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1. Multiple infection theory rather than 'socio‐virology'? A commentary on Leeks et al. 2023.

Author

Alizon, Samuel

Subjects
*LEEK, *LIFE history theory, *SOCIAL context, *INFECTION, *EPIDEMIOLOGY, *MOLECULAR virology
Abstract

The article explores the concept of "sociovirology" and its application in studying the evolution of virus life-history traits. It emphasizes the social nature of viruses and the importance of understanding their interactions with hosts. The article discusses the challenges of measuring virus fitness in a social context and the role of relatedness in shaping selective pressures on virus populations. It also suggests that evolutionary epidemiology and multiple infection theory can provide useful frameworks for studying virus evolution. The article raises questions about the appropriateness of the term "sociovirology" and highlights the need for a formal definition of fitness in this context. [Extracted from the article]

Published
2023
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4. Epidemiological and clinical insights from SARS-CoV-2 RT-PCR cycle amplification values

Author

Alizon, Samuel, Selinger, Christian, Sofonea, Mircea, Haim-Boukobza, Stéphanie, Giannoli, Jean-Marc, Ninove, Laetitia, Pillet, Sylvie, Thibault, V., De Rougemont, Alexis, tumiotto, camille, Solis, Morgane, L’honneur, Anne-Sophie, Stephan, Robin, Bressollette-Bodin, Céline, Salmona, Maud, L'Honneur, Anne-Sophie, Behillil, Sylvie, Lefeuvre, Caroline, Dina, Julia, Hantz, Sébastien, Hartard, Cédric, Veyer, David, Delagrèverie, Héloïse, Fourati, Slim, Visseaux, Benoît, Henquell, Cécile, Lina, Bruno, Foulongne, Vincent, Burrel, Sonia, Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Institut de Recherche pour le Développement (IRD), Laboratoire CERBA [Saint Ouen l'Aumône], Biogroupe, Université de Brest (UBO), Unité des Virus Emergents (UVE), Aix Marseille Université (AMU)-Institut de Recherche pour le Développement (IRD)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Hospitalier Universitaire Méditerranée Infection (IHU Marseille), Centre Hospitalier Universitaire de Saint-Etienne (CHU de Saint-Etienne), CHU Pontchaillou [Rennes], CHU Dijon, Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon), Microbiologie cellulaire et moléculaire et pathogénicité (MCMP), Université Bordeaux Segalen - Bordeaux 2-Centre National de la Recherche Scientifique (CNRS), CHU Bordeaux [Bordeaux], Centre Hospitalier Universitaire de Nîmes (CHU Nîmes), Centre hospitalier universitaire de Nantes (CHU Nantes), Centre de Recherche en Transplantation et Immunologie (U1064 Inserm - CRTI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Nantes - UFR de Médecine et des Techniques Médicales (UFR MEDECINE), Université de Nantes (UN)-Université de Nantes (UN), Centre d’Investigation Clinique de Nantes (CIC Nantes), Université de Nantes (UN)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre hospitalier universitaire de Nantes (CHU Nantes), Hopital Saint-Louis [AP-HP] (AP-HP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Immunologie humaine, physiopathologie & immunothérapie (HIPI (UMR_S_976 / U976)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), Hôpital Cochin [AP-HP], Génétique Moléculaire des Virus à ARN - Molecular Genetics of RNA Viruses (GMV-ARN (UMR_3569 / U-Pasteur_2)), Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris]-Université de Paris (UP), Centre National de Référence des virus des infections respiratoires (dont la grippe) - National Reference Center Virus Influenzae [Paris] (CNR), Institut Pasteur [Paris], Centre Hospitalier Universitaire d'Angers (CHU Angers), PRES Université Nantes Angers Le Mans (UNAM), Hémodynamique, Interaction Fibrose et Invasivité tumorales Hépatiques (HIFIH), Université d'Angers (UA), Groupe de Recherche sur l'Adaptation Microbienne (GRAM 2.0), Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU), CHU Caen, Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN), CHU Limoges, Anti-infectieux : supports moléculaires des résistances et innovations thérapeutiques (RESINFIT), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Génomique, Environnement, Immunité, Santé, Thérapeutique (GEIST), Université de Limoges (UNILIM)-Université de Limoges (UNILIM)-CHU Limoges, Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Laboratoire de Chimie Physique et Microbiologie pour les Matériaux et l'Environnement (LCPME), Institut de Chimie du CNRS (INC)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Hôpital Avicenne [AP-HP], Hôpital Henri Mondor, Infection, Anti-microbiens, Modélisation, Evolution (IAME (UMR_S_1137 / U1137)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP)-Université Sorbonne Paris Nord, AP-HP - Hôpital Bichat - Claude Bernard [Paris], Infection Inflammation et Interaction Hôtes Pathogènes [CHU Clermont-Ferrand] (3IHP ), Direction de la recherche clinique et de l’innovation [CHU Clermont-Ferrand] (DRCI), CHU Clermont-Ferrand-CHU Clermont-Ferrand, CHU Clermont-Ferrand, Centre National de Référence des Virus des Infections Respiratoires (dont la Grippe) [Lyon] (CNR), Institut des Agents Infectieux [Lyon] (IAI), Hospices Civils de Lyon (HCL)-Hospices Civils de Lyon (HCL), Hôpital de la Croix-Rousse [CHU - HCL], Hospices Civils de Lyon (HCL), CHU Montpellier, Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), CHU Limoges-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Génomique, Environnement, Immunité, Santé, Thérapeutique (GEIST), and Université de Limoges (UNILIM)-Université de Limoges (UNILIM)

Subjects
medicine.medical_specialty, 2019-20 coronavirus outbreak, Coronavirus disease 2019 (COVID-19), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Biology, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, 03 medical and health sciences, 0302 clinical medicine, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, Patient age, Epidemiology, Pandemic, medicine, 030212 general & internal medicine, 030304 developmental biology, [SDV.EE.SANT]Life Sciences [q-bio]/Ecology, environment/Health, 0303 health sciences, [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], Virology, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 3. Good health, Real-time polymerase chain reaction, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, National database, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, [SDV.EE.IEO]Life Sciences [q-bio]/Ecology, environment/Symbiosis
Abstract

The SARS-CoV-2 pandemic has led to an unprecedented daily use of molecular RT-PCR tests. These tests are interpreted qualitatively for diagnosis, and the relevance of the test result intensity, i.e. the number of amplification cycles (Ct), is debated because of strong potential biases. We analyze a national database of tests performed on more than 2 million individuals between January and November 2020. Although we findCtvalues to vary depending on the testing laboratory or the assay used, we detect strong significant trends with patient age, number of days after symptoms onset, or the state of the epidemic (the temporal reproduction number) at the time of the test. These results suggest thatCtvalues can be used to improve short-term predictions for epidemic surveillance.

Published
2021

5. Analyzing and Modeling the Spread of SARS-CoV-2 Omicron Lineages BA.1 and BA.2, France, September 2021-February 2022.

Author

Sofonea, Mircea T., Roquebert, Bénédicte, Foulongne, Vincent, Morquin, David, Verdurme, Laura, Trombert-Paolantoni, Sabine, Roussel, Mathilde, Bonetti, Jean-Christophe, Zerah, Judith, Haim-Boukobza, Stéphanie, and Alizon, Samuel

Abstract

We analyzed 324,734 SARS-CoV-2 variant screening tests from France enriched with 16,973 whole-genome sequences sampled during September 1, 2021-February 28, 2022. Results showed the estimated growth advantage of the Omicron variant over the Delta variant to be 105% (95% CI 96%-114%) and that of the BA.2 lineage over the BA.1 lineage to be 49% (95% CI 44%-52%). Quantitative PCR cycle threshold values were consistent with an increased ability of Omicron to generate breakthrough infections. Epidemiologic modeling shows that, in spite of its decreased virulence, the Omicron variant can generate important critical COVID-19 activity in hospitals in France. The magnitude of the BA.2 wave in hospitals depends on the level of relaxing of control measures but remains lower than that of BA.1 in median scenarios. [ABSTRACT FROM AUTHOR]

Published
2022
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6. The source of individual heterogeneity shapes infectious disease outbreaks.

