are defined. The movement log is analysed to measure the plausibility of
any such facts. However, the facts are checked over different temporal
instances, since the facts or relations of the knowledge graph change with
time-instances. The backbone SDI helps in sharing heterogeneous data,
maintaining the authenticity and integrity of the data to build PKG, and
facilitating services seamlessly to the user.

###  3.4 Spatio-temporal Data Analytics Engine: Deep Learning Architecture to
find out Hotspots

“You cannot fight a fire blindfolded. And we cannot stop this pandemic if we
don’t know who is infected.”121212WHO Director-General’s opening remarks at
the media briefing on COVID-19, dated March 16, 2020 Until there is no vaccine
of this contagious disease to curb the pandemic, identifying the affected
person and restricting the mobility to reduce human-to-human transmission is
the only optimistic solution. Analyzing spatio-temporal pattern of disease
outbreak is an integral part of pandemic control, which helps in identifying
spatio-temporal hotspots (disease emerging areas), and subsequently assists
the planning of emergency measures for monitoring, surveillance, and
prevention of the disease spread. Most of the governments have enforced
partial or complete lockdown to prevent the disease spread. However, the
complete lockdown measure has negative impact on the socio-economic condition
of a country, and more feasible solution is required, such as identifying
high-risk zones (large number of affected person) and impose lockdown at those
regions. In such a scenario, it is absolutely necessary to find the
correlation of the disease spread and other additional information from
spatio-temporal context. For instance, whether the disease spread is related
to population density, demography data etc. Moreover, whether specific regions
like, railway junction, commercial area is more susceptible to infection -
needs to be analyzed.

### Predict the hotspots areas

The primary objective of our analytics module is to identify the high-risk
areas of disease spread in a region. We aim to find out the correlation of
contextual information such as mobility with the probable spread of the
disease.

Of late, deep learning has gained significant research interest to utilize the
correlations between related tasks and improve the classification accuracy by
jointly learning more than one task. Inspired by this paradigm, STOPPAGE
models the deep learning module to predict the hot-spot areas. It may be
noted, that in our problem set-up, the mobility patterns have significant
impact, and learning the representation of these contextual variables is one
of the important aspects of our deep learning architecture. Fig. 5 illustrates
the recurrent architecture of the proposed deep learning module. In our
framework, we have considered the following features:

Figure 5: Deep Learning Architecture of STOPPAGE

  1. 1.

Demographic data ($D$): Demographic data is the statistical data collected
about the characteristics of the population. We have considered $age$,
$gender$, $employment\;status$ and $literacy\;rate$ of people of a region.

  2. 2.

Population density ($PD$): It provides the number of people in each region
along with the condition of their household (slum area/houseless etc.). The
datasets $D$ and $PD$ have been collected from the census information board of
India131313https://www.censusindia.gov.in/(S(esr3lm45pksguc451d45sp55))/2011census/population_enumeration.aspx,
where district and zone-wise (specific locality) population and demographic
data are available.

  3. 3.

Point of Interest (POI): The POI information, such as the locations of the
airports, railway junctions, hospitals, and other commercial areas are
analysed to extract insights into the possible disease spread pattern and in
identifying the hotspot/containment zones. The data has been extracted from
OSM and Google place API141414https://developers.google.com/places/web-
service/search.

  4. 4.

Mobility Report ($MR$): The individual and aggregated mobility information are
analysed for predicting hotspots. The aggregated mobility report is collected
from Google Community mobility
Report151515https://www.google.com/covid19/mobility/. The individual mobility
history is collected from users’ smart-device and Google map timeline of
individuals161616https://www.google.com/maps/timeline?pb in the study-region.

All of these information is managed using an efficient storage method. The
region is segmented into different grids, and the fog devices store the
information of all the grids within its coverage using a hashing scheme [34].
Thus, the information is segregated into different hierarchical segments, and
data extraction becomes efficient.

