1
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Chardès V, Mazzolini A, Mora T, Walczak AM. Evolutionary stability of antigenically escaping viruses. Proc Natl Acad Sci U S A 2023; 120:e2307712120. [PMID: 37871216 PMCID: PMC10622963 DOI: 10.1073/pnas.2307712120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 08/24/2023] [Indexed: 10/25/2023] Open
Abstract
Antigenic variation is the main immune escape mechanism for RNA viruses like influenza or SARS-CoV-2. While high mutation rates promote antigenic escape, they also induce large mutational loads and reduced fitness. It remains unclear how this cost-benefit trade-off selects the mutation rate of viruses. Using a traveling wave model for the coevolution of viruses and host immune systems in a finite population, we investigate how immunity affects the evolution of the mutation rate and other nonantigenic traits, such as virulence. We first show that the nature of the wave depends on how cross-reactive immune systems are, reconciling previous approaches. The immune-virus system behaves like a Fisher wave at low cross-reactivities, and like a fitness wave at high cross-reactivities. These regimes predict different outcomes for the evolution of nonantigenic traits. At low cross-reactivities, the evolutionarily stable strategy is to maximize the speed of the wave, implying a higher mutation rate and increased virulence. At large cross-reactivities, where our estimates place H3N2 influenza, the stable strategy is to increase the basic reproductive number, keeping the mutation rate to a minimum and virulence low.
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Affiliation(s)
- Victor Chardès
- Laboratoire de Physique de l’École Normale Supérieure, CNRS, Paris Sciences & Lettres University, Sorbonne Université, and Université Paris-Cité, 75005Paris, France
- Center for Computational Biology, Flatiron Institute, New York, NY10010
| | - Andrea Mazzolini
- Laboratoire de Physique de l’École Normale Supérieure, CNRS, Paris Sciences & Lettres University, Sorbonne Université, and Université Paris-Cité, 75005Paris, France
| | - Thierry Mora
- Laboratoire de Physique de l’École Normale Supérieure, CNRS, Paris Sciences & Lettres University, Sorbonne Université, and Université Paris-Cité, 75005Paris, France
| | - Aleksandra M. Walczak
- Laboratoire de Physique de l’École Normale Supérieure, CNRS, Paris Sciences & Lettres University, Sorbonne Université, and Université Paris-Cité, 75005Paris, France
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2
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Baccili AP, Monteiro LHA. Social Pressure from a Core Group can Cause Self-Sustained Oscillations in an Epidemic Model. Acta Biotheor 2023; 71:18. [PMID: 37347302 DOI: 10.1007/s10441-023-09469-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 06/08/2023] [Indexed: 06/23/2023]
Abstract
Let the individuals of a population be divided into two groups with different personal habits. The core group is associated with health risk behaviors; the non-core group avoids unhealthy activities. Assume that the infected individuals of the core group can spread a contagious disease to the whole population. Also, assume that cure does not confer immunity. Here, an epidemiological model written as a set of ordinary differential equations is proposed to investigate the infection propagation in this population. In the model, migrations between these two groups are allowed; however, the transitions from the non-core group into the core group prevail. These migrations can be either spontaneous or stimulated by social pressure. It is analytically shown that, in the scenario of spontaneous migration, the disease is either naturally eradicated or chronically persists at a constant level. In the scenario of stimulated migration, in addition to eradication and constant persistence, self-sustained oscillations in the number of sick individuals can also be found. These analytical results are illustrated by numerical simulations and discussed from a public health perspective.
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Affiliation(s)
- A P Baccili
- Universidade Presbiteriana Mackenzie, PPGEEC, Escola de Engenharia, Rua da Consolação, n.896, 01302-907, São Paulo, SP, Brazil
| | - L H A Monteiro
- Universidade Presbiteriana Mackenzie, PPGEEC, Escola de Engenharia, Rua da Consolação, n.896, 01302-907, São Paulo, SP, Brazil.
- Universidade de São Paulo, Escola Politécnica, São Paulo, SP, Brazil.
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3
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Kumata R, Sasaki A. Antigenic escape is accelerated by the presence of immunocompromised hosts. Proc Biol Sci 2022; 289:20221437. [PMID: 36350217 PMCID: PMC9653221 DOI: 10.1098/rspb.2022.1437] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 10/17/2022] [Indexed: 04/01/2024] Open
Abstract
The repeated emergence of SARS-CoV-2 escape mutants from host immunity has obstructed the containment of the current pandemic and poses a serious threat to humanity. Prolonged infection in immunocompromised patients has received increasing attention as a driver of immune escape, and accumulating evidence suggests that viral genomic diversity and emergence of immune-escape mutants are promoted in immunocompromised patients. However, because immunocompromised patients comprise a small proportion of the host population, whether they have a significant impact on antigenic evolution at the population level is unknown. We consider an evolutionary epidemiological model that combines antigenic evolution and epidemiological dynamics. Applying this model to a heterogeneous host population, we study the impact of immunocompromised hosts on the evolutionary dynamics of pathogen antigenic escape from host immunity. We derived analytical formulae of the speed of antigenic evolution in heterogeneous host populations and found that even a small number of immunocompromised hosts in the population significantly accelerates antigenic evolution. Our results demonstrate that immunocompromised hosts play a key role in viral adaptation at the population level and emphasize the importance of critical care and surveillance of immunocompromised hosts.
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Affiliation(s)
- Ryuichi Kumata
- Department of Evolutionary Studies of Biosystems, The Graduate University of Advanced Studies, SOKENDAI, Hayama, Kanagawa 2400139, Japan
| | - Akira Sasaki
- Department of Evolutionary Studies of Biosystems, The Graduate University of Advanced Studies, SOKENDAI, Hayama, Kanagawa 2400139, Japan
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4
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Azizi A, Kazanci C, Komarova NL, Wodarz D. Effect of Human Behavior on the Evolution of Viral Strains During an Epidemic. Bull Math Biol 2022; 84:144. [PMID: 36334172 PMCID: PMC9638455 DOI: 10.1007/s11538-022-01102-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 10/17/2022] [Indexed: 11/08/2022]
Abstract
It is well known in the literature that human behavior can change as a reaction to disease observed in others, and that such behavioral changes can be an important factor in the spread of an epidemic. It has been noted that human behavioral traits in disease avoidance are under selection in the presence of infectious diseases. Here, we explore a complementary trend: the pathogen itself might experience a force of selection to become less “visible,” or less “symptomatic,” in the presence of such human behavioral trends. Using a stochastic SIR agent-based model, we investigated the co-evolution of two viral strains with cross-immunity, where the resident strain is symptomatic while the mutant strain is asymptomatic. We assumed that individuals exercised self-regulated social distancing (SD) behavior if one of their neighbors was infected with a symptomatic strain. We observed that the proportion of asymptomatic carriers increased over time with a stronger effect corresponding to higher levels of self-regulated SD. Adding mandated SD made the effect more significant, while the existence of a time-delay between the onset of infection and the change of behavior reduced the advantage of the asymptomatic strain. These results were consistent under random geometric networks, scale-free networks, and a synthetic network that represented the social behavior of the residents of New Orleans.
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Affiliation(s)
- Asma Azizi
- Department of Mathematics, Kennesaw State University, Marietta, GA, 30060, USA.
| | - Caner Kazanci
- Department of Mathematics, University of Georgia, Athens, GA, 30602, USA.,College of Engineering, University of Georgia, Athens, GA, 30602, USA
| | - Natalia L Komarova
- Department of Mathematics, University of California Irvine, Irvine, CA, 92697, USA
| | - Dominik Wodarz
- Department of Population Health and Disease Prevention Program in Public Health, Susan and Henry Samueli College of Health Sciences, University of California, Irvine, CA, 92697, USA
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5
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Stockdale JE, Liu P, Colijn C. The potential of genomics for infectious disease forecasting. Nat Microbiol 2022; 7:1736-1743. [PMID: 36266338 DOI: 10.1038/s41564-022-01233-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 08/18/2022] [Indexed: 11/09/2022]
Abstract
Genomic technologies have led to tremendous gains in understanding how pathogens function, evolve and interact. Pathogen diversity is now measurable at high precision and resolution, in part because over the past decade, sequencing technologies have increased in speed and capacity, at decreased cost. Alongside this, the use of models that can forecast emergence and size of infectious disease outbreaks has risen, highlighted by the coronavirus disease 2019 pandemic but also due to modelling advances that allow for rapid estimates in emerging outbreaks to inform monitoring, coordination and resource deployment. However, genomics studies have remained largely retrospective. While they contain high-resolution views of pathogen diversification and evolution in the context of selection, they are often not aligned with designing interventions. This is a missed opportunity because pathogen diversification is at the core of the most pressing infectious public health challenges, and interventions need to take the mechanisms of virulence and understanding of pathogen diversification into account. In this Perspective, we assess these converging fields, discuss current challenges facing both surveillance specialists and modellers who want to harness genomic data, and propose next steps for integrating longitudinally sampled genomic data with statistical learning and interpretable modelling to make reliable predictions into the future.
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Affiliation(s)
- Jessica E Stockdale
- Department of Mathematics, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Pengyu Liu
- Department of Mathematics, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Caroline Colijn
- Department of Mathematics, Simon Fraser University, Burnaby, British Columbia, Canada.
