Computational Forecasting Methodology for Acute Respiratory Infectious Disease Dynamics
Bibliographic Data
| ID | 15465372 |
|---|---|
| Authors | Daniel Alejandro González Bandala (0000-0001-9400-2426, Autonomous University of San Luis Potosí), Juan C Cuevas‐Tello (0000-0002-7566-0412, Autonomous University of San Luis Potosí, corresponding author), Daniel E Noyola (0000-0002-4840-6742, Autonomous University of San Luis Potosí), Andreu Comas‐García (0000-0002-2368-1529, Autonomous University of San Luis Potosí), Christian A García‐Sepúlveda (0000-0002-1169-7857, Autonomous University of San Luis Potosí), Christian A García-Sepúlveda (Autonomous University of San Luis Potosí) |
| Year | 2020 |
| Volume | 17 |
| Issue | 12 |
| Pages | 4540-4540 |
| Publication date | 2020-06-24 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph17124540 |
| PMID | 32599746 |
| OpenAlex | W3037192120 |
| Language | EN |
| Citations received | 1 |
| References cited | 53 |
The study of infectious disease behavior has been a scientific concern for many years as early identification of outbreaks provides great advantages including timely implementation of public health measures to limit the spread of an epidemic. We propose a methodology that merges the predictions of (i) a computational model with machine learning, (ii) a projection model, and (iii) a proposed smoothed endemic channel calculation. The predictions are made on weekly acute respiratory infection (ARI) data obtained from epidemiological reports in Mexico, along with the usage of key terms in the Google search engine. The results obtained with this methodology were compared with state-of-the-art techniques resulting in reduced root mean squared percentage error (RMPSE) and maximum absolute percent error (MAPE) metrics, achieving a MAPE of 21.7%. This methodology could be extended to detect and raise alerts on possible outbreaks on ARI as well as for other seasonal infectious diseases
Disease · Econometrics · Infectious disease (medical specialty · Machine learning · Mean absolute percentage error · Mean squared error · Outbreak · Pathology · Statistics · Computer Science · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Influenza Virus Research Studies · Mathematics · Medicine · Epidemiology
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| Unique citing works | 1 |
|---|---|
| Citations per year | 0,25 |
| Citation span | 2022 - 2022 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |