Other examples


Before having a look at these examples, it’s highly recommended that you go through the Writing your First Program section!

Houses on a lattice

The model discussed earlier has no sense of geography: each house and office is practically isolated from all the others, as you can only infect people in your location. A simple way to try to introduce this into the model is by using houses on a grid, with the following characteristics:

  • Houses are arranged periodically in rows and columns

  • Each house has 4 nearest neighbours - namely the houses north, south, east, and west of them

  • Assume that people in a house can only infect their nearest neighbours

  • The inhabitants of a house are less likely to infect their neighbours than they are to their housemates

The house arrangement

The lattice of houses - each square represents a house. Here, houses 2,6,8 and 12 are neighbours of 7, and could potentially infect or get infected by it.

To simplify what happens at the boundaries, we implement periodic boundary conditions. This means the left edge and the right edge are identified, and likewise for the top and bottom edges of the lattice. Apart from being easy to implement, a system with periodic bondary conditions can be thought of as one which feels the influence of it’s environment, rather than being isolated.

Depiction of periodic boundary conditions

With periodic boundary conditions, house 24 is now to the left of 20, and 0 is below 20.

We use a 50 × 50 lattice, which totals to 2500 houses. We can also add an intervention to the model, to make it more realistic. We divide the lattice into larger ‘blocks’ of 100 houses each, so that a 5 × 5 arrangement of 25 blocks gives us our original lattice.


The implementation below uses a block attribute in the house class. The blocks are numbered in the same way the houses are.

We can keep track of the number of infected in each block, and block it off if the infected count rises too high.

Depiction of a block being blockaded

If we split a 4 × 4 lattice into 4 blocks, we can see the boundary cordoned off if block 1 has a large number of infected.

We shall cordon off a block in the following way:

  • If infectedCountToTriggerBlockade is above a threshold (we use 30), then the block needs to be closed off

  • The block is closed off for blockadeDuration from the instant where the threshold is breached, regardless of the current levels of infected

  • After the block comes out of the blockade, it cannot get locked down again for blockadeCooldown days, regardless of the current levels of infected

Rather than having the initial infected people spread around, we’ll set them to be clustered in an area to better see the spread. We’ll set the four corners as the infected area: despite looking like 4 separate regions they’re actually all connected due to the periodic boundary conditions.

The model is built over an SIR model, with most of the changes being made to three classes: Main, SusceptibleState and GISOutputSpec.

There are several changes made to this class, the most important one being the addition of the intervention.

We first initialize isBlockadedList, a boolean array of length 25. If isBlockadedList[i] == True, that means that block number i is currently blockaded.

We use an IntervalBasedIntervention to set up our required intervention, and check for whether a block needs to be cordoned off every tick with whenActiveActionFunc.


We start the intervention at tick 1, and end it at tick 5000. The simulation is assumed to have ended by then, as after numerous trials it never seemed to reach 5000 ticks. However, this is not good practice: we ideally should force the simulation to end when the intervention does, by adding the required condition in StopSimulation.

When ingesting the input data, we define a NEIGHBOURS relation between two neighbouring houses. However, we cannot register a relation between two nodes if only one of them has been registered. Thus, for every row in the input csv file, we register not just the house mentioned on the row but also all of it’s neighbours.


If you refer back to the final note in the section on Reading inputs from a synthetic population, you’ll see that registering an identical node multiple times does not lead to the node being duplicated in the graph.

What does what?

Here’s a breakdown of the primary methods in the Main class, and what they do.

  • blockadeBlock: Defines and registers the intervention described above

  • create12HourSchedules: Defines and registers the agent registerSchedules

  • csvDataExtractor: Creates the graph using the CSV input file

  • getLeftNeighbour, getRightNeighbour, getUpNeighbour, getDownNeighbour: Take a houses’ ID and returns the ID of the appropriate neighbour, after taking periodic boundary conditions into account

  • getHouseBlock: Returns the block number a given house is part of

The source code for these classes can be found below:

package com.bharatsim.examples.epidemiology.latticeHousesModel

import com.bharatsim.engine.ContextBuilder._
import com.bharatsim.engine._
import com.bharatsim.engine.actions.StopSimulation
import com.bharatsim.engine.basicConversions.decoders.DefaultDecoders._
import com.bharatsim.engine.basicConversions.encoders.DefaultEncoders._
import com.bharatsim.engine.dsl.SyntaxHelpers._
import com.bharatsim.engine.execution.Simulation
import com.bharatsim.engine.graph.ingestion.{GraphData, Relation}
import com.bharatsim.engine.graph.patternMatcher.MatchCondition._
import com.bharatsim.engine.intervention.{IntervalBasedIntervention, SingleInvocationIntervention}
import com.bharatsim.engine.listeners.{CsvOutputGenerator, SimulationListenerRegistry}
import com.bharatsim.engine.models.{Agent, Node}
import com.bharatsim.examples.epidemiology.latticeHousesModel.DiseaseStates.{InfectedState, SusceptibleState}
import com.bharatsim.examples.epidemiology.latticeHousesModel.InfectionStatus._
import com.typesafe.scalalogging.LazyLogging

