Modeling serological testing to inform relaxation of social distancing for COVID-19 control

Abstract

Serological testing remains a passive component of the current public health response to the COVID-19 pandemic. Using a transmission model, we examined how serology can be implemented to allow seropositive individuals to increase levels of social interaction while offsetting transmission risks. We simulated the use of widespread serological testing in three metropolitan areas with different initial outbreak timing and severity characteristics: New York City, South Florida, and Washington Puget Sound. In our model, we use realistic serological assay characteristics, in which tested seropositive individuals partially restore their social contacts and act as immunological 'shields'. Compared to a scenario with no intervention, beginning a mass serological testing program on November 1, 2020 was predicted to avert 15,000 deaths (28% reduction, 95% CrI: 0.4%-30.2%) in New York City, 3,000 (31.1% reduction, 95% CrI: 26.4%-33.3%) in South Florida and 10,000 (60.3% reduction, 95% CrI: 50.2%-60.7%) in Washington State by June 2021. In all three sites, widespread serological testing substantially blunted new waves of transmission. Serological testing has the potential to mitigate the impacts of the COVID-19 pandemic while also allowing a substantial number of individuals to safely return to social interactions and economic activity.

Figure 1.

Methods diagram overview A) Overall model diagram. Serological antibody testing is shown by dashed arrows. Red dashed arrows indicate either false positives (i.e., someone is not immune, but is moved to the test-positive group) and occur at a rate that is a function of 1-specificity, or false negatives (i.e., someone is recovered, but stays in the test-negative group). True positives occur at a rate that is a function of the sensitivity. *Symptomatic infections in the test-positive group have similar severity to symptomatic infections in the not tested/test-negative group, but symptoms are not recognized as being caused by SARS-CoV-2 unless they are severe enough to warrant hospitalization. Note that the hospitalization compartments are located in the ‘Not tested/test-negative’ layer for simplicity, though individuals who incorrectly test positive could move to these compartments after developing symptomatic infection. B) All three municipalities enacted social distancing regulations in mid-March (–23). Under these measures, we assume all contacts at school and daycare were eliminated and contacts outside of home, work, and school (‘other’) locations were substantially reduced (24) while contacts at home remained unchanged, with distancing starting at the time that stay-at-home orders were enacted in each site. (Figure 1A) We used the ‘reopening’ date, or date that stay-at-home orders were lifted in each location, as the time at which social distancing was partially relaxed. In accordance with school reopening plans in each location, we assume that schools and daycares remained closed until September 1, 2020 in South Florida and October 1, 2020 in Washington and New York, after which they reopen at 50% capacity. This 50% reduction is meant to capture that interactions in schools in Fall 2020 are substantially reduced from pre-pandemic levels due to the combination of online instruction as well as physical distancing and mask-wearing for students attending in-person. We assumed a serological testing and shielding strategy starts on November 1, 2020.

Figure 2.

The consistency between the fitted model and the deaths/seroprevalence data Sound. Daily critical care cases through July 1 are shown in the first row. In the second row, the cumulative number of recovered (previously infected) individuals is shown. A red square shows the seroprevalence estimates from Havers et al. in each location (12). In the third row, the cumulative deaths are shown, with death data shown in blue squares (20). Grey bands show 95% credible intervals, derived from the last 5,000 iterations of converged MCMC chains (ten chains from New York City and South Florida, nine chains from Washington Puget Sound).

Figure 3.

Critical care cases over time by testing level (colors) and location (panels) assuming 5:1 shielding. Dates corresponding to the start of general social distancing in March 2020 and ‘reopening’ or lifting at stay-at-home (SAH) orders in May and June 2020, are based on the dates that policies were enacted, or restrictions lifted, in each location. We assume that schools reopen at 50% capacity on September 1, 2020 in South Florida and October 1, 2020 in Washington and New York. Dotted lines show the impacts of a test with 96% specificity and solid lines show a test with 99.8% specificity.

Figure 4.

The top row shows cumulative deaths by location (panels) from March 2020 to February 2021 for the scenario with 5:1 shielding, assuming schools reopen on September 1, 2020 in South Florida and October 1, 2020 in Washington and New York. Colored lines show test specificity.

Figure 5.

Contour plot of cumulative deaths in each location from November 1, 2020 to June 1, 2021 (left column) and number of people released from social distancing (right column) as a function of the degree of relaxation of social distancing and number of tests per day. The far right of the x-axis corresponds to a pre-pandemic level of contact and the far left corresponds to the contact levels in each location during stay-at-home orders in March-June. The reduction in contact during stay-at-home orders was fitted for each location and is shown by the vertical red dotted line. Both panels assume a test specificity of 99.8% and a shielding factor of 5:1.