Modeling the effect of exposure notification and non-pharmaceutical interventions on COVID-19 transmission in Washington state

Estimating vaccine efficacy during open-label follow-up of COVID-19 vaccine trials based on population-level surveillance data

While rapid development and roll out of COVID-19 vaccines is necessary in a pandemic, the process limits the ability of clinical trials to assess longer-term vaccine efficacy. We leveraged COVID-19 surveillance data in the U.S. to evaluate vaccine efficacy in U.S. Government-funded COVID-19 vaccine efficacy trials with a three-step estimation process. First, we used a compartmental epidemiological model informed by county-level surveillance data, a "population model", to estimate SARS-CoV-2 incidence among the unvaccinated. Second, a "cohort model" was used to adjust the population SARS-CoV-2 incidence to the vaccine trial cohort, taking into account individual participant characteristics and the difference between SARS-CoV-2 infection and COVID-19 disease. Third, we fit a regression model estimating the offset between the cohort-model-based COVID-19 incidence in the unvaccinated with the placebo-group COVID-19 incidence in the trial during blinded follow-up. Counterfactual placebo COVID-19 incidence was estimated during open-label follow-up by adjusting the cohort-model-based incidence rate by the estimated offset. Vaccine efficacy during open-label follow-up was estimated by contrasting the vaccine group COVID-19 incidence with the counterfactual placebo COVID-19 incidence. We documented good performance of the methodology in a simulation study. We also applied the methodology to estimate vaccine efficacy for the two-dose AZD1222 COVID-19 vaccine using data from the phase 3 U.S. trial (ClinicalTrials.gov # NCT04516746). We estimated AZD1222 vaccine efficacy of 59.1% (95% uncertainty interval (UI): 40.4%-74.3%) in April, 2021 (mean 106 days post-second dose), which reduced to 35.7% (95% UI: 15.0%-51.7%) in July, 2021 (mean 198 days post-second-dose). We developed and evaluated a methodology for estimating longer-term vaccine efficacy. This methodology could be applied to estimating counterfactual placebo incidence for future placebo-controlled vaccine efficacy trials of emerging pathogens with early termination of blinded follow-up, to active-controlled or uncontrolled COVID-19 vaccine efficacy trials, and to other clinical endpoints influenced by vaccination.

COVSIM: A stochastic agent-based COVID-19 SIMulation model for North Carolina

We document the evolution and use of the stochastic agent-based COVID-19 simulation model (COVSIM) to study the impact of population behaviors and public health policy on disease spread within age, race/ethnicity, and urbanicity subpopulations in North Carolina. We detail the methodologies used to model the complexities of COVID-19, including multiple agent attributes (i.e., age, race/ethnicity, high-risk medical status), census tract-level interaction network, disease state network, agent behavior (i.e., masking, pharmaceutical intervention (PI) uptake, quarantine, mobility), and variants. We describe its uses outside of the COVID-19 Scenario Modeling Hub (CSMH), which has focused on the interplay of nonpharmaceutical and pharmaceutical interventions, equitability of vaccine distribution, and supporting local county decision-makers in North Carolina. This work has led to multiple publications and meetings with a variety of local stakeholders. When COVSIM joined the CSMH in January 2022, we found it was a sustainable way to support new COVID-19 challenges and allowed the group to focus on broader scientific questions. The CSMH has informed adaptions to our modeling approach, including redesigning our high-performance computing implementation.

Interplay of Digital Proximity App Use and SARS-CoV-2 Vaccine Uptake in Switzerland: Analysis of Two Population-Based Cohort Studies

Objectives: Our study aims to evaluate developments in vaccine uptake and digital proximity tracing app use in a localized context of the SARS-CoV-2 pandemic. Methods: We report findings from two population-based longitudinal cohorts in Switzerland from January to December 2021. Failure time analyses and Cox proportional hazards regression models were conducted to assess vaccine uptake and digital proximity tracing app (SwissCovid) uninstalling outcomes. Results: We observed a dichotomy of individuals who did not use the SwissCovid app and did not get vaccinated, and who used the SwissCovid app and got vaccinated during the study period. Increased vaccine uptake was observed with SwissCovid app use (aHR, 1.51; 95% CI: 1.40-1.62 [CI-DFU]; aHR, 1.79; 95% CI: 1.62-1.99 [CSM]) compared to SwissCovid app non-use. Decreased SwissCovid uninstallation risk was observed for participants who got vaccinated (aHR, 0.55; 95% CI: 0.38-0.81 [CI-DFU]; aHR, 0.45; 95% CI: 0.27-0.78 [CSM]) compared to participants who did not get vaccinated. Conclusion: In evolving epidemic contexts, these findings underscore the need for communication strategies as well as flexible digital proximity tracing app adjustments that accommodate different preventive measures and their anticipated interactions.

Exposure notification system activity as a leading indicator for SARS-COV-2 caseload forecasting

PURPOSE

Digital methods to augment traditional contact tracing approaches were developed and deployed globally during the COVID-19 pandemic. These "Exposure Notification (EN)" systems present new opportunities to support public health interventions. To date, there have been attempts to model the impact of such systems, yet no reports have explored the value of real-time system data for predictive epidemiological modeling.

METHODS

We investigated the potential to short-term forecast COVID-19 caseloads using data from California's implementation of the Google Apple Exposure Notification (GAEN) platform, branded as CA Notify. CA Notify is a digital public health intervention leveraging resident's smartphones for anonymous EN. We extended a published statistical model that uses prior case counts to investigate the possibility of predicting short-term future case counts and then added EN activity to test for improved forecast performance. Additional predictive value was assessed by comparing the pandemic forecasting models with and without EN activity to the actual reported caseloads from 1-7 days in the future.

RESULTS

Observation of time series presents noticeable evidence for temporal association of system activity and caseloads. Incorporating earlier ENs in our model improved prediction of the caseload counts. Using Bayesian inference, we found nonzero influence of EN terms with probability one. Furthermore, we found a reduction in both the mean absolute percentage error and the mean squared prediction error, the latter of at least 5% and up to 32% when using ENs over the model without.

CONCLUSIONS

This preliminary investigation suggests smartphone based ENs can significantly improve the accuracy of short-term forecasting. These predictive models can be readily deployed as local early warning systems to triage resources and interventions.

Phase-wise evaluation and optimization of non-pharmaceutical interventions to contain the COVID-19 pandemic in the U.S

Given that the effectiveness of COVID-19 vaccines and other therapies is greatly limited by the continuously emerging variants, non-pharmaceutical interventions have been adopted as primary control strategies in the global fight against the COVID-19 pandemic. However, implementing strict interventions over extended periods of time is inevitably hurting the economy. Many countries are faced with the dilemma of how to take appropriate policy actions for socio-economic recovery while curbing the further spread of COVID-19. With an aim to solve this multi-objective decision-making problem, we investigate the underlying temporal dynamics and associations between policies, mobility patterns, and virus transmission through vector autoregressive models and the Toda-Yamamoto Granger causality test. Our findings reveal the presence of temporal lagged effects and Granger causality relationships among various transmission and human mobility variables. We further assess the effectiveness of existing COVID-19 control measures and explore potential optimal strategies that strike a balance between public health and socio-economic recovery for individual states in the U.S. by employing the Pareto optimality and genetic algorithms. The results highlight the joint power of the state of emergency declaration, wearing face masks, and the closure of bars, and emphasize the necessity of pursuing tailor-made strategies for different states and phases of epidemiological transmission. Our framework enables policymakers to create more refined designs of COVID-19 strategies and can be extended to other countries regarding best practices in pandemic response.

