Table 1.
Parameter values and initial conditions.
Fig 1.
Representative structures of ER and BA graphs.
a: Erdős–Rényi (ER) graph with 100 nodes and link probability of 0.1. b: Barabási-Albert (BA) graph with 100 nodes and 10 edges attached from each new node to existing nodes. The purple nodes represent non-poor agents and the yellow nodes represent poor agents.
Fig 2.
Simulated system outcomes in non-intervention scenario.
Yellow line represents overall population; dashed blue line represents poor population; and dashed green line represents non-poor population. a: Current proportion of population infected over time on ER graph. Depicts the current infected caseload (as of each day) as a proportion of the population. All sections of the population contribute proportionally to caseloads. b: Cumulative proportion of population infected over time in ER graph. Depicts the temporal evolution of cumulative populations infected. All sections of the population display the same trend in cumulative infection rates. c: Current proportion of population infected over time in BA graph. Shows exponential growth and decay with a much higher peak than in the ER graph. d: Cumulative proportion of population infected over time in BA graph. Fractions of population affected are exactly the same across poor and non-poor categories, though much higher than in ER graph. Overall, in this non-intervention scenario, both categories of the population are similarly affected, per expectation, across all network types.
Fig 3.
Simulated system outcomes in transmission probability reduction scenario.
Yellow line represents overall population; dashed blue line represents poor population; dashed green line represents non-poor population; and thin black line represents non-intervention scenario for comparison. a: Current proportion of population infected over time on ER graph. Peak overall caseload is lower than non-intervention scenario, but poor population has a higher peak than non-poor. b: Cumulative proportion of population infected over time on ER graph. Cumulative infected is lower than under non-intervention, but the poor suffer a higher cumulative infection rate than the non-poor (76% v. 69%). c: Current proportion of population infected over time on BA graph. As in the case of the ER graph outcomes, peak caseload is lower than non-intervention scenario, but with a higher peak for the poor. d: Cumulative proportion of population infected over time on BA graph. Overall infected is lower than under non-intervention, with poor having an overall higher cumulative infection rate (89% v. 86%).
Fig 4.
Evolution of mean-field effective transmission rate under transmission rate reduction scenario.
Until the intervention begins, effective transmission rate of all sections of the population follow the same trajectory, but then show differing paths with transmission rates for poor higher during the period of intervention. Post-intervention, again, the trajectories merge and effective transmission rate remains flat, under 1.
Fig 5.
Simulated system outcomes for contact rate reduction scenario.
Yellow line represents overall population; dashed blue line represents poor population; dashed green line represents non-poor population; and thin black line represents non-intervention scenario for comparison. a: Current proportion of population infected over time on ER graph. Peak overall caseload is lower than non-intervention scenario with poor population having a higher peak than non-poor, but we see a second peak post intervention, where non-poor are also affected. b: Cumulative proportion of population infected over time on ER graph. Cumulative infected is lower than under non-intervention, but the poor suffer a much higher cumulative infection rate than the non-poor (68% v. 61%). c: Current proportion of population infected over time on BA graph. Poor have higher peak than non-poor, but overall peak is lower than under non-intervention. d: Cumulative proportion of population infected over time on BA graph. Poor have much higher cumulative infection rate than the non-poor (88% v. 81%).
Fig 6.
Evolution of mean-field effective transmission rate under contact rate reduction scenario.
Until the intervention begins, effective transmission rate of all sections of the population follow the same trajectory, but then show differing paths with transmission rates for poor higher during the period of intervention. Post-intervention, again, the trajectories merge and effective transmission rate rises to above 1, meaning that infections are again taking hold in the population leading to the second, albeit smaller, peak in caseloads.
Table 2.
Simulation outcomes.