Machine learning mathematical models for incidence estimation during pandemics.
Accurate estimates of the incidence of infectious diseases are key for the control of epidemics. However, healthcare systems are often unable to test the population exhaustively, especially when asymptomatic and paucisymptomatic cases are widespread; this leads to significant and systematic under-re...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2024-12-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012687 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841533237629812736 |
|---|---|
| author | Oscar Fajardo-Fontiveros Mattia Mattei Giulio Burgio Clara Granell Sergio Gómez Alex Arenas Marta Sales-Pardo Roger Guimerà |
| author_facet | Oscar Fajardo-Fontiveros Mattia Mattei Giulio Burgio Clara Granell Sergio Gómez Alex Arenas Marta Sales-Pardo Roger Guimerà |
| author_sort | Oscar Fajardo-Fontiveros |
| collection | DOAJ |
| description | Accurate estimates of the incidence of infectious diseases are key for the control of epidemics. However, healthcare systems are often unable to test the population exhaustively, especially when asymptomatic and paucisymptomatic cases are widespread; this leads to significant and systematic under-reporting of the real incidence. Here, we propose a machine learning approach to estimate the incidence of a pandemic in real-time, using reported cases and the overall test rate. In particular, we use Bayesian symbolic regression to automatically learn the closed-form mathematical models that most parsimoniously describe incidence. We develop and validate our models using COVID-19 incidence values for nine different countries, confirming their ability to accurately predict daily incidence. Remarkably, despite the differences in epidemic trajectories and dynamics across countries, we find that a single model for all countries offers a more parsimonious description and is more predictive of actual incidence compared to separate models for each country. Our results show the potential to accurately model incidence in real-time using closed-form mathematical models, providing a valuable tool for public health decision-makers. |
| format | Article |
| id | doaj-art-68562ab43d2e441baf48b555c85f4f91 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-68562ab43d2e441baf48b555c85f4f912025-01-17T05:30:56ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-12-012012e101268710.1371/journal.pcbi.1012687Machine learning mathematical models for incidence estimation during pandemics.Oscar Fajardo-FontiverosMattia MatteiGiulio BurgioClara GranellSergio GómezAlex ArenasMarta Sales-PardoRoger GuimeràAccurate estimates of the incidence of infectious diseases are key for the control of epidemics. However, healthcare systems are often unable to test the population exhaustively, especially when asymptomatic and paucisymptomatic cases are widespread; this leads to significant and systematic under-reporting of the real incidence. Here, we propose a machine learning approach to estimate the incidence of a pandemic in real-time, using reported cases and the overall test rate. In particular, we use Bayesian symbolic regression to automatically learn the closed-form mathematical models that most parsimoniously describe incidence. We develop and validate our models using COVID-19 incidence values for nine different countries, confirming their ability to accurately predict daily incidence. Remarkably, despite the differences in epidemic trajectories and dynamics across countries, we find that a single model for all countries offers a more parsimonious description and is more predictive of actual incidence compared to separate models for each country. Our results show the potential to accurately model incidence in real-time using closed-form mathematical models, providing a valuable tool for public health decision-makers.https://doi.org/10.1371/journal.pcbi.1012687 |
| spellingShingle | Oscar Fajardo-Fontiveros Mattia Mattei Giulio Burgio Clara Granell Sergio Gómez Alex Arenas Marta Sales-Pardo Roger Guimerà Machine learning mathematical models for incidence estimation during pandemics. PLoS Computational Biology |
| title | Machine learning mathematical models for incidence estimation during pandemics. |
| title_full | Machine learning mathematical models for incidence estimation during pandemics. |
| title_fullStr | Machine learning mathematical models for incidence estimation during pandemics. |
| title_full_unstemmed | Machine learning mathematical models for incidence estimation during pandemics. |
| title_short | Machine learning mathematical models for incidence estimation during pandemics. |
| title_sort | machine learning mathematical models for incidence estimation during pandemics |
| url | https://doi.org/10.1371/journal.pcbi.1012687 |
| work_keys_str_mv | AT oscarfajardofontiveros machinelearningmathematicalmodelsforincidenceestimationduringpandemics AT mattiamattei machinelearningmathematicalmodelsforincidenceestimationduringpandemics AT giulioburgio machinelearningmathematicalmodelsforincidenceestimationduringpandemics AT claragranell machinelearningmathematicalmodelsforincidenceestimationduringpandemics AT sergiogomez machinelearningmathematicalmodelsforincidenceestimationduringpandemics AT alexarenas machinelearningmathematicalmodelsforincidenceestimationduringpandemics AT martasalespardo machinelearningmathematicalmodelsforincidenceestimationduringpandemics AT rogerguimera machinelearningmathematicalmodelsforincidenceestimationduringpandemics |