Vaccine effectiveness against emerging COVID-19 variants using digital health data.

BACKGROUND: Participatory surveillance of self-reported symptoms and vaccination status can be used to supplement traditional public health surveillance and provide insights into vaccine effectiveness and changes in the symptoms produced by an infectious disease. The University of Maryland COVID Trends and Impact Survey provides an example of participatory surveillance that leveraged Facebook's active user base to provide self-reported symptom and vaccination data in near real-time. METHODS: Here, we develop a methodology for identifying changes in vaccine effectiveness and COVID-19 symptomatology using the University of Maryland COVID Trends and Impact Survey data from three middle-income countries (Guatemala, Mexico, and South Africa). We implement conditional logistic regression to develop estimates of vaccine effectiveness conditioned on the prevalence of various definitions of self-reported COVID-like illness in lieu of confirmed diagnostic test results. RESULTS: We highlight a reduction in vaccine effectiveness during Omicron-dominated waves of infections when compared to periods dominated by the Delta variant (median change across COVID-like illness definitions: -0.40, IQR[-0.45, -0.35]. Further, we identify a shift in COVID-19 symptomatology towards upper respiratory type symptoms (i.e., cough and sore throat) during Omicron periods of infections. Stratifying COVID-like illness by the National Institutes of Health's (NIH) description of mild and severe COVID-19 symptoms reveals a similar level of vaccine protection across different levels of COVID-19 severity during the Omicron period. CONCLUSIONS: Participatory surveillance data alongside methodologies described in this study are particularly useful for resource-constrained settings where diagnostic testing results may be delayed or limited.

Surveys that are sent out to users of social media can be used to supplement traditional methods to monitor the spread of infectious diseases. This has the potential to be particularly useful in areas where other data is unavailable, such as areas with less surveillance of infectious disease prevalence and access to infectious disease diagnostics. We used data from a survey available to users of the social media platform Facebook to collect information about any potential symptoms of COVID-19 infection and vaccines received during the COVID-19 pandemic. We found a potential reduction in vaccine effectiveness and change in symptoms when the Omicron variant was known to be circulating compared to the earlier Delta variant. This method could be adapted to monitor the spread of COVID-19 and other infectious diseases in the future, which might enable the impact of infectious diseases to be recognized more quickly.

Decreased Seasonal Influenza Rates Detected in a Crowdsourced Influenza-Like Illness Surveillance System During the COVID-19 Pandemic: Prospective Cohort Study.

BACKGROUND: Seasonal respiratory viruses had lower incidence during their 2019-2020 and 2020-2021 seasons, which overlapped with the COVID-19 pandemic. The widespread implementation of precautionary measures to prevent transmission of SARS-CoV-2 has been seen to also mitigate transmission of seasonal influenza. The COVID-19 pandemic also led to changes in care seeking and access. Participatory surveillance systems have historically captured mild illnesses that are often missed by surveillance systems that rely on encounters with a health care provider for detection. OBJECTIVE: This study aimed to assess if a crowdsourced syndromic surveillance system capable of detecting mild influenza-like illness (ILI) also captured the globally observed decrease in ILI in the 2019-2020 and 2020-2021 influenza seasons, concurrent with the COVID-19 pandemic. METHODS: Flu Near You (FNY) is a web-based participatory syndromic surveillance system that allows participants in the United States to report their health information using a brief weekly survey. Reminder emails are sent to registered FNY participants to report on their symptoms and the symptoms of household members. Guest participants may also report. ILI was defined as fever and sore throat or fever and cough. ILI rates were determined as the number of ILI reports over the total number of reports and assessed for the 2016-2017, 2017-2018, 2018-2019, 2019-2020, and 2020-2021 influenza seasons. Baseline season (2016-2017, 2017-2018, and 2018-2019) rates were compared to the 2019-2020 and 2020-2021 influenza seasons. Self-reported influenza diagnosis and vaccination status were captured and assessed as the total number of reported events over the total number of reports submitted. CIs for all proportions were calculated via a 1-sample test of proportions. RESULTS: ILI was detected in 3.8% (32,239/848,878) of participants in the baseline seasons (2016-2019), 2.58% (7418/287,909) in the 2019-2020 season, and 0.27% (546/201,079) in the 2020-2021 season. Both influenza seasons that overlapped with the COVID-19 pandemic had lower ILI rates than the baseline seasons. ILI decline was observed during the months with widespread implementation of COVID-19 precautions, starting in February 2020. Self-reported influenza diagnoses decreased from early 2020 through the influenza season. Self-reported influenza positivity among ILI cases varied over the observed time period. Self-reported influenza vaccination rates in FNY were high across all observed seasons. CONCLUSIONS: A decrease in ILI was detected in the crowdsourced FNY surveillance system during the 2019-2020 and 2020-2021 influenza seasons, mirroring trends observed in other influenza surveillance systems. Specifically, the months within seasons that overlapped with widespread pandemic precautions showed decreases in ILI and confirmed influenza. Concerns persist regarding respiratory pathogens re-emerging with changes to COVID-19 guidelines. Traditional surveillance is subject to changes in health care behaviors. Systems like FNY are uniquely situated to detect disease across disease severity and care seeking, providing key insights during public health emergencies.

