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Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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Propagation of Uncertainty from Random Error

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

Propagation of Uncertainty from Systematic Error

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...

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:

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Updated: Oct 3, 2025

Arbovirus Infections As Screening Tools for the Identification of Viral Immunomodulators and Host Antiviral Factors

Arbovirus Infections As Screening Tools for the Identification of Viral Immunomodulators and Host Antiviral Factors

Published on: September 13, 2018

ARBO: Arbovirus modeling and uncertainty quantification toolbox.

Michel Tosin¹, Eber Dantas², Americo Cunha¹

¹Rio de Janeiro State University, Rio de Janeiro, Brazil.

Software Impacts

|February 21, 2022

Summary

This summary is machine-generated.

This study introduces ARBO, a new software package for simulating and analyzing arbovirus outbreaks. ARBO aids in understanding infectious disease dynamics, crucial for predicting future epidemics like Zika.

Keywords:

Arbovirus Mathematical epidemiology Model calibration Model discrepancy Uncertainty quantification

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Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes

Published on: September 27, 2014

Area of Science:

Epidemiology
Mathematical Biology
Computational Science

Background:

The COVID-19 pandemic underscored the need for robust mathematical tools in infectious disease outbreak analysis.
Arboviruses, such as Zika, pose significant public health threats requiring advanced predictive modeling.
Existing tools may not adequately capture the complex nonlinear dynamics of vector-borne diseases.

Purpose of the Study:

To introduce ARBO, a novel software package designed for the simulation and analysis of arbovirus nonlinear dynamics.
To provide an intuitive and extensible platform for studying vector-borne disease outbreaks.
To demonstrate the utility of ARBO through its application to the Brazilian Zika outbreak.

Main Methods:

Development of the ARBO package with a minimalist and extensible design.
Utilizing nonlinear dynamics principles for arbovirus outbreak modeling.
Application of ARBO to analyze real-world data from the Brazilian Zika outbreak.

Main Results:

ARBO offers a versatile framework for simulating and analyzing arbovirus transmission.
The package's capabilities were effectively demonstrated through case studies of the Brazilian Zika outbreak.
The minimalist and intuitive design facilitates broader adoption and application in epidemiological research.

Conclusions:

ARBO provides a valuable tool for mathematical epidemiologists studying arbovirus dynamics.
The package has the potential to significantly impact future research on vector-borne disease prediction and control.
ARBO enhances our capacity to understand and respond to arbovirus epidemics.