scholarly journals Modeling the combined effect of digital exposure notification and non-pharmaceutical interventions on the COVID-19 epidemic in Washington state

2020 ◽  
Author(s):  
Matthew Abueg ◽  
Robert Hinch ◽  
Neo Wu ◽  
Luyang Liu ◽  
William J M Probert ◽  
...  
Keyword(s):  
Computational Models ◽  
Washington State ◽  
Health Interventions ◽  
Disease Spread ◽  
Contact Tracing ◽  
Notification System ◽  
Health Authorities ◽  
Smartphone Technology ◽  
Representative Model ◽  
Pharmaceutical Interventions

Contact tracing is increasingly being used to combat COVID-19, and digital implementations are now being deployed, many of them based on Apple and Google's Exposure Notification System. These systems are new and are based on smartphone technology that has not traditionally been used for this purpose, presenting challenges in understanding possible outcomes. In this work, we use individual-based computational models to explore how digital exposure notifications can be used in conjunction with non-pharmaceutical interventions, such as traditional contact tracing and social distancing, to influence COVID-19 disease spread in a population. Specifically, we use a representative model of the household and occupational structure of three counties in the state of Washington together with a proposed digital exposure notifications deployment to quantify impacts under a range of scenarios of adoption, compliance, and mobility. In a model in which 15% of the population participated, we found that digital exposure notification systems could reduce infections and deaths by approximately 8% and 6%, effectively complementing traditional contact tracing. We believe this can serve as guidance to health authorities in Washington state and beyond on how exposure notification systems can complement traditional public health interventions to suppress the spread of COVID-19.

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

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Matthew Abueg ◽  
Robert Hinch ◽  
Neo Wu ◽  
Luyang Liu ◽  
William Probert ◽  
...  
Keyword(s):  
Washington State ◽  
Disease Spread ◽  
Contact Tracing ◽  
Notification System ◽  
Health Authorities ◽  
Individual Based Models ◽  
Pharmaceutical Interventions

AbstractContact tracing is increasingly used to combat COVID-19, and digital implementations are now being deployed, many based on Apple and Google’s Exposure Notification System. These systems utilize non-traditional smartphone-based technology, presenting challenges in understanding possible outcomes. In this work, we create individual-based models of three Washington state counties to explore how digital exposure notifications combined with other non-pharmaceutical interventions influence COVID-19 disease spread under various adoption, compliance, and mobility scenarios. In a model with 15% participation, we found that exposure notification could reduce infections and deaths by approximately 8% and 6% and could effectively complement traditional contact tracing. We believe this can provide health authorities in Washington state and beyond with guidance on how exposure notification can complement traditional interventions to suppress the spread of COVID-19.

scholarly journals Investigation of the Efficiency of Mask Wearing, Contact Tracing, and Case Isolation during the COVID-19 Outbreak

2021 ◽  
Vol 10 (13) ◽  
pp. 2761
Author(s):  
Tatiana Filonets ◽  
Maxim Solovchuk ◽  
Wayne Gao ◽  
Tony Wen-Hann Sheu
Keyword(s):  
Mathematical Model ◽  
Reproduction Number ◽  
Control Measures ◽  
Disease Spread ◽  
Contact Tracing ◽  
Imported Cases ◽  
Quantitative Results ◽  
High Level ◽  
Pharmaceutical Interventions ◽  
The Basic Reproduction Number

Case isolation and contact tracing are two essential parts of control measures to prevent the spread of COVID-19, however, additional interventions, such as mask wearing, are required. Taiwan successfully contained local COVID-19 transmission after the initial imported cases in the country in early 2020 after applying the above-mentioned interventions. In order to explain the containment of the disease spread in Taiwan and understand the efficiency of different non-pharmaceutical interventions, a mathematical model has been developed. A stochastic model was implemented in order to estimate the effectiveness of mask wearing together with case isolation and contact tracing. We investigated different approaches towards mask usage, estimated the effect of the interventions on the basic reproduction number (R0), and simulated the possibility of controlling the outbreak. With the assumption that non-medical and medical masks have 20% and 50% efficiency, respectively, case isolation works on 100%, 70% of all people wear medical masks, and R0 = 2.5, there is almost 80% probability of outbreak control with 60% contact tracing, whereas for non-medical masks the highest probability is only about 20%. With a large proportion of infectiousness before the onset of symptoms (40%) and the presence of asymptomatic cases, the investigated interventions (isolation of cases, contact tracing, and mask wearing by all people), implemented on a high level, can help to control the disease spread. Superspreading events have also been included in our model in order to estimate their impact on the outbreak and to understand how restrictions on gathering and social distancing can help to control the outbreak. The obtained quantitative results are in agreement with the empirical COVID-19 data in Taiwan.