Author

Elie, Baptiste, Selinger, Christian, and Alizon, Samuel

Subjects
DISEASE outbreaks, COMMUNICABLE diseases, INFECTIOUS disease transmission, HETEROGENEITY, EMERGING infectious diseases
Abstract

There is known heterogeneity between individuals in infectious disease transmission patterns. The source of this heterogeneity is thought to affect epidemiological dynamics but studies tend not to control for the overall heterogeneity in the number of secondary cases caused by an infection. To explore the role of individual variation in infection duration and transmission rate in parasite emergence and spread, while controlling for this potential bias, we simulate stochastic outbreaks with and without parasite evolution. As expected, heterogeneity in the number of secondary cases decreases the probability of outbreak emergence. Furthermore, for epidemics that do emerge, assuming more realistic infection duration distributions leads to faster outbreaks and higher epidemic peaks. When parasites require adaptive mutations to cause large epidemics, the impact of heterogeneity depends on the underlying evolutionary model. If emergence relies on within-host evolution, decreasing the infection duration variance decreases the probability of emergence. These results underline the importance of accounting for realistic distributions of transmission rates to anticipate the effect of individual heterogeneity on epidemiological dynamics. [ABSTRACT FROM AUTHOR]

Published
2022
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7. Non-Markovian modelling highlights the importance of age structure on Covid-19 epidemiological dynamics.

Author

REYNÉ, BASTIEN, RICHARD, QUENTIN, SELINGER, CHRISTIAN, SOFONEA, MIRCEA T., DJIDJOU-DEMASSÉ, RAMSES, and ALIZON, SAMUEL

Subjects
COVID-19, PARTIAL differential equations, ORDINARY differential equations, COVID-19 pandemic, HEALTH policy, DYNAMICS
Abstract

The Covid-19 pandemic outbreak was followed by a huge amount of modelling studies in order to rapidly gain insights to implement the best public health policies. Most of these compartmental models involved ordinary differential equations (ODEs) systems. Such a formalism implicitly assumes that the time spent in each compartment does not depend on the time already spent in it, which is at odds with the clinical data. To overcome this "memoryless" issue, a widely used solution is to increase and chain the number of compartments of a unique reality (e.g. have infected individual move between several compartments). This allows for greater heterogeneity and thus be closer to the observed situation, but also tends to make the whole model more difficult to apprehend and parameterize. We develop a non-Markovian alternative formalism based on partial differential equations (PDEs) instead of ODEs, which, by construction, provides a memory structure for each compartment thereby allowing us to limit the number of compartments. We apply our model to the French 2021 SARS-CoV-2 epidemic and, while accounting for vaccine-induced and natural immunity, we analyse and determine the major components that contributed to the Covid-19 hospital admissions. The results indicate that the observed vaccination rate alone is not enough to control the epidemic, and a global sensitivity analysis highlights a huge uncertainty attributable to the age-structured contact matrix. Our study shows the flexibility and robustness of PDE formalism to capture national COVID-19 dynamics and opens perspectives to study medium or long-term scenarios involving immune waning or virus evolution. [ABSTRACT FROM AUTHOR]

Published
2022
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8. SARS‐CoV‐2 virulence evolution: Avirulence theory, immunity and trade‐offs.

Author

Alizon, Samuel and Sofonea, Mircea T.

Subjects
*SARS-CoV-2, *PANDEMICS, *COVID-19 pandemic, *IMMUNITY, *EPIDEMICS
Abstract

The COVID‐19 pandemic has led to a resurgence of the debate on whether host–parasite interactions should evolve towards avirulence. In this review, we first show that SARS‐CoV‐2 virulence is evolving, before explaining why some expect the mortality caused by the epidemic to converge towards that of human seasonal alphacoronaviruses. Leaning on existing theory, we then include viral evolution into the picture and discuss hypotheses explaining why the virulence has increased since the beginning of the pandemic. Finally, we mention some potential scenarios for the future. [ABSTRACT FROM AUTHOR]

Published
2021
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9. Detecting Rapid Spread of SARS-CoV-2 Variants, France, January 26-February 16, 2021.

Author

Haim-Boukobza, Stéphanie, Roquebert, Bénédicte, Trombert-Paolantoni, Sabine, Lecorche, Emmanuel, Verdurme, Laura, Foulongne, Vincent, Selinger, Christian, Michalakis, Yannis, Sofonea, Mircea T., and Alizon, Samuel

Subjects
SARS-CoV-2, COVID-19, COVID-19 pandemic
Abstract

Variants of severe acute respiratory syndrome coronavirus 2 raise concerns regarding the control of coronavirus disease epidemics. We analyzed 40,000 specific reverse transcription PCR tests performed on positive samples during January 26-February 16, 2021, in France. We found high transmission advantage of variants and more advanced spread than anticipated. [ABSTRACT FROM AUTHOR]

Published
2021
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10. Inferring epidemiological parameters from phylogenies using regression-ABC: a comparative study

Author

Saulnier, Emma, Gascuel, Olivier, Alizon, Samuel, Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Evolution Théorique et Expérimentale (MIVEGEC-ETE), Perturbations, Evolution, Virulence (PEV), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris], Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Centre interdisciplinaire de recherche en biologie (CIRB), Labex MemoLife, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Collège de France (CdF (institution))-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)

Subjects
Computer and Information Sciences, Leaves, Epidemiology, Plant Science, Research and Analysis Methods, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, Disease Outbreaks, Sierra Leone, Kernel Methods, Risk Factors, Epidemiological Statistics, Medicine and Health Sciences, Humans, Computer Simulation, Evolutionary Systematics, lcsh:QH301-705.5, Phylogeny, Taxonomy, Data Management, Evolutionary Biology, Models, Statistical, Incidence, Plant Anatomy, Simulation and Modeling, Applied Mathematics, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], Biology and Life Sciences, Bayes Theorem, Phylogenetic Analysis, Hemorrhagic Fever, Ebola, Ebolavirus, Virus Latency, Phylogenetics, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, lcsh:Biology (General), Physical Sciences, Regression Analysis, Epidemiological Methods and Statistics, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Algorithms, Mathematics, Research Article
Abstract