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6
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Nuismer SL, Basinski AJ, Schreiner C, Whitlock A, Remien CH. Reservoir population ecology, viral evolution and the risk of emerging infectious disease. Proc Biol Sci 2022; 289:20221080. [PMID: 36100013 PMCID: PMC9470272 DOI: 10.1098/rspb.2022.1080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/18/2022] [Indexed: 11/12/2022] Open
Abstract
The ecology and life history of wild animals influences their potential to harbour infectious disease. This observation has motivated studies identifying empirical relationships between traits of wild animals and historical patterns of spillover and emergence into humans. Although these studies have identified compelling broad-scale patterns, they are generally agnostic with respect to underlying mechanisms. Here, we develop mathematical models that couple reservoir population ecology with viral epidemiology and evolution to clarify existing verbal arguments and pinpoint the conditions that favour spillover and emergence. Our results support the idea that average lifespan influences the likelihood of an animal serving as a reservoir for human infectious disease. At the same time, however, our results show that the magnitude of this effect is sensitive to the rate of viral mutation. Our results also demonstrate that viral pathogens causing persistent infections or a transient immune response within the reservoir are more likely to fuel emergence. Genetically explicit stochastic simulations enrich these mathematical results by identifying relationships between the genetic basis of transmission and the risk of spillover and emergence. Together, our results clarify the scope of applicability for existing hypotheses and refine our understanding of emergence risk.
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Affiliation(s)
- Scott L. Nuismer
- Department of Biological Sciences, University of Idaho, Moscow, ID 83844, USA
| | - Andrew J. Basinski
- Institute for Interdisciplinary Data Science, University of Idaho, Moscow, ID 83844, USA
| | - Courtney Schreiner
- Bioinformatics and Computational Biology, University of Idaho, Moscow, ID 83844, USA
| | - Alexander Whitlock
- Department of Biological Sciences, University of Idaho, Moscow, ID 83844, USA
| | - Christopher H. Remien
- Department of Mathematics and Statistical Science, University of Idaho, Moscow, ID 83844, USA
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7
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Saubin M, Louet C, Bousset L, Fabre F, Frey P, Fudal I, Grognard F, Hamelin F, Mailleret L, Stoeckel S, Touzeau S, Petre B, Halkett F. Improving sustainable crop protection using population genetics concepts. Mol Ecol 2022; 32:2461-2471. [PMID: 35906846 DOI: 10.1111/mec.16634] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 06/23/2022] [Accepted: 06/24/2022] [Indexed: 10/16/2022]
Abstract
Growing genetically resistant plants allows pathogen populations to be controlled and reduces the use of pesticides. However, pathogens can quickly overcome such resistance. In this context, how can we achieve sustainable crop protection? This crucial question has remained largely unanswered despite decades of intense debate and research effort. In this study, we used a bibliographic analysis to show that the research field of resistance durability has evolved into three subfields: (i) 'plant breeding' (generating new genetic material), (ii) 'molecular interactions' (exploring the molecular dialogue governing plant-pathogen interactions) and (iii) 'epidemiology and evolution' (explaining and forecasting of pathogen population dynamics resulting from selection pressure(s) exerted by resistant plants). We argue that this triple split of the field impedes integrated research progress and ultimately compromises the sustainable management of genetic resistance. After identifying a gap among the three subfields, we argue that the theoretical framework of population genetics could bridge this gap. Indeed, population genetics formally explains the evolution of all heritable traits, and allows genetic changes to be tracked along with variation in population dynamics. This provides an integrated view of pathogen adaptation, in particular via evolutionary-epidemiological feedbacks. In this Opinion Note, we detail examples illustrating how such a framework can better inform best practices for developing and managing genetically resistant cultivars.
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Affiliation(s)
| | - Clémentine Louet
- Université de Lorraine, INRAE, IAM, Nancy, France.,Université Paris Saclay, INRAE, BIOGER, Thiverval-Grignon, France
| | - Lydia Bousset
- INRAE, Agrocampus Ouest, Université de Rennes, IGEPP, Le Rheu, France
| | - Frédéric Fabre
- INRAE, Bordeaux Sciences Agro, SAVE, F-33882 Villenave d'Ornon, France
| | - Pascal Frey
- Université de Lorraine, INRAE, IAM, Nancy, France
| | - Isabelle Fudal
- Université Paris Saclay, INRAE, BIOGER, Thiverval-Grignon, France
| | - Frédéric Grognard
- Université Côte d'Azur, Inria, INRAE, CNRS, Sorbonne Université, Biocore team, Sophia Antipolis, France
| | - Frédéric Hamelin
- INRAE, Agrocampus Ouest, Université de Rennes, IGEPP, Le Rheu, France
| | - Ludovic Mailleret
- Université Côte d'Azur, Inria, INRAE, CNRS, Sorbonne Université, Biocore team, Sophia Antipolis, France.,Université Côte d'Azur, INRAE, CNRS, ISA, Sophia Antipolis, France
| | - Solenn Stoeckel
- INRAE, Agrocampus Ouest, Université de Rennes, IGEPP, Le Rheu, France
| | - Suzanne Touzeau
- Université Côte d'Azur, Inria, INRAE, CNRS, Sorbonne Université, Biocore team, Sophia Antipolis, France
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8
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Miller JK, Elenberg K, Dubrawski A. Forecasting emergence of COVID-19 variants of concern. PLoS One 2022; 17:e0264198. [PMID: 35202422 PMCID: PMC8870573 DOI: 10.1371/journal.pone.0264198] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 02/04/2022] [Indexed: 12/02/2022] Open
Abstract
We consider whether one can forecast the emergence of variants of concern in the SARS-CoV-2 outbreak and similar pandemics. We explore methods of population genetics and identify key relevant principles in both deterministic and stochastic models of spread of infectious disease. Finally, we demonstrate that fitness variation, defined as a trait for which an increase in its value is associated with an increase in net Darwinian fitness if the value of other traits are held constant, is a strong indicator of imminent transition in the viral population.
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Affiliation(s)
- James Kyle Miller
- Auton Systems LLC, Pittsburgh, PA, United States of America
- * E-mail:
| | - Kimberly Elenberg
- United States Department of Defense Covid Task Force, Washington, DC, United States of America
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9
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Rumschlag SL, Roth SA, McMahon TA, Rohr JR, Civitello DJ. Variability in environmental persistence but not per capita transmission rates of the amphibian chytrid fungus leads to differences in host infection prevalence. J Anim Ecol 2021; 91:170-181. [PMID: 34668575 DOI: 10.1111/1365-2656.13612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 09/27/2021] [Indexed: 11/28/2022]
Abstract
Heterogeneities in infections among host populations may arise through differences in environmental conditions through two mechanisms. First, environmental conditions may alter host exposure to pathogens via effects on survival. Second, environmental conditions may alter host susceptibility, making infection more or less likely if contact between a host and pathogen occurs. Further, host susceptibility might be altered through acquired resistance, which hosts can develop, in some systems, through exposure to dead or decaying pathogens and their metabolites. Environmental conditions may alter the rates of pathogen decomposition, influencing the likelihood of hosts developing acquired resistance. The present study primarily tests how environmental context influences the relative contributions of pathogen survival and per capita transmission on host infection prevalence using the amphibian chytrid fungus (Batrachochytrium dendrobatidis; Bd) as a model system. Secondarily, we evaluate how environmental context influences the decomposition of Bd because previous studies have shown that dead Bd and its metabolites can illicit acquired resistance in hosts. We conducted Bd survival and infection experiments and then fit models to discern how Bd mortality, decomposition and per capita transmission rates vary among water sources [e.g. artificial spring water (ASW) or water from three ponds]. We found that infection prevalence differed among water sources, which was driven by differences in mortality rates of Bd, rather than differences in per capita transmission rates. Bd mortality rates varied among pond water treatments and were lower in ASW compared to pond water. These results suggest that variation in Bd infection dynamics could be a function of environmental factors in waterbodies that result in differences in exposure of hosts to live Bd. In contrast to the persistence of live Bd, we found that the rates of decomposition of dead Bd did not vary among water sources, which may suggest that exposure of hosts to dead Bd or its metabolites might not commonly vary among nearby sites. Ultimately, a mechanistic understanding of the environmental dependence of free-living pathogens could lead to a deeper understanding of the patterns of outbreak heterogeneity, which could inform surveillance and management strategies.
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Affiliation(s)
- Samantha L Rumschlag
- Department of Biological Sciences, Environmental Change Initiative, and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA.,Department of Integrative Biology, University of South Florida, Tampa, FL, USA
| | - Sadie A Roth
- Department of Integrative Biology, University of South Florida, Tampa, FL, USA.,Department of Natural Resources Management, Texas Tech University, Lubbock, TX, USA
| | - Taegan A McMahon
- Department of Biology, University of Tampa, Tampa, FL, USA.,Department of Biology, Connecticut College, New London, CT, USA
| | - Jason R Rohr
- Department of Biological Sciences, Environmental Change Initiative, and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA.,Department of Integrative Biology, University of South Florida, Tampa, FL, USA
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10
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Correa AMS, Howard-Varona C, Coy SR, Buchan A, Sullivan MB, Weitz JS. Revisiting the rules of life for viruses of microorganisms. Nat Rev Microbiol 2021; 19:501-513. [PMID: 33762712 DOI: 10.1038/s41579-021-00530-x] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/15/2021] [Indexed: 02/01/2023]
Abstract
Viruses that infect microbial hosts have traditionally been studied in laboratory settings with a focus on either obligate lysis or persistent lysogeny. In the environment, these infection archetypes are part of a continuum that spans antagonistic to beneficial modes. In this Review, we advance a framework to accommodate the context-dependent nature of virus-microorganism interactions in ecological communities by synthesizing knowledge from decades of virology research, eco-evolutionary theory and recent technological advances. We discuss that nuanced outcomes, rather than the extremes of the continuum, are particularly likely in natural communities given variability in abiotic factors, the availability of suboptimal hosts and the relevance of multitrophic partnerships. We revisit the 'rules of life' in terms of how long-term infections shape the fate of viruses and microbial cells, populations and ecosystems.
