Symptoms Compatible With Long Coronavirus Disease (COVID) in Healthcare Workers With and Without Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection-Results of a Prospective Multicenter Cohort.

BACKGROUND: The burden of long-term symptoms (ie, long COVID) in patients after mild COVID-19 is debated. Within a cohort of healthcare workers (HCWs), frequency and risk factors for symptoms compatible with long COVID are assessed. METHODS: Participants answered baseline (August/September 2020) and weekly questionnaires on SARS-CoV-2 nasopharyngeal swab (NPS) results and acute disease symptoms. In January 2021, SARS-CoV-2 serology was performed; in March, symptoms compatible with long COVID (including psychometric scores) were asked and compared between HCWs with positive NPS, seropositive HCWs without positive NPS (presumable asymptomatic/pauci-symptomatic infections), and negative controls. The effect of time since diagnosis and quantitative anti-spike protein antibodies (anti-S) was evaluated. Poisson regression was used to identify risk factors for symptom occurrence. RESULTS: Of 3334 HCWs (median, 41 years; 80% female), 556 (17%) had a positive NPS and 228 (7%) were only seropositive. HCWs with positive NPS more frequently reported ≥1 symptom compared with controls (73% vs 52%, P < .001); seropositive HCWs without positive NPS did not score higher than controls (58% vs 52%, P = .13), although impaired taste/olfaction (16% vs 6%, P < .001) and hair loss (17% vs 10%, P = .004) were more common. Exhaustion/burnout was reported by 24% of negative controls. Many symptoms remained elevated in those diagnosed >6 months ago; anti-S titers correlated with high symptom scores. Acute viral symptoms in weekly questionnaires best predicted long-COVID symptoms. Physical activity at baseline was negatively associated with neurocognitive impairment and fatigue scores. CONCLUSIONS: Seropositive HCWs without positive NPS are only mildly affected by long COVID. Exhaustion/burnout is common, even in noninfected HCWs. Physical activity might be protective against neurocognitive impairment/fatigue symptoms after COVID-19.

Risk analysis for occurrences of schistosomiasis in the coastal area of Porto de Galinhas, Pernambuco, Brazil.

BACKGROUND: Manson's schistosomiasis continues to be a severe public health problem in Brazil, where thousands of people live under the risk of contracting this parasitosis. In the Northeast of Brazil, schistosomiasis has expanded from rural areas to the coast of Pernambuco State, where the intermediate host is Biomphalaria glabrata snails. This study aims at presenting situational analyses on schistosomiasis at the coastal locality of Porto de Galinhas, Pernambuco, Brazil, by determining the risk factors relating to its occurrence from the epidemiological and spatial perspectives. METHODS: In order to gather prevalence data, a parasitological census surveys were conducted in 2010 in the light of the Kato-Katz technique. Furthermore, malacological surveys were also conducted in the same years so as to define the density and infection rates of the intermediate host. Lastly, socioeconomic-behavioral survey was also conducted to determine the odds ratio for infection by Schistosoma mansoni. Based on these data, spatial analyses were done, resulting in maps of the risk of disease transmission. To predict the risk of schistosomiasis occurrence, a multivariate logistic regression was performed using R 2.13 software. RESULTS: Based on prevalence, malacological and socioeconomic-behavioural surveys, it was identified a prevalence of 15.7% in the investigated population (2,757 individuals). Due to the malacological survey, 36 breeding sites were identified, of which 11 were classified as foci of schistosomiasis transmission since they pointed out snails which were infected by Schistosoma mansoni. Overall, 11,012 snails (Biomphalaria glabrata) were collected. The multivariate regression model identified six explanatory variables of environmental, socioeconomic and demographic nature. Spatial sweep analysis by means of the Bernoulli method identified one statistically significant cluster in Salinas (RR = 2.2; p-value < 0.000), the district with the highest occurrence of cases. CONCLUSIONS: Based on the resulting information from this study, the epidemiological dimensions of this disease are significant and severe, within the scenario of schistosomiasis in Pernambuco state. The risk factors which were identified in the predictive model made it clear that the environmental and social conditions influence on the schistosomiasis occurrences.

Digital Transformation of Public Health for Noncommunicable Diseases: Narrative Viewpoint of Challenges and Opportunities.

