Contact tracing reveals community transmission of COVID-19 in New York City.
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ABSTRACT:
SUBMITTER: Pei S
PROVIDER: S-EPMC9347284 | biostudies-literature | 2022 Jul
REPOSITORIES: biostudies-literature
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Contact tracing reveals community transmission of COVID-19 in New York City.
Pei Sen S Kandula Sasikiran S Vega Jaime Cascante JC Yang Wan W Foerster Steffen S Thompson Corinne C Baumgartner Jennifer J Ahuja Shama S Blaney Kathleen K Varma Jay J Long Theodore T Shaman Jeffrey J
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Understanding SARS-CoV-2 transmission within and among communities is critical for tailoring public health policies to local context. However, analysis of community transmission is challenging due to a lack of high-resolution surveillance and testing data. Here, using contact tracing records for 644,029 cases and their contacts in New York City during the second pandemic wave, we provide a detailed characterization of the operational performance of contact tracing and reconstruct exposure and tr ...[more]
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Project description:Understanding SARS-CoV-2 transmission within and among communities is critical for tailoring public health policies to local context. However, analysis of community transmission is challenging due to a lack of high-resolution surveillance and testing data. Here, using contact tracing records for 644,029 cases and their contacts in New York City during the second pandemic wave, we provide a detailed characterization of the operational performance of contact tracing and reconstruct exposure and transmission networks at individual and ZIP code scales. We find considerable heterogeneity in reported close contacts and secondary infections and evidence of extensive transmission across ZIP code areas. Our analysis reveals the spatial pattern of SARS-CoV-2 spread and communities that are tightly interconnected by exposure and transmission. We find that locations with higher vaccination coverage and lower numbers of visitors to points-of-interest had reduced within- and cross-ZIP code transmission events, highlighting potential measures for curtailing SARS-CoV-2 spread in urban settings.
| S-EPMC9588776 | biostudies-literature
Project description:ImportanceContact tracing is the process of identifying people who have recently been in contact with someone diagnosed with an infectious disease. During an outbreak, data collected from contact tracing can inform interventions to reduce the spread of infectious diseases. Understanding factors associated with completion rates of contact tracing surveys can help design improved interview protocols for ongoing and future programs.ObjectiveTo identify factors associated with completion rates of COVID-19 contact tracing surveys in New York City (NYC) and evaluate the utility of a predictive model to improve completion rates, we analyze laboratory-confirmed and probable COVID-19 cases and their self-reported contacts in NYC from October 1st 2020 to May 10th 2021.MethodsWe analyzed 742,807 case investigation calls made during the study period. Using a log-binomial regression model, we examined the impact of age, time of day of phone call, and zip code-level demographic and socioeconomic factors on interview completion rates. We further developed a random forest model to predict the best phone call time and performed a counterfactual analysis to evaluate the change of completion rates if the predicative model were used.ResultsThe percentage of contact tracing surveys that were completed was 79.4%, with substantial variations across ZIP code areas. Using a log-binomial regression model, we found that the age of index case (an individual who has tested positive through PCR or antigen testing and is thus subjected to a case investigation) had a significant effect on the completion of case investigation - compared with young adults (the reference group,24 years old < age < = 65 years old), the completion rate for seniors (age > 65 years old) were lower by 12.1% (95%CI: 11.1% - 13.3%), and the completion rate for youth group (age < = 24 years old) were lower by 1.6% (95%CI: 0.6% -2.6%). In addition, phone calls made from 6 to 9 pm had a 4.1% (95% CI: 1.8% - 6.3%) higher completion rate compared with the reference group of phone calls attempted from 12 and 3 pm. We further used a random forest algorithm to assess its potential utility for selecting the time of day of phone call. In counterfactual simulations, the overall completion rate in NYC was marginally improved by 1.2%; however, certain ZIP code areas had improvements up to 7.8%.ConclusionThese findings suggest that age and time of day of phone call were associated with completion rates of case investigations. It is possible to develop predictive models to estimate better phone call time for improving completion rates in certain communities.
