Event-based surveillance at health facility and community level in low-income and middle-income countries: a systematic review

Strengthening event-based surveillance (EBS): a case study from Afghanistan

The sustained instability in Afghanistan, along with ongoing disease outbreaks and the impact of the COVID-19 pandemic, has significantly affected the country.During the COVID-19 pandemic, the country's detection and response capacities faced challenges. Case identification was done in all health facilities from primary to tertiary levels but neglected cases at the community level, resulting in undetected and uncontrolled transmission from communities. This emphasizes a missed opportunity for early detection that Event-Based Surveillance (EBS) could have facilitated.Therefore, Afghanistan planned to strengthen the EBS component of the national public health surveillance system to enhance the capacity for the rapid detection and response to infectious disease outbreaks, including COVID-19 and other emerging diseases. This effort was undertaken to promptly mitigate the impact of such outbreaks.We conducted a landscape assessment of Afghanistan's public health surveillance system to identify the best way to enhance EBS, and then we crafted an implementation work plan. The work plan included the following steps: establishing an EBS multisectoral coordination and working group, identifying EBS information sources, prioritizing public health events of importance, defining signals, establishing reporting mechanisms, and developing standard operating procedures and training guides.EBS is currently being piloted in seven provinces in Afghanistan. The lessons learned from the pilot phase will support its overall expansion throughout the country.

Establishing an early warning surveillance system in jails in Calabarzon, the Philippines, 2021

The Philippines' Republic Act 11332 (2020) mandates prisons, jails and detention centres to participate in disease surveillance, but currently no surveillance system exists in these facilities. This report aims to describe the piloting of an early warning disease surveillance system in 21 selected jails in Calabarzon from July to September 2021. Sites were selected based on congestion, proximity to health facilities and logistical capacity. Data sources, collection mechanisms and reporting tools were determined and health personnel were trained in the operation of the system. During the implementation period, the system detected 10 health events, with influenza-like illness and foodborne illness being the most common. Nine of these events were reported within 24 hours. The local health unit provided medications for clinical management and instructed jail nurses on infection prevention and control measures, including active case finding, the isolation of cases and the inspection of food handling. Twelve sites reported over 8 of the 10 weeks, with all sites reporting zero cases promptly. The challenges identified included insufficient workforce, slow internet speed and multitasking. It was concluded that the jail-based early warning surveillance system is feasible and functional, but the perceived benefits of jail management are crucial to the acceptability and ownership of the system. It is recommended to replicate the surveillance system in other penitentiaries nationwide.

An indicator framework for the monitoring and evaluation of event-based surveillance systems

Event-based surveillance (EBS) systems have been implemented globally to support early warning surveillance across human, animal, and environmental health in diverse settings, including at the community level, within health facilities, at border points of entry, and through media monitoring of internet-based sources. EBS systems should be evaluated periodically to ensure that they meet the objectives related to the early detection of health threats and to identify areas for improvement in the quality, efficiency, and usefulness of the systems. However, to date, there has been no comprehensive framework to guide the monitoring and evaluation of EBS systems; this absence of standardisation has hindered progress in the field. The Africa Centres for Disease Control and Prevention and US Centers for Disease Control and Prevention have collaborated to develop an EBS monitoring and evaluation indicator framework, adaptable to specific country contexts, that uses measures relating to input, activity, output, outcome, and impact to map the processes and expected results of EBS systems. Through the implementation and continued refinement of these indicators, countries can ensure the early detection of health threats and improve their ability to measure and describe the impacts of EBS systems, thus filling the current evidence gap regarding their effectiveness.

Evaluation of a multi-component early warning system for pastoralist populations in Doolo zone, Ethiopia: mixed-methods study

BACKGROUND

This study evaluated an early warning, alert and response system for a crisis-affected population in Doolo zone, Somali Region, Ethiopia, in 2019-2021, with a history of epidemics of outbreak-prone diseases. To adequately cover an area populated by a semi-nomadic pastoralist, or livestock herding, population with sparse access to healthcare facilities, the surveillance system included four components: health facility indicator-based surveillance, community indicator- and event-based surveillance, and alerts from other actors in the area. This evaluation described the usefulness, acceptability, completeness, timeliness, positive predictive value, and representativeness of these components.

