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2022 Vol. 4, No. 52

Preplanned Studies
Longitudinal Participatory Surveillance Highlights Association Between Mask-Wearing and Lower COVID-19 Risk — United States, 2020
Makayla Swaciak, Zachary Popp, Autumn Gertz, Kara Sewalk, Marinanicole Schultheiss, Benjamin Rader, John S. Brownstein
2022, 4(52): 1169-1175. doi: 10.46234/ccdcw2022.235
Abstract(5710) HTML (181) PDF 385KB(118)
Abstract:
What is already known about this topic?

Numerous ecological and laboratory studies suggest face masks are an effective non-pharmaceutical intervention for reducing the spread of coronavirus disease 2019 (COVID-19), but cannot otherwise assess individual-level effects.

What is added by this report?

Using a prospective cohort of individuals enrolled in a participatory, syndromic surveillance tool prior to the first case of COVID-19 in the United States, we present a novel longitudinal assessment of the effectiveness of face masks.

What are the public health implications for public health practice?

Our analysis demonstrates an association between self-reported mask-wearing behavior and lower individual risk of syndromic COVID-19-like illness while adjusting for confounders at the individual level. Our results also highlight the dual utility of participatory syndromic surveillance systems as both disease trend monitors and tools that can aid in understanding the effectiveness of personal protective measures.

Hospital Strain and COVID-19 Fatality — England, April 2020–March 2022
Tengfei Lin, Ziyi Zhao, Zhirong Yang, Bingli Li, Chang Wei, Fuxiao Li, Yiwen Jiang, Di Liu, Zuyao Yang, Feng Sha, Jinling Tang
2022, 4(52): 1176-1180. doi: 10.46234/ccdcw2022.236
Abstract(4884) HTML (175) PDF 1406KB(80)
Abstract:
What is already known about this topic?

During the coronavirus disease 2019 (COVID-19) pandemic, tremendous efforts have been made in countries to suppress epidemic peaks and strengthen hospital services to avoid hospital strain and ultimately reduce the risk of death from COVID-19. However, there is limited empirical evidence that hospital strain increases COVID-19 deaths.

What is added by this report?

We found the risk of death from COVID-19 was linearly associated with the number of patients currently in hospitals, a measure of hospital strain, before the Omicron period. This risk could be increased by a maximum of 188.0%.

What are the implications for public health practice?

These findings suggest that any (additional) effort to reduce hospital strain would be beneficial during early large COVID-19 outbreaks and possibly also others alike. During an Omicron outbreak, vigilance remains necessary to prevent excess deaths caused by hospital strain as happened in Hong Kong Special Administrative Region, China.

Perspectives
Methods and Applications
Comparing COVID-19 Case Prediction Between ARIMA Model and Compartment Model — China, December 2019–April 2020
Bangguo Qi, Nankun Liu, Shicheng Yu, Feng Tan
2022, 4(52): 1185-1188. doi: 10.46234/ccdcw2022.239
Abstract(4463) HTML (161) PDF 544KB(70)
Abstract:
Introduction

To compare the performance between the compartment model and the autoregressive integrated moving average (ARIMA) model that were applied to the prediction of new infections during the coronavirus disease 2019 (COVID-19) epidemic.

Methods

The compartment model and the ARIMA model were established based on the daily cases of new infection reported in China from December 2, 2019 to April 8, 2020. The goodness of fit of the two models was compared using the coefficient of determination (R2).

Results

The compartment model predicts that the number of new cases without a cordon sanitaire, i.e., a restriction of mobility to prevent spread of disease, will increase exponentially over 10 days starting from January 23, 2020, while the ARIMA model shows a linear increase. The calculated R2 values of the two models without cordon sanitaire were 0.990 and 0.981. The prediction results of the ARIMA model after February 2, 2020 have a large deviation. The R2 values of complete transmission process fit of the epidemic for the 2 models were 0.964 and 0.933, respectively.

Discussion

The two models fit well at different stages of the epidemic. The predictions of compartment model were more in line with highly contagious transmission characteristics of COVID-19. The accuracy of recent historical data had a large impact on the predictions of the ARIMA model as compared to those of the compartment model.

Notifiable Infectious Diseases Reports