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Related Concept Videos

Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
Life Tables01:22

Life Tables

A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
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Censoring Survival Data01:09

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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Survival Tree01:19

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
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Related Experiment Video

Updated: Jun 24, 2026

Windowing Chicken Eggs for Developmental Studies
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Modeling and Forecasting Dead-on-Arrival in Broilers Using Time Series Methods: A Case Study from Thailand.

Chalita Jainonthee1,2,3, Panneepa Sivapirunthep4, Pranee Pirompud5

  • 1Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai 50100, Thailand.

Animals : an Open Access Journal From MDPI
|April 26, 2025
PubMed
Summary

Antibiotic-free broiler production faces transport mortality challenges. Time series models like TBATS and ETS accurately forecast dead-on-arrival percentages, aiding proactive management and improving animal welfare.

Keywords:
ETSNNARSARIMATBATSXGBoostantibiotic-free broiler productiondead-on-arrivalpoultry welfareseasonal patterntime series forecasting

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Area of Science:

  • Poultry Science
  • Animal Welfare
  • Data Science

Background:

  • Antibiotic-free (ABF) broiler production is key for sustainable farming.
  • Transport stress increases mortality in ABF broilers.
  • Accurate prediction of transport mortality is crucial for welfare and efficiency.

Purpose of the Study:

  • Analyze time series data of monthly dead-on-arrival (%DOA) percentages.
  • Compare the forecasting performance of various time series models for %DOA.
  • Evaluate the utility of forecasting models as decision-support tools in ABF broiler production.

Main Methods:

  • Collected and aggregated monthly %DOA data from 127,578 broiler transports (2018-2024).
  • Decomposed time series data to identify trends and seasonality.
  • Trained and evaluated SARIMA, NNAR, TBATS, ETS, and XGBoost models using historical and test data.

Main Results:

  • A distinct seasonal pattern in %DOA was identified.
  • TBATS (21.2% MAPE) and ETS (22.1% MAPE) showed the highest forecasting accuracy.
  • These models significantly outperformed NNAR (54.4% MAPE) and XGBoost (29.3% MAPE).

Conclusions:

  • Time series forecasting models, particularly TBATS and ETS, are valuable for predicting transport mortality in ABF broiler production.
  • Accurate %DOA forecasts support proactive planning to reduce losses.
  • Improved planning enhances animal welfare and operational efficiency in sustainable poultry farming.