Comparative effectiveness of contact tracing interventions in the context of the COVID-19 pandemic: a systematic review

Contact tracing is a non-pharmaceutical intervention (NPI) widely used in the control of the COVID-19 pandemic. Its effectiveness may depend on a number of factors including the proportion of contacts traced, delays in tracing, the mode of contact tracing (e.g. forward, backward or bidirectional contact training), the types of contacts who are traced (e.g. contacts of index cases or contacts of contacts of index cases), or the setting where contacts are traced (e.g. the household or the workplace). We performed a systematic review of the evidence regarding the comparative effectiveness of contact tracing interventions. 78 studies were included in the review, 12 observational (ten ecological studies, one retrospective cohort study and one pre-post study with two patient cohorts) and 66 mathematical modelling studies. Based on the results from six of the 12 observational studies, contact tracing can be effective at controlling COVID-19. Two high quality ecological studies showed the incremental effectiveness of adding digital contact tracing to manual contact tracing. One ecological study of intermediate quality showed that increases in contact tracing were associated with a drop in COVID-19 mortality, and a pre-post study of acceptable quality showed that prompt contact tracing of contacts of COVID-19 case clusters / symptomatic individuals led to a reduction in the reproduction number R. Within the seven observational studies exploring the effectiveness of contact tracing in the context of the implementation of other non-pharmaceutical interventions, contact tracing was found to have an effect on COVID-19 epidemic control in two studies and not in the remaining five studies. However, a limitation in many of these studies is the lack of description of the extent of implementation of contact tracing interventions. Based on the results from the mathematical modelling studies, we identified the following highly effective policies: (1) manual contact tracing with high tracing coverage and either medium-term immunity, highly efficacious isolation/quarantine and/ or physical distancing (2) hybrid manual and digital contact tracing with high app adoption with highly effective isolation/ quarantine and social distancing, (3) secondary contact tracing, (4) eliminating contact tracing delays, (5) bidirectional contact tracing, (6) contact tracing with high coverage in reopening educational institutions. We also highlighted the role of social distancing to enhance the effectiveness of some of these interventions in the context of 2020 lockdown reopening. While limited, the evidence from observational studies shows a role for manual and digital contact tracing in controlling the COVID-19 epidemic. More empirical studies accounting for the extent of contact tracing implementation are required.

Large-scale calibration and simulation of COVID-19 epidemiologic scenarios to support healthcare planning

The COVID-19 pandemic has provided stiff challenges for planning and resourcing in health services in the UK and worldwide. Epidemiological models can provide simulations of how infectious disease might progress in a population given certain parameters. We adapted an agent-based model of COVID-19 to inform planning and decision-making within a healthcare setting, and created a software framework that automates processes for calibrating the model parameters to health data and allows the model to be run at national population scale on National Health Service (NHS) infrastructure. We developed a method for calibrating the model to three daily data streams (hospital admissions, intensive care occupancy, and deaths), and demonstrate that on cross-validation the model fits acceptably to unseen data streams including official estimates of COVID-19 incidence. Once calibrated, we use the model to simulate future scenarios of the spread of COVID-19 in England and show that the simulations provide useful projections of future COVID-19 clinical demand. These simulations were used to support operational planning in the NHS in England, and we present the example of the use of these simulations in projecting future clinical demand during the rollout of the national COVID-19 vaccination programme. Being able to investigate uncertainty and test sensitivities was particularly important to the operational planning team. This epidemiological model operates within an ecosystem of data technologies, drawing on a range of NHS, government and academic data sources, and provides results to strategists, planners and downstream data systems. We discuss the data resources that enabled this work and the data challenges that were faced.

Impact of Privacy Messaging on COVID-19 Exposure Notification App Downloads: Evidence From a Randomized Experiment

Introduction

Digital contact-tracing smartphone apps have the potential to slow the spread of disease but are not widely used. We tested whether messages describing how a COVID-19 digital contact-tracing app protects users' privacy led to increased or decreased intentions to download the app by either calming privacy concerns or increasing their saliency.

Design

Randomized controlled trial.

Setting/participants

We recruited adult smartphone owners in the U.S. (oversampled for younger adults aged 18-34 years) in November 2020 through an online panel.

Intervention

Survey software randomly assigned 860 participants to 1 of 2 parallel messaging conditions (n=430 privacy assured, n=430 no privacy described).

Main outcome measures

4-point scale of intention to use the app "if public health officials released a COVID Exposure Notification app in their state" that averaged likelihood to (1) download and install the app on their phone; (2) keep the app active on their phone; and (3) keep Bluetooth active on their phone (needed for the app to work).

Results

After removing incompletes, those who failed the manipulation checks, or those who had already downloaded a COVID-19 digital contact-tracing app, we analyzed 671 participants (n=330 privacy, n=341 no privacy) in 2021. There was no relationship between privacy condition and download intention (mean_privacy=2.69, mean_noprivacy=2.69, b=0.01, 95% CI= -0.13, 0.15, p=0.922) but also no evidence that describing the app's security increased context-dependent privacy concerns (measured 3 ways). Instead, we found increased endorsement of data security in the privacy condition using a scale of beliefs about the app keeping privacy secure (mean_privacy=2.74, mean_noprivacy=2.58, b=0.16, 95% CI=0.04, 0.28, p=0.009, small effect ω²=0.009).

Conclusions

This study provides some evidence that people developing contact-tracing messaging campaigns do not need to worry that describing a digital contact-tracing app's privacy protections will backfire. Future mixed methods testing of messages about who has access to information-and for how long-may uncover new communication strategies to increase public trust in contact-tracing apps.

Trial registration

This study is registered with AsPredicted#51826.

A hospital demand and capacity intervention approach for COVID-19

The mathematical interpretation of interventions for the mitigation of epidemics in the literature often involves finding the optimal time to initiate an intervention and/or the use of the number of infections to manage impact. Whilst these methods may work in theory, in order to implement effectively they may require information which is not likely to be available in the midst of an epidemic, or they may require impeccable data about infection levels in the community. In reality, testing and cases data can only be as good as the policy of implementation and the compliance of the individuals, which implies that accurately estimating the levels of infections becomes difficult or complicated from the data that is provided. In this paper, we demonstrate a different approach to the mathematical modelling of interventions, not based on optimality or cases, but based on demand and capacity of hospitals who have to deal with the epidemic on a day to day basis. In particular, we use data-driven modelling to calibrate a susceptible-exposed-infectious-recovered-died type model to infer parameters that depict the dynamics of the epidemic in several regions of the UK. We use the calibrated parameters for forecasting scenarios and understand, given a maximum capacity of hospital healthcare services, how the timing of interventions, severity of interventions, and conditions for the releasing of interventions affect the overall epidemic-picture. We provide an optimisation method to capture when, in terms of healthcare demand, an intervention should be put into place given a maximum capacity on the service. By using an equivalent agent-based approach, we demonstrate uncertainty quantification on the likelihood that capacity is not breached, by how much if it does, and the limit on demand that almost guarantees capacity is not breached.

