Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

A real-time search strategy for finding urban disease vector infestations

Erica Billig Rose, Jason A. Roy, Ricardo Castillo-Neyra, Michelle E. Ross, Carlos Condori-Pino, View ORCID ProfileJennifer K. Peterson, Cesar Naquira-Velarde, View ORCID ProfileMichael Z. Levy
doi: https://doi.org/10.1101/2020.01.20.911974
Erica Billig Rose
1University of Pennsylvania, Department of Biostatistics, Epidemiology & Informatics, Philadelphia, Pennsylvania, US
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jason A. Roy
2Department of Biostatistics and Epidemiology, Rutgers School of Public Health, Piscataway, NJ, US
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ricardo Castillo-Neyra
1University of Pennsylvania, Department of Biostatistics, Epidemiology & Informatics, Philadelphia, Pennsylvania, US
3Zoonotic Disease Research Laboratory, One Health Unit, Facultad de Salud Pública y Administración, Universidad Peruana Cayetano Heredia, Lima, Perú
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Michelle E. Ross
1University of Pennsylvania, Department of Biostatistics, Epidemiology & Informatics, Philadelphia, Pennsylvania, US
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Carlos Condori-Pino
3Zoonotic Disease Research Laboratory, One Health Unit, Facultad de Salud Pública y Administración, Universidad Peruana Cayetano Heredia, Lima, Perú
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jennifer K. Peterson
1University of Pennsylvania, Department of Biostatistics, Epidemiology & Informatics, Philadelphia, Pennsylvania, US
4University Honors College, Portland State University, Portland, OR, US
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Jennifer K. Peterson
Cesar Naquira-Velarde
3Zoonotic Disease Research Laboratory, One Health Unit, Facultad de Salud Pública y Administración, Universidad Peruana Cayetano Heredia, Lima, Perú
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Michael Z. Levy
1University of Pennsylvania, Department of Biostatistics, Epidemiology & Informatics, Philadelphia, Pennsylvania, US
3Zoonotic Disease Research Laboratory, One Health Unit, Facultad de Salud Pública y Administración, Universidad Peruana Cayetano Heredia, Lima, Perú
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Michael Z. Levy
  • For correspondence: mzlevy@upenn.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

Abstract

Containing domestic vector infestation requires the ability to swiftly locate and treat infested homes. In urban settings where vectors are heterogeneously distributed throughout a dense housing matrix, the task of locating infestations can be challenging. Here, we present a novel stochastic compartmental model developed to help locate infested homes in urban areas. We designed the model using infestation data for the Chagas disease vector species Triatoma infestans in Arequipa, Peru. Our approach incorporates disease vector counts at each observed house, and the vector’s complex spatial dispersal dynamics. We used a Bayesian method to augment the observed data, estimate the insect population growth and dispersal parameters, and determine posterior infestation probabilities of households. We investigated the properties of the model through simulation studies, followed by field testing in Arequipa. Simulation studies showed the model to be accurate in its estimates of two parameters of interest: the growth rate of a domestic triatomine bug colony and the probability of a triatomine bug successfully invading a new home after dispersing from an infested home. When testing the model in the field, data collection using model estimates was hindered by low household participation rates, which severely limited the algorithm and in turn, the model’s predictive power. While future optimization efforts must improve the model’s capabilities when household participation is low, our approach is nonetheless an important step toward integrating data with predictive modeling to carry out evidence-based vector surveillance in cities.

Footnotes

  • ↵* erica.w.b.rose{at}gmail.com, mzlevy{at}pennmedicine.upenn.edu

  • https://github.com/ebillig/Search-Strategy

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted January 24, 2020.
Download PDF
Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
A real-time search strategy for finding urban disease vector infestations
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
A real-time search strategy for finding urban disease vector infestations
Erica Billig Rose, Jason A. Roy, Ricardo Castillo-Neyra, Michelle E. Ross, Carlos Condori-Pino, Jennifer K. Peterson, Cesar Naquira-Velarde, Michael Z. Levy
bioRxiv 2020.01.20.911974; doi: https://doi.org/10.1101/2020.01.20.911974
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
A real-time search strategy for finding urban disease vector infestations
Erica Billig Rose, Jason A. Roy, Ricardo Castillo-Neyra, Michelle E. Ross, Carlos Condori-Pino, Jennifer K. Peterson, Cesar Naquira-Velarde, Michael Z. Levy
bioRxiv 2020.01.20.911974; doi: https://doi.org/10.1101/2020.01.20.911974

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4095)
  • Biochemistry (8787)
  • Bioengineering (6493)
  • Bioinformatics (23388)
  • Biophysics (11766)
  • Cancer Biology (9168)
  • Cell Biology (13292)
  • Clinical Trials (138)
  • Developmental Biology (7423)
  • Ecology (11386)
  • Epidemiology (2066)
  • Evolutionary Biology (15120)
  • Genetics (10414)
  • Genomics (14024)
  • Immunology (9145)
  • Microbiology (22109)
  • Molecular Biology (8793)
  • Neuroscience (47449)
  • Paleontology (350)
  • Pathology (1423)
  • Pharmacology and Toxicology (2483)
  • Physiology (3711)
  • Plant Biology (8063)
  • Scientific Communication and Education (1433)
  • Synthetic Biology (2215)
  • Systems Biology (6021)
  • Zoology (1251)