COVID-19 Vaccine Acceptance and Uptake in Bangkok, Thailand: Cross-sectional Online Survey.

BACKGROUND: The third most severe COVID-19 wave in the middle of 2021 coincided with the dual challenges of limited vaccine supply and lagging acceptance in Bangkok, Thailand. Understanding of persistent vaccine hesitancy during the "608" campaign to vaccinate those aged over 60 years and 8 medical risk groups was needed. On-the-ground surveys place further demands on resources and are scale limited. We leveraged the University of Maryland COVID-19 Trends and Impact Survey (UMD-CTIS), a digital health survey conducted among daily Facebook user samples, to fill this need and inform regional vaccine rollout policy. OBJECTIVE: The aims of this study were to characterize COVID-19 vaccine hesitancy, frequent reasons for hesitancy, mitigating risk behaviors, and the most trusted sources of COVID-19 information through which to combat vaccine hesitancy in Bangkok, Thailand during the 608 vaccine campaign. METHODS: We analyzed 34,423 Bangkok UMD-CTIS responses between June and October 2021, coinciding with the third COVID-19 wave. Sampling consistency and representativeness of the UMD-CTIS respondents were evaluated by comparing distributions of demographics, 608 priority groups, and vaccine uptake over time with source population data. Estimates of vaccine hesitancy in Bangkok and 608 priority groups were tracked over time. Frequently cited hesitancy reasons and trusted information sources were identified according to the 608 group and degree of hesitancy. Kendall tau was used to test statistical associations between vaccine acceptance and vaccine hesitancy. RESULTS: The Bangkok UMD-CTIS respondents had similar demographics over weekly samples and compared to the Bangkok source population. Respondents self-reported fewer pre-existing health conditions compared to census data overall but had a similar prevalence of the important COVID-19 risk factor diabetes. UMD-CTIS vaccine uptake rose in parallel with national vaccination statistics, while vaccine hesitancy and degree of hesitancy declined (-7% hesitant per week). Concerns about vaccination side effects (2334/3883, 60.1%) and wanting to wait and see (2410/3883, 62.1%) were selected most frequently, while "not liking vaccines" (281/3883, 7.2%) and "religious objections" (52/3883, 1.3%) were selected least frequently. Greater vaccine acceptance was associated positively with wanting to "wait and see" and negatively with "don't believe I need (the vaccine)" (Kendall tau 0.21 and -0.22, respectively; adjusted P<.001). Scientists and health experts were most frequently cited as trusted COVID-19 information sources (13,600/14,033, 96.9%), even among vaccine hesitant respondents. CONCLUSIONS: Our findings provide policy and health experts with evidence that vaccine hesitancy was declining over the study timeframe. Hesitancy and trust analyses among the unvaccinated support Bangkok policy measures to address vaccine safety and efficacy concerns through health experts rather than government or religious officials. Large-scale surveys enabled by existing widespread digital networks offer an insightful minimal-infrastructure resource for informing region-specific health policy needs.

Use of At-Home COVID-19 Tests - United States, August 23, 2021-March 12, 2022.