Inferring epidemiological parameters such as the R0 from time-scaled phylogenies is a timely challenge. Most current approaches rely on likelihood functions, which raise specific issues that range from computing these functions to finding their maxima numerically. Here, we present a new regression-based Approximate Bayesian Computation (ABC) approach, which we base on a large variety of summary statistics intended to capture the information contained in the phylogeny and its corresponding lineage-through-time plot. The regression step involves the Least Absolute Shrinkage and Selection Operator (LASSO) method, which is a robust machine learning technique. It allows us to readily deal with the large number of summary statistics, while avoiding resorting to Markov Chain Monte Carlo (MCMC) techniques. To compare our approach to existing ones, we simulated target trees under a variety of epidemiological models and settings, and inferred parameters of interest using the same priors. We found that, for large phylogenies, the accuracy of our regression-ABC is comparable to that of likelihood-based approaches involving birth-death processes implemented in BEAST2. Our approach even outperformed these when inferring the host population size with a Susceptible-Infected-Removed epidemiological model. It also clearly outperformed a recent kernel-ABC approach when assuming a Susceptible-Infected epidemiological model with two host types. Lastly, by re-analyzing data from the early stages of the recent Ebola epidemic in Sierra Leone, we showed that regression-ABC provides more realistic estimates for the duration parameters (latency and infectiousness) than the likelihood-based method. Overall, ABC based on a large variety of summary statistics and a regression method able to perform variable selection and avoid overfitting is a promising approach to analyze large phylogenies., Author summary Given the rapid evolution of many pathogens, analysing their genomes by means of phylogenies can inform us about how they spread. This is the focus of the field known as “phylodynamics”. Most existing methods inferring epidemiological parameters from virus phylogenies are limited by the difficulty of handling complex likelihood functions, which commonly incorporate latent variables. Here, we use an alternative method known as regression-based Approximate Bayesian Computation (ABC), which circumvents this problem by using simulations and dataset comparisons. Since phylogenies are difficult to compare to one another, we introduce many summary statistics to describe them and take advantage of current machine learning techniques able to perform variable selection. We show that the accuracy we reach is comparable to that of existing methods. This accuracy increases with phylogeny size and can even be higher than that of existing methods for some parameters. Overall, regression-based ABC opens new perspectives to infer epidemiological parameters from large phylogenies.

Published
2017

11. Why is HIV not vector-borne?

Author

Day, Troy, Mideo, Nicole, Alizon, Samuel, and Queen's University [Kingston, Canada]

Subjects
disease, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], transmission, HIV, insect-borne transmission, mosquito, Original Articles, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, virulence, AIDS, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, evolutionary medicine, evolution, epidemiology, vector, ComputingMilieux_MISCELLANEOUS, trade-off
Abstract

Many pathogens of humans are blood borne, including HIV, Malaria, Hepatitis B and C, West Nile virus, Dengue, and other viral hemorrhagic fevers. Although several of these pathogens are transmitted by blood-feeding arthropods, HIV is not. A number of properties of HIV and its life cycle have been identified as proximate explanations for the absence of arthropod transmission, but little consideration has been given to why HIV has not evolved this form of transmission. We consider the empirical evidence for arthropod transmission, and suggest that mechanical transmission has not evolved in HIV because such strains would induce a faster onset of AIDS during infection, which would thereby limit their ability to spread. On the other hand, it is not as clear why biological transmission has not occurred. Available data suggests that a lack of appropriate genetic variation in HIV is one explanation, but it is also possible that a conflict between natural selection occurring within and between infected individuals has prevented its evolution instead. We discuss the potential significance of these ideas, and argue that taking such an evolutionary perspective broadens our understanding of infectious diseases and the potential consequences of public health interventions.

Published
2008

12. Towards a multi-level and a multi-disciplinary approach to DNA oncovirus virulence.

Author

Alizon, Samuel, Bravo, Ignacio G., Farrell, Paul J., and Roberts, Sally

Subjects
*RETROVIRUSES, *ONCOGENIC viruses, *TRANSMISSIBLE tumors, *VIRUS diseases, *IMMUNIZATION
Abstract

One out of 10 cancers is estimated to arise frominfections by a handful of oncogenic viruses. These infectious cancers constitute an opportunity for primary prevention through immunization against the viral infection, for early screening through molecular detection of the infectious agent, and potentially for specific treatments, by targeting the virus as a marker of cancer cells. Accomplishing these objectives will require a detailed understanding of the natural history of infections, the mechanisms by which the viruses contribute to disease, the mutual adaptation of viruses and hosts, and the possible viral evolution in the absence and in the presence of the public health interventions conceived to target them. This issue showcases the current developments in experimental tissue-like and animal systems, mathematical models and evolutionary approaches to understand DNA oncoviruses. Our global aim is to provide proximate explanations to the present-day interface and interactions between virus and host, as well as ultimate explanations about the adaptive value of these interactions and about the evolutionary pathways that have led to the current malignant phenotype of oncoviral infections. This article is part of the theme issue 'Silent cancer agents: multidisciplinary modelling of human DNA oncoviruses'. [ABSTRACT FROM AUTHOR]

Published
2019
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13. Parasite co-transmission and the evolutionary epidemiology of virulence

Author

Alizon, Samuel, Evolution Théorique et Expérimentale (MIVEGEC-ETE), Perturbations, Evolution, Virulence (PEV), Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), and Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)

Subjects
virulence, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], evolution, transmission, epidemiology, multiple infections, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, ComputingMilieux_MISCELLANEOUS
Abstract

International audience; no abstract

Published
2013

14. Coevolution of virulence and immunosuppression in multiple infections.

Author

Kamiya, Tsukushi, Mideo, Nicole, and Alizon, Samuel

Subjects
COEVOLUTION, IMMUNOSUPPRESSION, MICROBIAL virulence, PHYSIOLOGICAL adaptation, PARASITIC diseases, EPIDEMIOLOGY
Abstract

Abstract: Many components of host–parasite interactions have been shown to affect the way virulence (i.e. parasite‐induced harm to the host) evolves. However, coevolution of multiple parasite traits is often neglected. We explore how an immunosuppressive adaptation of parasites affects and coevolves with virulence in multiple infections. Applying the adaptive dynamics framework to epidemiological models with coinfection, we show that immunosuppression is a double‐edged sword for the evolution of virulence. On one hand, it amplifies the adaptive benefit of virulence by increasing the abundance of coinfections through epidemiological feedbacks. On the other hand, immunosuppression hinders host recovery, prolonging the duration of infection and elevating the cost of killing the host (as more opportunities for transmission will be forgone if the host dies). The balance between the cost and benefit of immunosuppression varies across different background mortality rates of hosts. In addition, we find that immunosuppression evolution is influenced considerably by the precise trade‐off shape determining the effect of immunosuppression on host recovery and susceptibility to further infection. These results demonstrate that the evolution of virulence is shaped by immunosuppression while highlighting that the evolution of immune evasion mechanisms deserves further research attention. [ABSTRACT FROM AUTHOR]

Published
2018
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15. Bridging scales in the evolution of infectious disease life histories: theory

Author

Day, Troy, Alizon, Samuel, Mideo, Nicole, Evolution Théorique et Expérimentale (MIVEGEC-ETE), Perturbations, Evolution, Virulence (PEV), Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), and Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)

Subjects
Time Factors, model, Models, Genetic, Virulence, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], Population Dynamics, Models, Theoretical, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, Biological Evolution, Communicable Diseases, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, within-host, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, evolution, Price equation, Animals, epidemiology, Epidemics, nested model
Abstract

International audience; A significant goal of recent theoretical research on pathogen evolution has been to develop theory that bridges within- and between-host dynamics. The main approach used to date is one that nests within-host models of pathogen replication in models for the between-host spread of infectious diseases. Although this provides an elegant approach, it nevertheless suffers from some practical difficulties. In particular, the information required to satisfactorily model the mechanistic details of the within-host dynamics is not often available. Here, we present a theoretical approach that circumvents these difficulties by quantifying the relevant within-host factors in an empirically tractable way. The approach is closely related to quantitative genetic models for function-valued traits, and it also allows for the prediction of general characteristics of disease life history, including the timing of virulence, transmission, and host recovery. In a companion paper, we illustrate the approach by applying it to data from a model system of malaria.

