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Morgan ER, Dillard D, Lofgren E, Maddison BK, Riklon S, McElfish P, Sinclair K. Moana: Alternate surveillance for COVID-19 in a Unique Population (MASC-UP). Contemp Clin Trials Commun 2024; 37:101246. [PMID: 38222877 PMCID: PMC10784670 DOI: 10.1016/j.conctc.2023.101246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 11/20/2023] [Accepted: 12/17/2023] [Indexed: 01/16/2024] Open
Abstract
Objective Create a longitudinal, multi-modal and multi-level surveillance cohort that targets early detection of symptomatic and asymptomatic COVID-19 cases among Native Hawaiian and Pacific Islander adults in the Continental US and identify effective modalities for participatory disease surveillance and sustainably integrate them into ongoing COVID-19 and other public health surveillance efforts. Materials and methods We recruited cohorts from three sites: Federal Way, WA; Springdale, AR; and remotely. Participants received a survey that included demographic characteristics and questions regarding COVID-19. Participants completed symptom checks via text message every month and recorded their temperature daily using a Kinsa smart thermometer. Results Recruitment and data collection is ongoing. Presently, 441 adults have consented to participate. One-third of participants were classified as essential workers during the pandemic. Discussion Over the past 18 months, we have improved our strategies to elicit better data from participants and have learned from some of the weaknesses in our initial deployment of this type of surveillance system. Other limitations stem from historic inequities and barriers which limited Native Hawaiian and Pacific Island representation in academic and clinical environments. One manifestation of this was the limited ability to provide study materials and support in multiple languages. We hope that continued partnership with the community will allow further opportunities to help restore trust in academic and medical institutions, thus generating knowledge to advance health equity. Conclusion This participatory disease surveillance mechanism complements traditional surveillance systems by engaging underserved communities. We may also gain insights generalizable to other pathogens of concern.
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Affiliation(s)
- Erin R. Morgan
- Institute for Research and Education to Advance Community Health, Elson S. Floyd College of Medicine, Washington State University, Seattle, WA, USA
| | - Denise Dillard
- Institute for Research and Education to Advance Community Health, Elson S. Floyd College of Medicine, Washington State University, Seattle, WA, USA
| | - Eric Lofgren
- Paul G. Allen School for Global Health, College of Veterinary Medicine, Washington State University, Pullman, WA, USA
| | | | - Sheldon Riklon
- Department of Family and Preventive Medicine, University of Arkansas for the Medical Sciences, Fayetteville, AR, USA
| | - Pearl McElfish
- Department of Internal Medicine, University of Arkansas for the Medical Sciences, Fayetteville, AR, USA
| | - Ka`imi Sinclair
- Institute for Research and Education to Advance Community Health, College of Nursing, Washington State University, Seattle, WA, USA
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2
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Aushev A, Pesonen H, Heinonen M, Corander J, Kaski S. Likelihood-free inference with deep Gaussian processes. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2022.107529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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3
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Avecilla G, Chuong JN, Li F, Sherlock G, Gresham D, Ram Y. Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics. PLoS Biol 2022; 20:e3001633. [PMID: 35622868 PMCID: PMC9140244 DOI: 10.1371/journal.pbio.3001633] [Citation(s) in RCA: 2] [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: 09/30/2021] [Accepted: 04/14/2022] [Indexed: 11/24/2022] Open
Abstract
The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to empirically quantify. As these 2 parameters determine the pace of evolutionary change in a population, the dynamics of adaptive evolution may enable inference of their values. Copy number variants (CNVs) are a pervasive source of heritable variation that can facilitate rapid adaptive evolution. Previously, we developed a locus-specific fluorescent CNV reporter to quantify CNV dynamics in evolving populations maintained in nutrient-limiting conditions using chemostats. Here, we use CNV adaptation dynamics to estimate the rate at which beneficial CNVs are introduced through de novo mutation and their fitness effects using simulation-based likelihood-free inference approaches. We tested the suitability of 2 evolutionary models: a standard Wright-Fisher model and a chemostat model. We evaluated 2 likelihood-free inference algorithms: the well-established Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) algorithm, and the recently developed Neural Posterior Estimation (NPE) algorithm, which applies an artificial neural network to directly estimate the posterior distribution. By systematically evaluating the suitability of different inference methods and models, we show that NPE has several advantages over ABC-SMC and that a Wright-Fisher evolutionary model suffices in most cases. Using our validated inference framework, we estimate the CNV formation rate at the GAP1 locus in the yeast Saccharomyces cerevisiae to be 10-4.7 to 10-4 CNVs per cell division and a fitness coefficient of 0.04 to 0.1 per generation for GAP1 CNVs in glutamine-limited chemostats. We experimentally validated our inference-based estimates using 2 distinct experimental methods-barcode lineage tracking and pairwise fitness assays-which provide independent confirmation of the accuracy of our approach. Our results are consistent with a beneficial CNV supply rate that is 10-fold greater than the estimated rates of beneficial single-nucleotide mutations, explaining the outsized importance of CNVs in rapid adaptive evolution. More generally, our study demonstrates the utility of novel neural network-based likelihood-free inference methods for inferring the rates and effects of evolutionary processes from empirical data with possible applications ranging from tumor to viral evolution.
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Affiliation(s)
- Grace Avecilla
- Department of Biology, New York University, New York, New York, United States of America
- Center for Genomics and Systems Biology, New York University, New York, New York, United States of America
| | - Julie N. Chuong
- Department of Biology, New York University, New York, New York, United States of America
- Center for Genomics and Systems Biology, New York University, New York, New York, United States of America
| | - Fangfei Li
- Department of Genetics, Stanford University, California, Stanford, United States of America
| | - Gavin Sherlock
- Department of Genetics, Stanford University, California, Stanford, United States of America
| | - David Gresham
- Department of Biology, New York University, New York, New York, United States of America
- Center for Genomics and Systems Biology, New York University, New York, New York, United States of America
| | - Yoav Ram
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
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4
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Csűrös M. Gain-loss-duplication models for copy number evolution on a phylogeny: Exact algorithms for computing the likelihood and its gradient. Theor Popul Biol 2022; 145:80-94. [DOI: 10.1016/j.tpb.2022.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 03/07/2022] [Accepted: 03/10/2022] [Indexed: 10/18/2022]
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5
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Bi J, Shen W, Zhu W. Random Forest Adjustment for Approximate Bayesian Computation. J Comput Graph Stat 2021. [DOI: 10.1080/10618600.2021.1981341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Jiefeng Bi
- Wang Yanan Institute for Studies in Economics (WISE), Xiamen University, Xiamen, China
| | - Weining Shen
- Department of Statistics, University of California, Irvine, CA
| | - Weixuan Zhu
- Wang Yanan Institute for Studies in Economics (WISE), Department of Statistics and Data Science, School of Economics, Xiamen University, Xiamen, China
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6
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Goodness of fit for models with intractable likelihood. TEST-SPAIN 2021. [DOI: 10.1007/s11749-020-00747-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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7
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Park M, Vinaroz M, Jitkrittum W. ABCDP: Approximate Bayesian Computation with Differential Privacy. ENTROPY (BASEL, SWITZERLAND) 2021; 23:961. [PMID: 34441101 PMCID: PMC8391538 DOI: 10.3390/e23080961] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 07/15/2021] [Accepted: 07/20/2021] [Indexed: 11/17/2022]
Abstract
We developed a novel approximate Bayesian computation (ABC) framework, ABCDP, which produces differentially private (DP) and approximate posterior samples. Our framework takes advantage of the sparse vector technique (SVT), widely studied in the differential privacy literature. SVT incurs the privacy cost only when a condition (whether a quantity of interest is above/below a threshold) is met. If the condition is sparsely met during the repeated queries, SVT can drastically reduce the cumulative privacy loss, unlike the usual case where every query incurs the privacy loss. In ABC, the quantity of interest is the distance between observed and simulated data, and only when the distance is below a threshold can we take the corresponding prior sample as a posterior sample. Hence, applying SVT to ABC is an organic way to transform an ABC algorithm to a privacy-preserving variant with minimal modification, but yields the posterior samples with a high privacy level. We theoretically analyzed the interplay between the noise added for privacy and the accuracy of the posterior samples. We apply ABCDP to several data simulators and show the efficacy of the proposed framework.
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Affiliation(s)
- Mijung Park
- Computer Science, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Margarita Vinaroz
- Max Planck Institute for Intelligent Systems, 72076 Tübingen, Germany;
- Department of Computer Science, University of Tübingen, 72076 Tübingen, Germany
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8
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Camacho-Aguilar E, Warmflash A, Rand DA. Quantifying cell transitions in C. elegans with data-fitted landscape models. PLoS Comput Biol 2021; 17:e1009034. [PMID: 34061834 PMCID: PMC8195438 DOI: 10.1371/journal.pcbi.1009034] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Revised: 06/11/2021] [Accepted: 05/03/2021] [Indexed: 12/19/2022] Open
Abstract
Increasing interest has emerged in new mathematical approaches that simplify the study of complex differentiation processes by formalizing Waddington's landscape metaphor. However, a rational method to build these landscape models remains an open problem. Here we study vulval development in C. elegans by developing a framework based on Catastrophe Theory (CT) and approximate Bayesian computation (ABC) to build data-fitted landscape models. We first identify the candidate qualitative landscapes, and then use CT to build the simplest model consistent with the data, which we quantitatively fit using ABC. The resulting model suggests that the underlying mechanism is a quantifiable two-step decision controlled by EGF and Notch-Delta signals, where a non-vulval/vulval decision is followed by a bistable transition to the two vulval states. This new model fits a broad set of data and makes several novel predictions.
