scholarly journals The Three Ts of Pathogen Evolution During Zoonotic Emergence

2021 ◽  
Author(s):  
Elisa Visher ◽  
Claire Evensen ◽  
Sarah Guth ◽  
Edith Lai ◽  
Marina Norfolk ◽  
...  
Keyword(s):  
Time Scales ◽  
Time Scale ◽  
Zoonotic Diseases ◽  
Control Measures ◽  
Effective Control ◽  
Human Populations ◽  
Selection Pressures ◽  
Trade Off ◽  
Pathogen Evolution ◽  
Trade Offs

When novel zoonotic diseases like Sars-CoV-2 emerge, they are likely to be poorly adapted to humans. Effective control measures will suppress transmission before significant evolution can occur, but extended transmission in human populations allows time for selection pressures to act. In this review, we discuss these selection pressures with the aim of better understanding the factors shaping both transmission and virulence in zoonotic pathogens as they become established. We discuss how selection pressures during epidemics of emerging zoonotic disease are determined by the three Ts: trade-offs, transmission, and time scales. In short, virulence and transmission may trade-off, but transmission is likely to be favored by selection early in emergence. However, the relative selection pressures on transmission and virulence shift depending on the time scale of the epidemic. Predicting pathogen evolution in zoonoses therefore depends critically on understanding both the trade-offs of transmission-improving mutations and the time scales of selection.

scholarly journals The three Ts of virulence evolution during zoonotic emergence

2021 ◽  
Vol 288 (1956) ◽  
pp. 20210900
Author(s):  
Elisa Visher ◽  
Claire Evensen ◽  
Sarah Guth ◽  
Edith Lai ◽  
Marina Norfolk ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Time Scales ◽  
Evolutionary Theory ◽  
Transmission Rate ◽  
Trade Off ◽  
Transmission Rates ◽  
Zoonotic Pathogens ◽  
Virulence Evolution ◽  
Trade Offs ◽  
Considerable Confusion

There is increasing interest in the role that evolution may play in current and future pandemics, but there is often also considerable confusion about the actual evolutionary predictions. This may be, in part, due to a historical separation of evolutionary and medical fields, but there is a large, somewhat nuanced body of evidence-supported theory on the evolution of infectious disease. In this review, we synthesize this evolutionary theory in order to provide a framework for clearer understanding of the key principles. Specifically, we discuss the selection acting on zoonotic pathogens' transmission rates and virulence at spillover and during emergence. We explain how the direction and strength of selection during epidemics of emerging zoonotic disease can be understood by a three Ts framework: trade-offs, transmission, and time scales. Virulence and transmission rate may trade-off, but transmission rate is likely to be favoured by selection early in emergence, particularly if maladapted zoonotic pathogens have ‘no-cost’ transmission rate improving mutations available to them. Additionally, the optimal virulence and transmission rates can shift with the time scale of the epidemic. Predicting pathogen evolution, therefore, depends on understanding both the trade-offs of transmission-improving mutations and the time scales of selection.

scholarly journals Stochastic SIS Modelling: Coinfection of Two Pathogens in Two-Host Communities

Entropy ◽  
2019 ◽  
Vol 22 (1) ◽  
pp. 54 ◽  
Author(s):  
Auwal Abdullahi ◽  
Shamarina Shohaimi ◽  
Adem Kilicman ◽  
Mohd Hafiz Ibrahim ◽  
Nader Salari
Keyword(s):  
Branching Process ◽  
Zoonotic Diseases ◽  
Control Measures ◽  
Effective Control ◽  
Initial Population ◽  
Continuous Time Markov Chain ◽  
Reproduction Numbers ◽  
Single Host ◽  
The Mean ◽  
Probability Of Extinction

A pathogen can infect multiple hosts. For example, zoonotic diseases like rabies often colonize both humans and animals. Meanwhile, a single host can sometimes be infected with many pathogens, such as malaria and meningitis. Therefore, we studied two susceptible classes S 1 ( t ) and S 2 ( t ) , each of which can be infected when interacting with two different infectious groups I 1 ( t ) and I 2 ( t ) . The stochastic models were formulated through the continuous time Markov chain (CTMC) along with their deterministic analogues. The statistics for the developed model were studied using the multi-type branching process. Since each epidemic class was assumed to transmit only its own type of pathogen, two reproduction numbers were obtained, in addition to the probability-generating functions of offspring. Thus, these, together with the mean number of infections, were used to estimate the probability of extinction. The initial population of infectious classes can influence their probability of extinction. Understanding the disease extinctions and outbreaks could result in rapid intervention by the management for effective control measures.

scholarly journals Understanding eco-immunology of bacterial zoonoses and alternative therapeutics toward "One Health"

2021 ◽  
Vol 7 (1) ◽  
pp. 104-115
Author(s):  
Manisha Behera ◽  
Soma Mondal Ghorai ◽  
Sachinandan De ◽  
Hardeep Kaur
Keyword(s):  
Anthropogenic Activities ◽  
Zoonotic Diseases ◽  
Control Measures ◽  
Fine Tuning ◽  
Disease Emergence ◽  
Host Pathogen Interactions ◽  
Trade Offs ◽  
Host Pathogen ◽  
Comparative Immunity ◽  
And Control

The current review identifies key bacterial zoonoses, the understanding of comparative immunology, evolutionary trade-offs between emerging bacterial pathogens and their dynamics on both arms of immunity. The several gaps in the literature limit our understanding of spread of prominent bacterial zoonotic diseases and the host-pathogen interactions that may change in response to environmental and social factors. Gaining a more comprehensive understanding of how anthropogenic activities affects the spread of emerging zoonotic diseases, is essential for predicting and mitigating future disease emergence through fine-tuning of surveillance and control measures with respect to different pathogens. This review highlights the urgent need to increase understanding of the comparative immunity of animal reservoirs, design of vaccines according to the homology in host-pathogen interactions, and the alternative strategies to counter the risk of bacterial pathogenic spillover to humans with eventual spread of zoonotic diseases.

scholarly journals A Mathematical Model with Quarantine States for the Dynamics of Ebola Virus Disease in Human Populations

2016 ◽  
Vol 2016 ◽  
pp. 1-29 ◽  
Author(s):  
Gideon A. Ngwa ◽  
Miranda I. Teboh-Ewungkem
Keyword(s):  
Ebola Virus ◽  
Emergency Services ◽  
Reproduction Number ◽  
Virus Disease ◽  
Initial Response ◽  
Control Measures ◽  
Ebola Virus Disease ◽  
Effective Control ◽  
Human Populations ◽  
Threshold Parameter

A deterministic ordinary differential equation model for the dynamics and spread of Ebola Virus Disease is derived and studied. The model contains quarantine and nonquarantine states and can be used to evaluate transmission both in treatment centres and in the community. Possible sources of exposure to infection, including cadavers of Ebola Virus victims, are included in the model derivation and analysis. Our model’s results show that there exists a threshold parameter,R0, with the property that when its value is above unity, an endemic equilibrium exists whose value and size are determined by the size of this threshold parameter, and when its value is less than unity, the infection does not spread into the community. The equilibrium state, when it exists, is locally and asymptotically stable with oscillatory returns to the equilibrium point. The basic reproduction number,R0, is shown to be strongly dependent on the initial response of the emergency services to suspected cases of Ebola infection. When intervention measures such as quarantining are instituted fully at the beginning, the value of the reproduction number reduces and any further infections can only occur at the treatment centres. Effective control measures, to reduceR0to values below unity, are discussed.

Dual Goals for Speed and Accuracy on the Same Performance Task

2012 ◽  
Vol 11 (3) ◽  
pp. 118-126 ◽  
Author(s):  
Olive Emil Wetter ◽  
Jürgen Wegge ◽  
Klaus Jonas ◽  
Klaus-Helmut Schmidt
Keyword(s):  
Memory Scanning ◽  
Performance Task ◽  
Performance Tasks ◽  
Trade Off ◽  
Test Experiment ◽  
Trade Offs ◽  
New Finding ◽  
Sternberg Paradigm ◽  
Speed Accuracy ◽  
Speed And Accuracy

In most work contexts, several performance goals coexist, and conflicts between them and trade-offs can occur. Our paper is the first to contrast a dual goal for speed and accuracy with a single goal for speed on the same task. The Sternberg paradigm (Experiment 1, n = 57) and the d2 test (Experiment 2, n = 19) were used as performance tasks. Speed measures and errors revealed in both experiments that dual as well as single goals increase performance by enhancing memory scanning. However, the single speed goal triggered a speed-accuracy trade-off, favoring speed over accuracy, whereas this was not the case with the dual goal. In difficult trials, dual goals slowed down scanning processes again so that errors could be prevented. This new finding is particularly relevant for security domains, where both aspects have to be managed simultaneously.

The Effects of Decision Time on Perceptions of Decisions and Decision Makers in (Moral) Trade-Off Scenarios

2019 ◽  
Author(s):  
Anna Katharina Spälti ◽  
Mark John Brandt ◽  
Marcel Zeelenberg
Keyword(s):  
Decision Process ◽  
Decision Time ◽  
Decision Makers ◽  
Process Information ◽  
Total N ◽  
Trade Off ◽  
Financial Gain ◽  
Trade Offs ◽  
Sacred Values ◽  
Moral Context

People often have to make trade-offs. We study three types of trade-offs: 1) "secular trade-offs" where no moral or sacred values are at stake, 2) "taboo trade-offs" where sacred values are pitted against financial gain, and 3) "tragic trade-offs" where sacred values are pitted against other sacred values. Previous research (Critcher et al., 2011; Tetlock et al., 2000) demonstrated that tragic and taboo trade-offs are not only evaluated by their outcomes, but are also evaluated based on the time it took to make the choice. We investigate two outstanding questions: 1) whether the effect of decision time differs for evaluations of decisions compared to decision makers and 2) whether moral contexts are unique in their ability to influence character evaluations through decision process information. In two experiments (total N = 1434) we find that decision time affects character evaluations, but not evaluations of the decision itself. There were no significant differences between tragic trade-offs and secular trade-offs, suggesting that the decisions structure may be more important in evaluations than moral context. Additionally, the magnitude of the effect of decision time shows us that decision time, may be of less practical use than expected. We thus urge, to take a closer examination of the processes underlying decision time and its perception.