The recent SARS-CoV-2 pandemic underscored the effectiveness and rapid deployment of digital public health interventions, notably the digital proximity tracing apps, leveraging Bluetooth capabilities to trace and notify users about potential infection exposures. Backed by renowned organizations such as the World Health Organization and the European Union, digital proximity tracings showcased the promise of digital public health. As the world pivots from pandemic responses, it becomes imperative to address noncommunicable diseases (NCDs) that account for a vast majority of health care expenses and premature disability-adjusted life years lost. The narrative of digital transformation in the realm of NCD public health is distinct from infectious diseases. Public health, with its multifaceted approach from disciplines such as medicine, epidemiology, and psychology, focuses on promoting healthy living and choices through functions categorized as "Assessment," "Policy Development," "Resource Allocation," "Assurance," and "Access." The power of artificial intelligence (AI) in this digital transformation is noteworthy. AI can automate repetitive tasks, facilitating health care providers to prioritize personal interactions, especially those that cannot be digitalized like emotional support. Moreover, AI presents tools for individuals to be proactive in their health management. However, the human touch remains irreplaceable; AI serves as a companion guiding through the health care landscape. Digital evolution, while revolutionary, poses its own set of challenges. Issues of equity and access are at the forefront. Vulnerable populations, whether due to economic constraints, geographical barriers, or digital illiteracy, face the threat of being marginalized further. This transformation mandates an inclusive strategy, focusing on not amplifying existing health disparities but eliminating them. Population-level digital interventions in NCD prevention demand societal agreement. Policies, like smoking bans or sugar taxes, though effective, might affect those not directly benefiting. Hence, all involved parties, from policy makers to the public, should have a balanced perspective on the advantages, risks, and expenses of these digital shifts. For a successful digital shift in public health, especially concerning NCDs, AI's potential to enhance efficiency, effectiveness, user experience, and equity-the "quadruple aim"-is undeniable. However, it is vital that AI-driven initiatives in public health domains remain purposeful, offering improvements without compromising other objectives. The broader success of digital public health hinges on transparent benchmarks and criteria, ensuring maximum benefits without sidelining minorities or vulnerable groups. Especially in population-centric decisions, like resource allocation, AI's ability to avoid bias is paramount. Therefore, the continuous involvement of stakeholders, including patients and minority groups, remains pivotal in the progression of AI-integrated digital public health.

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.

Using EpiCore to Enable Rapid Verification of Potential Health Threats: Illustrated Use Cases and Summary Statistics.

BACKGROUND: The proliferation of digital disease-detection systems has led to an increase in earlier warning signals, which subsequently have resulted in swifter responses to emerging threats. Such highly sensitive systems can also produce weak signals needing additional information for action. The delays in the response to a genuine health threat are often due to the time it takes to verify a health event. It was the delay in outbreak verification that was the main impetus for creating EpiCore. OBJECTIVE: This paper describes the potential of crowdsourcing information through EpiCore, a network of voluntary human, animal, and environmental health professionals supporting the verification of early warning signals of potential outbreaks and informing risk assessments by monitoring ongoing threats. METHODS: This paper uses summary statistics to assess whether EpiCore is meeting its goal to accelerate the time to verification of identified potential health events for epidemic and pandemic intelligence purposes from around the world. Data from the EpiCore platform from January 2018 to December 2022 were analyzed to capture request for information response rates and verification rates. Illustrated use cases are provided to describe how EpiCore members provide information to facilitate the verification of early warning signals of potential outbreaks and for the monitoring and risk assessment of ongoing threats through EpiCore and its utilities. RESULTS: Since its launch in 2016, EpiCore network membership grew to over 3300 individuals during the first 2 years, consisting of professionals in human, animal, and environmental health, spanning 161 countries. The overall EpiCore response rate to requests for information increased by year between 2018 and 2022 from 65.4% to 68.8% with an initial response typically received within 24 hours (in 2022, 94% of responded requests received a first contribution within 24 h). Five illustrated use cases highlight the various uses of EpiCore. CONCLUSIONS: As the global demand for data to facilitate disease prevention and control continues to grow, it will be crucial for traditional and nontraditional methods of disease surveillance to work together to ensure health threats are captured earlier. EpiCore is an innovative approach that can support health authorities in decision-making when used complementarily with official early detection and verification systems. EpiCore can shorten the time to verification by confirming early detection signals, informing risk-assessment activities, and monitoring ongoing events.

