The impact of contact data resolution on disease transmission intervention strategy evaluations

Abstract

Collecting and analyzing contact data during epidemics are important before implementing disease transmission intervention strategies. However, how much contact data should be collected and how the amount affects the implementation and evaluation of interventions remains a question. This study investigates whether the implementation of disease intervention will be affected by different contact resolutions, which are defined by the duration of interaction between individuals, and whether collecting data with varying precision will affect the outcome of interventions. Considering these questions, this study simulates disease outbreaks with Singapore ‘cruise to nowhere’ dataset (3.96 million contacts) and the SEIR model with fixed disease-specific parameters. There will be 50 iterations of the same simulations with no intervention, PCR + Quarantine, PCR + Vaccination. The baseline parameter value will be first tested, followed by sensitivity analysis with intervention parameters for all resolutions. The impact on the disease outbreak will be measured using peak infectious, outbreak duration, and resources utilized. The baseline analysis result shows that the ranking of intervention preferred is constant, regardless of resolution. In sensitivity analysis, increased PCR sensitivity decreases peak infections, as expected. However, increasing PCR specificity ironically increases peak infectious due to false positives for all resolutions. The PCR testing frequency for the non-quarantined population is also more critical than that for the quarantined population, and the result is more evident for ‘close’ resolution. Hypothetically, there is a tradeoff between vaccination coverage and the number of vaccinations used, but the result in the report states otherwise.

In conclusion, several results do not follow the hypothesis in sensitivity analysis, but the result mostly remains consistent for all resolutions. Thus, this report challenges the need for high-resolution data for policy decision-making and advocates for more efficient resource usage, especially during crucial periods like epidemics.