Predicting Undesired Treatment Outcome in Mental Healthcare: Machine Learning Study (Preprint)

2019 ◽  
Author(s):  
Kasper Van Mens ◽  
Joran Lokkerbol ◽  
Richard Janssen ◽  
Robert de Lange ◽  
Bea Tiemens
Keyword(s):  
Machine Learning ◽  
Treatment Outcome ◽  
Mental Health Treatment ◽  
Mental Healthcare ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Trade Off ◽  
Trade Offs ◽  
Outcome Monitoring ◽  
Extreme Gradient Boosting

BACKGROUND It remains a challenge to predict which treatment will work for which patient in mental healthcare. OBJECTIVE In this study we compare machine algorithms to predict during treatment which patients will not benefit from brief mental health treatment and present trade-offs that must be considered before an algorithm can be used in clinical practice. METHODS Using an anonymized dataset containing routine outcome monitoring data from a mental healthcare organization in the Netherlands (n = 2,655), we applied three machine learning algorithms to predict treatment outcome. The algorithms were internally validated with cross-validation on a training sample (n = 1,860) and externally validated on an unseen test sample (n = 795). RESULTS The performance of the three algorithms did not significantly differ on the test set. With a default classification cut-off at 0.5 predicted probability, the extreme gradient boosting algorithm showed the highest positive predictive value (ppv) of 0.71(0.61 – 0.77) with a sensitivity of 0.35 (0.29 – 0.41) and area under the curve of 0.78. A trade-off can be made between ppv and sensitivity by choosing different cut-off probabilities. With a cut-off at 0.63, the ppv increased to 0.87 and the sensitivity dropped to 0.17. With a cut-off of at 0.38, the ppv decreased to 0.61 and the sensitivity increased to 0.57. CONCLUSIONS Machine learning can be used to predict treatment outcomes based on routine monitoring data.This allows practitioners to choose their own trade-off between being selective and more certain versus inclusive and less certain.

Characterizing the effectiveness of COVID-19 mitigation measures: A data-centric approach (Preprint)

2020 ◽  
Author(s):  
Lukman Olagoke ◽  
Ahmet E. Topcu
Keyword(s):  
Control Measure ◽  
Control Measures ◽  
World Health ◽  
Mitigation Measures ◽  
Effective Control ◽  
The World ◽  
Decision Making Processes ◽  
And Performance ◽  
Health Organization ◽  
The Impact

BACKGROUND COVID-19 represents a serious threat to both national health and economic systems. To curb this pandemic, the World Health Organization (WHO) issued a series of COVID-19 public safety guidelines. Different countries around the world initiated different measures in line with the WHO guidelines to mitigate and investigate the spread of COVID-19 in their territories. OBJECTIVE The aim of this paper is to quantitatively evaluate the effectiveness of these control measures using a data-centric approach. METHODS We begin with a simple text analysis of coronavirus-related articles and show that reports on similar outbreaks in the past strongly proposed similar control measures. This reaffirms the fact that these control measures are in order. Subsequently, we propose a simple performance statistic that quantifies general performance and performance under the different measures that were initiated. A density based clustering of based on performance statistic was carried out to group countries based on performance. RESULTS The performance statistic helps evaluate quantitatively the impact of COVID-19 control measures. Countries tend show variability in performance under different control measures. The performance statistic has negative correlation with cases of death which is a useful characteristics for COVID-19 control measure performance analysis. A web-based time-line visualization that enables comparison of performances and cases across continents and subregions is presented. CONCLUSIONS The performance metric is relevant for the analysis of the impact of COVID-19 control measures. This can help caregivers and policymakers identify effective control measures and reduce cases of death due to COVID-19. The interactive web visualizer provides easily digested and quick feedback to augment decision-making processes in the COVID-19 response measures evaluation. CLINICALTRIAL Not Applicable

Challenges in the Empirical Study of Global Governance Legitimacy

2018 ◽  
Author(s):  
Steven Bernstein
Keyword(s):  
Empirical Study ◽  
Global Governance ◽  
Research Agenda ◽  
Cultural Factors ◽  
Theory Building ◽  
Trade Off ◽  
Trade Offs ◽  
Different Types ◽  
Political Communities

This commentary discusses three challenges for the promising and ambitious research agenda outlined in the volume. First, it interrogates the volume’s attempts to differentiate political communities of legitimation, which may vary widely in composition, power, and relevance across institutions and geographies, with important implications not only for who matters, but also for what gets legitimated, and with what consequences. Second, it examines avenues to overcome possible trade-offs from gains in empirical tractability achieved through the volume’s focus on actor beliefs and strategies. One such trade-off is less attention to evolving norms and cultural factors that may underpin actors’ expectations about what legitimacy requires. Third, it addresses the challenge of theory building that can link legitimacy sources, (de)legitimation practices, audiences, and consequences of legitimacy across different types of institutions.

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