Agent-based model of human-to-human disease transmission across a network(s) represented by a symmetric matrix
Individuals can be members of different networks such as work, family/household, school that are constant through time in the absence of interventions. They also participate in random networks that are generated at every time step based on parameter r.net.prob
. School closures eliminate the school-based network (e.g. all transmission probabilities in school network to 0), increase familial contacts (e.g. within household), and reduce random edge generation from r.net.prob
to r.net.prob.sc
. Shelter in place reduces the generation of random edges to r.net.prob.sip
and also increases household contacts between household members who are not in essential workforce and therefore either work from home or are laid off (NOT IMPLEMENTED YET). The contact matrix is an
The transmission model across the network is a modified SEIR that allows for variability in symptom severity by including presymptomatic (t.___
parameters draw from a distribution to generate a time until the next event occurs and p.___
parameters return a probability of transitioning to one state or another at each transition (e.g. asymptomatic or not). Probability of being asymptomatic, of being severely symptomatic (eventually hospitalized), and of dying are age-dependent. As an example: p.asymp
gets a probability that the infected individual will be asymptomatic, this probability is then used in a bernouli trial where success is defined as the individual transitioning to t.asymp
to determine how long they will remain asymptomatic before recovering.
Transition times are stored in the t.til.nxt
matrix and infection status is stored in the inf.mat
matrix, each with dimensions t.til.nxt
matrix becomes negative, the function next.state
is implemented to determine the next state and the time that will be spent in that state. Following these updates to infection status, the network is updated. School closures and shelter in place interventions alter the network as described above. Infection status influences the network by quarantining individuals with symptomatic infection (e.g. add.r.edges
with probabilities influenced by interventions and individual heterogeneities (NOT IMPLEMENTED YET). Finally, transmission is simulated across the network using function new.infection
which reduces the contact matrix to only those columns corresonding to infectious individuals, then performs a Bernoulli trial row by row to simulate the probability a contact results in transmission. New infections are then added to inf.mat
if the Bernoulli trial is successful and the corresponding row is susceptible. For these new infections, t.latent
is also sampled and added to the t.til.nxt
matrix.