| S-EPMC10854191 | biostudies-literature
Project description:ImportanceContact tracing is a core strategy for preventing the spread of many infectious diseases of public health concern. Better understanding of the outcomes of contact tracing for COVID-19 as well as the operational opportunities and challenges in establishing a program for a jurisdiction as large as New York City (NYC) is important for the evaluation of this strategy.ObjectiveTo describe the establishment, scaling, and maintenance of Trace, NYC's contact tracing program, and share data on outcomes during its first 17 months.Design, setting, and participantsThis cross-sectional study included people with laboratory test-confirmed and probable COVID-19 and their contacts in NYC between June 1, 2020, and October 31, 2021. Trace launched on June 1, 2020, and had a workforce of 4147 contact tracers, with the majority of the workforce performing their jobs completely remotely. Data were analyzed in March 2022.Main outcomes and measuresNumber and proportion of persons with COVID-19 and contacts on whom investigations were attempted and completed; timeliness of interviews relative to symptom onset or exposure for symptomatic cases and contacts, respectively.ResultsCase investigations were attempted for 941 035 persons. Of those, 840 922 (89.4%) were reached and 711 353 (75.6%) completed an intake interview (women and girls, 358 775 [50.4%]; 60 178 [8.5%] Asian, 110 636 [15.6%] Black, 210 489 [28.3%] Hispanic or Latino, 157 349 [22.1%] White). Interviews were attempted for 1 218 650 contacts. Of those, 904 927 (74.3%) were reached, and 590 333 (48.4%) completed intake (women and girls, 219 261 [37.2%]; 47 403 [8.0%] Asian, 98 916 [16.8%] Black, 177 600 [30.1%] Hispanic or Latino, 116 559 [19.7%] White). Completion rates were consistent over time and resistant to changes related to vaccination as well as isolation and quarantine guidance. Among symptomatic cases, median time from symptom onset to intake completion was 4.7 days; a median 1.4 contacts were identified per case. Median time from contacts' last date of exposure to intake completion was 2.3 days. Among contacts, 30.1% were tested within 14 days of notification. Among cases, 27.8% were known to Trace as contacts. The overall expense for Trace from May 6, 2020, through October 31, 2021, was approximately $600 million.Conclusions and relevanceDespite the complexity of developing a contact tracing program in a diverse city with a population of over 8 million people, in this case study we were able to identify 1.4 contacts per case and offer resources to safely isolate and quarantine to over 1 million cases and contacts in this study period.
| S-EPMC9631107 | biostudies-literature
Project description:BackgroundNew York City (NYC) was the first COVID-19 epicenter in the United States and home to one of the country's largest contact tracing programs, NYC Test & Trace (T2). Understanding points of attrition along the stages of program implementation and follow-up can inform contact tracing efforts for future epidemics or pandemics. The objective of this study was to evaluate the completeness and timeliness of T2 case and contact notification and monitoring using a "cascade of care" approach.MethodsThis cross-sectional study included all SARS-CoV-2 cases and contacts reported to T2 from May 31, 2020 to January 1, 2022. Attrition along the "cascade of care" was defined as: (1) attempted, (2) reached, (3) completed intake (main outcome), (4) eligible for monitoring, and (5) successfully monitored. Timeliness was assessed: (1) by median days from a case's date of testing until their positive result was reported to T2, (2) from result until the case was notified by T2, and (3) from a case report of a contact until notification of the contact.ResultsA total of 1.45 million cases and 1.38 million contacts were reported to T2 during this period. For cases, attrition occurred evenly across the first three cascade steps (~-12%) and did not change substantially until the Omicron wave in December 2021. During the Omicron wave, the proportion of cases attempted dropped precipitously. For contacts, the largest attrition occurred between attempting and reaching (-27%), and attrition rose with each COVID-19 wave as contact volumes increased. Attempts to reach contacts discontinued entirely during the Omicron wave. Overall, 67% of cases and 49% of contacts completed intake interviews (79% and 57% prior to Omicron). T2 was timely, with a median of 1 day to receive lab results, 2 days to notify cases, and < 1 day to notify contacts.ConclusionsT2 provided a large volume of NYC residents with timely notification and monitoring. Engagement in the program was lower for contacts than cases, with the largest gap coming from inability to reach individuals during call attempts. To strengthen future test-and-trace efforts, strategies are needed to encourage acceptance of local contact tracer outreach attempts.
