When is contact tracing not enough to stop an outbreak?

COVID-19 contact-tracing smartphone application usage-The New Zealand COVID Tracer experience

Contact tracing has been a central pillar of the nonpharmaceutical health system response to the COVID-19 pandemic. Countries around the world have devoted unprecedented levels of resources to build up their testing and tracing capabilities, including the development and deployment of smartphone-based applications. Yet despite these nontrivial investments, the body of academic literature evaluating the effects of the smartphone-based applications remains scant and many apps have not delivered the promised benefits (Bano et al. 2021). We contribute to this body of empirical evidence by analysing data on uptake and usage of New Zealand's QR code-based application New Zealand COVID Tracer (NZCT). Our paper uses descriptive statistics and regression analysis to focus on the likelihood of an individual registering to use the application, the likelihood that a registered user will use the application to scan QR codes, and the extent of that usage, measured as the number of codes scanned daily. In addition, we take advantage of the "natural experiment" offered by the decision in August 2021 following the establishment of endemic community transmission of COVID-19 to make use of the application mandatory from 7 September 2021 to assess the effect of this policy on usage. We find that despite increasing numbers of individuals downloading the application over time, the number of active devices and scanning activity was very low, even when community outbreaks occurred (i.e. actual infection risk was higher). Mandating use (separate and distinct from increased infection risk) led to an increase in the number of scans in total via an increase in the number of active devices only.

Downsizing of COVID-19 contact tracing in highly immune populations

Contact tracing is a key component of successful management of COVID-19. Contacts of infected individuals are asked to quarantine, which can significantly slow down (or prevent) community spread. Contact tracing is particularly effective when infections are detected quickly, when contacts are traced with high probability, when the initial number of cases is low, and when social distancing and border restrictions are in place. However, the magnitude of the individual contribution of these factors in reducing epidemic spread and the impact of population immunity (due to either previous infection or vaccination), in determining contact tracing outputs is not fully understood. We present a delayed differential equation model to investigate how the immunity status and the relaxation of social distancing requirements affect contact tracing practices. We investigate how the minimal contact tracing efficiency required to keep an outbreak under control depends on the contact rate and on the proportion of immune individuals. Additionally, we consider how delays in outbreak detection and increased case importation rates affect the number of contacts to be traced daily. We show that in communities that have reached a certain immunity status, a lower contact tracing efficiency is required to avoid a major outbreak, and delayed outbreak detection and relaxation of border restrictions do not lead to a significantly higher risk of overwhelming contact tracing. We find that investing in testing programs, rather than increasing the contact tracing capacity, has a larger impact in determining whether an outbreak will be controllable. This is because early detection activates contact tracing, which will slow, and eventually reverse exponential growth, while the contact tracing capacity is a threshold that will easily become overwhelmed if exponential growth is not curbed. Finally, we evaluate quarantine effectiveness in relation to the immunity status of the population and for different viral variants. We show that quarantine effectiveness decreases with increasing proportion of immune individuals, and increases in the presence of more transmissible variants. These results suggest that a cost-effective approach is to establish different quarantine rules for immune and nonimmune individuals, where rules should depend on viral transmissibility after vaccination or infection. Altogether, our study provides quantitative information for contact tracing downsizing in vaccinated populations or in populations that have already experienced large community outbreaks, to guide COVID-19 exit strategies.

Listening to bluetooth beacons for epidemic risk mitigation

The ongoing COVID-19 pandemic let to efforts to develop and deploy digital contact tracing systems to expedite contact tracing and risk notification. Unfortunately, the success of these systems has been limited, partly owing to poor interoperability with manual contact tracing, low adoption rates, and a societally sensitive trade-off between utility and privacy. In this work, we introduce a new privacy-preserving and inclusive system for epidemic risk assessment and notification that aims to address these limitations. Rather than capturing pairwise encounters between user devices as done by existing systems, our system captures encounters between user devices and beacons placed in strategic locations where infection clusters may originate. Epidemiological simulations using an agent-based model demonstrate that, by utilizing location and environmental information and interoperating with manual contact tracing, our system can increase the accuracy of contact tracing actions and may help reduce epidemic spread already at low adoption.

