BACKGROUND: Contact tracing in association with quarantine and isolation is an important public health tool to control outbreaks of infectious diseases. This strategy has been widely implemented during the current COVID-19 pandemic. The effectiveness of this nonpharmaceutical intervention is largely dependent on social interactions within the population and its combination with other interventions. Given the high transmissibility of SARS-CoV-2, short serial intervals, and asymptomatic transmission patterns, the effectiveness of contact tracing for this novel viral agent is largely unknown. OBJECTIVE: This study aims to identify and synthesize evidence regarding the effectiveness of contact tracing on infectious viral disease outcomes based on prior scientific literature. METHODS: An evidence-based review was conducted to identify studies from the PubMed database, including preprint medRxiv server content, related to the effectiveness of contact tracing in viral outbreaks. The search dates were from database inception to July 24, 2020. Outcomes of interest included measures of incidence, transmission, hospitalization, and mortality. RESULTS: Out of 159 unique records retrieved, 45 (28.3%) records were reviewed at the full-text level, and 24 (15.1%) records met all inclusion criteria. The studies included utilized mathematical modeling (n=14), observational (n=8), and systematic review (n=2) approaches. Only 2 studies considered digital contact tracing. Contact tracing was mostly evaluated in combination with other nonpharmaceutical interventions and/or pharmaceutical interventions. Although some degree of effectiveness in decreasing viral disease incidence, transmission, and resulting hospitalizations and mortality was observed, these results were highly dependent on epidemic severity (R0 value), number of contacts traced (including presymptomatic and asymptomatic cases), timeliness, duration, and compliance with combined interventions (eg, isolation, quarantine, and treatment). Contact tracing effectiveness was particularly limited by logistical challenges associated with increased outbreak size and speed of infection spread. CONCLUSIONS: Timely deployment of contact tracing strategically layered with other nonpharmaceutical interventions could be an effective public health tool for mitigating and suppressing infectious outbreaks by decreasing viral disease incidence, transmission, and resulting hospitalizations and mortality.
Subject(s)Communicable Disease Control/methods , Contact Tracing , Virus Diseases/prevention & control , COVID-19/prevention & control , Humans
BACKGROUND: Previous studies have suggested that haemodynamic-guided management using an implantable pulmonary artery pressure monitor reduces heart failure hospitalisations in patients with moderately symptomatic (New York Heart Association [NYHA] functional class III) chronic heart failure and a hospitalisation in the past year, irrespective of ejection fraction. It is unclear if these benefits extend to patients with mild (NYHA functional class II) or severe (NYHA functional class IV) symptoms of heart failure or to patients with elevated natriuretic peptides without a recent heart failure hospitalisation. This trial was designed to evaluate whether haemodynamic-guided management using remote pulmonary artery pressure monitoring could reduce heart failure events and mortality in patients with heart failure across the spectrum of symptom severity (NYHA funational class II-IV), including those with elevated natriuretic peptides but without a recent heart failure hospitalisation. METHODS: The randomised arm of the haemodynamic-GUIDEed management of Heart Failure (GUIDE-HF) trial was a multicentre, single-blind study at 118 centres in the USA and Canada. Following successful implantation of a pulmonary artery pressure monitor, patients with all ejection fractions, NYHA functional class II-IV chronic heart failure, and either a recent heart failure hospitalisation or elevated natriuretic peptides (based on a-priori thresholds) were randomly assigned (1:1) to either haemodynamic-guided heart failure management based on pulmonary artery pressure or a usual care control group. Patients were masked to their study group assignment. Investigators were aware of treatment assignment but did not have access to pulmonary artery pressure data for control patients. The primary endpoint was a composite of all-cause mortality and total heart failure events (heart failure hospitalisations and urgent heart failure hospital visits) at 12 months assessed in all randomly assigned patients. Safety was assessed in all patients. A pre-COVID-19 impact analysis for the primary and secondary outcomes was prespecified. This study is registered with ClinicalTrials.gov, NCT03387813. FINDINGS: Between March 15, 2018, and Dec 20, 2019, 1022 patients were enrolled, with 1000 patients implanted successfully, and follow-up was completed on Jan 8, 2021. There were 253 primary endpoint events (0·563 per patient-year) among 497 patients in the haemodynamic-guided management group (treatment group) and 289 (0·640 per patient-year) in 503 patients in the control group (hazard ratio [HR] 0·88, 95% CI 0·74-1·05; p=0·16). A prespecified COVID-19 sensitivity analysis using a time-dependent variable to compare events before COVID-19 and during the pandemic suggested a treatment interaction (pinteraction=0·11) due to a change in the primary endpoint event rate during the pandemic phase of the trial, warranting a pre-COVID-19 impact analysis. In the pre-COVID-19 impact analysis, there were 177 primary events (0·553 per patient-year) in the intervention group and 224 events (0·682 per patient-year) in the control group (HR 0·81, 95% CI 0·66-1·00; p=0·049). This difference in primary events almost disappeared during COVID-19, with a 21% decrease in the control group (0·536 per patient-year) relative to pre-COVID-19, virtually no change in the treatment group (0·597 per patient-year), and no difference between groups (HR 1·11, 95% CI 0·80-1·55; p=0·53). The cumulative incidence of heart failure events was not reduced by haemodynamic-guided management (0·85, 0·70-1·03; p=0·096) in the overall study analysis but was significantly decreased in the pre-COVID-19 impact analysis (0·76, 0·61-0·95; p=0·014). 1014 (99%) of 1022 patients had freedom from device or system-related complications. INTERPRETATION: Haemodynamic-guided management of heart failure did not result in a lower composite endpoint rate of mortality and total heart failure events compared with the control group in the overall study analysis. However, a pre-COVID-19 impact analysis indicated a possible benefit of haemodynamic-guided management on the primary outcome in the pre-COVID-19 period, primarily driven by a lower heart failure hospitalisation rate compared with the control group. FUNDING: Abbott.
Subject(s)Electrodes, Implanted , Heart Failure , Hemodynamics , Hospitalization/statistics & numerical data , Pulmonary Artery , Aged , COVID-19 , Female , Heart Failure/classification , Heart Failure/physiopathology , Hemodynamics/physiology , Hospitalization/trends , Humans , Male , Mortality/trends , Remote Sensing Technology
Artificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics.