coronavirus positivity predicting bat
TRANSCRIPT
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The Dark Knight - Timothy Quek, Ryan Kim, Peter Wang, Isaac Law
Predicting Bat Coronavirus Positivity
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Background
● Bats comprise ~20% of mammal species (> 1,400 species)
● Serve as reservoir hosts of many deadly viruses (e.g. Ebola, Hendra, Nipah)
● The SARS-CoV-2 virus that led to the current COVID-19 pandemic likely originated from an Asian bat species
● Scientists do research on bats worldwide to study relationships between bats and viruses
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Predict factors that make it more likely for a particular bat species to be a potential coronavirus reservoir host
- Geographical and Environmental Characteristics- Morphological and Other Biological Traits- Phylogenetic Group
Problem Statement
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Data Analysis
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01.Bat CoV PositivityDataset manually collected from 100+ published papersLook for coronavirus positivity rates among samples from bats
PanTHERIADataset on global mammalian species-level dataset of life-history, ecological and geographical traits
02.
Datasets
Bat Ecology / Viral DiversityBat specific dataset used in a study on viral diversity and reservoir status in a Canadian study
04.
EltonTraits 1.0Dataset on global species-level foraging attributes of mammals
03.
Zoonotic Infectious DiseasesDataset used in a study on zoonotic emerging infectious diseases, including geographical / environmental features
05.
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Main Features Included: 13 features selected out of 72
Weather- Weather cluster (Precipitation + Temperature)- Actual / Potential Evapotranspiration Rate
Location Cluster (approximately corresponds to continent)- Geographical cluster
Phylogenetic Cluster (56 million years ago)- Factorized cluster
Land Use and Environment- Land-Barren- Evergreen Broadleaf- Managed Vegetation- Crop Change
Species Diversity / Livestock- Mammalian Diversity, - # of Poultry (log)- # of mammals livestock (log)
Human Population- Human Population Density change (for each grid cell)
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Weather- Precipitation and Temperature
● Each dot represents one particular bat species● High prevalence: ≥5% coronavirus positivity (red dots); Low prevalence: <5% positivity (blue dots)
Low PrevalenceHigh Prevalence
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Effect of Temperature and Humidity on Coronavirus Infectivity
Adv Virol 2011;2011:734690. doi: 10.1155/2011/734690. Epub 2011 Oct 1.
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Land Use and Environment
Land Use and Environment- Proportion of Barren land- Broadleaf Evergreen Forest- Managed Vegetation- Cropland Change
● Higher proportion of barren land in the geographical distribution of bat species associated with higher coronavirus prevalence
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Phylogenetic Cluster (56 million years ago)
● PhyloClust56-Phylogenetic clusters based on evolutionary relationships between bats 56 million years ago
● “PC3” showed a lower coronavirus positivity compared to the other phylogenetic clusters on univariate analysis
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Ecology of Mammals
Ecology of Mammals- Mammalian Diversity- # of Poultry (log)- # of mammals livestock (log)
● (1) Mammalian diversity and(2) Poultry / mammalian livestock headcounts show statistically significant relationships with bat coronavirus prevalence
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Mammalian Diversity and Emerging Infectious Diseases
● High mammalian biodiversity is associated with lower prevalence of bat coronavirus positivity
● Previous research- biodiversity loss increases disease transmission● Mechanism is unclear- one speculation:
○ Species better at buffering disease transmission are affected more with biodiversity reduction
○ Conversely, species with higher rates of reproduction (and spend less resources on host immunity) may survive longer during reductions in biodiversity
Nature. 2010 Dec 2;468(7324):647-52.