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Affiliation(s)
| | | | - Samantha R Coy
- BioSciences Department, Rice University, Houston, TX, USA
| | - Alison Buchan
- Department of Microbiology, University of Tennessee, Knoxville, TN, USA.
| | - Matthew B Sullivan
- Department of Microbiology, The Ohio State University, Columbus, OH, USA. .,Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, Columbus, OH, USA.
| | - Joshua S Weitz
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA. .,School of Physics, Georgia Institute of Technology, Atlanta, GA, USA.
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11
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Turtle J, Riley P, Ben-Nun M, Riley S. Accurate influenza forecasts using type-specific incidence data for small geographic units. PLoS Comput Biol 2021; 17:e1009230. [PMID: 34324487 PMCID: PMC8354478 DOI: 10.1371/journal.pcbi.1009230] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 08/10/2021] [Accepted: 06/30/2021] [Indexed: 11/24/2022] Open
Abstract
Influenza incidence forecasting is used to facilitate better health system planning and could potentially be used to allow at-risk individuals to modify their behavior during a severe seasonal influenza epidemic or a novel respiratory pandemic. For example, the US Centers for Disease Control and Prevention (CDC) runs an annual competition to forecast influenza-like illness (ILI) at the regional and national levels in the US, based on a standard discretized incidence scale. Here, we use a suite of forecasting models to analyze type-specific incidence at the smaller spatial scale of clusters of nearby counties. We used data from point-of-care (POC) diagnostic machines over three seasons, in 10 clusters, capturing: 57 counties; 1,061,891 total specimens; and 173,909 specimens positive for Influenza A. Total specimens were closely correlated with comparable CDC ILI data. Mechanistic models were substantially more accurate when forecasting influenza A positive POC data than total specimen POC data, especially at longer lead times. Also, models that fit subpopulations of the cluster (individual counties) separately were better able to forecast clusters than were models that directly fit to aggregated cluster data. Public health authorities may wish to consider developing forecasting pipelines for type-specific POC data in addition to ILI data. Simple mechanistic models will likely improve forecast accuracy when applied at small spatial scales to pathogen-specific data before being scaled to larger geographical units and broader syndromic data. Highly local forecasts may enable new public health messaging to encourage at-risk individuals to temporarily reduce their social mixing during seasonal peaks and guide public health intervention policy during potentially severe novel influenza pandemics.
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Affiliation(s)
- James Turtle
- Infectious Disease Group, Predictive Science Inc., San Diego, California, United States
- * E-mail:
| | - Pete Riley
- Infectious Disease Group, Predictive Science Inc., San Diego, California, United States
| | - Michal Ben-Nun
- Infectious Disease Group, Predictive Science Inc., San Diego, California, United States
| | - Steven Riley
- Infectious Disease Group, Predictive Science Inc., San Diego, California, United States
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, United Kingdom
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12
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Abstract
The evolution of many microbes and pathogens, including circulating viruses such as seasonal influenza, is driven by immune pressure from the host population. In turn, the immune systems of infected populations get updated, chasing viruses even farther away. Quantitatively understanding how these dynamics result in observed patterns of rapid pathogen and immune adaptation is instrumental to epidemiological and evolutionary forecasting. Here we present a mathematical theory of coevolution between immune systems and viruses in a finite-dimensional antigenic space, which describes the cross-reactivity of viral strains and immune systems primed by previous infections. We show the emergence of an antigenic wave that is pushed forward and canalized by cross-reactivity. We obtain analytical results for shape, speed, and angular diffusion of the wave. In particular, we show that viral-immune coevolution generates an emergent timescale, the persistence time of the wave's direction in antigenic space, which can be much longer than the coalescence time of the viral population. We compare these dynamics to the observed antigenic turnover of influenza strains, and we discuss how the dimensionality of antigenic space impacts the predictability of the evolutionary dynamics. Our results provide a concrete and tractable framework to describe pathogen-host coevolution.
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13
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Ding C, Liu X, Yang S. The value of infectious disease modeling and trend assessment: a public health perspective. Expert Rev Anti Infect Ther 2021; 19:1135-1145. [PMID: 33522327 DOI: 10.1080/14787210.2021.1882850] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Disease outbreaks of acquired immunodeficiency syndrome, severe acute respiratory syndrome, pandemic H1N1, H7N9, H5N1, Ebola, Zika, Middle East respiratory syndrome, and recently COVID-19 have raised the attention of the public over the past half-century. Revealing the characteristics and epidemic trends are important parts of disease control. The biological scenarios including transmission characteristics can be constructed and translated into mathematical models, which can help to predict and gain a deeper understanding of diseases. AREAS COVERED This review discusses the models for infectious diseases and highlights their values in the field of public health. This information will be of interest to mathematicians and clinicians, and make a significant contribution toward the development of more specific and effective models. Literature searches were performed using the online database of PubMed (inception to August 2020). EXPERT OPINION Modeling could contribute to infectious disease control by means of predicting the scales of disease epidemics, indicating the characteristics of disease transmission, evaluating the effectiveness of interventions or policies, and warning or forecasting during the pre-outbreak of diseases. With the development of theories and the ability of calculations, infectious disease modeling would play a much more important role in disease prevention and control of public health.
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Affiliation(s)
- Cheng Ding
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases,National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaoxiao Liu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases,National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shigui Yang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases,National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
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14
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Huddleston J, Barnes JR, Rowe T, Xu X, Kondor R, Wentworth DE, Whittaker L, Ermetal B, Daniels RS, McCauley JW, Fujisaki S, Nakamura K, Kishida N, Watanabe S, Hasegawa H, Barr I, Subbarao K, Barrat-Charlaix P, Neher RA, Bedford T. Integrating genotypes and phenotypes improves long-term forecasts of seasonal influenza A/H3N2 evolution. eLife 2020; 9:e60067. [PMID: 32876050 PMCID: PMC7553778 DOI: 10.7554/elife.60067] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 08/24/2020] [Indexed: 12/17/2022] Open
Abstract
Seasonal influenza virus A/H3N2 is a major cause of death globally. Vaccination remains the most effective preventative. Rapid mutation of hemagglutinin allows viruses to escape adaptive immunity. This antigenic drift necessitates regular vaccine updates. Effective vaccine strains need to represent H3N2 populations circulating one year after strain selection. Experts select strains based on experimental measurements of antigenic drift and predictions made by models from hemagglutinin sequences. We developed a novel influenza forecasting framework that integrates phenotypic measures of antigenic drift and functional constraint with previously published sequence-only fitness estimates. Forecasts informed by phenotypic measures of antigenic drift consistently outperformed previous sequence-only estimates, while sequence-only estimates of functional constraint surpassed more comprehensive experimentally-informed estimates. Importantly, the best models integrated estimates of both functional constraint and either antigenic drift phenotypes or recent population growth.
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Affiliation(s)
- John Huddleston
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research CenterSeattleUnited States
- Molecular and Cell Biology Program, University of WashingtonSeattleUnited States
| | - John R Barnes
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC)AtlantaUnited States
| | - Thomas Rowe
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC)AtlantaUnited States
| | - Xiyan Xu
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC)AtlantaUnited States
| | - Rebecca Kondor
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC)AtlantaUnited States
| | - David E Wentworth
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC)AtlantaUnited States
| | - Lynne Whittaker
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick InstituteLondonUnited Kingdom
| | - Burcu Ermetal
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick InstituteLondonUnited Kingdom
| | - Rodney Stuart Daniels
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick InstituteLondonUnited Kingdom
| | - John W McCauley
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick InstituteLondonUnited Kingdom
| | - Seiichiro Fujisaki
- Influenza Virus Research Center, National Institute of Infectious DiseasesTokyoJapan
| | - Kazuya Nakamura
- Influenza Virus Research Center, National Institute of Infectious DiseasesTokyoJapan
| | - Noriko Kishida
- Influenza Virus Research Center, National Institute of Infectious DiseasesTokyoJapan
| | - Shinji Watanabe
- Influenza Virus Research Center, National Institute of Infectious DiseasesTokyoJapan
| | - Hideki Hasegawa
- Influenza Virus Research Center, National Institute of Infectious DiseasesTokyoJapan
| | - Ian Barr
- The WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Department of Microbiology and Immunology, The University of Melbourne, The Peter Doherty Institute for Infection and ImmunityMelbourneAustralia
| | - Kanta Subbarao
- The WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Department of Microbiology and Immunology, The University of Melbourne, The Peter Doherty Institute for Infection and ImmunityMelbourneAustralia
| | - Pierre Barrat-Charlaix
- Biozentrum, University of BaselBaselSwitzerland
- Swiss Institute of BioinformaticsBaselSwitzerland
| | - Richard A Neher
- Biozentrum, University of BaselBaselSwitzerland
- Swiss Institute of BioinformaticsBaselSwitzerland
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research CenterSeattleUnited States
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15
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Abstract
There is no doubt that the novel coronavirus SARS-CoV-2 that causes COVID-19 is mutating and thus has the potential to adapt during the current pandemic. Whether this evolution will lead to changes in the transmission, the duration, or the severity of the disease is not clear. This has led to considerable scientific and media debate, from raising alarms about evolutionary change to dismissing it. Here we review what little is currently known about the evolution of SARS-CoV-2 and extend existing evolutionary theory to consider how selection might be acting upon the virus during the COVID-19 pandemic. Although there is currently no definitive evidence that SARS-CoV-2 is undergoing further adaptation, continued evidence-based analysis of evolutionary change is important so that public health measures can be adjusted in response to substantive changes in the infectivity or severity of COVID-19.
