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Conquet E, Ozgul A, Blumstein DT, Armitage KB, Oli MK, Martin JGA, Clutton-Brock TH, Paniw M. Demographic consequences of changes in environmental periodicity. Ecology 2023; 104:e3894. [PMID: 36208282 DOI: 10.1002/ecy.3894] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 07/27/2022] [Accepted: 08/04/2022] [Indexed: 01/24/2023]
Abstract
The fate of natural populations is mediated by complex interactions among vital rates, which can vary within and among years. Although the effects of random, among-year variation in vital rates have been studied extensively, relatively little is known about how periodic, nonrandom variation in vital rates affects populations. This knowledge gap is potentially alarming as global environmental change is projected to alter common periodic variations, such as seasonality. We investigated the effects of changes in vital-rate periodicity on populations of three species representing different forms of adaptation to periodic environments: the yellow-bellied marmot (Marmota flaviventer), adapted to strong seasonality in snowfall; the meerkat (Suricata suricatta), adapted to inter-annual stochasticity as well as seasonal patterns in rainfall; and the dewy pine (Drosophyllum lusitanicum), adapted to fire regimes and periodic post-fire habitat succession. To assess how changes in periodicity affect population growth, we parameterized periodic matrix population models and projected population dynamics under different scenarios of perturbations in the strength of vital-rate periodicity. We assessed the effects of such perturbations on various metrics describing population dynamics, including the stochastic growth rate, log λS . Overall, perturbing the strength of periodicity had strong effects on population dynamics in all three study species. For the marmots, log λS decreased with increased seasonal differences in adult survival. For the meerkats, density dependence buffered the effects of perturbations of periodicity on log λS . Finally, dewy pines were negatively affected by changes in natural post-fire succession under stochastic or periodic fire regimes with fires occurring every 30 years, but were buffered by density dependence from such changes under presumed more frequent fires or large-scale disturbances. We show that changes in the strength of vital-rate periodicity can have diverse but strong effects on population dynamics across different life histories. Populations buffered from inter-annual vital-rate variation can be affected substantially by changes in environmentally driven vital-rate periodic patterns; however, the effects of such changes can be masked in analyses focusing on inter-annual variation. As most ecosystems are affected by periodic variations in the environment such as seasonality, assessing their contributions to population viability for future global-change research is crucial.
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Affiliation(s)
- Eva Conquet
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
| | - Arpat Ozgul
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
| | - Daniel T Blumstein
- Department of Ecology and Evolutionary Biology, University of California Los Angeles, Los Angeles, California, USA.,The Rocky Mountain Biological Laboratory, Crested Butte, Colorado, USA
| | - Kenneth B Armitage
- Department of Ecology and Evolutionary Biology, The University of Kansas, Lawrence, Kansas, USA
| | - Madan K Oli
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, USA
| | - Julien G A Martin
- Department of Biology, University of Ottawa, Ottawa, Ontario, Canada.,School of Biological Sciences, University of Aberdeen, Aberdeen, UK
| | - Tim H Clutton-Brock
- Department of Zoology, University of Cambridge, Cambridge, UK.,Kalahari Research Trust, Kuruman River Reserve, Northern Cape, South Africa.,Mammal Research Institute, University of Pretoria, Pretoria, South Africa
| | - Maria Paniw
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.,Department of Conservation and Global Change, Doñana Biological Station (EBD-CSIC), Seville, Spain
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2
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Capera-Aragones P, Tyson RC, Foxall E. The maximum entropy principle to predict forager spatial distributions: an alternate perspective for movement ecology. THEOR ECOL-NETH 2023. [DOI: 10.1007/s12080-023-00552-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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3
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Tavakoli M, Tavakkoli-Moghaddam R, Mesbahi R, Ghanavati-Nejad M, Tajally A. Simulation of the COVID-19 patient flow and investigation of the future patient arrival using a time-series prediction model: a real-case study. Med Biol Eng Comput 2022; 60:969-990. [PMID: 35152366 PMCID: PMC8853249 DOI: 10.1007/s11517-022-02525-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 02/01/2022] [Indexed: 12/12/2022]
Abstract
COVID-19 looks to be the worst pandemic disease in the last decades due to its number of infected people, deaths, and the staggering demand for healthcare services, especially hospitals. The first and most important step is to identify the patient flow through a certain process. For the second step, there is a crucial need for predicting the future patient arrivals for planning especially at the administrative level of a hospital. This study aims to first simulate the patient flow process and then predict the future entry of patients in a hospital as the case study. Also, according to the system status, this study suggests some policies based on different probable scenarios and assesses the outcome of each decision to improve the policies. The simulation model is conducted by Arena.15 software. The seasonal auto-regressive integrated moving average (SARIMA) model is used for patient’s arrival prediction within 30 days. Different scenarios are evaluated through a data envelopment analysis (DEA) method. The simulation model runs for predicted patient’s arrival for the least efficient scenario and the outputs compare the base run scenario. Results show that the system collapses after 14 days according to the predictions and simulation and the bottleneck of the ICU and CCU departments becomes problematic. Hospitals can use simulation and also prediction tools to avoid the crisis to plan for the future in the pandemic.