Understanding the impact of digital contact tracing during the COVID-19 pandemic

Digital contact tracing (DCT) applications have been introduced in many countries to aid the containment of COVID-19 outbreaks. Initially, enthusiasm was high regarding their implementation as a non-pharmaceutical intervention (NPI). However, no country was able to prevent larger outbreaks without falling back to harsher NPIs. Here, we discuss results of a stochastic infectious-disease model that provide insights in how the progression of an outbreak and key parameters such as detection probability, app participation and its distribution, as well as engagement of users impact DCT efficacy informed by results of empirical studies. We further show how contact heterogeneity and local contact clustering impact the intervention's efficacy. We conclude that DCT apps might have prevented cases on the order of single-digit percentages during single outbreaks for empirically plausible ranges of parameters, ignoring that a substantial part of these contacts would have been identified by manual contact tracing. This result is generally robust against changes in network topology with exceptions for homogeneous-degree, locally-clustered contact networks, on which the intervention prevents more infections. An improvement of efficacy is similarly observed when app participation is highly clustered. We find that DCT typically averts more cases during the super-critical phase of an epidemic when case counts are rising and the measured efficacy therefore depends on the time of evaluation.

A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine

Since 2019, the COVID-19 pandemic has had an extremely high impact on all facets of the society and will potentially have an everlasting impact for years to come. In response to this, over the past years, there have been a significant number of research efforts on exploring approaches to combat COVID-19. In this paper, we present a survey of the current research efforts on using mobile Internet of Thing (IoT) devices, Artificial Intelligence (AI), and telemedicine for COVID-19 detection and prediction. We first present the background and then present current research in this field. Specifically, we present the research on COVID-19 monitoring and detection, contact tracing, machine learning based approaches, telemedicine, and security. We finally discuss the challenges and the future work that lay ahead in this field before concluding this paper.

The Apple Mobility Trends Data in Human Mobility Patterns during Restrictions and Prediction of COVID-19: A Systematic Review and Meta-Analysis

The objective of this systematic review with PRISMA guidelines is to discover how population movement information has epidemiological implications for the spread of COVID-19. In November 2022, the Web of Science and Scopus databases were searched for relevant reports for the review. The inclusion criteria are: (1) the study uses data from Apple Mobility Trends Reports, (2) the context of the study is about COVID-19 mobility patterns, and (3) the report is published in a peer-reviewed venue in the form of an article or conference paper in English. The review included 35 studies in the period of 2020-2022. The main strategy used for data extraction in this review is a matrix proposal to present each study from a perspective of research objective and outcome, study context, country, time span, and conducted research method. We conclude by pointing out that these data are not often used in studies and it is better to study a single country instead of doing multiple-country research. We propose topic classifications for the context of the studies as transmission rate, transport policy, air quality, re-increased activities, economic activities, and financial markets.

Defining Key Performance Indicators for the California COVID-19 Exposure Notification System (CA Notify)

OBJECTIVES

Toward common methods for system monitoring and evaluation, we proposed a key performance indicator framework and discussed lessons learned while implementing a statewide exposure notification (EN) system in California during the COVID-19 epidemic.

MATERIALS AND METHODS

California deployed the Google Apple Exposure Notification framework, branded CA Notify, on December 10, 2020, to supplement traditional COVID-19 contact tracing programs. For system evaluation, we defined 6 key performance indicators: adoption, retention, sharing of unique codes, identification of potential contacts, behavior change, and impact. We aggregated and analyzed data from December 10, 2020, to July 1, 2021, in compliance with the CA Notify privacy policy.

RESULTS

We estimated CA Notify adoption at nearly 11 million smartphone activations during the study period. Among 1 654 201 CA Notify users who received a positive test result for SARS-CoV-2, 446 634 (27%) shared their unique code, leading to ENs for other CA Notify users who were in close proximity to the SARS-CoV-2-positive individual. We identified at least 122 970 CA Notify users as contacts through this process. Contact identification occurred a median of 4 days after symptom onset or specimen collection date of the user who received a positive test result for SARS-CoV-2.

PRACTICE IMPLICATIONS

Smartphone-based EN systems are promising new tools to supplement traditional contact tracing and public health interventions, particularly when efficient scaling is not feasible for other approaches. Methods to collect and interpret appropriate measures of system performance must be refined while maintaining trust and privacy.

Estimating SARS-CoV-2 variant fitness and the impact of interventions in England using statistical and geo-spatial agent-based models

The SARS-CoV-2 epidemic has been extended by the evolution of more transmissible viral variants. In autumn 2020, the B.1.177 lineage became the dominant variant in England, before being replaced by the B.1.1.7 (Alpha) lineage in late 2020, with the sweep occurring at different times in each region. This period coincided with a large number of non-pharmaceutical interventions (e.g. lockdowns) to control the epidemic, making it difficult to estimate the relative transmissibility of variants. In this paper, we model the spatial spread of these variants in England using a meta-population agent-based model which correctly characterizes the regional variation in cases and distribution of variants. As a test of robustness, we additionally estimated the relative transmissibility of multiple variants using a statistical model based on the renewal equation, which simultaneously estimates the effective reproduction number R. Relative to earlier variants, the transmissibility of B.1.177 is estimated to have increased by 1.14 (1.12-1.16) and that of Alpha by 1.71 (1.65-1.77). The vaccination programme starting in December 2020 is also modelled. Counterfactual simulations demonstrate that the vaccination programme was essential for reopening in March 2021, and that if the January lockdown had started one month earlier, up to 30 k (24 k-38 k) deaths could have been prevented. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Estimating SARS-CoV-2 variant fitness and the impact of interventions in England using statistical and geo-spatial agent-based models

The SARS-CoV-2 epidemic has been extended by the evolution of more transmissible viral variants. In autumn 2020, the B.1.177 lineage became the dominant variant in England, before being replaced by the B.1.1.7 (Alpha) lineage in late 2020, with the sweep occurring at different times in each region. This period coincided with a large number of non-pharmaceutical interventions (e.g. lockdowns) to control the epidemic, making it difficult to estimate the relative transmissibility of variants. In this paper, we model the spatial spread of these variants in England using a meta-population agent-based model which correctly characterizes the regional variation in cases and distribution of variants. As a test of robustness, we additionally estimated the relative transmissibility of multiple variants using a statistical model based on the renewal equation, which simultaneously estimates the effective reproduction number R. Relative to earlier variants, the transmissibility of B.1.177 is estimated to have increased by 1.14 (1.12-1.16) and that of Alpha by 1.71 (1.65-1.77). The vaccination programme starting in December 2020 is also modelled. Counterfactual simulations demonstrate that the vaccination programme was essential for reopening in March 2021, and that if the January lockdown had started one month earlier, up to 30 k (24 k-38 k) deaths could have been prevented. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Equity and Accessibility of Washington State’s COVID-19 Digital Exposure Notification Tool (WA Notify): Survey and Listening Sessions Among Community Leaders

Background

In November 2020, WA Notify, Washington State’s COVID-19 digital exposure notification tool, was launched statewide to mitigate ongoing COVID-19 transmission. WA Notify uses the Bluetooth proximity–triggered, Google/Apple Exposure Notification Express framework to distribute notifications to users who have added or activated this tool on their smartphones. This smartphone-based tool relies on sufficient population-level activation to be effective; however, little is known about its adoption among communities disproportionately impacted by the COVID-19 pandemic or what barriers might limit its adoption and use among diverse populations.

Objective

We sought to (1) conduct a formative exploration of equity-related issues that may influence the access, adoption, and use of WA Notify, as perceived by community leaders of populations disproportionately impacted by the COVID-19 pandemic; and (2) generate recommendations for promoting the equitable access to and impact of this novel intervention for these communities.

