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 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 Chronic contamination decreases disease spread: a Daphnia –fungus–copper case study

2012 ◽  
Vol 279 (1741) ◽  
pp. 3146-3153 ◽  
Author(s):  
David J. Civitello ◽  
Philip Forys ◽  
Adam P. Johnson ◽  
Spencer R. Hall
Keyword(s):  
Disease Outbreaks ◽  
Population Level ◽  
Disease Spread ◽  
Chemical Contamination ◽  
Single Generation ◽  
Individual Level ◽  
Enhanced Transmission ◽  
Generational Effects ◽  
Parasite Reproduction

Chemical contamination and disease outbreaks have increased in many ecosystems. However, connecting pollution to disease spread remains difficult, in part, because contaminants can simultaneously exert direct and multi-generational effects on several host and parasite traits. To address these challenges, we parametrized a model using a zooplankton–fungus–copper system. In individual-level assays, we considered three sublethal contamination scenarios: no contamination, single-generation contamination (hosts and parasites exposed only during the assays) and multi-generational contamination (hosts and parasites exposed for several generations prior to and during the assays). Contamination boosted transmission by increasing contact of hosts with parasites. However, it diminished parasite reproduction by reducing the size and lifespan of infected hosts. Multi-generational contamination further reduced parasite reproduction. The parametrized model predicted that a single generation of contamination would enhance disease spread (via enhanced transmission), whereas multi-generational contamination would inhibit epidemics relative to unpolluted conditions (through greatly depressed parasite reproduction). In a population-level experiment, multi-generational contamination reduced the size of experimental epidemics but did not affect Daphnia populations without disease. This result highlights the importance of multi-generational effects for disease dynamics. Such integration of models with experiments can provide predictive power for disease problems in contaminated environments.

scholarly journals How effective was Newfoundland & Labrador’s travel ban to prevent the spread of COVID-19? An agent-based analysis

2021 ◽  
Author(s):  
Dionne M. Aleman ◽  
Benjamin Z. Tham ◽  
Sean J. Wagner ◽  
Justin Semelhago ◽  
Asghar Mohammadi ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Spatial Location ◽  
Contact Tracing ◽  
Infection Rates ◽  
Case Scenario ◽  
Agent Based ◽  
Individual Level ◽  
Transmission Rates ◽  
Imported Cases ◽  
The Uk

AbstractBackgroundTo prevent the spread of COVID-19 in Newfoundland & Labrador (NL), NL implemented a wide travel ban in May 2020. We estimate the effectiveness of this travel ban using a customized agent-based simulation (ABS).MethodsWe built an individual-level ABS to simulate the movements and behaviors of every member of the NL population, including arriving and departing travellers. The model considers individual properties (spatial location, age, comorbidities) and movements between environments, as well as age-based disease transmission with pre-symptomatic, symptomatic, and asymptomatic transmission rates. We examine low, medium, and high travel volume, traveller infection rates, and traveller quarantine compliance rates to determine the effect of travellers on COVID spread, and the ability of contact tracing to contain outbreaks.ResultsInfected travellers increased COVID cases by 2-52x (8-96x) times and peak hospitalizations by 2-49x (8-94x), with (without) contact tracing. Although contact tracing was highly effective at reducing spread, it was insufficient to stop outbreaks caused by travellers in even the best-case scenario, and the likelihood of exceeding contact tracing capacity was a concern in most scenarios. Quarantine compliance had only a small impact on COVID spread; travel volume and infection rate drove spread.InterpretationNL’s travel ban was likely a critically important intervention to prevent COVID spread. Even a small number of infected travellers can play a significant role in introducing new chains of transmission, resulting in exponential community spread and significant increases in hospitalizations, while outpacing contact tracing capabilities. With the presence of more transmissible variants, e.g., the UK variant, prevention of imported cases is even more critical.

scholarly journals Extracting Data from Disparate Sources for Agent-Based Disease Spread Models

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
M. Laskowski ◽  
B. C. P. Demianyk ◽  
J. Benavides ◽  
M. R. Friesen ◽  
R. D. McLeod ◽  
...  
Keyword(s):  
Demographic Data ◽  
Population Level ◽  
Real Data ◽  
Smartphone Application ◽  
Geographic Area ◽  
Disease Spread ◽  
Telecommunication Service ◽  
Interaction Patterns ◽  
Agent Based ◽  
Contact Data

This paper presents a review and evaluation of real data sources relative to their role and applicability in an agent-based model (ABM) simulating respiratory infection spread a large geographic area. The ABM is a spatial-temporal model inclusive of behavior and interaction patterns between individual agents. The agent behaviours in the model (movements and interactions) are fed by census/demographic data, integrated with real data from a telecommunication service provider (cellular records), traffic survey data, as well as person-person contact data obtained via a custom 3G smartphone application that logs Bluetooth connectivity between devices. Each source provides data of varying type and granularity, thereby enhancing the robustness of the model. The work demonstrates opportunities in data mining and fusion and the role of data in calibrating and validating ABMs. The data become real-world inputs into susceptible-exposed-infected-recovered (SEIR) disease spread models and their variants, thereby building credible and nonintrusive models to qualitatively model public health interventions at the population level.