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Affiliation(s)
- Elena Camacho-Aguilar
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
- Department of Biosciences, Rice University, Houston, Texas, United States of America
| | - Aryeh Warmflash
- Department of Biosciences, Rice University, Houston, Texas, United States of America
- Department of Bioengineering, Rice University, Houston, Texas, United States of America
| | - David A. Rand
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
- Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
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9
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Lofgren ET, Mietchen M, Dicks KV, Moehring R, Anderson D. Estimated Methicillin-Resistant Staphylococcus aureus Decolonization in Intensive Care Units Associated With Single-Application Chlorhexidine Gluconate or Mupirocin. JAMA Netw Open 2021; 4:e210652. [PMID: 33662133 PMCID: PMC7933999 DOI: 10.1001/jamanetworkopen.2021.0652] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
IMPORTANCE Chlorhexidine gluconate (CHG) and mupirocin are widely used to decolonize patients with methicillin-resistant Staphylococcus aureus (MRSA) and reduce risks associated with infection in hospitalized populations. Quantifying the association of an application of CHG alone or in combination with mupirocin with risk of MRSA infection is important for studies evaluating alternative decolonization strategies or schedules and for identifying whether there is room for improved decolonizing agents. OBJECTIVE To estimate the proportion of patients with MRSA decolonized per application of CHG and mupirocin from existing population-level studies. DESIGN, SETTING, AND PARTICIPANTS A stochastic mathematical model of an 18-bed intensive care unit (ICU) in an academic medical center operating over 1 year was used to estimate parameters for the proportion of simulated patients with MRSA decolonized per application of CHG and mupirocin. The model was conducted using approximate bayesian computation with data from an existing meta-analysis of studies conducted from February 2005 through January 2015. Data were analyzed from January 2018 through November 2019. EXPOSURE A universal decolonization protocol for colonized patients in the ICU using CHG or CHG and mupirocin in combination was simulated. MAIN OUTCOMES AND MEASURES The proportion of patients with MRSA decolonized per application of CHG and mupirocin was estimated. RESULTS The estimated proportion of patients with MRSA decolonized per application of CHG was 0.15 (95% credible interval, 0.01-0.42), and the estimated proportion per application of mupirocin in conjunction with CHG was 0.15 (95% credible interval, 0.01-0.54). A lag in colonization detection was associated with decreases in the CHG estimate (0.11; 95% credible interval, 0.01-0.30) and mupirocin estimate (0.10; 95% credible interval, 0.00-0.34), which were sensitive to the value of the modeled contact rate between nurses and patients. A 1% increase in the value of this parameter was associated with a 0.73% increase in the estimated combined outcomes associated with CHG and mupirocin (95% CI: 0.71, 0.75). Gaps longer than 24 hours in the administration of decolonizing agents were associated with a decrease of within-ICU MRSA transmission. Compared with a mean (SD) of 1.23 (0.27) acquisitions per 1000 patient-days in scenarios with no decolonizing bathing, a bathing protocol administering CHG and mupirocin every 120 hours was associated with a mean (SD) acquisition rate of 1.03 (0.24) acquisitions per 1000 patient days, a 16.3% decrease (95% CI, 14.7%-18.0%; P > .001). CONCLUSIONS AND RELEVANCE These findings suggest that there may be room for significant improvement in anti-MRSA disinfectants, including the compounds themselves and their delivery mechanisms. Despite the decolonization estimates found in this study, these agents are associated with robust outcomes after delays in administration, which may help in alleviating concerns over patient comfort and toxic effects.
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Affiliation(s)
- Eric T. Lofgren
- Paul G. Allen School for Global Animal Health, Washington State University, Pullman, Washington
| | - Matthew Mietchen
- Paul G. Allen School for Global Animal Health, Washington State University, Pullman, Washington
| | - Kristen V. Dicks
- Duke Center for Antimicrobial Stewardship and Infection Prevention, Durham, North Carolina
| | - Rebekah Moehring
- Duke Center for Antimicrobial Stewardship and Infection Prevention, Durham, North Carolina
| | - Deverick Anderson
- Duke Center for Antimicrobial Stewardship and Infection Prevention, Durham, North Carolina
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10
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Wu Y, Huang M, Wang X, Li Y, Jiang L, Yuan Y. The prevention and control of tuberculosis: an analysis based on a tuberculosis dynamic model derived from the cases of Americans. BMC Public Health 2020; 20:1173. [PMID: 32723305 PMCID: PMC7385980 DOI: 10.1186/s12889-020-09260-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2019] [Accepted: 07/14/2020] [Indexed: 11/25/2022] Open
Abstract
Background Tuberculosis (TB), a preventable and curable disease, is claimed as the second largest number of fatalities, and there are 9,025 cases reported in the United States in 2018. Many researchers have done a lot of research and achieved remarkable results, but TB is still a severe problem for human beings. The study is a further exploration of the prevention and control of tuberculosis. Methods In the paper, we propose a new dynamic model to study the transmission dynamics of TB, and then use global differential evolution and local sequential quadratic programming (DESQP) optimization algorithm to estimate parameters of the model. Finally, we use Latin hypercube sampling (LHS) and partial rank correlation coefficients (PRCC) to analyze the influence of parameters on the basic reproduction number (\documentclass[12pt]{minimal}
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\begin{document}$\mathcal R_{0}$\end{document}R0) and the total infectious (including the diagnosed, undiagnosed and incomplete treatment infectious), respectively. Results According to the research, the basic reproduction number is computed as 2.3597 from 1984 to 2018, which means TB is also an epidemic in the US. The diagnosed rate is 0.6082, which means the undiagnosed will be diagnosed after 1.6442 years. The diagnosed will recover after an average of 1.9912 years. Moreover, some diagnosed will end the treatment after 1.7550 years for some reason. From the study, it’s shown that 2.40% of the recovered will be reactivated, and 13.88% of the newborn will be vaccinated. However, the immune system will be lost after about 19.6078 years. Conclusion Through the results of this study, we give some suggestions to help prevent and control the TB epidemic in the United States, such as prolonging the protection period of the vaccine by developing new and more effective vaccines to prevent TB; using the Chemoprophylaxis for incubation patients to prevent their conversion into active TB; raising people’s awareness of the prevention and control of TB and treatment after illness; isolating the infected to reduce the spread of TB. According to the latest report in the announcement that came at the first WHO Global Ministerial Conference on Ending tuberculosis in the Sustainable Development Era, we predict that it is challenging to control TB by 2030.
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Affiliation(s)
- Yan Wu
- School of Information and Mathematics, Yangtze University, Jingzhou 434023, China, Nanhuan Road, Jingzhou, 434023, China
| | - Meng Huang
- School of Information and Mathematics, Yangtze University, Jingzhou 434023, China, Nanhuan Road, Jingzhou, 434023, China
| | - Ximei Wang
- School of Information and Mathematics, Yangtze University, Jingzhou 434023, China, Nanhuan Road, Jingzhou, 434023, China
| | - Yong Li
- School of Information and Mathematics, Yangtze University, Jingzhou 434023, China, Nanhuan Road, Jingzhou, 434023, China.,Institute of Applied Mathematics, Yangtze University, Nanhuan Road, Jingzhou, 434023, China
| | - Lei Jiang
- Department of Respiratory Medicine, Jingzhou Hospital of Traditional Chinese Medicine, Jiangjin East Road, Jingzhou, 434000, China
| | - Yuan Yuan
- Laboratory Department, Jingzhou Maternal and Child Health Hospital, Jingzhong Road, Jingzhou, 434000, China
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11
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Warne DJ, Baker RE, Simpson MJ. Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art. J R Soc Interface 2020; 16:20180943. [PMID: 30958205 DOI: 10.1098/rsif.2018.0943] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Stochasticity is a key characteristic of intracellular processes such as gene regulation and chemical signalling. Therefore, characterizing stochastic effects in biochemical systems is essential to understand the complex dynamics of living things. Mathematical idealizations of biochemically reacting systems must be able to capture stochastic phenomena. While robust theory exists to describe such stochastic models, the computational challenges in exploring these models can be a significant burden in practice since realistic models are analytically intractable. Determining the expected behaviour and variability of a stochastic biochemical reaction network requires many probabilistic simulations of its evolution. Using a biochemical reaction network model to assist in the interpretation of time-course data from a biological experiment is an even greater challenge due to the intractability of the likelihood function for determining observation probabilities. These computational challenges have been subjects of active research for over four decades. In this review, we present an accessible discussion of the major historical developments and state-of-the-art computational techniques relevant to simulation and inference problems for stochastic biochemical reaction network models. Detailed algorithms for particularly important methods are described and complemented with Matlab® implementations. As a result, this review provides a practical and accessible introduction to computational methods for stochastic models within the life sciences community.
