scholarly journals Predicting plant disease epidemics from functionally represented weather series

2019 ◽  
Vol 374 (1775) ◽  
pp. 20180273 ◽  
Author(s):  
D. A. Shah ◽  
P. A. Paul ◽  
E. D. De Wolf ◽  
L. V. Madden
Keyword(s):  
Infectious Disease ◽  
Time Series ◽  
Disease Risk ◽  
Search Algorithm ◽  
Disease Outbreaks ◽  
Plant Diseases ◽  
Theme Issue ◽  
Weather Patterns ◽  
Infectious Disease Outbreaks ◽  
Disease Epidemics

Epidemics are often triggered by specific weather patterns favouring the pathogen on susceptible hosts. For plant diseases, models predicting epidemics have therefore often emphasized the identification of early season weather patterns that are correlated with a disease outcome at some later point. Toward that end, window-pane analysis is an exhaustive search algorithm traditionally used in plant pathology for mining correlations in a weather series with respect to a disease endpoint. Here we show, with reference to Fusarium head blight (FHB) of wheat, that a functional approach is a more principled analytical method for understanding the relationship between disease epidemics and environmental conditions over an extended time series. We used scalar-on-function regression to model a binary outcome (FHB epidemic or non-epidemic) relative to weather time series spanning 140 days relative to flowering (when FHB infection primarily occurs). The functional models overall fit the data better than previously described standard logistic regression (lr) models. Periods much earlier than heretofore realized were associated with FHB epidemics. The findings were used to create novel weather summary variables which, when incorporated into lr models, yielded a new set of models that performed as well as existing lr models for real-time predictions of disease risk. 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 Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases

2019 ◽  
Vol 374 (1775) ◽  
pp. 20180258 ◽  
Author(s):  
M. Alamil ◽  
J. Hughes ◽  
K. Berthier ◽  
C. Desbiez ◽  
G. Thébaud ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Statistical Learning ◽  
Sequence Data ◽  
Disease Outbreaks ◽  
Plant Diseases ◽  
Training Data ◽  
Contact Tracing ◽  
Sequencing Data ◽  
Theme Issue ◽  
Infectious Disease Outbreaks

Pathogen sequence data have been exploited to infer who infected whom, by using empirical and model-based approaches. Most of these approaches exploit one pathogen sequence per infected host (e.g. individual, household, field). However, modern sequencing techniques can reveal the polymorphic nature of within-host populations of pathogens. Thus, these techniques provide a subsample of the pathogen variants that were present in the host at the sampling time. Such data are expected to give more insight on epidemiological links than a single sequence per host. In general, a mechanistic viewpoint to transmission and micro-evolution has been followed to infer epidemiological links from these data. Here, we investigate an alternative approach grounded on statistical learning. The idea consists of learning the structure of epidemiological links with a pseudo-evolutionary model applied to training data obtained from contact tracing, for example, and using this initial stage to infer links for the whole dataset. Such an approach has the potential to be particularly valuable in the case of a risk of erroneous mechanistic assumptions, it is sufficiently parsimonious to allow the handling of big datasets in the future, and it is versatile enough to be applied to very different contexts from animal, human and plant epidemiology. 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 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 A new mechanistic model of weather-dependent Septoria tritici blotch disease risk

2019 ◽  
Vol 374 (1775) ◽  
pp. 20180266 ◽  
Author(s):  
Thomas M. Chaloner ◽  
Helen N. Fones ◽  
Varun Varma ◽  
Daniel P. Bebber ◽  
Sarah J. Gurr
Keyword(s):  
Infectious Disease ◽  
Disease Risk ◽  
Mechanistic Model ◽  
Disease Outbreaks ◽  
Septoria Tritici Blotch ◽  
Septoria Tritici ◽  
Climate Data ◽  
Theme Issue ◽  
Zymoseptoria Tritici ◽  
Infectious Disease Outbreaks

We present a new mechanistic model for predicting Septoria tritici blotch (STB) disease, parameterized with experimentally derived data for temperature- and wetness-dependent germination, growth and death of the causal agent, Zymoseptoria tritici . The output of this model (A) was compared with observed disease data for UK wheat over the period 2002–2016. In addition, we compared the output of a second model (B), in which experimentally derived parameters were replaced by a modified version of a published Z. tritici thermal performance equation, with the same observed disease data. Neither model predicted observed annual disease, but model A was able to differentiate UK regions with differing average disease risks over the entire period. The greatest limitations of both models are: broad spatial resolution of the climate data, and lack of host parameters. Model B is further limited by its lack of explicitly defined pathogen death, leading to a cumulative overestimation of disease over the course of the growing season. Comparison of models A and B demonstrates the importance of accounting for the temperature-dependency of pathogen processes important in the initiation and progression of disease. However, effective modelling of STB will probably require similar experimentally derived parameters for host and environmental factors, completing the disease triangle. 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 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 Translating surveillance data into incidence estimates

