Evolving Face Mask Guidance During a Pandemic and Potential Harm to Public Perception: Infodemiology Study of Sentiment and Emotion on Twitter.

BACKGROUND: Throughout the COVID-19 pandemic, US Centers for Disease Control and Prevention policies on face mask use fluctuated. Understanding how public health communications evolve around key policy decisions may inform future decisions on preventative measures by aiding the design of communication strategies (eg, wording, timing, and channel) that ensure rapid dissemination and maximize both widespread adoption and sustained adherence. OBJECTIVE: We aimed to assess how sentiment on masks evolved surrounding 2 changes to mask guidelines: (1) the recommendation for mask use on April 3, 2020, and (2) the relaxation of mask use on May 13, 2021. METHODS: We applied an interrupted time series method to US Twitter data surrounding each guideline change. Outcomes were changes in the (1) proportion of positive, negative, and neutral tweets and (2) number of words within a tweet tagged with a given emotion (eg, trust). Results were compared to COVID-19 Twitter data without mask keywords for the same period. RESULTS: There were fewer neutral mask-related tweets in 2020 (ß=-3.94 percentage points, 95% CI -4.68 to -3.21; P<.001) and 2021 (ß=-8.74, 95% CI -9.31 to -8.17; P<.001). Following the April 3 recommendation (ß=.51, 95% CI .43-.59; P<.001) and May 13 relaxation (ß=3.43, 95% CI 1.61-5.26; P<.001), the percent of negative mask-related tweets increased. The quantity of trust-related terms decreased following the policy change on April 3 (ß=-.004, 95% CI -.004 to -.003; P<.001) and May 13 (ß=-.001, 95% CI -.002 to 0; P=.008). CONCLUSIONS: The US Twitter population responded negatively and with less trust following guideline shifts related to masking, regardless of whether the guidelines recommended or relaxed mask usage. Federal agencies should ensure that changes in public health recommendations are communicated concisely and rapidly.

Evaluating criminal justice reform during COVID-19: The need for a novel sentiment analysis package.

The health and safety of incarcerated persons and correctional personnel have been prominent in the U.S. news media discourse during the COVID-19 pandemic. Examining changing attitudes toward the health of the incarcerated population is imperative to better assess the extent to which the general public favors criminal justice reform. However, existing natural language processing lexicons that underlie current sentiment analysis (SA) algorithms may not perform adequately on news articles related to criminal justice due to contextual complexities. News discourse during the pandemic has highlighted the need for a novel SA lexicon and algorithm (i.e., an SA package) tailored for examining public health policy in the context of the criminal justice system. We analyzed the performance of existing SA packages on a corpus of news articles at the intersection of COVID-19 and criminal justice collected from state-level outlets between January and May 2020. Our results demonstrated that sentence sentiment scores provided by three popular SA packages can differ considerably from manually-curated ratings. This dissimilarity was especially pronounced when the text was more polarized, whether negatively or positively. A randomly selected set of 1,000 manually scored sentences, and the corresponding binary document term matrices, were used to train two new sentiment prediction algorithms (i.e., linear regression and random forest regression) to verify the performance of the manually-curated ratings. By better accounting for the unique context in which incarceration-related terminologies are used in news media, both of our proposed models outperformed all existing SA packages considered for comparison. Our findings suggest that there is a need to develop a novel lexicon, and potentially an accompanying algorithm, for analysis of text related to public health within the criminal justice system, as well as criminal justice more broadly.

Syndromic surveillance of respiratory infections during protracted conflict: experiences from northern Syria 2016-2021.

OBJECTIVE: Northern Syria faces a large burden of influenza-like illness (ILI) and severe acute respiratory illness (SARI). This study aimed to investigate the trends of Early Warning and Response Network (EWARN) reported ILI and SARI in northern Syria between 2016 and 2021 and the potential impact of SARS-CoV-2. METHODS: We extracted weekly EWARN data on ILI/ SARI and aggregated cases and consultations into 4-week intervals to calculate case positivity. We conducted a seasonal-trend decomposition to assess case trends in the presence of seasonal fluctuations. RESULTS: It was observed that 4-week aggregates of ILI cases (n = 5,942,012), SARI cases (n = 114,939), ILI case positivity, and SARI case positivity exhibited seasonal fluctuations with peaks in the winter months. ILI and SARI cases in individuals aged ≥5 years surpassed those in individuals aged <5 years in late 2019. ILI cases clustered primarily in Aleppo and Idlib, whereas SARI cases clustered in Aleppo, Idlib, Deir Ezzor, and Hassakeh. SARI cases increased sharply in 2021, corresponding with a severe SARS-CoV-2 wave, compared with the steady increase in ILI cases over time. CONCLUSION: Respiratory infections cause widespread morbidity and mortality throughout northern Syria, particularly with the emergence of SARS-CoV-2. Strengthened surveillance and access to testing and treatment are critical to manage outbreaks among conflict-affected populations.

Risk factors for increased COVID-19 case-fatality in the United States: A county-level analysis during the first wave.

The ongoing COVID-19 pandemic is causing significant morbidity and mortality across the US. In this ecological study, we identified county-level variables associated with the COVID-19 case-fatality rate (CFR) using publicly available datasets and a negative binomial generalized linear model. Variables associated with decreased CFR included a greater number of hospitals per 10,000 people, banning religious gatherings, a higher percentage of people living in mobile homes, and a higher percentage of uninsured people. Variables associated with increased CFR included a higher percentage of the population over age 65, a higher percentage of Black or African Americans, a higher asthma prevalence, and a greater number of hospitals in a county. By identifying factors that are associated with COVID-19 CFR in US counties, we hope to help officials target public health interventions and healthcare resources to locations that are at increased risk of COVID-19 fatalities.

