Analyzing the Effect of Vaccination Over COVID Cases and Deaths in Asian Countries Using Machine Learning Models

Real-time forecasting of COVID-19 spread according to protective behavior and vaccination: autoregressive integrated moving average models

BACKGROUND

Mathematical and statistical models are used to predict trends in epidemic spread and determine the effectiveness of control measures. Automatic regressive integrated moving average (ARIMA) models are used for time-series forecasting, but only few models of the 2019 coronavirus disease (COVID-19) pandemic have incorporated protective behaviors or vaccination, known to be effective for pandemic control.

METHODS

To improve the accuracy of prediction, we applied newly developed ARIMA models with predictors (mask wearing, avoiding going out, and vaccination) to forecast weekly COVID-19 case growth rates in Canada, France, Italy, and Israel between January 2021 and March 2022. The open-source data was sourced from the YouGov survey and Our World in Data. Prediction performance was evaluated using the root mean square error (RMSE) and the corrected Akaike information criterion (AICc).

RESULTS

A model with mask wearing and vaccination variables performed best for the pandemic period in which the Alpha and Delta viral variants were predominant (before November 2021). A model using only past case growth rates as autoregressive predictors performed best for the Omicron period (after December 2021). The models suggested that protective behaviors and vaccination are associated with the reduction of COVID-19 case growth rates, with booster vaccine coverage playing a particularly vital role during the Omicron period. For example, each unit increase in mask wearing and avoiding going out significantly reduced the case growth rate during the Alpha/Delta period in Canada (-0.81 and -0.54, respectively; both p < 0.05). In the Omicron period, each unit increase in the number of booster doses resulted in a significant reduction of the case growth rate in Canada (-0.03), Israel (-0.12), Italy (-0.02), and France (-0.03); all p < 0.05.

CONCLUSIONS

The key findings of this study are incorporating behavior and vaccination as predictors led to accurate predictions and highlighted their significant role in controlling the pandemic. These models are easily interpretable and can be embedded in a "real-time" schedule with weekly data updates. They can support timely decision making about policies to control dynamically changing epidemics.

Outcome prediction in hospitalized COVID-19 patients: Comparison of the performance of five severity scores

Background

The aim of our study was to externally validate the predictive capability of five developed coronavirus disease 2019 (COVID-19)-specific prognostic tools, including the COVID-19 Spanish Society of Infectious Diseases and Clinical Microbiology (SEIMC), Shang COVID severity score, COVID-intubation risk score-neutrophil/lymphocyte ratio (IRS-NLR), inflammation-based score, and ventilation in COVID estimator (VICE) score.

Methods

The medical records of all patients hospitalized for a laboratory-confirmed COVID-19 diagnosis between May 2021 and June 2021 were retrospectively analyzed. Data were extracted within the first 24 h of admission, and five different scores were calculated. The primary and secondary outcomes were 30-day mortality and mechanical ventilation, respectively.

Results

A total of 285 patients were enrolled in our cohort. Sixty-five patients (22.8%) were intubated with ventilator support, and the 30-day mortality rate was 8.8%. The Shang COVID severity score had the highest numerical area under the receiver operator characteristic (AUC-ROC) (AUC 0.836) curve to predict 30-day mortality, followed by the SEIMC score (AUC 0.807) and VICE score (AUC 0.804). For intubation, both the VICE and COVID-IRS-NLR scores had the highest AUC (AUC 0.82) compared to the inflammation-based score (AUC 0.69). The 30-day mortality increased steadily according to higher Shang COVID severity scores and SEIMC scores. The intubation rate exceeded 50% in the patients stratified by higher VICE scores and COVID-IRS-NLR score quintiles.

Conclusion

The discriminative performances of the SEIMC score and Shang COVID severity score are good for predicting the 30-day mortality of hospitalized COVID-19 patients. The COVID-IRS-NLR and VICE showed good performance for predicting invasive mechanical ventilation (IMV).

Different patterns of excess all-cause mortality by age and sex in Hungary during the 2nd and 3rd waves of the COVID-19 pandemic

It is well accepted that COVID-19-related mortality shows a strong age dependency. However, temporal changes in the age distribution of excess relative mortality between waves of the pandemic are less frequently investigated. We aimed to assess excess absolute mortality and the age-distribution of all-cause mortality during the second and third waves of the COVID-19 pandemic in Hungary compared to the same periods of non-pandemic years. Rate ratios for excess all-cause mortality with 95% confidence intervals and the number of excess deaths for the second (week 41 of 2020 through week 4 of 2021) and third waves (weeks 7-21 of 2021) of the COVID pandemic for the whole of Hungary compared to the same periods of the pre-pandemic years were estimated for 10-year age strata using Poisson regression. Altogether, 9771 (95% CI: 9554-9988) excess deaths were recorded during the second wave of the pandemic, while it was lower, 8143 (95% CI: 7953-8333) during the third wave. During the second wave, relative mortality peaked for ages 65-74 and 75-84 (RR 1.37, 95%CI 1.33-1.41, RR 1.38, 95%CI 1.34-1.42). Conversely, during the third wave, relative mortality peaked for ages 35-44 (RR 1.43, 95%CI 1.33-1.55), while those ≥65 had substantially lower relative risks compared to the second wave. The reduced relative mortality among the elderly during the third wave is likely a consequence of the rapidly increasing vaccination coverage of the elderly coinciding with the third wave. The hugely increased relative mortality of those 35-44 could point to non-biological causes, such as less stringent adherence to non-pharmaceutical measures in this population.