Next, it is important to find out the spatio-temporal correlation (SC) factor
of the COVID-19 spread in neighbouring regions, which has been computed based
on Moran’s I [35] as follows:

$SC=\frac{o}{\sum_{a}\sum_{b}w_{ab}}\times\frac{\sum_{a}\sum_{b}w_{ab}(v_{a}-\bar{v})(v_{b}-\bar{v})}{\sum_{a}(v_{b}-\bar{v})^{2}}$
(7)

Here, $v_{a}$ represents the number of events/ incidence (here, number of new
cases reported in region $a$) observed in a region $a$, the mean of the newly
confirmed cases in the whole region is $\bar{v}$, the total number of
observations is $o$. $w_{ab}$ represents the spatial adjacency between $a$ and
$b$. Here, we have used different types of adjacency relation instead of only
distance measure. The direct spatial distance measures as follows: (1) two
regions share a common border; (2) direct route is present to reach $b$
directly from $a$. Next, we sort the regions based on their (3) population
density, (4) literacy rate, (5) available medical facilities, (6) aggregate
movement flow. Based on each of the variable, we compute $SC$ where a region
is adjacent with its previous ranked region and the next ranked region. We
compute these values at different time-spans (specifically in each week of the
study period).

$SC=\left\\{\begin{matrix}0&No\;Spatial\;Autocorrelation\\\
>0&Positive\;Spatial\;Autocorrelation\\\
<0&Negative\;Spatial\;Autocorrelation\\\ \end{matrix}\right.$ (8)

As shown in the equation (8), when the computed value of SC is 0, it
represents no spatial autocorrelation. Similarly, the larger absolute SC value
demonstrates stronger spatial autocorrelation. This information (SC on six
different adjacency metric) is used in the deep learning module to efficiently
predict next possible hotspot zones.

Specifically, a trajectory segment/ movement history of an individual records
the consecutive stay-points in the path of the user, which are continuous
spatial locations. Each grid is identified with unique id. Thus, we discretize
the continuous spatial location information. We use the skip-gram model to
learn the representation of spatial locations ($l_{1},l_{2},\dots,l_{T}$) in
the training dataset:

$\frac{1}{T}\sum_{t=1}^{T}\log[p(l_{t-c},\dots,l_{t-1},l_{t+1},\dots,l_{t+c}|l_{t})]$
(9)

which can be written as:

$\begin{split}=\frac{1}{T}\sum_{t=1}\sum_{con}\log p(l_{t+j}|l_{t})\\\
where\;context:-c\leq j\leq c,j\neq 0\end{split}$ (10)

where $l_{t+j}$ is the neighboring location of the present location $l_{t}$.
Based on the spatial proximity rule that two neighboring locations have
similar representation, we use softmax function as defined:

$p(l_{t+j}|l_{t})=\frac{\exp(a_{l_{t+j}}^{N}a_{l_{t}})}{\sum_{l_{i}\in
l}\exp(a_{l_{i}}^{N}a_{l_{t}})}$ (11)

Thus, the embedding layer converts the spatial locations into:

$a_{l_{1}},a_{l_{2}}\dots,a_{l_{N}}$ (12)

where $N$ is the number of the regions. Next, we embed the temporal
information. The representation of the temporal information should comprises
of day of the week, timestamp and time-duration spent in a location. Here, the
skip-gram model is not efficient, and we use paragraph-vector model and get
the vector representation of temporal information. After embedding the spatial
and temporal features, we deploy a bidirectional LSTM layer to capture the
shared information of both the tasks. The basic building block of the LSTM
layer is as follows:

$\begin{split}i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}),\;f_{t}=\sigma(W_{f}^{\cdot}[h_{t-1},x_{t}]+b_{f})\\\
o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o});\;\\\ c_{t}=f_{t}\cdot
c_{t-1}+i_{t}\cdot\tanh(W_{c}\cdot[h_{t-1},x_{t}]+b_{c})\end{split}$ (13)

where the input, output, and forget gates are represented by $i,o$ and $f$,
respectively. The hidden representation is denoted by $h_{t}$. Since, STOPPAGE
uses a bidirectional LSTM, the output of the layer is modified as:

$h_{t}=\vv{h_{t}}+\overleftarrow{h_{t}}$ (14)

Here, the output from the forward and backward propagation layer are
represented by $\vv{h_{t}}$ and $\overleftarrow{h_{t}}$ respectively.

