scholarly journals How decision makers can use quantitative approaches to guide outbreak responses

2019 ◽  
Vol 374 (1776) ◽  
pp. 20180365 ◽  
Author(s):  
Oliver Morgan
Keyword(s):  
Infectious Disease ◽  
Disease Outbreaks ◽  
Decision Makers ◽  
Specific Response ◽  
Outbreak Response ◽  
Theme Issue ◽  
Infectious Disease Outbreaks ◽  
Response Planning ◽  
And Control ◽  
Quantitative Approaches

Decision makers are responsible for directing staffing, logistics, selecting public health interventions, communicating to professionals and the public, planning future response needs, and establishing strategic and tactical priorities along with their funding requirements. Decision makers need to rapidly synthesize data from different experts across multiple disciplines, bridge data gaps and translate epidemiological analysis into an operational set of decisions for disease control. Analytic approaches can be defined for specific response phases: investigation, scale-up and control. These approaches include: improved applications of quantitative methods to generate insightful epidemiological descriptions of outbreaks; robust investigations of causal agents and risk factors; tools to assess response needs; identifying and monitoring optimal interventions or combinations of interventions; and forecasting for response planning. Data science and quantitative approaches can improve decision-making in outbreak response. To realize these benefits, we need to develop a structured approach that will improve the quality and timeliness of data collected during outbreaks, establish analytic teams within the response structure and define a research agenda for data analytics in outbreak response. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

scholarly journals Context matters: using reinforcement learning to develop human-readable, state-dependent outbreak response policies

2019 ◽  
Vol 374 (1776) ◽  
pp. 20180277 ◽  
Author(s):  
W. J. M. Probert ◽  
S. Lakkur ◽  
C. J. Fonnesbeck ◽  
K. Shea ◽  
M. C. Runge ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Reinforcement Learning ◽  
Disease Outbreaks ◽  
Decision Makers ◽  
Outbreak Response ◽  
Theme Issue ◽  
Infectious Disease Outbreaks ◽  
Human Decision ◽  
Context Dependent ◽  
Machine Readable

The number of all possible epidemics of a given infectious disease that could occur on a given landscape is large for systems of real-world complexity. Furthermore, there is no guarantee that the control actions that are optimal, on average, over all possible epidemics are also best for each possible epidemic. Reinforcement learning (RL) and Monte Carlo control have been used to develop machine-readable context-dependent solutions for complex problems with many possible realizations ranging from video-games to the game of Go. RL could be a valuable tool to generate context-dependent policies for outbreak response, though translating the resulting policies into simple rules that can be read and interpreted by human decision-makers remains a challenge. Here we illustrate the application of RL to the development of context-dependent outbreak response policies to minimize outbreaks of foot-and-mouth disease. We show that control based on the resulting context-dependent policies, which adapt interventions to the specific outbreak, result in smaller outbreaks than static policies. We further illustrate two approaches for translating the complex machine-readable policies into simple heuristics that can be evaluated by human decision-makers. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

scholarly journals Rigorous surveillance is necessary for high confidence in end-of-outbreak declarations for Ebola and other infectious diseases

2019 ◽  
Vol 374 (1776) ◽  
pp. 20180431 ◽  
Author(s):  
Robin N. Thompson ◽  
Oliver W. Morgan ◽  
Katri Jalava
Keyword(s):  
Infectious Disease ◽  
Disease Outbreaks ◽  
Decision Makers ◽  
World Health ◽  
Theme Issue ◽  
Public Health Decision ◽  
Infectious Disease Outbreaks ◽  
Travel Restrictions ◽  
Health Decision ◽  
Health Organization

The World Health Organization considers an Ebola outbreak to have ended once 42 days have passed since the last possible exposure to a confirmed case. Benefits of a quick end-of-outbreak declaration, such as reductions in trade/travel restrictions, must be balanced against the chance of flare-ups from undetected residual cases. We show how epidemiological modelling can be used to estimate the surveillance level required for decision-makers to be confident that an outbreak is over. Results from a simple model characterizing an Ebola outbreak suggest that a surveillance sensitivity (i.e. case reporting percentage) of 79% is necessary for 95% confidence that an outbreak is over after 42 days without symptomatic cases. With weaker surveillance, unrecognized transmission may still occur: if the surveillance sensitivity is only 40%, then 62 days must be waited for 95% certainty. By quantifying the certainty in end-of-outbreak declarations, public health decision-makers can plan and communicate more effectively.This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This issue is linked with the earlier theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

scholarly journals Optimizing spatial and seasonal deployment of vaccination campaigns to eliminate wildlife rabies

