Related Experiment Video
Updated: Dec 19, 2025
07:13
Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs
Published on: April 9, 2021
4.5K
U.S. county-level characteristics to inform equitable COVID-19 response
Taylor Chin1, Rebecca Kahn1, Ruoran Li1
1Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA.
Medrxiv : the Preprint Server for Health Sciences
|June 9, 2020
Summary
COVID-19 risk varies across US counties due to biological, demographic, and socioeconomic factors. Understanding this variation is crucial for effective public health strategies and resource allocation to vulnerable populations.
Area of Science:
- Epidemiology
- Public Health
- Sociology
Background:
- Coronavirus Disease 2019 (COVID-19) spread reveals unequal risk across the US.
- Biological, demographic, and socioeconomic factors influence COVID-19 susceptibility.
- These factors reflect and are exacerbated by structural inequities.
Conclusions:
- Decentralized COVID-19 response requires recognizing intra-state, inter-county risk variations.
- Structural inequalities significantly contribute to COVID-19 burden and healthcare demand.
- Targeted strategies are needed to support vulnerable communities and ensure resource allocation.
Related Concept Videos
01:26Principles of Disease Surveillance
393
Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
393
01:29Bias in Epidemiological Studies
1.1K
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:
1.1K
01:25Statistical Methods for Analyzing Epidemiological Data
799
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:
799
00:52Pareto Chart
7.5K
A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
7.5K
01:04Pie Chart
15.6K
A pie chart (or a pie graph) is a circular graphical chart or a pictorial representation of categorical data. It is divided into slices of pie each indicating numerical proportions. It is also used to show the relative sizes of data in a single chart.
In a pie chart, the central angle, the arc length of each slice, and the area are directly proportional to the quantity or percentage it represents. Some real-world examples that can be depicted using pie charts include marks obtained by students...
In a pie chart, the central angle, the arc length of each slice, and the area are directly proportional to the quantity or percentage it represents. Some real-world examples that can be depicted using pie charts include marks obtained by students...
15.6K
01:09Types of Skewness
16.5K
If the frequency distribution of a data set is more inclined towards smaller or larger values, the distribution is said to be skewed. If data values are skewed to the right, then the distribution is called positively skewed. Conversely, if the plot is skewed to the left, the distribution is called negatively skewed.
For instance, in the middle of a pandemic, the geographical distribution of vaccine coverage may be positively skewed towards populations in the global north countries. However,...
For instance, in the middle of a pandemic, the geographical distribution of vaccine coverage may be positively skewed towards populations in the global north countries. However,...
16.5K