Participatory Disease Surveillance Research Articles

Moana: Alternate surveillance for COVID-19 in a Unique Population (MASC-UP)

ObjectiveCreate a longitudinal, multi-modal and multi-level surveillance cohort that targets early detection of symptomatic and asymptomatic COVID-19 cases among Native Hawaiian and Pacific Islander adults in the Continental US and identify effective modalities for participatory disease surveillance and sustainably integrate them into ongoing COVID-19 and other public health surveillance efforts. Materials and methodsWe recruited cohorts from three sites: Federal Way, WA; Springdale, AR; and remotely. Participants received a survey that included demographic characteristics and questions regarding COVID-19. Participants completed symptom checks via text message every month and recorded their temperature daily using a Kinsa smart thermometer. ResultsRecruitment and data collection is ongoing. Presently, 441 adults have consented to participate. One-third of participants were classified as essential workers during the pandemic. DiscussionOver the past 18 months, we have improved our strategies to elicit better data from participants and have learned from some of the weaknesses in our initial deployment of this type of surveillance system. Other limitations stem from historic inequities and barriers which limited Native Hawaiian and Pacific Island representation in academic and clinical environments. One manifestation of this was the limited ability to provide study materials and support in multiple languages. We hope that continued partnership with the community will allow further opportunities to help restore trust in academic and medical institutions, thus generating knowledge to advance health equity. ConclusionThis participatory disease surveillance mechanism complements traditional surveillance systems by engaging underserved communities. We may also gain insights generalizable to other pathogens of concern.

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.

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

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. 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. 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. 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. 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.

Participatory disease surveillance for a mass gathering — a prospective cohort study on COVID-19, Germany 2021

BackgroundMass gatherings (MGs) such as music festivals and sports events have been associated with a high risk of SARS-CoV-2 transmission. On-site research can foster knowledge of risk factors for infections and improve risk assessments and precautionary measures at future MGs. We tested a web-based participatory disease surveillance tool to detect COVID-19 infections at and after an outdoor MG by collecting self-reported COVID-19 symptoms and tests.MethodsWe conducted a digital prospective observational cohort study among fully immunized attendees of a sports festival that took place from September 2 to 5, 2021 in Saxony-Anhalt, Germany. Participants used our study app to report demographic data, COVID-19 tests, symptoms, and their contact behavior. This self-reported data was used to define probable and confirmed COVID-19 cases for the full “study period” (08/12/2021 – 10/31/2021) and within the 14-day “surveillance period” during and after the MG, with the highest likelihood of an MG-related COVID-19 outbreak (09/04/2021 – 09/17/2021).ResultsA total of 2,808 of 9,242 (30.4%) event attendees participated in the study. Within the study period, 776 individual symptoms and 5,255 COVID-19 tests were reported. During the 14-day surveillance period around and after the MG, seven probable and seven PCR-confirmed COVID-19 cases were detected. The confirmed cases translated to an estimated seven-day incidence of 125 per 100,000 participants (95% CI [67.7/100,000, 223/100,000]), which was comparable to the average age-matched incidence in Germany during this time. Overall, weekly numbers of COVID-19 cases were fluctuating over the study period, with another increase at the end of the study period.ConclusionCOVID-19 cases attributable to the mass gathering were comparable to the Germany-wide age-matched incidence, implicating that our active participatory disease surveillance tool was able to detect MG-related infections. Further studies are needed to evaluate and apply our participatory disease surveillance tool in other mass gathering settings.

Digital COVID-19 surveillance tool for a mass gathering event - a prospective cohort study

