The impact of social distancing, contact tracing, and case isolation interventions to suppress the COVID-19 epidemic: A modeling study

Abstract

Introduction: Most countries are dependent on nonpharmaceutical public health interventions such as social distancing, contact tracing, and case isolation to mitigate COVID-19 spread until medicines or vaccines widely available. Minimal research has been performed on the independent and combined impact of each of these interventions based on empirical case data.

Methods: We obtained data from all confirmed COVID-19 cases from January 7th to February 22nd 2020 in Zhejiang Province, China, to fit an age-stratified compartmental model using human contact information before and during the outbreak. The effectiveness of social distancing, contact tracing, and case isolation was studied and compared in simulation. We also simulated a two-phase reopening scenario to assess whether various strategies combining nonpharmaceutical interventions are likely to achieve population-level control of a second-wave epidemic.

Results: Our study sample included 1,218 symptomatic cases with COVID-19, of which 664 had no inter-province travel history. Results suggest that 36.5 % (95 % CI, 12.8-57.1) of contacts were quarantined, and approximately five days (95 % CI, 2.2-11.0) were needed to detect and isolate a case. As contact networks would increase after societal and economic reopening, avoiding a second wave without strengthening nonpharmaceutical interventions compared to the first wave it would be exceedingly difficult.

Conclusions: Continuous attention and further improvement of nonpharmaceutical interventions are needed in second-wave prevention. Specifically, contact tracing merits further attention.

Keywords: COVID-19; Case isolation; Contact tracing; Modeling; Social distancing.

Fig. 1

Model compartments diagram.

Fig. 2

Epidemic curve of the COVID-19 outbreak in Zhejiang and model fitting results. (A) Fitted sigmoid function of social contact change. Zhejiang changed its infectious disease alert category to the highest level on January 23rd, 2020 (left red vertical line) and started a comprehensive set of restrictions on February 1st, 2020 (right red vertical line). (B) The distribution of isolation speed (days). (C) The distribution of contact tracing proportion. (D) Model fitting curve and observed counts of confirmed symptomatic cases by their symptom onset date.

Fig. 3

Scenarios of the epidemic curve with varied case isolation speed ($\epsilon$) and contact tracing proportion (p) by increasing or decreasing 20 %. For example, contact tracing proportion decreased by 20 % means the proportion changed from p to 0.8*p; isolation speed decreased by 20 % means days need for a case’s isolation changed from ε to 0.8*ε days. Line: mean value, color band: 95 % prediction interval.

Fig. 4

Heatmap of the ratio of the predicted total number of cases over observed values with different contact tracing proportion, case isolation speed and social contact strength during the outbreak. When the strength of the contact matrix (the CNT level during the outbreak) increased, potential alternative strategies related to case tracing and isolation speed which could suppress the epidemic curve closed to the observed level (ratio close to 1, the blue area) drop quickly.

Fig. 5

The two-phases reopening with varied case isolation speed and contact quarantine proportion. Green area was the 1st phase, purple area was the 2nd phase. Case isolation speed and contact quarantine proportion were hold at the fitted value of the outbreak period through reopening phases in the scenario 1 but varied in scenario 2 and 3. There was an apparent second wave in the first scenario, but much smaller in the second one and almost avoid in the last scenario. In these theoretical simulations, the second wave heavily depended on case isolation speed and contact quarantine proportion.