Measles outbreak investigation process in low- and middle-income countries: a systematic review of the methods and costs of contact tracing

Aim

The occurrence of measles outbreaks has increased, and previously measles-free countries are experiencing a resurgence, making measles elimination by 2020 unlikely. Therefore, outbreak prevention and rapid response strategies will need to be intensified. This systematic review therefore examines whether contact tracing (CT) as compared to no CT is an effective means of reducing measles spread during outbreaks in low- and middle-income countries (LMICs).

Subject and methods

A systematic review was conducted by searching six databases (CINAHL, Global Health, Medline, Cochrane Library, Web of Science and PubMed). The 17 included articles were appraised using the Critical Appraisal Skills Programme checklists and analysed using a narrative synthesis.

Results

CT is often used alongside mass communication strategies and hospital record checks. Interviewing measles cases to identify contacts, and considering everyone who has shared a space with a case as a contact are common CT methods. Also, CT can be done backwards and/or forwards with the measles case as the focal point of the investigation process. The cost per case of an outbreak response dominated by CT is high especially in terms of labour for the health sector and productivity losses for households. However, overall outbreak expenditure can be low if CT results in fewer and less severe measles cases and a short outbreak duration.

Conclusion

CT data as a standalone and comparative active surveillance approach in LMICs is scarce. If CT is initiated early, it can prevent large outbreaks, thereby reducing the economic burden of measles and drive LMICs towards measles elimination.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10389-021-01590-2.

COVID-19 contact tracing: The Welsh experience

Objective

Contact tracing is one of the key public health response actions to control the outbreak of a novel virus. This paper describes the preparation process, activation and operational experience for contact tracing of individuals in response to confirmed COVID-19 cases in Wales.

Study design

A descriptive approach has been adopted and lessons learned from our initial public health response to COVID-19 will be used to develop a new operational model for contact tracing in Wales.

Methods

As part of preparations for the response in Wales, Public Health Wales formed a Contact Tracing Cell (CTC) ready to be mobilised in the event of a confirmed case.

Results

Trial activation of the CTC during the preparation period helped to resolve some issues before ‘real’ activation. A highly flexible approach was needed due to the constant changes to the guidance that required rapid understanding, updates to pathways and clear communication to contact tracers.

Conclusions

Our experience and recommendations may benefit future efforts to control the spread of the virus in Wales and elsewhere, particularly in supporting COVID-19 outbreaks in enclosed settings such as care homes or in geographically localised areas. Learning from the initial public health response to COVID-19 will guide the delivery and implementation of a new contact tracing model as we move to a later stage of the pandemic when containment measures become feasible in localised outbreaks. This may include scaling-up the CTC to mobilise contact tracers to local teams and the potential use of digital technologies to support the next operational model of the CTC in Wales.

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Contact tracing is one of the key public health response actions to control the outbreak of a novel virus.

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For a CTC to be effective, identification and training of adequate numbers of staff needs to be done as early as possible.

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A highly flexible approach was needed due to the logistical complexities of dealing with constant changes to the guidelines.

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The mobilisation of non-health protection staff freed up health protection teams to deal with more complex issues.

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Learning is needed to enhance future contact tracing activities when containment becomes feasible in localised outbreaks.

Implementation and Process of a COVID-19 Contact Tracing Initiative: Leveraging Health Professional Students to Extend the Workforce During a Pandemic

Background

The Centers for Disease Control and Prevention recommends aggressive contact tracing to control the COVID-19 pandemic. In this work, we (1) describe the development of a COVID-19 contact tracing initiative that includes medical, nursing, and public health students, and is led by clinicians and infectious disease epidemiologists within our health system, and, (2) articulate process steps for contact tracing including workflows and telephone scripts, and, (3) highlight the key challenges and strategies to overcome these challenges.

Methods

A single academic institution-based contact tracing initiative was rapidly scaled to 110 health professional students, four physicians, two epidemiologists, and a research team. Following training, students called patients who were COVID-19 positive and the individuals they were in contact with to ensure proper isolation and quarantine measures. Students also assisted those who faced barriers to quarantine.

Implications

In total, between March 24 and May 28 – this initiative completed contact tracing for 536 confirmed cases, which resulted in the identification of 953 contacts. We aim to disseminate this process, including telephone scripts and workflow, to other health systems for use in their initiatives to respond to the COVID-19 pandemic and future public health emergencies.