Abstract Introduction Mass gatherings have been associated with a high risk of SARS-CoV-2 transmission. On-site research can foster knowledge of risk factors for infections and improve risk assessments and precautionary measures at future events. We tested a web-based participatory disease surveillance tool to detect COVID-19 infections at and after an outdoor mass gathering by collecting self-reported COVID-19 symptoms and tests. Methods We conducted a digital prospective observational cohort study among fully immunized attendees of a sports event that took place from September 2 to 5, 2021 in Thuringia, Germany. Participants used our study app to report demographic data, COVID-19 tests, symptoms, and their contact behavior. This self-reported data was used to define probable and confirmed COVID-19 cases during the full “study period” (08/12/2021 - 10/31/2021) and within the 14-day “surveillance period” during and after the event, in which the highest likelihood of an event related COVID-19 outbreak could be expected (09/04/2021 - 09/17/2021). Results A total of 2,808 of 9,242 (30.4%) event attendees participated in the study. During the study period, 776 symptoms and 5,255 COVID-19 tests were reported in the study app. During the surveillance period, seven PCR positive COVID-19 cases were found to be associated with the event. This translated to an estimated seven-day incidence of ∼125/100,000 cases (95% CI [67.7/100,000, 223/100,000]), which was comparable to the average age-matched incidence in Germany during this time (118.3/100,000). Conclusions COVID-19 cases attributable to the mass gathering were comparable to the German-wide age-matched incidence, implicating that our active participatory surveillance tool was able to detect mass gathering related infections. Further studies are needed to evaluate and apply our participatory disease surveillance tool in other mass gathering settings. Key messages • Our digital COVID-19 surveillance tool for mass gathering events was easy to implement within the organizational structure of the event and well accepted amongst event attendees. • Our active participatory surveillance tool was able to detect mass gathering related infections comparable to the Germany-wide incidence.

The Impact of the First Year of the COVID-19 Pandemic on Canine Rabies Control Efforts: A Mixed-Methods Study of Observations About the Present and Lessons for the Future

Achieving zero human deaths from dog-mediated rabies has been set as a global target for 2030. However, the COVID-19 pandemic has disrupted essential health services across the world, with disproportionate impacts on Neglected Tropical Diseases. Through a mixed-method study using stakeholder questionnaires and in-depth interviews, we examined the scale and nature of disruption from the first year of the pandemic to rabies control programs, and reflected on lessons for the future. Study participants included practitioners and policymakers working in government, academia, international organizations, and the pharmaceutical industry across 48 countries, mainly in Africa and Asia. Mass dog vaccination, essential to rabies control, was most heavily impacted and in 2020, was carried out as planned in just 5% of surveyed countries. Access to post-exposure prophylaxis (PEP) also decreased due to fear of COVID-19 infection and difficulties in reaching health care centers. Dog vaccination and PEP delivery suffered from disruptions to the importation and distribution of vaccines. School closures affected rabies awareness activities and, when public events moved online, they could not reach the most disadvantaged groups. Surveillance, already weak, was severely disrupted by movement restrictions which, together with reduced demand for PEP, exacerbated under-reporting. Participants reported growing complaints around free-roaming dogs, with numbers likely to have increased in some settings. In some countries, dog rabies outbreaks and human rabies cases were already ascribed to the pandemic, but further impacts are likely still to be realized. Meanwhile, decreased demand for PEP from COVID-19 constraints could lead to reduced procurement in future. In the wake of post-COVID-19 demands on health services, there is an opportunity for veterinary services to show leadership in progressing the Zero by 30 agenda, particularly in scaling up mass dog vaccination within and across countries, as well as potential to make better use of community-based vaccinators. Countries must further secure stable procurement of dog and human vaccines, classifying them as essential goods prioritized for import and where needed, through sharing of stocks. Dedicated telemedicine services also show promise, for example through fostering participatory disease surveillance, including Integrated Bite Case Management, and delivering up-to-date instructions on the closest sources of PEP.

Representativeness of the FluWatchers Participatory Disease Surveillance Program 2015-2016 to 2018-2019: How do participants compare with the Canadian population?

FluWatch is Canada's national surveillance system that monitors the spread of influenza. Its syndromic surveillance component monitors the spread of influenza-like illness (ILI) in near-real time for signals of unusual or increased activity. Syndromic surveillance data are collected from two main sources: the Sentinel Practitioner ILI Reporting System and FluWatchers.We evaluated the representativeness of the most recent participant population to understand changes in representativeness since 2015, to identify demographic and geographic gaps and correlates/determinants of participation to characterize a typical participant. In this serial cross-sectional study, characteristics of participants during four consecutive influenza seasons (2015-2016, 2016-2017, 2017-2018 and 2018-2019) were compared with the 2016 Canadian Census and the 2015-2016, 2016-2017, 2017-2018 and 2018-2019 National Seasonal Influenza Vaccination Coverage Surveys. Associations between demographic factors and the level of user participation were also analyzed among the 2018-2019 FluWatchers population. Infants (0-4 years) and older adults (65 years and older) were under-represented in FluWatchers across all four influenza seasons. Female and urban participants were significantly over-represented. Vaccination coverage remained significantly higher among the FluWatchers populations from the past four influenza seasons across all age groups. Level of participation among FluWatchers was associated with age and vaccination status, but not with sex or geography. Over its four years of implementation, the FluWatchers participant population became more representative of the Canadian population with respect to age and geography (urban/rural and provincial/territorial). FluWatchers participants under-represent the tails of Canada's age distribution and over-represent those who engage in health promoting behaviours as indicated by high influenza vaccine coverage, consistent with typical volunteer-based survey response biases. Representativeness would likely improve with targeted recruitment of under-represented groups, such as males, older adults and Canadians living in rural areas.

