A norovirus gastroenteritis outbreak in an Australian child-care center: A household-level analysis

Nicolas Roydon Smoll1, Arifuzzman Khan1, Jacina Walker1,2

  • 1Central Queensland Public Health Unit, Rockhampton, Queensland, Australia.

Plos One
|November 2, 2021
PubMed

Insights

Norovirus outbreaks in childcare centers are common. This study found that each sick child likely infected one other child or staff member and one household contact, highlighting transmission risks.

Area of Science:

  • Public Health
  • Infectious Disease Epidemiology
  • Pediatric Infectious Diseases

Background:

  • Norovirus outbreaks pose a significant health burden in childcare settings globally.
  • Limited data exists on the transmission dynamics and overall impact within childcare facilities and associated households.

Purpose of the Study:

  • To analyze transmission patterns, household attack rates, and the basic reproduction number (R0) of a norovirus outbreak in an Australian childcare center.

Main Methods:

  • Conducted an outbreak investigation using parental interviews for suspected gastroenteritis cases.
  • Collected data from symptomatic individuals within childcare and their household clusters.
  • Estimated serial intervals and calculated the basic reproduction number (R0) during the outbreak's growth phase.

Main Results:

  • A total of 52 individuals across 19 households reported symptoms.
  • 46.9% of transmissions originated within the childcare center.
  • The household attack rate was 36.5%, and the estimated R0 was 2.4.

Conclusions:

  • Childcare norovirus outbreaks involve approximately double the number of affected individuals compared to symptomatic staff and children alone.
  • Each symptomatic childcare attendee infected, on average, one other attendee/staff member and slightly more than one household contact.

Related Concept Videos

Steps in Outbreak Investigation01:18

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:
253
Factors Affecting the Risk of Infection01:26

Factors Affecting the Risk of Infection

The hosts' susceptibility to infection depends on several factors. The integrity of the skin and mucous membranes helps protect the body against microbial attacks. When the skin is altered, the chance of infection, limb loss, and even death increases.
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
12.8K
Statistical Methods for Analyzing Epidemiological Data01:25

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:
601
Causality in Epidemiology01:21

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.1K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
779
Prevalence and Incidence01:08

Prevalence and Incidence

In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
948