Published
2011

16. Phylodynamique des infections virales.

Author

Alizon, Samuel and Saulnier, Emma

Abstract

Phylodynamics is a recent field that aims at using genetic sequence data to estimate epidemiological parameters such as the viral population growth rate, the number of infections in the population or even their duration. Its main underlying assumption is that the way viruses spread leaves marks in their genome. In this review, we first introduce the originality of phylodynamics inferences compared to 'classical' phylogenetic approaches. Then, we present the novelty of using phylogenies of infections compared to species trees, while giving some directions to infer of such objects. We discuss the birth of phylodynamics and its first successes, in order to present some of the questions the approach can address. Finally, we highlight some future challenges for the field. [ABSTRACT FROM AUTHOR]

Published
2017
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17. Exposing the diversity of multiple infection patterns.

Author

Sofonea, Mircea T., Alizon, Samuel, and Michalakis, Yannis

Subjects
*BIODIVERSITY, *INFECTION, *GAUSSIAN distribution, *INJECTIONS, *GENOTYPES
Abstract

Natural populations often have to cope with genetically distinct parasites that can coexist, or not, within the same hosts. Theoretical models addressing the evolution of virulence have considered two within host infection outcomes, namely superinfection and coinfection. The field somehow became limited by this dichotomy that does not correspond to an empirical reality as other infection patterns, namely sets of within-host infection outcomes, are possible. We indeed formally prove there are over one hundred different infection patterns solely for recoverable chronic infections caused by two genetically distinct horizontally-transmitted microparasites. We afterwards highlight eight infection patterns using an explicit modelling of within-host dynamics that captures a large range of ecological interactions, five of which have been neglected so far. To clarify the terminology related to multiple infections, we introduce terms describing these new relevant patterns and illustrate them with existing biological systems. These infection patterns constitute a new framework for linking within-host and between-host dynamics, which is a requirement to forward our understanding of the epidemiology and the evolution of parasites. [ABSTRACT FROM AUTHOR]

Published
2017
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18. From within-host interactions to epidemiological competition: a general model for multiple infections.

Author

Sofonea, Mircea T., Alizon, Samuel, and Michalakis, Yannis

Subjects
*HOST-parasite relationships, *EPIDEMIOLOGICAL research, *MICROBIAL virulence, *INFECTION, *GENOTYPE-environment interaction
Abstract

Many hosts are infected by several parasite genotypes at a time. In these co-infected hosts, parasites can interact in various ways thus creating diverse within-host dynamics, making it difficult to predict the expression and the evolution of virulence. Moreover, multiple infections generate a combinatorial diversity of cotransmission routes at the host population level, which complicates the epidemiology and may lead to non-trivial outcomes. We introduce a new model for multiple infections, which allows any number of parasite genotypes to infect hosts and potentially coexist in the population. In our model, parasites affect one another's within-host growth through density-dependent interactions and by means of public goods and spite. These within-host interactions determine virulence, recovery and transmission rates, which are then integrated in a transmission network. We use analytical solutions and numerical simulations to investigate epidemiological feedbacks in host populations infected by several parasite genotypes. Finally, we discuss general perspectives on multiple infections. [ABSTRACT FROM AUTHOR]

Published
2015
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19. Adaptive virulence evolution: the good old fitness-based approach.

Author

Alizon, Samuel and Michalakis, Yannis

Subjects
*MICROBIAL virulence, *LIFE cycles (Biology), *BIOLOGICAL fitness, *EPIDEMIOLOGY, *HOSTS (Biology), *PATHOGENIC microorganisms, *BIOLOGICAL evolution
Abstract

Infectious diseases could be expected to evolve towards complete avirulence to their hosts if given enough time. However, this is not the case. Often, virulence is maintained because it is linked to adaptive advantages to the parasite, a situation that is often associated with the hypothesis known as the transmission–virulence trade-off hypothesis. Here, we argue that this hypothesis has three limitations, which are related to how virulence is defined, the possibility of multiple trade-offs, and the difficulty of testing the hypothesis empirically. By adopting a fitness-based approach, where the relation between virulence and the fitness of the parasite throughout its life cycle is directly assessed, it is possible to address these limitations and to determine directly whether virulence is adaptive. [ABSTRACT FROM AUTHOR]

Published
2015
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20. Epidemic Spread on Weighted Networks.

Author

Kamp, Christel, Moslonka-Lefebvre, Mathieu, and Alizon, Samuel

Subjects
EPIDEMICS, DISTRIBUTION (Probability theory), EPIDEMIOLOGY, COMPUTER simulation, COMMUNICABLE diseases
Abstract

The contact structure between hosts shapes disease spread. Most network-based models used in epidemiology tend to ignore heterogeneity in the weighting of contacts between two individuals. However, this assumption is known to be at odds with the data for many networks (e.g. sexual contact networks) and to have a critical influence on epidemics' behavior. One of the reasons why models usually ignore heterogeneity in transmission is that we currently lack tools to analyze weighted networks, such that most studies rely on numerical simulations. Here, we present a novel framework to estimate key epidemiological variables, such as the rate of early epidemic expansion () and the basic reproductive ratio (), from joint probability distributions of number of partners (contacts) and number of interaction events through which contacts are weighted. These distributions are much easier to infer than the exact shape of the network, which makes the approach widely applicable. The framework also allows for a derivation of the full time course of epidemic prevalence and contact behaviour, which we validate with numerical simulations on networks. Overall, incorporating more realistic contact networks into epidemiological models can improve our understanding of the emergence and spread of infectious diseases. [ABSTRACT FROM AUTHOR]

Published
2013
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21. Introducing the Outbreak Threshold in Epidemiology.

Author

Hartfield, Matthew and Alizon, Samuel

Subjects
*EPIDEMIOLOGICAL research, *EPIDEMIC research, *DISEASE outbreaks, *PATHOGENIC microorganisms, *MICROORGANISMS
Abstract

When a pathogen is rare in a host population, there is a chance that it will die out because of stochastic effects instead of causing a major epidemic. Yet no criteria exist to determine when the pathogen increases to a risky level, from which it has a large chance of dying out, to when a major outbreak is almost certain. We introduce such an outbreak threshold (T0), and find that for large and homogeneous host populations, in which the pathogen has a reproductive ratio R0, on the order of 1/Log(R0) infected individuals are needed to prevent stochastic fade-out during the early stages of an epidemic. We also show how this threshold scales with higher heterogeneity and R0 in the host population. These results have implications for controlling emerging and re-emerging pathogens. [ABSTRACT FROM AUTHOR]

Published
2013
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22. Multiple infections and the evolution of virulence.

Author

Alizon, Samuel, de Roode, Jacobus C., Michalakis, Yannis, and Drake, John

Subjects
*BIOLOGICAL evolution, *MICROBIAL virulence, *PUBLIC health, *COMMUNICABLE diseases, *EPIDEMIOLOGY, *HEALTH outcome assessment
Abstract

Infections that consist of multiple parasite strains or species are common in the wild and are a major public health concern. Theory suggests that these infections have a key influence on the evolution of infectious diseases and, more specifically, on virulence evolution. However, we still lack an overall vision of the empirical support for these predictions. We argue that within-host interactions between parasites largely determine how virulence evolves and that experimental data support model predictions. Then, we explore the main limitation of the experimental study of such 'mixed infections', which is that it draws conclusions on evolutionary outcomes from studies conducted at the individual level. We also discuss differences between coinfections caused by different strains of the same species or by different species. Overall, we argue that it is possible to make sense out of the complexity inherent to multiple infections and that experimental evolution settings may provide the best opportunity to further our understanding of virulence evolution. [ABSTRACT FROM AUTHOR]

Published
2013
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23. Within-host parasite cooperation and the evolution of virulence.

