A Smartphone App for Real-Time Assessment of Malaria Prophylaxis Adverse Events.

Background: Growth of international travel to malarial areas over the last decades has contributed to more travelers taking malaria prophylaxis. Travel-related symptoms may be wrongly attributed to malaria prophylaxis and hinder compliance. Here, we aimed to assess the frequency of real-time reporting of symptoms by travelers following malaria prophylaxis using a smartphone app. Method: Adult international travelers included in this single-center study (Barcelona, Spain) used the smartphone Trip Doctor® app developed by our group for real-time tracking of symptoms and adherence to prophylaxis. Results: Six hundred four (n = 604) international travelers were included in the study; 74.3% (449) used the app daily, and for one-quarter of travelers, malaria prophylaxis was prescribed. Participants from the prophylaxis group traveled more to Africa (86.7% vs. 4.3%; p < 0.01) and to high travel medical risk countries (60.8% vs. 18%; p < 0.01) and reported more immunosuppression (30.8% vs. 23.1% p < 0.01). Regarding symptoms, no significant intergroup differences were observed, and no relationship was found between the total number of malarial pills taken and reported symptoms. Conclusions: In our cohort, the number of symptoms due to malaria prophylaxis was not significantly higher than in participants for whom prophylaxis was not prescribed, and the overall proportion of symptoms is higher compared with other studies.

Comparison of Influenza-Like Illness (ILI) incidence data from the novel LeCellPHIA participatory surveillance system with COVID-19 case count data, Lesotho, July 2020 - July 2021.

BACKGROUND: While laboratory testing for infectious diseases such as COVID-19 is the surveillance gold standard, it is not always feasible, particularly in settings where resources are scarce. In the small country of Lesotho, located in sub-Saharan Africa, COVID-19 testing has been limited, thus surveillance data available to local authorities are limited. The goal of this study was to compare a participatory influenza-like illness (ILI) surveillance system in Lesotho with COVID-19 case count data, and ultimately to determine whether the participatory surveillance system adequately estimates the case count data. METHODS: A nationally-representative sample was called on their mobile phones weekly to create an estimate of incidence of ILI between July 2020 and July 2021. Case counts from the website Our World in Data (OWID) were used as the gold standard to which our participatory surveillance data were compared. We calculated Spearman's and Pearson's correlation coefficients to compare the weekly incidence of ILI reports to COVID-19 case count data. RESULTS: Over course of the study period, an ILI symptom was reported 1,085 times via participatory surveillance for an average annual cumulative incidence of 45.7 per 100 people (95% Confidence Interval [CI]: 40.7 - 51.4). The cumulative incidence of reports of ILI symptoms was similar among males (46.5, 95% CI: 39.6 - 54.4) and females (45.1, 95% CI: 39.8 - 51.1). There was a slightly higher annual cumulative incidence of ILI among persons living in peri-urban (49.5, 95% CI: 31.7 - 77.3) and urban settings compared to rural areas. The January peak of the participatory surveillance system ILI estimates correlated significantly with the January peak of the COVID-19 case count data (Spearman's correlation coefficient = 0.49; P < 0.001) (Pearson's correlation coefficient = 0.67; P < 0.0001). CONCLUSIONS: The ILI trends captured by the participatory surveillance system in Lesotho mirrored trends of the COVID-19 case count data from Our World in Data. Public health practitioners in geographies that lack the resources to conduct direct surveillance of infectious diseases may be able to use cell phone-based data collection to monitor trends.

Enhanced passive surveillance for early detection of African and classical swine fevers.

African swine fever (ASF) and classical swine fever (CSF) are transboundary animal diseases (TADs) of pigs. Much effort and resources are regularly put into preventing these diseases' introduction in free areas. Passive surveillance activities bring the highest chances for the early detection of TAD incursions because they are routinely and widely conducted at farms, and because these activities focus on the time between introduction and when the first sample is sent for diagnostic testing. The authors proposed the implementation of an enhanced passive surveillance (EPS) protocol based on collecting data through participatory surveillance actions using an objective and adaptable scoring system to aid the early detection of ASF or CSF at the farm level. The protocol was applied in two commercial pig farms for ten weeks in the Dominican Republic, which is a CSF- and ASF-infected country. This study was a proof of concept, based on the EPS protocol to aid detection of substantial variations in the risk score triggering testing. One of the followed farms had score variation, which triggered testing of the animals, although the test results were negative. The study enables assessment of some of the weaknesses associated with passive surveillance and provides lessons applicable to the problem. Results demonstrate the potential for overcoming some issues preventing the broad application of EPS protocols and suggest that standardised approaches may contribute to the early detection of CSF and ASF introductions.

