Contact tracing in NYC
The NYC Test & Trace Corps initiative was launched in June 202019. Established as an operation to provide contact tracing, testing, and resources to support isolation and quarantine, the contact tracing program was integrated with a set of intervention efforts designed to limit morbidity and mortality from COVID-19 in NYC (Supplementary Information). We analyzed data obtained from case investigations and COVID-19 testing results (molecular and antigen) collected between October 1, 2020 and May 10, 2021 (Extended Data Fig. 1, Supplementary Information). During this period, 691,834 confirmed and probable cases were reported to the New York City Department of Health and Mental Hygiene (DOHMH)20. After excluding cases residing in residential congregate settings, cases were sent to the NYC Test & Trace Corps for contact tracing. Among these cases, 644,029 were reached by tracers and 450,415 completed an interview. In total, 779,011 contacts with confirmed and probable cases were self-reported via case investigations, of whom 20.9% (162,659/779,011) were subsequently tested. The median time from specimen collection to reporting results to DOHMH was 2 days. 97% of index patients were called by tracers within two days of reporting to DOHMH (Fig. 1a) and 68.4% of contacts were called the day of reporting to the Test & Trace team (Fig. 1b). Among tested contacts, 66.6% sought testing within one week of notification (Fig. 1d). For traced symptomatic infections, 86.7% were tested after symptom onset, and 13.3% were tested before symptom development (Fig. 1d).
Adults aged 20 to 49 years old constituted the majority of index cases (Fig. 1e), a finding in agreement with the age distribution of confirmed infections in the United States21. Self-reported contacts were more uniformly distributed among the population under 50 years old (Fig. 1f). The age-stratified contact matrix highlights more frequent interactions among individuals of similar age and inter-generation mixing within the household (Fig. 1g), a pattern also observed in other countries22.
Exposure and transmission networks
We reconstructed the self-reported exposure network at the individual level for the study period. The exposure network was highly fragmented, with 947,042 individuals in 242,486 disjoint clusters. Cluster size showed considerable heterogeneity (Fig. 2a), as did the number of contacts reported by each index case (Fig. 2b). We visualize several large exposure clusters in Fig. 2c, color-coded by the home borough of each person. Exposure clusters exhibit diverse structures ranging from hub-and-spoke networks with a single spreader to networks with multiple spreaders. Over half of the clusters shown in Fig. 2c were in Queens and Brooklyn. Within the large exposure clusters in Fig. 2c, 1,195 index patients (59.4%) reported contacts living in the same borough, but 817 (40.6%) cross-borough contacts were also recorded.
We further reconstructed transmission chains by linking the contact tracing records and the laboratory-confirmed cases (molecular and antigen). Due to asymptomatic and pre-symptomatic shedding23–25, index cases were not necessarily the source of infections in these putative transmission events. To infer the direction of transmission, we estimated the infection date of lab-positive cases. For symptomatic cases, infection date was estimated using an empirical incubation period distribution obtained from a prior study17; for asymptomatic cases, we used specimen collection date to estimate infection date using a model of viral load dynamics coupled with a Bayesian inference (Extended Data Fig. 2)26. We sampled an ensemble of possible transmission networks compatible with the estimated chronological order of infections and selected the network with maximum likelihood based on transmission probabilities across age groups (Extended Data Table 1, Extended Data Fig. 3). More details on the transmission network reconstruction are provided in the Supplementary Information.
During the study period, we identified 58,474 potential transmission clusters formed by exposures that resulted in lab-confirmed infections. On average, these transmission clusters had a mean size of 2.3 individuals, representing 19.6% (135,478/691,834) recorded cases during the study period. However, transmission cluster size and the number of secondary cases linked to each index case had large variance (Fig. 2d-e) – only 0.20% of transmission clusters involved more than 6 infections. The largest transmission cluster identified consisted of 12 cases, and the maximum number of secondary cases for each single index case was 7. Transmission clusters with at least 6 infections are visualized in Fig. 2f.
