Evaluating the Association between Routine Pneumococcal Vaccination and COVID-19 Severity among Older Adults in the United States (preprint)

BackgroundThe relationship between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Streptococcus pneumoniae remains uncertain. This study investigates the association between routine pneumococcal vaccination and the progression to severe COVID-19 outcomes in a cohort of older adults in the United States. MethodsOur cohort study includes adults aged 65 and older from a subset of adults covered by Medicare in the United States with a documented COVID-19 diagnosis. Logistic regression models were employed to assess the association between pneumococcal vaccination (13-valent conjugate vaccine [PCV13] and 23-valent pneumococcal polysaccharide vaccine [PPSV23]) and COVID-19 severity. ResultsAmong 90,070 Medicare enrollees with a COVID-19 diagnosis, 28,124 individuals exhibited severe respiratory symptoms or were admitted to the intensive care unit (ICU). The odds ratio (OR) for progression from non-severe symptoms to respiratory symptoms with or without ICU admission with prior PCV13 receipt was 0.91 (95% confidence interval [CI], 0.88, 0.93), the OR for progression from severe respiratory symptoms to ICU critical care with prior PCV13 receipt was 0.92 (95% CI, 0.88, 0.97), and the OR for progression from non-severe symptoms to ICU critical care with prior PCV13 receipt was 0.85 (95% CI, 0.81, 0.90). There was no association between PPSV23 received more than five years before the COVID-19 diagnosis and the COVID-19 outcomes. ConclusionsOverall, our findings indicate moderate to no association between PCV vaccination and COVID-19 severity.

Characterizing spatiotemporal trends in self-reported mask-wearing behavior in the United States (preprint)

Although face mask-wearing has been adopted throughout the U.S. to prevent the spread of COVID-19, reliable spatial estimates of mask-wearing through different phases of the pandemic do not yet exist. Using 8+ million survey responses, survey raking, and debiasing with ground-truth data on a different mitigation behavior, we generate fine-scale spatiotemporal estimates of mask-wearing across the U.S. from September 2020 to May 2021. We find that county-level masking behavior is spatially heterogeneous along an urbanrural gradient and moderately temporally heterogeneous. Because these survey data could be prone to social desirability and non-response biases, we evaluate whether a question about community mask-wearing could be a less biased alternative and find support for this social sensing approach to behavioral surveillance. Our work highlights the need to characterize public health behaviors at fine spatiotemporal scales to capture heterogeneities driving outbreak trajectories, and the role of behavioral big data to inform public health efforts.

Trends in non-COVID-19 hospitalizations prior to and during the COVID-19 pandemic period, United States, 2017-2021 (preprint)

COVID-19 pandemic-related shifts in healthcare utilization, in combination with trends in non-COVID-19 disease transmission and NPI use, had clear impacts on infectious and chronic disease hospitalization rates. Using a national healthcare billing database (C19RDB), we estimated the monthly incidence rate ratio of hospitalizations between March 2020 and June 2021 according to 19 ICD-10 diagnostic chapters and 189 subchapters. The majority of hospitalization causes showed an immediate decline in incidence during March 2020. Hospitalizations for diagnoses such as reproductive neoplasms, hypertension, and diabetes returned to pre-pandemic norms in incidence during late 2020 and early 2021, while others, like those for infectious respiratory disease, never returned to pre-pandemic norms. These results are crucial for contextualizing future research, particularly time series analyses, utilizing surveillance and hospitalization data for non-COVID-19 disease. Our assessment of subchapter level primary hospitalization codes offers new insight into trends among less frequent causes of hospitalization during the COVID-19 pandemic.