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.

Schistosomiasis transmission and environmental change: a spatio-temporal analysis in Porto de Galinhas, Pernambuco--Brazil.

BACKGROUND: In Brazil, schistosomiasis mansoni infection is an endemic disease that mainly affects the country's rural populations who carry out domestic and social activities in rivers and water accumulations that provide shelter for the snails of the disease. The process of rural migration to urban centers and the disorderly occupation of natural environments by these populations from endemic areas have favored expansion of schistosomiasis to locations that had been considered to be disease-free. Based on environmental changes that have occurred in consequent to an occupation and urbanization process in the locality of Porto de Galinhas, the present study sought to identify the relationship between those chances, measure by remote-sensing techniques, and establish a new endemic area for schistosomiasis on the coast of Pernambuco State--Brazil. METHODS: To gather prevalence data, two parasitological census surveys were conducted (2000 and 2010) using the Kato-Katz technique. Two malacological surveys were also conducted in the same years in order to define the density and infection rate of the intermediate host. Based on these data, spatial analyses were done, resulting in maps of the risk of disease transmission. To ascertain the environmental changes that have occurred at the locality, images from the QuickBird satellite were analyzed, thus resulting in land use maps. RESULTS: Over this 10-year period, the foci of schistosomiasis became more concentrated in the Salinas district. This area was considered to be at the greatest risk of schistosomiasis transmission and had the highest prevalence rates over this period. The study illustrated that this was the area most affected by the environmental changes resulting from the disorderly urbanization process, which gave rise to unsanitary environments that favored the establishment and maintenance of foci of schistosomiasis transmission, thereby consolidating the process of expansion and endemization of this parasitosis.

The impacts of remote learning in secondary education during the pandemic in Brazil.

The transition to remote learning in the context of coronavirus disease 2019 (COVID-19) might have led to dramatic setbacks in education. Taking advantage of the fact that São Paulo State featured in-person classes for most of the first school quarter of 2020 but not thereafter, we estimate the effects of remote learning in secondary education using a differences-in-differences strategy that contrasts variation in students' outcomes across different school quarters, before and during the pandemic. We also estimate intention-to-treat effects of reopening schools in the pandemic through a triple-differences strategy, contrasting changes in educational outcomes across municipalities and grades that resumed in-person classes or not over the last school quarter in 2020. We find that, under remote learning, dropout risk increased by 365% while test scores decreased by 0.32 s.d., as if students had only learned 27.5% of the in-person equivalent. Partially resuming in-person classes increased test scores by 20% relative to the control group.

Association of COVID-19 Incidence and Mortality Rates With School Reopening in Brazil During the COVID-19 Pandemic.

Importance: School closures because of COVID-19 have left 1.6 billion students around the world without in-person classes for a prolonged period. To our knowledge, no study has documented whether reopening schools in low- and middle-income countries during the pandemic was associated with increased aggregate COVID-19 incidence and mortality with appropriate counterfactuals. Objective: To test whether reopening schools under appropriate protocols during the COVID-19 pandemic was associated with increased municipal-level COVID-19 cases and deaths in São Paulo State, Brazil. Design Setting and Participants: This observational study of municipalities in São Paulo State, Brazil, uses a difference-in-differences analysis to examine the association between municipal decisions to reopen schools during the COVID-19 pandemic and municipal-level COVID-19 case and death rates between October and December 2020. The study compared 129 municipalities that reopened schools in 2020 with 514 that did not and excluded data for 2 municipalities that reopened schools and closed then again. Main Outcomes and Measures: New COVID-19 cases and deaths per 10 000 inhabitants up to 12 weeks after school reopenings and municipal-level aggregate mobility for a subset of municipalities. Results: There were 8764 schools in the 129 municipalities that reopened schools compared with 9997 in the control group of 514 municipalities that did not reopen schools. The municipalities that reopened schools had a cumulative COVID-19 incidence of 20 cases per 1000 inhabitants and mortality of 0.5 deaths per 1000 inhabitants in September 2020 (the baseline period) compared with an incidence of 18 cases per 1000 inhabitants and mortality of 0.45 deaths per 1000 inhabitants during the baseline period in the comparison group. The findings indicated that there were no statistically significant differences between municipalities that authorized schools to reopen and those that did not for (1) weekly new cases (difference-in-differences, -0.03; 95% CI, -0.09 to 0.03) and (2) weekly new deaths (difference-in-differences, -0.003; 95% CI, -0.011 to 0.004) before and after October 2020. Reopening schools was not associated with higher disease activity, even in relatively vulnerable municipalities, nor aggregate mobility. Conclusions and Relevance: The findings from this study suggest that keeping schools open during the COVID-19 pandemic did not contribute to the aggregate disease activity.