METHODS

We carried out a mixed-methods study retrospectively analysing data from the surveillance system February 2019-January 2021 along with key informant interviews with system implementers, and focus group discussions with local communities. Transcripts were analyzed using a mixed deductive and inductive approach. Surveillance quality indicators assessed included completeness, timeliness, and positive predictive value, among others.

RESULTS

1010 signals were analysed; these resulted in 168 verified events, 58 alerts, and 29 responses. Most of the alerts (46/58) and responses (22/29) were initiated through the community event-based branch of the surveillance system. In comparison, one alert and one response was initiated via the community indicator-based branch. Positive predictive value of signals received was about 6%. About 80% of signals were verified within 24 h of reports, and 40% were risk assessed within 48 h. System responses included new mobile clinic sites, measles vaccination catch-ups, and water and sanitation-related interventions. Focus group discussions emphasized that responses generated were an expected return by participant communities for their role in data collection and reporting. Participant communities found the system acceptable when it led to the responses they expected. Some event types, such as those around animal health, led to the community's response expectations not being met.

CONCLUSIONS

Event-based surveillance can produce useful data for localized public health action for pastoralist populations. Improvements could include greater community involvement in the system design and potentially incorporating One Health approaches.

Evaluating event-based surveillance capacity in Africa: Use of the Africa CDC scorecard, 2022-2023

Introduction

Event-based surveillance (EBS) is a critical component of Early Warning, Alert and Response (EWAR) capacity needed for outbreak prevention and control. To better understand existing EBS and monitor the progress of capacity-building efforts over time, Africa CDC developed an EBS scorecard as part of a revision to the EBS Framework.

Methods

We distributed the scorecard to African Union (AU) Member States (MSs). Survey responses from the MSs' human health sector were aggregated, cleaned, and analysed. MS, regional, and continental EBS capacity was assessed.

Results

Between 21 July 2022 and 4 April 2023, a total of 63 respondents representing 49 (89%) of 55 MSs completed the survey. Given Africa CDC's public health mandate, we acknowledged the importance of One Health collaboration in MSs but focused on and analysed only the human health sector responses. Thirty-four (71%) MSs stated having EBS in place; hotline was the most common type of EBS implemented (76%). Seventeen (50%) MSs reported multisectoral, One Health collaboration as part of EBS implementation. Scorecard outcomes showed a minimal (score of <60%) to average (score between 60-80%) level of EBS capacity in 29 and five (5) MSs respectively.

Discussion

Current EBS capacity levels need to be strengthened in Africa to ensure the continent remains prepared for future public health threats. The Africa CDC EBS scorecard provides a useful way to measure and track this capacity over time. Results can be used to advocate for and target resources for capacity building to foster public health emergency preparedness efforts.

Early-stage pregnancy recognition on microblogs: Machine learning and lexicon-based approaches

Pregnancy carries high medical and psychosocial risks that could lead pregnant women to experience serious health consequences. Providing protective measures for pregnant women is one of the critical tasks during the pregnancy period. This study proposes an emotion-based mechanism to detect the early stage of pregnancy using real-time data from Twitter. Pregnancy-related emotions (e.g., anger, fear, sadness, joy, and surprise) and polarity (positive and negative) were extracted from users' tweets using NRC Affect Intensity Lexicon and SentiStrength techniques. Then, pregnancy-related terms were extracted and mapped with pregnancy-related sentiments using part-of-speech tagging and association rules mining techniques. The results showed that pregnancy tweets contained high positivity, as well as significant amounts of joy, sadness, and fear. The classification results demonstrated the possibility of using users' sentiments for early-stage pregnancy recognition on microblogs. The proposed mechanism offers valuable insights to healthcare decision-makers, allowing them to develop a comprehensive understanding of users' health status based on social media posts.

SARS-CoV-2 case detection using community event-based surveillance system-February-September 2020: lessons learned from Senegal

The COVID-19 pandemic necessitated the rapid development and implementation of effective surveillance systems to detect and respond to the outbreak in Senegal. In this documentation, we describe the design and implementation of the Community Event-Based Surveillance (CEBS) system in Senegal to strengthen the existing Integrated Disease Surveillance and Response system. The CEBS system used a hotline and toll-free number to collect and triage COVID-19-related calls from the community. Data from the CEBS system were integrated with the national system for further investigation and laboratory testing. From February to September 2020, a total of 10 760 calls were received by the CEBS system, with 10 751 calls related to COVID-19. The majority of calls came from the Dakar region, which was the epicentre of the outbreak in Senegal. Of the COVID-19 calls, 50.2% were validated and referred to health districts for further investigation, and 25% of validated calls were laboratory-confirmed cases of SARS-CoV-2. The implementation of the CEBS system allowed for timely detection and response to potential COVID-19 cases, contributing to the overall surveillance efforts in the country. Lessons learned from this experience include the importance of decentralised CEBS, population sensitisation on hotlines and toll-free usage, and the potential role of Community Health Workers in triaging alerts that needs further analysis. This experience highlights the contribution of a CEBS system in Senegal and provides insights into the design and operation of such a system. The findings can inform other countries in strengthening their surveillance systems and response strategies.

