scholarly journals Temporal Contact Graph Reveals The Evolving Epidemic Situation of COVID-19

2021 ◽  
Author(s):  
Mincheng Wu ◽  
Chao Li ◽  
Zhangchong Shen ◽  
Shibo He ◽  
Lingling Tang ◽  
...  
Keyword(s):  
Participation Rate ◽  
Population Level ◽  
User Participation ◽  
Contact Tracing ◽  
Prevention Measures ◽  
Contact Graph ◽  
Individual Level ◽  
Related Data ◽  
Epidemic Size ◽  
Travel Restrictions

Abstract Digital contact tracing has been recently advocated by China and many countries as part of digital prevention measures on COVID-19. Controversies have been raised about their effectiveness in practice as it remains open how they can be fully utilized to control COVID-19. In this article, we show that an abundance of information can be extracted from digital contact tracing for COVID-19 prevention and control. Specifically, we construct a temporal contact graph that quantifies the daily contacts between infectious and susceptible individuals by exploiting a large volume of location-related data contributed by 10,527,737 smartphone users in Wuhan, China. The temporal contact graph reveals five time-varying indicators can accurately capture actual contact trends at population level, demonstrating that travel restrictions (e.g., city lockdown) in Wuhan played an important role in containing COVID-19. We reveal a strong correlation between the contacts level and the epidemic size, and estimate several significant epidemiological parameters (e.g., serial interval). We also show that user participation rate exerts higher influence on situation evaluation than user upload rate does. At individual level, however, the temporal contact graph plays a limited role, since the behavior distinction between the infected and uninfected contacted individuals are not substantial. The revealed results can tell the effectiveness of digital contact tracing against COVID-19, providing guidelines for governments to implement interventions using information technology.

scholarly journals Temporal Contact Graph Reveals the Evolving Epidemic Situation of COVID-19

2020 ◽  
Author(s):  
Mincheng Wu ◽  
Chao Li ◽  
Zhangchong Shen ◽  
Shibo He ◽  
Lingling Tang ◽  
...  
Keyword(s):  
User Involvement ◽  
Evaluation Framework ◽  
Control Measures ◽  
Contact Tracing ◽  
Contact Graph ◽  
Individual Level ◽  
Related Data ◽  
The United Kingdom ◽  
Pearson Coefficient ◽  
And Control

Abstract Contact tracing APPs have been recently advocated by many countries (e.g., the United Kingdom, Australia, etc.) as part of control measures on COVID-19. Controversies have been raised about their effectiveness in practice as it still remains unclear how they can be fully utilized to fuel the fight against COVID-19. In this article, we show that an abundance of information can be extracted from contact tracing for COVID-19 prevention and control, providing the first data-driven evidence that supports the wide implementation of such APPs. Specifically, we construct a temporal contact graph that quantifies the daily contacts between infectious and susceptible individuals by exploiting a large volume of location related data contributed by 10,527,737 smartphone users in Wuhan, China. Five time-varying indicators we introduce can accurately capture actual contact trends at individual and population levels, demonstrating that travel restriction in Wuhan played an important role in containing COVID-19. We reveal a strong correlation (Pearson coefficient 0.929) between daily confirmed cases and daily total contacts, which can be utilized as a new and efficient way to evaluate and predict the evolving epidemic situation of COVID-19. Further, we find that there is a prominent distinction of contact behaviors between the infected and uninfected contacted individuals, and design an infection risk evaluation framework to identify infected ones. This can help narrow down the search of high risk contacted individuals for quarantine. Our results indicate that user involvement has an explicit impact on individual-level contact trend estimation while minor impact on situation evaluation, offering guidelines for governments to implement contact tracing APPs.