Author

Alizon, Samuel and Lion, Sébastien

Subjects
*HOST-parasite relationships, *BIOLOGICAL evolution, *MICROBIAL virulence, *EPIDEMIOLOGY, *KIN selection (Evolution), *SIDEROPHORES
Abstract

Infections by multiple genotypes are common in nature and are known to select for higher levels of virulence for some parasites. When parasites produce public goods (PGs) within the host, such co-infections have been predicted to select for lower levels of virulence. However, this prediction is based on simplifying assumptions regarding epidemiological feedbacks on the multiplicity of infections (MOI). Here, we analyse the case of parasites producing a PG (for example, siderophore-producing bacteria) using a nested model that ties together within-host and epidemiological processes. We find that the prediction that co-infection should select for less virulent strains for PG-producing parasites is only valid if both parasite transmission and virulence are linear functions of parasite density. If there is a trade-off relationship such that virulence increases more rapidly than transmission, or if virulence also depends on the total amount of PGs produced, then more complex relationships between virulence and the MOI are predicted. Our results reveal that explicitly taking into account the distribution of parasite strains among hosts could help better understand the selective pressures faced by parasites at the population level. [ABSTRACT FROM AUTHOR]

Published
2011
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24. Epidemiological and clinical consequences of within-host evolution

Author

Alizon, Samuel, Luciani, Fabio, and Regoes, Roland R.

Subjects
*EPIDEMIOLOGY, *HOSTS (Biology), *BIOLOGICAL evolution, *BACTERIA, *VIRUS diseases, *PATHOGENIC microorganisms, *MICROBIAL virulence, *EMPIRICAL research
Abstract

Many viruses and bacteria are known to evolve rapidly over the course of an infection. However, epidemiological studies generally assume that within-host evolution is an instantaneous process. We argue that the dynamics of within-host evolution has implications at the within-host and at the between-host levels. We first show that epidemiologists should consider within-host evolution, notably because it affects the genotype of the pathogen that is transmitted. We then present studies that investigate evolution at the within-host level and examine the extent to which these studies can help to understand infection traits involved in the epidemiology (e.g. transmission rate, virulence, recovery rate). Finally, we discuss how new techniques for data acquisition can open new perspectives for empirical and theoretical research. [ABSTRACT FROM AUTHOR]

Published
2011
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25. Multiple Infections, Immune Dynamics, and the Evolution of Virulence.

Author

Alizon, Samuel and van Baalen, Minus

Subjects
*MICROBIAL virulence, *PARASITISM, *IMMUNE system, *ANIMAL species, *EPIDEMIOLOGY, *ANIMAL population estimates, *ECOLOGICAL heterogeneity, *SCIENTIFIC method, HOSTS of parasitic gastropods
Abstract

Understanding the effect of multiple infections is essential for the prediction (and eventual control) of virulence evolution. Some theoretical studies have considered the possibility that several strains coexist in the same host (coinfection), but few have taken their within-host dynamics explicitly into account. Here, we develop a nested approach based on a simple model for the interaction of parasite strains with their host's immune system. We study virulence evolution by linking the within-host dynamics to an epidemiological framework that incorporates multiple infections. Our model suggests that antigenically similar parasite strains cannot coexist in the long term inside a host. We also find that the optimal level of virulence increases with the efficiency of multiple infections. Finally, we notice that coinfections create heterogeneity in the host population (with susceptible hosts and infected hosts), which can lead to evolutionary branching in the parasite population and the emergence of a hypervirulent parasite strategy. We interpret this result as a parasite specialization to the infectious state of the hosts. Our study has experimental and theoretical implications in a virulence management perspective. [ABSTRACT FROM AUTHOR]

Published
2008
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26. Linking within- and between-host dynamics in the evolutionary epidemiology of infectious diseases

Author

Mideo, Nicole, Alizon, Samuel, and Day, Troy

Subjects
*EPIDEMIOLOGY, *MATHEMATICAL models, *COMMUNICABLE diseases, *PATHOGENIC microorganisms, *HOST-parasite relationships, *BIOLOGICAL evolution
Abstract

Nested models (also called embedded models) explicitly link dynamical processes that occur at different scales. Recently there has been considerable interest in linking within- and between-host levels of disease dynamics in the study of pathogen evolution. Here we review the extent to which these nested models have increased our understanding of pathogen evolution. We suggest that, although such models have been useful for determining the nature of tradeoffs between epidemiological parameters and for evaluating the consequences of conflicting selection pressures at different scales, the vast majority of previous results could likely have been obtained without the use of nested models per se. Nevertheless, these models have proven very useful through their highlighting of the importance of within-host disease dynamics on pathogen evolution. [Copyright &y& Elsevier]

Published
2008
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27. Transmission–virulence trade-offs in vector-borne diseases

Author

Alizon, Samuel and van Baalen, Minus

Subjects
*MICROBIAL virulence genetics, *PARASITES, *EPIDEMIOLOGICAL research
Abstract

Abstract: Though it is commonly supposed that there is a trade-off between virulence and transmission, there is little data and little insight into what it should look like. Here, we consider the specific case of vector-borne parasites (inspired by human malaria) and analyse an embedded model to understand how specific life-cycle aspects may affect this trade-off. First, we find that, for such parasites, the transmission function may have an S-shape. Second, we find that the trade-off obtained for vector-borne parasites is less sensitive to parameter variations than the trade-off obtained for directly transmitted parasites. Third, we find that other parasite traits, such as the conversion from replicative to infective stages, could have important epidemiological implications. Finally, we compare the effect of treatments targeting either the asexual or the sexual parasite life-stage. [Copyright &y& Elsevier]

Published
2008
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28. Quantifying the epidemic spread of Ebola virus (EBOV) in Sierra Leone using phylodynamics.

Author

Alizon, Samuel, Lion, Sébastien, Murall, Carmen Lía, and Abbate, Jessica L

Subjects
*EBOLA virus, *PHYLOGENY, *EPIDEMICS, *GENOMES, *SEQUENCE analysis
Abstract

Measuring epidemic parameters early in an outbreak is essential to inform control efforts. Using the viral genome sequence and collection date from 78 infections in the 2014 Ebola virus outbreak in Sierra Leone, we estimate key epidemiological parameters such as infectious period duration (approximately 71 hours) and date of the first case in Sierra Leone (approximately April 25th). We also estimate the effective reproduction number, Re, (approximately 1.26), which is the number of secondary infections effectively caused by an infected individual and accounts for public health control measures. This study illustrates that phylodynamics methods, applied during the initial phase of an outbreak on fewer and more easily attainable data, can yield similar estimates to count-based epidemiological studies. [ABSTRACT FROM AUTHOR]

Published
2014
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30. Weighting for sex acts to understand the spread of STI on networks

Author

Moslonka-Lefebvre, Mathieu, Bonhoeffer, Sebastian, and Alizon, Samuel

Subjects
*SEXUALLY transmitted diseases, *SEX (Biology), *STATISTICAL weighting, *EPIDEMIOLOGY, *SEXUAL intercourse, *COMMUNICABLE diseases
Abstract

Abstract: Human sexual networks exhibit a heterogeneous structure where few individuals have many partners and many individuals have few partners. Network theory predicts that the spread of sexually transmitted infections (STI) on such networks should exhibit striking properties (e.g. rapid spread). However, these properties cannot be found in epidemiological data. Current network models typically assume a constant STI transmission risk per partnership, which is unrealistic because it implies that sexual activity is proportional to the number of partners and that individuals have the same activity with each partner. We develop a framework that allows us to weight any sexual network based on biological assumptions. Our results indicate that STI spreading on the resulting weighted networks do not have heterogeneous-related properties, which is consistent with data and earlier studies. [Copyright &y& Elsevier]