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

2021 ◽  
Vol 17 (7) ◽  
pp. e1009146
Author(s):  
Robert Hinch ◽  
William J. M. Probert ◽  
Anel Nurtay ◽  
Michelle Kendall ◽  
Chris Wymant ◽  
...  
Keyword(s):  
Computational Models ◽  
Contact Tracing ◽  
Agent Based ◽  
Health Measures ◽  
The World ◽  
The Uk ◽  
Spread Of Infection ◽  
The Impact ◽  
Age Stratification ◽  
Pharmaceutical Interventions

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.

scholarly journals Observations and conversations: how communities learn about infection risk can impact the success of non-pharmaceutical interventions against epidemics

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Matthew J. Silk ◽  
Simon Carrignon ◽  
R. Alexander Bentley ◽  
Nina H. Fefferman
Keyword(s):  
Social Norms ◽  
Social Communication ◽  
Physical Contact ◽  
Disease Spread ◽  
Contact Tracing ◽  
Accurate Information ◽  
The Social ◽  
Communication Layer ◽  
Perceived Social Norms ◽  
Pharmaceutical Interventions

Abstract Background Individual behavioural decisions are responses to a person’s perceived social norms that could be shaped by both their physical and social environment. In the context of the COVID-19 pandemic, these environments correspond to epidemiological risk from contacts and the social construction of risk by communication within networks of friends. Understanding the circumstances under which the influence of these different social networks can promote the acceptance of non-pharmaceutical interventions and consequently the adoption of protective behaviours is critical for guiding useful, practical public health messaging. Methods We explore how information from both physical contact and social communication layers of a multiplex network can contribute to flattening the epidemic curve in a community. Connections in the physical contact layer represent opportunities for transmission, while connections in the communication layer represent social interactions through which individuals may gain information, e.g. messaging friends. Results We show that maintaining focus on awareness of risk among each individual’s physical contacts promotes the greatest reduction in disease spread, but only when an individual is aware of the symptoms of a non-trivial proportion of their physical contacts (~ ≥ 20%). Information from the social communication layer without was less useful when these connections matched less well with physical contacts and contributed little in combination with accurate information from physical contacts. Conclusions We conclude that maintaining social focus on local outbreak status will allow individuals to structure their perceived social norms appropriately and respond more rapidly when risk increases. Finding ways to relay accurate local information from trusted community leaders could improve mitigation even where more intrusive/costly strategies, such as contact-tracing, are not possible.

scholarly journals An analysis of COVID-19 clusters in India

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pooja Sengupta ◽  
Bhaswati Ganguli ◽  
Sugata SenRoy ◽  
Aditya Chatterjee
Keyword(s):  
Public Health ◽  
Cluster Analysis ◽  
Population Density ◽  
Case Studies ◽  
Compartment Model ◽  
Health Interventions ◽  
Disease Spread ◽  
Second Phase ◽  
Public Health Interventions ◽  
Homogeneous Groups

Abstract Background In this study we cluster the districts of India in terms of the spread of COVID-19 and related variables such as population density and the number of specialty hospitals. Simulation using a compartment model is used to provide insight into differences in response to public health interventions. Two case studies of interest from Nizamuddin and Dharavi provide contrasting pictures of the success in curbing spread. Methods A cluster analysis of the worst affected districts in India provides insight about the similarities between them. The effects of public health interventions in flattening the curve in their respective states is studied using the individual contact SEIQHRF model, a stochastic individual compartment model which simulates disease prevalence in the susceptible, infected, recovered and fatal compartments. Results The clustering of hotspot districts provide homogeneous groups that can be discriminated in terms of number of cases and related covariates. The cluster analysis reveal that the distribution of number of COVID-19 hospitals in the districts does not correlate with the distribution of confirmed COVID-19 cases. From the SEIQHRF model for Nizamuddin we observe in the second phase the number of infected individuals had seen a multitudinous increase in the states where Nizamuddin attendees returned, increasing the risk of the disease spread. However, the simulations reveal that implementing administrative interventions, flatten the curve. In Dharavi, through tracing, tracking, testing and treating, massive breakout of COVID-19 was brought under control. Conclusions The cluster analysis performed on the districts reveal homogeneous groups of districts that can be ranked based on the burden placed on the healthcare system in terms of number of confirmed cases, population density and number of hospitals dedicated to COVID-19 treatment. The study rounds up with two important case studies on Nizamuddin basti and Dharavi to illustrate the growth curve of COVID-19 in two very densely populated regions in India. In the case of Nizamuddin, the study showed that there was a manifold increase in the risk of infection. In contrast it is seen that there was a rapid decline in the number of cases in Dharavi within a span of about one month.