Structure, functions, performance and gaps of event-based surveillance (EBS) in Sudan, 2021: a cross-sectional review

BACKGROUND

Event-based surveillance (EBS) is an essential component of Early Warning Alert and Response (EWAR) as per the International Health Regulations (IHR), 2005. EBS was established in Sudan in 2016 as a complementary system for Indicator-based surveillance (IBS). This review will provide an overview of the current EBS structure, functions and performance in Sudan and identify the gaps and ways forward.  METHODS: The review followed the WHO/EMRO guidelines and tools. Structured discussions, observation and review of records and guidelines were done at national and state levels. Community volunteers were interviewed through phone calls. Directors of Health Emergency and Epidemic Control, surveillance officers and focal persons for EBS at the state level were also interviewed. SPSS software was used to perform descriptive statistical analysis for quantitative data, while qualitative data was analysed manually using thematic analysis, paying particular attention to the health system level allowing for an exploration of how and why experiences differ across levels. Written and verbal consents were obtained from all participants as appropriate.

RESULTS

Sudan has a functioning EBS; however, there is an underestimation of its contribution and importance at the national and states levels. The link between the national level and states is ad hoc or is driven by the need for reports. While community event-based surveillance (CEBS) is functioning, EBS from health facilities and from non-health sectors is not currently active. The integration of EBS into overall surveillance was not addressed, and the pathway from detection to action is not clear. The use of electronic databases and platforms is generally limited. Factors that would improve performance include training, presence of a trained focal person at state level, and regular follow-up from the national level. Factors such as staff turnover, income in relation to expenses and not having a high academic qualification (Diploma or MSc) were noticed as inhibiting factors.

CONCLUSION

The review recommended revisiting the surveillance structure at national and state levels to put EBS as an essential component and to update guidelines and standard operation procedures SOPs to foster the integration between EBS components and the overall surveillance system. The need for strengthening the link with states, capacity building and re-addressing the training modalities was highlighted.

Community-based surveillance of infectious diseases: a systematic review of drivers of success

Introduction

Community-based surveillance may improve early detection and response to disease outbreaks by leveraging the capacity of community members to carry out surveillance activities within their communities. In 2021, the WHO published a report detailing the evidence gaps and research priorities around community-centred approaches to health emergencies. In response, we carried out a systematic review and narrative synthesis of the evidence describing the drivers of success of community-based surveillance systems.

Methods

We included grey literature and peer-reviewed sources presenting empirical findings of the drivers of success of community-based surveillance systems for the detection and reporting of infectious disease-related events. We searched for peer-reviewed literature via MEDLINE, EMBASE, Global Health, SCOPUS and ReliefWeb. We carried out grey literature searches using Google Search and DuckDuckGo. We used an evaluation quality checklist to assess quality.

Results

Nineteen sources (17 peer-reviewed and 2 grey literature) met our inclusion criteria. Included sources reported on community-based surveillance for the detection and reporting of a variety of diseases in 15 countries (including three conflict settings). The drivers of success were grouped based on factors relating to: (1) surveillance workers, (2) the community, (3) case detection and reporting, (4) and integration.

Discussion

The drivers of success were found to map closely to principles of participatory community engagement with success factors reflecting high levels of acceptability, collaboration, communication, local ownership, and trust. Other factors included: strong supervision and training, a strong sense of responsibility for community health, effective engagement of community informants, close proximity of surveillance workers to communities, the use of simple and adaptable case definitions, quality assurance, effective use of technology, and the use of data for real-time decision-making. Our findings highlight strategies for improving the design and implementation of community-based surveillance. We suggest that investment in participatory community engagement more broadly may be a key surveillance preparedness activity.

PROSPERO registration number

CRD42022303971.

