Designing and Evaluating a Hierarchical Framework for Matching Food Outlets across Multi-sourced Geospatial Datasets: a Case Study of San Diego County.

Research on retail food environment (RFE) relies on data availability and accuracy. However, the discrepancies in RFE datasets may lead to imprecision when measuring association with health outcomes. In this research, we present a two-tier hierarchical point of interest (POI) matching framework to compare and triangulate food outlets across multiple geospatial data sources. Two matching parameters were used including the geodesic distance between businesses and the similarity of business names according to Levenshtein distance (LD) and Double Metaphone (DM). Sensitivity analysis was conducted to determine thresholds of matching parameters. Our Tier 1 matching used more restricted parameters to generate high confidence-matched POIs, whereas in Tier 2 we opted for relaxed matching parameters and applied a weighted multi-attribute model on the previously unmatched records. Our case study in San Diego County, California used government, commercial, and crowdsourced data and returned 20.2% matched records from Tier 1 and 18.6% matched from Tier 2. Our manual validation shows a 100% matching rate for Tier 1 and up to 30.6% for Tier 2. Matched and unmatched records from Tier 1 were further analyzed for spatial patterns and categorical differences. Our hierarchical POI matching framework generated highly confident food POIs by conflating datasets and identified some food POIs that are unique to specific data sources. Triangulating RFE data can reduce uncertain and invalid POI listings when representing food environment using multiple data sources. Studies investigating associations between food environment and health outcomes may benefit from improved quality of RFE.

Vehicle Localization Using Crowdsourced Data Collected on Urban Roads.

Vehicle localization using mounted sensors is an essential technology for various applications, including autonomous vehicles and road mapping. Achieving high positioning accuracy through the fusion of low-cost sensors is a topic of considerable interest. Recently, applications based on crowdsourced data from a large number of vehicles have received significant attention. Equipping standard vehicles with low-cost onboard sensors offers the advantage of collecting data from multiple drives over extensive road networks at a low operational cost. These vehicle trajectories and road observations can be utilized for traffic surveys, road inspections, and mapping. However, data obtained from low-cost devices are likely to be highly inaccurate. On urban roads, unlike highways, complex road structures and GNSS signal obstructions caused by buildings are common. This study proposes a reliable vehicle localization method using a large amount of crowdsourced data collected from urban roads. The proposed localization method is designed with consideration for the high inaccuracy of the data, the complexity of road structures, and the partial use of high-definition (HD) maps that account for environmental changes. The high inaccuracy of sensor data affects the reliability of localization. Therefore, the proposed method includes a reliability assessment of the localized vehicle poses. The performance of the proposed method was evaluated using data collected from buses operating in Seoul, Korea. The data used for the evaluation were collected 18 months after the creation of the HD maps.

Mobility Dynamics amid COVID-19 with a Case Study in Tennessee.

The year 2020 has marked the spread of a global pandemic, COVID-19, challenging many aspects of our daily lives. Different organizations have been involved in controlling this outbreak. The social distancing intervention is deemed to be the most effective policy in reducing face-to-face contact and slowing down the rate of infections. Stay-at-home and shelter-in-place orders have been implemented in different states and cities, affecting daily traffic patterns. Social distancing interventions and fear of the disease resulted in a traffic decline in cities and counties. However, after stay-at-home orders ended and some public places reopened, traffic gradually started to revert to pre-pandemic levels. It can be shown that counties have diverse patterns in the decline and recovery phases. This study analyzes county-level mobility change after the pandemic, explores the contributing factors, and identifies possible spatial heterogeneity. To this end, 95 counties in Tennessee have been selected as the study area to perform geographically weighted regressions (GWR) models. The results show that density on non-freeway roads, median household income, percent of unemployment, population density, percent of people over age 65, percent of people under age 18, percent of work from home, and mean time to work are significantly correlated with vehicle miles traveled change magnitude in both decline and recovery phases. Also, the GWR estimation captures the spatial heterogeneity and local variation in coefficients among counties. Finally, the results imply that the recovery phase could be estimated depending on the identified spatial attributes. The proposed model can help agencies and researchers estimate and manage decline and recovery based on spatial factors in similar events in the future.

Pavement Quality Evaluation Using Connected Vehicle Data.

Modern vehicles have extensive instrumentation that can be used to actively assess the condition of infrastructure such as pavement markings, signs, and pavement smoothness. Currently, pavement condition evaluations are performed by state and federal officials typically using the industry standard of the International Roughness Index (IRI) or visual inspections. This paper looks at the use of on-board sensors integrated in Original Equipment Manufacturer (OEM) connected vehicles to obtain crowdsource estimates of ride quality using the International Rough Index (IRI). This paper presents a case study where over 112 km (70 mi) of Interstate-65 in Indiana were assessed, utilizing both an inertial profiler and connected production vehicle data. By comparing the inertial profiler to crowdsourced connected vehicle data, there was a linear correlation with an R2 of 0.79 and a p-value of <0.001. Although there are no published standards for using connected vehicle roughness data to evaluate pavement quality, these results suggest that connected vehicle roughness data is a viable tool for network level monitoring of pavement quality.

Combining Participatory Mapping and Geospatial Analysis Techniques to Assess Wildfire Risk in Rural North Vietnam.

Participatory mapping (PM) is a valuable research tool for assessing fire risk, especially in regions where data are difficult to collect or inconsistent; in such areas, the integration between crowdsourced data and geospatial techniques plays a fundamental role in gathering more consistent and reliable information. This study combines a participatory (community-based) mapping approach with geospatial techniques to assess fire risk in Van Chan district, northern Vietnam, an area where the economy relies mainly on forestry activities. Local stakeholders designed a map of wildfires, which was modelled as a function of a set of physical and socio-economic variables. A fire-probability map of the district was obtained and compared with MODIS data (2000-2020). The results suggest that higher fire probability occurs in areas with lower human pressure, and they provide information on related socio-economic drivers that affect this phenomenon. This study highlights the importance of combining participatory approaches and geospatial techniques to assess fire dynamics and prevent wildfires in terms of understanding and predicting the risks. The involvement of local communities is fundamental to this innovative participatory approach with regard to better supporting decision-making and prevention actions and to developing fire control management guidelines.

Classifying and Mapping Cultural Ecosystem Services Using Artificial Intelligence and Social Media Data.

Quantifying and mapping cultural ecosystem services are complex because of their intangibility. Data from social media, such as geo-tagged photographs, has been proposed for mapping cultural use or appreciation of ecosystems. However, manual content analysis and classification of large numbers of photographs is time-consuming. The potential of deep learning for automating the analysis of crowdsourced social media content is still being explored in CES research. Here, we use a new deep learning model for automating the classification of natural and human elements relevant to CES from Flickr images. This approach applies a convolutional neural network architecture to analyze over 29,000 photographs from the Lithuanian coast and uses hierarchical clustering to group these photographs. The accuracy of the classification was assessed by comparison with manual classification. Over 37% of the photographs were taken for the landscape appreciation class, and 28% of the photographs were taken of nature, of animals or plants, which represent the nature appreciation class. The main clusters were identified in urban areas, more precisely in the main coastal cities of Lithuania. The distribution of the nature photographs was concentrated around particular natural attractions, and they were more likely to occur in parks and natural reserves with high levels of vegetation and animal cover. This approach that was developed for clustering the photographs was accurate and saved approximately 100 km of manual work. The method demonstrates how analyzing large numbers of digital photographs expands the analytical toolbox available to researchers and allows the quantification and mapping of CES at large geographical scales. Automated assessment and mapping of cultural ecosystem services could be used to inform urban planning and improve nature reserve management.

The Uncounted Casualties of a Hidden COVID-19 Epidemic in China: Cross-sectional Study on Deaths Related to Overwork.

BACKGROUND: During the COVID-19 response, nonclinical essential workers usually worked overtime and experienced significant work stress, which subsequently increased their risk of mortality due to cardiovascular diseases, stroke, and pre-existing conditions. Deaths on duty, including deaths due to overwork, during the COVID-19 response were usually reported on web-based platforms for public recognition and solidarity. Although no official statistics are available for these casualties, a list of on-duty deaths has been made publicly available on the web by crowdsourcing. OBJECTIVE: This study aims to understand the trends and characteristics of deaths related to overwork among the frontline nonclinical essential workers participating in nonpharmaceutical interventions during the first wave of COVID-19 in China. METHODS: Based on a web-based crowdsourced list of deaths on duty during the first wave of the COVID-19 response in China, we manually verified all overwork-related death records against the full-text web reports from credible sources. After excluding deaths caused by COVID-19 infection and accidents, a total of 340 deaths related to overwork among nonclinical essential workers were attributed to combatting the COVID-19 crisis. We coded the key characteristics of the deceased workers, including sex, age at death, location, causes of death, date of incidence, date of death, containment duties, working area, and occupation. The temporal and spatial correlations between deaths from overwork and COVID-19 cases in China were also examined using Pearson correlation coefficient. RESULTS: From January 20 to April 26, 2020, at least 340 nonclinical frontline workers in China were reported to have died as a result of overwork while combatting COVID-19. The weekly overwork mortality was positively correlated with weekly COVID-19 cases (r=0.79, P<.001). Two-thirds of deceased workers (230/340, 67.6%) were under 55 years old, and two major causes of deaths related to overwork were cardiovascular diseases (138/340, 40.6%) and cerebrovascular diseases (73/340, 21.5%). Outside of Hubei province, there were almost 2.5 times as many deaths caused by COVID-19-related overwork (308/340, 90.6%) than by COVID-19 itself (n=120). CONCLUSIONS: The high number of deaths related to overwork among nonclinical essential workers at the frontline of the COVID-19 epidemic is alarming. Policies for occupational health protection against work hazards should therefore be prioritized and enforced.

RSI-CB: A Large-Scale Remote Sensing Image Classification Benchmark Using Crowdsourced Data.

Image classification is a fundamental task in remote sensing image processing. In recent years, deep convolutional neural networks (DCNNs) have experienced significant breakthroughs in natural image recognition. The remote sensing field, however, is still lacking a large-scale benchmark similar to ImageNet. In this paper, we propose a remote sensing image classification benchmark (RSI-CB) based on massive, scalable, and diverse crowdsourced data. Using crowdsourced data, such as Open Street Map (OSM) data, ground objects in remote sensing images can be annotated effectively using points of interest, vector data from OSM, or other crowdsourced data. These annotated images can, then, be used in remote sensing image classification tasks. Based on this method, we construct a worldwide large-scale benchmark for remote sensing image classification. This benchmark has large-scale geographical distribution and large total image number. It contains six categories with 35 sub-classes of more than 24,000 images of size 256 × 256 pixels. This classification system of ground objects is defined according to the national standard of land-use classification in China and is inspired by the hierarchy mechanism of ImageNet. Finally, we conduct numerous experiments to compare RSI-CB with the SAT-4, SAT-6, and UC-Merced data sets. The experiments show that RSI-CB is more suitable as a benchmark for remote sensing image classification tasks than other benchmarks in the big data era and has many potential applications.

An Automated Machine-Learning Approach for Road Pothole Detection Using Smartphone Sensor Data.

Road surface monitoring and maintenance are essential for driving comfort, transport safety and preserving infrastructure integrity. Traditional road condition monitoring is regularly conducted by specially designed instrumented vehicles, which requires time and money and is only able to cover a limited proportion of the road network. In light of the ubiquitous use of smartphones, this paper proposes an automatic pothole detection system utilizing the built-in vibration sensors and global positioning system receivers in smartphones. We collected road condition data in a city using dedicated vehicles and smartphones with a purpose-built mobile application designed for this study. A series of processing methods were applied to the collected data, and features from different frequency domains were extracted, along with various machine-learning classifiers. The results indicated that features from the time and frequency domains outperformed other features for identifying potholes. Among the classifiers tested, the Random Forest method exhibited the best classification performance for potholes, with a precision of 88.5% and recall of 75%. Finally, we validated the proposed method using datasets generated from different road types and examined its universality and robustness.

A statistical model for helices with applications.

Motivated by a cutting edge problem related to the shape of α -helices in proteins, we formulate a parametric statistical model, which incorporates the cylindrical nature of the helix. Our focus is to detect a "kink," which is a drastic change in the axial direction of the helix. We propose a statistical model for the straight α -helix and derive the maximum likelihood estimation procedure. The cylinder is an accepted geometric model for α -helices, but our statistical formulation, for the first time, quantifies the uncertainty in atom positions around the cylinder. We propose a change point technique "Kink-Detector" to detect a kink location along the helix. Unlike classical change point problems, the change in direction of a helix depends on a simultaneous shift of multiple data points rather than a single data point, and is less straightforward. Our biological building block is crowdsourced data on straight and kinked helices; which has set a gold standard. We use this data to identify salient features to construct Kink-detector, test its performance and gain some insights. We find the performance of Kink-detector comparable to its computational competitor called "Kink-Finder." We highlight that identification of kinks by visual assessment can have limitations and Kink-detector may help in such cases. Further, an analysis of crowdsourced curved α -helices finds that Kink-detector is also effective in detecting moderate changes in axial directions.

The potential value of crowdsourced surveillance systems in supplementing sentinel influenza networks: the case of France.

IntroductionParticipatory surveillance systems provide rich crowdsourced data, profiling individuals and their health status at a given time. We explored the usefulness of data from GrippeNet.fr, a participatory surveillance system, to estimate influenza-related illness incidence in France. Methods: GrippeNet.fr is an online cohort since 2012 averaging ca. 5,000 weekly participants reporting signs/symptoms suggestive of influenza. GrippeNet.fr has flexible criteria to define influenza-related illness. Different case definitions based on reported signs/symptoms and inclusions of criteria accounting for individuals' reporting and participation were used to produce influenza-related illness incidence estimates, which were compared to those from sentinel networks. We focused on the 2012/13 and 2013/14 seasons when two sentinel networks, monitoring influenza-like-illness (ILI) and acute respiratory infections (ARI) existed in France. Results: GrippeNet.fr incidence estimates agreed well with official temporal trends, with a higher accuracy for ARI than ILI. The influenza epidemic peak was often anticipated by one week, despite irregular participation of individuals. The European Centre for Disease Prevention and Control ILI definition, commonly used by participatory surveillance in Europe, performed better in tracking ARI than ILI when applied to GrippeNet.fr data. Conclusion: Evaluation of the epidemic intensity from crowdsourced data requires epidemic and intensity threshold estimations from several consecutive seasons. The study provides a standardised analytical framework for crowdsourced surveillance showing high sensitivity in detecting influenza-related changes in the population. It contributes to improve the comparability of epidemics across seasons and with sentinel systems. In France, GrippeNet.fr may supplement the ILI sentinel network after ARI surveillance discontinuation in 2014.

Risk Prevention of Spreading Emerging Infectious Diseases Using a HybridCrowdsensing Paradigm, Optical Sensors, and Smartphone.

The risk of spreading diseases within (ad-hoc)crowds and the need to pervasively screen asymptomatic individuals to protect the population against emerging infectious diseases, request permanentcrowd surveillance., particularly in high-risk regions. Thecase of Ebola epidemic in West Africa in recent years has shown the need for pervasive screening. The trend today in diseases surveillance is consisting of epidemiological data collection about emerging infectious diseases using social media, wearable sensors systems, or mobile applications and data analysis. This approach presents various limitations. This paper proposes a novel approach for diseases monitoring and risk prevention of spreading infectious diseases. The proposed approach, aiming at overcoming the limitation of existing disease surveillance approaches, combines the hybrid crowdsensing paradigm with sensing individuals' bio-signals using optical sensors for monitoring any risks of spreading emerging infectious diseases in any (ad-hoc) crowds. A proof-of-concept has been performed using a drone armed with a cat s60 smartphone featuring a Forward Looking Infra-Red (FLIR) camera. According to the results of the conducted experiment, the concept has the potential to improve the conventional epidemiological data collection. The measurement is reliable, and the recorded data are valid. The measurement error rates are about 8%.

Participatory Syndromic Surveillance of Influenza in Europe.

The growth of digital communication technologies for public health is offering an unconventional means to engage the general public in monitoring community health. Here we present Influenzanet, a participatory system for the syndromic surveillance of influenza-like illness (ILI) in Europe. Through standardized online surveys, the system collects detailed profile information and self-reported symptoms volunteered by participants resident in the Influenzanet countries. Established in 2009, it now includes 10 countries representing more than half of the 28 member states of the European Union population. The experience of 7 influenza seasons illustrates how Influenzanet has become an adjunct to existing ILI surveillance networks, offering coherence across countries, inclusion of nonmedically attended ILI, flexibility in case definition, and facilitating individual-level epidemiological analyses generally not possible in standard systems. Having the sensitivity to timely detect substantial changes in population health, Influenzanet has the potential to become a viable instrument for a wide variety of applications in public health preparedness and control.

Sensing Urban Patterns with Antenna Mappings: The Case of Santiago, Chile.

