Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Choosing Between z and t Distribution01:25

Choosing Between z and t Distribution

2.9K
The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
2.9K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

513
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
513
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

85
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
85

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Prevalence, distribution, and phylogenetics of the tick-borne relapsing fever spirochete Borrelia turicatae in the soft tick Ornithodoros turicata americanus in Florida.

PLoS neglected tropical diseases·2026
Same author

Development and partial validation of a TaqMan quantitative PCR assay to detect Cervidpoxvirus.

BMC veterinary research·2026
Same author

Identification of the First CHeRI Orbivirus 3-5 Strain Isolated from a Dead Farmed White-Tailed Deer (<i>Odocoileus virginianus)</i> Whose Death Had Been Attributed to an Infection by Mule Deerpox Virus.

Viruses·2026
Same author

Xenomonitoring reveals mosquito-host feeding patterns in Serbia.

Medical and veterinary entomology·2026
Same author

Transmission Dynamics of Torque Teno Sus Virus 1 (TTSuV1) Between Wild and Farmed Pigs: A Molecular Tool for Monitoring Cross-Population Spillover in Swine.

Microorganisms·2025
Same author

Rat Coronavirus Epizootiology in Wild <i>Rattus norvegicus</i>.

Vector borne and zoonotic diseases (Larchmont, N.Y.)·2025

Related Experiment Video

Updated: Sep 7, 2025

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
07:21

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Published on: August 25, 2018

13.0K

Ensemble Models for Tick Vectors: Standard Surveys Compared with Convenience Samples.

William H Kessler1,2, Carrie De Jesus3, Samantha M Wisely1,3

  • 1Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611, USA.

Diseases (Basel, Switzerland)
|June 23, 2022
PubMed
Summary

Species Distribution Models (SDMs) for tick vectors like Amblyomma americanum and Ixodes scapularis vary significantly based on sampling methods. Using convenience samples for SDMs can lead to substantial underestimation of potential tick habitats, impacting public health strategies.

Keywords:
Amblyomma americanumFloridaIxodes scapularisSpecies Distribution Models SDMsbiased samplingensemble modelsstudy designticks

More Related Videos

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.4K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

Related Experiment Videos

Last Updated: Sep 7, 2025

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
07:21

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Published on: August 25, 2018

13.0K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.4K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

Area of Science:

  • Vector-borne disease ecology
  • Spatial modeling
  • Ecological forecasting

Background:

  • Species Distribution Models (SDMs) are crucial for mapping pathogen vector ranges.
  • Ensemble SDMs integrate various analytical methods with environmental data.
  • Data collection biases can significantly impact SDM accuracy.

Purpose of the Study:

  • To compare Ensemble SDM predictions for Ixodid tick vectors using different sampling data.
  • To assess the impact of convenience vs. standard survey sampling on predicted geographic distributions.
  • To evaluate discrepancies in SDMs for Amblyomma americanum and Ixodes scapularis in Florida.

Main Methods:

  • Developed Ensemble SDMs for A. americanum and I. scapularis in Florida.
  • Utilized two distinct datasets: convenience tick samples and U.S. CDC standard survey collections.
  • Quantified model agreement using Kappa statistics and compared predicted range overlaps.

Main Results:

  • Ensemble SDMs derived from convenience samples and standard surveys showed low agreement (Kappa < 0.07).
  • Convenience sample SDMs incorrectly predicted absence in areas with high occurrence likelihoods based on standard surveys.
  • Standard survey SDMs omitted a significant proportion of convenience sample sites, often due to geocoding issues.

Conclusions:

  • Sampling methodology profoundly influences SDM outputs for tick vectors.
  • Convenience sampling can lead to underestimation of vector distribution, potentially affecting disease risk assessment.
  • Improving data quality and geocoding accuracy is essential for reliable arthropod spatial modeling.