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Affiliation(s)
- Mahdieh Tavakoli
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | | | - Reza Mesbahi
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Mohssen Ghanavati-Nejad
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Amirreza Tajally
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
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4
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Sreeramula S, Rahardjo D. Estimating COVID-19 R t in Real-time: An Indonesia health policy perspective. MACHINE LEARNING WITH APPLICATIONS 2021; 6:100136. [PMID: 34939041 PMCID: PMC8378038 DOI: 10.1016/j.mlwa.2021.100136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 08/09/2021] [Accepted: 08/09/2021] [Indexed: 02/01/2023] Open
Abstract
COVID-19 (SARS COV2 n-corona virus) is the newfangled virus of the coronavirus family. COVID-19 can cause serious illness with symptoms of fever, cold, cough, and respiratory blockage. COVID-19 is a contagious virus, which originated in Wuhan, China. After one month, WHO declared it as a Pandemic due to its rapid spreading. Presently, Indonesia is also facing a hard time controlling the spread. Hence, it is essential to understand the spread rate in Indonesia and to analyze the strategies to minimize the virus spread. The proposed study can be used to assess variations in virus spread both nationally, and sub-nationally. This allows public health officials and policy-makers to track the progress of the outbreak in near real-time using an epidemiologically valid measure.
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Affiliation(s)
| | - Deny Rahardjo
- Strategic management and innovation lecturer and IT practitioner, Sinarmas Group, Jakarta, Indonesia
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5
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Andreassen HP, Sundell J, Ecke F, Halle S, Haapakoski M, Henttonen H, Huitu O, Jacob J, Johnsen K, Koskela E, Luque-Larena JJ, Lecomte N, Leirs H, Mariën J, Neby M, Rätti O, Sievert T, Singleton GR, van Cann J, Vanden Broecke B, Ylönen H. Population cycles and outbreaks of small rodents: ten essential questions we still need to solve. Oecologia 2021; 195:601-622. [PMID: 33369695 PMCID: PMC7940343 DOI: 10.1007/s00442-020-04810-w] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 11/19/2020] [Indexed: 12/25/2022]
Abstract
Most small rodent populations in the world have fascinating population dynamics. In the northern hemisphere, voles and lemmings tend to show population cycles with regular fluctuations in numbers. In the southern hemisphere, small rodents tend to have large amplitude outbreaks with less regular intervals. In the light of vast research and debate over almost a century, we here discuss the driving forces of these different rodent population dynamics. We highlight ten questions directly related to the various characteristics of relevant populations and ecosystems that still need to be answered. This overview is not intended as a complete list of questions but rather focuses on the most important issues that are essential for understanding the generality of small rodent population dynamics.