2019 ◽  
Vol 374 (1776) ◽  
pp. 20180280 ◽  
Author(s):  
Laurie Baker ◽  
Jason Matthiopoulos ◽  
Thomas Müller ◽  
Conrad Freuling ◽  
Katie Hampson
Keyword(s):  
Infectious Disease ◽  
Plant Pathogens ◽  
Herd Immunity ◽  
Disease Outbreaks ◽  
Vaccination Strategy ◽  
Theme Issue ◽  
Infectious Disease Outbreaks ◽  
Control Programmes ◽  
The Face ◽  
And Control

Understanding how the spatial deployment of interventions affects elimination time horizons and potential for disease re-emergence has broad application to control programmes targeting human, animal and plant pathogens. We previously developed an epidemiological model that captures the main features of rabies spread and the impacts of vaccination based on detailed records of fox rabies in eastern Germany during the implementation of an oral rabies vaccination (ORV) programme. Here, we use simulations from this fitted model to determine the best vaccination strategy, in terms of spatial placement and timing of ORV efforts, for three epidemiological scenarios representative of current situations in Europe. We found that consecutive and comprehensive twice-yearly vaccinations across all regions rapidly controlled and eliminated rabies and that the autumn campaigns had the greater impact on increasing the probability of elimination. This appears to result from the need to maintain sufficient herd immunity in the face of large birth pulses, as autumn vaccinations reach susceptible juveniles and therefore a larger proportion of the population than spring vaccinations. Incomplete vaccination compromised time to elimination requiring the same or more vaccination effort to meet similar timelines. Our results have important practical implications that could inform policies for rabies containment and elimination in Europe and elsewhere. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

scholarly journals Perfect counterfactuals for epidemic simulations

2019 ◽  
Vol 374 (1776) ◽  
pp. 20180279 ◽  
Author(s):  
Joshua Kaminsky ◽  
Lindsay T. Keegan ◽  
C. Jessica E. Metcalf ◽  
Justin Lessler
Keyword(s):  
Infectious Disease ◽  
Disease Outbreaks ◽  
Computation Time ◽  
Control Measures ◽  
Mathematical Representation ◽  
Theme Issue ◽  
Stochastic Variation ◽  
Infectious Disease Outbreaks ◽  
Negative Effect ◽  
And Control

Simulation studies are often used to predict the expected impact of control measures in infectious disease outbreaks. Typically, two independent sets of simulations are conducted, one with the intervention, and one without, and epidemic sizes (or some related metric) are compared to estimate the effect of the intervention. Since it is possible that controlled epidemics are larger than uncontrolled ones if there is substantial stochastic variation between epidemics, uncertainty intervals from this approach can include a negative effect even for an effective intervention. To more precisely estimate the number of cases an intervention will prevent within a single epidemic, here we develop a ‘single-world’ approach to matching simulations of controlled epidemics to their exact uncontrolled counterfactual. Our method borrows concepts from percolation approaches, prunes out possible epidemic histories and creates potential epidemic graphs (i.e. a mathematical representation of all consistent epidemics) that can be ‘realized’ to create perfectly matched controlled and uncontrolled epidemics. We present an implementation of this method for a common class of compartmental models (e.g. SIR models), and its application in a simple SIR model. Results illustrate how, at the cost of some computation time, this method substantially narrows confidence intervals and avoids nonsensical inferences. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

scholarly journals Increased frequency of travel in the presence of cross-immunity may act to decrease the chance of a global pandemic

2019 ◽  
Vol 374 (1775) ◽  
pp. 20180274 ◽  
Author(s):  
R. N. Thompson ◽  
C. P. Thompson ◽  
O. Pelerman ◽  
S. Gupta ◽  
U. Obolski
Keyword(s):  
Infectious Disease ◽  
Influenza Pandemic ◽  
Disease Outbreaks ◽  
Theme Issue ◽  
Infectious Disease Outbreaks ◽  
Global Pandemic ◽  
Cross Immunity ◽  
1918 Influenza ◽  
And Control ◽  
Epidemic Forecasting