Impact of respirator versus surgical masks on SARS-CoV-2 acquisition in healthcare workers: a prospective multicentre cohort.

BACKGROUND: There is insufficient evidence regarding the role of respirators in the prevention of SARS-CoV-2 infection. We analysed the impact of filtering facepiece class 2 (FFP2) versus surgical masks on the risk of SARS-CoV-2 acquisition among Swiss healthcare workers (HCW). METHODS: Our prospective multicentre cohort enrolled HCW from June to August 2020. Participants were asked about COVID-19 risk exposures/behaviours, including preferentially worn mask type when caring for COVID-19 patients outside of aerosol-generating procedures. The impact of FFP2 on (1) self-reported SARS-CoV-2-positive nasopharyngeal PCR/rapid antigen tests captured during weekly surveys, and (2) SARS-CoV-2 seroconversion between baseline and January/February 2021 was assessed. RESULTS: We enrolled 3259 participants from nine healthcare institutions, whereof 716 (22%) preferentially used FFP2. Among these, 81/716 (11%) reported a SARS-CoV-2-positive swab, compared to 352/2543 (14%) surgical mask users; seroconversion was documented in 85/656 (13%) FFP2 and 426/2255 (19%) surgical mask users. Adjusted for baseline characteristics, COVID-19 exposure, and risk behaviour, FFP2 use was non-significantly associated with decreased risk for SARS-CoV-2-positive swab (adjusted hazard ratio [aHR] 0.8, 95% CI 0.6-1.0) and seroconversion (adjusted odds ratio [aOR] 0.7, 95% CI 0.5-1.0); household exposure was the strongest risk factor (aHR 10.1, 95% CI 7.5-13.5; aOR 5.0, 95% CI 3.9-6.5). In subgroup analysis, FFP2 use was clearly protective among those with frequent (> 20 patients) COVID-19 exposure (aHR 0.7 for positive swab, 95% CI 0.5-0.8; aOR 0.6 for seroconversion, 95% CI 0.4-1.0). CONCLUSIONS: Respirators compared to surgical masks may convey additional protection from SARS-CoV-2 for HCW with frequent exposure to COVID-19 patients.

The endemisation of schistosomiasis in Porto de Galinhas, Pernambuco, Brazil, 10 years after the first epidemic outbreak.

In 2000, after heavy rains and floods in Porto de Galinhas, Pernambuco, Brazil, an outbreak of schistosomiasis was recorded, of which 62.2% (412 cases) were of the acute clinical form. Between 2001-2009, occasional findings of Biomphalaria snails parasitised by Schistosoma mansoni indicated that disease transmission was still occurring. This motivated a new epidemiological survey between August-December 2010 to provide an update of the occurrence of this health hazard and to investigate the process of disease endemisation at this locality. This survey gathered parasitological, clinical and malacological data. The results of this survey, compared with data from the year 2000 survey, showed the following: (i) over these 10 years, there were declines in the total percentage of cases and the percentage of acute forms, (ii) the acute clinical form now represents 23.3% in contrast with the 62.2% detected in 2000 and (iii) the current prevalence of schistosomiasis is 15.7%, while in 2000 32.1% of the individuals were diagnosed as parasitised. Today, the chronic clinical form represents 76.7% of the total number of cases diagnosed, thus showing that over the 10-year period the occurrences of clinical forms became inverted. These findings, together with visual observation of insalubrious environmental conditions, indicate that schistosomiasis has become endemic in Porto de Galinhas.

Digital SARS-CoV-2 Detection Among Hospital Employees: Participatory Surveillance Study.

