General protocol for predicting outbreaks of infectious diseases in social networks
Abstract Epidemic spreading on social networks with quenched connections is strongly influenced by dynamic correlations between connected nodes, posing theoretical challenges in predicting outbreaks of infectious diseases. The quenched connections introduce dynamic correlations, indicating that the...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-03-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-56340-7 |
| _version_ | 1797259379402080256 |
|---|---|
| author | Sungchul Kwon Jeong-Man Park |
| author_facet | Sungchul Kwon Jeong-Man Park |
| author_sort | Sungchul Kwon |
| collection | DOAJ |
| description | Abstract Epidemic spreading on social networks with quenched connections is strongly influenced by dynamic correlations between connected nodes, posing theoretical challenges in predicting outbreaks of infectious diseases. The quenched connections introduce dynamic correlations, indicating that the infection of one node increases the likelihood of infection among its neighboring nodes. These dynamic correlations pose significant difficulties in developing comprehensive theories for threshold determination. Determining the precise epidemic threshold is pivotal for diseases control. In this study, we propose a general protocol for accurately determining epidemic thresholds by introducing a new set of fundamental conditions, where the number of connections between individuals of each type remains constant in the stationary state, and by devising a rescaling method for infection rates. Our general protocol is applicable to diverse epidemic models, regardless of the number of stages and transmission modes. To validate our protocol’s effectiveness, we apply it to two widely recognized standard models, the susceptible–infected–recovered-susceptible model and the contact process model, both of which have eluded precise threshold determination using existing sophisticated theories. Our results offer essential tools to enhance disease control strategies and preparedness in an ever-evolving landscape of infectious diseases. |
| first_indexed | 2024-04-24T23:08:30Z |
| format | Article |
| id | doaj.art-cd64bc68e2d648be803c905c022b607c |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T23:08:30Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-cd64bc68e2d648be803c905c022b607c2024-03-17T12:21:40ZengNature PortfolioScientific Reports2045-23222024-03-011411810.1038/s41598-024-56340-7General protocol for predicting outbreaks of infectious diseases in social networksSungchul Kwon0Jeong-Man Park1Department of Physics, The Catholic University of KoreaDepartment of Physics, The Catholic University of KoreaAbstract Epidemic spreading on social networks with quenched connections is strongly influenced by dynamic correlations between connected nodes, posing theoretical challenges in predicting outbreaks of infectious diseases. The quenched connections introduce dynamic correlations, indicating that the infection of one node increases the likelihood of infection among its neighboring nodes. These dynamic correlations pose significant difficulties in developing comprehensive theories for threshold determination. Determining the precise epidemic threshold is pivotal for diseases control. In this study, we propose a general protocol for accurately determining epidemic thresholds by introducing a new set of fundamental conditions, where the number of connections between individuals of each type remains constant in the stationary state, and by devising a rescaling method for infection rates. Our general protocol is applicable to diverse epidemic models, regardless of the number of stages and transmission modes. To validate our protocol’s effectiveness, we apply it to two widely recognized standard models, the susceptible–infected–recovered-susceptible model and the contact process model, both of which have eluded precise threshold determination using existing sophisticated theories. Our results offer essential tools to enhance disease control strategies and preparedness in an ever-evolving landscape of infectious diseases.https://doi.org/10.1038/s41598-024-56340-7 |
| spellingShingle | Sungchul Kwon Jeong-Man Park General protocol for predicting outbreaks of infectious diseases in social networks Scientific Reports |
| title | General protocol for predicting outbreaks of infectious diseases in social networks |
| title_full | General protocol for predicting outbreaks of infectious diseases in social networks |
| title_fullStr | General protocol for predicting outbreaks of infectious diseases in social networks |
| title_full_unstemmed | General protocol for predicting outbreaks of infectious diseases in social networks |
| title_short | General protocol for predicting outbreaks of infectious diseases in social networks |
| title_sort | general protocol for predicting outbreaks of infectious diseases in social networks |
| url | https://doi.org/10.1038/s41598-024-56340-7 |
| work_keys_str_mv | AT sungchulkwon generalprotocolforpredictingoutbreaksofinfectiousdiseasesinsocialnetworks AT jeongmanpark generalprotocolforpredictingoutbreaksofinfectiousdiseasesinsocialnetworks |