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

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.

Healthcare-seeking behaviours of patients with acute respiratory infection: a cross-sectional survey in a rural area of southwest China

OBJECTIVES

This study aimed to assess the healthcare-seeking behaviour and related factors of people with acute respiratory symptoms in the rural areas of central and western China to estimate the disease burden of influenza more accurately.

DESIGN

Cross-sectional survey.

SETTINGS

Fifty-two communities/villages in the Wanzhou District, Chongqing, China, a rural area in southwest China, from May 2022 to July 2022.

PARTICIPANTS

The participants were those who had been living in Wanzhou District continuously for more than 6 months and consented to participate.

OUTCOME MEASURES

A semistructured questionnaire was used to determine the healthcare-seeking behaviour of participants, and the dichotomous response of 'yes' or 'no' was used to assess whether participants had acute respiratory symptoms and their healthcare-seeking behaviour.

RESULTS

Only 50.92% (360 of 707) of the patients with acute respiratory infection visited medical and health institutions for treatment, whereas 49.08% (347 of 707) avoided treatment or opted for self-medication. The primary reason for not seeing a doctor was that patients felt their condition was not serious and visiting a medical facility for treatment was unnecessary. Short distance (87.54%) and reasonable charges (49.48%) were ranked as the most important reasons for choosing treatment at primary medical and health facilities (80.27%). The primary reasons for which patients visited secondary and tertiary hospitals (7.78% and 8.61%, respectively) were that doctors in such facilities were better at diagnosis (57.14%) and at treatment (87.10%).

CONCLUSION

The findings provided in this study indicated that regular healthcare-seeking behaviour investigations should be conducted. The disease burden of influenza can be calculated more accurately when healthcare-seeking behaviour investigations are combined with surveillance in the hospitals.

Syndromic surveillance during 2022 Uganda Martyrs' commemoration

Mass gatherings frequently include close, prolonged interactions between people, which presents opportunities for infectious disease transmission. Over 20,000 pilgrims gathered at Namugongo Catholic and Protestant shrines to commemorate 2022 Uganda Martyr's Day. We described syndromes suggestive of key priority diseases particularly COVID-19 and viral hemorrhagic fever (VHF) among visiting pilgrims during May 25-June 5, 2022. We conducted a survey among pilgrims at the catholic and protestant shrines based on signs and symptoms for key priority diseases: COVID-19 and VHF. A suspected COVID-19 case was defined as acute respiratory illness (temperature greater 37.5°C and at least one sign/symptom of respiratory infection such as cough or shortness of breath) whereas a suspected VHF case was defined as fever >37.5°C and unexplained bleeding among pilgrims who visited Namugongo Catholic and Protestant shrines from May 25 to June 5, 2022. Pilgrims were sampled systematically at entrances and demarcated zonal areas to participate in the survey. Additionally, we extracted secondary data on pilgrims who sought emergency medical services from Health Management Information System registers. Descriptive analysis was conducted to identify syndromes suggestive of key priority diseases. Among 1,350 pilgrims interviewed, 767 (57%) were female. The mean age was 37.9 (±17.9) years. Nearly all pilgrims 1,331 (98.6%) were Ugandans. A total of 236 (18%) reported ≥1 case definition symptom and 42 (3%) reported ≥2 symptoms. Thirty-nine (2.9%) were suspected COVID-19 cases and three (0.2%) were suspected VHF cases from different regions of Uganda. Among 5,582 pilgrims who sought medical care from tents, 628 (11.3%) had suspected COVID-19 and one had suspected VHF. Almost one in fifty pilgrims at the 2022 Uganda Martyrs' commemoration had at least one symptom of COVID-19 or VHF. Intensified syndromic surveillance and planned laboratory testing capacity at mass gatherings is important for early detection of public health emergencies that could stem from such events.

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.

