Seasonal Trends of COVID-19 Deaths in Italy: A Confirmatory Linear Regression Study with Time Series Data from 2024/2025
Roccetti, M.
Show abstract
This study builds on and extends a previous research conducted with the time series data of COVID-19 deaths in Italy in the period 2021-2024. In those earlier works, weekly COVID-19 deaths showed pronounced seasonal increasing variations of COVID-19 mortality, rising in summer and high fall to peak in early winter, and then declining in the late winter and spring periods. Using a linear regression model, this current study has validated the seasonal mortality pattern mentioned before. We present original findings achieved with recent data from 53 weeks starting with the end of May 2024 until the end of May 2025. They confirm a recurring seasonal pattern of COVID-19 deaths, rising in early summer and peaking in high fall. Then, we document a steady decrease, starting in mid-winter until the end of the spring period. Since there is little published work from Italy in this context, having clearly shown the seasons with common increasing variations of COVID-19 mortality offers a contribution towards helping that subset of at risk population who are seasonally vulnerable. Our present study indicates that the COVID-19 mortality trend observed in previous years is still continuing, influenced by several factors that need further investigation.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- A new, simple method of describing COVID-19 trajectory and dynamics in any country based on Johnson Cumulative Distribution Function fitting 94%
- Assessing the Impact of Human Mobility to Predict Regional Excess Death in Ecuador 93%
- A multipurpose machine learning approach to predict COVID-19 negative prognosis in Sao Paulo, Brazil 93%
Similar papers in this journal
- Predicting mortality, duration of treatment, pulmonary embolism and required ceiling of ventilatory support for COVID-19 inpatients: A Machine-Learning Approach 93%
- A two-phase stochastic dynamic model for COVID-19 mid-term policy recommendations in Greece: a pathway towards mass vaccination 92%
- Associations Between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States 92%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.