Modeling the impact of surveillance activities combined with physical distancing interventions on COVID-19 epidemics at a local level

Abstract

Physical distancing and contact tracing are two key components in controlling the COVID-19 epidemics. Understanding their interaction at local level is important for policymakers. We propose a flexible modeling framework to assess the effect of combining contact tracing with different physical distancing strategies. Using scenario tree analyses, we compute the probability of COVID-19 detection using passive surveillance, with and without contact tracing, in metropolitan Barcelona. The estimates of detection probability and the frequency of daily social contacts are fitted into an age-structured susceptible-exposed-infectious-recovered compartmental model to simulate the epidemics considering different physical distancing scenarios over a period of 26 weeks. With the original Wuhan strain, the probability of detecting an infected individual without implementing physical distancing would have been 0.465, 0.515, 0.617, and 0.665 in designated age groups (0-14, 15-49, 50-64, and >65), respectively. As the physical distancing measures were reinforced and the disease circulation decreased, the interaction between the two interventions resulted in a reduction of the detection probabilities; however, despite this reduction, active contact tracing and isolation remained an effective supplement to physical distancing. If we relied solely on passive surveillance for diagnosing COVID-19, the model required a minimal 50% (95% credible interval, 39-69%) reduction of daily social contacts to keep the infected population under 5%, as compared to the 36% (95% credible interval, 22-56%) reduction with contact tracing systems. The simulation with the B.1.1.7 and B.1.167.2 strains shows similar results. Our simulations showed that a functioning contact tracing program would reduce the need for physical distancing and mitigate the COVID-19 epidemics.

Keywords: Contact tracing; Physical distancing; SARS-CoV-2; Scenario tree analysis; Social mixing pattern.

Fig. 1

Illustration of the modeling framework combining scenario tree analyses and a SEIR epidemiological model.

Fig. 2

Scenario tree diagram representing the probability of detecting cases of COVID-19 by passive surveillance. ^†The detection probability for each age stratum is calculated separately.

Fig. 3

Scenario tree diagram representing the probability of detecting positive cases by contact tracing. ^†The detection probability for each age stratum is calculated separately. ^‡According to the Spanish guidelines, contacts who were tested negative but developed symptoms of COVID-19 would be managed and re-tested as suspected cases.

Fig. 4

A, 4B, 4C. The estimated probability of disease detection reduced with the strengthening of physical distancing measures among different age strata with R0 = 2.6 (A), R0 = 3.4 (B), and R0 = 5.1 (C). The overall detection probabilities (${p.detect}_i$) were represented by solid lines. The detection probability with only passive surveillance (${p.detect.pass}_i$) were represented as dotted horizontal lines.

Fig. 4

A, 4B, 4C. The estimated probability of disease detection reduced with the strengthening of physical distancing measures among different age strata with R0 = 2.6 (A), R0 = 3.4 (B), and R0 = 5.1 (C). The overall detection probabilities (${p.detect}_i$) were represented by solid lines. The detection probability with only passive surveillance (${p.detect.pass}_i$) were represented as dotted horizontal lines.

Fig. 4

A, 4B, 4C. The estimated probability of disease detection reduced with the strengthening of physical distancing measures among different age strata with R0 = 2.6 (A), R0 = 3.4 (B), and R0 = 5.1 (C). The overall detection probabilities (${p.detect}_i$) were represented by solid lines. The detection probability with only passive surveillance (${p.detect.pass}_i$) were represented as dotted horizontal lines.

Fig. 5

The predicted overall attack rates with different level of physical distancing (represented by a reduced proportion of daily social contacts compared to a baseline) for different basic reproduction number (R0).