BACKGROUND: The implementation of novel techniques as a complement to traditional disease surveillance systems represents an additional opportunity for rapid analysis. OBJECTIVE: The objective of this work is to describe a web-based participatory surveillance strategy among health care workers (HCWs) in two Swiss hospitals during the first wave of COVID-19. METHODS: A prospective cohort of HCWs was recruited in March 2020 at the Cantonal Hospital of St. Gallen and the Eastern Switzerland Children's Hospital. For data analysis, we used a combination of the following techniques: locally estimated scatterplot smoothing (LOESS) regression, Spearman correlation, anomaly detection, and random forest. RESULTS: From March 23 to August 23, 2020, a total of 127,684 SMS text messages were sent, generating 90,414 valid reports among 1004 participants, achieving a weekly average of 4.5 (SD 1.9) reports per user. The symptom showing the strongest correlation with a positive polymerase chain reaction test result was loss of taste. Symptoms like red eyes or a runny nose were negatively associated with a positive test. The area under the receiver operating characteristic curve showed favorable performance of the classification tree, with an accuracy of 88% for the training data and 89% for the test data. Nevertheless, while the prediction matrix showed good specificity (80.0%), sensitivity was low (10.6%). CONCLUSIONS: Loss of taste was the symptom that was most aligned with COVID-19 activity at the population level. At the individual level-using machine learning-based random forest classification-reporting loss of taste and limb/muscle pain as well as the absence of runny nose and red eyes were the best predictors of COVID-19.

Prevalence of SARS-CoV-2 antibodies among Swiss hospital workers: Results of a prospective cohort study.

In this prospective cohort of 1,012 Swiss hospital employees, 3 different assays were used to screen serum for SARS-CoV-2 antibodies. Seropositivity was 1%; the positive predictive values of the lateral-flow immunoassay were 64% (IgG) and 13% (IgM). History of fever and myalgia most effectively differentiated seropositive and seronegative participants.

Non-occupational and occupational factors associated with specific SARS-CoV-2 antibodies among hospital workers - A multicentre cross-sectional study.

OBJECTIVES: Protecting healthcare workers (HCWs) from coronavirus disease-19 (COVID-19) is critical to preserve the functioning of healthcare systems. We therefore assessed seroprevalence and identified risk factors for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) seropositivity in this population. METHODS: Between 22 June 22 and 15 August 2020, HCWs from institutions in northern/eastern Switzerland were screened for SARS-CoV-2 antibodies. We recorded baseline characteristics, non-occupational and occupational risk factors. We used pairwise tests of associations and multivariable logistic regression to identify factors associated with seropositivity. RESULTS: Among 4664 HCWs from 23 healthcare facilities, 139 (3%) were seropositive. Non-occupational exposures independently associated with seropositivity were contact with a COVID-19-positive household (adjusted OR 59, 95% CI 33-106), stay in a COVID-19 hotspot (aOR 2.3, 95% CI 1.2-4.2) and male sex (aOR 1.9, 95% CI 1.1-3.1). Blood group 0 vs. non-0 (aOR 0.5, 95% CI 0.3-0.8), active smoking (aOR 0.4, 95% CI 0.2-0.7), living with children <12 years (aOR 0.3, 95% CI 0.2-0.6) and being a physician (aOR 0.2, 95% CI 0.1-0.5) were associated with decreased risk. Other occupational risk factors were close contact to COVID-19 patients (aOR 2.7, 95% CI 1.4-5.4), exposure to COVID-19-positive co-workers (aOR 1.9, 95% CI 1.1-2.9), poor knowledge of standard hygiene precautions (aOR 1.9, 95% CI 1.2-2.9) and frequent visits to the hospital canteen (aOR 2.3, 95% CI 1.4-3.8). DISCUSSION: Living with COVID-19-positive households showed the strongest association with SARS-CoV-2 seropositivity. We identified several potentially modifiable work-related risk factors, which might allow mitigation of the COVID-19 risk among HCWs. The lower risk among those living with children, even after correction for multiple confounders, is remarkable and merits further study.

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.

Participatory Surveillance Based on Crowdsourcing During the Rio 2016 Olympic Games Using the Guardians of Health Platform: Descriptive Study.

