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.

Suppression of the alpha, delta, and omicron variants of SARS-Cov-2 in Taiwan.

BACKGROUND: Taiwan was a coronavirus disease 2019 (COVID-19) outlier, with an extraordinarily long transmission-free record: 253 days without locally transmitted infections while the rest of the world battled wave after wave of infection. The appearance of the alpha variant in May 2021, closely followed by the delta variant, disrupted this transmission-free streak. However, despite low vaccination coverage (<1%), outbreaks were well-controlled. METHODS: This study analyzed the time to border closure and conducted one-sample t test to compare between Taiwan and Non-Taiwan countries prior to vaccine introduction. The study also collected case data to observe the dynamics of omicron transmission. Time-varying reproduction number,Rt, was calculated and was used to reflect infection impact at specified time points and model trends of future incidence. RESULTS: The study analyzed and compare the time to border closure in Taiwan and non-Taiwan countries. The mean times to any border closure from the first domestic case within each country were -21 and 5.98 days, respectively (P < .0001). The Taiwanese government invested in quick and effective contact tracing with a precise quarantine strategy in lieu of a strict lockdown. Residents followed recommendations based on self-discipline and unity. The self-discipline in action is evidenced in Google mobility reports. The central and local governments worked together to enact non-pharmaceutical interventions (NPIs), including universal masking, social distancing, limited unnecessary gatherings, systematic contact tracing, and enhanced quarantine measures. The people cooperated actively with pandemic-prevention regulations, including vaccination and preventive NPIs. CONCLUSIONS: This article describes four key factors underlying Taiwan's success in controlling COVID-19 transmission: quick responses; effective control measures with new technologies and rolling knowledge updates; unity and cooperation among Taiwanese government agencies, private companies and organizations, and individual citizens; and Taiwanese self-discipline.

Contribution of Testing Strategies and Contact Tracing towards COVID-19 Outbreaks Control: A Mathematical Modeling Study.

This modeling study considers different screening strategies, contact tracing, and the severity of novel epidemic outbreaks for various population sizes, providing insight into multinational containment effectiveness of emerging infectious diseases, prior to vaccines development. During the period of the ancestral SARS-Cov-2 virus, contact tracing alone is insufficient to achieve outbreak control. Although universal testing is proposed in multiple nations, its effectiveness accompanied by other measures is rarely examined. Our research investigates the necessity of universal testing when contact tracing and symptomatic screening measures are implemented. We used a stochastic transmission model to simulate COVID-19 transmission, evaluating containment strategies via contact tracing, one-time high risk symptomatic testing, and universal testing. Despite universal testing having the potential to identify subclinical cases, which is crucial for non-pharmaceutical interventions, our model suggests that universal testing only reduces the total number of cases by 0.0009% for countries with low COVID-19 prevalence and 0.025% for countries with high COVID-19 prevalence when rigorous contact tracing and symptomatic screening are also implemented. These findings highlight the effectiveness of testing strategies and contact tracing in reducing COVID-19 cases by identifying subclinical cases.