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Affiliation(s)
- Harry P Andreassen
- Faculty of Applied Ecology, Agricultural Sciences and Biotechnology, Inland Norway University of Applied Sciences, Campus Evenstad, 2480, Koppang, Norway
| | - Janne Sundell
- Lammi Biological Station, University of Helsinki, Pääjärventie 320, 16900, Lammi, Finland
| | - Fraucke Ecke
- Department of Wildlife, Fish, and Environmental Studies, Swedish University of Agricultural Sciences, Skogsmarksgränd, 90183, Umeå, Sweden
| | - Stefan Halle
- Institute of Ecology and Evolution, Friedrich Schiller University Jena, Dornburger Str. 159, 07743, Jena, Germany
| | - Marko Haapakoski
- Department of Biological and Environmental Science, Konnevesi Research Station, University of Jyväskylä, P.O. Box 35, 40014, Jyväskylä, Finland
| | - Heikki Henttonen
- Terrestrial Population Dynamics, Natural Resources Institute Finland, Latokartanonkaari 9, 00790, Helsinki, Finland
| | - Otso Huitu
- Terrestrial Population Dynamics, Natural Resources Institute Finland, Latokartanonkaari 9, 00790, Helsinki, Finland
| | - Jens Jacob
- Federal Research Centre for Cultivated Plants, Vertebrate Research, Julius Kühn-Institut, Toppheideweg 88, 48161, Münster, Germany
| | - Kaja Johnsen
- Faculty of Applied Ecology, Agricultural Sciences and Biotechnology, Inland Norway University of Applied Sciences, Campus Evenstad, 2480, Koppang, Norway
| | - Esa Koskela
- Department of Biological and Environmental Science, University of Jyväskylä, P.O. Box 35, 40014, Jyväskylä, Finland
| | - Juan Jose Luque-Larena
- Departamento de Ciencias Agroforestales, Escuela Tecnica Superior de Ingenierıas Agrarias, Universidad de Valladolid, Campus La Yutera, Avenida de Madrid 44, 34004, Palencia, Spain
| | - Nicolas Lecomte
- Canada Research Chair in Polar and Boreal Ecology and Centre D'Études Nordiques, Department of Biology, Université de Moncton, 18 Avenue Antonine-Maillet, Moncton, NB, E1A 3E9, Canada
| | - Herwig Leirs
- Evolutionary Ecology Group, Department of Biology, University of Antwerp, Universiteitslain 1, 2610, Wilrijk, Belgium
| | - Joachim Mariën
- Evolutionary Ecology Group, Department of Biology, University of Antwerp, Universiteitslain 1, 2610, Wilrijk, Belgium
| | - Magne Neby
- Faculty of Applied Ecology, Agricultural Sciences and Biotechnology, Inland Norway University of Applied Sciences, Campus Evenstad, 2480, Koppang, Norway
| | - Osmo Rätti
- Arctic Centre, University of Lapland, P.O. Box 122, 96101, Rovaniemi, Finland
| | - Thorbjörn Sievert
- Department of Biological and Environmental Science, Konnevesi Research Station, University of Jyväskylä, P.O. Box 35, 40014, Jyväskylä, Finland
| | - Grant R Singleton
- International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines
- Natural Resources Institute, University of Greenwich, Chatham Marine, Kent, ME4 4TB, UK
| | - Joannes van Cann
- Department of Biological and Environmental Science, University of Jyväskylä, P.O. Box 35, 40014, Jyväskylä, Finland
| | - Bram Vanden Broecke
- Evolutionary Ecology Group, Department of Biology, University of Antwerp, Universiteitslain 1, 2610, Wilrijk, Belgium
| | - Hannu Ylönen
- Department of Biological and Environmental Science, Konnevesi Research Station, University of Jyväskylä, P.O. Box 35, 40014, Jyväskylä, Finland.