Disentangling the rhythms of human activity in the built environment for airborne transmission risk (preprint)

Since the outset of the COVID-19 pandemic, substantial public attention has focused on the role of seasonality in suppressing transmission. Misconceptions have relied on seasonal mediation of respiratory diseases driven solely by environmental variables. However, seasonality is expected to be driven by host social behavior, particularly in highly susceptible populations. A key gap in understanding the role of social behavior in respiratory disease seasonality is our incomplete understanding of the seasonality of indoor human activity. We leverage a novel data stream on human mobility to characterize activity in indoor versus outdoor environments in the United States. We use a mobile app-based location dataset encompassing over 5 million locations nationally. We classify locations as primarily indoor (e.g. stores, offices) or outdoor (e.g. playgrounds, farmers markets), disentangling location-specific visitor counts into indoor and outdoor, to arrive at a fine-scale measure of indoor to outdoor human activity across time and space. We find the proportion of indoor to outdoor activity during a baseline year is seasonal, peaking in winter months. The measure displays a latitudinal gradient with stronger seasonality at northern latitudes and an additional summer peak in southern latitudes. We statistically fit this baseline indoor-outdoor activity measure to inform incorporation of this complex empirical pattern into infectious disease dynamic models. However, we find that the disruption of the COVID-19 pandemic caused these patterns to shift significantly from baseline, and the empirical patterns are necessary to predict spatio-temporal heterogeneity in disease dynamics. Our work empirically characterizes, for the first time, the seasonality of human social behavior at a large-scale with high spatio-temporal resolution, and provides a parsimonious parameterization of seasonal behavior that can be included in infectious disease dynamics models. We provide critical evidence and methods necessary to inform the public health of seasonal and pandemic respiratory pathogens and improve our understanding of the relationship between the physical environment and infection risk in the context of global ecological change.

Characterizing the spatiotemporal heterogeneity of the COVID-19 vaccination landscape (preprint)

It is critical that we maximize vaccination coverage across the United States so that SARS-CoV-2 transmission can be suppressed, and we can sustain the recent reopening of the nation. Maximizing vaccination requires that we track vaccination patterns to measure the progress of the vaccination campaign and target locations that may be undervaccinated. To improve efforts to track and characterize COVID-19 vaccination progress in the United States, we integrate CDC and state-provided vaccination data, identifying and rectifying discrepancies between these data sources. We find that COVID-19 vaccination coverage in the US exhibits significant spatial heterogeneity at the county level and statistically identify spatial clusters of undervaccination, all with foci in the southern US. Vaccination progress at the county level is also variable; many counties stalled in vaccination into June 2021 and few recovered by July, with transmission of the Delta variant rapidly rising. Using a comparison with a mechanistic growth model fitted to our integrated data, we classify vaccination dynamics across time at the county scale. Our findings underline the importance of curating accurate, fine-scale vaccination data and the continued need for widespread vaccination in the US, especially in the wake of the highly transmissible Delta variant.

Protecting health-care professionals and workers (other than COVID-19 management facilities) from contamination during COVID-19 pandemic (March 26, 2020 - India)

People should stay at home - just as our PM has appealed on TV to the nation. This saves people plus health-care professionals from getting infected. Patients with ongoing illnesses should continue with usual treatment, cancel routine medical appointments (doctor visits and investigations), and contact their respective doctors (through email or digital platforms) only in case of new significant problems. Hospitals to divide staff into teams that work in tandem on alternate days or every 3rd day (based on the number of healthy staff available, inpatient beds, and patient workload). In hospitals/clinics, all health-care professionals to wear regular surgical masks, wear gloves, follow hand hygiene, and disinfect all surfaces in between patients. Maintain social distancing at all times - with patients and with colleagues. At least 6 ft of space in waiting area between patients, in the outpatient department between patient and doctor, and other places in between staff. If someone is contaminated/positive, the hospital/facility is NOT shut down or everyone quarantined. Only those with close contact are tested and isolated (definition of close contact used in Hong Kong was at least 15 min interaction at <6 feet without surgical mask). Less significant contacts to be self-monitored for symptoms and temperature recorded twice a day. Goggles, headgear, N95 particle filter masks, and double gloves to be reserved for interaction with COVID-19 positive cases or for procedures where respiratory aerosols might be generated - like intubation. For patients with symptoms suggestive of COVID-19 (low-grade fever, dry cough, cold, body ache, fatigue, diarrhea, and breathlessness) or family contact with COVID-19 positive case need to be dealt with as per the government directive, referred to dedicated COVID-19 health-care facility and treated by their separate team of health-care professionals.