The next layer utilizes a Gated recurrent unit (GRU) which is similar to
$LSTM$. This layer is used for extracting the impact of the other contexts
such as demography, age and aggregated movement etc. GRU has reset and update
gate which are formally defined as:

$\begin{split}z_{t}^{{}^{\prime}}=\sigma(W_{z}^{{}^{\prime}}\cdot[h_{t-1}^{{}^{\prime}},x_{t}^{{}^{\prime}}])\;\;r_{t}^{{}^{\prime}}=\sigma(W_{r}^{{}^{\prime}}\cdot[h_{t-1}^{{}^{\prime}},x_{t}^{{}^{\prime}}])\\\
h_{t}^{{}^{\prime}}=(1-z_{t}^{{}^{\prime}})\ast
h_{t-1}^{{}^{\prime}}+z_{t}^{{}^{\prime}}\ast\tanh(W_{h}^{{}^{\prime}}\cdot[r_{t}^{{}^{\prime}}\ast
h_{t-1}^{{}^{\prime}},x_{t}^{{}^{\prime}}]\end{split}$ (15)

where $W_{z}^{{}^{\prime}}$, $W_{r}^{{}^{\prime}}$ and $W_{h}^{{}^{\prime}}$
are weight matrices. The update gate ($z_{t}^{{}^{\prime}}$) helps to extract
the required information from the past time-step and pass to the future. On
the other hand, reset gate ($r_{t}^{{}^{\prime}}$) is used to determine how
much past information needs to be eliminated. Therefore, in the proposed
model, the GRU is capable to filter and store information utilizing the reset
and forget gates. Here, we use specific information such as $SC$, $P_{ca}$,
$P_{co}$ etc. This is crucial to capture the different phase of the disease
spread, since GRU layer eliminates the vanishing gradient problem, but pass
the relevant information to the next steps of the network. Note that in the
proposed approach, deep LSTM architecture is used, allowing the network to
learn at different time scales over the input. Furthermore, they can make
better use of parameters by distributing over the space through multiple
layers.

Another important module of the architecture is attention mechanism. It is
used to capture the relationship between all of these contextual factors and
hotspots in different spatio-temporal resolutions. Here, we have used the dot
product attention function $f_{att}$ and the representation is defined as:

$r^{att}=\sum_{i=1}^{N}\frac{\exp(f_{att}(h_{t_{i}}^{{}^{\prime}},m_{t_{i}}))}{\sum_{i=1}^{N}\exp(f_{att}(h_{t_{i}}^{{}^{\prime}},m_{t_{i}}))}h_{t_{i}}^{{}^{\prime}}$
(16)

Here, $m$ is the vector representation of spatio-temporal features from the
previous layer. The GRU input layer is replaced by the weighted representation
($r_{att}$). On the other side, air travel connectivity index is utilized for
the initial phase of disease spread. Here, attention layer provides more
attention to the long-distance travels and connectivity between regions. For
both the phases of disease spread, the final layer is softmax layer where all
the outputs from different layers are fed.

$\begin{split}y^{{}^{\prime}}=softmax(W^{{}^{\prime}}h^{{}^{\prime}}+b^{{}^{\prime}})\end{split}$
(17)

The network is trained to minimize the cross-entropy loss as:

$L(y,y^{\prime})=-\sum_{i=1}^{TS}\sum_{j=1}^{Cl}y^{\prime
j}_{i}\log(y^{j}_{i})$ (18)

The number of training samples and number of classes are represented by $TS$
and $Cl$. We define 4 classes here: (i) $>20$ cases within 500m; (ii) $>50$
cases within 1km; (iii) $<5$ cases within 1km and (iv) $<10$ cases within 2km.
We define (i) and (ii) as hotspot zones. $y^{\prime}$ is the ground truth and
$y$ is the prediction probability.

It may be noted that, in the $MR$ feature analysis, we also combine the
information if any neighborhood region was a containment zone/ hotspot zone
(high infection rate) in last 14 days. This information is stored in the
respective fog devices of the region. Thus, the architecture encodes and
learns different mobility semantics and other parameters at varied contexts
and finds out whether the region is the next hotspot zone.

