scholarly journals Beyond R 0 : heterogeneity in secondary infections and probabilistic epidemic forecasting

2020 ◽  
Vol 17 (172) ◽  
pp. 20200393 ◽  
Author(s):  
Laurent Hébert-Dufresne ◽  
Benjamin M. Althouse ◽  
Samuel V. Scarpino ◽  
Antoine Allard
Keyword(s):  
Random Network ◽  
Emerging Infectious Diseases ◽  
Disease Outbreaks ◽  
Basic Reproductive Number ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Emerging Pathogens ◽  
Epidemic Size ◽  
Classic Result ◽  
Secondary Infections

The basic reproductive number, R 0 , is one of the most common and most commonly misapplied numbers in public health. Often used to compare outbreaks and forecast pandemic risk, this single number belies the complexity that different epidemics can exhibit, even when they have the same R 0 . Here, we reformulate and extend a classic result from random network theory to forecast the size of an epidemic using estimates of the distribution of secondary infections, leveraging both its average R 0 and the underlying heterogeneity. Importantly, epidemics with lower R 0 can be larger if they spread more homogeneously (and are therefore more robust to stochastic fluctuations). We illustrate the potential of this approach using different real epidemics with known estimates for R 0 , heterogeneity and epidemic size in the absence of significant intervention. Further, we discuss the different ways in which this framework can be implemented in the data-scarce reality of emerging pathogens. Lastly, we demonstrate that without data on the heterogeneity in secondary infections for emerging infectious diseases like COVID-19 the uncertainty in outbreak size ranges dramatically. Taken together, our work highlights the critical need for contact tracing during emerging infectious disease outbreaks and the need to look beyond R 0 .

scholarly journals Beyond R0: Heterogeneity in secondary infections and probabilistic epidemic forecasting

2020 ◽  
Author(s):  
Laurent Hébert-Dufresne ◽  
Benjamin M. Althouse ◽  
Samuel V. Scarpino ◽  
Antoine Allard
Keyword(s):  
Random Network ◽  
Emerging Infectious Diseases ◽  
Disease Outbreaks ◽  
Basic Reproductive Number ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Emerging Pathogens ◽  
Epidemic Size ◽  
Secondary Infections ◽  
Outbreak Size

The basic reproductive number — R0 — is one of the most common and most commonly misapplied numbers in public health. Although often used to compare outbreaks and forecast pandemic risk, this single number belies the complexity that two different pathogens can exhibit, even when they have the same R0 [1–3]. Here, we show how to predict outbreak size using estimates of the distribution of secondary infections, leveraging both its average R0 and the underlying heterogeneity. To do so, we reformulate and extend a classic result from random network theory [4] that relies on contact tracing data to simultaneously determine the first moment (R0) and the higher moments (representing the heterogeneity) in the distribution of secondary infections. Further, we show the different ways in which this framework can be implemented in the data-scarce reality of emerging pathogens. Lastly, we demonstrate that without data on the heterogeneity in secondary infections for emerging infectious diseases like COVID-19, the uncertainty in outbreak size ranges dramatically. Taken together, our work highlights the critical need for contact tracing during emerging infectious disease outbreaks and the need to look beyond R0 when predicting epidemic size.

scholarly journals Emerging infectious pathogens of wildlife

2001 ◽  
Vol 356 (1411) ◽  
pp. 1001-1012 ◽  
Author(s):  
A. Dobson ◽  
J. Foufopoulos
Keyword(s):  
United States ◽  
Anthropogenic Activities ◽  
Disease Outbreaks ◽  
The United States ◽  
Host Population ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Emerging Pathogens ◽  
Emergent Pathogen ◽  
Insight Into

The first part of this paper surveys emerging pathogens of wildlife recorded on the ProMED Web site for a 2–year period between 1998 and 2000. The majority of pathogens recorded as causing disease outbreaks in wildlife were viral in origin. Anthropogenic activities caused the outbreaks in a significant majority of cases. The second part of the paper develops some matrix models for quantifying the basic reproductive number, R 0 , for a variety of potential types of emergent pathogen that cause outbreaks in wildlife. These analyses emphasize the sensitivity of R 0 to heterogeneities created by either the spatial structure of the host population, or the ability of the pathogens to utilize multiple host species. At each stage we illustrate how the approach provides insight into the initial dynamics of emergent pathogens such as canine parvovirus, Lyme disease, and West Nile virus in the United States.