Methods

We used a 2-step data collection process to gather the perspectives of community leaders across Washington regarding the launch and implementation of WA Notify in their communities. A web-based, brief, and informational survey measured the perceptions of the community-level familiarity and effectiveness of WA Notify at slowing the spread of COVID-19 and identified potential barriers and concerns to accessing and adopting WA Notify (n=17). Semistructured listening sessions were conducted to expand upon survey findings and explore the community-level awareness, barriers, facilitators, and concerns related to activating WA Notify in greater depth (n=13).

Results

Our findings overlap considerably with those from previous mobile health equity studies. Digital literacy, trust, information accessibility, and misinformation were highlighted as key determinants of the adoption and use of WA Notify. Although WA Notify does not track users or share data, community leaders expressed concerns about security, data sharing, and personal privacy, which were cited as outweighing the potential benefits to adoption. Both the survey and informational sessions indicated low community-level awareness of WA Notify. Community leaders recommended the following approaches to improve engagement: tailoring informational materials for low-literacy levels, providing technology navigation, describing more clearly that WA Notify can help the community, and using trusted messengers who are already engaged with the communities to communicate about WA Notify.

Conclusions

As digital public health tools, such as WA Notify, emerge to address public health problems, understanding the key determinants of adoption and incorporating equity-focused recommendations into the development, implementation, and communication efforts around these tools will be instrumental to their adoption, use, and retention.

Engagement in Protective Behaviors by Digital Exposure Notification Users During the COVID-19 Pandemic, Washington State, January-June 2021

OBJECTIVES

Smartphone-based digital exposure notification (EN) tools were introduced during the COVID-19 pandemic to supplement strained case investigation and contact tracing efforts. We examined the influence of an EN tool implemented in Washington State, WA Notify, on user engagement in behaviors that protect against COVID-19 transmission.

METHODS

From January 25 through June 30, 2021, we administered 2 surveys to WA Notify users who received notification of a possible COVID-19 exposure. The initial survey, sent when users received a notification, focused on intent to engage in protective behaviors. The follow-up survey captured data on self-reported actual engagement in protective behaviors and contact by a public health contact tracer.

RESULTS

Of 1507 WA Notify users who completed the initial survey, 40.1% (n = 604) reported intending to seek COVID-19 testing and 67.1% (n = 1011) intended to watch for COVID-19 symptoms. Of 407 respondents to the follow-up survey, 57.5% (n = 234) reported getting tested and 84.3% (n = 343) reported watching for COVID-19 symptoms. Approximately 84% (n = 1266) of respondents to the initial survey received a notification from WA Notify before being reached by public health contact tracers; on follow-up, 42.5% (n = 173) of respondents reported never being contacted by public health.

CONCLUSIONS

Our findings suggest that WA Notify users may initiate protective behaviors earlier than nonusers who will not know of an exposure until notified by public health or by a known contact. Digital EN tools may be a valuable addition to existing public health outbreak investigation and response activities.

A Multifaceted Evaluation of a COVID-19 Contact Tracing Program in King County, Washington

CONTEXT

Despite the massive scale of COVID-19 case investigation and contact tracing (CI/CT) programs operating worldwide, the evidence supporting the intervention's public health impact is limited.

OBJECTIVE

To evaluate the Public Health-Seattle & King County (PHSKC) CI/CT program, including its reach, timeliness, effect on isolation and quarantine (I&Q) adherence, and potential to mitigate pandemic-related hardships.

DESIGN

This program evaluation used descriptive statistics to analyze surveillance records, case and contact interviews, referral records, and survey data provided by a sample of cases who had recently ended isolation.

SETTING

The PHSKC is one of the largest governmental local health departments in the United States. It serves more than 2.2 million people who reside in Seattle and 38 other municipalities.

PARTICIPANTS

King County residents who were diagnosed with COVID-19 between July 2020 and June 2021.

INTERVENTION

The PHSKC integrated COVID-19 CI/CT with prevention education and service provision.

RESULTS

The PHSKC CI/CT team interviewed 42 900 cases (82% of cases eligible for CI/CT), a mean of 6.1 days after symptom onset and 3.4 days after SARS-CoV-2 testing. Cases disclosed the names and addresses of 10 817 unique worksites (mean = 0.8/interview) and 11 432 other recently visited locations (mean = 0.5/interview) and provided contact information for 62 987 household members (mean = 2.7/interview) and 14 398 nonhousehold contacts (mean = 0.3/interview). The CI/CT team helped arrange COVID-19 testing for 5650 contacts, facilitated grocery delivery for 7253 households, and referred 9127 households for financial assistance. End of I&Q Survey participants (n = 304, 54% of sampled) reported self-notifying an average of 4 nonhousehold contacts and 69% agreed that the information and referrals provided by the CI/CT team helped them stay in isolation.

CONCLUSIONS

In the 12-month evaluation period, CI/CT reached 42 611 households and identified thousands of exposure venues. The timing of CI/CT relative to infectiousness and difficulty eliciting nonhousehold contacts may have attenuated the intervention's effect. Through promotion of I&Q guidance and services, CI/CT can help mitigate pandemic-related hardships.

Evaluating efficacy of indoor non-pharmaceutical interventions against COVID-19 outbreaks with a coupled spatial-SIR agent-based simulation framework

Contagious respiratory diseases, such as COVID-19, depend on sufficiently prolonged exposures for the successful transmission of the underlying pathogen. It is important that organizations evaluate the efficacy of non-pharmaceutical interventions aimed at mitigating viral transmission among their personnel. We have developed a operational risk assessment simulation framework that couples a spatial agent-based model of movement with an agent-based SIR model to assess the relative risks of different intervention strategies. By applying our model on MIT's Stata center, we assess the impacts of three possible dimensions of intervention: one-way vs unrestricted movement, population size allowed onsite, and frequency of leaving designated work location for breaks. We find that there is no significant impact made by one-way movement restrictions over unrestricted movement. Instead, we find that reducing the frequency at which individuals leave their workstations combined with lowering the number of individuals admitted below the current recommendations lowers the likelihood of highly connected individuals within the contact networks that emerge, which in turn lowers the overall risk of infection. We discover three classes of possible interventions based on their epidemiological effects. By assuming a direct relationship between data on secondary attack rates and transmissibility in the agent-based SIR model, we compare relative infection risk of four respiratory illnesses, MERS, SARS, COVID-19, and Measles, within the simulated area, and recommend appropriate intervention guidelines.

Epidemic spreading and digital contact tracing: Effects of heterogeneous mixing and quarantine failures

Contact tracing via digital tracking applications installed on mobile phones is an important tool for controlling epidemic spreading. Its effectivity can be quantified by modifying the standard methodology for analyzing percolation and connectivity of contact networks. We apply this framework to networks with varying degree distributions, numbers of application users, and probabilities of quarantine failures. Further, we study structured populations with homophily and heterophily and the possibility of degree-targeted application distribution. Our results are based on a combination of explicit simulations and mean-field analysis. They indicate that there can be major differences in the epidemic size and epidemic probabilities which are equivalent in the normal susceptible-infectious-recovered (SIR) processes. Further, degree heterogeneity is seen to be especially important for the epidemic threshold but not as much for the epidemic size. The probability that tracing leads to quarantines is not as important as the application adoption rate. Finally, both strong homophily and especially heterophily with regard to application adoption can be detrimental. Overall, epidemic dynamics are very sensitive to all of the parameter values we tested out, which makes the problem of estimating the effect of digital contact tracing an inherently multidimensional problem.