La peste porcine africaine (PPA) et la peste porcine classique (PPC) sont des maladies animales transfrontalières touchant les porcs. De nombreux efforts et ressources sont régulièrement alloués pour prévenir l'introduction de ces maladies dans des zones indemnes. Les activités de surveillance passive offrent les meilleures perspectives de détection précoce des incursions de maladies animales transfrontalières parce qu'elles sont menées de manière systématique et exhaustive dans les élevages, et parce qu'elles se concentrent sur la période entre l'introduction de la maladie et le moment où le premier échantillon est envoyé au laboratoire pour analyse. Les auteurs proposent la mise en oeuvre d'un protocole de surveillance passive renforcée fondé sur la collecte de données via des actions de surveillance participative utilisant un système de notation objectif et adaptable, en vue d'une détection précoce de la PPA et de la PPC dans les élevages. Ce protocole a été appliqué en République dominicaine, pays infecté par la PPA et la PPC, dans deux élevages porcins commerciaux pendant dix semaines. Cette étude était destinée à valider le principe de la méthode et se fondait sur le protocole de surveillance passive renforcée pour mieux détecter les variations substantielles de la note de risque qui conduisent à tester les animaux. L'un des élevages suivis a présenté une variation de cette note, ce qui a conduit à tester les animaux mais les tests se sont révélés négatifs. L'étude permet d'évaluer certaines des faiblesses associées à la surveillance passive et apporte des enseignements applicables à ce problème. Les résultats illustrent le potentiel de l'approche à surmonter certaines des problématiques empêchant l'application extensive des protocoles de surveillance passive renforcée. Ils suggèrent également que des approches normalisées pourraient contribuer à la détection précoce des cas d'introduction de la PPC et de la PPA.

La peste porcina africana (PPA) y la peste porcina clásica (PPC) son enfermedades animales transfronterizas que afectan al cerdo. Periódicamente se dedican grandes esfuerzos y cuantiosos recursos a evitar que estas enfermedades penetren en zonas que están exentas de ellas. Las actividades de vigilancia pasiva son las más eficaces para detectar con prontitud toda incursión de enfermedades animales transfronterizas, no solo por la regularidad y amplitud con que se llevan a cabo en las explotaciones, sino también porque inciden específicamente en el intervalo entre la penetración de una enfermedad y el momento en que se envía la primera muestra para que sea sometida a pruebas de diagnóstico. Los autores propusieron que se aplicara un protocolo de vigilancia pasiva reforzada que reposaba en la obtención de datos mediante actividades de vigilancia participativa, empleando para ello un sistema objetivo y adaptable de puntuación que ayudaba a detectar con prontitud la presencia en las explotaciones de PPA o PPC. Dicho protocolo fue aplicado a lo largo de diez semanas en dos explotaciones porcinas industriales de la República Dominicana, país en el que ambas infecciones están presentes. El estudio, que sirvió para poner a prueba la idea, pasaba por la aplicación del protocolo de vigilancia pasiva reforzada para ayudar a detectar variaciones sustanciales de la puntuación del nivel de riesgo que activa la realización de pruebas. En una de las explotaciones estudiadas se produjo una variación de la puntuación, cosa que activó la realización de pruebas en los animales, aunque estas arrojaron resultado negativo. El estudio aquí descrito permite evaluar algunos de los puntos débiles de la vigilancia pasiva y extraer enseñanzas aplicables al problema. Los resultados demuestran que es posible salvar algunas de las dificultades que impiden la aplicación generalizada de protocolos de vigilancia pasiva reforzada y dejan pensar que quizá el uso de planteamientos normalizados pueda ayudar a detectar con prontitud los casos de penetración de PPC o PPA.

A participatory surveillance of marsh deer (Blastocerus dichotomus) morbidity and mortality in Argentina: first results.