scholarly journals Testing, tracing and isolation in compartmental models

2021 ◽  
Vol 17 (3) ◽  
pp. e1008633
Author(s):  
Simone Sturniolo ◽  
William Waites ◽  
Tim Colbourn ◽  
David Manheim ◽  
Jasmina Panovska-Griffiths
Keyword(s):  
Computational Cost ◽  
Disease Outbreaks ◽  
Population Level ◽  
Compartmental Models ◽  
Contact Tracing ◽  
Adaptive Planning ◽  
Agent Based ◽  
Individual Level ◽  
Individual Based Models ◽  
The Individual

Existing compartmental mathematical modelling methods for epidemics, such as SEIR models, cannot accurately represent effects of contact tracing. This makes them inappropriate for evaluating testing and contact tracing strategies to contain an outbreak. An alternative used in practice is the application of agent- or individual-based models (ABM). However ABMs are complex, less well-understood and much more computationally expensive. This paper presents a new method for accurately including the effects of Testing, contact-Tracing and Isolation (TTI) strategies in standard compartmental models. We derive our method using a careful probabilistic argument to show how contact tracing at the individual level is reflected in aggregate on the population level. We show that the resultant SEIR-TTI model accurately approximates the behaviour of a mechanistic agent-based model at far less computational cost. The computational efficiency is such that it can be easily and cheaply used for exploratory modelling to quantify the required levels of testing and tracing, alone and with other interventions, to assist adaptive planning for managing disease outbreaks.

scholarly journals Surveillance Systems to Track and Evaluate Obesity Prevention Efforts

2017 ◽  
Vol 38 (1) ◽  
pp. 187-214 ◽  
Author(s):  
Deanna M. Hoelscher ◽  
Nalini Ranjit ◽  
Adriana Pérez
Keyword(s):  
Obesity Prevention ◽  
New Technologies ◽  
Underserved Populations ◽  
Population Level ◽  
Surveillance Systems ◽  
Obesity Epidemic ◽  
Individual Level ◽  
Related Data ◽  
International Trends ◽  
The Individual

To address the obesity epidemic, the public health community must develop surveillance systems that capture data at levels through which obesity prevention efforts are conducted. Current systems assess body mass index (BMI), diet, and physical activity behaviors at the individual level, but environmental and policy-related data are often lacking. The goal of this review is to describe US surveillance systems that evaluate obesity prevention efforts within the context of international trends in obesity monitoring, to identify potential data gaps, and to present recommendations to improve the evaluation of population-level initiatives. Our recommendations include adding environmental and policy measures to surveillance efforts with a focus on addressing underserved populations, harmonizing existing surveillance systems, including more sensitive measures of obesity outcomes, and developing a knowledgeable workforce. In addition, the widespread use of electronic health records and new technologies that allow self-quantification of behaviors offers opportunities for innovative surveillance methods.

scholarly journals Pruning Digital Contact Networks for Meso-scale Epidemic Surveillance Using Foursquare Data

2021 ◽  
Author(s):  
Sofia Hurtado ◽  
Radu Marculescu ◽  
Justin A. Drake ◽  
Ravi Srinivasan
Keyword(s):  
New York ◽  
Human Mobility ◽  
Smart Phone ◽  
Population Level ◽  
Dynamic Contact ◽  
Contact Tracing ◽  
Contact Networks ◽  
Individual Level ◽  
The Individual ◽  
Meso Scale

AbstractWith the recent boom in human sensing, the push to incorporate human mobility tracking with epidemic modeling highlights the lack of groundwork at the meso-scale (e.g., city-level) for both contact tracing and transmission dynamics. Although GPS data has been used to study city-level outbreaks, current approaches fail to capture the path of infection at the individual level. Consequently, in this paper, we extend the usefulness of epidemics prediction from estimating the size of an outbreak at the population level to estimating the individuals who may likely get infected within a finite period of time. To this end, we propose a network-based method to first build and then prune the dynamic contact networks for recurring interactions; these networks can serve as the backbone topology for mechanistic epidemics modeling. We test our method using Foursquare’s Points of Interest (POI) smart-phone geolocation data from over 1.3 million devices and show that we can recreate the COVID-19 infection curves for two major (yet very different) US cities (i.e., Austin and New York City) while maintaining the granularity of individual transmissions and reducing model uncertainty. Our method provides a foundation for building a disease prediction framework at the meso-scale that can help both policy makers and individuals of their estimated state of health and help with pandemic planning.

