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Understanding dynamics of an outbreak like that of COVID-19 is important indesigning effective control measures. This study aims to develop an agent basedmodel that compares changes in infection progression by manipulating differentparameters in a synthetic population. Model input includes populationcharacteristics like age, sex, working status etc. of each individual and otherfactors influencing disease dynamics. Depending on number of epicentres ofinfection, location of primary cases, sensitivity, proportion of asymptomaticand frequency or duration of lockdown, our simulator tracks every individualand hence infection progression through community over time.  In a closed community of 10000 people, it is seen that without any lockdown,number of cases peak around 6th week and wanes off around 15th week. If primarycase is located inside dense population cluster like slums, cases peak earlyand wane off slowly. With introduction of lockdown, cases peak at slower rate.If sensitivity of identifying infection decreases, cases and deaths increase.Number of cases declines with increase in proportion of asymptomatic cases. Themodel is robust and provides reproducible estimates with realistic parametervalues. It also guides in identifying measures to control outbreak in acommunity. It is flexible in accommodating different parameters likeinfectivity period, yield of testing, socio-economic strata, daily travel,awareness level, population density, social distancing, lockdown etc. and canbe tailored to study other infections with similar transmission pattern.

A Flexible Agent-Based Model to Study COVID-19 Outbreak -- A Generic Approach