An interactive national digital surveillance system to fight against COVID-19 in Bangladesh

Background

COVID-19 has affected many people globally, including in Bangladesh. Due to a lack of preparedness and resources, Bangladesh has experienced a catastrophic health crisis, and the devastation caused by this deadly virus has not yet been halted. Hence, precise and rapid diagnostics and infection tracing are essential for managing the condition and limiting its spread. The conventional screening procedure, such as reverse transcription polymerase chain reaction (RT-PCR), is not available in most rural areas and is time-consuming. Therefore, a data-driven intelligent surveillance system can be advantageous for rapid COVID-19 screening and risk estimation.

Objectives

This study describes the design, development, implementation, and characteristics of a nationwide web-based surveillance system for educating, screening, and tracking COVID-19 at the community level in Bangladesh.

Methods

The system consists of a mobile phone application and a cloud server. The data is collected by community health professionals via home visits or telephone calls and analyzed using rule-based artificial intelligence (AI). Depending on the results of the screening procedure, a further decision is made regarding the patient. This digital surveillance system in Bangladesh provides a platform to support government and non-government organizations, including health workers and healthcare facilities, in identifying patients at risk of COVID-19. It refers people to the nearest government healthcare facility, collecting and testing samples, tracking and tracing positive cases, following up with patients, and documenting patient outcomes.

Results

This study began in April 2020, and the results are provided in this paper till December 2022. The system has successfully completed 1,980,323 screenings. Our rule-based AI model categorized them into five separate risk groups based on the acquired patient information. According to the data, around 51% of the overall screened populations are safe, 35% are low risk, 9% are high risk, 4% are mid risk, and the remaining 1% is very high risk. The dashboard integrates all collected data from around the nation onto a single platform.

Conclusion

This screening can help the symptomatic patient take immediate action, such as isolation or hospitalization, depending on the severity. This surveillance system can also be utilized for risk mapping, planning, and allocating health resources to more vulnerable areas to reduce the virus's severity.

Participatory surveillance for COVID-19 trends detection in Brazil: Cross-section 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 healthcare 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 collections approaches.

OBJECTIVE

This study compares novel PS data on COVID-19 infection rates across nine Brazilian cities with official TS data to examine the opportunities and challenges of using the former, and the potential advantages of combining the two approaches.

METHODS

The traditional surveillance data for Brazil, prospectively called the TS data, is publicly accessible on GitHub. The participatory surveillance data was collected through the Brazil Sem Corona - a Colab platform. To gather information on an individual's health status, each participant was asked to fill out a daily questionnaire into the Colab app on symptoms as well as exposure.

RESULTS

We find that high participation rates are key for PS data to adequately mirror TS infection rates. Where participation was high, we document a significant trend correlation between lagged PS data and TS infection rates, suggesting that the former 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 horizon model based exclusively on TS data. Furthermore, we show that the PS data captures a population that significantly differs from the traditional observation.

CONCLUSIONS

In the traditional system, the new recorded COVID-19 cases per day are aggregated based on positive lab-confirmed tests. In contrast, the PS data shows a significant share of reports categorized as potential COVID-19 case that are not lab-confirmed. Quantifying the economic value of a PS system implementation remains hard. But scarce public funds as well as persisting constraints to the TS system motivate 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 trade-offs 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, but also shed lights on its limitations, and on the need for additional research to improve future implementations of PS platforms.

CLINICALTRIAL

Syndromic Surveillance Systems for Mass Gatherings: A Scoping Review

Syndromic surveillance involves the near-real-time collection of data from a potential multitude of sources to detect outbreaks of disease or adverse health events earlier than traditional forms of public health surveillance. The purpose of the present study is to elucidate the role of syndromic surveillance during mass gathering scenarios. In the present review, the use of syndromic surveillance for mass gathering scenarios is described, including characteristics such as methodologies of data collection and analysis, degree of preparation and collaboration, and the degree to which prior surveillance infrastructure is utilized. Nineteen publications were included for data extraction. The most common data source for the included syndromic surveillance systems was emergency departments, with first aid stations and event-based clinics also present. Data were often collected using custom reporting forms. While syndromic surveillance can potentially serve as a method of informing public health policy regarding specific mass gatherings based on the profile of syndromes ascertained, the present review does not indicate that this form of surveillance is a reliable method of detecting potentially critical public health events during mass gathering scenarios.

