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.

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.

Severe telecommunications disruption during floods can constrain help requests, emergency coordination, public information, and operational visibility at the same time that response needs intensify. This study analyzes the 2025 Southern Thailand flood telecommunications disruption through the Swiss Cheese Model to explain how infrastructure dependencies and protective layers interacted during breakdown and restoration. The article uses a qualitative-dominant single-case study design with descriptive quantitative support from a sanitized analytical extract prepared from a private operational coordination logbook maintained contemporaneously by the author. The extract combines chronological response records with daily anonymized mobile-network outage counts. In the recorded operational dataset, site-down counts rose from 335 on 2025-11-23 to a peak of 940 on 2025-11-26, then declined to the first all-zero row on 2025-12-06. These counts are reported as recorded site-down counts in the operational dataset and are not interpreted as population coverage, customer-level impact, or complete loss of service everywhere. The findings indicate severe multi-operator telecommunications disruption shaped by flood exposure, electrical power dependency, mobilesite vulnerability, field-access constraints, backup power and fuel logistics, emergency communication workarounds, data-platform interoperability, and institutional coordination. The study contributes by extending Swiss Cheese Model analysis to disaster telecommunications resilience and by showing why layered emergency communications should be treated as a core disaster risk reduction capacity in flood-prone regions.

Severe telecommunications disruption during floods can constrain help requests, emergency coordination, public information, and operational visibility at the same time that response needs intensify. This study analyzes the 2025 Southern Thailand flood telecommunications disruption through the Swiss Cheese Model to explain how infrastructure dependencies and protective layers interacted during breakdown and restoration. The article uses a qualitative-dominant single-case study design with descriptive quantitative support from a sanitized analytical extract prepared from a private operational coordination logbook maintained contemporaneously by the author. The extract combines chronological response records with daily anonymized mobile-network outage counts. In the recorded operational dataset, site-down counts rose from 335 on 2025-11-23 to a peak of 940 on 2025-11-26, then declined to the first all-zero row on 2025-12-06. These counts are reported as recorded site-down counts in the operational dataset and are not interpreted as population coverage, customer-level impact, or complete loss of service everywhere. The findings indicate severe multi-operator telecommunications disruption shaped by flood exposure, electrical power dependency, mobilesite vulnerability, field-access constraints, backup power and fuel logistics, emergency communication workarounds, data-platform interoperability, and institutional coordination. The study contributes by extending Swiss Cheese Model analysis to disaster telecommunications resilience.