Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Malaria

Malaria

Malaria pathogenesis in humans reflects a delicate interplay between parasite biology and host response. Clinical illness reflects a host’s immune response to the parasite’s asexual replication cycle, which is often asymptomatic in individuals with partial immunity. From the parasite's perspective, transmission between mosquito and human with minimal host pathology is evolutionarily advantageous. Among the six Plasmodium species infecting humans, P. falciparum and P. vivax dominate in global...

Statistical Methods for Analyzing Epidemiological Data

Statistical Methods for Analyzing Epidemiological Data

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:

Symbiosis

Symbiosis

Symbiotic relationships are long-term, close interactions between individuals of different species that affect the distribution and abundance of those species. When a relationship is beneficial to both species, this is called mutualism. When the relationship is beneficial to one species but neither beneficial nor harmful to the other species, this is called commensalism. When one organism is harmed to benefit another, the relationship is known as parasitism. These types of relationships often...

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...

Amebiasis

Amebiasis

Entamoeba histolytica, a protozoan parasite, is responsible for intestinal and extraintestinal amebiasis. Though a significant proportion of infections remain asymptomatic, approximately 50 million individuals annually are estimated to present with clinical disease, resulting in up to 100,000 deaths globally. The disease burden is disproportionately high in regions with lower socioeconomic status, such as parts of India, Africa, Mexico, and Latin America.Etiology and TransmissionThe infective...

Steps in Outbreak Investigation

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

You might also read

Related Articles

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

Sort by

Same author

Robot-assisted partial nephrectomy using the novel Hugo™ RAS system: Feasibility, setting and perioperative outcomes of the first off-clamp series.

Urologia·2024

Same author

HER2-low expression in patients with advanced or metastatic solid tumors.

Annals of oncology : official journal of the European Society for Medical Oncology·2023

Same author

Physical fitness and nutritional anthropometric status of children from disadvantaged communities in the Nelson Mandela Bay region.

South African journal of sports medicine·2023

Same author

[Postoperative imaging of the shoulder].

Radiologie (Heidelberg, Germany)·2022

Same author

Crowdsourcing snake identification with online communities of professional herpetologists and avocational snake enthusiasts.

Royal Society open science·2021

Same author

HIV-associated neurocognitive impairment in stable people living with HIV on ART in rural Tanzania.

HIV medicine·2020

Search research articles

Related Experiment Video

Updated: Jun 22, 2026

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

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

Published on: October 11, 2016

Bayesian geostatistical modelling for mapping schistosomiasis transmission.

P Vounatsou¹, G Raso, M Tanner

¹Department of Public Health and Epidemiology, Swiss Tropical Institute, P.O. Box, CH-4002 Basel, Switzerland. penelope.vounatsou@unibas.ch

|June 4, 2009

Summary

This study introduces a new Bayesian model to map schistosomiasis infection intensity more accurately. The zero-inflated model improves predictions of helminth infection risk, crucial for controlling disease morbidity.

More Related Videos

Chemical Cartography Approaches to Study Trypanosomatid Infection

Chemical Cartography Approaches to Study Trypanosomatid Infection

Published on: January 21, 2022

Cercarial Transformation and in vitro Cultivation of Schistosoma mansoni Schistosomules

Cercarial Transformation and in vitro Cultivation of Schistosoma mansoni Schistosomules

Published on: August 16, 2011

Related Experiment Videos

Last Updated: Jun 22, 2026

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

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

Published on: October 11, 2016

Chemical Cartography Approaches to Study Trypanosomatid Infection

Chemical Cartography Approaches to Study Trypanosomatid Infection

Published on: January 21, 2022

Cercarial Transformation and in vitro Cultivation of Schistosoma mansoni Schistosomules

Cercarial Transformation and in vitro Cultivation of Schistosoma mansoni Schistosomules

Published on: August 16, 2011

Area of Science:

Epidemiology
Spatial statistics
Parasitology

Background:

Schistosomiasis risk mapping often focuses on prevalence, neglecting infection intensity, which is vital for morbidity control.
Existing Bayesian geostatistical models for schistosomiasis intensity may use flawed assumptions, such as stationarity and standard negative binomial distributions, leading to inaccurate inference due to excess zeros.

Purpose of the Study:

To examine the assumptions in current schistosomiasis intensity mapping.
To develop and validate a novel Bayesian geostatistical zero-inflated (ZI) regression model with a non-stationary spatial process for mapping Schistosoma mansoni infection intensity.

Main Methods:

Examined assumptions of stationarity and negative binomial distributions in existing models.
Developed a Bayesian non-stationary ZI negative binomial regression model.
Validated the model using a georeferenced dataset from western Côte d'Ivoire, including demographic, environmental, parasitological, and socio-economic data from 3818 schoolchildren.

Main Results:

The study identified a high number of excess zeros in infection data, challenging standard negative binomial assumptions.
The developed Bayesian non-stationary ZI negative binomial model demonstrated a superior fit to the data compared to negative binomial, ZI Poisson, and standard ZI negative binomial models.
Nearly 40% of schoolchildren were infected, with a mean intensity of 162 eggs per gram of stool (EPG).

Conclusions:

The assumption of stationarity should be relaxed in schistosomiasis intensity mapping.
Geostatistical ZI models, particularly the non-stationary ZI negative binomial approach, provide more accurate maps of helminth infection intensity than traditional spatial negative binomial models.
Accurate mapping of infection intensity is essential for effective morbidity control strategies in schistosomiasis.