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

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

253
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:
253
Generation Time01:22

Generation Time

652
Bacterial generation time, the period required for a bacterial population to double during its exponential growth phase, serves as a critical measure of microbial growth dynamics under optimal conditions. This parameter varies significantly across bacterial species and can be influenced by factors such as temperature, pH, and the availability of nutrients. For example, Escherichia coli can achieve a generation time of approximately 20 minutes, while Mycobacterium tuberculosis exhibits a much...
652
Bacterial Growth Curve01:28

Bacterial Growth Curve

945
The bacterial growth curve is a fundamental concept in microbiology that describes the dynamics of bacterial population growth in a closed system with controlled environmental conditions, such as temperature and nutrient availability. This curve is divided into four distinct phases: lag, log (exponential), stationary, and death phases, each reflecting a unique stage of bacterial adaptation and growth. During the lag phase, bacteria acclimate to their surroundings by synthesizing essential...
945
Microbial Growth Measurement: Indirect Methods01:27

Microbial Growth Measurement: Indirect Methods

501
Estimating microbial growth is essential for understanding population dynamics and environmental adaptations. Indirect methods provide valuable insights by measuring parameters such as turbidity, metabolic activity, and biomass, enabling efficient and reproducible assessments.During exponential growth, microbial cells scatter light proportionally to their biomass, a principle used in turbidity measurements. About one million cells per milliliter produce detectable scattering, which a...
501
Drug Accumulation During Multiple Dosing: Repetitive IV Injections01:21

Drug Accumulation During Multiple Dosing: Repetitive IV Injections

34
Calculating drug dosage and accumulation in multiple-dose regimens is crucial for achieving therapeutic efficacy while avoiding toxicity. This involves determining the plasma drug concentrations over time to optimize dosing schedules. The principle of superposition is fundamental in this process, allowing for the prediction of drug concentration in plasma following multiple doses based on single-dose data.The principle of superposition asserts that the plasma concentration-time curves from...
34
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

701
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
701

You might also read

Related Articles

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

Sort by
Same author

Advanced Video-Based Processing for Low-Cost Damage Assessment of Buildings under Seismic Loading in Shaking Table Tests.

Sensors (Basel, Switzerland)·2023
Same author

Motion Magnification Applications for the Protection of Italian Cultural Heritage Assets.

Sensors (Basel, Switzerland)·2022
See all related articles

Related Experiment Video

Updated: Oct 12, 2025

Estimating Virus Production Rates in Aquatic Systems
10:49

Estimating Virus Production Rates in Aquatic Systems

Published on: September 22, 2010

12.8K

Estimating the epidemic growth dynamics within the first week.

Vincenzo Fioriti1, Marta Chinnici1, Andrea Arbore2

  • 1ENEA- C.R Casaccia, Via Anguillarese 301, Rome, 00123, Italy.

Heliyon
|November 24, 2021
PubMed
Summary

This study introduces a novel method using the Newcomb-Benford Law to predict infectious disease spread dynamics from just one week of data. This approach aids in early epidemic estimation, even with limited initial outbreak information.

Keywords:
Big dataComplex networkDynamical systemsEpidemic spreadingGraph theoryInfective diseases

More Related Videos

Precise, High-throughput Analysis of Bacterial Growth
09:00

Precise, High-throughput Analysis of Bacterial Growth

Published on: September 19, 2017

24.3K
Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins
09:40

Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins

Published on: June 11, 2015

12.4K

Related Experiment Videos

Last Updated: Oct 12, 2025

Estimating Virus Production Rates in Aquatic Systems
10:49

Estimating Virus Production Rates in Aquatic Systems

Published on: September 22, 2010

12.8K
Precise, High-throughput Analysis of Bacterial Growth
09:00

Precise, High-throughput Analysis of Bacterial Growth

Published on: September 19, 2017

24.3K
Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins
09:40

Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins

Published on: June 11, 2015

12.4K

Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Data Science

Background:

  • Accurate estimation of early infectious disease growth is crucial for predicting epidemic spread.
  • Existing mathematical models face challenges due to limited data during initial outbreak phases.
  • Diverse epidemic growth patterns, from sub-linear to super-exponential, complicate early-stage analysis.

Purpose of the Study:

  • To develop a straightforward methodology for estimating epidemic growth dynamics using minimal initial data.
  • To leverage the Newcomb-Benford Law for reliable inferences in the early stages of an outbreak.
  • To validate the proposed method on a real-world case study, the Italian COVID-19 epidemic.

Main Methods:

  • Application of the Newcomb-Benford Law to cumulative infected case data.
  • Utilizing only the first seven days of surveillance data from multiple locations.
  • Analysis of epidemic growth dynamics within a defined six-week scenario.

Main Results:

  • Demonstrated ability to discriminate between different epidemic growth dynamics using limited data.
  • Successfully applied the methodology to COVID-19 data from fifty Italian cities.
  • Identified the most probable approximating function for epidemic growth over a six-week period.

Conclusions:

  • The Newcomb-Benford Law provides a robust tool for early epidemic dynamic estimation.
  • A week's worth of surveillance data is sufficient for reliable outbreak growth pattern identification.
  • This methodology offers a practical approach to inform public health interventions during emerging infectious disease events.