##  4 Applications of STOPPAGE

In this section, we show how STOPPAGE can be utilized to combat pandemic
situation. We have discussed two use-cases, and how data analytics engine and
SDI of STOPPAGE can be utilized in such applications. An Android application
along with the API-endpoints have been developed to retrieve and share
information. We have also shown delay and power consumption to illustrate the
feasibility of STOPPAGE in terms of latency and energy consumption.

###  4.1 Use-Case: Find the Suspected Crowd

This is also observed that the risk of the infection spread depends on other
factors also. For instance, the probability of infecting other people are much
higher when the asymptomatic person is in the workplace or shopping mall
compared to when he is in a less crowded park or playground. Also, the time-
duration of contact with other person is also an important factor. Therefore,
STOPPAGE can also use the individual mobility history to find the suspected
people from the PKG analysis. If any of the users is tested COVID19-positive,
then the information is send to the nearby fog device and based on the PKG
analysis, all other users are notified. Alongside, using the reverse pruning
technique, we can also find out the asymptotic person by analysing the
mobility data. Analysing and assisting people in real-time is the major
challenge here. The fog devices keep track of a particular locality and assist
users within that region. Since, the dataset is huge, any compute-intensive
task is carried out in the distant cloud servers. However, since the fog nodes
store the results, the delay in communication is reduced.

###  4.2 Use-Case: Health Status Monitoring and Assistance at Home

In this crucial time, it may not be possible to clinically test everyone.
However, the preliminary testing can be done at home. The basic health
parameter values such as body temperature, blood pressure (systolic and
diastolic), pulse rate, SPO2 (oxygen saturation) level can be collected using
BAN and sent to the smart phone of the user. In case of COVID-19, the common
symptoms are fever, abnormality in breathing, cough etc. Here, fever and
breathing abnormality can be predicted from the collected body temperature,
pulse rate and SPO2 level. The collected health parameter values are sent to
the smart phone. The smart phones already have GPS (Global Positioning System)
tracking module, the environmental temperature and humidity detecting module.
It can check that whether the collected data values fall in the normal range
with respect to the user’s health profile and contextual information
(location, motion and acceleration, environmental temperature and humidity).
If the collected value falls in the normal range, the smart phone will show
that the health status is normal.

Figure 6: MyHealth: Android app for health monitoring

Let the normal range of a health parameter $hp$ is $hp_{up_{u}}$ to
$hp_{low_{u}}$, i.e. the upper value is $hp_{up_{u}}$ and lower value is
$hp_{low_{u}}$ for a user $u$ with respect his/her health profile and
contextual information, where $hp\in H$ and $H$ is the set of health
parameters. Let $hp_{cl_{u}}$ is the collected value of the health parameter
$hp$ for the user $u$. If $hp_{up_{u}}\geq hp_{cl_{u}}\geq hp_{low_{u}}$, then
the collected health parameter value resides in normal range. This checking is
done for all health parameters considered in the experiment. If $hp_{cl_{u}}$
falls in normal range for $hp$ $\forall H$, then the status is predicted as
normal. Otherwise, the predicted health status is abnormal and the smart phone
will show an alert message, and send the collected data with user’s health
profile and contextual information to the cloud. The health care centre of the
region where the user belongs will access the data and contact the user for
further check up. In this way a preliminary health monitoring service can be
provided to the user.

####  4.2.1 MyHealth: An Android App for Health Monitoring

We have designed an Android application (app) called, MyHealth which can be
used for personal health status checking. The collected health data, profile
and contextual information are processed, and if the user’s health status
seems to be abnormal (e.g. body temperature is high, pulse rate is abnormal,
SPO2 level is low), then the user is suggested to be at home, and medical
person will contact him/her soon for further medical tests (refer Fig. 6). It
indicates faster health care provisioning than the traditional system, where
the user has to contact a medical staff by himself/herself for further medical
tests all the time.