Mobile data has allowed us to sense urban dynamics at scales and granularities not known before, helping urban planners to cope with urban growth. A frequently used kind of dataset are Call Detail Records (CDR), used by telecommunication operators for billing purposes. Being an already extracted and processed dataset, it is inexpensive and reliable. A common assumption with respect to geography when working with CDR data is that the position of a device is the same as the Base Transceiver Station (BTS) it is connected to. Because the city is divided into a square grid, or by coverage zones approximated by Voronoi tessellations, CDR network events are assigned to corresponding areas according to BTS position. This geolocation may suffer from non negligible error in almost all cases. In this paper we propose "Antenna Virtual Placement" (AVP), a method to geolocate mobile devices according to their connections to BTS, based on decoupling antennas from its corresponding BTS according to its physical configuration (height, downtilt, and azimuth). We use AVP applied to CDR data as input for two different tasks: first, from an individual perspective, what places are meaningful for them? And second, from a global perspective, how to cluster city areas to understand land use using floating population flows? For both tasks we propose methods that complement or improve prior work in the literature. Our proposed methods are simple, yet not trivial, and work with daily CDR data from the biggest telecommunication operator in Chile. We evaluate them in Santiago, the capital of Chile, with data from working days from June 2015. We find that: (1) AVP improves city coverage of CDR data by geolocating devices to more city areas than using standard methods; (2) we find important places (home and work) for a 10% of the sample using just daily information, and recreate the population distribution as well as commuting trips; (3) the daily rhythms of floating population allow to cluster areas of the city, and explain them from a land use perspective by finding signature points of interest from crowdsourced geographical information. These results have implications for the design of applications based on CDR data like recommendation of places and routes, retail store placement, and estimation of transport effects from pollution alerts.

The Cyborg Method: A Method to Identify Fraudulent Responses from Crowdsourced Data.

Crowdsourcing is an essential data collection method for psychological research. Concerns about the validity and quality of crowdsourced data persist, however. A recent documented increase in the number of invalid responses within crowdsourced data has highlighted the need for quality control measures. Although a number of approaches are recommended, few have been empirically evaluated. The present study evaluated a Cyborg Method that used automated evaluation of participant meta-data and a review of short answer responses. Two samples were recruited - in the first, the Cyborg Method was applied after data collection to gauge the extent to which invalid responses were collected when a priori quality controls were absent. In the second, the Cyborg Method was applied during data collection to determine if the method would proactively screen invalid responses. Results suggested that Cyborg Method identified a substantial portion of invalid responses and both automated and human evaluation components was necessary. Furthermore, the Cyborg Method could be applied proactively to screen invalid responses and substantially reduced the per participant cost of data collection. These results suggest that the Cyborg Method is a promising means by which to collect high quality crowdsourced data.

Crowdsourced data as a strategic approach to include the human dimension in outdoor environmental quality assessments.

Outdoor environments extend living spaces as venues for various activities. Comfortable open public spaces can positively impact citizens' health and well-being, thereby improving the livability and resilience of cities. Considering the visitors' perception of these environments in comfort studies is crucial for ensuring their well-being and promoting the use of these spaces. However, traditional survey methods may be time- and resource-consuming to gather significant sample sizes, usually focusing on selected homogeneous samples. Crowdsourced data, then, has emerged as an alternative for assessing human perception, as it eases the collection of subjective feedback and potentially amplifies impact and inclusivity. This study presents a strategic approach for analyzing publicly available and willingly reported crowdsourced data from a digital mapping platform in outdoor comfort evaluations, aiming to verify whether these data are informative regarding environmental quality perception and to identify the environmental factors that people are most sensitive to. Urban parks located in New York City served as a case study. A multi-source, interdisciplinary information framework combined crowdsourced reviews with environmental data used to determine prevailing thermal conditions. Overall perception of parks was well-rated, revealing that their attractions and activities are probably the most appealing characteristics for park attendance. Regarding environmental perception, acoustic and thermal factors are clearly the most influential. Acoustics were well-rated, while the main aspect regarding the thermal domain is the recognition of shading as a mitigator for hot conditions. Environmental data provided complementary insights, particularly concerning the range of thermal sensations experienced in urban parks. The findings confirm that willingly reported crowdsourced data can provide valuable insights into urban crowd environmental perception, presenting a potentially suitable and effective method to include the human perspective in environmental quality assessments, as well as to evaluate and predict environmental-related risks.

Naturalistic psilocybin use is associated with persisting improvements in mental health and wellbeing: results from a prospective, longitudinal survey.