Shifting the Paradigm: The Dress-COV Telegram Bot as a Tool for Participatory Medicine

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic management is limited by great uncertainty, for both health systems and citizens. Facing this information gap requires a paradigm shift from traditional approaches to healthcare to the participatory model of improving health. This work describes the design and function of the Doing Risk sElf-assessment and Social health Support for COVID (Dress-COV) system. It aims to establish a lasting link between the user and the tool; thus, enabling modeling of the data to assess individual risk of infection, or developing complications, to improve the individual's self-empowerment. The system uses bot technology of the Telegram application. The risk assessment includes the collection of user responses and the modeling of data by machine learning models, with increasing appropriateness based on the number of users who join the system. The main results reflect: (a) the individual's compliance with the tool; (b) the security and versatility of the architecture; (c) support and promotion of self-management of behavior to accommodate surveillance system delays; (d) the potential to support territorial health providers, e.g., the daily efforts of general practitioners (during this pandemic, as well as in their routine practices). These results are unique to Dress-COV and distinguish our system from classical surveillance applications.

Beyond R0: heterogeneity in secondary infections and probabilistic epidemic forecasting

The basic reproductive number, R0, is one of the most common and most commonly misapplied numbers in public health. Often used to compare outbreaks and forecast pandemic risk, this single number belies the complexity that different epidemics can exhibit, even when they have the same R0. Here, we reformulate and extend a classic result from random network theory to forecast the size of an epidemic using estimates of the distribution of secondary infections, leveraging both its average R0 and the underlying heterogeneity. Importantly, epidemics with lower R0 can be larger if they spread more homogeneously (and are therefore more robust to stochastic fluctuations). We illustrate the potential of this approach using different real epidemics with known estimates for R0, heterogeneity and epidemic size in the absence of significant intervention. Further, we discuss the different ways in which this framework can be implemented in the data-scarce reality of emerging pathogens. Lastly, we demonstrate that without data on the heterogeneity in secondary infections for emerging infectious diseases like COVID-19 the uncertainty in outbreak size ranges dramatically. Taken together, our work highlights the critical need for contact tracing during emerging infectious disease outbreaks and the need to look beyond R0.

A data driven epidemic model to analyse the lockdown effect and predict the course of COVID-19 progress in India

We propose a data driven epidemic model using the real data on the infection, recovery and death cases for the analysis of COVID-19 progression in India. The model assumes continuation of existing control measures such as lockdown and quarantines, the suspected and confirmed cases and does not consider the scenario of 2nd surge of the epidemic due to any reason. The model is arrived after least square fitting of epidemic behaviour model based on theoretical formulation to the real data of cumulative infection cases reported between 24 March 2020 and 30May 2020. The predictive capability of the model has been validated with real data of infection cases reported during June 1-10, 2020. A detailed analysis of model predictions in terms of future trend of COVID-19 progress individually in 18 states of India and India as a whole has been attempted. Infection rate in India, as a whole, is continuously decreasing with time and has reached 3 times lower than the initial infection rate after 6 weeks of lock down suggesting the effectiveness of the lockdown in containing the epidemic. Results suggest that India, as a whole, could see the peak and end of the epidemic in the month of July 2020 and March 2021 respectively as per the current trend in the data. Active infected cases in India may touch 2 lakhs or little above at the peak time and total infected cases may reach over 19 lakhs as per current trend. State-wise results have been discussed in the manuscript. However, the prediction may deviate particularly for longer dates, as assumptions of model cannot be met always in a real scenario. In view of this, a real time application (COV-IND Predictor) has been developed which automatically syncs the latest data from the national COVID19 dash board on daily basis and updates the model input parameters and predictions instantaneously. This real time application can be accessed from the link: https://docs.google.com/spreadsheets/d/1fCwgnQ-dz4J0YWVDHUcbEW1423wOJjdEXm8TqJDWNAk/edit?usp=sharing and can serve as a practical tool for policy makers to track peak time and maximum active infected cases based on latest trend in data for medical readiness and taking epidemic management decisions.

Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts

BACKGROUND

Isolation of cases and contact tracing is used to control outbreaks of infectious diseases, and has been used for coronavirus disease 2019 (COVID-19). Whether this strategy will achieve control depends on characteristics of both the pathogen and the response. Here we use a mathematical model to assess if isolation and contact tracing are able to control onwards transmission from imported cases of COVID-19.