| S-EPMC11363647 | biostudies-literature
Project description:Simultaneously controlling COVID-19 epidemics and limiting economic and societal impacts presents a difficult challenge, especially with limited public health budgets. Testing, contact tracing, and isolating/quarantining is a key strategy that has been used to reduce transmission of SARS-CoV-2, the virus that causes COVID-19 and other pathogens. However, manual contact tracing is a time-consuming process and as case numbers increase a smaller fraction of cases' contacts can be traced, leading to additional virus spread. Delays between symptom onset and being tested (and receiving results), and a low fraction of symptomatic cases being tested and traced can also reduce the impact of contact tracing on transmission. We examined the relationship between increasing cases and delays and the pathogen reproductive number Rt, and the implications for infection dynamics using deterministic and stochastic compartmental models of SARS-CoV-2. We found that Rt increased sigmoidally with the number of cases due to decreasing contact tracing efficacy. This relationship results in accelerating epidemics because Rt initially increases, rather than declines, as infections increase. Shifting contact tracers from locations with high and low case burdens relative to capacity to locations with intermediate case burdens maximizes their impact in reducing Rt (but minimizing total infections may be more complicated). Contact tracing efficacy decreased sharply with increasing delays between symptom onset and tracing and with lower fraction of symptomatic infections being tested. Finally, testing and tracing reductions in Rt can sometimes greatly delay epidemics due to the highly heterogeneous transmission dynamics of SARS-CoV-2. These results demonstrate the importance of having an expandable or mobile team of contact tracers that can be used to control surges in cases. They also highlight the synergistic value of high capacity, easy access testing and rapid turn-around of testing results, and outreach efforts to encourage symptomatic cases to be tested immediately after symptom onset.
| S-EPMC8241027 | biostudies-literature
Project description: Not available
| S-EPMC7182018 | biostudies-literature
Project description:Contact tracing is currently one of the most effective measures to contain the COVID-19 pandemic. In order to identify persons that would otherwise not be known or remembered and to keep the time delay when reporting an infection and when contacting people as short as possible, digital contact tracing using smartphones seems to be a reasonable measure additional to manual contact tracing. Although first modelling studies predicted a positive effect in terms of prompt contact tracing, no empirically reliable data are as yet available, neither on the population-wide benefit nor on the potential risks of contact tracing apps. Risk-benefit assessment of such an app includes investigating whether such an app fulfils its purpose, as also research on the effectiveness, risks and side effects, and implementation processes (e.?g. planning and inclusion of different participants). The aim of this article was to give an overview of possible public health benefits as well as technical, social, legal and ethical aspects of a contact-tracing app in the context of the COVID-19 pandemic. Furthermore, conditions for the widest possible use of the app are presented.