PNS198 Arogya Setu Mobile Application for COVID-19 Self-Assessment, Education and Participatory Disease Surveillance: Perception Among Medical and Technology University Students.

Technology plays a tremendous role in the active surveillance and communication in COVID-19 containment. We estimate the perceptions of medical and technology university students from India towards the use of Arogya Setu mobile application developed by the Ministry of Health and Family Welfare, Government of India.

Participatory disease surveillance in India: A critical interpretative synthesis!

Participatory disease surveillance in India: A critical interpretative synthesis!

Exploring power and participation through informal livestock knowledge networks

Participatory epidemiology programmes aim to collect data by engaging local communities in knowledge sharing around livestock health. Critics of participatory approaches often cite the extractive nature of data collection and unequal power relations between researcher and researched as at odds with the original vision of participatory programming. This paper starts from the position that rural livestock owners are situated within multiple overlapping webs of relationships through which they exchange disease information and access resources. Participatory programmes are suggested as weaving new threads into these wider networks in a process that may be accepted or rejected by indigenous actors. Qualitative interviews were used to gather empirical data on the exchange of information around livestock health knowledge through indigenous relationships and a Participatory Disease Surveillance (PDS) programme within a Gabra pastoralist community in Northern Kenya. Subsequent analysis identified four pathway typologies; this paper provides a qualitative comparative analysis of each to explore the nature of participation within the study population. The paper concludes that social science approaches have a key role to play in understanding how relationships within and between indigenous and development actors can influence participation in development projects.

Postcode based participatory disease surveillance systems : a comparison with traditional risk-based surveillance and its application in the COVID-19 pandemic.

Background: The SARS-Cov-2 infection has rapidly saturated health systems and traditional surveillance networks are finding hard to keep pace with its spread. We designed a participatory disease surveillance (PDS) system, to capture symptoms of Influenza-like illness (ILI) to estimate SARS-CoV-2 infection in the community. While data generated by these platforms can help public health organisations find community hotspots and effectively direct control measures, it has never been compared to traditional systems. Methods and Objectives: A completely anonymised web based PDS system, www.trackcovid-19.org was developed. We evaluated the symptomatic responses received form the PDS system to the traditional risk based surveillance carried out by the Bruhat Bengaluru Mahanagara Palike over a period of 45 days in the South Indian city of Bengaluru. Methods and Objectives: A completely anonymised web based PDS system, www.trackcovid-19.org was developed. We evaluated the symptomatic responses received form the PDS system to the traditional risk based surveillance carried out by the Bruhat Bengaluru Mahanagara Palike over a period of 45 days in the South Indian city of Bengaluru. Results: The PDS system recorded 11062 entries from 106 Postal codes. A healthy response was obtained from 10863 users while 199 (1.8%) reported symptomatic. Subgroup analysis of a 14 day symptomatic window recorded 33 (0.29%) responses. Risk based surveillance was carried out covering a population of 605,284 with 209 (0.03%) individuals identified symptomatic. Conclusion: Web PDS platforms provide better visualisation of community infection when compared to traditional risk based surveillance systems. They are extremely useful by providing real time information in the extended battle against this pandemic. When integrated into national disease surveillance systems, they can provide long term community surveillance adding an important cost-effective layer to already available data sources.

A Case for Participatory Disease Surveillance of the COVID-19 Pandemic in India.