To quantify the spatial spread of SARS-CoV-2 in NYC at fine geographical scales, we mapped exposure and transmission networks across modified ZIP code tabulation areas (MODZCTAs, referred to as ZIP codes hereafter; Fig. 3a-b). Among 72,191 transmission events where place of residence was known, 7,826 (10.8%) included multiple ZIP codes. We observed several local clusters of ZIP codes that were tightly interconnected by exposure and transmission, centered around locations with high community prevalence. Infections in those high-prevalence ZIP code clusters were linked to self-reported contacts in nearby and far locations (Fig. 3a), which may have facilitated the spread of COVID-19 across the city (Fig. 3b). Among the cross-ZIP code transmission chains, we examined distributions of index cases who initiated transmission (Fig. 3c) and the infected contacts (Fig. 3d) across ZIP codes. A distinct skew in the distribution suggests that certain ZIP codes were more involved in the spatial spread of COVID-19. Geographically, most cross-ZIP code transmission events occurred within 10 km; however, long-distance transmission up to 40 km was also evident (Fig. 3e).
Evaluation of intervention measures
During the period from October 2020 to March 2021, a dynamic zone-based control strategy was adopted in New York State to limit viral spread in communities with high case growth rates while avoiding undue harm to the economy27. Three tiers of zones (yellow, orange, and red) were identified based on a set of metrics, collectively defined by test positivity rate, hospital admissions per capita, and hospital capacity27,28. Local restrictions on business and services were imposed based on zone conditions. Compliance to these restrictions can be reflected by population mobility in each ZIP code. In December 2020, vaccines became available to the population at highest risk for severe outcomes associated with COVID-19 in NYC and were subsequently available to all eligible individuals over 15 years old during early April 2021. With the support of the detailed contact tracing data, we evaluated the impact of these public health interventions on community transmission of SARS-CoV-2 in NYC.
We assessed the associations of the numbers of non-household within- and cross-ZIP code transmission events across NYC with demographic, socioeconomic, disease surveillance, vaccination coverage, and human mobility features (Supplementary Information). As non-household transmission contributed to the expansion of SARS-CoV-2 outside the household, we focused on 4,642 non-household transmission events. We used aggregated foot traffic records derived from mobile phone data29 documenting weekly numbers of individuals visiting points-of-interest (POIs, e.g., restaurants, grocery stores, gyms, and bars) in each ZIP code as an indicator of human mobility and compliance with the zone-based local restrictions (Supplementary Information). We used conditional autoregressive (CAR) models30 to assess the effects of the above factors on within- and cross-ZIP code transmission (Fig. 4). Specifically, for both within- and cross-ZIP code transmission, we fitted Poisson generalized linear mixed models (GLMM) with random effects and CAR priors to account for the inherent spatial-temporal autocorrelation in disease transmission data30,31 (Supplementary Information, Extended Data Figs. 4-5).
We found that higher vaccination coverage and fewer POI visitors were associated with reduced non-household within- and cross-ZIP code transmission in the same week (Fig. 4). Estimates of coefficients are provided in Extended Data Table 2. The model identifies a strong effect of vaccination on SARS-CoV-2 transmission: a 12.48% newly vaccinated population was associated with reductions of 28.0% (95% CI: 14.0% – 40.0%) and 14.8% (1.7% – 26.4%) for within- and cross-ZIP code non-household transmission events, respectively. In contrast, a 0.12 per capita increase of POI visitors was associated with increases of 9.6% (0.3% – 19.3%) and 14.4% (8.7% – 20.2%) for within- and cross-ZIP code transmission outside households, respectively. We further found that both within- and cross-ZIP code transmission had strong positive associations with log weekly cases per capita ( . Higher percentage of Hispanic residents and lower cumulative cases per capita were associated with higher non-household transmission ( ). For cross-ZIP code transmission, cumulative cases per capita had a stronger effect than vaccination and POI visitors (Fig. 4b, Extended Data Table 2), indicating that prior infections may result in reduced cross-ZIP code transmission in locations with a higher attack rate. These findings reveal how health inequities related to COVID-19 manifest across NYC communities. Results also indicate that promoting vaccination and capacity limits or temporary limits on local businesses, schools, and other POIs in high-prevalence communities were effective in reducing SARS-CoV-2 transmission in NYC. These findings were corroborated with an alternate random-effect model (Supplementary Information), and testing of effect lags of one week and two weeks (Extended Data Figs. 6-8).