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6
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Johnson‐Bice SM, Ferguson JM, Erb JD, Gable TD, Windels SK. Ecological forecasts reveal limitations of common model selection methods: predicting changes in beaver colony densities. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2021; 31:e02198. [PMID: 32583507 PMCID: PMC7816246 DOI: 10.1002/eap.2198] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2019] [Revised: 03/13/2020] [Accepted: 03/30/2020] [Indexed: 05/20/2023]
Abstract
Over the past two decades, there have been numerous calls to make ecology a more predictive science through direct empirical assessments of ecological models and predictions. While the widespread use of model selection using information criteria has pushed ecology toward placing a higher emphasis on prediction, few attempts have been made to validate the ability of information criteria to correctly identify the most parsimonious model with the greatest predictive accuracy. Here, we used an ecological forecasting framework to test the ability of information criteria to accurately predict the relative contribution of density dependence and density-independent factors (forage availability, harvest, weather, wolf [Canis lupus] density) on inter-annual fluctuations in beaver (Castor canadensis) colony densities. We modeled changes in colony densities using a discrete-time Gompertz model, and assessed the performance of four models using information criteria values: density-independent models with (1) and without (2) environmental covariates; and density-dependent models with (3) and without (4) environmental covariates. We then evaluated the forecasting accuracy of each model by withholding the final one-third of observations from each population and compared observed vs. predicted densities. Information criteria and our forecasting accuracy metrics both provided strong evidence of compensatory density dependence in the annual dynamics of beaver colony densities. However, despite strong within-sample performance by the most complex model (density-dependent with covariates) as determined using information criteria, hindcasts of colony densities revealed that the much simpler density-dependent model without covariates performed nearly as well predicting out-of-sample colony densities. The hindcast results indicated that the complex model over-fit our data, suggesting that parameters identified by information criteria as important predictor variables are only marginally valuable for predicting landscape-scale beaver colony dynamics. Our study demonstrates the importance of evaluating ecological models and predictions with long-term data and revealed how a known limitation of information criteria (over-fitting of complex models) can affect our interpretation of ecological dynamics. While incorporating knowledge of the factors that influence animal population dynamics can improve population forecasts, we suggest that comparing forecast performance metrics can likewise improve our knowledge of the factors driving population dynamics.
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Affiliation(s)
- Sean M. Johnson‐Bice
- Department of Biological SciencesUniversity of Manitoba50 Sifton RoadWinnipegManitobaR3T 2N2Canada
- Natural Resources Research InstituteUniversity of Minnesota Duluth5013 Miller Trunk HighwayDuluthMinnesota55812USA
| | - Jake M. Ferguson
- Department of BiologyUniversity of Hawai`i at Mānoa2538 McCarthy MallHonoluluHawaii96822USA
| | - John D. Erb
- Forest Wildlife Populations and Research GroupMinnesota Department of Natural Resources1201 E. highway 2Grand RapidsMinnesota55744USA
| | - Thomas D. Gable
- Department of Fisheries, Wildlife and Conservation BiologyUniversity of Minnesota Twin Cities2003 Upper Buford CircleSt. PaulMinnesota55108USA
| | - Steve K. Windels
- Natural Resources Research InstituteUniversity of Minnesota Duluth5013 Miller Trunk HighwayDuluthMinnesota55812USA
- Department of Fisheries, Wildlife and Conservation BiologyUniversity of Minnesota Twin Cities2003 Upper Buford CircleSt. PaulMinnesota55108USA
- Voyageurs National Park360 Highway 11 E.International FallsMinnesota56649USA
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7
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Nicolau PG, Sørbye SH, Yoccoz NG. Incorporating capture heterogeneity in the estimation of autoregressive coefficients of animal population dynamics using capture-recapture data. Ecol Evol 2020; 10:12710-12726. [PMID: 33304489 PMCID: PMC7713978 DOI: 10.1002/ece3.6642] [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: 03/09/2020] [Revised: 07/06/2020] [Accepted: 07/08/2020] [Indexed: 11/06/2022] Open
Abstract
Population dynamic models combine density dependence and environmental effects. Ignoring sampling uncertainty might lead to biased estimation of the strength of density dependence. This is typically addressed using state-space model approaches, which integrate sampling error and population process estimates. Such models seldom include an explicit link between the sampling procedures and the true abundance, which is common in capture-recapture settings. However, many of the models proposed to estimate abundance in the presence of capture heterogeneity lead to incomplete likelihood functions and cannot be straightforwardly included in state-space models. We assessed the importance of estimating sampling error explicitly by taking an intermediate approach between ignoring uncertainty in abundance estimates and fully specified state-space models for density-dependence estimation based on autoregressive processes. First, we estimated individual capture probabilities based on a heterogeneity model for a closed population, using a conditional multinomial likelihood, followed by a Horvitz-Thompson estimate for abundance. Second, we estimated coefficients of autoregressive models for the log abundance. Inference was performed using the methodology of integrated nested Laplace approximation (INLA). We performed an extensive simulation study to compare our approach with estimates disregarding capture history information, and using R-package VGAM, for different parameter specifications. The methods were then applied to a real data set of gray-sided voles Myodes rufocanus from Northern Norway. We found that density-dependence estimation was improved when explicitly modeling sampling error in scenarios with low process variances, in which differences in coverage reached up to 8% in estimating the coefficients of the autoregressive processes. In this case, the bias also increased assuming a Poisson distribution in the observational model. For high process variances, the differences between methods were small and it appeared less important to model heterogeneity.