import java.util.Date

object Main extends LazyLogging {

  final val numberOfTicksInADay: Int = 2
  final val dt: Double = 1/numberOfTicksInADay.toFloat

  private val myTick: ScheduleUnit = new ScheduleUnit(1)
  private val myDay: ScheduleUnit = new ScheduleUnit(myTick * numberOfTicksInADay)

  var isBlockadedList = new Array[Boolean](25)

  def main(args: Array[String]): Unit = {
    var beforeCount = 0
    val simulation = Simulation()

    simulation.ingestData(implicit context => {
      ingestCSVData("citizen10kLattice.csv", csvDataExtractor)
      logger.debug("Ingestion done")

    simulation.defineSimulation(implicit context => {


        (c: Context) => {
          getInfectedCount(c) == 0

      beforeCount = getInfectedCount(context)


      val currentTime = new Date().getTime

        new CsvOutputGenerator("src/main/resources/GISInfectedoutput_"+currentTime+".csv", new GISOutputSpec(context))

    simulation.onCompleteSimulation { implicit context =>

    val startTime = System.currentTimeMillis()
    val endTime = System.currentTimeMillis()
    logger.info("Total time: {} s", (endTime - startTime) / 1000)

  private def blockadeBlock(implicit context: Context): Unit = {

    val interventionName = "blockade"
    val infectedCountToTriggerBlockade = 30
    val blockadeDuration = 7 * numberOfTicksInADay
    val blockadeCooldown = 7 * numberOfTicksInADay
    var ticksSinceBlockade = Array.fill(25){0}

    def perTickAction(context: Context): Unit = {
      for (i <- 0 to 24)  {

        if (ticksSinceBlockade(i) == blockadeDuration) {
          isBlockadedList(i) = false

        if (ticksSinceBlockade(i) >= blockadeDuration + blockadeCooldown) {
          var infectedCountPerBlock: Long = 0
          var nodesInBlock = context.graphProvider.fetchNodes("House", "block" equ i)
          nodesInBlock.foreach(blockNode => {
            var tempvariable = fetchInfectedAndTotalPerLocation(blockNode.as[House], "House", context)
            infectedCountPerBlock += tempvariable._1.toLong

          if (infectedCountPerBlock >= infectedCountToTriggerBlockade) {
            isBlockadedList(i) = true
            ticksSinceBlockade(i) = 0

        else ticksSinceBlockade(i) += 1

    def fetchInfectedAndTotalPerLocation(node: Node, placeType: String, context: Context): (Double, Double) = {
      val cache = context.perTickCache
      val uniquekey = (placeType, node.internalId)
      cache.getOrUpdate(uniquekey, () => computeInfectedAndTotalPerLocation(node)).asInstanceOf[(Double, Double)]

    def computeInfectedAndTotalPerLocation(node: Node): (Double, Double) = {
      val totalNeighbourCount = node.getConnectionCount(node.getRelation[Person]().get)
      if (totalNeighbourCount == 0)
        return (0d, 1)  // toDo change to (0,0), add check for dividing by 0
      val infectedNeighbourCount = node.getConnectionCount(node.getRelation[Person]().get,
        "infectionState" equ Infected)
      return (infectedNeighbourCount.toDouble, totalNeighbourCount.toDouble)

    val intervention =
      IntervalBasedIntervention(interventionName, 1, 5000, whenActiveActionFunc = perTickAction)


  private def create12HourSchedules()(implicit context: Context): Unit = {

    val stayHomeSchedule = (myDay, myTick)
      .add[House](0, 1)