The coronavirus disease pandemic requires the deployment of novel surveillance strategies to curtail further spread of the disease in the community. Participatory disease surveillance mechanisms have already been adopted in countries for the current pandemic. India, with scarce resources, good telecom support, and a not-so-robust heath care system, makes a strong case for introducing participatory disease surveillance for the prevention and control of the pandemic. India has just launched Aarogya Setu, which is a first-of-its-kind participatory disease surveillance initiative in India. This will supplement the existing Integrated Disease Surveillance Programme in India by finding missing cases and having faster aggregation, analysis of data, and prompt response measures. This newly created platform empowers communities with the right information and guidance, enabling protection from infection and reducing unnecessary contact with the overburdened health care system. However, caution needs to be exercised to address participation from digitally isolated populations, ensure the reliability of data, and consider ethical concerns such as maintaining individual privacy.

Drivers, Risk Factors and Dynamics of African Swine Fever Outbreaks, Southern Highlands, Tanzania.

African swine fever remains an important pig disease globally in view of its rapid spread, economic impacts and food implications, with no option of vaccination or treatment. The Southern Highlands zone of Tanzania, an important pig-producing hub in East Africa, is endemic with African swine fever (ASF). From approximately the year 2010, the recurrence of outbreaks has been observed and it has now become a predictable pattern. We conducted exploratory participatory epidemiology and participatory disease surveillance in the Southern Highlands to understand the pig sector and the drivers and facilitators of infections, risk factors and dynamics of ASF in this important pig-producing area. Pigs continue to play a major role in rural livelihoods in the Southern Highlands and pork is a major animal protein source. Outbreaks of diseases, particularly ASF, have continued to militate against the scaling up of pig operations in the Southern Highlands. Intra- and inter-district and trans-border transnational outbreaks of ASF, the most common disease in the Southern Highlands, continue to occur. Trade and marketing systems, management systems, and lack of biosecurity, as well as anthropogenic (human) issues, animals and fomites, were identified as risk factors and facilitators of ASF infection. Changes in human behavior and communication in trade and marketing systems in the value chain, biosecurity and pig management practices are warranted. Relevant training must be implemented alongside the launch of the national ASF control strategy for Tanzania, which already established a roadmap for combating ASF in Tanzania. The high-risk points (slaughter slabs, border areas, and farms with poor biosecurity) and high-risk period (November–March) along the pig value chain must be targeted as critical control points for interventions in order to reduce the burden of infection.

Smartphone geospatial apps for dengue control, prevention, prediction, and education: MOSapp, DISapp, and the mosquito perception index (MPI).

India has the largest number of dengue cases in the world, contributing approximately 34% of the global burden. The framework for a geospatially enabled early warning and adaptive response system (EWARS) was first proposed in 2008. It was meant to be a decision support system for enhancing traditional surveillance methods for preventing mosquito-borne diseases in India by utilizing remote sensing data and fuzzy logic-based mathematical predictive modeling. This conceptual paper presents a significant evolution of EWARS such that it synthesizes inputs from not only traditional surveillance and reporting systems for dengue but also from the public via participatory disease surveillance. Two smartphone-based applications have been developed to support EWARS. The first-MOSapp-allows field health workers to upload surveillance data and collect key data on environmental parameters by both direct observation and via portable microclimate stations. The second-DISapp-collects relevant information directly from the community to support participatory disease surveillance. It also gives the user a real-time estimate of the risk of exposure to dengue in proximity to their home and has an educational component that provides information on relevant preventive measures. Both applications utilize a new mosquito abundance measure-the mosquito perception index (MPI)-as reported by the user. These data streams will feed into the EWARS model to generate dynamic risk maps that can guide resource optimization and strengthen disease surveillance, prevention, and response. It is anticipated that such an approach can assist in addressing gaps in the current system of dengue surveillance and control in India.

Global flu view: a platform to connect crowdsourced disease surveillance around the world

Purpose: Participatory disease surveillance actively engages the public in reporting on symptoms of their health to provide community-level data that complements traditional healthcare-based surveillance. Participatory surveillance is timely, low-cost, can account for non-medically attended populations, and allows for direct engagement with local populations. Three examples of participatory surveillance systems – Flu Near You in North America, Influenzanet in Europe, and Flu Tracking in Australia and New Zealand – have recently collaborated to develop Global Flu View, a shared platform for aggregation and dissemination of crowdsourced data on influenza-like illness. Methods & Materials: The collaborating surveillance systems have developed a shared application programming interface (API) for data exchange among systems. The API specifications outline specific variables that are called into a shared, cloud-based database. Data is processed via an ETL layer resulting in aggregate data at the postcode level. Key variables include reporting week, postal and country codes, user birth month and birth year, gender, vaccination status, and a series of symptom variables that include fever, cough, sore throat, headache, fatigue, and other indications of influenza-like illness. Processed data will be available to view in both graph and map formats on the website www.globalfluview.org. Results: The Global Flu View platform connects community-level influenza surveillance efforts across 15 countries on 3 continents. Tens of thousands of weekly reports are made available for users to explore through graphs, maps, and various filtering tools. The Global Flu View database also serves as a repository of influenza surveillance data that can be shared with disease modelers, researchers, and epidemiologists around the world. Conclusion: Global Flu View can highlight the value of the participatory surveillance approach, serving as a model for other countries. The platform is poised to incorporate data from additional countries with similar tools and serves as a case study for collaborative, multi-country data sharing for global health surveillance. As of July 2018 the platform is in beta-testing mode and will be made publicly available by late August 2018.