Published
2012
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31. Analyzing and Modeling the Spread of SARS-CoV-2 Omicron Lineages BA.1 and BA.2, France, September 2021–February 2022

Author

Mircea T. Sofonea, Bénédicte Roquebert, Vincent Foulongne, David Morquin, Laura Verdurme, Sabine Trombert-Paolantoni, Mathilde Roussel, Jean-Christophe Bonetti, Judith Zerah, Stéphanie Haim-Boukobza, Samuel Alizon, Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM), Laboratoire CERBA [Saint Ouen l'Aumône], CHU Montpellier, Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Centre interdisciplinaire de recherche en biologie (CIRB), Labex MemoLife, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Collège de France (CdF (institution))-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), projet MODVAR, financement ANRS0151, ANR-20-COVI-0098,PHYEPI,Intégration de données de séquences et d'incidence pour analyser et contrôler les épidémies virales(2020), Alizon, Samuel, and Intégration de données de séquences et d'incidence pour analyser et contrôler les épidémies virales - - PHYEPI2020 - ANR-20-COVI-0098 - COVID-19 - VALID

Subjects
Microbiology (medical), Epidemiology, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, [SDV.BID.SPT] Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, [SDV.BID.EVO] Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], [SDV.EE.ECO] Life Sciences [q-bio]/Ecology, environment/Ecosystems, Humans, [SDV.EE.SANT] Life Sciences [q-bio]/Ecology, environment/Health, [SDV.EE.SANT]Life Sciences [q-bio]/Ecology, environment/Health, [SDV.MP.VIR] Life Sciences [q-bio]/Microbiology and Parasitology/Virology, [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Virulence, SARS-CoV-2, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], COVID-19, [SDV.EE.IEO] Life Sciences [q-bio]/Ecology, environment/Symbiosis, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [SDE.BE] Environmental Sciences/Biodiversity and Ecology, Infectious Diseases, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, [SDV.GEN.GPO] Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, France, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, [SDV.EE.IEO]Life Sciences [q-bio]/Ecology, environment/Symbiosis
Abstract

We analyzed 324,734 SARS-CoV-2 variant screening tests from France enriched with 16,973 whole-genome sequences sampled during September 1, 2021-February 28, 2022. Results showed the estimated growth advantage of the Omicron variant over the Delta variant to be 105% (95% CI 96%-114%) and that of the BA.2 lineage over the BA.1 lineage to be 49% (95% CI 44%-52%). Quantitative PCR cycle threshold values were consistent with an increased ability of Omicron to generate breakthrough infections. Epidemiologic modeling shows that, in spite of its decreased virulence, the Omicron variant can generate important critical COVID-19 activity in hospitals in France. The magnitude of the BA.2 wave in hospitals depends on the level of relaxing of control measures but remains lower than that of BA.1 in median scenarios.

Published
2022

32. Analysing different exposures identifies that wearing masks and establishing COVID-19 areas reduce secondary-attack risk in aged-care facilities

Author

Mircea T. Sofonea, Hubert Blain, Amandine Pisoni, Samuel Alizon, Christian Selinger, Bastien Reyné, Stéphanie Miot, Edouard Tuaillon, Jean Bousquet, Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Pathogenesis and Control of Chronic and Emerging Infections (PCCEI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)-Université des Antilles (UA)-Etablissement français du don du sang [Montpellier], Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Humboldt University Of Berlin, Berlin Institute of Health (BIH), Contre les MAladies Chroniques pour un VIeillissement Actif en Languedoc-Roussillon (MACVIA-LR), Université Montpellier 1 (UM1)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Universitaire de Nîmes (CHU Nîmes)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-European Innovation Partnership on Active and Healthy Ageing Reference Site (EIP on AHA), Commission Européenne-Commission Européenne-Organisation Mondiale de la Santé / World Health Organization Office (OMS / WHO), EuroMov - Digital Health in Motion (Euromov DHM), IMT - MINES ALES (IMT - MINES ALES), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Montpellier (UM), Centre interdisciplinaire de recherche en biologie (CIRB), Labex MemoLife, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Collège de France (CdF (institution))-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), ANR-20-COVI-0098,PHYEPI,Intégration de données de séquences et d'incidence pour analyser et contrôler les épidémies virales(2020), Génétique et évolution des maladies infectieuses (GEMI), and Alizon, Samuel

Subjects
Epidemiology, Generalised Linear Mixed Models, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, Corrections, law.invention, 0302 clinical medicine, law, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Medicine, Infection control, aged care facilities, secondary-attack risk, 030212 general & internal medicine, aged-care facilities, [SDV.MHEP.ME] Life Sciences [q-bio]/Human health and pathology/Emerging diseases, Likelihood Functions, [SDV.MHEP.ME]Life Sciences [q-bio]/Human health and pathology/Emerging diseases, [SDV.MHEP.GEG] Life Sciences [q-bio]/Human health and pathology/Geriatry and gerontology, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], secondary attack risk, [SDV.MHEP.GEG]Life Sciences [q-bio]/Human health and pathology/Geriatry and gerontology, Masks, General Medicine, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 3. Good health, Transmission (mechanics), [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, [SDV.MHEP.MI] Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Original Article, 2019-20 coronavirus outbreak, Coronavirus disease 2019 (COVID-19), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), 03 medical and health sciences, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, Environmental health, Humans, AcademicSubjects/MED00860, Aged care, Epidemics, mask wearing, Aged, [SDV.EE.SANT]Life Sciences [q-bio]/Ecology, environment/Health, [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], SARS-CoV-2, business.industry, Outbreak, COVID-19, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, generalized linear mixed models, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, business, 030217 neurology & neurosurgery, [SDV.EE.IEO]Life Sciences [q-bio]/Ecology, environment/Symbiosis
Abstract

Background The COVID-19 epidemic has spread rapidly within aged-care facilities (ACFs), where the infection-fatality ratio is high. It is therefore urgent to evaluate the efficiency of infection prevention and control (IPC) measures in reducing SARS-CoV-2 transmission. Methods We analysed the COVID-19 outbreaks that took place between March and May 2020 in 12 ACFs using reverse transcription–polymerase chain reaction (RT–PCR) and serological tests for SARS-CoV-2 infection. Using maximum-likelihood approaches and generalized linear mixed models, we analysed the proportion of infected residents in ACFs and identified covariates associated with the proportion of infected residents. Results The secondary-attack risk was estimated at 4.1%, suggesting a high efficiency of the IPC measures implemented in the region. Mask wearing and the establishment of COVID-19 zones for infected residents were the two main covariates associated with lower secondary-attack risks. Conclusions Wearing masks and isolating potentially infected residents appear to be associated with a more limited spread of SARS-CoV-2 in ACFs.