The performance of COVID-19 Surveillance System as timely containment strategy in Western Oromia, Ethiopia

BACKGROUND

COVID-19 has been swiftly spreading throughout the world ever since it emerged in Wuhan, China, in late December 2019. Case detection and contact identification remain the key surveillance objectives for effective containment of the pandemic. This study was aimed at assessing performance of surveillance in early containment of COVID 19 in Western Oromia, Ethiopia.

METHODS

A cross-sectional study was conducted from August 1 to September 30, 2020, in the 7 kebeles of Nekemte and 2 kebeles of Shambu Town. Residents who lived there for at least the past six months were considered eligible for this study. Data were collected from community and health system at different levels using semi structured questionnaire and checklist, respectively. Participants' health facility usage (dependent variable) and perceived risk, awareness, Socioeconomic Status, and practices (independent variable) were assessed. Bivariable analysis was computed to test the presence of an association between dependent and independent variables. Independent predictors were identified on multivariable logistic regression using a p-value of (<0.05) significance level. We have checked the model goodness of fit test by Hosmer-lemeshow test.

RESULTS

One hundred seventy-nine (41%) of the participants believe that they have a high risk of contracting COVID-19 and 127 (29%) of them reported they have been visited by health extension worker. One hundred ninety-seven (45.2%) reported that they were not using health facilities for routine services during this pandemic. Except one hospital, all health facilities (92%) were using updated case definition. Three (33%) of the assessed health posts didn't have community volunteers. On multivariable logistic regression analysis, the source of income AOR=0.30, 95% CI (0.11, 0.86), perceived level of risk AOR=3.42, 95% CI (2.04, 5.7) and not visited by health extension workers AOR=0.46, 95% CI (0.29, 0.74) were found to be independent predictors of not using health facilities during this pandemic.

CONCLUSION

Event based surveillance, both at community and health facility level, was not performing optimally in identifying potential suspects. Therefore, for effective early containment of epidemic, it is critical to strengthen event based surveillance and make use of surveillance data for tailored intervention in settings where mass testing is not feasible.

Twenty years of integrated disease surveillance and response in Sub-Saharan Africa: challenges and opportunities for effective management of infectious disease epidemics

INTRODUCTION

This systematic review aimed to analyse the performance of the Integrated Disease Surveillance and Response (IDSR) strategy in Sub-Saharan Africa (SSA) and how its implementation has embraced advancement in information technology, big data analytics techniques and wealth of data sources.

METHODS

HINARI, PubMed, and advanced Google Scholar databases were searched for eligible articles. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols.

RESULTS

A total of 1,809 articles were identified and screened at two stages. Forty-five studies met the inclusion criteria, of which 35 were country-specific, seven covered the SSA region, and three covered 3-4 countries. Twenty-six studies assessed the IDSR core functions, 43 the support functions, while 24 addressed both functions. Most of the studies involved Tanzania (9), Ghana (6) and Uganda (5). The routine Health Management Information System (HMIS), which collects data from health care facilities, has remained the primary source of IDSR data. However, the system is characterised by inadequate data completeness, timeliness, quality, analysis and utilisation, and lack of integration of data from other sources. Under-use of advanced and big data analytical technologies in performing disease surveillance and relating multiple indicators minimises the optimisation of clinical and practice evidence-based decision-making.

CONCLUSIONS

This review indicates that most countries in SSA rely mainly on traditional indicator-based disease surveillance utilising data from healthcare facilities with limited use of data from other sources. It is high time that SSA countries consider and adopt multi-sectoral, multi-disease and multi-indicator platforms that integrate other sources of health information to provide support to effective detection and prompt response to public health threats.

Community-based surveillance in internally displaced people's camps and urban settings during a complex emergency in Yemen in 2020

Background

The need for early identification of coronavirus disease (COVID-19) cases in communities was high in Yemen during the first wave of the COVID-19 epidemic because most cases presenting to health facilities were severe. Early detection of cases would allow early interventions to interrupt the transmission chains. This study aimed to describe the implementation of community-based surveillance (CBS) in in internally displaced people (IDP) camps and urban settings in Yemen from 15 April 2020 to 30 September 2020.

Methods

Following the Centers for Disease Control and Prevention guidance for evaluation of surveillance systems, we assessed the usefulness and acceptability of CBS. For acceptability, we calculated the proportion of trained volunteers who reported disease alerts. To assess the usefulness, we compared the alerts reported through the electronic diseases early warning system (eDEWS) with the alerts reported through CBS and described the response activities implemented.