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Affiliation(s)
- David J Warne
- 1 School of Mathematical Sciences, Queensland University of Technology , Brisbane, Queensland 4001 , Australia
| | - Ruth E Baker
- 2 Mathematical Institute, University of Oxford , Oxford OX2 6GG , UK
| | - Matthew J Simpson
- 1 School of Mathematical Sciences, Queensland University of Technology , Brisbane, Queensland 4001 , Australia
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12
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Ruiz-Suarez S, Leos-Barajas V, Alvarez-Castro I, Morales JM. Using approximate Bayesian inference for a "steps and turns" continuous-time random walk observed at regular time intervals. PeerJ 2020; 8:e8452. [PMID: 32095333 PMCID: PMC7020826 DOI: 10.7717/peerj.8452] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 12/23/2019] [Indexed: 11/20/2022] Open
Abstract
The study of animal movement is challenging because movement is a process modulated by many factors acting at different spatial and temporal scales. In order to describe and analyse animal movement, several models have been proposed which differ primarily in the temporal conceptualization, namely continuous and discrete time formulations. Naturally, animal movement occurs in continuous time but we tend to observe it at fixed time intervals. To account for the temporal mismatch between observations and movement decisions, we used a state-space model where movement decisions (steps and turns) are made in continuous time. That is, at any time there is a non-zero probability of making a change in movement direction. The movement process is then observed at regular time intervals. As the likelihood function of this state-space model turned out to be intractable yet simulating data is straightforward, we conduct inference using different variations of Approximate Bayesian Computation (ABC). We explore the applicability of this approach as a function of the discrepancy between the temporal scale of the observations and that of the movement process in a simulation study. Simulation results suggest that the model parameters can be recovered if the observation time scale is moderately close to the average time between changes in movement direction. Good estimates were obtained when the scale of observation was up to five times that of the scale of changes in direction. We demonstrate the application of this model to a trajectory of a sheep that was reconstructed in high resolution using information from magnetometer and GPS devices. The state-space model used here allowed us to connect the scales of the observations and movement decisions in an intuitive and easy to interpret way. Our findings underscore the idea that the time scale at which animal movement decisions are made needs to be considered when designing data collection protocols. In principle, ABC methods allow to make inferences about movement processes defined in continuous time but in terms of easily interpreted steps and turns.
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Affiliation(s)
- Sofia Ruiz-Suarez
- INIBIOMA (CONICET-Universidad Nacional del Comahue), Rio Negro, Argentina
- Facultad de Ciencias Económicas, Universidad Nacional de Rosario, Rosario, Argentina
| | - Vianey Leos-Barajas
- Department of Statistics, North Carolina State University, Raleigh, United States of America
- Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, United States of America
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13
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Vihola M, Franks J. On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction. Biometrika 2020. [DOI: 10.1093/biomet/asz078] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
SummaryApproximate Bayesian computation enables inference for complicated probabilistic models with intractable likelihoods using model simulations. The Markov chain Monte Carlo implementation of approximate Bayesian computation is often sensitive to the tolerance parameter: low tolerance leads to poor mixing and large tolerance entails excess bias. We propose an approach that involves using a relatively large tolerance for the Markov chain Monte Carlo sampler to ensure sufficient mixing and post-processing the output, leading to estimators for a range of finer tolerances. We introduce an approximate confidence interval for the related post-corrected estimators and propose an adaptive approximate Bayesian computation Markov chain Monte Carlo algorithm, which finds a balanced tolerance level automatically based on acceptance rate optimization. Our experiments show that post-processing-based estimators can perform better than direct Markov chain Monte Carlo targeting a fine tolerance, that our confidence intervals are reliable, and that our adaptive algorithm leads to reliable inference with little user specification.
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Affiliation(s)
- Matti Vihola
- Department of Mathematics and Statistics, University of Jyväskylä, P.O. Box 35, FI-40014 University of Jyväskylä, Finland
| | - Jordan Franks
- Department of Mathematics and Statistics, University of Jyväskylä, P.O. Box 35, FI-40014 University of Jyväskylä, Finland
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14
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Lintusaari J, Blomstedt P, Rose B, Sivula T, Gutmann MU, Kaski S, Corander J. Resolving outbreak dynamics using approximate Bayesian computation for stochastic birth-death models. Wellcome Open Res 2019; 4:14. [PMID: 37744419 PMCID: PMC10514576 DOI: 10.12688/wellcomeopenres.15048.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/16/2019] [Indexed: 09/26/2023] Open
Abstract
Earlier research has suggested that approximate Bayesian computation (ABC) makes it possible to fit simulator-based intractable birth-death models to investigate communicable disease outbreak dynamics with accuracy comparable to that of exact Bayesian methods. However, recent findings have indicated that key parameters, such as the reproductive number R, may remain poorly identifiable with these models. Here we show that this identifiability issue can be resolved by taking into account disease-specific characteristics of the transmission process in closer detail. Using tuberculosis (TB) in the San Francisco Bay area as a case study, we consider a model that generates genotype data from a mixture of three stochastic processes, each with its own distinct dynamics and clear epidemiological interpretation. We show that our model allows for accurate posterior inferences about outbreak dynamics from aggregated annual case data with genotype information. As a byproduct of the inference, the model provides an estimate of the infectious population size at the time the data were collected. The acquired estimate is approximately two orders of magnitude smaller than assumed in earlier related studies, and it is much better aligned with epidemiological knowledge about active TB prevalence. Similarly, the reproductive number R related to the primary underlying transmission process is estimated to be nearly three times larger than previous estimates, which has a substantial impact on the interpretation of the fitted outbreak model.
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Affiliation(s)
- Jarno Lintusaari
- Helsinki Institute for Information Technology (HIIT), Department of Computer Science, Aalto University, Espoo, Finland
| | - Paul Blomstedt
- Helsinki Institute for Information Technology (HIIT), Department of Computer Science, Aalto University, Espoo, Finland
| | - Brittany Rose
- Department of Infectious Diseases Epidemiology and Modelling, Norwegian Institute of Public Health, Oslo, Norway
- Helsinki Institute for Information Technology (HIIT), Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Tuomas Sivula
- Helsinki Institute for Information Technology (HIIT), Department of Computer Science, Aalto University, Espoo, Finland
| | | | - Samuel Kaski
- Helsinki Institute for Information Technology (HIIT), Department of Computer Science, Aalto University, Espoo, Finland
| | - Jukka Corander
- Helsinki Institute for Information Technology (HIIT), Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
- Department of Biostatistics, University of Oslo, Oslo, Norway
- Infection Genomics, The Wellcome Trust Sanger Institute, Hinxton, UK
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15
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Tomczak JM, Węglarz‐Tomczak E. Estimating kinetic constants in the Michaelis–Menten model from one enzymatic assay using Approximate Bayesian Computation. FEBS Lett 2019; 593:2742-2750. [DOI: 10.1002/1873-3468.13531] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2019] [Revised: 06/05/2019] [Accepted: 06/27/2019] [Indexed: 01/04/2023]
Affiliation(s)
- Jakub M. Tomczak
- Institute of Informatics, Faculty of Science University of Amsterdam The Netherlands
| | - Ewelina Węglarz‐Tomczak
- Swammerdam Institute for Life Sciences, Faculty of Science University of Amsterdam The Netherlands
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Stepien TL, Lynch HE, Yancey SX, Dempsey L, Davidson LA. Using a continuum model to decipher the mechanics of embryonic tissue spreading from time-lapse image sequences: An approximate Bayesian computation approach. PLoS One 2019; 14:e0218021. [PMID: 31246967 PMCID: PMC6597152 DOI: 10.1371/journal.pone.0218021] [Citation(s) in RCA: 5] [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: 11/16/2018] [Accepted: 05/24/2019] [Indexed: 11/18/2022] Open
Abstract
Advanced imaging techniques generate large datasets capable of describing the structure and kinematics of tissue spreading in embryonic development, wound healing, and the progression of many diseases. These datasets can be integrated with mathematical models to infer biomechanical properties of the system, typically identifying an optimal set of parameters for an individual experiment. However, these methods offer little information on the robustness of the fit and are generally ill-suited for statistical tests of multiple experiments. To overcome this limitation and enable efficient use of large datasets in a rigorous experimental design, we use the approximate Bayesian computation rejection algorithm to construct probability density distributions that estimate model parameters for a defined theoretical model and set of experimental data. Here, we demonstrate this method with a 2D Eulerian continuum mechanical model of spreading embryonic tissue. The model is tightly integrated with quantitative image analysis of different sized embryonic tissue explants spreading on extracellular matrix (ECM) and is regulated by a small set of parameters including forces on the free edge, tissue stiffness, strength of cell-ECM adhesions, and active cell shape changes. We find statistically significant trends in key parameters that vary with initial size of the explant, e.g., for larger explants cell-ECM adhesion forces are weaker and free edge forces are stronger. Furthermore, we demonstrate that estimated parameters for one explant can be used to predict the behavior of other similarly sized explants. These predictive methods can be used to guide further experiments to better understand how collective cell migration is regulated during development.
