Epidemiological models are vital for forecasting the trajectory of the disease and consequently preparing the government for the future. Despite having well established models, these models require accurate values for parameters for average social contacts within a population and migration rates between populations to accurately predict the trajectory of cases and odds of new hotspots.
The effect of any state intervention takes 2-3 weeks to reflect in the trajectory of confirmed cases and even longer to equilibrate around the true steady-state effect. With near real-time aggregated social contact information we can reliably forecast the outcome within days of an intervention, without having to wait weeks to evaluate the effect.
Migration information can also be utilized to warn districts that have a high risk of importing cases from districts with widespread infections and also to predict cities likely to have local transmission chains starting out. The effects of various intervention strategies or relaxations including staggered lockdowns, lockdowns of only hotspots, opening/closing intra-city transport, and opening/closing inter-city transport can be effectively modelled using aggregated metrics.
We can use this information to ensure only the effective and appropriate restrictions are applied instead of blanket interventions across all communities.