\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage[natbibapa]{apacite}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[ngerman,english]{babel}



\usepackage{float}








\begin{document}

\title{TRACEABILITY BY SMART DEVICES, CELLULAR CHANNELS AND SOCIAL NETWORK
TOPOGRAPHY MAY HELP TO COUP COVID-19 TRANSMOGRIFY~~~}



\author[1]{Muhammad Aamir Shahzad }%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 08, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Epidemic diseases contiguously propagate between propitious substances,
along with sub-rosa characteristics specifically contiguousness and
replication speed may expound ferocity of epidemic diseases such as
COVID-19. Most likely, due to high contiguous velocity; in very short
time COVID-19 may have become global pandemic disease and key menace to
manhood. Deterrence of propagation may help in devastate virus
transmogrify using instantaneous traceability of individualized health
activities and trackability of public footprints using smart devices,
radiography, and social networks.%
\end{abstract}%



\sloppy


\section*{}

{\label{section}}

Coronavirus COVID-19; first uncovered in Wuhan, China by December 2019
and in very short period it became biggest threat to mankind and
emulated as a global pandemic by infecting several countries and
territories, and 1 international conveyance cruise ship harbored in
Yokohama, Japan {[}7,8{]}. Till February 10, 2020; COVID-19 has infected
43,099 humans and left 1,018 dead by this global pandemic {[}11{]}. Many
countries have lockdown, air travel have been abandoned and sealing of
national borders have been enforced in effort to isolate public and
geographical areas from the contagiousness of coronavirus eruption.
Scientific community and World Health Organization has published
precautionary measures for the possible prevention of COVID-19 infection
contagiousness {[}11{]}. In an effort to break the chain of COVID-19
infection and detection of infected persons; several safety measures
have been enforced globally; various territories has deployed handheld
and fixed thermal scanners in public transitioning areas such as
airports, railway stations and malls; by aiming to detect feverishness
and individualized body temperature rise pattern to screen and conclude
the presence of COVID-19 infection. Scientific community is assiduous to
evaluate grueling properties and characteristics of COVID-19 whilst
propagation and replication gimmick of the infection is still not much
known as ref. by {[}8{]}. It may be essential to construe virus
propagation to torpedo infection transmogrify. Some early symptoms of
COVID-19 infection may follow by flue, increased body temperature, sore
throat resulting in voice change, arrhythmic heart rate, shortness of
breathing, body aches, dullness, and physical inactivity {[}6,10{]}. But
most of the infected people initially remains veiled and realized,
reported, or screened on later stages till then public and surroundings
may have already been affected by suspected virus carrying person,
obscure propagating and multiplication of COVID-19 infection may
instigate due to said reasons {[}8{]}. Beside rapid proliferation of
COVID-19 infection; analogously, instantaneous surveillance and
traceability of public is indispensable to monitor and analyze physical
activities, individualized health, conditioned-stimulus and tracking by
geographic information system GIS to depict and understand social
propagation model of COVID-19 {[}2,8{]}. Precision technologies and
techniques has remarkable and proven contribution for mankind, these
smart technologies have changed everyday life; high-speed communications
and advancements has emerged world in digital era. Regardless of various
technologies, recent statistics are showing existence of 9.42 billion
mobile connections globally, which are 121\% of 7.75 billion existing
population of world and presence of unique mobile connections are 5.17
billion which is 66.7\% of world population, majority of users have
smart phones and similar gadgets. Radiography, smart phones and smart
watches such as Samsung, Apple, 3M, FitOn and Garmin which are mostly
equipped with precision body sensors and have unique features to support
healthy life by routinely tracking health activities such as body
temperature, heart rate, oxygen saturation, respiratory health, physical
activities, facial expression and voice recognition {[}4,5,12-15{]}.
Along with health tracking facilities, to support physical activities
and to help in medical emergency; smart devices retain location tracking
capabilities in collaborating with various service providers and popular
social networks. With strong privacy features, these smart devices and
radio signals may help in COVID-19 infection traceability, detection,
smart isolation and may alert to an established centralized pandemic
surveillance center for the traceability and detection of new and
unidentified infected individuals or specific infested community by
collecting individualized health data such as abnormal temperature,
unrhythmic heart rates, abnormal oxygen saturation, respiratory
distress, atypical voice pitch and unconscious facial recognition
{[}2,8,12-15{]}. Additionally, social network models plausible on real
life social structures, activities, relations and comprises on everyday
life activities for the wellbeing and socialization of mankind {[}3{]}.
Social network connectivity propagating and multiplication model may
prove to be a superlative model to understand and discover COVID-19
infection propagation pattern and may help in deterrence of COVID-19
infection proliferate by tracing societal footprints. Using smart
phones, public may also feed conditioned-stimulus data either on
existing social networks or using modified and specified COVID-19 social
network applications {[}9{]}. Location tracking services may help to
detect GIS based footprints and traceability in collaborating with
individualized health activities for the existence of infected persons,
specific infected location, and community. Such services may provision
calculation of infection patterns, spread rate and beckoning of
infectious areas to help in smart isolation, public protection, helping
patients and to support health authorities for medical assistances which
may lead the establishment of COVID-19 analytics. Furthermore,
applications of radio frequency identification RFID, Bluetooth and Wi-Fi
technology may help in automatic alerting, social distancing, and
proximity crowd analysis; automatic segregation, detection, and
isolation of healthy, infected, and cured persons even in crowds
{[}1,16,17{]}. Based on such instantaneous monitoring, data collection
using IoT and applications of big data, deep machine learning and data
mining may help to depict and predict COVID-19 infection propagation
prototype, its obstruction and characterization for the elimination of
COVID-19.