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Affiliation(s)
- Tracy L. Stepien
- Department of Mathematics, University of Arizona, Tucson, AZ, United States of America
- * E-mail: (LAD); (TLS); (HEL)
| | - Holley E. Lynch
- Department of Physics, Stetson University, DeLand, FL, United States of America
- * E-mail: (LAD); (TLS); (HEL)
| | - Shirley X. Yancey
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Laura Dempsey
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Lance A. Davidson
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States of America
- Department of Developmental Biology, University of Pittsburgh, Pittsburgh, PA, United States of America
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, United States of America
- * E-mail: (LAD); (TLS); (HEL)
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17
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Díaz Acosta CC, Russomando G, Candia N, Ritacco V, Vasconcellos SEG, de Berrêdo Pinho Moreira M, de Romero NJ, Morcillo N, De Waard JH, Gomes HM, Suffys PN. Exploring the "Latin American Mediterranean" family and the RD Rio lineage in Mycobacterium tuberculosis isolates from Paraguay, Argentina and Venezuela. BMC Microbiol 2019; 19:131. [PMID: 31195979 PMCID: PMC6567603 DOI: 10.1186/s12866-019-1479-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Accepted: 05/07/2019] [Indexed: 11/21/2022] Open
Abstract
Background The Latin American & Mediterranean (LAM) spoligotype family is one of the most successful genotype of Mycobacterium tuberculosis worldwide and particularly prevalent in South-America. Within this family, a sublineage named Region of Difference Rio (RDRio) was reported initially in Brazil and is characterized by a genomic deletion of about 26.3 kb. This lineage seems to show a specific adaptation to the Euro-Latin American population. In this context, we sought to evaluate the LAM family and the presence of the RDRio genotype in samples from three Latin American countries including Paraguay, Venezuela and Argentina. To detect LAM strains reliably we applied a typing scheme using spoligotyping, 12 loci MIRU-VNTR, the Ag85C103 SNP and the regions of difference RDRio and RD174. IS6110-RFLP results were also used when available. Results Genotyping of 413 M. tuberculosis isolates from three Latin-American countries detected LAM (46%) and the ill-defined T clade (16%) as the most frequent families. The highest clustering rate was detected in the sample population from the city of Caracas in Venezuela. We observed considerable differences in the presence of the RDRio lineage, with high frequency in Caracas-Venezuela (55%) and low frequency in Buenos Aires-Argentina (11%) and Paraguay (10%). The molecular markers (RD174, Ag85C103, MIRU02-MIRU40 signature) of the RDRio lineage were essentially confirmed. For the LAM family, the most polymorphic loci were MIRU40, MIRU31, MIRU10, MIRU26, MIRU16 and the least polymorphic MIRU24, MIRU20, MIRU04, MIRU23. Conclusions Our results suggest a differential adaptation of LAM-sublineages in neighboring populations and that RDRio strains spread regionally with different rates of distribution. The Ag85C SNP and RDs (RD174, RDRio) tested in this study can in fact facilitate molecular epidemiological studies of LAM strains in endemic settings and low-income countries. Electronic supplementary material The online version of this article (10.1186/s12866-019-1479-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Chyntia Carolina Díaz Acosta
- Departamento de Biología Molecular y Biotecnología. Instituto de Investigaciones en Ciencias de la Salud, Universidad Nacional de Asunción, Asunción, Paraguay.,Laboratório de Biologia Molecular aplicada às Micobactérias, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, 21045-900, Brazil
| | - Graciela Russomando
- Departamento de Biología Molecular y Biotecnología. Instituto de Investigaciones en Ciencias de la Salud, Universidad Nacional de Asunción, Asunción, Paraguay
| | - Norma Candia
- Departamento de Biología Molecular y Biotecnología. Instituto de Investigaciones en Ciencias de la Salud, Universidad Nacional de Asunción, Asunción, Paraguay
| | - Viviana Ritacco
- Servicio de Micobacterias, Instituto Nacional de Enfermedades Infecciosas, ANLIS "Carlos G. Malbran", Buenos Aires, Argentina
| | - Sidra E G Vasconcellos
- Laboratório de Biologia Molecular aplicada às Micobactérias, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, 21045-900, Brazil
| | | | | | - Nora Morcillo
- Instituto Nacional de Enfermedades Respiratorias Emilio Coni, Buenos Aires, Argentina
| | - Jacobus Henri De Waard
- Laboratorio de Tuberculosis, Instituto de Biomedicina, Caracas, Venezuela.,Present Address: One Health Research Group. Facultad de Ciencias de la Salud, Universidad de Las Américas (UDLA), Quito, Ecuador
| | - Harrison Magdinier Gomes
- Laboratório de Biologia Molecular aplicada às Micobactérias, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, 21045-900, Brazil
| | - Philip Noel Suffys
- Laboratório de Biologia Molecular aplicada às Micobactérias, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, 21045-900, Brazil.
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18
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Lintusaari J, Blomstedt P, Sivula T, Gutmann MU, Kaski S, Corander J. Resolving outbreak dynamics using approximate Bayesian computation for stochastic birth-death models. Wellcome Open Res 2019. [DOI: 10.12688/wellcomeopenres.15048.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Earlier research has suggested that approximate Bayesian computation (ABC) makes it possible to fit simulator-based intractable birth-death models to investigate communicable disease outbreak dynamics with accuracy comparable to that of exact Bayesian methods. However, recent findings have indicated that key parameters such as the reproductive number R may remain poorly identifiable with these models. Here we show that the identifiability issue can be resolved by taking into account disease-specific characteristics of the transmission process in closer detail. Using tuberculosis (TB) in the San Francisco Bay area as a case-study, we consider a model that generates genotype data from a mixture of three stochastic processes, each with their distinct dynamics and clear epidemiological interpretation. We show that our model allows for accurate posterior inferences about outbreak dynamics from aggregated annual case data with genotype information. As a by-product of the inference, the model provides an estimate of the infectious population size at the time the data was collected. The acquired estimate is approximately two orders of magnitude smaller compared to the assumptions made in the earlier related studies, and much better aligned with epidemiological knowledge about active TB prevalence. Similarly, the reproductive number R related to the primary underlying transmission process is estimated to be nearly three-fold compared with the previous estimates, which has a substantial impact on the interpretation of the fitted outbreak model.
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19
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Irvine MA, Hollingsworth TD. Kernel-density estimation and approximate Bayesian computation for flexible epidemiological model fitting in Python. Epidemics 2018; 25:80-88. [PMID: 29884470 PMCID: PMC6227249 DOI: 10.1016/j.epidem.2018.05.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Revised: 05/05/2018] [Accepted: 05/24/2018] [Indexed: 12/20/2022] Open
Abstract
Fitting complex models to epidemiological data is a challenging problem: methodologies can be inaccessible to all but specialists, there may be challenges in adequately describing uncertainty in model fitting, the complex models may take a long time to run, and it can be difficult to fully capture the heterogeneity in the data. We develop an adaptive approximate Bayesian computation scheme to fit a variety of epidemiologically relevant data with minimal hyper-parameter tuning by using an adaptive tolerance scheme. We implement a novel kernel density estimation scheme to capture both dispersed and multi-dimensional data, and directly compare this technique to standard Bayesian approaches. We then apply the procedure to a complex individual-based simulation of lymphatic filariasis, a human parasitic disease. The procedure and examples are released alongside this article as an open access library, with examples to aid researchers to rapidly fit models to data. This demonstrates that an adaptive ABC scheme with a general summary and distance metric is capable of performing model fitting for a variety of epidemiological data. It also does not require significant theoretical background to use and can be made accessible to the diverse epidemiological research community.
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Affiliation(s)
- Michael A Irvine
- Institute of Applied Mathematics, University of British Columbia, Vancouver, Canada.
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
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20
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21
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Warne DJ, Baker RE, Simpson MJ. Multilevel rejection sampling for approximate Bayesian computation. Comput Stat Data Anal 2018. [DOI: 10.1016/j.csda.2018.02.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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22
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23
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Quantifying TB transmission: a systematic review of reproduction number and serial interval estimates for tuberculosis. Epidemiol Infect 2018; 146:1478-1494. [PMID: 29970199 PMCID: PMC6092233 DOI: 10.1017/s0950268818001760] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Tuberculosis (TB) is the leading global infectious cause of death. Understanding TB transmission is critical to creating policies and monitoring the disease with the end goal of TB elimination. To our knowledge, there has been no systematic review of key transmission parameters for TB. We carried out a systematic review of the published literature to identify studies estimating either of the two key TB transmission parameters: the serial interval (SI) and the reproductive number. We identified five publications that estimated the SI and 56 publications that estimated the reproductive number. The SI estimates from four studies were: 0.57, 1.42, 1.44 and 1.65 years; the fifth paper presented age-specific estimates ranging from 20 to 30 years (for infants <1 year old) to <5 years (for adults). The reproductive number estimates ranged from 0.24 in the Netherlands (during 1933-2007) to 4.3 in China in 2012. We found a limited number of publications and many high TB burden settings were not represented. Certain features of TB dynamics, such as slow transmission, complicated parameter estimation, require novel methods. Additional efforts to estimate these parameters for TB are needed so that we can monitor and evaluate interventions designed to achieve TB elimination.