BACKGROUND: In an era of unprecedented socio-ecological changes, managing wildlife health demands high-quality data collection and the engagement of local communities. Blastocerus dichotomus, the largest South American deer, is Vulnerable to extinction mainly due to habitat loss. Diseases have been recognised as a potential threat, and winter mortality has been historically described in marsh deer populations from Argentina. Field difficulties have, however, prevented in-depth studies of their health status. RESULTS: Between May 2014 and April 2017, we investigated marsh deer morbidity and mortality in the two largest populations in Argentina. We collected data by means of a passive surveillance system that involved a network of researchers, field partners (veterinarians, park rangers, and local community), and decision makers. We sampled marsh deer during as well as outside mortality events. A total of 44 marsh deer with different body condition scores were evaluated. We obtained haematology and biochemistry values from animals with good body condition score. Marsh deer with poor body condition had a high burden of the ticks Amblyomma triste and Rhipicephalus microplus. Vector-borne agents such as Theileria cervi, Trypanosoma theileri, Trypanosoma evansi, Ehrlichia chaffeensis, Anaplasma platys, Anaplasma odocoilei, Anaplasma marginale, and Candidatus Anaplasma boleense were also found. Haemonchus spp., Ostertagia spp., and Trichostrongylus spp. were the most frequent gastrointestinal parasites in deer with poor body condition. A Multiple Correspondence Analysis reinforced a possible association of winter period with lower body score condition, high tick loads, infection with E. chaffeensis, and presence of harmful gastrointestinal parasites. CONCLUSIONS: Our approach allowed the establishment of a participatory surveillance network of marsh deer morbidity and mortality in Argentina. We report and analyse the first data obtained opportunistically within the framework of this network, providing information on the infectious and parasitic agents in marsh deer populations. The occurrence of Fasciola hepatica and Leptospira interrogans serovar pyrogenes is reported for the first time in wild marsh deer from Argentina. Our data will be useful to improve the interpretation of future mortality events. The field implementation of a surveillance network is key to a holistic approach to wildlife diseases.

Utilizing syndromic surveillance data for estimating levels of influenza circulation.

The availability of weekly Web-based participatory surveillance data on self-reported influenza-like illness (ILI), defined here as self-reported fever and cough/sore throat, over several influenza seasons allows for estimation of the incidence of influenza infection in population cohorts. We demonstrate this using syndromic data reported through the Influenzanet surveillance platform in the Netherlands. We used the 2011-2012 influenza season, a low-incidence season that began late, to assess the baseline rates of self-reported ILI during periods of low influenza circulation, and we used ILI rates above that baseline level from the 2012-1013 season, a major influenza season, to estimate influenza attack rates for that period. The latter conversion required estimates of age-specific probabilities of self-reported ILI given influenza (Flu) infection (P(ILI | Flu)), which were obtained from separate data (extracted from Hong Kong, China, household studies). For the 2012-2013 influenza season in the Netherlands, we estimated combined influenza A/B attack rates of 29.2% (95% credible interval (CI): 21.6, 37.9) among survey participants aged 20-49 years, 28.3% (95% CI: 20.7, 36.8) among participants aged 50-60 years, and 5.9% (95% CI: 0.4, 11.8) among participants aged ≥61 years. Estimates of influenza attack rates can be obtained in other settings using analogous, multiseason surveillance data on self-reported ILI together with separate, context-specific estimates of P(ILI | Flu).

Public health for the people: participatory infectious disease surveillance in the digital age.

The 21(st) century has seen the rise of Internet-based participatory surveillance systems for infectious diseases. These systems capture voluntarily submitted symptom data from the general public and can aggregate and communicate that data in near real-time. We reviewed participatory surveillance systems currently running in 13 different countries. These systems have a growing evidence base showing a high degree of accuracy and increased sensitivity and timeliness relative to traditional healthcare-based systems. They have also proven useful for assessing risk factors, vaccine effectiveness, and patterns of healthcare utilization while being less expensive, more flexible, and more scalable than traditional systems. Nonetheless, they present important challenges including biases associated with the population that chooses to participate, difficulty in adjusting for confounders, and limited specificity because of reliance only on syndromic definitions of disease limits. Overall, participatory disease surveillance data provides unique disease information that is not available through traditional surveillance sources.

Determinants of follow-up participation in the Internet-based European influenza surveillance platform Influenzanet.

BACKGROUND: "Influenzanet" is a network of Internet-based platforms aimed at collecting real-time data for influenza surveillance in several European countries. More than 30,000 European volunteers participate every year in the study, representing one of the largest existing Internet-based multicenter cohorts. Each week during the influenza season, participants are asked to report their symptoms (if any) along with a set of additional questions. OBJECTIVE: Focusing on the first influenza season of 2011-12, when the Influenzanet system was completely harmonized within a common framework in Sweden, the United Kingdom, the Netherlands, Belgium, France, Italy, and Portugal, we investigated the propensity of users to regularly come back to the platform to provide information about their health status. Our purpose was to investigate demographic and behavioral factors associated with participation in follow-up. METHODS: By means of a multilevel analysis, we evaluated the association between regular participation during the season and sociodemographic and behavioral characteristics as measured by a background questionnaire completed by participants on registration. RESULTS: We found that lower participation in follow-up was associated with lower educational status (odds ratio [OR] 0.80, 95% CI 0.75-0.85), smoking (OR 0.64, 95% CI 0.59-0.70), younger age (OR ranging from 0.30, 95% CI 0.26-0.33 to 0.70, 95% CI 0.64-0.77), not being vaccinated against seasonal influenza (OR 0.77, 95% CI 0.72-0.84), and living in a household with children (OR 0.69, 95% CI 0.65-0.74). Most of these results hold when single countries are analyzed separately. CONCLUSIONS: Given the opportunistic enrollment of self-selected volunteers in the Influenzanet study, we have investigated how sociodemographic and behavioral characteristics may be associated with follow-up participation in the Influenzanet cohort. The study described in this paper shows that, overall, the most important determinants of participation are related to education and lifestyle: smoking, lower education level, younger age, people living with children, and people who have not been vaccinated against seasonal influenza tend to have a lower participation in follow-up. Despite the cross-country variation, the main findings are similar in the different national cohorts, and indeed the results are found to be valid also when performing a single-country analysis. Differences between countries do not seem to play a crucial role in determining the factors associated with participation in follow-up.