      (stayHomeSchedule, (agent: Agent, _:Context) => agent.asInstanceOf[Person].age > 0, 1)

  private def csvDataExtractor(map: Map[String, String])(implicit context: Context): GraphData = {

    val citizenId = map("Agent_ID").toLong
    val age = map("Age").toInt

    val homeId = map("HHID").toLong
    val schoolId = map("school_id").toLong
    val officeId = map("WorkPlaceID").toLong
    val houseLatitude = map("H_Lat").toString
    val houseLongitude = map("H_Lon").toString

    val initialInfectionState = if ((houseLatitude=="0" || houseLatitude=="1" || houseLatitude=="49" ||
      houseLatitude=="2" || houseLatitude=="48") && (houseLongitude=="0" || houseLongitude=="1" || houseLongitude=="49" ||
      houseLongitude=="2" || houseLongitude=="48")) "Infected" else "Susceptible"

    val citizen: Person = Person(

    if (initialInfectionState == "Susceptible") {

    val home = House(homeId, getHouseBlock(homeId))
    val staysAt = Relation[Person, House](citizenId, "STAYS_AT", homeId)
    val memberOf = Relation[House, Person](homeId, "HOUSES", citizenId)

    val neighboursLeft = Relation[House, House](homeId, "NEIGHBOURS", getLeftNeighbour(homeId))
    val neighboursRight = Relation[House, House](homeId, "NEIGHBOURS", getRightNeighbour(homeId))
    val neighboursUp = Relation[House, House](homeId, "NEIGHBOURS", getUpNeighbour(homeId))
    val neighboursDown = Relation[House, House](homeId, "NEIGHBOURS", getDownNeighbour(homeId))

    val graphData = GraphData()
    graphData.addNode(citizenId, citizen)
    graphData.addNode(homeId, home)
    graphData.addRelations(staysAt, memberOf)

    var lHomeId = getLeftNeighbour(homeId)
    var rHomeId = getRightNeighbour(homeId)
    var uHomeId = getUpNeighbour(homeId)
    var dHomeId = getDownNeighbour(homeId)

    graphData.addNode(lHomeId, House(lHomeId, getHouseBlock(lHomeId)))
    graphData.addNode(rHomeId, House(rHomeId, getHouseBlock(rHomeId)))
    graphData.addNode(uHomeId, House(uHomeId, getHouseBlock(uHomeId)))
    graphData.addNode(dHomeId, House(dHomeId, getHouseBlock(dHomeId)))

    graphData.addRelations(staysAt, memberOf)
    graphData.addRelations(neighboursLeft, neighboursRight, neighboursUp, neighboursDown)

    if (age >= 25) {
      val office = Office(officeId)
      val worksAt = Relation[Person, Office](citizenId, "WORKS_AT", officeId)
      val employerOf = Relation[Office, Person](officeId, "EMPLOYER_OF", citizenId)

      graphData.addNode(officeId, office)
      graphData.addRelations(worksAt, employerOf)
    } else {
      val school = School(schoolId)
      val studiesAt = Relation[Person, School](citizenId, "STUDIES_AT", schoolId)
      val studentOf = Relation[School, Person](schoolId, "STUDENT_OF", citizenId)

      graphData.addNode(schoolId, school)
      graphData.addRelations(studiesAt, studentOf)


  private def getLeftNeighbour(houseID: Long) : Long = {
    if ((houseID + 1) % 50 == 0 ) {
      houseID + 1 - 50
    else houseID + 1

  private def getRightNeighbour(houseID: Long) : Long = {
    if (houseID % 50 == 0 ) {
      houseID - 1 + 50
    else houseID - 1

  private def getUpNeighbour(houseID: Long) : Long = {
    (houseID + 50) % 2500

  private def getDownNeighbour(houseID: Long) : Long = {
    (houseID - 50 + 2500) % 2500

  def getHouseBlock(houseID: Long) : Int = {
    val block_Lat = (houseID % 50) / 10
    val block_Lon = (houseID / 50) / 10
    5*block_Lat.toInt + block_Lon.toInt

  private def printStats(beforeCount: Int)(implicit context: Context): Unit = {
    val afterCountSusceptible = getSusceptibleCount(context)
    val afterCountInfected = getInfectedCount(context)
    val afterCountRecovered = getRemovedCount(context)

    logger.info("Infected before: {}", beforeCount)
    logger.info("Infected after: {}", afterCountInfected)
    logger.info("Susceptible: {}", afterCountSusceptible)
    logger.info("Recovered: {}", afterCountRecovered)

  private def getInitialRecoveryTick(state: String): Double = {
    if (state == "Susceptible") {
    else {

  private def getSusceptibleCount(context: Context) = {
    context.graphProvider.fetchCount("Person", "infectionState" equ Susceptible)

  private def getInfectedCount(context: Context) = {
    context.graphProvider.fetchCount("Person", "infectionState" equ Infected)

  private def getRemovedCount(context: Context) = {
    context.graphProvider.fetchCount("Person", "infectionState" equ Removed)