####  4.2.2 Delay in Health Status Prediction

Let the total delay in collecting health parameter data and transmission to
the smart phone is $Delay_{h}$, and the delay in collecting contextual
information is $Delay_{c}$. The health profile is already present inside the
smart phone. As the contextual information collection takes place
simultaneously while the smart phone receives health parameter data from BAN,
the total delay in collecting and accumulating health data with profile and
contextual information is given as:

$Delay_{ca}=Delay_{mob}+max(Delay_{h},Delay_{c})$ (19)

where $Delay_{mob}$ is the delay in data accumulation. Let the amount of data
transmitted in uplink communication $i$ is $D_{i}$, and in downlink
communication $j$ is $D_{j}$. Let the data transmission rate in uplink
communication $i$ is $R_{ui}$, in downlink communication $j$ is $R_{dj}$. Let
the failure rate in uplink communication $i$ is $f_{ui}$, in downlink
communication is $f_{dj}$ respectively. If there are $m$ uplink communications
and $n$ downlink communications take place, then the total communication delay
is given as,

$\begin{split}Delay_{com}=\sum_{i=1}^{m}{(1+f_{ui})\cdot(D_{i}/R_{ui})}+\\\
\sum_{j=1}^{n}{(1+f_{dj})\cdot(D_{j}/R_{dj})}\end{split}$ (20)

Let the amount of data processed inside the smart phone is $D_{mob}$, inside
the fog device is $D_{f}$, and inside the cloud is $D_{c}$, and the processing
speed of the smart phone, fog device and cloud are $S_{mob}$, $S_{f}$ and
$S_{c}$ respectively. Then the delay in processing the data is given as,

$Delay_{pro}=(D_{mob}/S_{mob})+(D_{f}/S_{f})+(D_{c}/S_{c})$ (21)

The total delay in predicting health status is given as,

$Delay_{tot}=Delay_{ca}+Delay_{com}+Delay_{pro}$ (22)

The delay in predicting health status in case of proposed system and in case
of cloud only system are presented in Fig. 7. This is observed that the use of
the proposed health care system reduces the delay $\sim$(21-60)% than the
cloud only health care system. In cloud only system the communication delay is
higher, where as in proposed system the intermediate nodes participate in data
processing, which in turn reduces the communication delay. Subsequently, the
total delay is reduced in the proposed system.

Figure 7: Delay in health status prediction Figure 8: Power consumption of the
smart phone during health status prediction period

####  4.2.3 Power Consumption of Smartphone during Health Status Prediction/
Monitoring

In the module, smartphones are used as an edge device, which collects,
accumulates and sends health and context related information. In this context,
it is important to measure and optimize the power consumption of the mobile
device. Let the power consumption of the smart phone in data transmission,
reception, active and idle mode are $P_{t}$, $P_{r}$, $P_{a}$, and $P_{i}$
respectively.

Figure 9: Experimental Result (Five regions have been selected and the
boundary of all hotspots within the regions have been predicted by STOPPAGE)

The power consumption of the smart phone during data collection and
accumulation period is given as,

$P_{ca}=(P_{a}\cdot Delay_{mob})+(P_{r}\cdot max(Delay_{h},Delay_{c}))$ (23)

Let for the smart phone the number of uplink communication is $k$ and downlink
communication is $q$. Then the power consumption of the smart phone during
communication period is given as,

$\begin{split}P_{com}=(P_{t}\cdot(\sum_{i=1}^{k}{(1+f_{ui})\cdot(D_{i}/R_{ui})}))+\\\
(P_{r}\cdot(\sum_{j=1}^{q}{(1+f_{dj})\cdot(D_{j}/R_{dj})}))+\\\
(P_{i}\cdot(\sum_{i=1}^{(m-k)}{(1+f_{ui})\cdot(D_{i}/R_{ui})}))+\\\
(P_{i}\cdot(\sum_{j=1}^{(n-q)}{(1+f_{dj})\cdot(D_{j}/R_{dj})}))\end{split}$
(24)

The power consumption of the smart phone during data processing period is
given as,

$P_{pro}=P_{a}\cdot(D_{mob}/S_{mob})+P_{i}\cdot(D_{f}/S_{f})+P_{i}\cdot(D_{c}/S_{c})$
(25)

The power consumption of the smart phone in the proposed system and cloud only
system are presented in Fig. 8. As in the proposed system the intermediate
nodes participate in data processing the delay is reduced, and consequently
the power consumption of the smart phone is also reduced. This is observed
that the proposed system reduces the power consumption of the smart phone by
$\sim$(19-50)% than the cloud only system.