Active Participatory Regional Surveillance for Notifiable Swine Pathogens.

We evaluated an active participatory design for the regional surveillance of notifiable swine pathogens based on testing 10 samples collected by farm personnel in each participating farm. To evaluate the performance of the design, public domain software was used to simulate the introduction and spread of a pathogen among 17,521 farms in a geographic region of 1,615,246 km2. Using the simulated pathogen spread data, the probability of detecting ≥ 1 positive farms in the region was estimated as a function of the percent of participating farms (20%, 40%, 60%, 80%, 100%), farm-level detection probability (10%, 20%, 30%, 40%, 50%), and regional farm-level prevalence. At 0.1% prevalence (18 positive farms among 17,521 farms) and a farm-level detection probability of 30%, the participatory surveillance design achieved 67%, 90%, and 97% probability of detecting ≥ 1 positive farms in the region when producer participation was 20%, 40%, and 60%, respectively. The cost analysis assumed that 10 individual pig samples per farm would be pooled into 2 samples (5 pigs each) for testing. Depending on the specimen collected (serum or swab sample) and test format (nucleic acid or antibody detection), the cost per round of sampling ranged from EUR 0.017 to EUR 0.032 (USD 0.017 to USD 0.034) per pig in the region. Thus, the analysis suggested that an active regional participatory surveillance design could achieve detection at low prevalence and at a sustainable cost.

Population Behavior Changes Underlying Phasic Shifts of SARS-CoV-2 Exposure Settings Across 3 Omicron Epidemic Waves in Hong Kong: Prospective Cohort Study.

BACKGROUND: Exposure risk was shown to have affected individual susceptibility and the epidemic spread of COVID-19. The dynamics of risk by and across exposure settings alongside the variations following the implementation of social distancing interventions are understudied. OBJECTIVE: This study aims to examine the population's trajectory of exposure risk in different settings and its association with SARS-CoV-2 infection across 3 consecutive Omicron epidemic waves in Hong Kong. METHODS: From March to June 2022, invitation letters were posted to 41,132 randomly selected residential addresses for the recruitment of households into a prospective population cohort. Through web-based monthly surveys coupled with email reminders, a representative from each enrolled household self-reported incidents of SARS-CoV-2 infections, COVID-19 vaccination uptake, their activity pattern in the workplace, and daily and social settings in the preceding month. As a proxy of their exposure risk, the reported activity trend in each setting was differentiated into trajectories based on latent class growth analyses. The associations of different trajectories of SARS-CoV-2 infection overall and by Omicron wave (wave 1: February-April; wave 2: May-September; wave 3: October-December) in 2022 were evaluated by using Cox proportional hazards models and Kaplan-Meier analysis. RESULTS: In total, 33,501 monthly responses in the observation period of February-December 2022 were collected from 5321 individuals, with 41.7% (2221/5321) being male and a median age of 46 (IQR 34-57) years. Against an expanding COVID-19 vaccination coverage from 81.9% to 95.9% for 2 doses and 20% to 77.7% for 3 doses, the cumulative incidence of SARS-CoV-2 infection escalated from <0.2% to 25.3%, 32.4%, and 43.8% by the end of waves 1, 2, and 3, respectively. Throughout February-December 2022, 52.2% (647/1240) of participants had worked regularly on-site, 28.7% (356/1240) worked remotely, and 19.1% (237/1240) showed an assorted pattern. For daily and social settings, 4 and 5 trajectories were identified, respectively, with 11.5% (142/1240) and 14.6% (181/1240) of the participants gauged to have a high exposure risk. Compared to remote working, working regularly on-site (adjusted hazard ratio [aHR] 1.47, 95% CI 1.19-1.80) and living in a larger household (aHR 1.12, 95% CI 1.06-1.18) were associated with a higher risk of SARS-CoV-2 infection in wave 1. Those from the highest daily exposure risk trajectory (aHR 1.46, 95% CI 1.07-2.00) and the second highest social exposure risk trajectory (aHR 1.52, 95% CI 1.18-1.97) were also at an increased risk of infection in waves 2 and 3, respectively, relative to the lowest risk trajectory. CONCLUSIONS: In an infection-naive population, SARS-CoV-2 transmission was predominantly initiated at the workplace, accelerated in the household, and perpetuated in the daily and social environments, as stringent restrictions were scaled down. These patterns highlight the phasic shift of exposure settings, which is important for informing the effective calibration of targeted social distancing measures as an alternative to lockdown.

