S: Background: The aim of this study was to evaluate the most effective combination of autoregressive integrated moving average (ARIMA), a time series model, and association rule mining (ARM) techniques to identify meaningful prognostic factors and predict the number of cases for efficient COVID-19 crisis management. Methods: The 3685 COVID-19 patients admitted at Thailand’s first university field hospital following the four waves of infections from March 2020 to August 2021 were analyzed using the autoregressive integrated moving average (ARIMA), its derivative to exogenous variables (ARIMAX), and association rule mining (ARM). Results: The ARIMA (2, 2, 2) model with an optimized parameter set predicted the number of the COVID-19 cases admitted at the hospital with acceptable error scores (R² = 0.5695, RMSE = 29.7605, MAE = 27.5102). Key features from ARM (symptoms, age, and underlying diseases) were selected to build an ARIMAX (1, 1, 1) model, which yielded better performance in predicting the number of admitted cases (R² = 0.5695, RMSE = 27.7508, MAE = 23.4642). The association analysis revealed that hospital stays of more than 14 days were related to the healthcare worker patients and the patients presented with underlying diseases. The worsening cases that required referral to the hospital ward were associated with the patients admitted with symptoms, pregnancy, metabolic syndrome, and age greater than 65 years old. Conclusions: This study demonstrated that the ARIMAX model has the potential to predict the number of COVID-19 cases by incorporating the most associated prognostic factors identified by ARM technique to the ARIMA model, which could be used for preparation and optimal management of hospital resources during pandemics.

Designing new medical devices with advanced humidity sensors is of great significance for patients with incontinence-associated dermatitis (IAD). The primary goal of this study is to test the humidity-sensing mattress system for patients with IAD in clinical settings. The design of the mattress is set at 203 cm, with 10 × 3 sensors, dimensions of 19 × 32 cm, and a weighted bearing of 200 kg. The main sensors consist of a humidity-sensing film, a thin-film electrode (6 × 0.1 mm), and a glass substrate (500 nm). The sensitivity of the test mattress system showed that the resistance-humidity sensor was at a temperature of 35 °C (${\mathrm{V}}_0$ = 30 V, ${\mathrm{V}}_0$= 350 mV), with slope at 1.13 V/fF, $f$ = 1 MHz, 20–90% RH, and a response time of 20 s at 2 $\mathsf{\mu }$m. In addition, the humidity sensor reached 90% RH, with a response time of less than 10 s, a magnitude of 1$0^7$–1$0^4$ $\mathsf{\Omega }$, 1 mol%, ${\mathrm{CrO}}_{1.5}$, and F${\mathrm{O}}_{1.5}$, respectively. This design is not only a simple, low-cost medical sensing device, but also opens a new pathway for developing humidity-sensing mattresses in the field of flexible sensors, wearable medical diagnostic devices, and health detection.

This study aims to analyze the patient characteristics and factors related to clinical outcomes in the crisis management of the COVID-19 pandemic in a field hospital. We conducted retrospective analysis of patient clinical data from March 2020 to August 2021 at the first university-based field hospital in Thailand. Multivariable logistic regression models were used to evaluate the factors associated with the field hospital discharge destination. Of a total of 3685 COVID-19 patients, 53.6% were women, with the median age of 30 years. General workers accounted for 97.5% of patients, while 2.5% were healthcare workers. Most of the patients were exposed to coronavirus from the community (84.6%). At the study end point, no patients had died, 97.7% had been discharged home, and 2.3% had been transferred to designated high-level hospitals due to their condition worsening. In multivariable logistic regression analysis, older patients with one or more underlying diseases who showed symptoms of COVID-19 and whose chest X-rays showed signs of pneumonia were in a worse condition than other patients. In conclusion, the university-based field hospital has the potential to fill acute gaps and prevent public agencies from being overwhelmed during crisis events.