Flu near you: crowdsourcing influenza-like illness reporting in the United States comparing the 2016-17 and 2017-18 influenza season with participant-reported symptoms

Purpose: The United States Centers for Disease Control and Prevention (CDC) estimates that influenza sickened over 30 million people in the United States during the 2016-17 influenza season. While vaccination remains one of the best defenses against it, early detection is crucial to halting its spread. Recognizing symptoms early can allow public health officials, and the communities they serve, to take steps to reduce transmission. Flu Near You is an online participatory disease surveillance system that allows volunteers in the United States and Canada to report their health information using a brief weekly survey. The system collects symptom data, which it publishes to the website, and offers an interface to compare its data with data from the CDC sentinel influenza network. Participating in Flu Near You is simple – individuals can enroll in the Flu Near You system by signing up at www.flunearyou.org. Methods & Materials: Here we compare self-reported symptoms during the 2017-18 influenza season to the 2016-17 influenza season, to determine any differences in symptom reporting and healthcare-seeking behavior observed between the two years. Symptoms include: fever, headache, diarrhea, fatigue, nausea, rash, cough, sore throat, body ache, chills/night sweats, shortness of breath, and runny nose. Influenza-like-illnesses (ILI) include: fever and cough and/or sore throat. Results: During the 2017-18 influenza season, 51.6% participants reported at least one symptom through FNY that was either self-reported (users) or submitted on their behalf (household members). Compared to the 2016-17 influenza season, 44.5% participants reported at least symptom through Flu Near You. We also observed that 22.7% participants with at least one symptom sought medical attention during the 2017-18 influenza season, compared to 26.5% participants during the 2016-17 influenza season. Conclusion: We observe an increase in symptom reporting during the 2017-18 influenza season to date, compared to the 2016-17 flu season. We also observed a decrease in participants experiencing symptoms who seek medical attention. We hope to provide an opportunity for the public to engage directly, as well as continue to be the trusted source for community-level disease surveillance.

A Smartphone App (AfyaData) for Innovative One Health Disease Surveillance from Community to National Levels in Africa: Intervention in Disease Surveillance

BackgroundWe describe the development and initial achievements of a participatory disease surveillance system that relies on mobile technology to promote Community Level One Health Security (CLOHS) in Africa.ObjectiveThe objective of this system, Enhancing Community-Based Disease Outbreak Detection and Response in East and Southern Africa (DODRES), is to empower community-based human and animal health reporters with training and information and communication technology (ICT)–based solutions to contribute to disease detection and response, thereby complementing strategies to improve the efficiency of infectious disease surveillance at national, regional, and global levels. In this study, we refer to techno-health as the application of ICT-based solutions to enhance early detection, timely reporting, and prompt response to health events in human and animal populations.MethodsAn EpiHack, involving human and animal health experts as well as ICT programmers, was held in Tanzania in 2014 to identify major challenges facing early detection, timely reporting, and prompt response to disease events. This was followed by a project inception workshop in 2015, which brought together key stakeholders, including policy makers and community representatives, to refine the objectives and implementation plan of the DODRES project. The digital ICT tools were developed and packaged together as the AfyaData app to support One Health disease surveillance. Community health reporters (CHRs) and officials from animal and human health sectors in Morogoro and Ngorongoro districts in Tanzania were trained to use the AfyaData app. The AfyaData supports near- to real-time data collection and submission at both community and health facility levels as well as the provision of feedback to reporters. The functionality of the One Health Knowledge Repository (OHKR) app has been integrated into the AfyaData app to provide health information on case definitions of diseases of humans and animals and to synthesize advice that can be transmitted to CHRs with next step response activities or interventions. Additionally, a WhatsApp social group was made to serve as a platform to sustain interactions between community members, local government officials, and DODRES team members.ResultsWithin the first 5 months (August-December 2016) of AfyaData tool deployment, a total of 1915 clinical cases in livestock (1816) and humans (99) were reported in Morogoro (83) and Ngorongoro (1832) districts.ConclusionsThese initial results suggest that the DODRES community-level model creates an opportunity for One Health engagement of people in their own communities in the detection of infectious human and animal disease threats. Participatory approaches supported by digital and mobile technologies should be promoted for early disease detection, timely reporting, and prompt response at the community, national, regional, and global levels.

