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Wu SL, Sánchez C HM, Henry JM, Citron DT, Zhang Q, Compton K, Liang B, Verma A, Cummings DAT, Le Menach A, Scott TW, Wilson AL, Lindsay SW, Moyes CL, Hancock PA, Russell TL, Burkot TR, Marshall JM, Kiware S, Reiner RC, Smith DL. Vector bionomics and vectorial capacity as emergent properties of mosquito behaviors and ecology. PLoS Comput Biol 2020; 16:e1007446. [PMID: 32320389 PMCID: PMC7197866 DOI: 10.1371/journal.pcbi.1007446] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 05/04/2020] [Accepted: 03/21/2020] [Indexed: 11/18/2022] Open
Abstract
Mosquitoes are important vectors for pathogens that infect humans and other vertebrate animals. Some aspects of adult mosquito behavior and mosquito ecology play an important role in determining the capacity of vector populations to transmit pathogens. Here, we re-examine factors affecting the transmission of pathogens by mosquitoes using a new approach. Unlike most previous models, this framework considers the behavioral states and state transitions of adult mosquitoes through a sequence of activity bouts. We developed a new framework for individual-based simulation models called MBITES (Mosquito Bout-based and Individual-based Transmission Ecology Simulator). In MBITES, it is possible to build models that simulate the behavior and ecology of adult mosquitoes in exquisite detail on complex resource landscapes generated by spatial point processes. We also developed an ordinary differential equation model which is the Kolmogorov forward equations for models developed in MBITES under a specific set of simplifying assumptions. While mosquito infection and pathogen development are one possible part of a mosquito's state, that is not our main focus. Using extensive simulation using some models developed in MBITES, we show that vectorial capacity can be understood as an emergent property of simple behavioral algorithms interacting with complex resource landscapes, and that relative density or sparsity of resources and the need to search can have profound consequences for mosquito populations' capacity to transmit pathogens.
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Affiliation(s)
- Sean L Wu
- Divisions of Biostatistics & Epidemiology, University of California, Berkeley, Berkeley, California, United States of America
| | - Héctor M Sánchez C
- Divisions of Biostatistics & Epidemiology, University of California, Berkeley, Berkeley, California, United States of America.,Instituto Tecnológico y de Estudios Superiores de Monterrey, Monterrey, Mexico
| | - John M Henry
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
| | - Daniel T Citron
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
| | - Qian Zhang
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
| | - Kelly Compton
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
| | - Biyonka Liang
- Divisions of Biostatistics & Epidemiology, University of California, Berkeley, Berkeley, California, United States of America
| | - Amit Verma
- Emory University, Atlanta, Georgia, United States of America
| | - Derek A T Cummings
- Department of Biology, University of Florida, Gainesville, Florida, United States of America
| | - Arnaud Le Menach
- Clinton Health Access Initiative, Boston, Massachusetts, United States of America
| | - Thomas W Scott
- University of California, Davis, California, United States of America
| | - Anne L Wilson
- Liverpool School of Tropical Tropical Medicine, Liverpool, United Kingdom
| | - Steven W Lindsay
- Department of Biosciences, University of Durham, Durham, United Kingdom
| | | | - Penny A Hancock
- Big Data Institute, University of Oxford, Oxford, United Kingdom
| | - Tanya L Russell
- Australian Institute of Tropical Health and Medicine, James Cook University, Cairns, Australia
| | - Thomas R Burkot
- Australian Institute of Tropical Health and Medicine, James Cook University, Cairns, Australia
| | - John M Marshall
- Divisions of Biostatistics & Epidemiology, University of California, Berkeley, Berkeley, California, United States of America
| | - Samson Kiware
- Ifakara Health Institute, Environmental Health and Ecological Sciences Thematic Group, Ifakara, Tanzania
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America.,Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, Washington, United States of America
| | - David L Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America.,Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, Washington, United States of America
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Salter JM, Williamson DB, Scinocca J, Kharin V. Uncertainty Quantification for Computer Models With Spatial Output Using Calibration-Optimal Bases. J Am Stat Assoc 2019. [DOI: 10.1080/01621459.2018.1514306] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- James M. Salter
- Department of Mathematics, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
| | - Daniel B. Williamson
- Department of Mathematics, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
| | - John Scinocca
- Canadian Centre for Climate Modelling and Analysis, Victoria, BC, Canada
| | - Viatcheslav Kharin
- Canadian Centre for Climate Modelling and Analysis, Victoria, BC, Canada
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Kooperman GJ, Pritchard MS, O'Brien TA, Timmermans BW. Rainfall From Resolved Rather Than Parameterized Processes Better Represents the Present-Day and Climate Change Response of Moderate Rates in the Community Atmosphere Model. JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS 2018; 10:971-988. [PMID: 29861837 PMCID: PMC5969264 DOI: 10.1002/2017ms001188] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 02/28/2018] [Indexed: 06/08/2023]
Abstract