Ignoring spatial heterogeneity in drivers of SARS-CoV-2 transmission in the US will impede sustained elimination (preprint)

To dissect the transmission dynamics of SARS-CoV-2 in the United States, we integrate parallel streams of high-resolution data on contact, mobility, seasonality, vaccination and seroprevalence within a metapopulation network. We find the COVID-19 pandemic in the US is characterized by a geographically localized mosaic of transmission along an urban-rural gradient, with many outbreaks sustained by between-county transmission. We detect a dynamic tension between the spatial scale of public health interventions and population susceptibility as pre-pandemic contact is resumed. Further, we identify regions rendered particularly at risk from invasion by variants of concern due to spatial connectivity. These findings emphasize the public health importance of accounting for the hierarchy of spatial scales in transmission and the heterogeneous impacts of mobility on the landscape of contagion risk.

Characterizing superspreading of SARS-CoV-2: from mechanism to measurement (preprint)

Superspreading is a ubiquitous feature of SARS-CoV-2 transmission dynamics, with a few primary infectors leading to a large proportion of secondary infections. Despite the superspreading events observed in previous coronavirus outbreaks, the mechanisms behind the phenomenon are still poorly understood. Here, we show that superspreading is largely driven by heterogeneity in contact behavior rather than heterogeneity in susceptibility or infectivity caused by biological factors. We find that highly heterogeneous contact behavior is required to produce the extreme superspreading estimated from recent COVID-19 outbreaks. However, we show that superspreading estimates are noisy and subject to biases in data collection and public health capacity, potentially leading to an overestimation of superspreading. These results suggest that superspreading for COVID-19 is substantial, but less than previously estimated. Our findings highlight the complexity inherent to quantitative measurement of epidemic dynamics and the necessity of robust theory to guide public health intervention.

Socio-economic disparities in social distancing during the COVID-19 pandemic in the United States (preprint)

Importance: Eliminating disparities in the burden of COVID-19 requires equitable access to control measures across socio-economic groups. Limited research on socio-economic differences in mobility hampers our ability to understand whether inequalities in social distancing are occurring during the SARS-CoV-2 pandemic. Objective: To assess how mobility patterns have varied across the United States during the COVID-19 pandemic, and identify associations with socio-economic factors of populations. Design, Setting, and Participants: We used anonymized mobility data from tens of millions of devices to measure the speed and depth of social distancing at the county level between February and May 2020. Using linear mixed models, we assessed the associations between social distancing and socio-economic variables, including the proportion of people below the poverty level, the proportion of Black people, the proportion of essential workers, and the population density. Main outcomes and Results: We find that the speed, depth, and duration of social distancing in the United States is heterogeneous. We particularly show that social distancing is slower and less intense in counties with higher proportions of people below the poverty level and essential workers; and in contrast, that social distancing is intense in counties with higher population densities and larger Black populations. Conclusions and relevance: Socio-economic inequalities appear to be associated with the levels of adoption of social distancing, potentially resulting in wide-ranging differences in the impact of COVID-19 in communities across the United States. This is likely to amplify existing health disparities, and needs to be addressed to ensure the success of ongoing pandemic mitigation efforts.

