Forecasting COVID‐19 Transmission: Insights From the Logistic Smooth Transition Model

ABSTRACT This study aims to explain the transmission mechanism of the COVID‐19 pandemic through short‐term forecasts based on the logistic smooth transition (LST) model. The model simulation was applied to data from the first seven waves in Japan, as well as the first waves of the other G7 countries. The model provided forecasts with significant precision, with an average of 59 days ahead of the actual number of COVID‐19 cases. The model effectively estimated transition speed and identified critical thresholds for each pandemic wave, facilitating early predictions of peak timings. This framework not only enhances our understanding of COVID‐19 transmission dynamics but also offers a replicable method for managing future pandemics.

Authors

Institutions

Publication Details

Journal
Journal of Forecasting
Published
2026-05-29
DOI
https://doi.org/10.1002/for.70182
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Forecasting COVID‐19 Transmission: Insights From the Logistic Smooth Transition Model

Tolga Omay, Yeliz Uymaz
Journal of Forecasting
COVID-19 epidemiological studies
article

Forecasting COVID‐19 Transmission: Insights From the Logistic Smooth Transition Model

Tolga Omay, Yeliz Uymaz
article en

Abstract

ABSTRACT This study aims to explain the transmission mechanism of the COVID‐19 pandemic through short‐term forecasts based on the logistic smooth transition (LST) model. The model simulation was applied to data from the first seven waves in Japan, as well as the first waves of the other G7 countries. The model provided forecasts with significant precision, with an average of 59 days ahead of the actual number of COVID‐19 cases. The model effectively estimated transition speed and identified critical thresholds for each pandemic wave, facilitating early predictions of peak timings. This framework not only enhances our understanding of COVID‐19 transmission dynamics but also offers a replicable method for managing future pandemics.

Journal of Forecasting
Ankara University (TR), Brunel University of London (GB)
Good health and well-being
Openalex Percentile: Top 5%
COVID-19 epidemiological studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.