Deficiencies in the parameterizations of convection used in global climate models often lead to a distorted representation of the simulated rainfall intensity distribution (i.e., too much rainfall from weak rain rates). While encouraging improvements in high percentile rainfall intensity have been found as the horizontal resolution of the Community Atmosphere Model is increased to ∼25 km, we demonstrate no corresponding improvement in the moderate rain rates that generate the majority of accumulated rainfall. Using a statistical framework designed to emphasize links between precipitation intensity and accumulated rainfall beyond just the frequency distribution, we show that CAM cannot realistically simulate moderate rain rates, and cannot capture their intensification with climate change, even as resolution is increased. However, by separating the parameterized convective and large-scale resolved contributions to total rainfall, we find that the intensity, geographic pattern, and climate change response of CAM's large-scale rain rates are more consistent with observations (TRMM 3B42), superparameterization, and theoretical expectations, despite issues with parameterized convection. Increasing CAM's horizontal resolution does improve the representation of total rainfall intensity, but not due to changes in the intensity of large-scale rain rates, which are surprisingly insensitive to horizontal resolution. Rather, improvements occur through an increase in the relative contribution of the large-scale component to the total amount of accumulated rainfall. Analysis of sensitivities to convective timescale and entrainment rate confirm the importance of these parameters in the possible development of scale-aware parameterizations, but also reveal unrecognized trade-offs from the entanglement of precipitation frequency and total amount.
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Affiliation(s)
| | | | - Travis A. O'Brien
- Climate and Ecosystems Science DivisionLawrence Berkeley National LaboratoryBerkeleyCAUSA
- Department of Land Air and Water ResourcesUniversity of California DavisDavisCAUSA
| | - Ben W. Timmermans
- Climate and Ecosystems Science DivisionLawrence Berkeley National LaboratoryBerkeleyCAUSA
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Affiliation(s)
- Ming Yang
- Control and Simulation Center, Harbin Institute of Technology, Harbin, People's Republic of China
| | - Zhizhao Liu
- Control and Simulation Center, Harbin Institute of Technology, Harbin, People's Republic of China
| | - Wei Li
- Control and Simulation Center, Harbin Institute of Technology, Harbin, People's Republic of China
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5
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Li W, Lu L, Xie X, Yang M. A novel extension algorithm for optimized Latin hypercube sampling. J STAT COMPUT SIM 2017. [DOI: 10.1080/00949655.2017.1340475] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Wei Li
- Control and Simulation Center, Harbin Institute of Technology, Harbin, People’s Republic of China
| | - Lingyun Lu
- Control and Simulation Center, Harbin Institute of Technology, Harbin, People’s Republic of China
| | - Xiaotian Xie
- Control and Simulation Center, Harbin Institute of Technology, Harbin, People’s Republic of China
| | - Ming Yang
- Control and Simulation Center, Harbin Institute of Technology, Harbin, People’s Republic of China
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Salter JM, Williamson D. A comparison of statistical emulation methodologies for multi-wave calibration of environmental models. ENVIRONMETRICS 2016; 27:507-523. [PMID: 28042255 PMCID: PMC5157755 DOI: 10.1002/env.2405] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Revised: 07/01/2016] [Accepted: 07/06/2016] [Indexed: 05/08/2023]
Abstract
Expensive computer codes, particularly those used for simulating environmental or geological processes, such as climate models, require calibration (sometimes called tuning). When calibrating expensive simulators using uncertainty quantification methods, it is usually necessary to use a statistical model called an emulator in place of the computer code when running the calibration algorithm. Though emulators based on Gaussian processes are typically many orders of magnitude faster to evaluate than the simulator they mimic, many applications have sought to speed up the computations by using regression-only emulators within the calculations instead, arguing that the extra sophistication brought using the Gaussian process is not worth the extra computational power. This was the case for the analysis that produced the UK climate projections in 2009. In this paper, we compare the effectiveness of both emulation approaches upon a multi-wave calibration framework that is becoming popular in the climate modeling community called "history matching." We find that Gaussian processes offer significant benefits to the reduction of parametric uncertainty over regression-only approaches. We find that in a multi-wave experiment, a combination of regression-only emulators initially, followed by Gaussian process emulators for refocussing experiments can be nearly as effective as using Gaussian processes throughout for a fraction of the computational cost. We also discover a number of design and emulator-dependent features of the multi-wave history matching approach that can cause apparent, yet premature, convergence of our estimates of parametric uncertainty. We compare these approaches to calibration in idealized examples and apply it to a well-known geological reservoir model.
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Affiliation(s)
- James M. Salter
- College of Engineering, Mathematics and Physical SciencesUniversity of ExeterExeterU.K.
| | - Daniel Williamson
- College of Engineering, Mathematics and Physical SciencesUniversity of ExeterExeterU.K.
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