Introduction: The classic psychedelic psilocybin, found in some mushroom species, has received renewed interest in clinical research, showing potential mental health benefits in preliminary trials. Naturalistic use of psilocybin outside of research settings has increased in recent years, though data on the public health impact of such use remain limited. Methods: This prospective, longitudinal study comprised six sequential automated web-based surveys that collected data from adults planning to take psilocybin outside clinical research: at time of consent, 2 weeks before, the day before, 1-3 days after, 2-4 weeks after, and 2-3 months after psilocybin use. Results: A sample of 2,833 respondents completed all baseline assessments approximately 2 weeks before psilocybin use, 1,182 completed the 2-4 week post-use survey, and 657 completed the final follow-up survey 2-3 months after psilocybin use. Participants were primarily college-educated White men residing in the United States with a prior history of psychedelic use; mean age = 40 years. Participants primarily used dried psilocybin mushrooms (mean dose = 3.1 grams) for "self-exploration" purposes. Prospective longitudinal data collected before and after a planned psilocybin experience on average showed persisting reductions in anxiety, depression, and alcohol misuse, increased cognitive flexibility, emotion regulation, spiritual wellbeing, and extraversion, and reduced neuroticism and burnout after psilocybin use. However, a minority of participants (11% at 2-4 weeks and 7% at 2-3 months) reported persisting negative effects after psilocybin use (e.g., mood fluctuations, depressive symptoms). Discussion: Results from this study, the largest prospective survey of naturalistic psilocybin use to date, support the potential for psilocybin to produce lasting improvements in mental health symptoms and general wellbeing.

Urban Versus Lake Impacts on Heat Stress and Its Disparities in a Shoreline City.

Shoreline cities are influenced by both urban-scale processes and land-water interactions, with consequences on heat exposure and its disparities. Heat exposure studies over these cities have focused on air and skin temperature, even though moisture advection from water bodies can also modulate heat stress. Here, using an ensemble of model simulations covering Chicago, we find that Lake Michigan strongly reduces heat exposure (2.75°C reduction in maximum average air temperature in Chicago) and heat stress (maximum average wet bulb globe temperature reduced by 0.86°C) during the day, while urbanization enhances them at night (2.75 and 1.57°C increases in minimum average air and wet bulb globe temperature, respectively). We also demonstrate that urban and lake impacts on temperature (particularly skin temperature), including their extremes, and lake-to-land gradients, are stronger than the corresponding impacts on heat stress, partly due to humidity-related feedback. Likewise, environmental disparities across community areas in Chicago seen for skin temperature are much higher (1.29°C increase for maximum average values per $10,000 higher median income per capita) than disparities in air temperature (0.50°C increase) and wet bulb globe temperature (0.23°C increase). The results call for consistent use of physiologically relevant heat exposure metrics to accurately capture the public health implications of urbanization.

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.

Seasonal influenza vaccination in pharmacy in France: description and determinants of the vaccinated at-risk population using this service, 1 year after implementation.

OBJECTIVES: Vaccination of the at-risk population against influenza by pharmacists was widely implemented in France in 2019. Only little data are available about the population using this service. We have explored the characteristics and determinants of the at-risk population vaccinated in pharmacy through a web-based cohort during the 2019-20 winter season. METHODS: This study is based on the data of the profile survey of at-risk over-18 vaccinated participants of the cohort GrippeNet.fr, for the 2019-20 winter season. Population characteristics were described using the inclusion questionnaire data. Factors associated with pharmacy influenza vaccination were analysed through a logistic regression model. KEY FINDINGS: In total, 3144 people were included in the study. 50.2% (N = 1577) of them were women and 65.5% (N = 2060) were over 65 years old. 29.5% (N = 928) of participants were vaccinated in pharmacy. 73.1% (N = 678) of participants vaccinated in pharmacy were over 65 years old and 46.6% (N = 432) had a treatment for one or more chronic disease. Factors positively associated with being vaccinated by a pharmacist were: being a man (OR = 1.25, 95% confidence interval [1.06-1.47]), being over 65 years old (OR = 1.97 [1.49-2.63]), living in a test region (OR = 1.62 [1.29-2.02] and 1.72 [1.43-2.07] depending on the year of the implementation of the experimentation) and being vaccinated against influenza in 2018/2019 (OR = 1.71 [1.32-2.21]). Factors negatively associated were: taking a chronic treatment (OR = 0.83 [0.70-0.97]), and living alone (OR = 1.40 [1.17-1.67] and being in contact with sick people (OR = 0.68 [0.50-0.93]). CONCLUSIONS: This study confirmed some factors associated with pharmacy influenza vaccination and feeds the debate on other uncertain factors. These findings can support public health authorities' willingness to enhance pharmacists' involvement in the future country-wide vaccination campaign. Our study also highlights the necessity to further investigate the impact of this measure in a few years.