Participatory Disease Surveillance for the Early Detection of Cholera-Like Diarrheal Disease Outbreaks in Rural Villages in Malawi: Prospective Cohort Study.

BACKGROUND: Cholera-like diarrheal disease (CLDD) outbreaks are complex and influenced by environmental factors, socioeconomic conditions, and population dynamics, leading to limitations in traditional surveillance methods. In Malawi, cholera is considered an endemic disease. Its epidemiological profile is characterized by seasonal patterns, often coinciding with the rainy season when contamination of water sources is more likely. However, the outbreak that began in March 2022 has extended to the dry season, with deaths reported in all 29 districts. It is considered the worst outbreak in the past 10 years. OBJECTIVE: This study aims to evaluate the feasibility and outcomes of participatory surveillance (PS) using interactive voice response (IVR) technology for the early detection of CLDD outbreaks in Malawi. METHODS: This longitudinal cohort study followed 740 households in rural settings in Malawi for 24 weeks. The survey tool was designed to have 10 symptom questions collected every week. The proxies' rationale was related to exanthematic, ictero-hemorragica for endemic diseases or events, diarrhea and respiratory/targeting acute diseases or events, and diarrhea and respiratory/targeting seasonal diseases or events. This work will focus only on the CLDD as a proxy for gastroenteritis and cholera. In this study, CLDD was defined as cases where reports indicated diarrhea combined with either fever or vomiting/nausea. RESULTS: During the study period, our data comprised 16,280 observations, with an average weekly participation rate of 35%. Maganga TA had the highest average of completed calls, at 144.83 (SD 10.587), while Ndindi TA had an average of 123.66 (SD 13.176) completed calls. Our findings demonstrate that this method might be effective in identifying CLDD with a notable and consistent signal captured over time (R2=0.681404). Participation rates were slightly higher at the beginning of the study and decreased over time, thanks to the sensitization activities rolled out at the CBCCs level. In terms of the attack rates for CLDD, we observed similar rates between Maganga TA and Ndindi TA, at 16% and 15%, respectively. CONCLUSIONS: PS has proven to be valuable for the early detection of epidemics. IVR technology is a promising approach for disease surveillance in rural villages in Africa, where access to health care and traditional disease surveillance methods may be limited. This study highlights the feasibility and potential of IVR technology for the timely and comprehensive reporting of disease incidence, symptoms, and behaviors in resource-limited settings.

Syndromic surveillance performance in Canada throughout the COVID-19 pandemic, March 1, 2020 to March 4, 2023.

The coronavirus disease 2019 (COVID-19) pandemic has highlighted the need for robust surveillance of respiratory viruses. Syndromic surveillance continues to be an important surveillance component recommended by the World Health Organization (WHO). While FluWatchers, Canada's syndromic surveillance system, has been in place since 2015, the COVID-19 pandemic provided a valuable opportunity to expand the program's scope and underlying technology infrastructure. Following some structural changes to FluWatchers syndromic questionnaire, participants are now able to contribute valuable data to the non-specific surveillance of respiratory virus activity across Canada. This article examines the performance of FluWatchers' syndromic surveillance over the three years of the COVID-19 pandemic in Canada. More specifically, this article examines FluWatchers' performance with respect to the correlation between the FluWatchers influenza-like illness (ILI) and acute respiratory infection (ARI) indicators and total respiratory virus detections (RVDs) in Canada, including influenza, respiratory syncytial virus (RSV), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and other respiratory viruses.

Participatory Surveillance for COVID-19 Trend Detection in Brazil: Cross-sectional Study.