Figure 10: Mobility pattern change across India

In the conventional strategies followed in most of the developing countries,
the user sees the parameter values, answers to the questions regarding health
monitoring over voice call/message, and then based on his/her answers, the
health status is predicted, and if it seems to be abnormal, then the user is
asked to visit a health centre for further check up. It is quite obvious this
whole procedure takes much time. If the mobile health monitoring takes place,
the latency is quite less than the conventional health monitoring system.
Moreover, as the data collection and processing entirely takes place by the
device, the accuracy of the prediction and reliability are also higher. On the
other hand, the user can do this residing at home; no need to visit the health
centre for preliminary check up until and unless an emergency occurs. This in
turn helps to avoid unnecessary gathering and social distancing. Thus, we
strongly recommend to use mobile health monitoring in such situations to avoid
unnecessary delay and gathering.

Table 2: Comparison of Accuracy Measure and Ablation Study of the Deep Learning Module for Identifying COVID-19 Hotspots Model | Precision | Recall | Accuracy
---|---|---|---
KNN | 54.03% | 54.8% | 56.05%
DT | 50.08% | 51.87% | 54.9%
SVM | 60.04% | 58.56% | 62.48%
CNN | 75.12% | 72.30% | 77.15%
ST-RNN | 77.05% | 72.68% | 78.72%
Ablation Study of the Deep Learning Module of STOPPAGE
w/o PKG Integration | 82.8% | 83.74% | 86.18%
w/o Attention Layer | 84.4% | 85.78% | 88.09%
w/o bi-LSTM | 85.20% | 87.51% | 93.01%
w/o two-phase Analysis | 82.04% | 86.18% | 90.45%
STOPPAGE (Full) |  88.15% |  90.25% |  93.12%

##  5 Implementation and Evaluation

In this section, we present the implementation details of the proposed
framework, STOPPAGE. We have used the compute engine and app engine of Google
Cloud Platform (GCP) to carry out the spatio-temporal data analysis. In the
test-bed, we have used Raspberry Pi 3 as the fog device. We have also designed
an Android application, using Android Studio 4.1 with Firebase database
support, which collects the location, acceleration, proximity, temperature and
light sensor data from the smartphone’s in-built sensors using the Android
sensor framework. The application is also capable to communicate with the
wearable devices such as smart-watch (Fitbit). In the Raspberry Pi 3, we have
installed the Eddystone Bluetooth Beacon, for sending data periodically.

Table 3: Comparison of Execution time of STOPPAGE for retrieving PKG
information

No. of Entities | Execution Time (in minute)
---|---
| Huang et al. [33] | Trivedi et al. [36] | STOPPAGE
$1\times 10^{3}$ | 2.2 | 2.5 | 2.6
$5\times 10^{3}$ | 8.9 | 7.4 | 3.23
$10\times 10^{3}$ | 26.01 | 20.4 | 5.2
$20\times 10^{3}$ | 35.18 | 28.7 | 9.12
$40\times 10^{3}$ | 40.54 | 34.2 | 16.5
$50\times 10^{3}$ | 49.2 | 40.4 | 21.32

For training the deep learning module, we have used popular Adam algorithm to
update the network weights iteratively and optimize the parameters. The run-
time (training time) of the model is 86minutes for all five regions for 100
rounds (epochs). We also observe the performance (accuracy@5 for hotspot
prediction) variation of the model using different cell size and batch size,
and get optimal results in cell size $64$ and batch size $10$.