How the Field of Infectious Diseases Can Leverage Digital Strategy and Social Media Use During a Pandemic.

Rapid information dissemination is critical in a world changing rapidly due to global threats. Ubiquitous internet access has created new methods of information dissemination that are rapid, far-reaching, and universally accessible. However, inaccuracies may accompany rapid information dissemination, and rigorous evaluation of primary data through various forms of peer review is crucial. In an era in which high-quality information can save lives, it is critical that infectious diseases specialists are well versed in digital strategy to effectively disseminate information to colleagues and the community and diminish voices spreading misinformation. In this study, we review how social media can be used for rapid dissemination of quality information, benefits and pitfalls of social media use, and general recommendations for developing a digital strategy as an infectious diseases specialist. We will describe how the Infectious Diseases Society of America has leveraged digital strategy and social media and how individuals can amplify these resources to disseminate information, provide clinical knowledge, community guidance, and build their own person brand. We conclude in providing guidance to infectious diseases specialists in aiming to build and preserve public trust, consider their audience and specific goals, and use social media to highlight the value of the field of infectious diseases.

Did Clostridioides difficile testing and infection rates change during the COVID-19 pandemic?

Testing for and incidence of Clostridioides difficile infection (CDI) was examined at a single center before and during the first surge of the COVID-19 pandemic. Incidence of CDI remained stable but testing statistically significantly decreased during the first surge despite an increase in antibiotic use. There were no new CDI-focused antimicrobial stewardship interventions introduced during this time.

Analyzing Digital Evidence From a Telemental Health Platform to Assess Complex Psychological Responses to the COVID-19 Pandemic: Content Analysis of Text Messages.

BACKGROUND: The novel COVID-19 disease has negatively impacted mortality, economic conditions, and mental health. These impacts are likely to continue after the COVID-19 pandemic ends. There are no methods for characterizing the mental health burden of the COVID-19 pandemic, and differentiating this burden from that of the prepandemic era. Accurate illness detection methods are critical for facilitating pandemic-related treatment and preventing the worsening of symptoms. OBJECTIVE: We aimed to identify major themes and symptom clusters in the SMS text messages that patients send to therapists. We assessed patients who were seeking treatment for pandemic-related distress on Talkspace, which is a popular telemental health platform. METHODS: We used a machine learning algorithm to identify patients' pandemic-related concerns, based on their SMS text messages in a large, digital mental health service platform (ie, Talkspace). This platform uses natural language processing methods to analyze unstructured therapy transcript data, in parallel with brief clinical assessment methods for analyzing depression and anxiety symptoms. RESULTS: Our results show a significant increase in the incidence of COVID-19-related intake anxiety symptoms (P<.001), but no significant differences in the incidence of intake depression symptoms (P=.79). During our transcript analyses, we identified terms that were related to 24 symptoms outside of those included in the diagnostic criteria for anxiety and depression. CONCLUSIONS: Our findings for Talkspace suggest that people who seek treatment during the pandemic experience more severe intake anxiety than they did before the COVID-19 outbreak. It is important to monitor the symptoms that we identified in this study and the symptoms of anxiety and depression, to fully understand the effects of the COVID-19 pandemic on mental health.

Modeling between-population variation in COVID-19 dynamics in Hubei, Lombardy, and New York City.

As the COVID-19 pandemic continues, formulating targeted policy interventions that are informed by differential severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission dynamics will be of vital importance to national and regional governments. We develop an individual-level model for SARS-CoV-2 transmission that accounts for location-dependent distributions of age, household structure, and comorbidities. We use these distributions together with age-stratified contact matrices to instantiate specific models for Hubei, China; Lombardy, Italy; and New York City, United States. Using data on reported deaths to obtain a posterior distribution over unknown parameters, we infer differences in the progression of the epidemic in the three locations. We also examine the role of transmission due to particular age groups on total infections and deaths. The effect of limiting contacts by a particular age group varies by location, indicating that strategies to reduce transmission should be tailored based on population-specific demography and social structure. These findings highlight the role of between-population variation in formulating policy interventions. Across the three populations, though, we find that targeted "salutary sheltering" by 50% of a single age group may substantially curtail transmission when combined with the adoption of physical distancing measures by the rest of the population.

Machine Learning Maps Research Needs in COVID-19 Literature.

As of August 2020, thousands of COVID-19 (coronavirus disease 2019) publications have been produced. Manual assessment of their scope is an overwhelming task, and shortcuts through metadata analysis (e.g., keywords) assume that studies are properly tagged. However, machine learning approaches can rapidly survey the actual text of publication abstracts to identify research overlap between COVID-19 and other coronaviruses, research hotspots, and areas warranting exploration. We propose a fast, scalable, and reusable framework to parse novel disease literature. When applied to the COVID-19 Open Research Dataset, dimensionality reduction suggests that COVID-19 studies to date are primarily clinical, modeling, or field based, in contrast to the vast quantity of laboratory-driven research for other (non-COVID-19) coronavirus diseases. Furthermore, topic modeling indicates that COVID-19 publications have focused on public health, outbreak reporting, clinical care, and testing for coronaviruses, as opposed to the more limited number focused on basic microbiology, including pathogenesis and transmission.