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16
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Lam EKS, Morris DH, Hurt AC, Barr IG, Russell CA. The impact of climate and antigenic evolution on seasonal influenza virus epidemics in Australia. Nat Commun 2020; 11:2741. [PMID: 32488106 PMCID: PMC7265451 DOI: 10.1038/s41467-020-16545-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Accepted: 05/09/2020] [Indexed: 11/08/2022] Open
Abstract
Although seasonal influenza viruses circulate globally, prevention and treatment occur at the level of regions, cities, and communities. At these scales, the timing, duration and magnitude of epidemics vary substantially, but the underlying causes of this variation are poorly understood. Here, based on analyses of a 15-year city-level dataset of 18,250 laboratory-confirmed and antigenically-characterised influenza virus infections from Australia, we investigate the effects of previously hypothesised environmental and virological drivers of influenza epidemics. We find that anomalous fluctuations in temperature and humidity do not predict local epidemic onset timings. We also find that virus antigenic change has no consistent effect on epidemic size. In contrast, epidemic onset time and heterosubtypic competition have substantial effects on epidemic size and composition. Our findings suggest that the relationship between influenza population immunity and epidemiology is more complex than previously supposed and that the strong influence of short-term processes may hinder long-term epidemiological forecasts.
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Affiliation(s)
- Edward K S Lam
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Dylan H Morris
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Aeron C Hurt
- WHO Collaborating Centre for Reference and Research on Influenza, VIDRL, Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
- Department of Microbiology and Immunology, University of Melbourne, Parkville, VIC, Australia
| | - Ian G Barr
- WHO Collaborating Centre for Reference and Research on Influenza, VIDRL, Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
- Department of Microbiology and Immunology, University of Melbourne, Parkville, VIC, Australia
- School of Applied Biomedical Sciences, Federation University, Churchill, VIC, Australia
| | - Colin A Russell
- Department of Medical Microbiology, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
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17
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Day T, Parsons T, Lambert A, Gandon S. The Price equation and evolutionary epidemiology. Philos Trans R Soc Lond B Biol Sci 2020; 375:20190357. [PMID: 32146879 DOI: 10.1098/rstb.2019.0357] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The Price equation has found widespread application in many areas of evolutionary biology, including the evolutionary epidemiology of infectious diseases. In this paper, we illustrate the utility of this approach to modelling disease evolution by first deriving a version of Price's equation that can be applied in continuous time and to populations with overlapping generations. We then show how this version of Price's equation provides an alternative perspective on pathogen evolution by considering the epidemiological meaning of each of its terms. Finally, we extend these results to the case where population size is small and generates demographic stochasticity. We show that the particular partitioning of evolutionary change given by Price's equation is also a natural way to partition the evolutionary consequences of demographic stochasticity, and demonstrate how such stochasticity tends to weaken selection on birth rate (e.g. the transmission rate of an infectious disease) and enhance selection on mortality rate (e.g. factors, like virulence, that cause the end of an infection). In the long term, if there is a trade-off between virulence and transmission across parasite strains, the weaker selection on transmission and stronger selection on virulence that arises from demographic stochasticity will tend to drive the evolution of lower levels of virulence. This article is part of the theme issue 'Fifty years of the Price equation'.
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Affiliation(s)
- Troy Day
- Department of Mathematics and Statistics, Queen's University, Jeffery Hall, Kingston, Ontario, Canada K7L 3N6.,Department of Biology, Queen's University, Jeffery Hall, Kingston, Ontario, Canada K7L 3N6
| | - Todd Parsons
- Sorbonne Université, Laboratoire de Probabilités, Statistique et Modélisation (LPSM), CNRS UMR 8001, 75005 Paris, France
| | - Amaury Lambert
- Sorbonne Université, Laboratoire de Probabilités, Statistique et Modélisation (LPSM), CNRS UMR 8001, 75005 Paris, France
| | - Sylvain Gandon
- CEFE UMR 5175, CNRS - Université de Montpellier - Université Paul-Valéry Montpellier - EPHE, 1919, route de Mende, 34293 Montpellier Cedex 5, France
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18
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Early prediction of antigenic transitions for influenza A/H3N2. PLoS Comput Biol 2020; 16:e1007683. [PMID: 32069282 PMCID: PMC7048310 DOI: 10.1371/journal.pcbi.1007683] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 02/28/2020] [Accepted: 01/26/2020] [Indexed: 11/20/2022] Open
Abstract
Influenza A/H3N2 is a rapidly evolving virus which experiences major antigenic transitions every two to eight years. Anticipating the timing and outcome of transitions is critical to developing effective seasonal influenza vaccines. Using a published phylodynamic model of influenza transmission, we identified indicators of future evolutionary success for an emerging antigenic cluster and quantified fundamental trade-offs in our ability to make such predictions. The eventual fate of a new cluster depends on its initial epidemiological growth rate––which is a function of mutational load and population susceptibility to the cluster––along with the variance in growth rate across co-circulating viruses. Logistic regression can predict whether a cluster at 5% relative frequency will eventually succeed with ~80% sensitivity, providing up to eight months advance warning. As a cluster expands, the predictions improve while the lead-time for vaccine development and other interventions decreases. However, attempts to make comparable predictions from 12 years of empirical influenza surveillance data, which are far sparser and more coarse-grained, achieve only 56% sensitivity. By expanding influenza surveillance to obtain more granular estimates of the frequencies of and population-wide susceptibility to emerging viruses, we can better anticipate major antigenic transitions. This provides added incentives for accelerating the vaccine production cycle to reduce the lead time required for strain selection. The efficacy of annual seasonal influenza vaccines depends on selecting the strain that best matches circulating viruses. This selection takes place 9–12 months prior to the influenza season. To advise this decision, we used an influenza A/H3N2 phylodynamic simulation to explore how reliably and how far in advance can we identify strains that will dominate future influenza seasons? What data should we collect to accelerate and improve the accuracy of such forecasts? And importantly, what is the gap between the theoretical limit of prediction and prediction based on current influenza surveillance? Our results suggest that even with detailed virological information, the tight race between the antigenic turnover dynamics and the vaccine development timeline limits early detection of emerging viruses. Predictions based on current influenza surveillance do not achieve the theoretical limit and thus our results provide impetus for denser sampling and the development of rapid methods for estimating viral fitness.
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19
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Olson DR, Lopman BA, Konty KJ, Mathes RW, Papadouka V, Ternier A, Zucker JR, Simonsen L, Grenfell BT, Pitzer VE. Surveillance data confirm multiyear predictions of rotavirus dynamics in New York City. SCIENCE ADVANCES 2020; 6:eaax0586. [PMID: 32133392 PMCID: PMC7043922 DOI: 10.1126/sciadv.aax0586] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Accepted: 12/06/2019] [Indexed: 05/17/2023]
Abstract
Prediction skill is a key test of models for epidemic dynamics. However, future validation of models against out-of-sample data is rare, partly because of a lack of timely surveillance data. We address this gap by analyzing the response of rotavirus dynamics to infant vaccination. Syndromic surveillance of emergency department visits for diarrhea in New York City reveals a marked decline in diarrheal incidence among infants and young children, in line with data on rotavirus-coded hospitalizations and laboratory-confirmed cases, and a shift from annual to biennial epidemics increasingly affecting older children and adults. A published mechanistic model qualitatively predicted these patterns more than 2 years in advance. Future efforts to increase vaccination coverage may disrupt these patterns and lead to further declines in the incidence of rotavirus-attributable gastroenteritis.
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Affiliation(s)
- Donald R. Olson
- New York City Department of Health and Mental Hygiene, New York City, NY, USA
- Corresponding author. (D.R.O.); (V.E.P.)
| | - Benjamin A. Lopman
- Centers for Disease Control and Prevention, Atlanta, GA, USA
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Kevin J. Konty
- New York City Department of Health and Mental Hygiene, New York City, NY, USA
| | - Robert W. Mathes
- New York City Department of Health and Mental Hygiene, New York City, NY, USA
| | - Vikki Papadouka
- New York City Department of Health and Mental Hygiene, New York City, NY, USA
| | - Alexandra Ternier
- New York City Department of Health and Mental Hygiene, New York City, NY, USA
| | - Jane R. Zucker
- New York City Department of Health and Mental Hygiene, New York City, NY, USA
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Lone Simonsen
- Department of Science and Environment, Roskilde University, Rodskilde, Denmark
- Department of Global Health, George Washington University, Washington, DC, USA
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Virginia E. Pitzer
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
- Corresponding author. (D.R.O.); (V.E.P.)