EpiHacks, a Process for Technologists and Health Experts to Cocreate Optimal Solutions for Disease Prevention and Control: User-Centered Design Approach

Background

Technology-based innovations that are created collaboratively by local technology specialists and health experts can optimize the addressing of priority needs for disease prevention and control. An EpiHack is a distinct, collaborative approach to developing solutions that combines the science of epidemiology with the format of a hackathon. Since 2013, a total of 12 EpiHacks have collectively brought together over 500 technology and health professionals from 29 countries.

Objective

We aimed to define the EpiHack process and summarize the impacts of the technology-based innovations that have been created through this approach.

Methods

The key components and timeline of an EpiHack were described in detail. The focus areas, outputs, and impacts of the twelve EpiHacks that were conducted between 2013 and 2021 were summarized.

Results

EpiHack solutions have served to improve surveillance for influenza, dengue, and mass gatherings, as well as laboratory sample tracking and One Health surveillance, in rural and urban communities. Several EpiHack tools were scaled during the COVID-19 pandemic to support local governments in conducting active surveillance. All tools were designed to be open source to allow for easy replication and adaptation by other governments or parties.

Conclusions

EpiHacks provide an efficient, flexible, and replicable new approach to generating relevant and timely innovations that are locally developed and owned, are scalable, and are sustainable.

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.

Detection of Spatiotemporal Clusters of COVID-19-Associated Symptoms and Prevention Using a Participatory Surveillance App: Protocol for the @choum Study

BACKGROUND

The early detection of clusters of infectious diseases such as the SARS-CoV-2-related COVID-19 disease can promote timely testing recommendation compliance and help to prevent disease outbreaks. Prior research revealed the potential of COVID-19 participatory syndromic surveillance systems to complement traditional surveillance systems. However, most existing systems did not integrate geographic information at a local scale, which could improve the management of the SARS-CoV-2 pandemic.

OBJECTIVE

The aim of this study is to detect active and emerging spatiotemporal clusters of COVID-19-associated symptoms, and to examine (a posteriori) the association between the clusters' characteristics and sociodemographic and environmental determinants.

METHODS

This report presents the methodology and development of the @choum (English: "achoo") study, evaluating an epidemiological digital surveillance tool to detect and prevent clusters of individuals (target sample size, N=5000), aged 18 years or above, with COVID-19-associated symptoms living and/or working in the canton of Geneva, Switzerland. The tool is a 5-minute survey integrated into a free and secure mobile app (CoronApp-HUG). Participants are enrolled through a comprehensive communication campaign conducted throughout the 12-month data collection phase. Participants register to the tool by providing electronic informed consent and nonsensitive information (gender, age, geographically masked addresses). Symptomatic participants can then report COVID-19-associated symptoms at their onset (eg, symptoms type, test date) by tapping on the @choum button. Those who have not yet been tested are offered the possibility to be informed on their cluster status (information returned by daily automated clustering analysis). At each participation step, participants are redirected to the official COVID-19 recommendations websites. Geospatial clustering analyses are performed using the modified space-time density-based spatial clustering of applications with noise (MST-DBSCAN) algorithm.

RESULTS

The study began on September 1, 2020, and will be completed on February 28, 2022. Multiple tests performed at various time points throughout the 5-month preparation phase have helped to improve the tool's user experience and the accuracy of the clustering analyses. A 1-month pilot study performed among 38 pharmacists working in 7 Geneva-based pharmacies confirmed the proper functioning of the tool. Since the tool's launch to the entire population of Geneva on February 11, 2021, data are being collected and clusters are being carefully monitored. The primary study outcomes are expected to be published in mid-2022.

CONCLUSIONS

The @choum study evaluates an innovative participatory epidemiological digital surveillance tool to detect and prevent clusters of COVID-19-associated symptoms. @choum collects precise geographic information while protecting the user's privacy by using geomasking methods. By providing an evidence base to inform citizens and local authorities on areas potentially facing a high COVID-19 burden, the tool supports the targeted allocation of public health resources and promotes testing.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/30444.