The high frequency of modern travel has led to concerns about a devastating pandemic since a lethal pathogen strain could spread worldwide quickly. Many historical pandemics have arisen following pathogen evolution to a more virulent form. However, some pathogen strains invoke immune responses that provide partial cross-immunity against infection with related strains. Here, we consider a mathematical model of successive outbreaks of two strains—a low virulence (LV) strain outbreak followed by a high virulence (HV) strain outbreak. Under these circumstances, we investigate the impacts of varying travel rates and cross-immunity on the probability that a major epidemic of the HV strain occurs, and the size of that outbreak. Frequent travel between subpopulations can lead to widespread immunity to the HV strain, driven by exposure to the LV strain. As a result, major epidemics of the HV strain are less likely, and can potentially be smaller, with more connected subpopulations. Cross-immunity may be a factor contributing to the absence of a global pandemic as severe as the 1918 influenza pandemic in the century since. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.

scholarly journals Outbreak analytics: a developing data science for informing the response to emerging pathogens

2019 ◽  
Vol 374 (1776) ◽  
pp. 20180276 ◽  
Author(s):  
Jonathan A. Polonsky ◽  
Amrish Baidjoe ◽  
Zhian N. Kamvar ◽  
Anne Cori ◽  
Kara Durski ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Data Science ◽  
Disease Outbreaks ◽  
Health Concern ◽  
Emerging Pathogens ◽  
Outbreak Response ◽  
Theme Issue ◽  
Effective Response ◽  
Infectious Disease Outbreaks ◽  
Challenges And Opportunities

Despite continued efforts to improve health systems worldwide, emerging pathogen epidemics remain a major public health concern. Effective response to such outbreaks relies on timely intervention, ideally informed by all available sources of data. The collection, visualization and analysis of outbreak data are becoming increasingly complex, owing to the diversity in types of data, questions and available methods to address them. Recent advances have led to the rise of outbreak analytics , an emerging data science focused on the technological and methodological aspects of the outbreak data pipeline, from collection to analysis, modelling and reporting to inform outbreak response. In this article, we assess the current state of the field. After laying out the context of outbreak response, we critically review the most common analytics components, their inter-dependencies, data requirements and the type of information they can provide to inform operations in real time. We discuss some challenges and opportunities and conclude on the potential role of outbreak analytics for improving our understanding of, and response to outbreaks of emerging pathogens. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control‘. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

scholarly journals Critical transitions in malaria transmission models are consistently generated by superinfection

2019 ◽  
Vol 374 (1775) ◽  
pp. 20180275 ◽  
Author(s):  
David Alonso ◽  
Andy Dobson ◽  
Mercedes Pascual
Keyword(s):  
Infectious Disease ◽  
Disease Outbreaks ◽  
Distribution Patterns ◽  
Past Century ◽  
Theme Issue ◽  
Vector Borne Diseases ◽  
Infectious Disease Outbreaks ◽  
Critical Transitions ◽  
Vector Borne ◽  
And Control

The history of modelling vector-borne infections essentially begins with the papers by Ross on malaria. His models assume that the dynamics of malaria can most simply be characterized by two equations that describe the prevalence of malaria in the human and mosquito hosts. This structure has formed the central core of models for malaria and most other vector-borne diseases for the past century, with additions acknowledging important aetiological details. We partially add to this tradition by describing a malaria model that provides for vital dynamics in the vector and the possibility of super-infection in the human host: reinfection of asymptomatic hosts before they have cleared a prior infection. These key features of malaria aetiology create the potential for break points in the prevalence of infected hosts, sudden transitions that seem to characterize malaria’s response to control in different locations. We show that this potential for critical transitions is a general and underappreciated feature of any model for vector-borne diseases with incomplete immunity, including the canonical Ross–McDonald model. Ignoring these details of the host’s immune response to infection can potentially lead to serious misunderstanding in the interpretation of malaria distribution patterns and the design of control schemes for other vector-borne diseases.This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.

scholarly journals Analysing livestock network data for infectious disease control: an argument for routine data collection in emerging economies

2019 ◽  
Vol 374 (1776) ◽  
pp. 20180264 ◽  
Author(s):  
G. L. Chaters ◽  
P. C. D. Johnson ◽  
S. Cleaveland ◽  
J. Crispell ◽  
W. A. de Glanville ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Disease Control ◽  
Disease Transmission ◽  
Disease Outbreaks ◽  
Routine Data ◽  
Contact Layer ◽  
Theme Issue ◽  
Targeted Interventions ◽  
Infectious Disease Outbreaks ◽  
And Control

