Seasonal synchronization and unpredictability in epidemic models with waning immunity and healthcare thresholds
Abstract This paper explores a model integrating healthcare capacity thresholds and seasonal effects to investigate the synchronization of epidemic cycles with seasonal transmission rates, using parameters reflective of the COVID-19 pandemic. Through bifurcation analysis in the epi-seasonal domain,...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-01467-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849726146245033984 |
|---|---|
| author | Veronika Eclerová Deeptajyoti Sen Lenka Přibylová |
| author_facet | Veronika Eclerová Deeptajyoti Sen Lenka Přibylová |
| author_sort | Veronika Eclerová |
| collection | DOAJ |
| description | Abstract This paper explores a model integrating healthcare capacity thresholds and seasonal effects to investigate the synchronization of epidemic cycles with seasonal transmission rates, using parameters reflective of the COVID-19 pandemic. Through bifurcation analysis in the epi-seasonal domain, we identify regions of significant seasonal synchronization related to transmission rate fluctuations, waning immunity, and healthcare capacity thresholds. The model highlights four sources of unpredictability: chaotic regimes, quasiperiodicity, proximity to SNIC or transcritical bifurcations, and bistability. Our findings reveal that chaotic regimes are more predictable than quasiperiodic regimes in epidemiological terms. Synchronizing outbreaks with seasonal cycles, even in chaotic regimes, predominantly results in significant winter outbreaks. Conversely, quasiperiodicity allows outbreaks to occur at any time of the year. Near eradication unpredictability aligns with historical pertussis data, underscoring the model’s relevance to real-world epidemics and vaccine schedules. Additionally, we identify a bistability region with potential for abrupt shifts in disease prevalence, triggered by superspreading events or migration. |
| format | Article |
| id | doaj-art-1a9b404efe924be6bd37804c52f068d8 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1a9b404efe924be6bd37804c52f068d82025-08-20T03:10:17ZengNature PortfolioScientific Reports2045-23222025-05-0115112010.1038/s41598-025-01467-4Seasonal synchronization and unpredictability in epidemic models with waning immunity and healthcare thresholdsVeronika Eclerová0Deeptajyoti Sen1Lenka Přibylová2Department of Mathematics and Statistics, Faculty of Science, Masaryk UniversityDepartment of Mathematics and Statistics, Faculty of Science, Masaryk UniversityDepartment of Mathematics and Statistics, Faculty of Science, Masaryk UniversityAbstract This paper explores a model integrating healthcare capacity thresholds and seasonal effects to investigate the synchronization of epidemic cycles with seasonal transmission rates, using parameters reflective of the COVID-19 pandemic. Through bifurcation analysis in the epi-seasonal domain, we identify regions of significant seasonal synchronization related to transmission rate fluctuations, waning immunity, and healthcare capacity thresholds. The model highlights four sources of unpredictability: chaotic regimes, quasiperiodicity, proximity to SNIC or transcritical bifurcations, and bistability. Our findings reveal that chaotic regimes are more predictable than quasiperiodic regimes in epidemiological terms. Synchronizing outbreaks with seasonal cycles, even in chaotic regimes, predominantly results in significant winter outbreaks. Conversely, quasiperiodicity allows outbreaks to occur at any time of the year. Near eradication unpredictability aligns with historical pertussis data, underscoring the model’s relevance to real-world epidemics and vaccine schedules. Additionally, we identify a bistability region with potential for abrupt shifts in disease prevalence, triggered by superspreading events or migration.https://doi.org/10.1038/s41598-025-01467-4SIRS modelSeasonalityBifurcationChaosQuasiperiodicity |
| spellingShingle | Veronika Eclerová Deeptajyoti Sen Lenka Přibylová Seasonal synchronization and unpredictability in epidemic models with waning immunity and healthcare thresholds Scientific Reports SIRS model Seasonality Bifurcation Chaos Quasiperiodicity |
| title | Seasonal synchronization and unpredictability in epidemic models with waning immunity and healthcare thresholds |
| title_full | Seasonal synchronization and unpredictability in epidemic models with waning immunity and healthcare thresholds |
| title_fullStr | Seasonal synchronization and unpredictability in epidemic models with waning immunity and healthcare thresholds |
| title_full_unstemmed | Seasonal synchronization and unpredictability in epidemic models with waning immunity and healthcare thresholds |
| title_short | Seasonal synchronization and unpredictability in epidemic models with waning immunity and healthcare thresholds |
| title_sort | seasonal synchronization and unpredictability in epidemic models with waning immunity and healthcare thresholds |
| topic | SIRS model Seasonality Bifurcation Chaos Quasiperiodicity |
| url | https://doi.org/10.1038/s41598-025-01467-4 |
| work_keys_str_mv | AT veronikaeclerova seasonalsynchronizationandunpredictabilityinepidemicmodelswithwaningimmunityandhealthcarethresholds AT deeptajyotisen seasonalsynchronizationandunpredictabilityinepidemicmodelswithwaningimmunityandhealthcarethresholds AT lenkapribylova seasonalsynchronizationandunpredictabilityinepidemicmodelswithwaningimmunityandhealthcarethresholds |