scholarly journals Fueling the Covid-19 pandemic: summer school holidays and incidence rates in German districts

2021 ◽  
Author(s):  
Thomas Plümper ◽  
Eric Neumayer
Keyword(s):  
Public Health ◽  
Fixed Effects ◽  
Summer School ◽  
Incidence Rates ◽  
Contact Tracing ◽  
Robert Koch Institute ◽  
Cross Sectional ◽  
Holiday Period ◽  
Health Authorities ◽  
Public Health Authorities

AbstractBackgroundThe Robert-Koch-Institute reports that during the summer holiday period a foreign country is stated as the most likely place of infection for an average of 27 and a maximum of 49% of new SARS-CoV-2 infections in Germany.MethodsCross-sectional study on observational data. In Germany, summer school holidays are coordinated between states and spread out over 13 weeks. Employing a dynamic model with district fixed effects, we analyze the association between these holidays and weekly incidence rates across 401 German districts.ResultsWe find effects of the holiday period of around 45% of the average district incidence rates in Germany during their respective final week of holidays and the 2 weeks after holidays end. Western states tend to experience stronger effects than Eastern states. We also find statistically significant interaction effects of school holidays with per capita taxable income and the share of foreign residents in a district’s population.ConclusionsOur results suggest that changed behavior during the holiday season accelerated the pandemic and made it considerably more difficult for public health authorities to contain the spread of the virus by means of contact tracing. Germany’s public health authorities did not prepare adequately for this acceleration.

scholarly journals How detection ranges and usage stops impact digital contact tracing effectiveness for COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Konstantin D. Pandl ◽  
Scott Thiebes ◽  
Manuel Schmidt-Kraepelin ◽  
Ali Sunyaev
Keyword(s):  
Disease Spread ◽  
Contact Tracing ◽  
Behavioral Characteristics ◽  
Policy Makers ◽  
Detection Range ◽  
Proximity Detection ◽  
Effective Technology ◽  
Different Types ◽  
Effectiveness And Efficiency ◽  
The Impact

AbstractTo combat the COVID-19 pandemic, many countries around the globe have adopted digital contact tracing apps. Various technologies exist to trace contacts that are potentially prone to different types of tracing errors. Here, we study the impact of different proximity detection ranges on the effectiveness and efficiency of digital contact tracing apps. Furthermore, we study a usage stop effect induced by a false positive quarantine. Our results reveal that policy makers should adjust digital contact tracing apps to the behavioral characteristics of a society. Based on this, the proximity detection range should at least cover the range of a disease spread, and be much wider in certain cases. The widely used Bluetooth Low Energy protocol may not necessarily be the most effective technology for contact tracing.

scholarly journals Spatiotemporal patterns and progression of the Delta variant of COVID-19 and their health intervention linkages in Southeast Asia

2021 ◽  
Author(s):  
Wei Luo ◽  
Zhaoyin Liu ◽  
Yuxuan Zhou ◽  
Yumin Zhao ◽  
Yunyue Elita Li ◽  
...  
Keyword(s):  
Public Health ◽  
Southeast Asia ◽  
Spatiotemporal Analysis ◽  
Health Interventions ◽  
Disease Spread ◽  
Epidemic Dynamics ◽  
Health Measures ◽  
Global Pandemic ◽  
Public Health Strategies ◽  
Time Scan

The global pandemic of COVID-19 presented an unprecedented challenge to all countries in the world, among which Southeast Asia (SEA) countries managed to maintain and mitigate the first wave of COVID-19 in 2020. However, these countries were caught in the crisis after the Delta variant was introduced to SEA, though many countries had immediately implemented non-pharmaceutical intervention (NPI) measures along with vaccination in order to contain the disease spread. To investigate the potential linkages between epidemic dynamics and public health interventions, we adopted a prospective space-time scan method to conduct spatiotemporal analysis at the district level in the seven selected countries in SEA from June 2021 to October 2021. Results reveal the spatial and temporal propagation and progression of COVID-19 risks relative to public health measures implemented by different countries. Our research benefits continuous improvements of public health strategies in preventing and containing this pandemic.