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20
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Fang X, Ai J, Liu W, Ji H, Zhang X, Peng Z, Wu Y, Shi Y, Shen W, Bao C. Epidemiology of infectious diarrhoea and the relationship with etiological and meteorological factors in Jiangsu Province, China. Sci Rep 2019; 9:19571. [PMID: 31862956 PMCID: PMC6925108 DOI: 10.1038/s41598-019-56207-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Accepted: 12/04/2019] [Indexed: 11/24/2022] Open
Abstract
We depicted the epidemiological characteristics of infectious diarrhoea in Jiangsu Province, China. Generalized additive models were employed to evaluate the age-specific effects of etiological and meteorological factors on prevalence. A long-term increasing prevalence with strong seasonality was observed. In those aged 0–5 years, disease risk increased rapidly with the positive rate of virus (rotavirus, norovirus, sapovirus, astrovirus) in the 20–50% range. In those aged > 20 years, disease risk increased with the positive rate of adenovirus and bacteria (Vibrio parahaemolyticus, Salmonella, Escherichia coli, Campylobacter jejuni) until reaching 5%, and thereafter stayed stable. The mean temperature, relative humidity, temperature range, and rainfall were all related to two-month lag morbidity in the group aged 0–5 years. Disease risk increased with relative humidity between 67–78%. Synchronous climate affected the incidence in those aged >20 years. Mean temperature and rainfall showed U-shape associations with disease risk (with threshold 15 °C and 100 mm per month, respectively). Meanwhile, disease risk increased gradually with sunshine duration over 150 hours per month. However, no associations were found in the group aged 6–19 years. In brief, etiological and meteorological factors had age-specific effects on the prevalence of infectious diarrhoea in Jiangsu. Surveillance efforts are needed to prevent its spread.
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Affiliation(s)
- Xinyu Fang
- School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, 210009, China
| | - Jing Ai
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, 210009, China
| | - Wendong Liu
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, 210009, China
| | - Hong Ji
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, 210009, China
| | - Xuefeng Zhang
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, 210009, China
| | - Zhihang Peng
- School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Ying Wu
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, 210009, China
| | - Yingying Shi
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, 210009, China
| | - Wenqi Shen
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, 210009, China
| | - Changjun Bao
- School of Public Health, Nanjing Medical University, Nanjing, 211166, China. .,Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, 210009, China. .,NHC Key laboratory of Enteric Pathogenic Microbiology, Nanjing, 210009, China.
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21
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Živković D, John S, Verin M, Stephan W, Tellier A. Neutral genomic signatures of host-parasite coevolution. BMC Evol Biol 2019; 19:230. [PMID: 31856710 PMCID: PMC6924072 DOI: 10.1186/s12862-019-1556-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 12/09/2019] [Indexed: 12/21/2022] Open
Abstract
Background Coevolution is a selective process of reciprocal adaptation in hosts and parasites or in mutualistic symbionts. Classic population genetics theory predicts the signatures of selection at the interacting loci of both species, but not the neutral genome-wide polymorphism patterns. To bridge this gap, we build an eco-evolutionary model, where neutral genomic changes over time are driven by a single selected locus in hosts and parasites via a simple biallelic gene-for-gene or matching-allele interaction. This coevolutionary process may lead to cyclic changes in the sizes of the interacting populations. Results We investigate if and when these changes can be observed in the site frequency spectrum of neutral polymorphisms from host and parasite full genome data. We show that changes of the host population size are too smooth to be observable in its polymorphism pattern over the course of time. Conversely, the parasite population may undergo a series of strong bottlenecks occurring on a slower relative time scale, which may lead to observable changes in a time series sample. We also extend our results to cases with 1) several parasites per host accelerating relative time, and 2) multiple parasite generations per host generation slowing down rescaled time. Conclusions Our results show that time series sampling of host and parasite populations with full genome data are crucial to understand if and how coevolution occurs. This model provides therefore a framework to interpret and draw inference from genome-wide polymorphism data of interacting species.
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Affiliation(s)
- Daniel Živković
- Section of Population Genetics, Technical University of Munich, Freising, Germany.
| | - Sona John
- Section of Population Genetics, Technical University of Munich, Freising, Germany
| | - Mélissa Verin
- Section of Population Genetics, Technical University of Munich, Freising, Germany.,Department of Mathematics and Statistics, Queen's University, Kingston, Ontario, Canada
| | - Wolfgang Stephan
- Leibniz Institute for Evolution and Biodiversity Science, Berlin, Germany
| | - Aurélien Tellier
- Section of Population Genetics, Technical University of Munich, Freising, Germany.
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22
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Marchi J, Lässig M, Mora T, Walczak AM. Multi-Lineage Evolution in Viral Populations Driven by Host Immune Systems. Pathogens 2019; 8:E115. [PMID: 31362404 PMCID: PMC6789611 DOI: 10.3390/pathogens8030115] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 07/23/2019] [Accepted: 07/24/2019] [Indexed: 12/20/2022] Open
Abstract
Viruses evolve in the background of host immune systems that exert selective pressure and drive viral evolutionary trajectories. This interaction leads to different evolutionary patterns in antigenic space. Examples observed in nature include the effectively one-dimensional escape characteristic of influenza A and the prolonged coexistence of lineages in influenza B. Here, we use an evolutionary model for viruses in the presence of immune host systems with finite memory to obtain a phase diagram of evolutionary patterns in a two-dimensional antigenic space. We find that, for small effective mutation rates and mutation jump ranges, a single lineage is the only stable solution. Large effective mutation rates combined with large mutational jumps in antigenic space lead to multiple stably co-existing lineages over prolonged evolutionary periods. These results combined with observations from data constrain the parameter regimes for the adaptation of viruses, including influenza.
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Affiliation(s)
- Jacopo Marchi
- Laboratoire de physique de l'École normale supérieure (PSL University), CNRS, Sorbonne Université, and Université de Paris, 75005 Paris, France
| | - Michael Lässig
- Institute of Theoretical Physics, University of Cologne, 50937 Cologne, Germany
| | - Thierry Mora
- Laboratoire de physique de l'École normale supérieure (PSL University), CNRS, Sorbonne Université, and Université de Paris, 75005 Paris, France.
| | - Aleksandra M Walczak
- Laboratoire de physique de l'École normale supérieure (PSL University), CNRS, Sorbonne Université, and Université de Paris, 75005 Paris, France.
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23
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Clavijo MJ, Davies P, Morrison R, Bruner L, Olson S, Rosey E, Rovira A. Temporal patterns of colonization and infection with Mycoplasma hyorhinis in two swine production systems in the USA. Vet Microbiol 2019; 234:110-118. [PMID: 31213266 DOI: 10.1016/j.vetmic.2019.05.021] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Revised: 05/24/2019] [Accepted: 05/26/2019] [Indexed: 02/03/2023]
Abstract
Control of Mycoplasma hyorhinis (M. hyorhinis) associated disease is currently hindered by limited knowledge of the epidemiology and ecology of this organism. A prospective longitudinal investigation was conducted to determine the dynamics of M. hyorhinis colonization in two swine production systems. In each system (A, B), 51 young sows (parities 1, 2) and 56 older sows (>parity 2) were selected at farrowing and tested by qPCR of nasal swabs and for antibodies by serum ELISA. From each sow, a piglet was randomly selected, and nasal and serum samples were collected at birth, weaning, and 10 days post-weaning. Two further samplings were performed in the nursery and finishing stages during the high-risk periods for M. hyorhinis-associated disease, and 12 pigs were euthanized and necropsied at these later sampling events. The prevalence of M. hyorhinis colonization in sows was low (<5%). No associations were found between sow parity or sow serum titer and piglet nasal colonization at either birth or weaning. In contrast to the low prevalence (0.95-2.70%) observed in piglets pre-weaning, most pigs became colonized during the first four weeks after weaning and remained positive throughout the nursery and finishing stages. The detection of M. hyorhinis in oral fluids followed similar patterns as those observed using nasal swabs. ELISA results showed decreased detection of maternal antibodies at around 3 weeks of age and a subsequent increase after natural exposure. The role of M. hyorhinis in polyserositis and arthritis was demonstrated in these two herds. Establishing the temporal dynamics of exposure and infection with M. hyorhinis in pigs will enable more strategic implementation of intervention strategies in affected herds.
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Affiliation(s)
- Maria Jose Clavijo
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St Paul, MN, United States.
| | - Peter Davies
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St Paul, MN, United States
| | - Robert Morrison
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St Paul, MN, United States
| | - Laura Bruner
- Swine Vet Center, 1608 S Minnesota Ave, St Peter, MN, 56082, United States
| | - Steve Olson
- Austin Veterinary Clinic, 3100 W Oakland Ave, Austin, MN, 55912, United States
| | - Everett Rosey
- Global Biologics Research, Zoetis Inc, 333 Portage Street, Kalamazoo, MI, 49007, United States
| | - Albert Rovira
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St Paul, MN, United States
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24
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Scarpino SV, Petri G. On the predictability of infectious disease outbreaks. Nat Commun 2019; 10:898. [PMID: 30796206 PMCID: PMC6385200 DOI: 10.1038/s41467-019-08616-0] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Accepted: 01/14/2019] [Indexed: 11/21/2022] Open
Abstract
Infectious disease outbreaks recapitulate biology: they emerge from the multi-level interaction of hosts, pathogens, and environment. Therefore, outbreak forecasting requires an integrative approach to modeling. While specific components of outbreaks are predictable, it remains unclear whether fundamental limits to outbreak prediction exist. Here, adopting permutation entropy as a model independent measure of predictability, we study the predictability of a diverse collection of outbreaks and identify a fundamental entropy barrier for disease time series forecasting. However, this barrier is often beyond the time scale of single outbreaks, implying prediction is likely to succeed. We show that forecast horizons vary by disease and that both shifting model structures and social network heterogeneity are likely mechanisms for differences in predictability. Our results highlight the importance of embracing dynamic modeling approaches, suggest challenges for performing model selection across long time series, and may relate more broadly to the predictability of complex adaptive systems.