Vaccines alone are no silver bullets: a modeling study on the impact of efficient contact tracing on COVID-19 infection and transmission in Malaysia

BACKGROUND

The computer simulation presented in this study aimed to investigate the effect of contact tracing on coronavirus disease 2019 (COVID-19) transmission and infection in the context of rising vaccination rates.

METHODS

This study proposed a deterministic, compartmental model with contact tracing and vaccination components. We defined contact tracing effectiveness as the proportion of contacts of a positive case that was successfully traced and the vaccination rate as the proportion of daily doses administered per population in Malaysia. Sensitivity analyses on the untraced and infectious populations were conducted.

RESULTS

At a vaccination rate of 1.4%, contact tracing with an effectiveness of 70% could delay the peak of untraced asymptomatic cases by 17 d and reduce it by 70% compared with 30% contact tracing effectiveness. A similar trend was observed for symptomatic cases when a similar experiment setting was used. We also performed sensitivity analyses by using different combinations of contact tracing effectiveness and vaccination rates. In all scenarios, the effect of contact tracing on COVID-19 incidence persisted for both asymptomatic and symptomatic cases.

CONCLUSIONS

While vaccines are progressively rolled out, efficient contact tracing must be rapidly implemented concurrently to reach, find, test, isolate and support the affected populations to bring COVID-19 under control.

Review on people's trust on home use medical devices during Covid-19 pandemic in India

With the rapid development of the medical device against COVID-19 is an excellent achievement. There are numerous obstacles effectively facing the worldwide population, from manufacture to distribution, deployment and, acceptance. Many manufacturers have entered the market rivalry as people's knowledge and demand for home-use medical equipment has increased. India represents a compelling market opportunity for global medical device manufacturers. Substantial growth for the Indian medical device industry is expected to be driven by the current low per-person spending rate for medical devices. The growth of the medical devices industry in India raises competition law issues (anti-trust) and therefore maintaining public trust in home-use medical devices during COVID-19 will be as essential. The review article aims to create awareness among people about commonly used medical devices during the COVID-19 pandemic and to survey people’s trust in home usable medical devices in India. In a worldwide pandemic, manufacturers of medical devices face insufficient storage and the impossibility of meeting the requirements of the health centre. The sale of some of the most significant medical devices has increased, making it more difficult for the medical device industry to satisfy demand with high-quality goods since the quality of COVID-19 items plays a vital part in the present scenario. Despite the difficulty in providing enough medical equipment during a pandemic, they are striving to adapt to the circumstance. After recognizing the need to promote awareness and grasp the selling, and production, handling of medical instruments during COVID-19 at home was conducted. In addition, medical equipment manufacturers and distributors look at this scenario as an opportunity to profit more. This review article would enable researchers during COVID-19 to build more knowledge and widespread trust in medical technologies respectively.

Characterizing the effective reproduction number during the COVID-19 pandemic: Insights from Qatar’s experience

Background

The effective reproduction number, R_t, is a tool to track and understand pandemic dynamics. This investigation of R_t estimations was conducted to guide the national COVID-19 response in Qatar, from the onset of the pandemic until August 18, 2021.

Methods

Real-time “empirical” R_t^Empirical was estimated using five methods, including the Robert Koch Institute, Cislaghi, Systrom-Bettencourt and Ribeiro, Wallinga and Teunis, and Cori et al. methods. R_t was also estimated using a transmission dynamics model (R_t^Model-based). Uncertainty and sensitivity analyses were conducted. Correlations between different R_t estimates were assessed by calculating correlation coefficients, and agreements between these estimates were assessed through Bland-Altman plots.

Results

R_t^Empirical captured the evolution of the pandemic through three waves, public health response landmarks, effects of major social events, transient fluctuations coinciding with significant clusters of infection, and introduction and expansion of the Alpha (B.1.1.7) variant. The various estimation methods produced consistent and overall comparable R_t^Empirical estimates with generally large correlation coefficients. The Wallinga and Teunis method was the fastest at detecting changes in pandemic dynamics. R_t^Empirical estimates were consistent whether using time series of symptomatic PCR-confirmed cases, all PCR-confirmed cases, acute-care hospital admissions, or ICU-care hospital admissions, to proxy trends in true infection incidence. R_t^Model-based correlated strongly with R_t^Empirical and provided an average R_t^Empirical.

Conclusions

R_t estimations were robust and generated consistent results regardless of the data source or the method of estimation. Findings affirmed an influential role for R_t estimations in guiding national responses to the COVID-19 pandemic, even in resource-limited settings.

Reasons for Nonuse, Discontinuation of Use, and Acceptance of Additional Functionalities of a COVID-19 Contact Tracing App: Cross-sectional Survey Study

BACKGROUND

In several countries, contact tracing apps (CTAs) have been introduced to warn users if they have had high-risk contacts that could expose them to SARS-CoV-2 and could, therefore, develop COVID-19 or further transmit the virus. For CTAs to be effective, a sufficient critical mass of users is needed. Until now, adoption of these apps in several countries has been limited, resulting in questions on which factors prevent app uptake or stimulate discontinuation of app use.

OBJECTIVE

The aim of this study was to investigate individuals' reasons for not using, or stopping use of, a CTA, in particular, the Coronalert app. Users' and nonusers' attitudes toward the app's potential impact was assessed in Belgium. To further stimulate interest and potential use of a CTA, the study also investigated the population's interest in new functionalities.

METHODS

An online survey was administered in Belgium to a sample of 1850 respondents aged 18 to 64 years. Data were collected between October 30 and November 2, 2020. Sociodemographic differences were assessed between users and nonusers. We analyzed both groups' attitudes toward the potential impact of CTAs and their acceptance of new app functionalities.

RESULTS

Our data showed that 64.9% (1201/1850) of our respondents were nonusers of the CTA under study; this included individuals who did not install the app, those who downloaded but did not activate the app, and those who uninstalled the app. While we did not find any sociodemographic differences between users and nonusers, attitudes toward the app and its functionalities seemed to differ. The main reasons for not downloading and using the app were a perceived lack of advantages (308/991, 31.1%), worries about privacy (290/991, 29.3%), and, to a lesser extent, not having a smartphone (183/991, 18.5%). Users of the CTA agreed more with the potential of such apps to mitigate the consequences of the pandemic. Overall, nonusers found the possibility of extending the CTA with future functionalities to be less acceptable than users. However, among users, acceptability also tended to differ. Among users, functionalities relating to access and control, such as digital certificates or "green cards" for events, were less accepted (358/649, 55.2%) than functionalities focusing on informing citizens about the spread of the virus (453/649, 69.8%) or making an appointment to get tested (525/649, 80.9%).

CONCLUSIONS

Our results show that app users were more convinced of the CTA's utility and more inclined to accept new app features than nonusers. Moreover, nonusers had more CTA-related privacy concerns. Therefore, to further stimulate app adoption and use, its potential advantages and privacy-preserving mechanisms need to be stressed. Building further knowledge on the forms of resistance among nonusers is important for responding to these barriers through the app's further development and communication campaigns.