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24
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Kühnert D, Coscolla M, Brites D, Stucki D, Metcalfe J, Fenner L, Gagneux S, Stadler T. Tuberculosis outbreak investigation using phylodynamic analysis. Epidemics 2018; 25:47-53. [PMID: 29880306 PMCID: PMC6227250 DOI: 10.1016/j.epidem.2018.05.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Revised: 05/07/2018] [Accepted: 05/13/2018] [Indexed: 01/08/2023] Open
Abstract
Phylodynamic analysis gives insight into mycobacterium tuberculosis outbreaks. Robust estimation of epidemiological parameters in Bern thanks to high sampling rate. Infectious period for WTK cases significantly longer than in Bernese outbreak.
The fast evolution of pathogenic viruses has allowed for the development of phylodynamic approaches that extract information about the epidemiological characteristics of viral genomes. Thanks to advances in whole genome sequencing, they can be applied to slowly evolving bacterial pathogens like Mycobacterium tuberculosis. In this study, we investigate and compare the epidemiological dynamics underlying two M. tuberculosis outbreaks using phylodynamic methods. Specifically, we (i) test if the outbreak data sets contain enough genetic variation to estimate short-term evolutionary rates and (ii) reconstruct epidemiological parameters such as the effective reproduction number. The first outbreak occurred in the Swiss city of Bern (1987–2012) and was caused by a drug-susceptible strain belonging to the phylogenetic M. tuberculosis Lineage 4. The second outbreak was caused by a multidrug-resistant (MDR) strain of Lineage 2, imported from the Wat Tham Krabok (WTK) refugee camp in Thailand into California. There is little temporal signal in the Bern data set and moderate temporal signal in the WTK data set. Thanks to its high sampling proportion (90%) the Bern outbreak allows robust estimation of epidemiological parameters despite the poor temporal signal. Conversely, there is much uncertainty in the epidemiological estimates concerning the sparsely sampled (9%) WTK outbreak. Our results suggest that both outbreaks peaked around 1990, although they were only recognized as outbreaks in 1993 (Bern) and 2004 (WTK). Furthermore, individuals were infected for a significantly longer period (around 9 years) in the WTK outbreak than in the Bern outbreak (4–5 years). Our work highlights both the limitations and opportunities of phylodynamic analysis of outbreaks involving slowly evolving pathogens: (i) estimation of the evolutionary rate is difficult on outbreak time scales and (ii) a high sampling proportion allows quantification of the age of the outbreak based on the sampling times, and thus allows for robust estimation of epidemiological parameters.
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Affiliation(s)
- Denise Kühnert
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zürich, Zürich, Switzerland; Institute of Medical Virology, University of Zürich, Zürich, Switzerland; Institute of Integrative Biology, ETH Zürich, Zürich, Switzerland; Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
| | - Mireia Coscolla
- Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Switzerland; University of Basel, Switzerland
| | - Daniela Brites
- Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Switzerland; University of Basel, Switzerland
| | - David Stucki
- Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Switzerland; University of Basel, Switzerland
| | - John Metcalfe
- University of California, San Francisco, School of Medicine, United States
| | - Lukas Fenner
- Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Switzerland; University of Basel, Switzerland; Institute of Social and Preventive Medicine, University of Bern, 3012 Bern, Switzerland
| | - Sebastien Gagneux
- Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Switzerland; University of Basel, Switzerland
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
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25
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Lintusaari J, Gutmann MU, Dutta R, Kaski S, Corander J. Fundamentals and Recent Developments in Approximate Bayesian Computation. Syst Biol 2018; 66:e66-e82. [PMID: 28175922 PMCID: PMC5837704 DOI: 10.1093/sysbio/syw077] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2015] [Revised: 08/09/2016] [Accepted: 08/09/2016] [Indexed: 12/16/2022] Open
Abstract
Bayesian inference plays an important role in phylogenetics, evolutionary biology, and in many other branches of science. It provides a principled framework for dealing with uncertainty and quantifying how it changes in the light of new evidence. For many complex models and inference problems, however, only approximate quantitative answers are obtainable. Approximate Bayesian computation (ABC) refers to a family of algorithms for approximate inference that makes a minimal set of assumptions by only requiring that sampling from a model is possible. We explain here the fundamentals of ABC, review the classical algorithms, and highlight recent developments. [ABC; approximate Bayesian computation; Bayesian inference; likelihood-free inference; phylogenetics; simulator-based models; stochastic simulation models; tree-based models.]
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Affiliation(s)
- Jarno Lintusaari
- Department of Computer Science, Aalto University, Espoo, Finland.,Helsinki Institute for Information Technology HIIT, Espoo, Finland
| | - Michael U Gutmann
- Department of Computer Science, Aalto University, Espoo, Finland.,Helsinki Institute for Information Technology HIIT, Espoo, Finland.,Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Ritabrata Dutta
- Department of Computer Science, Aalto University, Espoo, Finland.,Helsinki Institute for Information Technology HIIT, Espoo, Finland
| | - Samuel Kaski
- Department of Computer Science, Aalto University, Espoo, Finland.,Helsinki Institute for Information Technology HIIT, Espoo, Finland
| | - Jukka Corander
- Helsinki Institute for Information Technology HIIT, Espoo, Finland.,Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.,Department of Biostatistics, University of Oslo, Oslo, Norway
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26
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Karabatsos G, Leisen F. An approximate likelihood perspective on ABC methods. STATISTICS SURVEYS 2018. [DOI: 10.1214/18-ss120] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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27
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Ericok OB, Cemgil AT, Erturk H. Approximate Bayesian computation techniques for optical characterization of nanoparticle clusters. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2018; 35:88-97. [PMID: 29328096 DOI: 10.1364/josaa.35.000088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Accepted: 11/10/2017] [Indexed: 06/07/2023]
Abstract
Characterization of nanoparticle aggregates from observed scattered light leads to a highly complex inverse problem. Even the forward model is so complex that it prohibits the use of classical likelihood-based inference methods. In this study, we compare four so-called likelihood-free methods based on approximate Bayesian computation (ABC) that requires only numeric simulation of the forward model without the need of evaluating a likelihood. In particular, rejection, Markov chain Monte Carlo, population Monte Carlo, and adaptive population Monte Carlo (APMC) are compared in terms of accuracy. In the current model, we assume that the nanoparticle aggregates are mutually well separated and made up of particles of same size. Filippov's particle-cluster algorithm is used to generate aggregates, and discrete dipole approximation is used to estimate scattering behavior. It is found that the APMC algorithm is superior to others in terms of time and acceptance rates, although all algorithms produce similar posterior distributions. Using ABC techniques and utilizing unpolarized light experiments at 266 nm wavelength, characterization of soot aggregates is performed with less than 2 nm deviation in nanoparticle radius and 3-4 deviation in number of nanoparticles forming the monodisperse aggregates. Promising results are also observed for the polydisperse aggregate with log-normal particle size distribution.
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28
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Browning AP, McCue SW, Binny RN, Plank MJ, Shah ET, Simpson MJ. Inferring parameters for a lattice-free model of cell migration and proliferation using experimental data. J Theor Biol 2018; 437:251-260. [DOI: 10.1016/j.jtbi.2017.10.032] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Revised: 10/30/2017] [Accepted: 10/31/2017] [Indexed: 12/20/2022]
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29
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Kandler A, Wilder B, Fortunato L. Inferring individual-level processes from population-level patterns in cultural evolution. ROYAL SOCIETY OPEN SCIENCE 2017; 4:170949. [PMID: 28989786 PMCID: PMC5627126 DOI: 10.1098/rsos.170949] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Accepted: 08/04/2017] [Indexed: 05/24/2023]
Abstract
Our species is characterized by a great degree of cultural variation, both within and between populations. Understanding how group-level patterns of culture emerge from individual-level behaviour is a long-standing question in the biological and social sciences. We develop a simulation model capturing demographic and cultural dynamics relevant to human cultural evolution, focusing on the interface between population-level patterns and individual-level processes. The model tracks the distribution of variants of cultural traits across individuals in a population over time, conditioned on different pathways for the transmission of information between individuals. From these data, we obtain theoretical expectations for a range of statistics commonly used to capture population-level characteristics (e.g. the degree of cultural diversity). Consistent with previous theoretical work, our results show that the patterns observed at the level of groups are rooted in the interplay between the transmission pathways and the age structure of the population. We also explore whether, and under what conditions, the different pathways can be distinguished based on their group-level signatures, in an effort to establish theoretical limits to inference. Our results show that the temporal dynamic of cultural change over time retains a stronger signature than the cultural composition of the population at a specific point in time. Overall, the results suggest a shift in focus from identifying the one individual-level process that likely produced the observed data to excluding those that likely did not. We conclude by discussing the implications for empirical studies of human cultural evolution.
