  Understanding dynamics of an outbreak like that of COVID-19 is important in
designing effective control measures. This study aims to develop an agent based
model that compares changes in infection progression by manipulating different
parameters in a synthetic population. Model input includes population
characteristics like age, sex, working status etc. of each individual and other
factors influencing disease dynamics. Depending on number of epicentres of
infection, location of primary cases, sensitivity, proportion of asymptomatic
and frequency or duration of lockdown, our simulator tracks every individual
and 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 primary
case is located inside dense population cluster like slums, cases peak early
and 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. The
model is robust and provides reproducible estimates with realistic parameter
values. It also guides in identifying measures to control outbreak in a
community. It is flexible in accommodating different parameters like
infectivity period, yield of testing, socio-economic strata, daily travel,
awareness level, population density, social distancing, lockdown etc. and can
be tailored to study other infections with similar transmission pattern.