For evaluating the spatio-temporal analysis, we have implemented the methods
in GCP VM and QGIS framework. The overall mobility change in India is shown in
Fig. 10. It also shows the phase-wise lockdown and unlockdown measures taken
by the government. Based on the overall situation of the disease spread, we
have evaluated the prediction in two phases: (i) initial phase of the disease
spread (upto unlock 1.0) and (ii) next phase (till mid Dec). The accuracy of
predicting the hotspots is shown in Fig. 9. For this prediction, we have
considered the study region as West Bengal, a state of India. We have executed
the proposed method on five selected regions of the state where we have the
access of individual mobility history of the residents. The precision and
recall value computation captures the efficacy of STOPPAGE to predict the
boundary of the hotspot region. The ground-truth data has been collected from
the state government dashboard171717https://wb.gov.in/containment-zones-in-
west-bengal.aspx in daily basis. It is observed that the precision and recall
in both phases of the disease spread is quite high. STOPPAGE can identify the
hotspot areas apriori with more than $85\%$ precision and recall values. Table
2 presents the accuracy measure of STOPPAGE along with the baseline methods
namely, KNN, Decision Tree (DT), SVM, CNN[37] and ST-RNN[38]. The parameter
for KNN is selected as $5$. We have chosen radial basis function (RF) as the
activation function in NN. A linear kernel is selected for SVM. The results
for different runs are captured and average precision, recall and accuracy
measure is reported. We have performed the ablation study of the deep learning
architecture of STOPPAGE to demonstrate the significance of each of the
modules. Table 2 shows the comparison values of the baselines and the ablation
result. It is observed that STOPPAGE outperforms all other baselines in a
significant margin. The ablation study proves that the architecture is
suitable and better compared to all other possible set-ups. This shows the
novelty and the significance of the deep learning module of STOPPAGE in
COVID-19 context. The Android application can be used to monitor the current
pandemic status of the country, and for assisting the users. It retrieves the
data from the nearest fog device regarding the predicted hotspot areas and
sends the notification to the users within the predicted hotspots.

Figure 11: Accuracy of PKG

One of the major contributions of this paper is constructing the Pandemic-
Knowledge graph (PKG) and summarizing the information in the knowledge graph
structure. We have implemented PKG with real-life dataset and compared the
execution time to extract relations or facts with different number of entities
ranging from 1000 to 50,000. The performance of PKG is compared with baseline
knowledge graph [33] and temporal knowledge graph [36]. The execution time to
extract the facts or relations is shown in TABLE 3. It has been observed that
our proposed framework STOPPAGE has lower execution time than the other
existing approaches, which indicates STOPPAGE provides better time-efficiency.
Fig. 11 shows the accuracy measures for two tasks namely, relation resolution
and entity resolution. The relation resolution finds out facts or relations
from the knowledge graph, and entity resolution refines the entity information
effectively. In the temporal knowledge graph [36], the authors present a novel
deep learning based framework to combine the evolving entities and their
dynamically changing relationships over time, and represented that as
knowledge evolution. However, in the context of COVID-19, this approach does
not work well. It is observed that the baseline and temporal KG have achieved
accuracy within $0.42-0.72$ range, while STOPPAGE has an accuracy of
$0.91-0.93$ range. Therefore, it is observed empirically that STOPPAGE has
outperformed other existing approaches in a larger margin in terms of accuracy
and execution time.

##  6 Conclusions and Future work

In this paper, we have presented an efficient pandemic management and
monitoring framework, namely, STOPPAGE leveraging the Internet of Spatio-
Health Things - a Cloud/Fog/Edge/IoT based efficient SDI to overcome the
various present challenges due to the outbreak of COVID-19. Specifically, we
have presented a novel spatio-temporal data analytics method using deep
learning to augment varied contextual information to identify probable
hotspots for taking preventive measures. A novel pandemic knowledge graph
(PKG) is proposed to find out the correlations of disease outbreak and
semantic spatio-temporal information. STOPPAGE presents varied types of
movement semantics (cascading and co-occurrence patterns), and spatio-temporal
correlation with the COVID-19 spread which are potent features to predict next
hotspot zones. The proposed fog/edge based architecture outperforms existing
approaches in terms of delay, power consumption and accuracy. It is shown that
SDI is one of the major components to combat the pandemic situation. In the
future, we will explore the possibility of incorporating climatology data to
enhance the accuracy of STOPPAGE. STOPPAGE facilitates a spatio-temporal data-
driven analytics engine running over SDI backbone which is capable to extract
meaningful insights from available data utilizing technologies like IoT,
mobility analytics in spatial context. However, in future, we will extend
STOPPAGE by incorporating domain knowledge from medical and epidemiology
experts to make the system more robust and reliable to combat any type of
pandemic situation efficiently.

## Acknowledgment

The work is partially supported by DST Research Grant. The authors are
thankful to the local medical authority and stakeholders for supporting this
work.

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