scholarly journals A stochastic epidemiological model to estimate the size of an outbreak at the first case identification

2020 ◽  
Author(s):  
Peter Czuppon ◽  
François Blanquart ◽  
Florence Débarre
Keyword(s):  
Branching Processes ◽  
Single Point ◽  
Basic Reproductive Number ◽  
Contact Tracing ◽  
Upper And Lower Bounds ◽  
Reproductive Number ◽  
Related Case ◽  
Epidemic Size ◽  
First Case ◽  
Over Time

AbstractThe identification of a first case (e.g. by a disease-related death or hospitalization event) raises the question of the actual size of a local outbreak. Quick estimates of the outbreak size are required to assess the necessary testing, contact tracing and potential containment effort. Using general branching processes and assuming that epidemic parameters (including the basic reproductive number) are constant over time, we characterize the distribution of the first hospitalization time and of the epidemic size at this random time. We find that previous estimates either overestimate or largely underestimate the actual epidemic size. In addition, we provide upper and lower bounds for the number of infectious individuals of the local outbreak over time. The upper bound is the cumulative epidemic size, and the lower bound is a constant fraction of it. Lastly, we compute the number of detectable cases if one were to test the whole local outbreak at a single point in time. In a growing epidemic, most individuals have been infected recently, which can strongly limit the detection of infected individuals when there is a delay between an infection and its potential detection. Overall, our analysis provides new analytical estimates about the epidemic size at identification of a first disease-related case. This piece of information is important to inform policy makers during the early stages of an epidemic outbreak.

scholarly journals Adapting an Agent-Based Model of Infectious Disease Spread in an Irish County to COVID-19

Systems ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 41
Author(s):  
Elizabeth Hunter ◽  
John D. Kelleher
Keyword(s):  
Complex System ◽  
Infectious Disease ◽  
Emerging Infectious Diseases ◽  
Disease Outbreaks ◽  
Basic Reproductive Number ◽  
Disease Spread ◽  
Reproductive Number ◽  
Agent Based Model ◽  
School Closures ◽  
Agent Based

The dynamics that lead to the spread of an infectious disease through a population can be characterized as a complex system. One way to model such a system, in order to improve preparedness, and learn more about how an infectious disease, such as COVID-19, might spread through a population, is agent-based epidemiological modelling. When a pandemic is caused by an emerging disease, it takes time to develop a completely new model that captures the complexity of the system. In this paper, we discuss adapting an existing agent-based model for the spread of measles in Ireland to simulate the spread of COVID-19. The model already captures the population structure and commuting patterns of the Irish population, and therefore, once adapted to COVID-19, it can provide important insight on the pandemic, specifically in Ireland. We first investigate the different disease parameters that need to be adjusted to simulate the spread of COVID-19 instead of measles and then run a set of experiments initially comparing the model output for our original measles model with that from the adjusted COVID-19 model. We then report on experiments on how the different values of the basic reproductive number, R0, influence the simulated outbreaks, and find that our model behaves as expected: the higher the R0, the more agents are infected. Then, we demonstrate how different intervention strategies, such as vaccinations and school closures, influence the spread of measles and COVID-19 and how we can simulate real pandemic timings and interventions in our model. We show that with the same society, environment and transportation components among the different disease components lead to very different results for the two diseases, and that our COVID-19 model, when run for Leitrim County, Ireland, predicts a similar outbreak length to a real outbreak in Leitrim County, Ireland, but the model results in a higher number of infected agents compared to the real outbreak. This difference in cases is most likely due to identifying all cases of COVID-19 in the model opposed to only those tested. Once an agent-based model is created to simulate a specific complex system or society, the disease component can be adapted to simulate different infectious disease outbreaks. This makes agent-based models a powerful tool that can be used to help understand the spread of new and emerging infectious diseases.

scholarly journals Digital proximity tracing on empirical contact networks for pandemic control