scholarly journals SARS-CoV-2 Load does not Predict Transmissibility in College Students

2021 ◽  
Author(s):  
Di Tian ◽  
Zhen Lin ◽  
Ellie M. Kriner ◽  
Dalton J. Esneault ◽  
Jonathan Tran ◽  
...  
Keyword(s):  
College Students ◽  
Viral Load ◽  
Population Level ◽  
Viral Transmission ◽  
Contact Tracing ◽  
Infected People ◽  
Individual Level ◽  
Ct Value ◽  
The Individual

AbstractSARS-CoV2 is highly contagious and the global spread has caused significant medical, social and economic impacts. Other than vaccination, effective public health measures, including contact tracing, isolation and quarantine, is critical for deterring viral transmission, preventing infection progression and resuming normal activities. Viral transmission is affected by many factors but the viral load and vitality could be among the most important ones. Although in vitro culture studies have indicated that the amount of virus isolated from infected people determines the successful rate of virus isolation, whether the viral load carried at the individual level would affect the transmissibility was not known. We aimed to determine whether the Ct value, a measurement of viral load by RT-PCR assay, could differentiate the spreader from the non-spreader in a population of college students. Our results indicate that while at the population level the Ct value is lower, suggesting a higher viral load, in the symptomatic spreaders than the asymptomatic non-spreaders, there is a significant overlap in the Ct values between the two groups. Thus Ct values, or the viral load, at the individual level could not predict the transmissibility. Our studies also suggest that a sensitive method to detect the presence of virus is needed to identify asymptomatic persons who may carry a low viral load but can still be infectious.

scholarly journals Testing, tracing and isolation in compartmental models

2020 ◽  
Author(s):  
Simone Sturniolo ◽  
William Waites ◽  
Tim Colbourn ◽  
David Manheim ◽  
Jasmina Panovska-Griffiths
Keyword(s):  
Computational Cost ◽  
Disease Outbreaks ◽  
Population Level ◽  
Compartmental Models ◽  
Disease Spread ◽  
Contact Tracing ◽  
Probabilistic Argument ◽  
Adaptive Planning ◽  
Agent Based ◽  
Individual Level

AbstractExisting compartmental mathematical modelling methods for epidemics, such as SEIR models, cannot accurately represent effects of contact tracing. This makes them inappropriate for evaluating testing and contact tracing strategies to contain an outbreak. An alternative used in practice is the application of agent- or individual-based models (ABM). However ABMs are complex, less well-understood and much more computationally expensive. This paper presents a new method for accurately including the effects of Testing, contact-Tracing and Isolation (TTI) strategies in standard compartmental models. We derive our method using a careful probabilistic argument to show how contact tracing at the individual level is reflected in aggregate on the population level. We show that the resultant SEIR-TTI model accurately approximates the behaviour of a mechanistic agent-based model at far less computational cost. The computational efficiency is such that it can be easily and cheaply used for exploratory modelling to quantify the required levels of testing and tracing, alone and with other interventions, to assist adaptive planning for managing disease outbreaks.Author SummaryThe importance of modeling to inform and support decision making is widely acknowledged. Understanding how to enhance contact tracing as part of the Testing-Tracing-Isolation (TTI) strategy for mitigation of COVID is a key public policy questions. Our work develops the SEIR-TTI model as an extension of the classic Susceptible, Exposed, Infected and Recovered (SEIR) model to include tracing of contacts of people exposed to and infectious with COVID-19. We use probabilistic argument to derive contact tracing rates within a compartmental model as aggregates of contact tracing at an individual level. Our adaptation is applicable across compartmental models for infectious diseases spread. We show that our novel SEIR-TTI model can accurately approximate the behaviour of mechanistic agent-based models at far less computational cost. The SEIR-TTI model represents an important addition to the theoretical methodology of modelling infectious disease spread and we anticipate that it will be immediately applicable to the management of the COVID-19 pandemic.