Electronic data collection, management and analysis tools used for outbreak response in low- and middle-income countries: a systematic review and stakeholder survey

BACKGROUND

Use of electronic data collection, management and analysis tools to support outbreak response is limited, especially in low income countries. This can hamper timely decision-making during outbreak response. Identifying available tools and assessing their functions in the context of outbreak response would support appropriate selection and use, and likely more timely data-driven decision-making during outbreaks.

METHODS

We conducted a systematic review and a stakeholder survey of the Global Outbreak Alert and Response Network and other partners to identify and describe the use of, and technical characteristics of, electronic data tools used for outbreak response in low- and middle-income countries. Databases included were MEDLINE, EMBASE, Global Health, Web of Science and CINAHL with publications related to tools for outbreak response included from January 2010-May 2020. Software tool websites of identified tools were also reviewed. Inclusion and exclusion criteria were applied and counts, and proportions of data obtained from the review or stakeholder survey were calculated.

RESULTS

We identified 75 electronic tools including for data collection (33/75), management (13/75) and analysis (49/75) based on data from the review and survey. Twenty-eight tools integrated all three functionalities upon collection of additional information from the tool developer websites. The majority were open source, capable of offline data collection and data visualisation. EpiInfo, KoBoCollect and Open Data Kit had the broadest use, including for health promotion, infection prevention and control, and surveillance data capture. Survey participants highlighted harmonisation of data tools as a key challenge in outbreaks and the need for preparedness through training front-line responders on data tools. In partnership with the Global Health Network, we created an online interactive decision-making tool using data derived from the survey and review.

CONCLUSIONS

Many electronic tools are available for data -collection, -management and -analysis in outbreak response, but appropriate tool selection depends on knowledge of tools' functionalities and capabilities. The online decision-making tool created to assist selection of the most appropriate tool(s) for outbreak response helps by matching requirements with functionality. Applying the tool together with harmonisation of data formats, and training of front-line responders outside of epidemic periods can support more timely data-driven decision making in outbreaks.

Data and Digital Solutions to Support Surveillance Strategies in the Context of the COVID-19 Pandemic

Background: In order to prevent spread and improve control of infectious diseases, public health experts need to closely monitor human and animal populations. Infectious disease surveillance is an established, routine data collection process essential for early warning, rapid response, and disease control. The quantity of data potentially useful for early warning and surveillance has increased exponentially due to social media and other big data streams. Digital epidemiology is a novel discipline that includes harvesting, analysing, and interpreting data that were not initially collected for healthcare needs to enhance traditional surveillance. During the current COVID-19 pandemic, the importance of digital epidemiology complementing traditional public health approaches has been highlighted. Objective: The aim of this paper is to provide a comprehensive overview for the application of data and digital solutions to support surveillance strategies and draw implications for surveillance in the context of the COVID-19 pandemic and beyond. Methods: A search was conducted in PubMed databases. Articles published between January 2005 and May 2020 on the use of digital solutions to support surveillance strategies in pandemic settings and health emergencies were evaluated. Results: In this paper, we provide a comprehensive overview of digital epidemiology, available data sources, and components of 21st-century digital surveillance, early warning and response, outbreak management and control, and digital interventions. Conclusions: Our main purpose was to highlight the plausible use of new surveillance strategies, with implications for the COVID-19 pandemic strategies and then to identify opportunities and challenges for the successful development and implementation of digital solutions during non-emergency times of routine surveillance, with readiness for early-warning and response for future pandemics. The enhancement of traditional surveillance systems with novel digital surveillance methods opens a direction for the most effective framework for preparedness and response to future pandemics.

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.

Challenges for Nontechnical Implementation of Digital Proximity Tracing During the COVID-19 Pandemic: Media Analysis of the SwissCovid App

BACKGROUND

Several countries have released digital proximity tracing (DPT) apps to complement manual contact tracing for combatting the SARS-CoV-2 pandemic. DPT aims to notify app users about proximity exposures to persons infected with SARS-CoV-2 so that they can self-quarantine. The success of DPT apps depends on user acceptance and the embedding of DPT into the pandemic mitigation strategy.