Estimating the impact of interventions against COVID-19: From lockdown to vaccination

Coronavirus disease 2019 (COVID-19) is an infectious disease of humans caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since the first case was identified in China in December 2019 the disease has spread worldwide, leading to an ongoing pandemic. In this article, we present an agent-based model of COVID-19 in Luxembourg, and use it to estimate the impact, on cases and deaths, of interventions including testing, contact tracing, lockdown, curfew and vaccination. Our model is based on collation, with agents performing activities and moving between locations accordingly. The model is highly heterogeneous, featuring spatial clustering, over 2000 behavioural types and a 10 minute time resolution. The model is validated against COVID-19 clinical monitoring data collected in Luxembourg in 2020. Our model predicts far fewer cases and deaths than the equivalent equation-based SEIR model. In particular, with R0 = 2.45, the SEIR model infects 87% of the resident population while our agent-based model infects only around 23% of the resident population. Our simulations suggest that testing and contract tracing reduce cases substantially, but are less effective at reducing deaths. Lockdowns are very effective although costly, while the impact of an 11pm-6am curfew is relatively small. When vaccinating against a future outbreak, our results suggest that herd immunity can be achieved at relatively low coverage, with substantial levels of protection achieved with only 30% of the population fully immune. When vaccinating in the midst of an outbreak, the challenge is more difficult. In this context, we investigate the impact of vaccine efficacy, capacity, hesitancy and strategy. We conclude that, short of a permanent lockdown, vaccination is by far the most effective way to suppress and ultimately control the spread of COVID-19.

Effect of Nudges on Downloads of COVID-19 Exposure Notification Apps: A Randomized Clinical Trial

This randomized clinical trial examines the effect of digital contact tracing using smartphone app nudges to increase downloads of Pennsylvania’s COVID Alert PA app.

Modelling digital and manual contact tracing for COVID-19. Are low uptakes and missed contacts deal-breakers?

Comprehensive testing schemes, followed by adequate contact tracing and isolation, represent the best public health interventions we can employ to reduce the impact of an ongoing epidemic when no or limited vaccine supplies are available and the implications of a full lockdown are to be avoided. However, the process of tracing can prove feckless for highly-contagious viruses such as SARS-CoV-2. The interview-based approaches often miss contacts and involve significant delays, while digital solutions can suffer from insufficient adoption rates or inadequate usage patterns. Here we present a novel way of modelling different contact tracing strategies, using a generalized multi-site mean-field model, which can naturally assess the impact of manual and digital approaches alike. Our methodology can readily be applied to any compartmental formulation, thus enabling the study of more complex pathogen dynamics. We use this technique to simulate a newly-defined epidemiological model, SEIR-T, and show that, given the right conditions, tracing in a COVID-19 epidemic can be effective even when digital uptakes are sub-optimal or interviewers miss a fair proportion of the contacts.

Strategies to increase downloads of COVID-19 exposure notification apps: A discrete choice experiment

Exposure notification apps have been developed to assist in notifying individuals of recent exposures to SARS-CoV-2. However, in several countries, such apps have had limited uptake. We assessed whether strategies to increase downloads of exposure notification apps should emphasize improving the accuracy of the apps in recording contacts and exposures, strengthening privacy protections and/or offering financial incentives to potential users. In a discrete choice experiment with potential app users in the US, financial incentives were more than twice as important in decision-making about app downloads, than privacy protections, and app accuracy. The probability that a potential user would download an exposure notification app increased by 40% when offered a $100 reward to download (relative to a reference scenario in which the app is free). Financial incentives might help exposure notification apps reach uptake levels that improve the effectiveness of contact tracing programs and ultimately enhance efforts to control SARS-CoV-2. Rapid, pragmatic trials of financial incentives for app downloads in real-life settings are warranted.

Toward a Common Performance and Effectiveness Terminology for Digital Proximity Tracing Applications

Digital proximity tracing (DPT) for Sars-CoV-2 pandemic mitigation is a complex intervention with the primary goal to notify app users about possible risk exposures to infected persons. DPT not only relies on the technical functioning of the proximity tracing application and its backend server, but also on seamless integration of health system processes such as laboratory testing, communication of results (and their validation), generation of notification codes, manual contact tracing, and management of app-notified users. Policymakers and DPT operators need to know whether their system works as expected in terms of speed or yield (performance) and whether DPT is making an effective contribution to pandemic mitigation (also in comparison to and beyond established mitigation measures, particularly manual contact tracing). Thereby, performance and effectiveness are not to be confused. Not only are there conceptual differences but also diverse data requirements. For example, comparative effectiveness measures may require information generated outside the DPT system, e.g., from manual contact tracing. This article describes differences between performance and effectiveness measures and attempts to develop a terminology and classification system for DPT evaluation. We discuss key aspects for critical assessments of whether the integration of additional data measurements into DPT apps may facilitate understanding of performance and effectiveness of planned and deployed DPT apps. Therefore, the terminology and a classification system may offer some guidance to DPT system operators regarding which measurements to prioritize. DPT developers and operators may also make conscious decisions to integrate measures for epidemic monitoring but should be aware that this introduces a secondary purpose to DPT. Ultimately, the integration of further information (e.g., regarding exact exposure time) into DPT involves a trade-off between data granularity and linkage on the one hand, and privacy on the other. More data may lead to better epidemiological information but may also increase the privacy risks associated with the system, and thus decrease public DPT acceptance. Decision-makers should be aware of the trade-off and take it into account when planning and developing DPT systems or intending to assess the added value of DPT relative to the existing contact tracing systems.

Epidemiological Predictive Modeling of COVID-19 Infection: Development, Testing, and Implementation on the Population of the Benelux Union

Since the outbreak of coronavirus disease-2019 (COVID-19), the whole world has taken interest in the mechanisms of its spread and development. Mathematical models have been valuable instruments for the study of the spread and control of infectious diseases. For that purpose, we propose a two-way approach in modeling COVID-19 spread: a susceptible, exposed, infected, recovered, deceased (SEIRD) model based on differential equations and a long short-term memory (LSTM) deep learning model. The SEIRD model is a compartmental epidemiological model with included components: susceptible, exposed, infected, recovered, deceased. In the case of the SEIRD model, official statistical data available online for countries of Belgium, Netherlands, and Luxembourg (Benelux) in the period of March 15 2020 to March 15 2021 were used. Based on them, we have calculated key parameters and forward them to the epidemiological model, which will predict the number of infected, deceased, and recovered people. Results show that the SEIRD model is able to accurately predict several peaks for all the three countries of interest, with very small root mean square error (RMSE), except for the mild cases (maximum RMSE was 240.79 ± 90.556), which can be explained by the fact that no official data were available for mild cases, but this number was derived from other statistics. On the other hand, LSTM represents a special kind of recurrent neural network structure that can comparatively learn long-term temporal dependencies. Results show that LSTM is capable of predicting several peaks based on the position of previous peaks with low values of RMSE. Higher values of RMSE are observed in the number of infected cases in Belgium (RMSE was 535.93) and Netherlands (RMSE was 434.28), and are expected because of thousands of people getting infected per day in those countries. In future studies, we will extend the models to include mobility information, variants of concern, as well as a medical intervention, etc. A prognostic model could help us predict epidemic peaks. In that way, we could react in a timely manner by introducing new or tightening existing measures before the health system is overloaded.

Use of clinical data to augment healthcare worker contact tracing during the COVID-19 pandemic

OBJECTIVE

This work examined the secondary use of clinical data from the electronic health record (EHR) for screening our healthcare worker (HCW) population for potential exposures to patients with coronavirus disease 2019.