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Affiliation(s)
- Pedro G. Nicolau
- Department of Mathematics and StatisticsFaculty of Science and TechnologyUiT The Arctic University of NorwayTromsoNorway
| | - Sigrunn H. Sørbye
- Department of Mathematics and StatisticsFaculty of Science and TechnologyUiT The Arctic University of NorwayTromsoNorway
| | - Nigel G. Yoccoz
- Department of Arctic and Marine BiologyFaculty of Biosciences, Fisheries and EconomicsUiT The Arctic University of NorwayTromsoNorway
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8
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Avgar T, Betini GS, Fryxell JM. Habitat selection patterns are density dependent under the ideal free distribution. J Anim Ecol 2020; 89:2777-2787. [PMID: 32961607 PMCID: PMC7756284 DOI: 10.1111/1365-2656.13352] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Accepted: 08/07/2020] [Indexed: 11/27/2022]
Abstract
Despite being widely used, habitat selection models are rarely reliable and informative when applied across different ecosystems or over time. One possible explanation is that habitat selection is context-dependent due to variation in consumer density and/or resource availability. The goal of this paper is to provide a general theoretical perspective on the contributory mechanisms of consumer and resource density-dependent habitat selection, as well as on our capacity to account for their effects. Towards this goal we revisit the ideal free distribution (IFD), where consumers are assumed to be omniscient, equally competitive and freely moving, and are hence expected to instantaneously distribute themselves across a heterogeneous landscape such that fitness is equalised across the population. Although these assumptions are clearly unrealistic to some degree, the simplicity of the structure in IFD provides a useful theoretical vantage point to help clarify our understanding of more complex spatial processes. Of equal importance, IFD assumptions are compatible with the assumptions underlying common habitat selection models. Here we show how a fitness-maximising space use model, based on IFD, gives rise to resource and consumer density-dependent shifts in consumer distribution, providing a mechanistic explanation for the context-dependent outcomes often reported in habitat selection analysis. Our model suggests that adaptive shifts in consumer distribution patterns would be expected to lead to nonlinear and often non-monotonic patterns of habitat selection. These results indicate that even under the simplest of assumptions about adaptive organismal behaviour, habitat selection strength should critically depend on system-wide characteristics. Clarifying the impact of adaptive behavioural responses may be pivotal in making meaningful ecological inferences about observed patterns of habitat selection and allow reliable transferability of habitat selection predictions across time and space.
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Affiliation(s)
- Tal Avgar
- Department of Wildland ResourcesUtah State UniversityLoganUTUSA
| | | | - John M. Fryxell
- Department of Integrative BiologyUniversity of GuelphGuelphCanada
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9
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Henden J, Ehrich D, Soininen EM, Ims RA. Accounting for food web dynamics when assessing the impact of mesopredator control on declining prey populations. J Appl Ecol 2020. [DOI: 10.1111/1365-2664.13793] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- John‐André Henden
- Department of Arctic and Marine Biology UiT The Arctic University of Norway Tromsø Norway
| | - Dorothee Ehrich
- Department of Arctic and Marine Biology UiT The Arctic University of Norway Tromsø Norway
| | - Eeva M. Soininen
- Department of Arctic and Marine Biology UiT The Arctic University of Norway Tromsø Norway
| | - Rolf A. Ims
- Department of Arctic and Marine Biology UiT The Arctic University of Norway Tromsø Norway
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10
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Abstract
Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and these models are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models need to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to susceptible–infected–recovered (SIR) and susceptible-exposed-infectious-removed (SEIR) models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP; and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior across nations, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. This paper further suggests that a genuine novelty in outbreak prediction can be realized by integrating machine learning and SEIR models.