scholarly journals A HYBRID MODEL FOR STUDYING SPATIAL ASPECTS OF INFECTIOUS DISEASES

2012 ◽  
Vol 54 (1-2) ◽  
pp. 37-49 ◽  
Author(s):  
BENJAMIN J. BINDER ◽  
JOSHUA V. ROSS ◽  
MATTHEW J. SIMPSON
Keyword(s):  
Infectious Disease ◽  
Spatial Analysis ◽  
Infectious Diseases ◽  
Hybrid Model ◽  
Population Level ◽  
Infectious Agents ◽  
Agent Based ◽  
Individual Level ◽  
The Individual ◽  
Spread Of Infection

AbstractWe consider a hybrid model, created by coupling a continuum and an agent-based model of infectious disease. The framework of the hybrid model provides a mechanism to study the spread of infection at both the individual and population levels. This approach captures the stochastic spatial heterogeneity at the individual level, which is directly related to deterministic population level properties. This facilitates the study of spatial aspects of the epidemic process. A spatial analysis, involving counting the number of infectious agents in equally sized bins, reveals when the spatial domain is nonhomogeneous.

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 A modelling framework to assess the likely effectiveness of facemasks in combination with ‘lock-down’ in managing the COVID-19 pandemic

2020 ◽  
Vol 476 (2238) ◽  
pp. 20200376 ◽  
Author(s):  
Richard O. J. H. Stutt ◽  
Renata Retkute ◽  
Michael Bradley ◽  
Christopher A. Gilligan ◽  
John Colvin
Keyword(s):  
Mobile Apps ◽  
Population Level ◽  
Disease Spread ◽  
Contact Tracing ◽  
Lower Efficiency ◽  
The Public ◽  
Molecular Tests ◽  
Modelling Framework ◽  
Increased Risk ◽  
The Impact

COVID-19 is characterized by an infectious pre-symptomatic period, when newly infected individuals can unwittingly infect others. We are interested in what benefits facemasks could offer as a non-pharmaceutical intervention, especially in the settings where high-technology interventions, such as contact tracing using mobile apps or rapid case detection via molecular tests, are not sustainable. Here, we report the results of two mathematical models and show that facemask use by the public could make a major contribution to reducing the impact of the COVID-19 pandemic. Our intention is to provide a simple modelling framework to examine the dynamics of COVID-19 epidemics when facemasks are worn by the public, with or without imposed ‘lock-down’ periods. Our results are illustrated for a number of plausible values for parameter ranges describing epidemiological processes and mechanistic properties of facemasks, in the absence of current measurements for these values. We show that, when facemasks are used by the public all the time (not just from when symptoms first appear), the effective reproduction number, R e , can be decreased below 1, leading to the mitigation of epidemic spread. Under certain conditions, when lock-down periods are implemented in combination with 100% facemask use, there is vastly less disease spread, secondary and tertiary waves are flattened and the epidemic is brought under control. The effect occurs even when it is assumed that facemasks are only 50% effective at capturing exhaled virus inoculum with an equal or lower efficiency on inhalation. Facemask use by the public has been suggested to be ineffective because wearers may touch their faces more often, thus increasing the probability of contracting COVID-19. For completeness, our models show that facemask adoption provides population-level benefits, even in circumstances where wearers are placed at increased risk. At the time of writing, facemask use by the public has not been recommended in many countries, but a recommendation for wearing face-coverings has just been announced for Scotland. Even if facemask use began after the start of the first lock-down period, our results show that benefits could still accrue by reducing the risk of the occurrence of further COVID-19 waves. We examine the effects of different rates of facemask adoption without lock-down periods and show that, even at lower levels of adoption, benefits accrue to the facemask wearers. These analyses may explain why some countries, where adoption of facemask use by the public is around 100%, have experienced significantly lower rates of COVID-19 spread and associated deaths. We conclude that facemask use by the public, when used in combination with physical distancing or periods of lock-down, may provide an acceptable way of managing the COVID-19 pandemic and re-opening economic activity. These results are relevant to the developed as well as the developing world, where large numbers of people are resource poor, but fabrication of home-made, effective facemasks is possible. A key message from our analyses to aid the widespread adoption of facemasks would be: ‘my mask protects you, your mask protects me’.

scholarly journals Spatial aggregation choice in the era of digital and administrative surveillance data

2021 ◽  
Author(s):  
Elizabeth C. Lee ◽  
Ali Arab ◽  
Vittoria Colizza ◽  
Shweta Bansal
Keyword(s):  
Spatial Autocorrelation ◽  
Disease Surveillance ◽  
Disease Outbreaks ◽  
State Level ◽  
The United States ◽  
Relative Magnitude ◽  
Spatial Aggregation ◽  
Disease Spread ◽  
Medical Claims ◽  
Individual Level