scholarly journals Tracking the COVID-19 pandemic in Australia using genomics

2020 ◽  
Author(s):  
Torsten Seemann ◽  
Courtney Lane ◽  
Norelle Sherry ◽  
Sebastian Duchene ◽  
Anders Goncalves da Silva ◽  
...  
Keyword(s):  
Public Health ◽  
Genome Sequencing ◽  
Population Level ◽  
Genomic Diversity ◽  
Contact Tracing ◽  
Internal Quality ◽  
Reproductive Number ◽  
Healthcare Facilities ◽  
Travel Restrictions ◽  
Response Team

BACKGROUND: Whole-genome sequencing of pathogens can improve resolution of outbreak clusters and define possible transmission networks. We applied high-throughput genome sequencing of SARS-CoV-2 to 75% of cases in the State of Victoria (population 6.24 million) in Australia. METHODS: Cases of SARS-CoV-2 infection were detected through active case finding and contact tracing. A dedicated SARS-CoV-2 multidisciplinary genomic response team was formed to enable rapid integration of epidemiological and genomic data. Phylodynamic analysis was performed to assess the putative impact of social restrictions. RESULTS: Between 25 January and 14 April 2020, 1,333 COVID-19 cases were reported in Victoria, with a peak in late March. After applying internal quality control parameters, 903 samples were included in genomic analyses. Sequenced samples from Australia were representative of the global diversity of SARS-CoV-2, consistent with epidemiological findings of multiple importations and limited onward transmission. In total, 76 distinct genomic clusters were identified; these included large clusters associated with social venues, healthcare facilities and cruise ships. Sequencing of sequential samples from 98 patients revealed minimal intra-patient SARS-CoV-2 genomic diversity. Phylodynamic modelling indicated a significant reduction in the effective viral reproductive number (Re) from 1.63 to 0.48 after the implementation of travel restrictions and population-level physical distancing. CONCLUSIONS: Our data provide a comprehensive framework for the use of SARS-CoV-2 genomics in public health responses. The application of genomics to rapidly identify SARS-CoV-2 transmission chains will become critically important as social restrictions ease globally. Public health responses to emergent cases must be swift, highly focused and effective.

scholarly journals Substantial Decreases in NO2 Pollution Measured by Ground-Based Monitors in US Cities During COVID-19 Shutdowns from Reduced Transportation Volumes

2020 ◽  
Author(s):  
Asrah Heintzelman ◽  
Vijay Lulla ◽  
Gabriel Filippelli
Keyword(s):  
Substantial Reduction ◽  
Population Level ◽  
Severe Form ◽  
Air Pollutant ◽  
Sensor Data ◽  
Traffic Patterns ◽  
Sharp Drop ◽  
Related Data ◽  
Travel Restrictions ◽  
Us Cities

The air pollutant NO2 is derived largely from transportation sources and is known to cause respiratory disease. A substantial reduction in transport and industrial processes around the globe from the novel SARS-CoV-2 coronavirus and subsequent pandemic resulted in sharp declines in emissions, including for NO2. Additionally, the COVID-19 disease that results from the coronavirus may present in its most severe form in those who have been exposed to high levels of air pollution. To explore these links, we compared ground-based NO2 sensor data from 11 US cities from a two-month window (March-April) over the previous five years versus the same window during 2020 shutdowns. NO2 declined roughly 12-41% in the 11 cities. This decreased coincided with a sharp drop in vehicular traffic from shutdown-related travel restrictions. To explore this link more closely, we gathered more detailed traffic count data in one city, Indianapolis, Indiana, and found a strong correlation between traffic counts/classification and vehicle miles travelled, and a moderate correlation between NO2 and traffic related data. This finding indicates that we can use such analysis in targeting reduction in pollutants like NO2 by examining and manipulating traffic patterns, thus potentially leading to more population-level health resilience in the future.