BACKGROUND: The ongoing COVID-19 pandemic has emphasized the necessity of a well-functioning surveillance system to detect and mitigate disease outbreaks. Traditional surveillance (TS) usually relies on health care providers and generally suffers from reporting lags that prevent immediate response plans. Participatory surveillance (PS), an innovative digital approach whereby individuals voluntarily monitor and report on their own health status via web-based surveys, has emerged in the past decade to complement traditional data collection approaches. OBJECTIVE: This study compared novel PS data on COVID-19 infection rates across 9 Brazilian cities with official TS data to examine the opportunities and challenges of using PS data, and the potential advantages of combining the 2 approaches. METHODS: The TS data for Brazil are publicly accessible on GitHub. The PS data were collected through the Brazil Sem Corona platform, a Colab platform. To gather information on an individual's health status, each participant was asked to fill out a daily questionnaire on symptoms and exposure in the Colab app. RESULTS: We found that high participation rates are key for PS data to adequately mirror TS infection rates. Where participation was high, we documented a significant trend correlation between lagged PS data and TS infection rates, suggesting that PS data could be used for early detection. In our data, forecasting models integrating both approaches increased accuracy up to 3% relative to a 14-day forecast model based exclusively on TS data. Furthermore, we showed that PS data captured a population that significantly differed from a traditional observation. CONCLUSIONS: In the traditional system, the new recorded COVID-19 cases per day are aggregated based on positive laboratory-confirmed tests. In contrast, PS data show a significant share of reports categorized as potential COVID-19 cases that are not laboratory confirmed. Quantifying the economic value of PS system implementation remains difficult. However, scarce public funds and persisting constraints to the TS system provide motivation for a PS system, making it an important avenue for future research. The decision to set up a PS system requires careful evaluation of its expected benefits, relative to the costs of setting up platforms and incentivizing engagement to increase both coverage and consistent reporting over time. The ability to compute such economic tradeoffs might be key to have PS become a more integral part of policy toolkits moving forward. These results corroborate previous studies when it comes to the benefits of an integrated and comprehensive surveillance system, and shed light on its limitations and on the need for additional research to improve future implementations of PS platforms.

Enabling Multicentric Participatory Disease Surveillance for Global Health Enhancement: Viewpoint on Global Flu View.

Participatory surveillance (PS) has been defined as the bidirectional process of transmitting and receiving data for action by directly engaging the target population. Often represented as self-reported symptoms directly from the public, PS can provide evidence of an emerging disease or concentration of symptoms in certain areas, potentially identifying signs of an early outbreak. The construction of sets of symptoms to represent various disease syndromes provides a mechanism for the early detection of multiple health threats. Global Flu View (GFV) is the first-ever system that merges influenza-like illness (ILI) data from more than 8 countries plus 1 region (Hong Kong) on 4 continents for global monitoring of this annual health threat. GFV provides a digital ecosystem for spatial and temporal visualization of syndromic aggregates compatible with ILI from the various systems currently participating in GFV in near real time, updated weekly. In 2018, the first prototype of a digital platform to combine data from several ILI PS programs was created. At that time, the priority was to have a digital environment that brought together different programs through an application program interface, providing a real time map of syndromic trends that could demonstrate where and when ILI was spreading in various regions of the globe. After 2 years running as an experimental model and incorporating feedback from partner programs, GFV was restructured to empower the community of public health practitioners, data scientists, and researchers by providing an open data channel among these contributors for sharing experiences across the network. GFV was redesigned to serve not only as a data hub but also as a dynamic knowledge network around participatory ILI surveillance by providing knowledge exchange among programs. Connectivity between existing PS systems enables a network of cooperation and collaboration with great potential for continuous public health impact. The exchange of knowledge within this network is not limited only to health professionals and researchers but also provides an opportunity for the general public to have an active voice in the collective construction of health settings. The focus on preparing the next generation of epidemiologists will be of great importance to scale innovative approaches like PS. GFV provides a useful example of the value of globally integrated PS data to help reduce the risks and damages of the next pandemic.

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.

Longitudinal Participatory Surveillance Highlights Association Between Mask-Wearing and Lower COVID-19 Risk - United States, 2020.

What is already known about this topic?: Numerous ecological and laboratory studies suggest face masks are an effective non-pharmaceutical intervention for reducing the spread of coronavirus disease 2019 (COVID-19), but cannot otherwise assess individual-level effects. What is added by this report?: Using a prospective cohort of individuals enrolled in a participatory, syndromic surveillance tool prior to the first case of COVID-19 in the United States, we present a novel longitudinal assessment of the effectiveness of face masks. What are the public health implications for public health practice?: Our analysis demonstrates an association between self-reported mask-wearing behavior and lower individual risk of syndromic COVID-19-like illness while adjusting for confounders at the individual level. Our results also highlight the dual utility of participatory syndromic surveillance systems as both disease trend monitors and tools that can aid in understanding the effectiveness of personal protective measures.