AbstractBackgroundTraditional disease surveillance is increasingly being complemented by data from non-traditional sources like medical claims, electronic health records, and participatory syndromic data platforms. As non-traditional data are often collected at the individual-level and are convenience samples from a population, choices must be made on the aggregation of these data for epidemiological inference. Our study seeks to understand the influence of spatial aggregation choice on our understanding of disease spread with a case study of influenza-like illness in the United States.MethodsUsing U.S. medical claims data from 2002 to 2009, we examined the epidemic source location, onset and peak season timing, and epidemic duration of influenza seasons for data aggregated to the county and state scales. We also compared spatial autocorrelation and tested the relative magnitude of spatial aggregation differences between onset and peak measures of disease burden.ResultsWe found discrepancies in the inferred epidemic source locations and estimated influenza season onsets and peaks when comparing county and state-level data. Spatial autocorrelation was detected across more expansive geographic ranges during the peak season as compared to the early flu season, and there were greater spatial aggregation differences in early season measures as well.ConclusionsEpidemiological inferences are more sensitive to spatial scale early on during U.S. influenza seasons, when there is greater heterogeneity in timing, intensity, and geographic spread of the epidemics. Users of non-traditional disease surveillance should carefully consider how to extract accurate disease signals from finer-scaled data for early use in disease outbreaks.

scholarly journals Shared behavioral mechanisms underlie C. elegans aggregation and swarming

2018 ◽  
Author(s):  
S Serena Ding ◽  
Linus J. Schumacher ◽  
Avelino E. Javer ◽  
Robert G. Endres ◽  
André EX Brown
Keyword(s):  
A Priori ◽  
Population Level ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Individual Level ◽  
C Elegans ◽  
Behavioral Rules ◽  
Level Statistics ◽  
Approximate Bayesian ◽  
Behavioral Mechanisms

AbstractIn complex biological systems, simple individual-level behavioral rules can give rise to emergent group-level behavior. While such collective behavior has been well studied in cells and larger organisms, the mesoscopic scale is less understood, as it is unclear which sensory inputs and physical processes matter a priori. Here, we investigate collective feeding in the roundworm C. elegans at this intermediate scale, using quantitative phenotyping and agent-based modeling to identify behavioral rules underlying both aggregation and swarming—a dynamic phenotype only observed at longer timescales. Using fluorescent multi-worm tracking, we quantify aggregation behavior in terms of individual dynamics and population-level statistics. Based on our quantification, we use agent-based simulations and approximate Bayesian inference to identify three key behavioral rules that give rise to aggregation: cluster-edge reversals, a density-dependent switch between crawling speeds, and taxis towards neighboring worms. Our simulations suggest that swarming is simply driven by local food depletion but otherwise employs the same behavioral mechanisms as the initial aggregation. Hence, mesoscopic C. elegans uses mechanisms familiar from microscopic systems for aggregation, but implemented via more complex behaviors characteristic of macroscopic organisms.

scholarly journals Face Masks Against COVID-19: An Evidence Review

2020 ◽  
Author(s):  
Jeremy Howard ◽  
Austin Huang ◽  
Zhiyuan Li ◽  
Zeynep Tufekci ◽  
Vladimir Zdimal ◽  
...  
Keyword(s):  
Relevant Literature ◽  
Population Level ◽  
Transmission Probability ◽  
Analytical Framework ◽  
Disease Spread ◽  
Contact Tracing ◽  
Source Control ◽  
Individual Outcomes ◽  
Route Of Transmission ◽  
Asymptomatic Individuals

The science around the use of masks by the general public to impede COVID-19 transmission is advancing rapidly. Policymakers need guidance on how masks should be used by the general population to combat the COVID-19 pandemic. Here,we develop an analytical framework to examine an overlooked aspect of mask usage: masks as source-control targeting egress from the wearer with benefits at the population-level, rather than as PPE used for ingress control for health-care workers with focus on individual outcomes. We consider and synthesize the relevant literature to inform multiple areas: 1) transmission characteristics of COVID-19, 2) filtering characteristics and efficacy of masks, 3) estimated population impacts of widespread community mask use, and 4) sociological considerations for policies concerning mask-wearing. A primary route of transmission of COVID-19 is likely via respiratory droplets, and is known to be transmissible from presymptomatic and asymptomatic individuals. Reducing disease spread requires two things: first, limit contacts of infected individuals via physical distancing and other measures, and second, reduce the transmission probability per contact. The preponderance of evidence indicates that mask wearing reduces the transmissibility per contact by reducing transmission of infected droplets in both laboratory and clinical contexts. Public mask wearing is most effective at reducing spread of the virus when compliance is high. The decreased transmissibility could substantially reduce the death toll and economic impact while the cost of the intervention is low. Given the current shortages of medical masks we recommend the adoption of public cloth mask wearing, as an effective form of source control for now, in conjunction with existing hygiene, distancing, and contact tracing strategies. We recommend that public officials and governments strongly encourage the use of widespread face masks in public, including the use of appropriate regulation.

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