scholarly journals Individual preventive social distancing during an epidemic may have negative population-level outcomes

2018 ◽  
Vol 15 (145) ◽  
pp. 20180296 ◽  
Author(s):  
Ka Yin Leung ◽  
Frank Ball ◽  
David Sirl ◽  
Tom Britton
Keyword(s):  
Preventive Measures ◽  
Network Models ◽  
Population Level ◽  
Careful Analysis ◽  
High Rate ◽  
Negative Consequences ◽  
Social Distancing ◽  
Individual Level ◽  
Epidemic Size ◽  
Negative Effect

The outbreak of an infectious disease in a human population can lead to individuals responding with preventive measures in an attempt to avoid getting infected. This leads to changes in contact patterns. However, as we show in this paper, rational behaviour at the individual level, such as social distancing from infectious contacts, may not always be beneficial for the population as a whole. We use epidemic network models to demonstrate the potential negative consequences at the population level. We take into account the social structure of the population through several network models. As the epidemic evolves, susceptible individuals may distance themselves from their infectious contacts. Some individuals replace their lost social connections by seeking new ties. If social distancing occurs at a high rate at the beginning of an epidemic, then this can prevent an outbreak from occurring. However, we show that moderate social distancing can worsen the disease outcome, both in the initial phase of an outbreak and the final epidemic size. Moreover, the same negative effect can arise in real-world networks. Our results suggest that one needs to be careful when targeting behavioural changes as they could potentially worsen the epidemic outcome. Furthermore, network structure crucially influences the way that individual-level measures impact the epidemic at the population level. These findings highlight the importance of careful analysis of preventive measures in epidemic models.

scholarly journals Substantial Decreases in U.S. Cities’ Ground-Based NO2 Concentrations during COVID-19 from Reduced Transportation

2021 ◽  
Vol 13 (16) ◽  
pp. 9030
Author(s):  
Asrah Heintzelman ◽  
Gabriel Filippelli ◽  
Vijay Lulla
Keyword(s):  
Substantial Reduction ◽  
Population Level ◽  
Adverse Outcomes ◽  
Sensor Data ◽  
The Novel ◽  
Traffic Patterns ◽  
Sharp Drop ◽  
Related Data ◽  
Global Transport ◽  
Travel Restrictions

A substantial reduction in global transport and industrial processes stemming from the novel SARS-CoV-2 coronavirus and subsequent pandemic resulted in sharp declines in emissions, including for NO2. This has implications for human health, given the role that this gas plays in pulmonary disease and the findings that past exposure to air pollutants has been linked to the most adverse outcomes from COVID-19 disease, likely via various co-morbidities. To explore how much COVID-19 shutdown policies impacted urban air quality, we examined ground-based NO2 sensor data from 11 U.S. cities from a two-month window (March–April) during shutdown in 2020, controlling for natural seasonal variability by using average changes in NO2 over the previous five years for these cities. Levels of NO2 and VMT reduction in March and April compared to January 2020 ranged between 11–65% and 11–89%, consistent with a sharp drop in vehicular traffic from shutdown-related travel restrictions. To explore this link closely, we gathered detailed traffic count data in one city—Indianapolis, Indiana—and found a strong correlation (0.90) between traffic counts/classification and vehicle miles travelled, a moderate correlation (0.54) between NO2 and traffic related data, and an average reduction of 1.11 ppb of NO2 linked to vehicular data. This finding indicates that targeted reduction in pollutants like NO2 can be made by manipulating traffic patterns, thus potentially leading to more population-level health resilience in the future.

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