Study highlights need for integration of policy, behavior change, and risk communication in pandemic response

In a recent study posted to the medRxiv* server, researchers measured the association between reproduction number (Rt), a weekly measure of real-time severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and time-varying coronavirus disease 2019 (COVID-19) mitigation policies between September 6, 2020, and November 27, 2021, a period of 64 weeks across 51 states in the United States and District of Columbia (DC).

Study: COVID-19 mitigation behaviors and policies limited SARS-CoV-2 transmission in the United States from September 2020 through November 2021. Image Credit: Nhemz/Shutterstock.comStudy: COVID-19 mitigation behaviors and policies limited SARS-CoV-2 transmission in the United States from September 2020 through November 2021. Image Credit: Nhemz/Shutterstock.com

*Important notice: medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Background

During the COVID-19 pandemic, nonpharmaceutical intervention (NPI) policies evolved and encompassed spatially and temporally diverse intervention policies, e.g., cancellation of large-scale public events, restriction on the scale of social gatherings, closing of schools, workplaces, non-essential businesses and mandatory use of mask in public places.

Evidence suggests that the effect of stay-at-home orders on SARS-CoV-2 transmission increased when combined with specific NPI policies.

Over time, the role of post-infection and post-vaccination immunity and viral evolution became apparent. Other factors, like weather, added further complexity to these dynamics.

Most importantly, the U.S. governance implemented diverse COVID-19 mitigation policies, and that too heterogeneously across its jurisdictions. The COVID-19 case and death data for each jurisdiction reflected the effect of this heterogeneity.

It raises the need to evaluate whether COVID-19 mitigation policies reduced SARS-CoV-2 transmission within the broader context of personal behavior, social factors, and weather conditions. 

About the study

In the present study, researchers pursued evidence of the spatiotemporal impact of COVID-19 mitigation policies on SARS-CoV-2 transmission across 51 U.S. states and DC for 2020-2021. The team explored the associations between Rt and selected determinants using two Bayesian Gaussian multilevel regression models.

First, they sampled the onset date of each COVID-19 case reported to the Centers for Disease Control and Prevention (CDC) for each U.S. jurisdiction to estimate Rt. Next, using log standard deviation=0.5 in published data, they used a log-normal distribution to sample infection dates.

The first study model used the Oxford COVID-19 government response tracker to retrieve standardized policy data encompassing a composite indicator (OSI) of the overall strictness of COVID-19 mitigation policies and the strength of their communication.

The second model focused on four individual policies, cancellation of public events, restrictions of gatherings, mask mandates, and stay-at-home orders. Furthermore, the team used the COVID-19 Trends and Impact Survey of Facebook users and similar sources to gather jurisdiction-level personal behavior data.

Likewise, they used the community mobility report data to estimate the proportional reduction in weekly median mobility relative to baseline mobility between January and February 2020. They also covered the weekly median reduction in national airline travel from 2019.

They fitted genome sequence data to a multinomial logistic regression model for estimates of weekly circulation of SARS-CoV-2 variants, Alpha and Delta, during the study period.

Furthermore, for their modeling predictions, the team pulled temperature (°C) data from weather stations, calculated the weekly median temperature in each U.S. jurisdiction, and assessed its associations with relative and absolute humidity to guide modeling.

From vaccination data, the team used data on the weekly proportion of individuals who completed a primary COVID-19 vaccine series.

Results

Rt estimates exhibited spatiotemporal variability across the U.S., with sustained increases (Rt>1) in late 2020 during the SARS-CoV-2 Alpha variant wave, followed by fluctuations, and rising again during the summer of 2021 during the Delta variant wave.

Similarly, stay-at-home orders, social gathering restrictions, cancellation of public events, and mask mandates exhibited spatiotemporal variability. Some states had implemented all four (e.g., Virginia), while others had none (e.g., Florida).

Nonetheless, the strictness of mitigation policies declined slightly in October 2020, markedly between March and June 2021, but overall remained highly variable throughout the study duration, with the lowest and highest median values in South Dakota and Hawaii (0.09; 0.66).

Furthermore, personal COVID-19 mitigation behaviors showed spatiotemporal variability. Until March 2021, they were relatively stable, but then airline travel and personal mobility returned to pre-pandemic levels. 

With moderate values (0.5) for Community COVID-19 Vulnerability Index (CCVI) indicators and at 12°C, the estimated Rt in the absence of mitigation was 2.6 and 2.5 for the OSI and the Policy model. In addition, the implementation of 50% of the strictest policies relative to OSI at 0% decreased Rt by 6.7%.

In the Policy Model, public event cancellation, restrictions on gathering sizes, and stay-at-home orders decreased Rt by 2.6%, 1.2%, and 2.6%, respectively. The effect of mask mandates reduced Rt by 0.7% but did not reach statistical significance.

However, personal mitigation behaviors showed strong effects in both models. For the OSI Model, Rt decreased by 22% with a 50% reduction in airline travel, 2.9%, and 14% if the local mobility decreased by 10%, and mask use increased to 50%.

Conclusions

The U.S. implemented diverse interventions during 2020-2021 to mitigate SARS-CoV-2 transmission. This diversity and the complex interplay between many contributing factors make precise estimates of a single intervention and its combinations infeasible.

Even though, at all-time points, a single measure could not reduce SARS-CoV-2 transmission, various layered measures did; nationwide vaccination helped mitigate SARS-CoV-2 transmission in most jurisdictions by late 2021 to emerge as a measure equivalent to behavioral modification.

Thus, most policies might not be adequate for mitigating respiratory pathogens in the unfortunate event of another future pandemic, as in the case of COVID-19.

However, a combination of policies and communication efforts that promote, favor, and reinstate behavioral change might prove helpful. It showcases the importance of integrating multi-layered mitigation approaches in public health at governance and community-level. 

*Important notice: medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Journal reference:
Neha Mathur

Written by

Neha Mathur

Neha is a digital marketing professional based in Gurugram, India. She has a Master’s degree from the University of Rajasthan with a specialization in Biotechnology in 2008. She has experience in pre-clinical research as part of her research project in The Department of Toxicology at the prestigious Central Drug Research Institute (CDRI), Lucknow, India. She also holds a certification in C++ programming.

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