2019 ◽  
Vol 374 (1776) ◽  
pp. 20180262 ◽  
Author(s):  
Y. Bourhis ◽  
T. Gottwald ◽  
F. van den Bosch
Keyword(s):  
Infectious Disease ◽  
Disease Incidence ◽  
Disease Outbreaks ◽  
Estimation Methods ◽  
Estimation Model ◽  
Theme Issue ◽  
Online Application ◽  
Infectious Disease Outbreaks ◽  
Sampling Series ◽  
Wide Range

Monitoring a population for a disease requires the hosts to be sampled and tested for the pathogen. This results in sampling series from which we may estimate the disease incidence, i.e. the proportion of hosts infected. Existing estimation methods assume that disease incidence does not change between monitoring rounds, resulting in an underestimation of the disease incidence. In this paper, we develop an incidence estimation model accounting for epidemic growth with monitoring rounds that sample varying incidence. We also show how to accommodate the asymptomatic period that is the characteristic of most diseases. For practical use, we produce an approximation of the model, which is subsequently shown to be accurate for relevant epidemic and sampling parameters. Both the approximation and the full model are applied to stochastic spatial simulations of epidemics. The results prove their consistency for a very wide range of situations. The estimation model is made available as an online application. 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 Adequacy of SEIR models when epidemics have spatial structure: Ebola in Sierra Leone

2019 ◽  
Vol 374 (1775) ◽  
pp. 20180282 ◽  
Author(s):  
Wayne M. Getz ◽  
Richard Salter ◽  
Whitney Mgbara
Keyword(s):  
Infectious Disease ◽  
Transmission Function ◽  
Disease Outbreaks ◽  
District Level ◽  
Transmission Model ◽  
Theme Issue ◽  
Infectious Disease Outbreaks ◽  
Country Level ◽  
Level Data ◽  
Populations At Risk

Dynamic SEIR (Susceptible, Exposed, Infectious, Removed) compartmental models provide a tool for predicting the size and duration of both unfettered and managed outbreaks—the latter in the context of interventions such as case detection, patient isolation, vaccination and treatment. The reliability of this tool depends on the validity of key assumptions that include homogeneity of individuals and spatio-temporal homogeneity. Although the SEIR compartmental framework can easily be extended to include demographic (e.g. age) and additional disease (e.g. healthcare workers) classes, dependence of transmission rates on time, and metapopulation structure, fitting such extended models is hampered by both a proliferation of free parameters and insufficient or inappropriate data. This raises the question of how effective a tool the basic SEIR framework may actually be. We go some way here to answering this question in the context of the 2014–2015 outbreak of Ebola in West Africa by comparing fits of an SEIR time-dependent transmission model to both country- and district-level weekly incidence data. Our novel approach in estimating the effective-size-of-the-populations-at-risk ( N eff ) and initial number of exposed individuals ( E 0 ) at both district and country levels, as well as the transmission function parameters, including a time-to-halving-the-force-of-infection ( t f/2 ) parameter, provides new insights into this Ebola outbreak. It reveals that the estimate R 0 ≈ 1.7 from country-level data appears to seriously underestimate R 0 ≈ 3.3 − 4.3 obtained from more spatially homogeneous district-level data. Country-level data also overestimate t f/2 ≈ 22 weeks, compared with 8–10 weeks from district-level data. Additionally, estimates for the duration of individual infectiousness is around two weeks from spatially inhomogeneous country-level data compared with 2.4–4.5 weeks from spatially more homogeneous district-level data, which estimates are rather high compared with most values reported in the literature. 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 The accelerated infectious disease risk in the Anthropocene: more outbreaks and wider global spread

2020 ◽  
Author(s):  
Serge Morand ◽  
Bruno A. Walther
Keyword(s):  
Infectious Disease ◽  
Economic Growth ◽  
Disease Risk ◽  
Disease Outbreaks ◽  
Well Being ◽  
Infectious Disease Outbreaks ◽  
Disease Networks ◽  
Increased Risk ◽  
Climate Crisis ◽  
Human Infectious Disease

The greatly accelerated economic growth during the Anthropocene has resulted in astonishing improvements in many aspects of human well-being, but has also caused the acceleration of risks, such as the interlinked biodiversity and climate crisis. Here, we report on another risk: the accelerated infectious disease risk associated with the number and geographic spread of human infectious disease outbreaks. Using the most complete, reliable, and up-to-date database on human infectious disease outbreaks (GIDEON), we show that the number of disease outbreaks, the number of diseases involved in these outbreaks, and the number of countries affected have increased during the entire Anthropocene. Furthermore, the spatial distribution of these outbreaks is becoming more globalized in the sense that the overall modularity of the disease networks across the globe has decreased, meaning disease outbreaks have become increasingly pandemic in their nature. This decrease in modularity is correlated with the increase in air traffic. We finally show that those countries and regions which are most central within these disease networks tend to be countries with higher GDPs. Therefore, one cost of increased global mobility and greater economic growth is the increased risk of disease outbreaks and their faster and wider spread. We briefly discuss three different scenarios which decision-makers might follow in light of our results.

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’.

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