MATERIALS AND METHODS

We conducted a cross-sectional study at a free-standing, quaternary care pediatric hospital comparing first-degree, patient-HCW pairs identified by the hospital's COVID-19 contact tracing team (CTT) to those identified using EHR clinical event data (EHR Report). The primary outcome was the number of patient-HCW pairs detected by each process.

RESULTS

Among 233 patients with COVID-19, our EHR Report identified 4,116 patient-HCW pairs, including 2,365 (30.0%) of the 7,890 pairs detected by the CTT. The EHR Report also revealed 1,751 pairs not identified by the CTT. The highest number of patient-HCW pairs per patient was detected in the inpatient care venue. Nurses comprised the most frequently identified HCW role overall.

CONCLUSION

Automated methods to screen HCWs for potential exposure to patients with COVID-19 using clinical event data from the EHR are likely to improve epidemiologic surveillance by contact tracing programs and represent a viable and readily available strategy which should be considered by other institutions.

The Roles of General Health and COVID-19 Proximity in Contact Tracing App Usage: Cross-sectional Survey Study

Background

Contact tracing apps are considered useful means to monitor SARS-CoV-2 infections during the off-peak stages of the COVID-19 pandemic. Their effectiveness is, however, dependent on the uptake of such COVID-19 apps.

Objective

We examined the role of individuals’ general health status in their willingness to use a COVID-19 tracing app as well as the roles of socioeconomic characteristics and COVID-19 proximity.

Methods

We drew data from the WageIndicator Foundation Living and Working in Coronavirus Times survey. The survey collected data on labor market status as well as the potential confounders of the relationship between general health and COVID-19 tracing app usage, such as sociodemographics and regular smartphone usage data. The survey also contained information that allowed us to examine the role of COVID-19 proximity, such as whether an individual has contracted SARS-CoV-2, whether an individual has family members and colleagues with COVID-19, and whether an individual exhibits COVID-19 pandemic–induced depressive and anxiety symptoms. We selected data that were collected in Spain, Italy, Germany, and the Netherlands from individuals aged between 18 and 70 years (N=4504). Logistic regressions were used to measure individuals’ willingness to use a COVID-19 tracing app.

Results

We found that the influence that socioeconomic factors have on COVID-19 tracing app usage varied dramatically between the four countries, although individuals experiencing forms of not being employed (ie, recent job loss and inactivity) consistently had a lower willingness to use a contact tracing app (effect size: 24.6%) compared to that of employees (effect size: 33.4%; P<.001). Among the selected COVID-19 proximity indicators, having a close family member with SARS-CoV-2 infection was associated with higher contact tracing app usage (effect size: 36.3% vs 27.1%; P<.001). After accounting for these proximity factors and the country-based variations therein, we found that having a poorer general health status was significantly associated with a much higher likelihood of contact tracing app usage; compared to a self-reported “very good” health status (estimated probability of contact tracing app use: 29.6%), the “good” (estimated probability: +4.6%; 95% CI 1.2%-8.1%) and “fair or bad” (estimated probability: +6.3%; 95% CI 2.3%-10.3%) health statuses were associated with a markedly higher willingness to use a COVID-19 tracing app.

Conclusions

Current public health policies aim to promote the use of smartphone-based contact tracing apps during the off-peak periods of the COVID-19 pandemic. Campaigns that emphasize the health benefits of COVID-19 tracing apps may contribute the most to the uptake of such apps. Public health campaigns that rely on digital platforms would also benefit from seriously considering the country-specific distribution of privacy concerns.

Digital contact tracing, community uptake, and proximity awareness technology to fight COVID-19: a systematic review

Digital contact tracing provides an expeditious and comprehensive way to collect and analyze data on people's proximity, location, movement, and health status. However, this technique raises concerns about data privacy and its overall effectiveness. This paper contributes to this debate as it provides a systematic review of digital contact tracing studies between January 1, 2020, and March 31, 2021. Following the PRISMA protocol for systematic reviews and the CHEERS statement for quality assessment, 580 papers were initially screened, and 19 papers were included in a qualitative synthesis. We add to the current literature in three ways. First, we evaluate whether digital contact tracing can mitigate COVID-19 by either reducing the effective reproductive number or the infected cases. Second, we study whether digital is more effective than manual contact tracing. Third, we analyze how proximity/location awareness technologies affect data privacy and population participation. We also discuss proximity/location accuracy problems arising when these technologies are applied in different built environments (i.e., home, transport, mall, park). This review provides a strong rationale for using digital contact tracing under specific requirements. Outcomes may inform current digital contact tracing implementation efforts worldwide regarding the potential benefits, technical limitations, and trade-offs between effectiveness and privacy.

Covasim: An agent-based model of COVID-19 dynamics and interventions

The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address these questions. Covasim includes country-specific demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, long-term care facilities, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing and protective equipment; pharmaceutical interventions, including vaccination; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. Implemented in pure Python, Covasim has been designed with equal emphasis on performance, ease of use, and flexibility: realistic and highly customized scenarios can be run on a standard laptop in under a minute. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine epidemic dynamics and inform policy decisions in more than a dozen countries in Africa, Asia-Pacific, Europe, and North America.

OpenABM-Covid19-An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing

SARS-CoV-2 has spread across the world, causing high mortality and unprecedented restrictions on social and economic activity. Policymakers are assessing how best to navigate through the ongoing epidemic, with computational models being used to predict the spread of infection and assess the impact of public health measures. Here, we present OpenABM-Covid19: an agent-based simulation of the epidemic including detailed age-stratification and realistic social networks. By default the model is parameterised to UK demographics and calibrated to the UK epidemic, however, it can easily be re-parameterised for other countries. OpenABM-Covid19 can evaluate non-pharmaceutical interventions, including both manual and digital contact tracing, and vaccination programmes. It can simulate a population of 1 million people in seconds per day, allowing parameter sweeps and formal statistical model-based inference. The code is open-source and has been developed by teams both inside and outside academia, with an emphasis on formal testing, documentation, modularity and transparency. A key feature of OpenABM-Covid19 are its Python and R interfaces, which has allowed scientists and policymakers to simulate dynamic packages of interventions and help compare options to suppress the COVID-19 epidemic.

Covasim: An agent-based model of COVID-19 dynamics and interventions

The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address these questions. Covasim includes country-specific demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, long-term care facilities, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing and protective equipment; pharmaceutical interventions, including vaccination; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. Implemented in pure Python, Covasim has been designed with equal emphasis on performance, ease of use, and flexibility: realistic and highly customized scenarios can be run on a standard laptop in under a minute. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine epidemic dynamics and inform policy decisions in more than a dozen countries in Africa, Asia-Pacific, Europe, and North America.

OpenABM-Covid19-An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing

SARS-CoV-2 has spread across the world, causing high mortality and unprecedented restrictions on social and economic activity. Policymakers are assessing how best to navigate through the ongoing epidemic, with computational models being used to predict the spread of infection and assess the impact of public health measures. Here, we present OpenABM-Covid19: an agent-based simulation of the epidemic including detailed age-stratification and realistic social networks. By default the model is parameterised to UK demographics and calibrated to the UK epidemic, however, it can easily be re-parameterised for other countries. OpenABM-Covid19 can evaluate non-pharmaceutical interventions, including both manual and digital contact tracing, and vaccination programmes. It can simulate a population of 1 million people in seconds per day, allowing parameter sweeps and formal statistical model-based inference. The code is open-source and has been developed by teams both inside and outside academia, with an emphasis on formal testing, documentation, modularity and transparency. A key feature of OpenABM-Covid19 are its Python and R interfaces, which has allowed scientists and policymakers to simulate dynamic packages of interventions and help compare options to suppress the COVID-19 epidemic.