COVID-19 testing provides information regarding exposure and transmission risks, guides preventative measures (e.g., if and when to start and end isolation and quarantine), identifies opportunities for appropriate treatments, and helps assess disease prevalence (1). At-home rapid COVID-19 antigen tests (at-home tests) are a convenient and accessible alternative to laboratory-based diagnostic nucleic acid amplification tests (NAATs) for SARS-CoV-2, the virus that causes COVID-19 (2-4). With the emergence of the SARS-CoV-2 B.1.617.2 (Delta) and B.1.1.529 (Omicron) variants in 2021, demand for at-home tests increased† (5). At-home tests are commonly used for school- or employer-mandated testing and for confirmation of SARS-CoV-2 infection in a COVID-19-like illness or following exposure (6). Mandated COVID-19 reporting requirements omit at-home tests, and there are no standard processes for test takers or manufacturers to share results with appropriate health officials (2). Therefore, with increased COVID-19 at-home test use, laboratory-based reporting systems might increasingly underreport the actual incidence of infection. Data from a cross-sectional, nonprobability-based online survey (August 23, 2021-March 12, 2022) of U.S. adults aged ≥18 years were used to estimate self-reported at-home test use over time, and by demographic characteristics, geography, symptoms/syndromes, and reasons for testing. From the Delta-predominant period (August 23-December 11, 2021) to the Omicron-predominant period (December 19, 2021-March 12, 2022)§ (7), at-home test use among respondents with self-reported COVID-19-like illness¶ more than tripled from 5.7% to 20.1%. The two most commonly reported reasons for testing among persons who used an at-home test were COVID-19 exposure (39.4%) and COVID-19-like symptoms (28.9%). At-home test use differed by race (e.g., self-identified as White [5.9%] versus self-identified as Black [2.8%]), age (adults aged 30-39 years [6.4%] versus adults aged ≥75 years [3.6%]), household income (>$150,000 [9.5%] versus $50,000-$74,999 [4.7%]), education (postgraduate degree [8.4%] versus high school or less [3.5%]), and geography (New England division [9.6%] versus West South Central division [3.7%]). COVID-19 testing, including at-home tests, along with prevention measures, such as quarantine and isolation when warranted, wearing a well-fitted mask when recommended after a positive test or known exposure, and staying up to date with vaccination,** can help reduce the spread of COVID-19. Further, providing reliable and low-cost or free at-home test kits to underserved populations with otherwise limited access to COVID-19 testing could assist with continued prevention efforts.

Quantifying the effectiveness of betaherpesvirus-vectored transmissible vaccines.

Transmissible vaccines have the potential to revolutionize how zoonotic pathogens are controlled within wildlife reservoirs. A key challenge that must be overcome is identifying viral vectors that can rapidly spread immunity through a reservoir population. Because they are broadly distributed taxonomically, species specific, and stable to genetic manipulation, betaherpesviruses are leading candidates for use as transmissible vaccine vectors. Here we evaluate the likely effectiveness of betaherpesvirus-vectored transmissible vaccines by developing and parameterizing a mathematical model using data from captive and free-living mouse populations infected with murine cytomegalovirus (MCMV). Simulations of our parameterized model demonstrate rapid and effective control for a range of pathogens, with pathogen elimination frequently occurring within a year of vaccine introduction. Our results also suggest, however, that the effectiveness of transmissible vaccines may vary across reservoir populations and with respect to the specific vector strain used to construct the vaccine.

Global monitoring of the impact of the COVID-19 pandemic through online surveys sampled from the Facebook user base.

Simultaneously tracking the global impact of COVID-19 is challenging because of regional variation in resources and reporting. Leveraging self-reported survey outcomes via an existing international social media network has the potential to provide standardized data streams to support monitoring and decision-making worldwide, in real time, and with limited local resources. The University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS), in partnership with Facebook, has invited daily cross-sectional samples from the social media platform's active users to participate in the survey since its launch on April 23, 2020. We analyzed UMD-CTIS survey data through December 20, 2020, from 31,142,582 responses representing 114 countries/territories weighted for nonresponse and adjusted to basic demographics. We show consistent respondent demographics over time for many countries/territories. Machine Learning models trained on national and pooled global data verified known symptom indicators. COVID-like illness (CLI) signals were correlated with government benchmark data. Importantly, the best benchmarked UMD-CTIS signal uses a single survey item whereby respondents report on CLI in their local community. In regions with strained health infrastructure but active social media users, we show it is possible to define COVID-19 impact trajectories using a remote platform independent of local government resources. This syndromic surveillance public health tool is the largest global health survey to date and, with brief participant engagement, can provide meaningful, timely insights into the global COVID-19 pandemic at a local scale.

Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus spillover in West Africa.

Forecasting the risk of pathogen spillover from reservoir populations of wild or domestic animals is essential for the effective deployment of interventions such as wildlife vaccination or culling. Due to the sporadic nature of spillover events and limited availability of data, developing and validating robust, spatially explicit, predictions is challenging. Recent efforts have begun to make progress in this direction by capitalizing on machine learning methodologies. An important weakness of existing approaches, however, is that they generally rely on combining human and reservoir infection data during the training process and thus conflate risk attributable to the prevalence of the pathogen in the reservoir population with the risk attributed to the realized rate of spillover into the human population. Because effective planning of interventions requires that these components of risk be disentangled, we developed a multi-layer machine learning framework that separates these processes. Our approach begins by training models to predict the geographic range of the primary reservoir and the subset of this range in which the pathogen occurs. The spillover risk predicted by the product of these reservoir specific models is then fit to data on realized patterns of historical spillover into the human population. The result is a geographically specific spillover risk forecast that can be easily decomposed and used to guide effective intervention. Applying our method to Lassa virus, a zoonotic pathogen that regularly spills over into the human population across West Africa, results in a model that explains a modest but statistically significant portion of geographic variation in historical patterns of spillover. When combined with a mechanistic mathematical model of infection dynamics, our spillover risk model predicts that 897,700 humans are infected by Lassa virus each year across West Africa, with Nigeria accounting for more than half of these human infections.

Transmissible vaccines in heterogeneous populations: Implications for vaccine design.

Transmissible vaccines may provide a promising solution for improving the control of infectious disease, particularly zoonotic pathogens with wildlife reservoirs. Although it is well known that heterogeneity in pathogen transmission impacts the spread of infectious disease, the effects of heterogeneity on vaccine transmission are largely unknown. Here we develop and analyze a mathematical model that quantifies the potential benefits of a transmissible vaccine in a population where transmission is heterogeneous between two subgroups. Our results demonstrate that the effect of heterogeneity on the benefit of vaccine transmission largely depends on the vaccine design and the pattern of vaccine administration across subgroups. Specifically, our results show that in most cases a transmissible vaccine designed to mirror the transmission of the pathogen is optimal. If the vaccination effort can be preferentially biased towards a given subgroup, a vaccine with a pattern of transmission opposite to that of the pathogen can become optimal in some cases. To better understand the consequences of heterogeneity on the effectiveness of a transmissible vaccine in the real world, we parameterized our model using data from Sin Nombre virus in deer mice (Peromyscus maniculatus). The results of this analysis reveal that when a vaccination campaign is limited in vaccine availability, a traditional vaccine must be administered primarily to males for the spread of Sin Nombre virus to be prevented. In contrast, a transmissible vaccine remains effective even when it cannot be preferentially administered to males.

Evaluating the promise of recombinant transmissible vaccines.

Transmissible vaccines have the potential to revolutionize infectious disease control by reducing the vaccination effort required to protect a population against a disease. Recent efforts to develop transmissible vaccines focus on recombinant transmissible vaccine designs (RTVs) because they pose reduced risk if intra-host evolution causes the vaccine to revert to its vector form. However, the shared antigenicity of the vaccine and vector may confer vaccine-immunity to hosts infected with the vector, thwarting the ability of the vaccine to spread through the population. We build a mathematical model to test whether a RTV can facilitate disease management in instances where reversion is likely to introduce the vector into the population or when the vector organism is already established in the host population, and the vector and vaccine share perfect cross-immunity. Our results show that a RTV can autonomously eradicate a pathogen, or protect a population from pathogen invasion, when cross-immunity between vaccine and vector is absent. If cross-immunity between vaccine and vector exists, however, our results show that a RTV can substantially reduce the vaccination effort necessary to control or eradicate a pathogen only when continuously augmented with direct manual vaccination. These results demonstrate that estimating the extent of cross-immunity between vector and vaccine is a critical step in RTV design, and that herpesvirus vectors showing facile reinfection and weak cross-immunity are promising.