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Affiliation(s)
- Samuel V Scarpino
- Network Science Institute, Northeastern University, Boston, MA, 02115, USA.
- Marine & Environmental Sciences, Northeastern University, Boston, MA, 02115, USA.
- Physics, Northeastern University, Boston, MA, 02115, USA.
- Health Sciences, Northeastern University, Boston, MA, 02115, USA.
- Dharma Platform, Washington, DC, 20005, USA.
- ISI Foundation, 10126, Turin, Italy.
| | - Giovanni Petri
- ISI Foundation, 10126, Turin, Italy.
- ISI Global Science Foundation, New York, NY, 10018, USA.
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25
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Singer A, Bradter U, Fabritius H, Snäll T. Dating past colonization events to project future species distributions. Methods Ecol Evol 2019. [DOI: 10.1111/2041-210x.13134] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Alexander Singer
- Swedish Species Information CentreSwedish University of Agricultural Sciences Uppsala Sweden
| | - Ute Bradter
- Swedish Species Information CentreSwedish University of Agricultural Sciences Uppsala Sweden
| | - Henna Fabritius
- Swedish Species Information CentreSwedish University of Agricultural Sciences Uppsala Sweden
| | - Tord Snäll
- Swedish Species Information CentreSwedish University of Agricultural Sciences Uppsala Sweden
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26
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Dalziel BD, Kissler S, Gog JR, Viboud C, Bjørnstad ON, Metcalf CJE, Grenfell BT. Urbanization and humidity shape the intensity of influenza epidemics in U.S. cities. Science 2019; 362:75-79. [PMID: 30287659 PMCID: PMC6510303 DOI: 10.1126/science.aat6030] [Citation(s) in RCA: 175] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Accepted: 08/10/2018] [Indexed: 01/14/2023]
Abstract
Influenza epidemics vary in intensity from year to year, driven by climatic conditions and by viral antigenic evolution. However, important spatial variation remains unexplained. Here we show predictable differences in influenza incidence among cities, driven by population size and structure. Weekly incidence data from 603 cities in the United States reveal that epidemics in smaller cities are focused on shorter periods of the influenza season, whereas in larger cities, incidence is more diffuse. Base transmission potential estimated from city-level incidence data is positively correlated with population size and with spatiotemporal organization in population density, indicating a milder response to climate forcing in metropolises. This suggests that urban centers incubate critical chains of transmission outside of peak climatic conditions, altering the spatiotemporal geometry of herd immunity.
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Affiliation(s)
- Benjamin D Dalziel
- Department of Integrative Biology, Oregon State University, Corvallis, OR, USA. .,Department of Mathematics, Oregon State University, Corvallis, OR, USA
| | - Stephen Kissler
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Julia R Gog
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Cecile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Ottar N Bjørnstad
- Department of Entomology, Pennsylvania State University, State College, PA, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Bryan T Grenfell
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.,Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, USA
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27
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Benhaiem S, Marescot L, East ML, Kramer-Schadt S, Gimenez O, Lebreton JD, Hofer H. Slow recovery from a disease epidemic in the spotted hyena, a keystone social carnivore. Commun Biol 2018; 1:201. [PMID: 30480102 PMCID: PMC6244218 DOI: 10.1038/s42003-018-0197-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Accepted: 10/19/2018] [Indexed: 12/21/2022] Open
Abstract
Predicting the impact of disease epidemics on wildlife populations is one of the twenty-first century's main conservation challenges. The long-term demographic responses of wildlife populations to epidemics and the life history and social traits modulating these responses are generally unknown, particularly for K-selected social species. Here we develop a stage-structured matrix population model to provide a long-term projection of demographic responses by a keystone social predator, the spotted hyena, to a virulent epidemic of canine distemper virus (CDV) in the Serengeti ecosystem in 1993/1994 and predict the recovery time for the population following the epidemic. Using two decades of longitudinal data from 625 known hyenas, we demonstrate that although the reduction in population size was moderate, i.e., the population showed high ecological 'resistance' to the novel CDV genotype present, recovery was slow. Interestingly, high-ranking females accelerated the population's recovery, thereby lessening the impact of the epidemic on the population.
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Affiliation(s)
- Sarah Benhaiem
- Department of Ecological Dynamics, Leibniz Institute for Zoo and Wildlife Research, Alfred-Kowalke-Strasse 17, D-10315, Berlin, Germany.
| | - Lucile Marescot
- Department of Ecological Dynamics, Leibniz Institute for Zoo and Wildlife Research, Alfred-Kowalke-Strasse 17, D-10315, Berlin, Germany
- CEFE, CNRS, University Montpellier, University Paul Valéry Montpellier 3, EPHE, IRD, Montpellier, 34090, France
| | - Marion L East
- Department of Ecological Dynamics, Leibniz Institute for Zoo and Wildlife Research, Alfred-Kowalke-Strasse 17, D-10315, Berlin, Germany
| | - Stephanie Kramer-Schadt
- Department of Ecological Dynamics, Leibniz Institute for Zoo and Wildlife Research, Alfred-Kowalke-Strasse 17, D-10315, Berlin, Germany
- Department of Ecology, Technische Universität Berlin, Rothenburgstr. 12, 12165, Berlin, Germany
| | - Olivier Gimenez
- CEFE, CNRS, University Montpellier, University Paul Valéry Montpellier 3, EPHE, IRD, Montpellier, 34090, France
| | - Jean-Dominique Lebreton
- CEFE, CNRS, University Montpellier, University Paul Valéry Montpellier 3, EPHE, IRD, Montpellier, 34090, France
| | - Heribert Hofer
- Department of Ecological Dynamics, Leibniz Institute for Zoo and Wildlife Research, Alfred-Kowalke-Strasse 17, D-10315, Berlin, Germany
- Department of Veterinary Medicine, Freie Universität Berlin, Oertzenweg 19b, Berlin, 14163, Germany
- Department of Biology, Chemistry, Pharmacy, Freie Universität Berlin, Takustr. 3, Berlin, 14195, Germany
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28
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Parsons TL, Lambert A, Day T, Gandon S. Pathogen evolution in finite populations: slow and steady spreads the best. J R Soc Interface 2018; 15:20180135. [PMID: 30282758 PMCID: PMC6228476 DOI: 10.1098/rsif.2018.0135] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Accepted: 09/11/2018] [Indexed: 01/02/2023] Open
Abstract
The theory of life-history evolution provides a powerful framework to understand the evolutionary dynamics of pathogens. It assumes, however, that host populations are large and that one can neglect the effects of demographic stochasticity. Here, we expand the theory to account for the effects of finite population size on the evolution of pathogen virulence. We show that demographic stochasticity introduces additional evolutionary forces that can qualitatively affect the dynamics and the evolutionary outcome. We discuss the importance of the shape of the pathogen fitness landscape on the balance between mutation, selection and genetic drift. This analysis reconciles Adaptive Dynamics with population genetics in finite populations and provides a new theoretical toolbox to study life-history evolution in realistic ecological scenarios.
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Affiliation(s)
- Todd L Parsons
- Laboratoire de Probabilités, Statistique et Modélisation (LPSM), Sorbonne Université, CNRS UMR 8001, Paris, France
| | - Amaury Lambert
- Laboratoire de Probabilités, Statistique et Modélisation (LPSM), Sorbonne Université, CNRS UMR 8001, Paris, France
- Center for Interdisciplinary Research in Biology (CIRB), Collège de France, PSL Research University, CNRS UMR 7241, INSERM U1050, Paris, France
| | - Troy Day
- Department of Mathematics and Statistics, Queen's University, Kingston, Canada
- Department of Biology, Queen's University, Kingston, Canada
| | - Sylvain Gandon
- Centre d'Ecologie Fonctionnelle et Evolutive (CEFE), Université de Montpellier-Université Paul-Valéry Montpellier-EPHE, CNRS UMR 5175, Montpellier, France
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29
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Takahashi S, Metcalf CJE, Arima Y, Fujimoto T, Shimizu H, Rogier van Doorn H, Le Van T, Chan YF, Farrar JJ, Oishi K, Grenfell BT. Epidemic dynamics, interactions and predictability of enteroviruses associated with hand, foot and mouth disease in Japan. J R Soc Interface 2018; 15:rsif.2018.0507. [PMID: 30209044 PMCID: PMC6170776 DOI: 10.1098/rsif.2018.0507] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Accepted: 08/20/2018] [Indexed: 12/28/2022] Open
Abstract
Outbreaks of hand, foot and mouth disease have been documented in Japan since 1963. This disease is primarily caused by the two closely related serotypes of Enterovirus A71 (EV-A71) and Coxsackievirus A16 (CV-A16). Here, we analyse Japanese virologic and syndromic surveillance time-series data from 1982 to 2015. As in some other countries in the Asia Pacific region, EV-A71 in Japan has a 3 year cyclical component, whereas CV-A16 is predominantly annual. We observe empirical signatures of an inhibitory interaction between the serotypes; virologic lines of evidence suggest they may indeed interact immunologically. We fit the time series to mechanistic epidemiological models: as a first-order effect, we find the data consistent with single-serotype susceptible–infected–recovered dynamics. We then extend the modelling to incorporate an inhibitory interaction between serotypes. Our results suggest the existence of a transient cross-protection and possible asymmetry in its strength such that CV-A16 serves as a stronger forcing on EV-A71. Allowing for asymmetry yields accurate out-of-sample predictions and the directionality of this effect is consistent with the virologic literature. Confirmation of these hypothesized interactions would have important implications for understanding enterovirus epidemiology and informing vaccine development. Our results highlight the general implication that even subtle interactions could have qualitative impacts on epidemic dynamics and predictability.