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Geographical Location
Location Cluster - Bat species found in the location cluster corresponding to Africa have a higher coronavirus positivity (after correction for other factors)
※ Red dots : ≥5% positivity※ Blue dots: <5% positivity
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Modeling and Results
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● Prevalence Rate Modeling- Poisson Regression
● High vs Low Coronavirus Prevalence Classification- Generalized Boosted Model
Two Main Analyses
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Feature Correlation & Feature Engineering
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● Modelled outcome-Number of positive bats (out of 100 bats)
● Stepwise forward inclusion based on AIC
● RMSE ~ 5.5
● Reasonable fit- except under-fitting at both extremes
Poisson Regression - Count Response Modeling
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● Model accuracy: ~ 74% ● GBM does not provide p-values or
coefficients, but ranks variables by relative influence
● Mammal & poultry ecological variables have heavy influence on bat coronavirus positivity (mammal biodiversity, mammal livestock / poultry headcount)
● Consistent with previous studies
Generalized Boosted Model - Binary Classification
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● Mammalian biodiversity plays an important role in both models● Bats in geographical ranges with HIGHER mammal biodiversity => lower CoV prevalence● Weather, land use and ecological factors come after mammalian diversity
Model InferenceRegression Classification
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Predictions
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● Prediction Process:Construct 95% C.I. with Poisson Regression, cross-check with GBM model
● Bat species flagged as “high CoV risk” when both models converge
● Attempted to predict the coronavirus risk in Rhinolophus bats- thought to be a major reservoir of SARS related coronaviruses
Predictions
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Model PredictionsRhinolophus inops
Rhinolophus subrufus
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Findings● Factors increasing the risk of high bat
coronavirus prevalence include reduced mammalian diversity and low temperature / humidity
● Weather, land use and ecological factors have higher explanatory power than bat characteristics
● Our models predict that 5 species of Rhinolophus bats from the Philippines likely have a high coronavirus prevalence
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● Deforestation and destruction of animal habitats likely contribute to the higher incidence of emerging infectious diseases
● The importance of the loss of mammalian diversity to predict the outcome likely reflects this point specifically
● “...global changes in the mode and the intensity of land use are creating expanding hazardous interfaces between people, livestock and wildlife reservoirs of zoonotic disease.”
Footnote
Nature. 2020 Aug; 584(7821): 398-402.
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● Professor Maria Cristina Rulli, Politecnico di Milano● Professor Paolo D’Odorico, University of California, Berkeley● Dr. Amanda Adams, Bat Conservation International● Dr. Natasha Spottiswoode, University of California, San Francisco● Authors of all the papers that we used in this Capstone project
● Dr. Fred Nugen, University of California, Berkeley● Dr. Alberto Todeschini, University of California, Berkeley● Our wonderful section mates● Our families● The bats
ACKNOWLEDGEMENTS
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CREDITS: This presentation template was created by Slidesgo, including icons by Flaticon, and infographics & images by Freepik
THANKS!http://bat-cov-positivity.org/home
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Main Features to Include
Weather- Mean Precipitation- Mean Temperature (squared)
● Using K-means clustering, put bat species into 2 clusters based on temperature and precipitation
● Monthly mean precipitation / temperature of species habitat:Low temperature and precipitation associated with higher bat coronavirus prevalence
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Contributions By Team Members
All of the team members contributed actively in the following areas of the project:
Development and refinement of conceptLiterature ReviewCoordination with ProfessorsData collectionData Cleaning and missing Data imputationVisualizationsMachine Learning AlgorithmsWebsite DesignWriting of Paper
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EDA of Selected Features in Final Merged Dataset
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Change in Human Population Density
Human Population- Rate of Change in Human Population Density between 1990 and 1995
● “HuPopDen_Chg” shows the rate of change of human population density between 1990 and 1995
● Interestingly, a lower change in human density (between 1990 to 1995) tends to be associated with a higher bat coronavirus prevalence
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Land Use and Environment
Land Use and Environment- Land-Barren- Evergreen Broadleaf- Managed Vegetation- Crop Change
● Change in land use for cropland (grid cell, 1900-2000) and the proportion of area covered by barren land/ evergreen/ cultivate vegetation show significance
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Possible Future Studies (and Capstone Projects?)
● Using species distribution and land use data, predict potential intermediate hosts that may result in coronavirus spillover infections from bats to humans
● Choosing a specific bat related zoonosis with well mapped out index cases, aim to predict areas with a high likelihood of future cases