Results

In Al-Mukalla City, 18% (14/78) of the volunteers reported at least one alert. In IDP camps, 58% (18/31) of volunteers reported at least one alert. In Al-Mukalla City, CBS detected 49 alerts of influenza-like illness, whereas health facilities detected 561 cases of COVID-19. In IDP camps, CBS detected 91 alerts of influenza-like illness, compared to 10 alerts detected through eDEWS. In IDP camps, CBS detected three other syndromes besides influenza-like illness (febrile illness outbreak suspicion, acute diarrhoea, and skin disease). In IDP camps, public health actions were implemented for each disease detected and no further cases were reported.

Conclusions

In Yemen, CBS was useful for detecting suspected outbreaks in IDP camps. CBS implementation did not yield expected results in general communities in urban areas in the early stage of the COVID-19 pandemic when little was known about the disease. In the urban setting, the system failed to detect suspected COVID-19 cases and other diseases despite the ongoing outbreaks reported through eDEWS. In Yemen, as in other countries, feasibility and acceptability studies should be conducted few months before CBS expansion in urban communities. The project should be expanded in IDP camps, by creating COVID-19 and other disease outbreak reporting sites.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13031-021-00394-1.

Humanitarian led community-based surveillance: case study in Ekondo-titi, Cameroon

BACKGROUND

Community-based surveillance (CBS) has been used successfully in many situations to strengthen existing health systems as well as in humanitarian crises. The Anglophone crisis of Northwest Southwest Cameroon, led to burning of villages, targeting of health personnel and destruction of health facilities which, in combination with distrust for the government services led to a collapse of surveillance for outbreak prone diseases.

METHODS

We evaluated the ability of the CBS system to identify suspected cases of outbreak prone diseases (OPD) as compared to the facility-based surveillance, evaluated the timeliness of the CBS system in identifying an OPD, reporting of OPD to District Health Service (DHS) and timeliness in outbreak response. The paper also assessed the collaboration with the DHS and contribution of the CBS system with regards to strengthening the overall surveillance of the health district and also determine the interventions undertaken to contain suspected/confirmed outbreaks.

RESULTS

In total 9 alerts of suspected OPDs were generated by the CBS system as compared to 0 by the DHS, with 8 investigated, 5 responses and 3 confirmed outbreaks. Average time from first symptoms to alert generation by the CBS system was 7.3 days. Average time lag from alert generation from the CBS to the DHS was 0.3 days which was essentially within 24 h. There was extensive and synergistic collaboration with the DHS.

DISCUSSION

CBS generated a higher number of alerts than traditional outbreak reported used in the region, and had timely investigations and if appropriate, responses. Careful selection of CHWs with strong community engagement led to the success of the project, and the use of the mobile health team in situ allowed for rapid responses to potential outbreaks, as well as for feedback to CHWs and communities. CBS was also well utilized for identification of other events, such as displacement and malnutrition.

CONCLUSION

In conflict settings, CBS can help in outbreak identification as well as other events, and a mobile health team is crucial to the success of the CBS due to the ability to rapidly response to generated alerts. The mobile health team provided timely investigation of 8 of 9 alerts generated. Collaboration with existing DHS structures is important for systems strengthening in such settings.

Evaluation of community based surveillance in the Rohingya refugee camps in Cox's Bazar, Bangladesh, 2019

BACKGROUND

Following an influx of an estimated 742,000 Rohingya refugees in Bangladesh, Médecins sans Frontières (MSF) established an active indicator-based Community Based Surveillance (CBS) in 13 sub-camps in Cox's Bazar in August 2017. Its objective was to detect epidemic prone diseases early for rapid response. We describe the surveillance, alert and response in place from epidemiological week 20 (12 May 2019) until 44 (2 November 2019).

METHODS

Suspected cases were identified through passive health facility surveillance and active indicator-based CBS. CBS-teams conducted active case finding for suspected cases of acute watery diarrhea (AWD), acute jaundice syndrome (AJS), acute flaccid paralysis (AFP), dengue, diphtheria, measles and meningitis. We evaluate the following surveillance system attributes: usefulness, Positive Predictive Value (PPV), timeliness, simplicity, flexibility, acceptability, representativeness and stability.