OBJECTIVE

By searching for media articles published during the first 3 months after DPT launch, the implementation of DPT in Switzerland was evaluated to inform similar undertakings in other countries. The second aim of the study was to create a link between reported DPT implementation challenges and normalization process theory for planning and optimizing complex digital health interventions, which can provide useful guidance for decision-making in DPT design and implementation.

METHODS

A Swiss media database was searched for articles on the Swiss DPT app (SwissCovid) published in German or French between July 4 and October 3, 2020. In a structured process, topics were extracted and clustered manually from articles that were deemed pertinent. Extracted topics were mapped to four NPT constructs, which reflected the flow of intervention development from planning, stakeholder onboarding, and execution to critical appraisal. Coherence constructs describe sense-making by stakeholders, cognitive participation constructs reflect participants' efforts to create engagement with the intervention, collective actions refer to intervention execution and joint stakeholder efforts to make the intervention work, and reflexive monitoring refers to collective risk-benefit appraisals to create improvements.

RESULTS

Out of 94 articles deemed pertinent and selected for closer inspection, 38 provided unique information on implementation challenges. Five challenge areas were identified: communication challenges, challenges for DPT to interface with other processes, fear of resource competition with established pandemic mitigation measures, unclear DPT effectiveness, and obstacles to greater user coverage and compliance. Specifically, several articles mentioned unclear DPT benefits to affect commitment and to raise fears among different health system actors regarding resource competition. Moreover, media reports indicated process interface challenges such as delays or unclear responsibilities in the notification cascade, as well as misunderstandings and unmet communication needs from health system actors. Finally, reports suggested misaligned incentives, not only for app usage by the public but also for process engagement by other actors in the app notification cascade. NPT provided a well-fitting framework to contextualize the different DPT implementation challenges and to highlight improvement strategies, namely a better alignment of stakeholder incentives, or stakeholder-specific communication to address their concerns about DPT.

CONCLUSIONS

Early experiences from one of the first adopters of DPT indicate that nontechnical implementation challenges may affect the effectiveness of DPT. The NPT analysis provides a novel perspective on DPT implementation and stresses the need for stakeholder inclusion in development and operationalization.

Scaling up of Eco-Bio-Social Strategy to Control Aedes aegypti in Highly Vulnerable Areas in Fortaleza, Brazil: A Cluster, Non-Randomized Controlled Trial Protocol

Aedes aegypti is a cosmopolitan vector for arboviruses dengue, Zika and chikungunya, disseminated in all Brazilian states. The Eco-Bio-Social (EBS) strategy is vital in Aedes aegypti control as it mobilizes stakeholders (government, professionals, society, and academics) to promote healthy environments. This paper describes the rationale and methods of expanding the EBS strategy for Aedes aegypti control in Fortaleza, Northeast Brazil. A cluster, non-randomized controlled clinical trial was developed to analyze the strategy’s effectiveness in vulnerable territories (high incidence of dengue and violent deaths; low HDI; substandard urban infrastructure, high population density, and water scarcity). We selected two intervention and two control groups, resulting in a sample of approximately 16,000 properties. The intervention consisted of environmental management by sealing large elevated water tanks, introduction of beta fish in waterholes, elimination of potential breeding sites, and mobilization and training of schoolchildren, endemic disease workers, health workers, social mobilizers, and community leaders; community surveillance of arboviruses; construction and validation of a booklet for the prevention of arboviruses in pregnant women. We analyzed the costs of arboviruses to government and households, the intervention cost-effectiveness, chikungunya’s chronicity, and acceptance, sustainability, and governance of vector control actions. The primary outcome (infestation) was analyzed using the house, container, and Breteau indices. We hope that this study will help us understand how to scale up strategies to fight Aedes aegypti in vulnerable areas.