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Affiliation(s)
- Saki Takahashi
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Yuzo Arima
- Infectious Disease Surveillance Center, National Institute of Infectious Diseases, Tokyo, Japan
| | - Tsuguto Fujimoto
- Infectious Disease Surveillance Center, National Institute of Infectious Diseases, Tokyo, Japan
| | - Hiroyuki Shimizu
- Department of Virology II, National Institute of Infectious Diseases, Tokyo, Japan
| | - H Rogier van Doorn
- Oxford University Clinical Research Unit-Wellcome Trust Major Overseas Programme, National Hospital for Tropical Diseases, Ha Noi, Viet Nam.,Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Tan Le Van
- Oxford University Clinical Research Unit-Wellcome Trust Major Overseas Programme, National Hospital for Tropical Diseases, Ha Noi, Viet Nam
| | - Yoke-Fun Chan
- Department of Medical Microbiology, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Jeremy J Farrar
- Oxford University Clinical Research Unit-Wellcome Trust Major Overseas Programme, National Hospital for Tropical Diseases, Ha Noi, Viet Nam.,Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Kazunori Oishi
- Infectious Disease Surveillance Center, National Institute of Infectious Diseases, Tokyo, Japan
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA .,Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
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30
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Bellavite P. Factors that influenced the historical trends of tetanus and diphtheria. Vaccine 2018; 36:5506. [DOI: 10.1016/j.vaccine.2018.07.061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Revised: 04/11/2018] [Accepted: 07/19/2018] [Indexed: 11/30/2022]
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31
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Gustafson KB, Proctor JL. Identifying spatio-temporal dynamics of Ebola in Sierra Leone using virus genomes. J R Soc Interface 2018; 14:rsif.2017.0583. [PMID: 29187639 PMCID: PMC5721162 DOI: 10.1098/rsif.2017.0583] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Accepted: 11/02/2017] [Indexed: 01/19/2023] Open
Abstract
Containing the recent West African outbreak of Ebola virus (EBOV) required the deployment of substantial global resources. Despite recent progress in analysing and modelling EBOV epidemiological data, a complete characterization of the spatio-temporal spread of Ebola cases remains a challenge. In this work, we offer a novel perspective on the EBOV epidemic in Sierra Leone that uses individual virus genome sequences to inform population-level, spatial models. Calibrated to phylogenetic linkages of virus genomes, these spatial models provide unique insight into the disease mobility of EBOV in Sierra Leone without the need for human mobility data. Consistent with other investigations, our results show that the spread of EBOV during the beginning and middle portions of the epidemic strongly depended on the size of and distance between populations. Our phylodynamic analysis also revealed a change in model preference towards a spatial model with power-law characteristics in the latter portion of the epidemic, correlated with the timing of major intervention campaigns. More generally, we believe this framework, pairing molecular diagnostics with a dynamic model selection procedure, has the potential to be a powerful forecasting tool along with offering operationally relevant guidance for surveillance and sampling strategies during an epidemic.
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32
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Silva DN, Várzea V, Paulo OS, Batista D. Population genomic footprints of host adaptation, introgression and recombination in coffee leaf rust. MOLECULAR PLANT PATHOLOGY 2018; 19:1742-1753. [PMID: 29328532 PMCID: PMC6638104 DOI: 10.1111/mpp.12657] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Revised: 01/04/2018] [Accepted: 01/09/2018] [Indexed: 05/30/2023]
Abstract
Coffee leaf rust, caused by Hemileia vastatrix (Hv), represents the biggest threat to coffee production worldwide and ranks amongst the most serious fungal diseases in history. Despite a recent series of outbreaks and emergence of hypervirulent strains, the population evolutionary history and potential of this pathogen remain poorly understood. To address this issue, we used restriction site-associated DNA sequencing (RADseq) to generate ∼19 000 single nucleotide polymorphisms (SNPs) across a worldwide collection of 37 Hv samples. Contrary to the long-standing idea that Hv represents a genetically unstructured and cosmopolitan species, our results reveal the existence of a cryptic species complex with marked host tropism. Using phylogenetic and pathological data, we show that one of these lineages (C3) infects almost exclusively the most economically valuable coffee species (tetraploids that include Coffea arabica and interspecific hybrids), whereas the other lineages (C1 and C2) are severely maladapted to these hosts, but successfully infect diploid coffee species. Population dynamic analyses suggest that the C3 group may be a recent 'domesticated' lineage that emerged via host shift from diploid coffee hosts. We also found evidence of recombination occurring within this group, which could explain the high pace of pathotype emergence despite the low genetic variation. Moreover, genomic footprints of introgression between the C3 and C2 groups were discovered and raise the possibility that virulence factors may be quickly exchanged between groups with different pathogenic abilities. This work advances our understanding of the evolutionary strategies used by plant pathogens in agro-ecosystems with direct and far-reaching implications for disease control.
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Affiliation(s)
- Diogo Nuno Silva
- Departamento de Biologia Animal, Centre for Ecology, Evolution and Environmental Changes (cE3c), Computational Biology and Population Genomics Group (CoBiG)Faculdade de Ciências, Universidade de LisboaLisboaPortugal
- Centro de Investigação das Ferrugens do Cafeeiro (CIFC), Instituto Superior de AgronomiaUniversidade de LisboaOeirasPortugal
- Linking Landscape, Environment, Agriculture and Food (LEAF), Instituto Superior de AgronomiaUniversidade de LisboaLisboaPortugal
| | - Vítor Várzea
- Centro de Investigação das Ferrugens do Cafeeiro (CIFC), Instituto Superior de AgronomiaUniversidade de LisboaOeirasPortugal
- Linking Landscape, Environment, Agriculture and Food (LEAF), Instituto Superior de AgronomiaUniversidade de LisboaLisboaPortugal
| | - Octávio Salgueiro Paulo
- Departamento de Biologia Animal, Centre for Ecology, Evolution and Environmental Changes (cE3c), Computational Biology and Population Genomics Group (CoBiG)Faculdade de Ciências, Universidade de LisboaLisboaPortugal
| | - Dora Batista
- Departamento de Biologia Animal, Centre for Ecology, Evolution and Environmental Changes (cE3c), Computational Biology and Population Genomics Group (CoBiG)Faculdade de Ciências, Universidade de LisboaLisboaPortugal
- Centro de Investigação das Ferrugens do Cafeeiro (CIFC), Instituto Superior de AgronomiaUniversidade de LisboaOeirasPortugal
- Linking Landscape, Environment, Agriculture and Food (LEAF), Instituto Superior de AgronomiaUniversidade de LisboaLisboaPortugal
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33
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Du X, King AA, Woods RJ, Pascual M. Evolution-informed forecasting of seasonal influenza A (H3N2). Sci Transl Med 2018; 9:9/413/eaan5325. [PMID: 29070700 DOI: 10.1126/scitranslmed.aan5325] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 05/26/2017] [Accepted: 08/16/2017] [Indexed: 12/13/2022]
Abstract
Interpandemic or seasonal influenza A, currently subtypes H3N2 and H1N1, exacts an enormous annual burden both in terms of human health and economic impact. Incidence prediction ahead of season remains a challenge largely because of the virus' antigenic evolution. We propose a forecasting approach that incorporates evolutionary change into a mechanistic epidemiological model. The proposed models are simple enough that their parameters can be estimated from retrospective surveillance data. These models link amino acid sequences of hemagglutinin epitopes with a transmission model for seasonal H3N2 influenza, also informed by H1N1 levels. With a monthly time series of H3N2 incidence in the United States for more than 10 years, we demonstrate the feasibility of skillful prediction for total cases ahead of season, with a tendency to underpredict monthly peak epidemic size, and an accurate real-time forecast for the 2016/2017 influenza season.
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Affiliation(s)
- Xiangjun Du
- Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA
| | - Aaron A King
- Departments of Ecology and Evolutionary Biology and Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Robert J Woods
- University of Michigan Health System, University of Michigan, Ann Arbor, MI 48109, USA
| | - Mercedes Pascual
- Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA. .,Santa Fe Institute, Santa Fe, NM 87501, USA
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34
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Borlase A, Webster JP, Rudge JW. Opportunities and challenges for modelling epidemiological and evolutionary dynamics in a multihost, multiparasite system: Zoonotic hybrid schistosomiasis in West Africa. Evol Appl 2018; 11:501-515. [PMID: 29636802 PMCID: PMC5891036 DOI: 10.1111/eva.12529] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2017] [Accepted: 08/01/2017] [Indexed: 01/01/2023] Open
Abstract
Multihost multiparasite systems are evolutionarily and ecologically dynamic, which presents substantial trans-disciplinary challenges for elucidating their epidemiology and designing appropriate control. Evidence for hybridizations and introgressions between parasite species is gathering, in part in line with improvements in molecular diagnostics and genome sequencing. One major system where this is becoming apparent is within the Genus Schistosoma, where schistosomiasis represents a disease of considerable medical and veterinary importance, the greatest burden of which occurs in sub-Saharan Africa. Interspecific hybridizations and introgressions bring an increased level of complexity over and above that already inherent within multihost, multiparasite systems, also representing an additional source of genetic variation that can drive evolution. This has the potential for profound implications for the control of parasitic diseases, including, but not exclusive to, widening host range, increased transmission potential and altered responses to drug therapy. Here, we present the challenging case example of haematobium group Schistosoma spp. hybrids in West Africa, a system involving multiple interacting parasites and multiple definitive hosts, in a region where zoonotic reservoirs of schistosomiasis were not previously considered to be of importance. We consider how existing mathematical model frameworks for schistosome transmission could be expanded and adapted to zoonotic hybrid systems, exploring how such model frameworks can utilize molecular and epidemiological data, as well as the complexities and challenges this presents. We also highlight the opportunities and value such mathematical models could bring to this and a range of similar multihost, multi and cross-hybridizing parasites systems in our changing world.