Published
2021

33. Treating symptomatic infections and the co-evolution of virulence and drug resistance

Author

Samuel Alizon, Evolution Théorique et Expérimentale (MIVEGEC-ETE), Perturbations, Evolution, Virulence (PEV), Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Centre interdisciplinaire de recherche en biologie (CIRB), Labex MemoLife, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Collège de France (CdF (institution))-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), European Project: 648963,H2020,ERC-2014-CoG,EVOLPROOF(2015), Génétique et évolution des maladies infectieuses (GEMI), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Génétique et évolution des maladies infectieuses (GEMI), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Alizon, Samuel, and Are HPV vaccines ‘evolution-proof’? Multilevel evolutionary ecology of human oncoviruses - EVOLPROOF - - H20202015-09-01 - 2020-08-31 - 648963 - VALID

Subjects
0106 biological sciences, medicine.medical_specialty, Population, Virulence, Context (language use), Drug resistance, Biology, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, 010603 evolutionary biology, 01 natural sciences, Mathematical modelling of infectious disease, resistance, 03 medical and health sciences, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, [SDV.BID.SPT] Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, Epidemiology, [SDV.EE.ECO] Life Sciences [q-bio]/Ecology, environment/Ecosystems, [SDV.BID.EVO] Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], medicine, [SDV.EE.SANT] Life Sciences [q-bio]/Ecology, environment/Health, Evolutionary dynamics, education, 030304 developmental biology, [SDV.MP.VIR] Life Sciences [q-bio]/Microbiology and Parasitology/Virology, Genetics, [SDV.EE.SANT]Life Sciences [q-bio]/Ecology, environment/Health, 0303 health sciences, education.field_of_study, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], treatment, Mortality rate, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], [SDV.EE.IEO] Life Sciences [q-bio]/Ecology, environment/Symbiosis, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 3. Good health, [SDE.BE] Environmental Sciences/Biodiversity and Ecology, virulence, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, coevolution, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, [SDV.GEN.GPO] Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], epidemiology, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, [SDV.EE.IEO]Life Sciences [q-bio]/Ecology, environment/Symbiosis
Abstract

Antimicrobial therapeutic treatments are by definition applied after the onset of symptoms, which tend to correlate with infection severity. Using mathematical epidemiology models, I explore how this link affects the coevolutionary dynamics between the virulence of an infection, measured via host mortality rate, and its susceptibility to chemotherapy. I show that unless resistance pre-exists in the population, drug-resistant infections are initially more virulent than drug-sensitive ones. As the epidemic unfolds, virulence is more counter-selected in drug-sensitive than in drug-resistant infections. This difference decreases over time and, eventually, the exact shape of genetic trade-offs govern long-term evolutionary dynamics. Using adaptive dynamics, I show that two types of evolutionary stable strategies (ESS) may be reached in the context of this simple model and that, depending on the parameter values, an ESS may only be locally stable. In general, the more the treatment rate increases with virulence, the lower the ESS value. Overall, both on the short-term and long-term, having treatment rate depend on infection virulence tend to favour less virulent strains in drug-sensitive infections. These results highlight the importance of the feedbacks between epidemiology, public health policies and parasite evolution, and have implications for the monitoring of virulence evolution., Cite as: Alizon S (2020). Treating symptomatic infections and the co-evolution of virulence and drug resistance. bioRxiv 2020.02.29.970905, ver. 3 peer-reviewed and recommended by PCI Evol Biol. ABSTRACT Antimicrobial therapeutic treatments are by definition applied after the onset of symptoms, which tend to correlate with infection severity. Using mathematical epidemiology models, I explore how this link affects the coevolutionary dynamics between the virulence of an infection, measured via host mortality rate, and its susceptibility to chemotherapy. I show that unless resistance pre-exists in the population, drug-resistant infections are initially more virulent than drug-sensitive ones. As the epidemic unfolds, virulence is more counter-selected in drug-sensitive than in drug-resistant infections. This difference decreases over time and, eventually, the exact shape of genetic trade-offs govern long-term evolutionary dynamics. Using adaptive dynamics, I show that two types of evolutionary stable strategies (ESS) may be reached in the context of this simple model and that, depending on the parameter values, an ESS may only be locally stable. In general, the more the treatment rate increases with virulence, the lower the ESS value. Overall, both on the short-term and long-term, having treatment rate depend on infection virulence tend to favour less virulent strains in drug-sensitive infections. These results highlight the importance of the feedbacks between epidemiology, public health policies and parasite evolution, and have implications for the monitoring of virulence evolution.

Published
2020

34. Natural history, dynamics, and ecology of human papillomaviruses in genital infections of young women: protocol of the PAPCLEAR cohort study

Author

Guillaume Christophe, Aziza Seddiki, Ignacio G. Bravo, Christian Selinger, Julie Lajoie, Mathilde Buisson, Vincent Foulongne, Jacques Ravel, Giuseppe D'Auria, Vanina Boué, Martine Teirlinck, Carmen Lía Murall, Marine Bonneau, Edouard Tuaillon, Rémy Froissart, Christelle Graf, Nathalie Jacobs, Frédérique Lorcy, Thérèse Rousset, Tim Waterboer, Florence De Taroni, Jacques Reynes, E. Picot, Nathalie Boulle, Soraya Groc, Samuel Alizon, Claire Bernat, Christophe Hirtz, Massilva Rahmoun, Audrey Jaussent, Monique Baldellou, Vincent Tribout, Michel Segondy, Sophie Grasset, Marie-Christine Picot, Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Virostyle (MIVEGEC-Virostyle), Perturbations, Evolution, Virulence (PEV), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Perturbations & Virulence in Populations & Communities (MIVEGEC-Perturbations & Virulence in Populations & Communities ), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), CIBER de Epidemiología y Salud Pública (CIBERESP), Pathogénèse et contrôle des infections chroniques (PCCI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)-Centre Hospitalier Universitaire de Montpellier (CHU Montpellier ), Laboratoire de Virologie [CHRU Montpellier], Du gène à l'écosystème (MIVEGEC-GeneSys), Pathogènes, Environnement, Santé Humaine (EPATH), Cellules Souches, Plasticité Cellulaire, Médecine Régénératrice et Immunothérapies (IRMB), Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), University of Manitoba [Winnipeg], University of Maryland School of Medicine, University of Maryland System, Recherches Translationnelles sur le VIH et les maladies infectieuses endémiques er émergentes (TransVIHMI), Université Cheikh Anta Diop [Dakar, Sénégal] (UCAD)-Institut de Recherche pour le Développement (IRD)-Université de Yaoundé I-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)-Université Montpellier 1 (UM1), Département Maladies Infectieuses et Tropicales, Hôpital Universitaire, Montpellier, France, German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), Groupe Interdisciplinaire de Génoprotéomique Appliquée (GIGA-Research), Université de Liège, Evolution Théorique et Expérimentale (MIVEGEC-ETE), Immunopathologie de l'Inflammation, Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM), University of Washington [Seattle], Centre d'Immunologie de Marseille - Luminy (CIML), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Centre International de Recherches Médicales de Franceville, Laboratoire de Pathologie Comparée (LPC), Université Montpellier 2 - Sciences et Techniques (UM2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institute of Research in Biotherapy, Laboratory of Biochemistry and Clinical Proteomics, Centre Hospitalier Universitaire de Montpellier (CHU Montpellier ), Institute for Genomic Sciences, School of Medicine (IGS), Recherches Translationnelles sur le VIH et les maladies infectieuses (TransVIHMI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 1 (UM1)-Université Cheikh Anta Diop [Dakar, Sénégal] (UCAD)-Universtié Yaoundé 1 [Cameroun]-Université de Montpellier (UM), Centre Hospitalier Universitaire de Montpellier (CHU Montpellier )-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Cellular and Molecular Immunology, GIGA-Research, university hospital, University Hospital, Alizon, Samuel, Génétique et évolution des maladies infectieuses (GEMI), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Génétique et évolution des maladies infectieuses (GEMI), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Institut de Recherche pour le Développement (IRD)-Université de Yaoundé I-Université Cheikh Anta Diop [Dakar, Sénégal] (UCAD)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Recherches Translationnelles sur le VIH et les maladies infectieuses endémiques et émergentes (TransVIHMI), Centre interdisciplinaire de recherche en biologie (CIRB), Labex MemoLife, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Collège de France (CdF (institution))-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), and Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Longitudinal study, viruses, [SDV]Life Sciences [q-bio], viral kinetics, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, 0302 clinical medicine, Clinical Protocols, Surveys and Questionnaires, Epidemiology, Protocol, [SDV.EE.ECO] Life Sciences [q-bio]/Ecology, environment/Ecosystems, Longitudinal Studies, 030212 general & internal medicine, ComputingMilieux_MISCELLANEOUS, [SDV.MP.VIR] Life Sciences [q-bio]/Microbiology and Parasitology/Virology, 0303 health sciences, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], persistence, General Medicine, Hydrogen-Ion Concentration, Viral Load, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 3. Good health, Natural history, Infectious Diseases, Vagina, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, Cytokines, Population study, Female, France, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, Viral load, Cohort study, HPV, medicine.medical_specialty, Adolescent, Young Adult, 03 medical and health sciences, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, [SDV.BID.SPT] Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, microbiota, [SDV.BID.EVO] Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], medicine, Humans, [SDV.EE.SANT] Life Sciences [q-bio]/Ecology, environment/Health, Genotyping, 030304 developmental biology, Gynecology, [SDV.EE.SANT]Life Sciences [q-bio]/Ecology, environment/Health, [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], business.industry, Papillomavirus Infections, [SDV.EE.IEO] Life Sciences [q-bio]/Ecology, environment/Symbiosis, virus load, immunity, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Human genetics, [SDE.BE] Environmental Sciences/Biodiversity and Ecology, Cross-Sectional Studies, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, [SDV.GEN.GPO] Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, business, acute infection, Genital Diseases, Female, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, [SDV.EE.IEO]Life Sciences [q-bio]/Ecology, environment/Symbiosis
Abstract