Assessing the interactions between COVID-19 and influenza in the United States (preprint)

The 2019-2020 influenza sentinel surveillance data exhibits unexpected trends. Typical influenza seasons have a small herald wave, followed by a decrease due to school closure during holidays, and then a main post-holiday peak that is significantly larger than the pre-holiday wave. During the 2019-2020 influenza season, influenza-like illness data in the United States appears to have a markedly lower main epidemic peak compared to what would be expected based on the pre-holiday peak. We hypothesize that the 2019-2020 influenza season does have a lower than expected burden and that this deflation is due to a behavioral or ecological interaction with COVID-19. We apply an intervention analysis to assess if this influenza season deviates from expectations, then we compare multiple hypothesized drivers of the decrease in influenza in a spatiotemporal regression model. Lastly, we develop a mechanistic metapopulation model, incorporating transmission reduction that scales with COVID-19 risk perception. We find that the 2019-2020 ILI season is smaller and decreases earlier than expected based on prior influenza seasons, and that the increase in COVID-19 risk perception is associated with this decrease. Additionally, we find that a 5% average reduction in transmission is sufficient to reproduce the observed flu dynamics. We propose that precautionary behaviors driven by COVID-19 risk perception or increased isolation driven by undetected COVID-19 spread dampened the influenza season. We suggest that when surveillance for a novel pathogen is limited, surveillance streams of co-circulating infections may provide a signal.

Health inequities in influenza transmission and surveillance (preprint)

The lower an individual’s socioeconomic position, the higher their risk of poor health in low-, middle-, and high-income settings alike. As health inequities grow, it is imperative that we develop an empirically-driven mechanistic understanding of the determinants of health disparities, and capture disease burden in at-risk populations to prevent exacerbation of disparities. Past work has been limited in data or scope and has thus fallen short of generalizable insights. Here, we integrate empirical data from observational studies and large-scale healthcare data with models to characterize the dynamics and spatial heterogeneity of health disparities in an infectious disease case study: influenza. We find that variation in social, behavioral, and physiological determinants exacerbates influenza epidemics, and that low socioeconomic status (SES) individuals disproportionately bear the burden of infection. We also identify geographical hotspots of influenza burden in low SES populations, much of which is overlooked in traditional influenza surveillance, and find that these differences are most predicted by variation in susceptibility and access to sickness absenteeism. Our results highlight that the effect of overlapping factors is synergistic and that reducing this intersectionality can significantly reduce inequities. Additionally, health disparities are expressed geographically, as targeting public health efforts spatially may be an efficient use of resources to abate inequities. The association between health and socioeconomic prosperity has a long history in the epidemiological literature; addressing health inequities in respiratory infectious disease burden is an important step towards social justice in public health, and ignoring them promises to pose a serious threat. Author summary Health inequities, or increased morbidity and mortality due to social factors, have been demonstrated for respiratory-transmitted infectious diseases, most recently evidenced by disparities in COVID-19 severe cases and deaths. Many potential causes of these inequities have been proposed, but they have not been compared, and we do not understand their mechanistic impacts. Our understanding of these issues is further hindered by epidemiological surveillance, which has been shown to overlook areas of low socioeconomic status. Here, we combine mechanistic and statistical modeling with high volume datasets to disentangle the drivers of respiratory transmitted infectious diseases, and to estimate locations where these health inequities are most severe, using influenza as a case study. We show that low socioeconomic individuals disproportionately bear the burden of influenza infection, and that all proposed factors are synergistic in causing these. Thus, public health intervention that targets any one of these drivers may alleviate other issues, as they are not mutually exclusive. Additionally, we provide geographical hotspots for improved surveillance. This work also demonstrates the imperative need to consider inequities and social drivers in data collection, epidemiological modeling, and public health work, as the most vulnerable populations may also be the most likely to be overlooked.

A first look at COVID-19 information and misinformation sharing on Twitter (preprint)

Since December 2019, COVID-19 has been spreading rapidly across the world. Not surprisingly, conversation about COVID-19 is also increasing. This article is a first look at the amount of conversation taking place on social media, specifically Twitter, with respect to COVID-19, the themes of discussion, where the discussion is emerging from, myths shared about the virus, and how much of it is connected to other high and low quality information on the Internet through shared URL links. Our preliminary findings suggest that a meaningful spatio-temporal relationship exists between information flow and new cases of COVID-19, and while discussions about myths and links to poor quality information exist, their presence is less dominant than other crisis specific themes. This research is a first step toward understanding social media conversation about COVID-19.