Application of Sarima model in forecasting and analyzing inpatient cases of acute mountain sickness
Bibliographic Data
| ID | 15358727 |
|---|---|
| Authors | Jianchao Liu (0000-0001-5647-3518), Fangfang Yu (Chinese PLA General Hospital), Han Song (0000-0001-6000-1572, Chinese PLA General Hospital, corresponding author) |
| Year | 2023 |
| Volume | 23 |
| Issue | 1 |
| Pages | 56-56 |
| Publication date | 2023-01-09 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | BMC Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1471-2458 • E-ISSN: 1471-2458 |
| Publisher | BioMed Central (PUBLISHER • GB) |
| DOI | 10.1186/s12889-023-14994-4 |
| PMID | 36624441 |
| OpenAlex | W4313827725 |
| Language | EN |
| Citations received | 3 |
| References cited | 2 |
AMS inpatients have obvious periodicity and seasonality. The SARIMA model has good fitting ability and high short-term prediction accuracy. It can help explore the characteristics of AMS disease and provide decision-making basis for allocation of relevant medical resources for AMS inpatients
Autoregressive integrated moving average · Biostatistics · Medical record · Statistics · Time series · Climate Change and Health Impacts · High Altitude and Hypoxia · Medicine · Travel-related health issues · Emergency Medicine · Epidemiology · Internal Medicine
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Prediction, lag and mixture effects of meteorology and pollutants on the incidence of pulmonary tuberculosis in Jining City, China
Epidemiology and Sarima model of deaths in a tertiary comprehensive hospital in Hangzhou from 2015 to 2022
| Citations per year | 1.5 |
|---|---|
| Citation span | 2024 - 2026 (3) |
| Highly cited | No |