IntroductionHuman papillomaviruses (HPVs) are responsible for one-third of all cancers caused by infections. Most HPV studies focus on chronic infections and cancers, and we know little about the early stages of the infection. Our main objective is to better understand the course and natural history of cervical HPV infections in healthy, unvaccinated and vaccinated, young women, by characterising the dynamics of various infection-related populations (virus, epithelial cells, vaginal microbiota and immune effectors). Another objective is to analyse HPV diversity within hosts, and in the study population, in relation to co-factors (lifestyle characteristics, vaccination status, vaginal microbiota, human genetics).Methods and analysisThe PAPCLEAR study is a single center longitudinal study following 150 women, aged 18–25 years, for up to 2 years. Visits occur every 2 or 4 months (depending on HPV status) during which several variables are measured, such as behaviours (via questionnaires), vaginal pH, HPV presence and viral load (via qPCR), local concentrations of cytokines (via MesoScale Discovery technology) and immune cells (via flow cytometry). Additional analyses are outsourced, such as titration of circulating anti-HPV antibodies, vaginal microbiota sequencing (16S and ITS1 loci) and human genotyping. To increase the statistical power of the epidemiological arm of the study, an additional 150 women are screened cross-sectionally. Finally, to maximise the resolution of the time series, participants are asked to perform weekly self-samples at home. Statistical analyses will involve classical tools in epidemiology, genomics and virus kinetics, and will be performed or coordinated by the Centre National de la Recherche Scientifique (CNRS) in Montpellier.Ethics and disseminationThis study has been approved by the Comité de Protection des Personnes Sud Méditerranée I (reference number 2016-A00712-49); by the Comité Consultatif sur le Traitement de l’Information en matière de Recherche dans le domaine de la Santé (reference number 16.504); by the Commission Nationale Informatique et Libertés (reference number MMS/ABD/AR1612278, decision number DR-2016–488) and by the Agence Nationale de Sécurité du Médicament et des Produits de Santé (reference 20160072000007). Results will be published in preprint servers, peer-reviewed journals and disseminated through conferences.Trial registration numberNCT02946346; Pre-results.

Published
2019

35. A human judgment approach to epidemiological forecasting

Author

Ryan J. Tibshirani, David C. Farrow, Sangwon Hyun, Donald S. Burke, Roni Rosenfeld, Logan C. Brooks, and Alizon, Samuel

Subjects
0301 basic medicine, Viral Diseases, Computer science, Epidemiology, Human judgment, Mathematical Sciences, Disease Outbreaks, 0302 clinical medicine, Mathematical and Statistical Techniques, Models, Epidemiological Statistics, Prevalence, Medicine and Health Sciences, lcsh:QH301-705.5, Ecology, Statistical, Biological Sciences, 3. Good health, Variety (cybernetics), Networking and Information Technology R&D, Infectious Diseases, Computational Theory and Mathematics, Modeling and Simulation, Physical Sciences, Epidemiological Methods and Statistics, Infection, Risk assessment, Statistics (Mathematics), Research Article, Neglected Tropical Diseases, Bioinformatics, Bioengineering, Research and Analysis Methods, Communicable Diseases, Risk Assessment, Sensitivity and Specificity, 03 medical and health sciences, Cellular and Molecular Neuroscience, Clinical Research, Information and Computing Sciences, Genetics, Humans, Statistical Methods, Molecular Biology, Ecology, Evolution, Behavior and Systematics, Government, Models, Statistical, Reproducibility of Results, Chikungunya Infection, Tropical Diseases, Data science, United States, Influenza, Emerging Infectious Diseases, Good Health and Well Being, 030104 developmental biology, Data access, lcsh:Biology (General), Predictive power, Generic health relevance, Epidemiologic Methods, 030217 neurology & neurosurgery, Mathematics, Forecasting
Abstract

Infectious diseases impose considerable burden on society, despite significant advances in technology and medicine over the past century. Advanced warning can be helpful in mitigating and preparing for an impending or ongoing epidemic. Historically, such a capability has lagged for many reasons, including in particular the uncertainty in the current state of the system and in the understanding of the processes that drive epidemic trajectories. Presently we have access to data, models, and computational resources that enable the development of epidemiological forecasting systems. Indeed, several recent challenges hosted by the U.S. government have fostered an open and collaborative environment for the development of these technologies. The primary focus of these challenges has been to develop statistical and computational methods for epidemiological forecasting, but here we consider a serious alternative based on collective human judgment. We created the web-based “Epicast” forecasting system which collects and aggregates epidemic predictions made in real-time by human participants, and with these forecasts we ask two questions: how accurate is human judgment, and how do these forecasts compare to their more computational, data-driven alternatives? To address the former, we assess by a variety of metrics how accurately humans are able to predict influenza and chikungunya trajectories. As for the latter, we show that real-time, combined human predictions of the 2014–2015 and 2015–2016 U.S. flu seasons are often more accurate than the same predictions made by several statistical systems, especially for short-term targets. We conclude that there is valuable predictive power in collective human judgment, and we discuss the benefits and drawbacks of this approach., Author summary Despite advanced and widely accessible health care, a large number of annual deaths in the United States are attributable to infectious diseases like influenza. Many of these cases could be easily prevented if sufficiently advanced warning was available. This is the main goal of epidemiological forecasting, a relatively new field that attempts to predict when and where disease outbreaks will occur. In response to growing interest in this endeavor, many forecasting frameworks have been developed for a variety of diseases. We ask whether an approach based on collective human judgment can be used to produce reasonable forecasts and how such forecasts compare with forecasts produced by purely data-driven systems. To answer this, we collected simple predictions in real-time from a set of expert and non-expert volunteers during the 2014–2015 and 2015–2016 U.S. flu seasons and during the 2014–2015 chikungunya invasion of Central America, and we report several measures of accuracy based on these predictions. By comparing these predictions with published forecasts of data-driven methods, we build an intuition for the difficulty of the task and learn that there is real value in collective human judgment.

Published
2017

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