BACKGROUND: With the evolution of digital media, areas such as public health are adding new platforms to complement traditional systems of epidemiological surveillance. Participatory surveillance and digital epidemiology have become innovative tools for the construction of epidemiological landscapes with citizens' participation, improving traditional sources of information. Strategies such as these promote the timely detection of warning signs for outbreaks and epidemics in the region. OBJECTIVE: This study aims to describe the participatory surveillance platform Guardians of Health, which was used in a project conducted during the 2016 Olympic and Paralympic Games in Rio de Janeiro, Brazil, and officially used by the Brazilian Ministry of Health for the monitoring of outbreaks and epidemics. METHODS: This is a descriptive study carried out using secondary data from Guardians of Health available in a public digital repository. Based on syndromic signals, the information subsidy for decision making by policy makers and health managers becomes more dynamic and assertive. This type of information source can be used as an early route to understand the epidemiological scenario. RESULTS: The main result of this research was demonstrating the use of the participatory surveillance platform as an additional source of information for the epidemiological surveillance performed in Brazil during a mass gathering. The platform Guardians of Health had 7848 users who generated 12,746 reports about their health status. Among these reports, the following were identified: 161 users with diarrheal syndrome, 68 users with respiratory syndrome, and 145 users with rash syndrome. CONCLUSIONS: It is hoped that epidemiological surveillance professionals, researchers, managers, and workers become aware of, and allow themselves to use, new tools that improve information management for decision making and knowledge production. This way, we may follow the path for a more intelligent, efficient, and pragmatic disease control system.

Saúde na Copa: The World's First Application of Participatory Surveillance for a Mass Gathering at FIFA World Cup 2014, Brazil.

BACKGROUND: The 2005 International Health Regulations (IHRs) established parameters for event assessments and notifications that may constitute public health emergencies of international concern. These requirements and parameters opened up space for the use of nonofficial mechanisms (such as websites, blogs, and social networks) and technological improvements of communication that can streamline the detection, monitoring, and response to health problems, and thus reduce damage caused by these problems. Specifically, the revised IHR created space for participatory surveillance to function, in addition to the traditional surveillance mechanisms of detection, monitoring, and response. Participatory surveillance is based on crowdsourcing methods that collect information from society and then return the collective knowledge gained from that information back to society. The spread of digital social networks and wiki-style knowledge platforms has created a very favorable environment for this model of production and social control of information. OBJECTIVE: The aim of this study was to describe the use of a participatory surveillance app, Healthy Cup, for the early detection of acute disease outbreaks during the Fédération Internationale de Football Association (FIFA) World Cup 2014. Our focus was on three specific syndromes (respiratory, diarrheal, and rash) related to six diseases that were considered important in a mass gathering context (influenza, measles, rubella, cholera, acute diarrhea, and dengue fever). METHODS: From May 12 to July 13, 2014, users from anywhere in the world were able to download the Healthy Cup app and record their health condition, reporting whether they were good, very good, ill, or very ill. For users that reported being ill or very ill, a screen with a list of 10 symptoms was displayed. Participatory surveillance allows for the real-time identification of aggregates of symptoms that indicate possible cases of infectious diseases. RESULTS: From May 12 through July 13, 2014, there were 9434 downloads of the Healthy Cup app and 7155 (75.84%) registered users. Among the registered users, 4706 (4706/7155, 65.77%) were active users who posted a total of 47,879 times during the study period. The maximum number of users that signed up in one day occurred on May 30, 2014, the day that the app was officially launched by the Minister of Health during a press conference. During this event, the Minister of Health announced the special government program Health in the World Cup on national television media. On that date, 3633 logins were recorded, which accounted for more than half of all sign-ups across the entire duration of the study (50.78%, 3633/7155). CONCLUSIONS: Participatory surveillance through community engagement is an innovative way to conduct epidemiological surveillance. Compared to traditional epidemiological surveillance, advantages include lower costs of data acquisition, timeliness of information collected and shared, platform scalability, and capacity for integration between the population being served and public health services.

Digital disease detection and participatory surveillance: overview and perspectives for Brazil.

This study aimed to describe the digital disease detection and participatory surveillance in different countries. The systems or platforms consolidated in the scientific field were analyzed by describing the strategy, type of data source, main objectives, and manner of interaction with users. Eleven systems or platforms, developed from 1996 to 2016, were analyzed. There was a higher frequency of data mining on the web and active crowdsourcing as well as a trend in the use of mobile applications. It is important to provoke debate in the academia and health services for the evolution of methods and insights into participatory surveillance in the digital age.