Telemedicine for international travelers through a Smartphone-based monitoring platform (Trip Doctor®).

BACKGROUND: Overall, more than 50% of international travelers develop symptoms while traveling and 55% of them seek medical assistance during the trip. We conducted a study to evaluate the usefulness of a Smartphone app called TRIP Doctor® to provide telemedicine to international travelers. METHODS: Participants over 18 years old attending our travel clinic at Hospital Clinic in Barcelona were invited to participate during 2017-2019. After downloading the app, the health status of the traveler was monitored on a daily basis, providing specific medical advice and offering remote contact with specialized physicians through an integrated chat, if needed. RESULTS: From 449 users, 59 (13%) contacted for medical assistance through the app during the trip. Main reasons for telemedicine were diarrhea (25.7%), skin conditions (19.7%) and fever (12.1%). Among patients who contacted, 90% of the travelers did not require to be referred to a local doctor. Symptomatic treatment was the main treatment prescribed (38%). In a 14.7% of the cases a follow-up was not required, a 63.2% recovered and 22.1% were loss of follow-up. After a multivariate analysis, duration of trip >14 days was found to be the only factor associated with the use of telemedicine (OR 2.2, CI 95% 1.1-4.5, p = 0.03). CONCLUSION: In conclusion, travelers using telemedicine travelled for longer periods of time and mostly contacted for mild symptoms which could be solved successfully by remote assistance with our specialized doctors.

The Landscape of Participatory Surveillance Systems Across the One Health Spectrum: Systematic Review.

BACKGROUND: Participatory surveillance systems augment traditional surveillance systems through bidirectional community engagement. The digital platform evolution has enabled the expansion of participatory surveillance systems, globally, for the detection of health events impacting people, animals, plants, and the environment, in other words, across the entire One Health spectrum. OBJECTIVE: The aim of this landscape was to identify and provide descriptive information regarding system focus, geography, users, technology, information shared, and perceived impact of ongoing participatory surveillance systems across the One Health spectrum. METHODS: This landscape began with a systematic literature review to identify potential ongoing participatory surveillance systems. A survey was sent to collect standardized data from the contacts of systems identified in the literature review and through direct outreach to stakeholders, experts, and professional organizations. Descriptive analyses of survey and literature review results were conducted across the programs. RESULTS: The landscape identified 60 ongoing single-sector and multisector participatory surveillance systems spanning five continents. Of these, 29 (48%) include data on human health, 26 (43%) include data on environmental health, and 24 (40%) include data on animal health. In total, 16 (27%) systems are multisectoral; of these, 9 (56%) collect animal and environmental health data; 3 (19%) collect human, animal, and environmental health data; 2 (13%) collect human and environmental health data; and 2 (13%) collect human and animal health data. Out of 60 systems, 31 (52%) are designed to cover a national scale, compared to those with a subnational (n=19, 32%) or multinational (n=10, 17%) focus. All systems use some form of digital technology. Email communication or websites (n=40, 67%) and smartphones (n=29, 48%) are the most common technologies used, with some using both. Systems have capabilities to download geolocation data (n=31, 52%), photographs (n=29, 48%), and videos (n=6, 10%), and can incorporate lab data or sample collection (n=15, 25%). In sharing information back with users, most use visualization, such as maps (n=43, 72%); training and educational materials (n=37, 62%); newsletters, blogs, and emails (n=34, 57%); and disease prevention information (n=32, 53%). Out of the 46 systems responding to the survey regarding perceived impacts of their systems, 36 (78%) noted "improved community knowledge and understanding" and 31 (67%) noted "earlier detection." CONCLUSIONS: The landscape demonstrated the breadth of applicability of participatory surveillance around the world to collect data from community members and trained volunteers in order to inform the detection of events, from invasive plant pests to weekly influenza symptoms. Acknowledging the importance of bidirectionality of information, these systems simultaneously share findings back with the users. Such directly engaged community detection systems capture events early and provide opportunities to stop outbreaks quickly.

Analysis of a Vaping-Associated Lung Injury Outbreak through Participatory Surveillance and Archival Internet Data.

The US Centers for Disease Control and Prevention alerted of a suspected outbreak of lung illness associated with using E-cigarette products in September 2019. At the time that the CDC published its alert little was known about the causes of the outbreak or who was at risk for it. Here we provide insights into the outbreak through analysis of passive reporting and participatory surveillance. We collected data about vaping habits and associated adverse reactions from four data sources pertaining to people in the USA: A participatory surveillance platform (YouVape), Reddit, Google Trends, and Bing. Data were analyzed to identify vaping behaviors and reported adverse events. These were correlated among sources and with prior reports. Data was obtained from 720 YouVape users, 4331 Reddit users, and over 1 million Bing users. Large geographic variation was observed across vaping products. Significant correlation was found among the data sources in reported adverse reactions. Models of participatory surveillance data found specific product and adverse reaction associations. Specifically, cannabidiol was found to be associated with fever, while tetrahydrocannabinol was found to be correlated with diarrhea. Our results demonstrate that utilization of different, complementary, online data sources provide a holistic view of vaping associated lung injury while augmenting traditional data sources.

Using farmer observations for animal health syndromic surveillance: Participation and performance of an online enhanced passive surveillance system.

The challenge of animal health surveillance is to provide the information necessary to appropriately inform disease prevention and control activities within the constraints of available resources. Syndromic surveillance of farmers' disease observations can improve animal health data capture from extensive livestock farming systems, especially where data are not otherwise being systematically collected or when data on confirmed aetiological diagnoses are unavailable at the disease level. As it is rarely feasible to recruit a truly random sample of farmers to provide observational reports, directing farmer sampling to align with the surveillance objectives is a reasonable and practical approach. As long as potential bias is recognised and managed, farmers who will report reliably can be desirable participants in a surveillance system. Thus, one early objective of a surveillance program should be to identify characteristics associated with reporting behaviour. Knowledge of the demographic and managerial characteristics of good reporters can inform efforts to recruit additional farms into the system or aid understanding of potential bias of system reports. We describe the operation of a farmer syndromic surveillance system in Victoria, Australia, over its first two years from 2014 to 2016. Survival analysis and classification and regression tree analysis were used to identify farm level factors associated with 'reliable' participation (low non-response rates in longitudinal reporting). Response rate and timeliness were not associated with whether farmers had disease to report, or with different months of the year. Farmers keeping only sheep were the most reliable and timely respondents. Farmers < 43 years of age had lower response rates than older farmers. Farmers with veterinary qualifications and those working full-time on-farm provided less timely reports than other educational backgrounds and farmers who worked part-time on-farm. These analyses provide a starting point to guide recruitment of participants for surveillance of farmers' observations using syndromic surveillance, and provide examples of strengths and weaknesses of syndromic surveillance systems for extensively-managed livestock. Once farm characteristics associated with reliable participation are known, they can be incorporated into surveillance system design in accordance with the objectives of the system.

Combining Wearable Devices and Mobile Surveys to Study Child and Youth Development in Malawi: Implementation Study of a Multimodal Approach.

BACKGROUND: Multimodal approaches have been shown to be a promising way to collect data on child development at high frequency, combining different data inputs (from phone surveys to signals from noninvasive biomarkers) to understand children's health and development outcomes more integrally from multiple perspectives. OBJECTIVE: The aim of this work was to describe an implementation study using a multimodal approach combining noninvasive biomarkers, social contact patterns, mobile surveying, and face-to-face interviews in order to validate technologies that help us better understand child development in poor countries at a high frequency. METHODS: We carried out a mixed study based on a transversal descriptive analysis and a longitudinal prospective analysis in Malawi. In each village, children were sampled to participate in weekly sessions in which data signals were collected through wearable devices (electrocardiography [ECG] hand pads and electroencephalography [EEG] headbands). Additionally, wearable proximity sensors to elicit the social network were deployed among children and their caregivers. Mobile surveys using interactive voice response calls were also used as an additional layer of data collection. An end-line face-to-face survey was conducted at the end of the study. RESULTS: During the implementation, 82 EEG/ECG data entry points were collected across four villages. The sampled children for EEG/ECG were 0 to 5 years old. EEG/ECG data were collected once a week. In every session, children wore the EEG headband for 5 minutes and the ECG hand pad for 3 minutes. In total, 3531 calls were sent over 5 weeks, with 2291 participants picking up the calls and 984 of those answering the consent question. In total, 585 people completed the surveys over the course of 5 weeks. CONCLUSIONS: This study achieved its objective of demonstrating the feasibility of generating data through the unprecedented use of a multimodal approach for tracking child development in Malawi, which is one of the poorest countries in the world. Above and beyond its multiple dimensions, the dynamics of child development are complex. It is the case not only that no data stream in isolation can accurately characterize it, but also that even if combined, infrequent data might miss critical inflection points and interactions between different conditions and behaviors. In turn, combining different modes at a sufficiently high frequency allows researchers to make progress by considering contact patterns, reported symptoms and behaviors, and critical biomarkers all at once. This application showcases that even in developing countries facing multiple constraints, complementary technologies can leverage and accelerate the digitalization of health, bringing benefits to populations that lack new tools for understanding child well-being and development.