WA Notify: the planning and implementation of a Bluetooth exposure notification tool for COVID-19 pandemic response in Washington State

Bluetooth exposure notification tools for mobile phones have emerged as one way to support public health contact tracing and mitigate the spread of COVID-19. Many states have launched their own versions of these tools. Washington State's exposure notification tool, WA Notify, became available on November 30, 2020, following a one-month Seattle campus pilot at the University of Washington. By the end of April 2021, 25% of the state's population had activated WA Notify, one of the highest adoption rates in the country. Washington State's formation of an Exposure Notification Advisory Committee, early pilot testing, and use of the EN Express system framework were all important factors in its adoption. Continuous monitoring and willingness to make early adjustments such as switching to automated texting of verification codes have also been important for improving the tool's value. Evaluation work is ongoing to determine and quantify WA Notify's effectiveness, timeliness, and accessibility.

The epidemiological impact of the NHS COVID-19 app

The COVID-19 pandemic has seen the emergence of digital contact tracing to help to prevent the spread of the disease. A mobile phone app records proximity events between app users, and when a user tests positive for COVID-19, their recent contacts can be notified instantly. Theoretical evidence has supported this new public health intervention^1-6, but its epidemiological impact has remained uncertain⁷. Here we investigate the impact of the National Health Service (NHS) COVID-19 app for England and Wales, from its launch on 24 September 2020 to the end of December 2020. It was used regularly by approximately 16.5 million users (28% of the total population), and sent approximately 1.7 million exposure notifications: 4.2 per index case consenting to contact tracing. We estimated that the fraction of individuals notified by the app who subsequently showed symptoms and tested positive (the secondary attack rate (SAR)) was 6%, similar to the SAR for manually traced close contacts. We estimated the number of cases averted by the app using two complementary approaches: modelling based on the notifications and SAR gave an estimate of 284,000 (central 95% range of sensitivity analyses 108,000-450,000), and statistical comparison of matched neighbouring local authorities gave an estimate of 594,000 (95% confidence interval 317,000-914,000). Approximately one case was averted for each case consenting to notification of their contacts. We estimated that for every percentage point increase in app uptake, the number of cases could be reduced by 0.8% (using modelling) or 2.3% (using statistical analysis). These findings support the continued development and deployment of such apps in populations that are awaiting full protection from vaccines.

Public health impact of delaying second dose of BNT162b2 or mRNA-1273 covid-19 vaccine: simulation agent based modeling study

OBJECTIVE

To estimate population health outcomes with delayed second dose versus standard schedule of SARS-CoV-2 mRNA vaccination.

DESIGN

Simulation agent based modeling study.

SETTING

Simulated population based on real world US county.

PARTICIPANTS

The simulation included 100 000 agents, with a representative distribution of demographics and occupations. Networks of contacts were established to simulate potentially infectious interactions though occupation, household, and random interactions.

INTERVENTIONS

Simulation of standard covid-19 vaccination versus delayed second dose vaccination prioritizing the first dose. The simulation runs were replicated 10 times. Sensitivity analyses included first dose vaccine efficacy of 50%, 60%, 70%, 80%, and 90% after day 12 post-vaccination; vaccination rate of 0.1%, 0.3%, and 1% of population per day; assuming the vaccine prevents only symptoms but not asymptomatic spread (that is, non-sterilizing vaccine); and an alternative vaccination strategy that implements delayed second dose for people under 65 years of age, but not until all those above this age have been vaccinated.

MAIN OUTCOME MEASURES

Cumulative covid-19 mortality, cumulative SARS-CoV-2 infections, and cumulative hospital admissions due to covid-19 over 180 days.

RESULTS

Over all simulation replications, the median cumulative mortality per 100 000 for standard dosing versus delayed second dose was 226 v 179, 233 v 207, and 235 v 236 for 90%, 80%, and 70% first dose efficacy, respectively. The delayed second dose strategy was optimal for vaccine efficacies at or above 80% and vaccination rates at or below 0.3% of the population per day, under both sterilizing and non-sterilizing vaccine assumptions, resulting in absolute cumulative mortality reductions between 26 and 47 per 100 000. The delayed second dose strategy for people under 65 performed consistently well under all vaccination rates tested.

CONCLUSIONS

A delayed second dose vaccination strategy, at least for people aged under 65, could result in reduced cumulative mortality under certain conditions.

Impact of close interpersonal contact on COVID-19 incidence: evidence from one year of mobile device data

Close contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). We sought to quantify interpersonal contact at the population-level by using anonymized mobile device geolocation data. We computed the frequency of contact (within six feet) between people in Connecticut during February 2020 - January 2021. Then we aggregated counts of contact events by area of residence to obtain an estimate of the total intensity of interpersonal contact experienced by residents of each town for each day. When incorporated into a susceptible-exposed-infective-removed (SEIR) model of COVID-19 transmission, the contact rate accurately predicted COVID-19 cases in Connecticut towns during the timespan. The pattern of contact rate in Connecticut explains the large initial wave of infections during March-April, the subsequent drop in cases during June-August, local outbreaks during August-September, broad statewide resurgence during September-December, and decline in January 2021. Contact rate data can help guide public health messaging campaigns to encourage social distancing and in the allocation of testing resources to detect or prevent emerging local outbreaks more quickly than traditional case investigation.

ONE SENTENCE SUMMARY

Close interpersonal contact measured using mobile device location data explains dynamics of COVID-19 transmission in Connecticut during the first year of the pandemic.

Estimating the impact of interventions against COVID-19: From lockdown to vaccination

Coronavirus disease 2019 (COVID-19) is an infectious disease of humans caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since the first case was identified in China in December 2019 the disease has spread worldwide, leading to an ongoing pandemic. In this article, we present an agent-based model of COVID-19 in Luxembourg, and use it to estimate the impact, on cases and deaths, of interventions including testing, contact tracing, lockdown, curfew and vaccination. Our model is based on collation, with agents performing activities and moving between locations accordingly. The model is highly heterogeneous, featuring spatial clustering, over 2000 behavioural types and a 10 minute time resolution. The model is validated against COVID-19 clinical monitoring data collected in Luxembourg in 2020. Our model predicts far fewer cases and deaths than the equivalent equation-based SEIR model. In particular, with R0 = 2.45, the SEIR model infects 87% of the resident population while our agent-based model infects only around 23% of the resident population. Our simulations suggest that testing and contract tracing reduce cases substantially, but are less effective at reducing deaths. Lockdowns are very effective although costly, while the impact of an 11pm-6am curfew is relatively small. When vaccinating against a future outbreak, our results suggest that herd immunity can be achieved at relatively low coverage, with substantial levels of protection achieved with only 30% of the population fully immune. When vaccinating in the midst of an outbreak, the challenge is more difficult. In this context, we investigate the impact of vaccine efficacy, capacity, hesitancy and strategy. We conclude that, short of a permanent lockdown, vaccination is by far the most effective way to suppress and ultimately control the spread of COVID-19.