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Affiliation(s)
- Anne Kandler
- Department of Human Behavior, Ecology and Culture, Max Planck Institute for Evolutionary Anthropology, Leipzig, Sachsen, Germany
- Santa Fe Institute, Santa Fe, NM, USA
| | - Bryan Wilder
- School of Engineering, University of Southern California, Los Angeles, CA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - Laura Fortunato
- Institute of Cognitive and Evolutionary Anthropology, University of Oxford, Oxford, UK
- Santa Fe Institute, Santa Fe, NM, USA
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30
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Browning AP, McCue SW, Simpson MJ. A Bayesian Computational Approach to Explore the Optimal Duration of a Cell Proliferation Assay. Bull Math Biol 2017; 79:1888-1906. [DOI: 10.1007/s11538-017-0311-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Accepted: 06/16/2017] [Indexed: 11/29/2022]
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31
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Tietäväinen A, Gutmann MU, Keski-Vakkuri E, Corander J, Hæggström E. Bayesian inference of physiologically meaningful parameters from body sway measurements. Sci Rep 2017. [PMID: 28630413 PMCID: PMC5476665 DOI: 10.1038/s41598-017-02372-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
The control of the human body sway by the central nervous system, muscles, and conscious brain is of interest since body sway carries information about the physiological status of a person. Several models have been proposed to describe body sway in an upright standing position, however, due to the statistical intractability of the more realistic models, no formal parameter inference has previously been conducted and the expressive power of such models for real human subjects remains unknown. Using the latest advances in Bayesian statistical inference for intractable models, we fitted a nonlinear control model to posturographic measurements, and we showed that it can accurately predict the sway characteristics of both simulated and real subjects. Our method provides a full statistical characterization of the uncertainty related to all model parameters as quantified by posterior probability density functions, which is useful for comparisons across subjects and test settings. The ability to infer intractable control models from sensor data opens new possibilities for monitoring and predicting body status in health applications.
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Affiliation(s)
- A Tietäväinen
- Department of Physics, University of Helsinki, FI-00014, Helsinki, Finland.
| | - M U Gutmann
- School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, UK
| | - E Keski-Vakkuri
- Department of Physics, University of Helsinki, FI-00014, Helsinki, Finland
| | - J Corander
- Department of Mathematics and Statistics, University of Helsinki, FI-00014, Helsinki, Finland.,Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, N-0317, Oslo, Norway
| | - E Hæggström
- Department of Physics, University of Helsinki, FI-00014, Helsinki, Finland
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32
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Crema ER, Kandler A, Shennan S. Revealing patterns of cultural transmission from frequency data: equilibrium and non-equilibrium assumptions. Sci Rep 2016; 6:39122. [PMID: 27974814 PMCID: PMC5156924 DOI: 10.1038/srep39122] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 11/17/2016] [Indexed: 12/15/2022] Open
Abstract
A long tradition of cultural evolutionary studies has developed a rich repertoire of mathematical models of social learning. Early studies have laid the foundation of more recent endeavours to infer patterns of cultural transmission from observed frequencies of a variety of cultural data, from decorative motifs on potsherds to baby names and musical preferences. While this wide range of applications provides an opportunity for the development of generalisable analytical workflows, archaeological data present new questions and challenges that require further methodological and theoretical discussion. Here we examine the decorative motifs of Neolithic pottery from an archaeological assemblage in Western Germany, and argue that the widely used (and relatively undiscussed) assumption that observed frequencies are the result of a system in equilibrium conditions is unwarranted, and can lead to incorrect conclusions. We analyse our data with a simulation-based inferential framework that can overcome some of the intrinsic limitations in archaeological data, as well as handle both equilibrium conditions and instances where the mode of cultural transmission is time-variant. Results suggest that none of the models examined can produce the observed pattern under equilibrium conditions, and suggest. instead temporal shifts in the patterns of cultural transmission.
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Affiliation(s)
- Enrico R Crema
- University of Cambridge, Department of Archaeology and Anthropology, CB2 3ER, Cambridge,United Kingdom
| | - Anne Kandler
- Max Planck Institute for Evolutionary Anthropology,Department of Human Behavior, Ecology and Culture, 04103,Leipzig,Germany
| | - Stephen Shennan
- UCL Institute of Archaeology WC1H 0PY,London, United Kingdom
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33
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Narula P, Piratla V, Bansal A, Azad S, Lio P. Parameter estimation of tuberculosis transmission model using Ensemble Kalman filter across Indian states and union territories. Infect Dis Health 2016. [DOI: 10.1016/j.idh.2016.11.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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34
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Neal P, Xiang F. Collapsing of Non‐centred Parameterized MCMC Algorithms with Applications to Epidemic Models. Scand Stat Theory Appl 2016. [DOI: 10.1111/sjos.12242] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Peter Neal
- Department of Mathematics and Statistics Lancaster University
| | - Fei Xiang
- Department of Veterinary Medicine University of Cambridge
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35
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Lintusaari J, Gutmann MU, Kaski S, Corander J. On the Identifiability of Transmission Dynamic Models for Infectious Diseases. Genetics 2016; 202:911-8. [PMID: 26739450 PMCID: PMC4788128 DOI: 10.1534/genetics.115.180034] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2015] [Accepted: 12/29/2015] [Indexed: 11/18/2022] Open
Abstract
Understanding the transmission dynamics of infectious diseases is important for both biological research and public health applications. It has been widely demonstrated that statistical modeling provides a firm basis for inferring relevant epidemiological quantities from incidence and molecular data. However, the complexity of transmission dynamic models presents two challenges: (1) the likelihood function of the models is generally not computable, and computationally intensive simulation-based inference methods need to be employed, and (2) the model may not be fully identifiable from the available data. While the first difficulty can be tackled by computational and algorithmic advances, the second obstacle is more fundamental. Identifiability issues may lead to inferences that are driven more by prior assumptions than by the data themselves. We consider a popular and relatively simple yet analytically intractable model for the spread of tuberculosis based on classical IS6110 fingerprinting data. We report on the identifiability of the model, also presenting some methodological advances regarding the inference. Using likelihood approximations, we show that the reproductive value cannot be identified from the data available and that the posterior distributions obtained in previous work have likely been substantially dominated by the assumed prior distribution. Further, we show that the inferences are influenced by the assumed infectious population size, which generally has been kept fixed in previous work. We demonstrate that the infectious population size can be inferred if the remaining epidemiological parameters are already known with sufficient precision.
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Affiliation(s)
- Jarno Lintusaari
- Helsinki Institute for Information Technology (HIIT) and Department of Computer Science, Aalto University, FI-00076 Aalto, Finland
| | - Michael U Gutmann
- Helsinki Institute for Information Technology (HIIT) and Department of Computer Science, Aalto University, FI-00076 Aalto, Finland Helsinki Institute for Information Technology (HIIT) and Department of Mathematics and Statistics, University of Helsinki, FI-00014 Helsinki, Finland
| | - Samuel Kaski
- Helsinki Institute for Information Technology (HIIT) and Department of Computer Science, Aalto University, FI-00076 Aalto, Finland
| | - Jukka Corander
- Helsinki Institute for Information Technology (HIIT) and Department of Mathematics and Statistics, University of Helsinki, FI-00014 Helsinki, Finland
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Kosmala M, Miller P, Ferreira S, Funston P, Keet D, Packer C. Estimating wildlife disease dynamics in complex systems using an Approximate Bayesian Computation framework. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2016; 26:295-308. [PMID: 27039526 DOI: 10.1890/14-1808] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Emerging infectious diseases of wildlife are of increasing concern to managers and conservation policy makers, but are often difficult to study and predict due to the complexity of host-disease systems and a paucity of empirical data. We demonstrate the use of an Approximate Bayesian Computation statistical framework to reconstruct the disease dynamics of bovine tuberculosis in Kruger National Park's lion population, despite limited empirical data on the disease's effects in lions. The modeling results suggest that, while a large proportion of the lion population will become infected with bovine tuberculosis, lions are a spillover host and long disease latency is common. In the absence of future aggravating factors, bovine tuberculosis is projected to cause a lion population decline of ~3% over the next 50 years, with the population stabilizing at this new equilibrium. The Approximate Bayesian Computation framework is a new tool for wildlife managers. It allows emerging infectious diseases to be modeled in complex systems by incorporating disparate knowledge about host demographics, behavior, and heterogeneous disease transmission, while allowing inference of unknown system parameters.