scholarly journals A window of opportunity for intensifying testing and tracing efforts to prevent new COVID-19 outbreaks due to more transmissible variants

2021 ◽  
Vol 47 (7/8) ◽  
pp. 329-338
Author(s):  
Jianhong Wu ◽  
Francesca Scarabel ◽  
Zachary McCarthy ◽  
Yanyu Xiao ◽  
Nicholas H Ogden
Keyword(s):  
Public Health ◽  
Reproduction Number ◽  
Health Interventions ◽  
Contact Tracing ◽  
Window Of Opportunity ◽  
New Wave ◽  
Public Health Interventions ◽  
Contact Rates ◽  
Large Outbreak ◽  
Restrictive Measures

Background: When public health interventions are being loosened after several days of decline in the number of coronavirus disease 2019 (COVID-19) cases, it is of critical importance to identify potential strategies to ease restrictions while mitigating a new wave of more transmissible variants of concern (VOCs). We estimated the necessary enhancements to public health interventions for a partial reopening of the economy while avoiding the worst consequences of a new outbreak, associated with more transmissible VOCs. Methods: We used a transmission dynamics model to quantify conditions that combined public health interventions must meet to reopen the economy without a large outbreak. These conditions are those that maintain the control reproduction number below unity, while accounting for an increase in transmissibility due to VOC. Results: We identified combinations of the proportion of individuals exposed to the virus who are traced and quarantined before becoming infectious, the proportion of symptomatic individuals confirmed and isolated, and individual daily contact rates needed to ensure the control reproduction number remains below unity. Conclusion: Our analysis indicates that the success of restrictive measures including lockdown and stay-at-home orders, as reflected by a reduction in number of cases, provides a narrow window of opportunity to intensify case detection and contact tracing efforts to prevent a new wave associated with circulation of more transmissible VOCs.

scholarly journals Transmission dynamics and the effects of non-pharmaceutical interventions in the COVID-19 outbreak resurged in Beijing, China: a descriptive and modelling study

BMJ Open ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. e047227
Author(s):  
Xiaoming Cui ◽  
Lin Zhao ◽  
Yuhao Zhou ◽  
Xin Lin ◽  
Runze Ye ◽  
...  
Keyword(s):  
Close Contact ◽  
Transmission Dynamics ◽  
Contact Tracing ◽  
Population Mobility ◽  
Wholesale Market ◽  
The People ◽  
Spatiotemporal Analyses ◽  
Epidemiological Characteristics ◽  
Pharmaceutical Interventions ◽  
Modelling Study

ObjectiveTo evaluate epidemiological characteristics and transmission dynamics of COVID-19 outbreak resurged in Beijing and to assess the effects of three non-pharmaceutical interventions.DesignDescriptive and modelling study based on surveillance data of COVID-19 in Beijing.SettingOutbreak in Beijing.ParticipantsThe database included 335 confirmed cases of COVID-19.MethodsTo conduct spatiotemporal analyses of the outbreak, we collected individual records on laboratory-confirmed cases of COVID-19 from 11 June 2020 to 5 July 2020 in Beijing, and visitor flow and products transportation data of Xinfadi Wholesale Market. We also built a modified susceptible-exposed-infected-removed model to investigate the effect of interventions deployed in Beijing.ResultsWe found that the staff working in the market (52.2%) and the people around 10 km to this epicentre (72.5%) were most affected, and the population mobility entering-exiting Xinfadi Wholesale Market significantly contributed to the spread of COVID-19 (p=0.021), but goods flow of the market had little impact on the virus spread (p=0.184). The prompt identification of Xinfadi Wholesale Market as the infection source could have avoided a total of 25 708 (95% CI 13 657 to 40 625) cases if unnoticed transmission lasted for a month. Based on the model, we found that active screening on targeted population by nucleic acid testing alone had the most significant effect.ConclusionsThe non-pharmaceutical interventions deployed in Beijing, including localised lockdown, close-contact tracing and community-based testing, were proved to be effective enough to contain the outbreak. Beijing has achieved an optimal balance between epidemic containment and economic protection.

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