RESULTS

Between epidemiological weeks 20 and 44, an average of 97,340 households were included in the CBS per surveillance cycle. Household coverage reached over 85%. Twenty-one RDT positive cholera cases and two clusters of AWD were identified by the CBS and health facility surveillance that triggered the response mechanism within 12 hours. The PPV of the CBS varied per disease between 41.7%-100%. The CBS required 354 full-time staff in 10 different roles. The CBS was sufficiently flexible to integrate dengue surveillance. The CBS was representative of the population in the catchment area due to its exhaustive character and high household coverage. All households consented to CBS participation, showing acceptability.

DISCUSSION

The CBS allowed for timely response but was resource intensive. Disease trends identified by the health facility surveillance and suspected diseases trends identified by CBS were similar, which might indicate limited additional value of the CBS in a dense and stable setting such as Cox's Bazar. Instead, a passive community-event-based surveillance mechanism combined with health facility-based surveillance could be more appropriate.

Early detection of cholera epidemics to support control in fragile states: estimation of delays and potential epidemic sizes

BACKGROUND

Cholera epidemics continue to challenge disease control, particularly in fragile and conflict-affected states. Rapid detection and response to small cholera clusters is key for efficient control before an epidemic propagates. To understand the capacity for early response in fragile states, we investigated delays in outbreak detection, investigation, response, and laboratory confirmation, and we estimated epidemic sizes. We assessed predictors of delays, and annual changes in response time.

METHODS

We compiled a list of cholera outbreaks in fragile and conflict-affected states from 2008 to 2019. We searched for peer-reviewed articles and epidemiological reports. We evaluated delays from the dates of symptom onset of the primary case, and the earliest dates of outbreak detection, investigation, response, and confirmation. Information on how the outbreak was alerted was summarized. A branching process model was used to estimate epidemic size at each delay. Regression models were used to investigate the association between predictors and delays to response.

RESULTS

Seventy-six outbreaks from 34 countries were included. Median delays spanned 1-2 weeks: from symptom onset of the primary case to presentation at the health facility (5 days, IQR 5-5), detection (5 days, IQR 5-6), investigation (7 days, IQR 5.8-13.3), response (10 days, IQR 7-18), and confirmation (11 days, IQR 7-16). In the model simulation, the median delay to response (10 days) with 3 seed cases led to a median epidemic size of 12 cases (upper range, 47) and 8% of outbreaks ≥ 20 cases (increasing to 32% with a 30-day delay to response). Increased outbreak size at detection (10 seed cases) and a 10-day median delay to response resulted in an epidemic size of 34 cases (upper range 67 cases) and < 1% of outbreaks < 20 cases. We estimated an annual global decrease in delay to response of 5.2% (95% CI 0.5-9.6, p = 0.03). Outbreaks signaled by immediate alerts were associated with a reduction in delay to response of 39.3% (95% CI 5.7-61.0, p = 0.03).

CONCLUSIONS

From 2008 to 2019, median delays from symptom onset of the primary case to case presentation and to response were 5 days and 10 days, respectively. Our model simulations suggest that depending on the outbreak size (3 versus 10 seed cases), in 8 to 99% of scenarios, a 10-day delay to response would result in large clusters that would be difficult to contain. Improving the delay to response involves rethinking the integration at local levels of event-based detection, rapid diagnostic testing for cluster validation, and integrated alert, investigation, and response.

People-centred surveillance: a narrative review of community-based surveillance among crisis-affected populations

Outbreaks of disease in settings affected by crises grow rapidly due to late detection and weakened public health systems. Where surveillance is underfunctioning, community-based surveillance can contribute to rapid outbreak detection and response, a core capacity of the International Health Regulations. We reviewed articles describing the potential for community-based surveillance to detect diseases of epidemic potential, outbreaks, and mortality among populations affected by crises. Surveillance objectives have included the early warning of outbreaks, active case finding during outbreaks, case finding for eradication programmes, and mortality surveillance. Community-based surveillance can provide sensitive and timely detection, identify valid signals for diseases with salient symptoms, and provide continuity in remote areas during cycles of insecurity. Effectiveness appears to be mediated by operational requirements for continuous supervision of large community networks, verification of a large number of signals, and integration of community-based surveillance within the routine investigation and response infrastructure. Similar to all community health systems, community-based surveillance requires simple design, reliable supervision, and early and routine monitoring and evaluation to ensure data validity. Research priorities include the evaluation of syndromic case definitions, electronic data collection for community members, sentinel site designs, and statistical techniques to counterbalance false positive signals.