Participatory Disease Surveillance in 21 Villages of Igabi and Chukun L.G.A.s of Kaduna State, Nigeria

Participatory Disease Surveillance in 21 Villages of Igabi and Chukun L.G.A.s of Kaduna State, Nigeria

Participatory Disease Surveillance: Engaging Communities Directly in Reporting, Monitoring, and Responding to Health Threats.

BackgroundSince 2012, the International Workshop on Participatory Surveillance (IWOPS) has served as an informal network to share best practices, consult on analytic methods, and catalyze innovation to advance the burgeoning method of direct engagement of populations in voluntary monitoring of disease.ObjectiveThis landscape provides an overview of participatory disease surveillance systems in the IWOPS network and orients readers to this growing field of practice.MethodsAuthors reviewed participatory approaches that include human and animal health surveillance, both syndromic (self- reported symptoms) and event-based, and how these tools have been leveraged for disease modeling and forecasting. The authors also discuss benefits, challenges, and future directions for participatory disease surveillance.ResultsThere are at least 23 distinct participatory surveillance tools or programs represented in the IWOPS network across 18 countries. Organizations supporting these tools are diverse in nature.ConclusionsParticipatory disease surveillance is a promising method to complement both traditional, facility-based surveillance and newer digital epidemiology systems.

Determinants of Participants' Follow-Up and Characterization of Representativeness in Flu Near You, A Participatory Disease Surveillance System.

BackgroundFlu Near You (FNY) is an Internet-based participatory surveillance system in the United States and Canada that allows volunteers to report influenza-like symptoms using a brief weekly symptom report.ObjectiveOur objective was to evaluate the representativeness of the FNY population compared with the general population of the United States, explore the demographic and behavioral characteristics associated with FNY’s high-participation users, and summarize results from a user survey of a cohort of FNY participants.MethodsWe compared (1) the representativeness of sex and age groups of FNY participants during the 2014-2015 flu season versus the general US population and (2) the distribution of Human Development Index (HDI) scores of FNY participants versus that of the general US population. We analyzed associations between demographic and behavioral factors and the level of participant follow-up (ie, high vs low). Finally, descriptive statistics of responses from FNY’s 2015 and 2016 end-of-season user surveys were calculated.ResultsDuring the 2014-2015 influenza season, 47,234 unique participants had at least one FNY symptom report that was either self-reported (users) or submitted on their behalf (household members). The proportion of female FNY participants was significantly higher than that of the general US population (n=28,906, 61.2% vs 51.1%, P<.001). Although each age group was represented in the FNY population, the age distribution was significantly different from that of the US population (P<.001). Compared with the US population, FNY had a greater proportion of individuals with HDI >5.0, signaling that the FNY user distribution was more affluent and educated than the US population baseline. We found that high-participation use (ie, higher participation in follow-up symptom reports) was associated with sex (females were 25% less likely than men to be high-participation users), higher HDI, not reporting an influenza-like illness at the first symptom report, older age, and reporting for household members (all differences between high- and low-participation users P<.001). Approximately 10% of FNY users completed an additional survey at the end of the flu season that assessed detailed user characteristics (3217/33,324 in 2015; 4850/44,313 in 2016). Of these users, most identified as being either retired or employed in the health, education, and social services sectors and indicated that they achieved a bachelor’s degree or higher.ConclusionsThe representativeness of the FNY population and characteristics of its high-participation users are consistent with what has been observed in other Internet-based influenza surveillance systems. With targeted recruitment of underrepresented populations, FNY may improve as a complementary system to timely tracking of flu activity, especially in populations that do not seek medical attention and in areas with poor official surveillance data.