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11
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Enhanced Gaussian process regression-based forecasting model for COVID-19 outbreak and significance of IoT for its detection. APPL INTELL 2020; 51:1492-1512. [PMID: 34764576 PMCID: PMC7785924 DOI: 10.1007/s10489-020-01889-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Virus based epidemic is one of the speedy and widely spread infectious disease which can affect the economy of the country as well as it is life-threatening too. So, there is a need to forecast the epidemic lifespan, which can help us in taking preventive measures and remedial action on time. These preventive measures and corrective action may consist of closing schools, closing malls, closing theaters, sealing of borders, suspension of public services, and suspension of traveling. Resuming such restrictions is depends upon the outbreak momentum and its decay rate. The accurate forecasting of the epidemic lifespan is one of the enormously essential and challenging tasks. It is a challenging task because the lack of knowledge about the novel virus-based diseases and its consequences with complicated societal-governmental factors can influence the widespread of this newly born disease. At this stage, any forecasting can play a vital role, and it will be reliable too. As we know, the novel virus-based diseases are in a growing phase, and we also do not have real-time data samples. Thus, the biggest challenge is to find out the machine learning-based best forecasting model, which could offer better forecasting with the limited training samples. In this paper, the Multi-Task Gaussian Process (MTGP) regression model with enhanced predictions of novel coronavirus (COVID-19) outbreak is proposed. The purpose of the proposed MTGP regression model is to predict the COVID-19 outbreak worldwide. It will help the countries in planning their preventive measures to reduce the overall impact of the speedy and widely spread infectious disease. The result of the proposed model has been compared with the other prediction model to find out its suitability and correctness. In subsequent analysis, the significance of IoT based devices in COVID-19 detection and prevention has been discussed.
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12
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COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach. MATHEMATICS 2020. [DOI: 10.3390/math8060890] [Citation(s) in RCA: 94] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to the lack of essential data and uncertainty, the epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19, and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are proposed to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for 9 days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.
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13
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Rodent population cycle as a determinant of gastrointestinal nematode abundance in a low-arctic population of the red fox. INTERNATIONAL JOURNAL FOR PARASITOLOGY-PARASITES AND WILDLIFE 2019; 9:36-41. [PMID: 30976515 PMCID: PMC6441723 DOI: 10.1016/j.ijppaw.2019.03.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 02/08/2019] [Accepted: 03/08/2019] [Indexed: 11/24/2022]
Abstract
We analyzed an 11-year time series (2005-2015) of parasite abundance for three intestinal nematode species in the red fox (Vulpes vulpes) as a function of the multi-annual rodent population cycle in low-arctic Norway, while correcting for other potential covariates that could influence prevalence and abundance. Rodents are paratenic and facultative intermediate hosts for the two Ascarididae species Toxascaris leonina and Toxocara canis, respectively and key prey for the red fox. Still the relative importance of indirect transmission through rodents and direct transmission through free-living stages is unclear. Abundance of these Ascarididae species in individual red foxes (N = 612) exhibited strongly cyclic dynamics that closely mirrored the 4-year rodent cycle. Negative binomial models provided evidence for a direct proportional increase in Ascarididae abundance with rodent density suggesting that predator functional response to rodent prey is the key transmission mechanism. In contrast, no cycles and constantly very low abundance were apparent for Uncinaria stenocephala - a third nematode species recorded without paratenic or intermediate stages.
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14
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Radchuk V, Kramer-Schadt S, Grimm V. Transferability of Mechanistic Ecological Models Is About Emergence. Trends Ecol Evol 2019; 34:487-488. [PMID: 30795841 DOI: 10.1016/j.tree.2019.01.010] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 01/18/2019] [Accepted: 01/23/2019] [Indexed: 11/25/2022]
Affiliation(s)
- Viktoriia Radchuk
- Department of Ecological Dynamics, Leibniz Institute for Zoo and Wildlife Research (IZW), Alfred-Kowalke-Straße 17, Berlin, Germany.
| | - Stephanie Kramer-Schadt
- Department of Ecological Dynamics, Leibniz Institute for Zoo and Wildlife Research (IZW), Alfred-Kowalke-Straße 17, Berlin, Germany; Department of Ecology, Technische Universität Berlin, Rothenburgstrasse 12, 12165 Berlin, Germany
| | - Volker Grimm
- Department of Ecological Modelling, Helmholtz Centre for Environmental Research - UFZ, Permoserstr. 15, Leipzig, Germany; Institute for Biochemistry and Biology, University of Potsdam, Maulbeerallee 2, Potsdam, Germany; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, Leipzig, Germany
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