## 1 Introduction
Some common parameter estimation methods for ABM calibration have been reported to reduce the uncertainty of the model (Table 1). \begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{1}{c}{**Study**} & \multicolumn{1}{c}{**Calibration Method**} & \multicolumn{1}{c}{\begin{tabular}{c} **Number of Unknown** \\ **Parameters** \\ \end{tabular} } & \multicolumn{1}{c}{**Field of ABM Application**} \\ \hline [PERSON] et al., 2007 [41] & Parameter Sweeping & 87 & Basic Immune Simulator \\ \hline [PERSON] et al., 2005 [42] & Pattern-Oriented Approach & unreported & Ecology \\ \hline [PERSON] et al., 2017 [43] & \begin{tabular}{l} Genetic algorithm with \\ random weighted sampling \\ \end{tabular} & 16 & Glioblastoma multiforme model \\ \hline [PERSON] et al., 2015 [44] & Bayesian Approach & 24 & Dynamics of acute inflammation \\ \hline [PERSON] et al., 2017 [45] & \begin{tabular}{l} Particle swarm optimization \\ (PSO) \\ \end{tabular} & 50 & Immune System \\ \hline [PERSON] et al., 2010 [46] & Genetic Algorithm & 6 & \begin{tabular}{l} Avian Influenza (H5N1) viruses \\ mutation \\ \end{tabular} \\ \hline [PERSON] et al., 2015 [47] & \begin{tabular}{l} Greedy algorithm and \\ Regression \\ \end{tabular} & 4 & Immune System \\ \hline [PERSON] et al., 2008 [48] & \
\begin{tabular}{l} Nonlinear multigrid/finite \\ difference method \\ \end{tabular} & 20 &
\begin{tabular}{l} Three-dimensional multispecies \\ nonlinear tumor growth \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter estimation methods in agent-based modeling (ABM). The pattern-oriented approach is used to estimate parameters by comparing observed trends and patterns in the model with empirical data [42; 49; 50]. For complex models having a large number of parameters, this approach has been limited to the identification of relevant patterns for discovering hidden information [50]. The particle swarm optimization (PSO) algorithm is a heuristic optimization method that is more suitable for computational models with a large number of parameters [45; 51]. The PSO investigates the mutual collaboration between particles for sharing the internal information and reducing parameter space by discarding implausible inputs [45; 51]. Since PSO has a low convergence rate in the iterative process, it is not considered as ideal for ABM, which would require thousands of iterations for each simulation [51]. Genetic algorithms (GA) are another heuristic optimization method that use a natural selection process to estimate as many solutions as possible simultaneously [46; 52]. In GA, the model parameter values are progressively modified using genetic operators namely mutation and crossover, to optimize the model's fitness in predicting the empirical data [46; 52]. One limitation of GA is parameter overfitting and large variation in the results for each run [52]. Other existing tools for parameter estimation in ABM include parameter sweeping, Bayesian approaches, greedy algorithms and regressions, hybrid approaches and nonlinear multi-grid/finite difference methods. These methods are often computationally expensive and are not ideal for biological models at large scales [41; 43; 44; 47; 48]. When the model has a large number of parameters with unknown values, model calibration can also require expensive computing resources. The parameter search space scales up with the number of parameters in the model [53; 54]. Lengthy simulation times, ambiguities in model design and a large number of unknown parameters can also limit the efficiency of model calibration [55]. Sensitivity analysis is thus required to reduce the number of parameters before model calibration. Sensitivity analysis is a statistical technique to explore the effects of systematically varying input parameters on the variability of the model outputs. Multiple sensitivity analysis methods have been reported for ABM [56; 57].