Livestock movements are an important mechanism of infectious disease transmission. Where these are well recorded, network analysis tools have been used to successfully identify system properties, highlight vulnerabilities to transmission, and inform targeted surveillance and control. Here we highlight the main uses of network properties in understanding livestock disease epidemiology and discuss statistical approaches to infer network characteristics from biased or fragmented datasets. We use a ‘hurdle model’ approach that predicts (i) the probability of movement and (ii) the number of livestock moved to generate synthetic ‘complete’ networks of movements between administrative wards, exploiting routinely collected government movement permit data from northern Tanzania. We demonstrate that this model captures a significant amount of the observed variation. Combining the cattle movement network with a spatial between-ward contact layer, we create a multiplex, over which we simulated the spread of ‘fast’ ( R 0 = 3) and ‘slow’ ( R 0 = 1.5) pathogens, and assess the effects of random versus targeted disease control interventions (vaccination and movement ban). The targeted interventions substantially outperform those randomly implemented for both fast and slow pathogens. Our findings provide motivation to encourage routine collection and centralization of movement data to construct representative networks. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

scholarly journals Detection, forecasting and control of infectious disease epidemics: modelling outbreaks in humans, animals and plants

2019 ◽  
Vol 374 (1775) ◽  
pp. 20190038 ◽  
Author(s):  
Robin N. Thompson ◽  
Ellen Brooks-Pollock
Keyword(s):  
Infectious Disease ◽  
Decision Making ◽  
Plant Disease ◽  
Influenza Pandemic ◽  
Disease Outbreaks ◽  
Theme Issue ◽  
Infectious Disease Outbreaks ◽  
Human Animal ◽  
Disease Epidemics ◽  
And Control

The 1918 influenza pandemic is one of the most devastating infectious disease epidemics on record, having caused approximately 50 million deaths worldwide. Control measures, including prohibiting non-essential gatherings as well as closing cinemas and music halls, were applied with varying success and limited knowledge of transmission dynamics. One hundred years later, following developments in the field of mathematical epidemiology, models are increasingly used to guide decision-making and devise appropriate interventions that mitigate the impacts of epidemics. Epidemiological models have been used as decision-making tools during outbreaks in human, animal and plant populations. However, as the subject has developed, human, animal and plant disease modelling have diverged. Approaches have been developed independently for pathogens of each host type, often despite similarities between the models used in these complementary fields. With the increased importance of a One Health approach that unifies human, animal and plant health, we argue that more inter-disciplinary collaboration would enhance each of the related disciplines. This pair of theme issues presents research articles written by human, animal and plant disease modellers. In this introductory article, we compare the questions pertinent to, and approaches used by, epidemiological modellers of human, animal and plant pathogens, and summarize the articles in these theme issues. We encourage future collaboration that transcends disciplinary boundaries and links the closely related areas of human, animal and plant disease epidemic modelling. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.

scholarly journals Using models to provide rapid programme support for California's efforts to suppress Huanglongbing disease of citrus

2019 ◽  
Vol 374 (1776) ◽  
pp. 20180281 ◽  
Author(s):  
Neil McRoberts ◽  
Sara Garcia Figuera ◽  
Sandra Olkowski ◽  
Brianna McGuire ◽  
Weiqi Luo ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Disease Outbreaks ◽  
Theme Issue ◽  
Citrus Industry ◽  
California Department ◽  
Considerable Uncertainty ◽  
Infectious Disease Outbreaks ◽  
Food And Agriculture ◽  
Quantitative Analyses ◽  
And Control

We describe a series of operational questions posed during the state-wide response in California to the arrival of the invasive citrus disease Huanglongbing. The response is coordinated by an elected committee from the citrus industry and operates in collaboration with the California Department of Food and Agriculture, which gives it regulatory authority to enforce the removal of infected trees. The paper reviews how surveillance for disease and resource allocation between detection and delimitation have been addressed, based on epidemiological principles. In addition, we describe how epidemiological analyses have been used to support rule-making to enact costly but beneficial regulations and we highlight two recurring themes in the programme support work: (i) data are often insufficient for quantitative analyses of questions and (ii) modellers and decision-makers alike may be forced to accept the need to make decisions on the basis of simple or incomplete analyses that are subject to considerable uncertainty. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

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