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\begin{document}
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\title{\Large\bf Survey of Models, Methods \& Techniques\\ for Computational Epidemiology }
%\author{\begin{tabular}[t]{c@{\extracolsep{8em}}c}
% Jason Weller & George Dimitoglou \\
% Department of Computer Science & Department of Computer Science \\
% Hood College & Hood College \\%
% Frederick, MD 21701 & Frederick, MD 21701
%\end{tabular}}
%for single author (just remove % characters)
\author{Jason A. Weller, George Dimitoglou \\
Department of Computer Science \\
Hood College \\
Frederick, MD 21701\\
\begin{small}$[$weller, dimitoglou$]$@cs.hood.edu\end{small}}
%for two authors (this is what is printed)
%\author{\begin{tabular}[t]{c@{\extracolsep{8em}}c}
%G. Dimitoglou & J. Weller \\
% Dept. of Computer Science & Coauthor Department \\
% Hood College & Coauthor Institute \\
% Frederick, MD 21701 & City, STATE~~zipcode
%\end{tabular}}
\maketitle
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\subsection*{\centering Abstract}
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{\em
This survey is an overview of current computer modeling and simulation trends.............
}
\section{Introduction}
Modeling and simulation provide ways to understand and predict how systems or phenomena may work.
In this survey, we attempt to provide an overview of the broad area of computational epidemiology. The objective is to
highlight the state of the art, identify open problems and propose the use of some unexplored methods, tools and approaches for
\section{Disease Categories}
Diseases are categorized into \textit{communicable} (infectious) and \textit{non-communicable}. A disease is infectious if it
Historically, there have been multiple instances of water being the vehicle for spreading cholera, infected fleas carried by
\section{Epidemiology Models}
We describe three different epidemiology models, all based on immunity acquisition.\\
\subsection{The SIR Model}
In the Susceptible-Infected-Removed (SIR) epidemiological model any member of the population is in one of three states: \textit{susceptible}, \textit{infected}, or \textit{removed} from the epidemic. The \textit{removed} state can only be reached by acquired immunity or death. The population, N, can be represented at any point in time \textit{t}, so that:
\begin{equation}
N = S(\textit{t}) + I(\textit{t}) + R(\textit{t})
\label{eqno1}
\end{equation}
\noindent where
This deterministic model works well for contact diseases such as measles and chickenpox, or any disease for which there are immunizations. As
individuals recover, they are no longer susceptible to infection and are removed from the population. Once an individual is exposed to the pathogen, they are modeled as infected and no latency or incubation period is taken into consideration
\begin{thebibliography}{9}
\bibitem{abbas} Abbas, K., Mikler, A., Gotti, R. (2005). Temporal Analysis of Infectious Diseases: Influenza. 2005 ACM Symposium on Applied Computing
\bibitem{anderson} Anderson, R. (1991). Discussion: The Kermack-McKendrick Epidemic Threshold Theorem. Bulletin of Mathematical Biology Vol. 53, No. 1/2, pp. 3-32.
\end{document}