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Vo BN, Drovandi CC, Pettitt AN, Pettet GJ. Melanoma Cell Colony Expansion Parameters Revealed by Approximate Bayesian Computation. PLoS Comput Biol 2015; 11:e1004635. [PMID: 26642072 PMCID: PMC4671693 DOI: 10.1371/journal.pcbi.1004635] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2015] [Accepted: 10/28/2015] [Indexed: 11/19/2022] Open
Abstract
In vitro studies and mathematical models are now being widely used to study the underlying mechanisms driving the expansion of cell colonies. This can improve our understanding of cancer formation and progression. Although much progress has been made in terms of developing and analysing mathematical models, far less progress has been made in terms of understanding how to estimate model parameters using experimental in vitro image-based data. To address this issue, a new approximate Bayesian computation (ABC) algorithm is proposed to estimate key parameters governing the expansion of melanoma cell (MM127) colonies, including cell diffusivity, D, cell proliferation rate, λ, and cell-to-cell adhesion, q, in two experimental scenarios, namely with and without a chemical treatment to suppress cell proliferation. Even when little prior biological knowledge about the parameters is assumed, all parameters are precisely inferred with a small posterior coefficient of variation, approximately 2–12%. The ABC analyses reveal that the posterior distributions of D and q depend on the experimental elapsed time, whereas the posterior distribution of λ does not. The posterior mean values of D and q are in the ranges 226–268 µm2h−1, 311–351 µm2h−1 and 0.23–0.39, 0.32–0.61 for the experimental periods of 0–24 h and 24–48 h, respectively. Furthermore, we found that the posterior distribution of q also depends on the initial cell density, whereas the posterior distributions of D and λ do not. The ABC approach also enables information from the two experiments to be combined, resulting in greater precision for all estimates of D and λ. Quantifying the underlying parameters that drive the expansion of melanoma cell colonies such as the cell diffusivity, cell proliferation rate and cell-to-cell adhesion strength can improve our understanding of melanoma biology and its response to treatment. We combine a simulation-based model of collective cell spreading with a novel Bayesian computational algorithm to estimate these parameters from carefully chosen summaries of collective cell image data and to quantify their associated uncertainty across different experimental conditions. Our summarisation of the image data leads to precise estimates for all parameters. Our analysis reveals that the cell diffusivity and the cell-to-cell adhesion strength estimates depend on experimental elapsed time. Furthermore, the cell-to-cell adhesion strength estimate appears to depend on the initial cell density, whereas the cell proliferation rate estimate is approximately the same over different experimental conditions.
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Affiliation(s)
- Brenda N. Vo
- School of Mathematical Sciences, Queensland University of Technology (QUT), Brisbane, Australia
- ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS), QUT, Brisbane, Australia
- * E-mail:
| | - Christopher C. Drovandi
- School of Mathematical Sciences, Queensland University of Technology (QUT), Brisbane, Australia
- ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS), QUT, Brisbane, Australia
| | - Anthony N. Pettitt
- School of Mathematical Sciences, Queensland University of Technology (QUT), Brisbane, Australia
- ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS), QUT, Brisbane, Australia
| | - Graeme J. Pettet
- School of Mathematical Sciences, Queensland University of Technology (QUT), Brisbane, Australia
- ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS), QUT, Brisbane, Australia
- Institute for Future Environments, Science and Engineering Centre, QUT, Brisbane, Australia
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Narula P, Azad S, Lio P. Bayesian Melding Approach to Estimate the Reproduction Number for Tuberculosis Transmission in Indian States and Union Territories. Asia Pac J Public Health 2015; 27:723-32. [PMID: 26182939 DOI: 10.1177/1010539515595068] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Tuberculosis (TB) is one of the most common infectious diseases and a leading cause of death in the world. Despite the full implementation of Revised National Tuberculosis Control Programme, the disease continues to be a leading cause of morality and economic burden in India. The basic reproduction is a fundamental key parameter that quantifies the spread of a disease. In this article, we present a Bayesian melding approach to estimate the basic reproduction number using a deterministic model of TB. We present a point estimate of the basic reproduction number of 35 states and union territories of India during 2006 to 2011. The basic reproduction number of TB for India is computed to be 0.92, which indicates the slow elimination of TB in India during 2006 to 2011.
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Affiliation(s)
| | - Sarita Azad
- Indian Institute of Technology Mandi, Mandi, India
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Barber S, Voss J, Webster M. The rate of convergence for approximate Bayesian computation. Electron J Stat 2015. [DOI: 10.1214/15-ejs988] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Wu Q, Smith-Miles K, Tian T. Approximate Bayesian computation schemes for parameter inference of discrete stochastic models using simulated likelihood density. BMC Bioinformatics 2014; 15 Suppl 12:S3. [PMID: 25473744 PMCID: PMC4243104 DOI: 10.1186/1471-2105-15-s12-s3] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Mathematical modeling is an important tool in systems biology to study the dynamic property of complex biological systems. However, one of the major challenges in systems biology is how to infer unknown parameters in mathematical models based on the experimental data sets, in particular, when the data are sparse and the regulatory network is stochastic. RESULTS To address this issue, this work proposed a new algorithm to estimate parameters in stochastic models using simulated likelihood density in the framework of approximate Bayesian computation. Two stochastic models were used to demonstrate the efficiency and effectiveness of the proposed method. In addition, we designed another algorithm based on a novel objective function to measure the accuracy of stochastic simulations. CONCLUSIONS Simulation results suggest that the usage of simulated likelihood density improves the accuracy of estimates substantially. When the error is measured at each observation time point individually, the estimated parameters have better accuracy than those obtained by a published method in which the error is measured using simulations over the entire observation time period.
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Bekara MEA, Courcoul A, Bénet JJ, Durand B. Modeling tuberculosis dynamics, detection and control in cattle herds. PLoS One 2014; 9:e108584. [PMID: 25254369 PMCID: PMC4177924 DOI: 10.1371/journal.pone.0108584] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2014] [Accepted: 09/02/2014] [Indexed: 11/18/2022] Open
Abstract
Epidemiological models are key tools for designing and evaluating detection and control strategies against animal infectious diseases. In France, after decades of decrease of bovine tuberculosis (bTB) incidence, the disease keeps circulating. Increasing prevalence levels are observed in several areas, where the detection and control strategy could be adapted. The objective of this work was to design and calibrate a model of the within-herd transmission of bTB. The proposed model is a stochastic model operating in discrete-time. Three health states were distinguished: susceptible, latent and infected. Dairy and beef herd dynamics and bTB detection and control programs were explicitly represented. Approximate Bayesian computation was used to estimate three model parameters from field data: the transmission parameter when animals are inside (βinside) and outside (βoutside) buildings, and the duration of the latent phase. An independent dataset was used for model validation. The estimated median was 0.43 [0.16–0.84] month−1 for βinside and 0.08 [0.01–0.32] month−1 for βoutside. The median duration of the latent period was estimated 3.5 [2]–[8] months. The sensitivity analysis showed only minor influences of fixed parameter values on these posterior estimates. Validation based on an independent dataset showed that in more than 80% of herds, the observed proportion of animals with detected lesions was between the 2.5% and 97.5% percentiles of the simulated distribution. In the absence of control program and once bTB has become enzootic within a herd, the median effective reproductive ratio was estimated to be 2.2 in beef herds and 1.7 in dairy herds. These low estimates are consistent with field observations of a low prevalence level in French bTB-infected herds.
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Affiliation(s)
- Mohammed El Amine Bekara
- University Paris Est, Anses, Laboratory of Animal Health, Epidemiology Unit, Maisons-Alfort, France
| | - Aurélie Courcoul
- University Paris Est, Anses, Laboratory of Animal Health, Epidemiology Unit, Maisons-Alfort, France
| | - Jean-Jacques Bénet
- University Paris Est, National Veterinary School of Alfort (ENVA), EpiMAI Unit, Maisons-Alfort, France
| | - Benoit Durand
- University Paris Est, Anses, Laboratory of Animal Health, Epidemiology Unit, Maisons-Alfort, France
- * E-mail:
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Demographic history and gene flow during silkworm domestication. BMC Evol Biol 2014; 14:185. [PMID: 25123546 PMCID: PMC4236568 DOI: 10.1186/s12862-014-0185-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2014] [Accepted: 08/05/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Gene flow plays an important role in domestication history of domesticated species. However, little is known about the demographic history of domesticated silkworm involving gene flow with its wild relative. RESULTS In this study, four model-based evolutionary scenarios to describe the demographic history of B. mori were hypothesized. Using Approximate Bayesian Computation method and DNA sequence data from 29 nuclear loci, we found that the gene flow at bottleneck model is the most likely scenario for silkworm domestication. The starting time of silkworm domestication was estimated to be approximate 7,500 years ago; the time of domestication termination was 3,984 years ago. Using coalescent simulation analysis, we also found that bi-directional gene flow occurred during silkworm domestication. CONCLUSIONS Estimates of silkworm domestication time are nearly consistent with the archeological evidence and our previous results. Importantly, we found that the bi-directional gene flow might occur during silkworm domestication. Our findings add a dimension to highlight the important role of gene flow in domestication of crops and animals.