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Affiliation(s)
- Anna Borlase
- Department of Pathobiology and Population SciencesCentre for Emerging, Endemic and Exotic DiseasesRoyal Veterinary CollegeUniversity of LondonLondonUK
- Department of Infectious Disease EpidemiologyLondon Centre for Neglected Tropical Disease ResearchSchool of Public HealthImperial College LondonLondonUK
| | - Joanne P. Webster
- Department of Pathobiology and Population SciencesCentre for Emerging, Endemic and Exotic DiseasesRoyal Veterinary CollegeUniversity of LondonLondonUK
- Department of Infectious Disease EpidemiologyLondon Centre for Neglected Tropical Disease ResearchSchool of Public HealthImperial College LondonLondonUK
| | - James W. Rudge
- Department of Infectious Disease EpidemiologyLondon Centre for Neglected Tropical Disease ResearchSchool of Public HealthImperial College LondonLondonUK
- Communicable Diseases Policy Research GroupLondon School of Hygiene and Tropical MedicineLondonUK
- Faculty of Public HealthMahidol UniversityBangkokThailand
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35
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Dolley S. Big Data's Role in Precision Public Health. Front Public Health 2018; 6:68. [PMID: 29594091 PMCID: PMC5859342 DOI: 10.3389/fpubh.2018.00068] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Accepted: 02/20/2018] [Indexed: 01/01/2023] Open
Abstract
Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous subpopulations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts.
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36
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Lion S, Gandon S. Spatial evolutionary epidemiology of spreading epidemics. Proc Biol Sci 2017; 283:rspb.2016.1170. [PMID: 27798295 DOI: 10.1098/rspb.2016.1170] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2016] [Accepted: 09/19/2016] [Indexed: 01/04/2023] Open
Abstract
Most spatial models of host-parasite interactions either neglect the possibility of pathogen evolution or consider that this process is slow enough for epidemiological dynamics to reach an equilibrium on a fast timescale. Here, we propose a novel approach to jointly model the epidemiological and evolutionary dynamics of spatially structured host and pathogen populations. Starting from a multi-strain epidemiological model, we use a combination of spatial moment equations and quantitative genetics to analyse the dynamics of mean transmission and virulence in the population. A key insight of our approach is that, even in the absence of long-term evolutionary consequences, spatial structure can affect the short-term evolution of pathogens because of the build-up of spatial differentiation in mean virulence. We show that spatial differentiation is driven by a balance between epidemiological and genetic effects, and this quantity is related to the effect of kin competition discussed in previous studies of parasite evolution in spatially structured host populations. Our analysis can be used to understand and predict the transient evolutionary dynamics of pathogens and the emergence of spatial patterns of phenotypic variation.
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Affiliation(s)
- S Lion
- CEFE UMR 5175, CNRS - Université de Montpellier - Université Paul-Valéry Montpellier - EPHE, 1919, route de Mende, 34293 Montpellier Cedex 5, France
| | - S Gandon
- CEFE UMR 5175, CNRS - Université de Montpellier - Université Paul-Valéry Montpellier - EPHE, 1919, route de Mende, 34293 Montpellier Cedex 5, France
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37
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Pennekamp F, Adamson MW, Petchey OL, Poggiale JC, Aguiar M, Kooi BW, Botkin DB, DeAngelis DL. The practice of prediction: What can ecologists learn from applied, ecology-related fields? ECOLOGICAL COMPLEXITY 2017. [DOI: 10.1016/j.ecocom.2016.12.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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38
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Bostanghadiri N, Pormohammad A, Chirani AS, Pouriran R, Erfanimanesh S, Hashemi A. Comprehensive review on the antimicrobial potency of the plant polyphenol Resveratrol. Biomed Pharmacother 2017; 95:1588-1595. [PMID: 28950659 DOI: 10.1016/j.biopha.2017.09.084] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2017] [Revised: 09/14/2017] [Accepted: 09/18/2017] [Indexed: 02/09/2023] Open
Abstract
Treatment of some infectious diseases are becoming more complicated because of increasing drug resistance rate and lack of proper antibiotics. Because of the rapid increase in drug-resistance trend, there is an urgent need for alternative microbicides to control infectious diseases. Resveratrol (RSV) is a small plant polyphenol that is naturally produced and distributed in 72 particular families of plants. The usage of natural derivatives such as RSV, have become popular among researchers for curing acute and chronic diseases. The purpose of the preset study was to comprehensively review and survey the antimicrobial potency of RSV. The present study demonstrates RSV as a natural antimicrobial agent.
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Affiliation(s)
- Narjess Bostanghadiri
- Department of Microbiology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ali Pormohammad
- Department of Microbiology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Alireza Salimi Chirani
- Department of Microbiology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ramin Pouriran
- School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Soroor Erfanimanesh
- Department of Microbiology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Hashemi
- Department of Microbiology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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39
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Vahdati AR, Sprouffske K, Wagner A. Effect of Population Size and Mutation Rate on the Evolution of RNA Sequences on an Adaptive Landscape Determined by RNA Folding. Int J Biol Sci 2017; 13:1138-1151. [PMID: 29104505 PMCID: PMC5666329 DOI: 10.7150/ijbs.19436] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Accepted: 07/05/2017] [Indexed: 02/04/2023] Open
Abstract
The dynamics of populations evolving on an adaptive landscape depends on multiple factors, including the structure of the landscape, the rate of mutations, and effective population size. Existing theoretical work often makes ad hoc and simplifying assumptions about landscape structure, whereas experimental work can vary important parameters only to a limited extent. We here overcome some of these limitations by simulating the adaptive evolution of RNA molecules, whose fitness is determined by the thermodynamics of RNA secondary structure folding. We study the influence of mutation rates and population sizes on final mean population fitness, on the substitution rates of mutations, and on population diversity. We show that evolutionary dynamics cannot be understood as a function of mutation rate µ, population size N, or population mutation rate Nµ alone. For example, at a given mutation rate, clonal interference prevents the fixation of beneficial mutations as population size increases, but larger populations still arrive at a higher mean fitness. In addition, at the highest population mutation rates we study, mean final fitness increases with population size, because small populations are driven to low fitness by the relatively higher incidence of mutations they experience. Our observations show that mutation rate and population size can interact in complex ways to influence the adaptive dynamics of a population on a biophysically motivated fitness landscape.
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Affiliation(s)
- Ali R Vahdati
- Institute of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.,The Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Kathleen Sprouffske
- Institute of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.,The Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Andreas Wagner
- Institute of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.,The Swiss Institute of Bioinformatics, Lausanne, Switzerland.,The Santa Fe Institute, Santa Fe, USA
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40
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41
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Does Antibiotic Resistance Evolve in Hospitals? Bull Math Biol 2016; 79:191-208. [PMID: 27924410 DOI: 10.1007/s11538-016-0232-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Accepted: 11/02/2016] [Indexed: 10/20/2022]
Abstract
Nosocomial outbreaks of bacteria are well documented. Based on these incidents, and the heavy usage of antibiotics in hospitals, it has been assumed that antibiotic resistance evolves in hospital environments. To test this assumption, we studied resistance phenotypes of bacteria collected from patient isolates at a community hospital over a 2.5-year period. A graphical model analysis shows no association between resistance and patient information other than time of arrival. This allows us to focus on time-course data. We introduce a hospital transmission model, based on negative binomial delay. Our main contribution is a statistical hypothesis test called the Nosocomial Evolution of Resistance Detector (NERD). It calculates the significance of resistance trends occurring in a hospital. It can inform hospital staff about the effects of various practices and interventions, can help detect clonal outbreaks, and is available as an R package. We applied the NERD method to each of the 16 antibiotics in the study via 16 hypothesis tests. For 13 of the antibiotics, we found that the hospital environment had no significant effect on the evolution of resistance; the hospital is merely a piece of the larger picture. The p-values obtained for the other three antibiotics (cefepime, ceftazidime, and gentamicin) indicate that particular care should be taken in hospital practices with these antibiotics. One of the three, ceftazidime, was significant after accounting for multiple hypotheses, indicating a trend of decreased resistance for this drug.
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42
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Li HJ, Cheng Q, Wang L. Understanding spatial spread of emerging infectious diseases in contemporary populations: Comment on "Pattern transitions in spatial epidemics: Mechanisms and emergent properties" by Gui-Quan Sun et al. Phys Life Rev 2016; 19:95-97. [PMID: 27818036 DOI: 10.1016/j.plrev.2016.10.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Accepted: 10/21/2016] [Indexed: 11/26/2022]
Affiliation(s)
- Hui-Jia Li
- School of Management Science and Engineering, Central University of Finance and Economics, Beijing 100080, China
| | - Qing Cheng
- Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China
| | - Lin Wang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region.
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