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
G. Cencetti ◽  
G. Santin ◽  
A. Longa ◽  
E. Pigani ◽  
A. Barrat ◽  
...  
Keyword(s):  
Large Scale ◽  
Disease Outbreaks ◽  
Physical Distance ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Modeling Framework ◽  
Infectious Disease Outbreaks ◽  
Contact Patterns ◽  
Contact Data ◽  
The Impact

AbstractDigital contact tracing is a relevant tool to control infectious disease outbreaks, including the COVID-19 epidemic. Early work evaluating digital contact tracing omitted important features and heterogeneities of real-world contact patterns influencing contagion dynamics. We fill this gap with a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread in real environments. We find that restrictive policies are more effective in containing the epidemic but come at the cost of unnecessary large-scale quarantines. Policy evaluation through their efficiency and cost results in optimized solutions which only consider contacts longer than 15–20 minutes and closer than 2–3 meters to be at risk. Our results show that isolation and tracing can help control re-emerging outbreaks when some conditions are met: (i) a reduction of the reproductive number through masks and physical distance; (ii) a low-delay isolation of infected individuals; (iii) a high compliance. Finally, we observe the inefficacy of a less privacy-preserving tracing involving second order contacts. Our results may inform digital contact tracing efforts currently being implemented across several countries worldwide.

scholarly journals Antiviral treatment for the control of pandemic influenza: some logistical constraints

2007 ◽  
Vol 5 (22) ◽  
pp. 545-553 ◽  
Author(s):  
N Arinaminpathy ◽  
A.R McLean
Keyword(s):  
Influenza Pandemic ◽  
Antiviral Treatment ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Epidemic Size ◽  
Conservative Policy ◽  
Control Programmes ◽  
Final Epidemic Size ◽  
Number Of Treatment ◽  
Modelling Approach

Disease control programmes for an influenza pandemic will rely initially on the deployment of antiviral drugs such as Tamiflu, until a vaccine becomes available. However, such control programmes may be severely hampered by logistical constraints such as a finite stockpile of drugs and a limit on the distribution rate. We study the effects of such constraints using a compartmental modelling approach. We find that the most aggressive possible antiviral programme minimizes the final epidemic size, even if this should lead to premature stockpile run-out. Moreover, if the basic reproductive number R 0 is not too high, such a policy can avoid run-out altogether. However, where run-out would occur, such benefits must be weighed against the possibility of a higher epidemic peak than if a more conservative policy were followed. Where there is a maximum number of treatment courses that can be dispensed per day, reflecting a manpower limit on antiviral distribution, our results suggest that such a constraint is unlikely to have a significant impact (i.e. increasing the final epidemic size by more than 10%), as long as drug courses sufficient to treat at least 6% of the population can be dispensed per day.

scholarly journals Digital Proximity Tracing in the COVID-19 Pandemic on Empirical Contact Networks

2020 ◽  
Author(s):  
Giulia Cencetti ◽  
Gabriele Santin ◽  
Antonio Longa ◽  
Emanuele Pigani ◽  
Alain Barrat ◽  
...  
Keyword(s):  
Disease Outbreaks ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Modeling Framework ◽  
Effective Measure ◽  
Epidemic Dynamics ◽  
Contact Patterns ◽  
Contact Data ◽  
High Level ◽  
The Impact

Abstract Digital contact tracing is increasingly considered as a tool to control infectious disease outbreaks. As part of a broader test, trace, isolate, and quarantine strategy, digital contract tracing apps have been proposed to alleviate lock-downs, and to return societies to a more normal situation in the ongoing COVID-19 crisis. Early work evaluating digital contact tracing did not consider important features and heterogeneities present in real-world contact patterns which impact epidemic dynamics. Here, we fill this gap by considering a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing apps in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread of COVID-19 in realistic scenarios such as a university campus, a workplace, or a high school. We find that restrictive policies are more effective in confining the epidemics but come at the cost of quarantining a large part of the population. It is possible to avoid this effect by considering less strict policies, which only consider contacts with longer exposure and at shorter distance to be at risk. Our results also show that isolation and tracing can help keep re-emerging outbreaks under control provided that hygiene and social distancing measures limit the reproductive number to 1.5. Moreover, we confirm that a high level of app adoption is crucial to make digital contact tracing an effective measure. Our results may inform app-based contact tracing efforts currently being implemented across several countries worldwide.

