Related Experiment Video
Updated: Oct 18, 2025

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
Published on: February 3, 2023
Inferring a cause-effect relationship between lockdown restrictions and COVID-19 pandemic trend during the first wave
1National Research Council, Institute of Biostructure and Bioimaging, Via Tommaso de Amicis, Naples 80145, Italy.
Abstract:
The large number of infected persons due to the COVID-19 pandemic and the need of hospital care for many of them induced the majority of world governments to implement lockdown measures. We developed an analytical model to evaluate the trend of the SARS-CoV-2 pandemic. This model was applied to the first four months of the epidemiological data of the most affected countries in Europe and Russia, in order to evaluate the effect of the lockdown on the epidemic curves during the first wave. According to our model, the difference between the beginning of the lockdown and the slope change of the curve representing the daily distribution of counts was: Germany and Spain 6 days, France 7 days, the United Kingdom 9 days, Italy 21 days, and Russia 30 days. On the basis of these results, we infer a possible cause-effect relationship between the lockdown imposed in countries taken into account and the curve representing the daily distribution of new cases. Lockdown measures imposed by governments slowed the spread of the pandemic and reduced the number of infected persons. In economic terms, the damage was considerable, with entire production sectors in crisis. On the other hand, the efforts and innovations implemented to produce vaccines and effective treatments against the pandemic could be applied also in other fields of public health.
Related Concept Videos
Causality in Epidemiology
Cause and Effect
Correlation
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
Residuals and Least-Squares Property
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Pareto Chart
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
Correlation and Causation
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...