| S-EPMC7536383 | biostudies-literature
Project description:BackgroundCOVID-19 has affected millions of people, and several chronic medical conditions appear to increase the risk of severe COVID-19. However, our understanding of COVID-19 outcomes in patients with CKD remains limited.MethodsThis was a retrospective cohort study of patients with and without CKD consecutively admitted with COVID-19 to three affiliated hospitals in New York City. Pre-COVID-19 CKD diagnoses were identified by billing codes and verified by manual chart review. In-hospital mortality was compared between patients with and without underlying CKD. Logistic regression was used to adjust this analysis for confounders and to identify patient characteristics associated with mortality.ResultsWe identified 280 patients with CKD, and 4098 patients without CKD hospitalized with COVID-19. The median age of the CKD group was 75 (65-84) years, and age of the non-CKD group 62 (48-75) years. Baseline (pre-COVID-19) serum creatinine in patients with CKD was 1.5 (1.2-2.2) mg/dl. In-hospital mortality was 30% in patients with CKD versus 20% in patients without CKD (P<0.001). The risk of in-hospital death in patients with CKD remained higher than in patients without CKD after adjustment for comorbidities (hypertension, diabetes mellitus, asthma, and chronic obstructive pulmonary disease), adjusted OR 1.4 (95% CI,1.1 to 1.9), P=0.01. When stratified by age, elderly patients with CKD (age >70 years) had higher mortality than their age-matched control patients without CKD. In patients with CKD, factors associated with in-hospital mortality were age (adjusted OR, 1.09 [95% CI, 1.06 to 1.12]), P<0.001, baseline and admission serum phosphorus (adjusted OR, 1.5 [95% CI, 1.03 to 2.1], P=0.03 and 1.4 [95% CI, 1.1 to 1.7], P=0.001), serum creatinine on admission >0.3 mg/dl above the baseline (adjusted OR 2.6 [95% CI, 1.2 to 5.4]P=0.01), and diagnosis of acute on chronic kidney injury during hospitalization (adjusted OR 4.6 [95% CI, 2.3 to 8.9], P<0.001).ConclusionsCKD is an independent risk factor for COVID-19-associated in-hospital mortality in elderly patients. Acute-on-chronic kidney injury increases the odds of in-hospital mortality in patients with CKD hospitalized with COVID-19.
| S-EPMC7901833 | biostudies-literature
Project description:This paper proposes an ensemble predictor for the weekly increase in the number of confirmed COVID-19 cases in the city of New York at zip code level. Within a Bayesian model averaging framework, the baseline is a Poisson regression for count data. The set of covariates includes autoregressive terms, spatial effects, and demographic and socioeconomic variables. Our results for the second wave of the coronavirus pandemic show that these regressors are more significant to predict the number of new confirmed cases as the pandemic unfolds. Both pointwise and interval forecasts exhibit strong predictive ability in-sample and out-of-sample.
| S-EPMC8242800 | biostudies-literature
Project description:Although testing, contact tracing, and case isolation programs can mitigate COVID-19 transmission and allow the relaxation of social distancing measures, few countries worldwide have succeeded in scaling such efforts to levels that suppress spread. The efficacy of test-trace-isolate likely depends on the speed and extent of follow-up and the prevalence of SARS-CoV-2 in the community. Here, we use a granular model of COVID-19 transmission to estimate the public health impacts of test-trace-isolate programs across a range of programmatic and epidemiological scenarios, based on testing and contact tracing data collected on a university campus and surrounding community in Austin, TX, between October 1, 2020, and January 1, 2021. The median time between specimen collection from a symptomatic case and quarantine of a traced contact was 2 days (interquartile range [IQR]: 2 to 3) on campus and 5 days (IQR: 3 to 8) in the community. Assuming a reproduction number of 1.2, we found that detection of 40% of all symptomatic cases followed by isolation is expected to avert 39% (IQR: 30% to 45%) of COVID-19 cases. Contact tracing is expected to increase the cases averted to 53% (IQR: 42% to 58%) or 40% (32% to 47%), assuming the 2- and 5-day delays estimated on campus and in the community, respectively. In a tracing-accelerated scenario, in which 75% of contacts are notified the day after specimen collection, cases averted increase to 68% (IQR: 55% to 72%). An accelerated contact tracing program leveraging rapid testing and electronic reporting of test results can significantly curtail local COVID-19 transmission.
| S-EPMC9407477 | biostudies-literature