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43
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Neal P, Terry Huang CL. Forward Simulation Markov Chain Monte Carlo with Applications to Stochastic Epidemic Models. Scand Stat Theory Appl 2014. [DOI: 10.1111/sjos.12111] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Peter Neal
- Department of Mathematics and Statistics Lancaster University
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Sandoval-Castellanos E, Palkopoulou E, Dalén L. Back to BaySICS: a user-friendly program for Bayesian Statistical Inference from Coalescent Simulations. PLoS One 2014; 9:e98011. [PMID: 24865457 PMCID: PMC4035278 DOI: 10.1371/journal.pone.0098011] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2013] [Accepted: 04/28/2014] [Indexed: 12/02/2022] Open
Abstract
Inference of population demographic history has vastly improved in recent years due to a number of technological and theoretical advances including the use of ancient DNA. Approximate Bayesian computation (ABC) stands among the most promising methods due to its simple theoretical fundament and exceptional flexibility. However, limited availability of user-friendly programs that perform ABC analysis renders it difficult to implement, and hence programming skills are frequently required. In addition, there is limited availability of programs able to deal with heterochronous data. Here we present the software BaySICS: Bayesian Statistical Inference of Coalescent Simulations. BaySICS provides an integrated and user-friendly platform that performs ABC analyses by means of coalescent simulations from DNA sequence data. It estimates historical demographic population parameters and performs hypothesis testing by means of Bayes factors obtained from model comparisons. Although providing specific features that improve inference from datasets with heterochronous data, BaySICS also has several capabilities making it a suitable tool for analysing contemporary genetic datasets. Those capabilities include joint analysis of independent tables, a graphical interface and the implementation of Markov-chain Monte Carlo without likelihoods.
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Affiliation(s)
- Edson Sandoval-Castellanos
- Department of Bioinformatics and Genetics, Swedish Museum of Natural History, Stockholm, Sweden; Department of Zoology, Stockholm University, Stockholm, Sweden
| | - Eleftheria Palkopoulou
- Department of Bioinformatics and Genetics, Swedish Museum of Natural History, Stockholm, Sweden; Department of Zoology, Stockholm University, Stockholm, Sweden
| | - Love Dalén
- Department of Bioinformatics and Genetics, Swedish Museum of Natural History, Stockholm, Sweden
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45
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McKinley TJ, Ross JV, Deardon R, Cook AR. Simulation-based Bayesian inference for epidemic models. Comput Stat Data Anal 2014. [DOI: 10.1016/j.csda.2012.12.012] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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46
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Exact vs. approximate computation: reconciling different estimates of Mycobacterium tuberculosis epidemiological parameters. Genetics 2014; 196:1227-30. [PMID: 24496011 PMCID: PMC3982679 DOI: 10.1534/genetics.113.158808] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Exact computational methods for inference in population genetics are intuitively preferable to approximate analyses. We reconcile two starkly different estimates of the reproductive number of tuberculosis from previous studies that used the same genotyping data and underlying model. This demonstrates the value of approximate analyses in validating exact methods.
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Bonello N, Sampson J, Burn J, Wilson IJ, McGrown G, Margison GP, Thorncroft M, Crossbie P, Povey AC, Santibanez-Koref M, Walters K. Bayesian inference supports a location and neighbour-dependent model of DNA methylation propagation at the MGMT gene promoter in lung tumours. J Theor Biol 2013; 336:87-95. [PMID: 23911575 DOI: 10.1016/j.jtbi.2013.07.019] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2012] [Revised: 07/12/2013] [Accepted: 07/19/2013] [Indexed: 11/29/2022]
Abstract
We exploit model-based Bayesian inference methodologies to analyse lung tumour-derived methylation data from a CpG island in the O6-methylguanine-DNA methyltransferase (MGMT) promoter. Interest is in modelling the changes in methylation patterns in a CpG island in the first exon of the promoter during lung tumour development. We propose four competils of methylation state propagation based on two mechanisms. The first is the location-dependence mechanism in which the probability of a gain or loss of methylation at a CpG within the promoter depends upon its location in the CpG sequence. The second mechanism is that of neighbour-dependence in which gain or loss of methylation at a CpG depends upon the methylation status of the immediately preceding CpG. Our data comprises the methylation status at 12 CpGs near the 5' end of the CpG island in two lung tumour samples for both alleles of a nearby polymorphism. We use approximate Bayesian computation, a computationally intensive rejection-sampling algorithm to infer model parameters and compare models without the need to evaluate the likelihood function. We compare the four proposed models using two criteria: the approximate Bayes factors and the distribution of the Euclidean distance between the summary statistics of the observed and simulated datasets. Our model-based analysis demonstrates compelling evidence for both location and neighbour dependence in the process of aberrant DNA methylation of this MGMT promoter CpG island in lung tumours. We find equivocal evidence to support the hypothesis that the methylation patterns of the two alleles evolve independently.
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Affiliation(s)
- Nicolas Bonello
- School of Mathematics and Statistics, University of Sheffield, Sheffield S3 7RH, UK
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48
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Selecting summary statistics in approximate Bayesian computation for calibrating stochastic models. BIOMED RESEARCH INTERNATIONAL 2013; 2013:210646. [PMID: 24288668 PMCID: PMC3830866 DOI: 10.1155/2013/210646] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2013] [Revised: 07/16/2013] [Accepted: 07/20/2013] [Indexed: 11/18/2022]
Abstract
Approximate Bayesian computation (ABC) is an approach for using measurement data to calibrate stochastic computer models, which are common in biology applications. ABC is becoming the “go-to” option when the data and/or parameter dimension is large because it relies on user-chosen summary statistics rather than the full data and is therefore computationally feasible. One technical challenge with ABC is that the quality of the approximation to the posterior distribution of model parameters depends on the user-chosen summary statistics. In this paper, the user requirement to choose effective summary statistics in order to accurately estimate the posterior distribution of model parameters is investigated and illustrated by example, using a model and corresponding real data of mitochondrial DNA population dynamics. We show that for some choices of summary statistics, the posterior distribution of model parameters is closely approximated and for other choices of summary statistics, the posterior distribution is not closely approximated. A strategy to choose effective summary statistics is suggested in cases where the stochastic computer model can be run at many trial parameter settings, as in the example.
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Luciani F, Sanders MT, Oveissi S, Pang KC, Chen W. Increasing viral dose causes a reversal in CD8+ T cell immunodominance during primary influenza infection due to differences in antigen presentation, T cell avidity, and precursor numbers. THE JOURNAL OF IMMUNOLOGY 2012; 190:36-47. [PMID: 23233728 DOI: 10.4049/jimmunol.1200089] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
T cell responses are characterized by the phenomenon of immunodominance (ID), whereby peptide-specific T cells are elicited in a reproducible hierarchy of dominant and subdominant responses. However, the mechanisms that give rise to ID are not well understood. We investigated the effect of viral dose on primary CD8(+) T cell (T(CD8+)) ID by injecting mice i.p. with various doses of influenza A virus and assessing the primary T(CD8+) response to five dominant and subdominant peptides. Increasing viral dose enhanced the overall strength of the T(CD8+) response, and it altered the ID hierarchy: specifically, NP(366-374) T(CD8+) were dominant at low viral doses but were supplanted by PA(224-233) T(CD8+) at high doses. To understand the basis for this reversal, we mathematically modeled these T(CD8+) responses and used Bayesian statistics to obtain estimates for Ag presentation, T(CD8+) precursor numbers, and avidity. Interestingly, at low viral doses, Ag presentation most critically shaped ID hierarchy, enabling T(CD8+) specific to the more abundantly presented NP(366-374) to dominate. By comparison, at high viral doses, T(CD8+) avidity and precursor numbers appeared to be the major influences on ID hierarchy, resulting in PA(224-233) T(CD8+) usurping NP(366-374) cells as the result of higher avidity and precursor numbers. These results demonstrate that the nature of primary T(CD8+) responses to influenza A virus is highly influenced by Ag dose, which, in turn, determines the relative importance of Ag presentation, T(CD8+) avidity, and precursor numbers in shaping the ID hierarchy. These findings provide valuable insights for future T(CD8+)-based vaccination strategies.
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Affiliation(s)
- Fabio Luciani
- Infection and Inflammation Research Centre, School of Medical Sciences, University of New South Wales, Sydney, New South Wales 2052, Australia
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50
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Speybroeck N, Williams CJ, Lafia KB, Devleesschauwer B, Berkvens D. Estimating the prevalence of infections in vector populations using pools of samples. MEDICAL AND VETERINARY ENTOMOLOGY 2012; 26:361-371. [PMID: 22486773 DOI: 10.1111/j.1365-2915.2012.01015.x] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Several statistical methods have been proposed for estimating the infection prevalence based on pooled samples, but these methods generally presume the application of perfect diagnostic tests, which in practice do not exist. To optimize prevalence estimation based on pooled samples, currently available and new statistical models were described and compared. Three groups were tested: (a) Frequentist models, (b) Monte Carlo Markov-Chain (MCMC) Bayesian models, and (c) Exact Bayesian Computation (EBC) models. Simulated data allowed the comparison of the models, including testing the performance under complex situations such as imperfect tests with a sensitivity varying according to the pool weight. In addition, all models were applied to data derived from the literature, to demonstrate the influence of the model on real-prevalence estimates. All models were implemented in the freely available R and OpenBUGS software and are presented in Appendix S1. Bayesian models can flexibly take into account the imperfect sensitivity and specificity of the diagnostic test (as well as the influence of pool-related or external variables) and are therefore the method of choice for calculating population prevalence based on pooled samples. However, when using such complex models, very precise information on test characteristics is needed, which may in general not be available.
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Affiliation(s)
- N Speybroeck
- Institut de Recherche Santé et Société (IRSS), Université catholique de Louvain, Brussels, Belgium.
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