METHODS

We developed a stochastic transmission model, parameterised to the COVID-19 outbreak. We used the model to quantify the potential effectiveness of contact tracing and isolation of cases at controlling a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-like pathogen. We considered scenarios that varied in the number of initial cases, the basic reproduction number (R₀), the delay from symptom onset to isolation, the probability that contacts were traced, the proportion of transmission that occurred before symptom onset, and the proportion of subclinical infections. We assumed isolation prevented all further transmission in the model. Outbreaks were deemed controlled if transmission ended within 12 weeks or before 5000 cases in total. We measured the success of controlling outbreaks using isolation and contact tracing, and quantified the weekly maximum number of cases traced to measure feasibility of public health effort.

FINDINGS

Simulated outbreaks starting with five initial cases, an R₀ of 1·5, and 0% transmission before symptom onset could be controlled even with low contact tracing probability; however, the probability of controlling an outbreak decreased with the number of initial cases, when R₀ was 2·5 or 3·5 and with more transmission before symptom onset. Across different initial numbers of cases, the majority of scenarios with an R₀ of 1·5 were controllable with less than 50% of contacts successfully traced. To control the majority of outbreaks, for R₀ of 2·5 more than 70% of contacts had to be traced, and for an R₀ of 3·5 more than 90% of contacts had to be traced. The delay between symptom onset and isolation had the largest role in determining whether an outbreak was controllable when R₀ was 1·5. For R₀ values of 2·5 or 3·5, if there were 40 initial cases, contact tracing and isolation were only potentially feasible when less than 1% of transmission occurred before symptom onset.

INTERPRETATION

In most scenarios, highly effective contact tracing and case isolation is enough to control a new outbreak of COVID-19 within 3 months. The probability of control decreases with long delays from symptom onset to isolation, fewer cases ascertained by contact tracing, and increasing transmission before symptoms. This model can be modified to reflect updated transmission characteristics and more specific definitions of outbreak control to assess the potential success of local response efforts.

FUNDING

Wellcome Trust, Global Challenges Research Fund, and Health Data Research UK.

Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts

BACKGROUND

Isolation of cases and contact tracing is used to control outbreaks of infectious diseases, and has been used for coronavirus disease 2019 (COVID-19). Whether this strategy will achieve control depends on characteristics of both the pathogen and the response. Here we use a mathematical model to assess if isolation and contact tracing are able to control onwards transmission from imported cases of COVID-19.

METHODS

We developed a stochastic transmission model, parameterised to the COVID-19 outbreak. We used the model to quantify the potential effectiveness of contact tracing and isolation of cases at controlling a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-like pathogen. We considered scenarios that varied in the number of initial cases, the basic reproduction number (R0), the delay from symptom onset to isolation, the probability that contacts were traced, the proportion of transmission that occurred before symptom onset, and the proportion of subclinical infections. We assumed isolation prevented all further transmission in the model. Outbreaks were deemed controlled if transmission ended within 12 weeks or before 5000 cases in total. We measured the success of controlling outbreaks using isolation and contact tracing, and quantified the weekly maximum number of cases traced to measure feasibility of public health effort.

FINDINGS

Simulated outbreaks starting with five initial cases, an R0 of 1·5, and 0% transmission before symptom onset could be controlled even with low contact tracing probability; however, the probability of controlling an outbreak decreased with the number of initial cases, when R0 was 2·5 or 3·5 and with more transmission before symptom onset. Across different initial numbers of cases, the majority of scenarios with an R0 of 1·5 were controllable with less than 50% of contacts successfully traced. To control the majority of outbreaks, for R0 of 2·5 more than 70% of contacts had to be traced, and for an R0 of 3·5 more than 90% of contacts had to be traced. The delay between symptom onset and isolation had the largest role in determining whether an outbreak was controllable when R0 was 1·5. For R0 values of 2·5 or 3·5, if there were 40 initial cases, contact tracing and isolation were only potentially feasible when less than 1% of transmission occurred before symptom onset.

INTERPRETATION

In most scenarios, highly effective contact tracing and case isolation is enough to control a new outbreak of COVID-19 within 3 months. The probability of control decreases with long delays from symptom onset to isolation, fewer cases ascertained by contact tracing, and increasing transmission before symptoms. This model can be modified to reflect updated transmission characteristics and more specific definitions of outbreak control to assess the potential success of local response efforts.

FUNDING

Wellcome Trust, Global Challenges Research Fund, and Health Data Research UK.