Citizen-Centered Mobile Health Apps Collecting Individual-Level Spatial Data for Infectious Disease Management: Scoping Review

BACKGROUND

The novel coronavirus SARS-CoV-2 rapidly spread around the world, causing the disease COVID-19. To contain the virus, much hope is placed on participatory surveillance using mobile apps, such as automated digital contact tracing, but broad adoption is an important prerequisite for associated interventions to be effective. Data protection aspects are a critical factor for adoption, and privacy risks of solutions developed often need to be balanced against their functionalities. This is reflected by an intensive discussion in the public and the scientific community about privacy-preserving approaches.

OBJECTIVE

Our aim is to inform the current discussions and to support the development of solutions providing an optimal balance between privacy protection and pandemic control. To this end, we present a systematic analysis of existing literature on citizen-centered surveillance solutions collecting individual-level spatial data. Our main hypothesis is that there are dependencies between the following dimensions: the use cases supported, the technology used to collect spatial data, the specific diseases focused on, and data protection measures implemented.

METHODS

We searched PubMed and IEEE Xplore with a search string combining terms from the area of infectious disease management with terms describing spatial surveillance technologies to identify studies published between 2010 and 2020. After a two-step eligibility assessment process, 27 articles were selected for the final analysis. We collected data on the four dimensions described as well as metadata, which we then analyzed by calculating univariate and bivariate frequency distributions.

RESULTS

We identified four different use cases, which focused on individual surveillance and public health (most common: digital contact tracing). We found that the solutions described were highly specialized, with 89% (24/27) of the articles covering one use case only. Moreover, we identified eight different technologies used for collecting spatial data (most common: GPS receivers) and five different diseases covered (most common: COVID-19). Finally, we also identified six different data protection measures (most common: pseudonymization). As hypothesized, we identified relationships between the dimensions. We found that for highly infectious diseases such as COVID-19 the most common use case was contact tracing, typically based on Bluetooth technology. For managing vector-borne diseases, use cases require absolute positions, which are typically measured using GPS. Absolute spatial locations are also important for further use cases relevant to the management of other infectious diseases.

CONCLUSIONS

We see a large potential for future solutions supporting multiple use cases by combining different technologies (eg, Bluetooth and GPS). For this to be successful, however, adequate privacy-protection measures must be implemented. Technologies currently used in this context can probably not offer enough protection. We, therefore, recommend that future solutions should consider the use of modern privacy-enhancing techniques (eg, from the area of secure multiparty computing and differential privacy).

Prioritizing COVID-19 tests based on participatory surveillance and spatial scanning

OBJECTIVES

This study aimed to identify, describe and analyze priority areas for COVID-19 testing combining participatory surveillance and traditional surveillance.

DESIGN

It was carried out a descriptive transversal study in the city of Caruaru, Pernambuco state, Brazil, within the period of 20/02/2020 to 05/05/2020. Data included all official reports for influenza-like illness notified by the municipality health department and the self-reports collected through the participatory surveillance platform Brasil Sem Corona.

METHODS

We used linear regression and loess regression to verify a correlation between Participatory Surveillance (PS) and Traditional Surveillance (TS). Also a spatial scanning approach was deployed in order to identify risk clusters for COVID-19.

RESULTS

In Caruaru, the PS had 861 active users, presenting an average of 1.2 reports per user per week. The platform Brasil Sem Corona started on March 20th and since then, has been officially used by the Caruaru health authority to improve the quality of information from the traditional surveillance system. Regarding the respiratory syndrome cases from TS, 1588 individuals were positive for this clinical outcome. The spatial scanning analysis detected 18 clusters and 6 of them presented statistical significance (p-value < 0.1). Clusters 3 and 4 presented an overlapping area that was chosen by the local authority to deploy the COVID-19 serology, where 50 individuals were tested. From there, 32 % (n = 16) presented reagent results for antibodies related to COVID-19.

CONCLUSION

Participatory surveillance is an effective epidemiological method to complement the traditional surveillance system in response to the COVID-19 pandemic by adding real-time spatial data to detect priority areas for COVID-19 testing.