scholarly journals The Novel Coronavirus, 2019-nCoV, is Highly Contagious and More Infectious Than Initially Estimated

2020 ◽  
Author(s):  
Steven Sanche ◽  
Yen Ting Lin ◽  
Chonggang Xu ◽  
Ethan Romero-Severson ◽  
Nick Hengartner ◽  
...  
Keyword(s):  
Case Reports ◽  
Individual Case ◽  
Control Measures ◽  
Basic Reproductive Number ◽  
Contact Tracing ◽  
Reproductive Number ◽  
The Novel ◽  
Transmission Potential ◽  
Novel Coronavirus ◽  
Strong Control

AbstractThe novel coronavirus (2019-nCoV) is a recently emerged human pathogen that has spread widely since January 2020. Initially, the basic reproductive number, R0, was estimated to be 2.2 to 2.7. Here we provide a new estimate of this quantity. We collected extensive individual case reports and estimated key epidemiology parameters, including the incubation period. Integrating these estimates and high-resolution real-time human travel and infection data with mathematical models, we estimated that the number of infected individuals during early epidemic double every 2.4 days, and the R0 value is likely to be between 4.7 and 6.6. We further show that quarantine and contact tracing of symptomatic individuals alone may not be effective and early, strong control measures are needed to stop transmission of the virus.One-sentence summaryBy collecting and analyzing spatiotemporal data, we estimated the transmission potential for 2019-nCoV.

scholarly journals MATHEMATICAL PREDICTIONS FOR COVID-19 AS A GLOBAL PANDEMIC

2020 ◽  
Author(s):  
Victor Alexander Okhuese
Keyword(s):  
Decision Makers ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Government Decision ◽  
Global Pandemic ◽  
Secondary Infections ◽  
Rate Of Recovery ◽  
Precautionary Measures ◽  
The Moment ◽  
Disease Free

ABSTRACTThis study shows that the disease free equilibrium (E0) for COVID-19 coronavirus does not satisfy the criteria for a locally or globally asymptotic stability. This implies that as a pandemic as declared by WHO (2020) the COVID-19 coronavirus does not have a curative vaccine yet and precautionary measures are advised through quarantine and observatory procedures. Also, the Basic Reproductive number (R0 < 1) by Equation (33) shows that there is a chance of decline of secondary infections when the ratio between the incidence rate in the population and the total number of infected population quarantined with observatory procedure.The effort to evaluate the disease equilibrium shows that unless there is a dedicated effort from government, decision makers and stakeholders, the world would hardly be reed of the COVID-19 coronavirus and further spread is eminent and the rate of infection will continue to increase despite the increased rate of recovery because of the absence of vaccine at the moment.

scholarly journals Infectious disease dynamics: what characterizes a successful invader?

2001 ◽  
Vol 356 (1410) ◽  
pp. 901-910 ◽  
Author(s):  
Robert M. May ◽  
Sunetra Gupta ◽  
Angela R. McLean
Keyword(s):  
Emerging Infectious Diseases ◽  
Infectious Agent ◽  
Immune Deficiency Syndrome ◽  
Deficiency Syndrome ◽  
Host Population ◽  
Hiv And Aids ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Species Barrier ◽  
Successful Invader

Against the background of human immunodeficiency virus (HIV) and acquired immune deficiency syndrome (AIDS) and other potentially emerging (or re–emerging) infectious diseases, this review will focus on the properties which enable an infectious agent to establish and maintain itself within a specified host population. We shall emphasize that for a pathogen to cross a species barrier is one thing, but for it successfully to maintain itself in the new population is must have a ‘basic reproductive number’, R 0 , which satisfies R 0 > 1. We shall further discuss how behavioural factors interweave with the basic biology of the production of transmission stages by the pathogen, all subject to possible secular changes, to determine the magnitude of R 0 . Although primarily focusing on HIV and AIDS, we shall review wider aspects of these questions.

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