\textbf{References:}

\begin{enumerate}
\tightlist
\item
  Ajami, S., \& Rajabzadeh, A. (2013). Radio Frequency Identification
  (RFID) technology and patient safety.~Journal of research in medical
  sciences: the official journal of Isfahan University of Medical
  Sciences,~18(9), 809.
\item
  Dong, E., Du, H., \& Gardner, L. (2020). An interactive web-based
  dashboard to track COVID-19 in real time.~The Lancet Infectious
  Diseases.
\item
  Garrido, M., \& Halavais, A. (2013). Mapping networks of support for
  the Zapatista movement: Applying social-networks analysis to study
  contemporary social movements. In~Cyberactivism~(pp. 175-194).
  Routledge.
\item
  Harvard Health. 2019.~Heart Rhythm Monitoring With A Smartwatch -
  Harvard Health. {[}online{]} Available at:
  \textless{}https://www.health.harvard.edu/heart-health/heart-rhythm-monitoring-with-a-smartwatch\textgreater{}
  {[}Accessed 20 February 2020{]}.
\item
  Iribarren, S. J., Schnall, R., Stone, P. W., \& Carballo-Di\selectlanguage{ngerman}éguez, A.
  (2016). Smartphone applications to support tuberculosis prevention and
  treatment: review and evaluation.~JMIR mHealth and uHealth,~4(2), e25.
\item
  Linton, N. M., Kobayashi, T., Yang, Y., Hayashi, K., Akhmetzhanov, A.
  R., Jung, S. M., \ldots{} \& Nishiura, H. (2020). Epidemiological
  characteristics of novel coronavirus infection: A statistical analysis
  of publicly available case data.~medRxiv.
\item
  Mizumoto, K., \& Chowell, G. (2020). Transmission potential of the
  novel coronavirus (COVID-19) onboard the Diamond Princess Cruises
  Ship, 2020.~Infectious Disease Modelling.
\item
  Sun, K., Chen, J., \& Viboud, C. (2020). Early epidemiological
  analysis of the coronavirus disease 2019 outbreak based on
  crowdsourced data: a population-level observational study.~The Lancet
  Digital Health.
\item
  West, D. M. (2015). Using mobile technology to improve maternal health
  and fight Ebola: A case study of mobile innovation in Nigeria.~Center
  for Technological Innovation at Brookings,~19, 308-312.
\item
  Yang, W., Cao, Q., Qin, L., Wang, X., Cheng, Z., Pan, A., \ldots{} \&
  Yan, F. (2020). Clinical characteristics and imaging manifestations of
  the 2019 novel coronavirus disease (COVID-19): A multi-center study in
  Wenzhou city, Zhejiang, China.~Journal of Infection.
\item
  Who.int. 2020.~Coronavirus. {[}online{]} Available at:
  \textless{}https://www.who.int/emergencies/diseases/novel-coronavirus-2019\textgreater{}
  {[}Accessed 10 February 2020{]}.
\item
  Mabrouk, M., Rajan, S., Bolic, M., Forouzanfar, M., Dajani, H. R., \&
  Batkin, I. (2016). Human breathing rate estimation from radar returns
  using harmonically related filters.~Journal of Sensors,~2016.
\item
  Yue, S., He, H., Wang, H., Rahul, H., \& Katabi, D. (2018). Extracting
  multi-person respiration from entangled RF signals.~Proceedings of the
  ACM on Interactive, Mobile, Wearable and Ubiquitous
  Technologies,~2(2), 1-22.
\item
  Wu, R., Liaqat, D., de Lara, E., Son, T., Rudzicz, F., Alshaer, H.,
  \ldots{} \& Gershon, A. S. (2018). Feasibility of using a smartwatch
  to intensively monitor patients with chronic obstructive pulmonary
  disease: prospective cohort study.~JMIR mHealth and uHealth,~6(6),
  e10046.
\item
  Adib, F., Mao, H., Kabelac, Z., Katabi, D., \& Miller, R. C. (2015,
  April). Smart homes that monitor breathing and heart rate.
  In~Proceedings of the 33rd annual ACM conference on human factors in
  computing systems~(pp. 837-846).
\item
  Faragher, R., \& Harle, R. (2014, September). An analysis of the
  accuracy of bluetooth low energy for indoor positioning applications.
  In~\emph{Proceedings of the 27th International Technical Meeting of
  The Satellite Division of the Institute of Navigation (ION GNSS+
  2014)} ~(Vol. 812, pp. 201-210).
\item
  Lee, B. (2016).~\emph{U.S. Patent No. 9,451,397} . Washington, DC:
  U.S. Patent and Trademark Office.
\end{enumerate}

\selectlanguage{english}
\FloatBarrier
\end{document}

