Traditional Surveillance Systems Research Articles

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.

Eliciting Disease Data from Wikipedia Articles

Traditional disease surveillance systems suffer from several disadvantages, including reporting lags and antiquated technology, that have caused a movement towards internet-based disease surveillance systems. Internet systems are particularly attractive for disease outbreaks because they can provide data in near real-time and can be verified by individuals around the globe. However, most existing systems have focused on disease monitoring and do not provide a data repository for policy makers or researchers. In order to fill this gap, we analyzed Wikipedia article content. We demonstrate how a named-entity recognizer can be trained to tag case counts, death counts, and hospitalization counts in the article narrative that achieves an F1 score of 0.753. We also show, using the the 2014 West African Ebola virus disease epidemic article as a case study, that there are detailed time series data that are consistently updated that closely align with ground truth data. We argue that Wikipedia can be used to create the first community-driven open-source emerging disease detection, monitoring, and repository system.

Characterizing HIV discussions and engagement on Twitter

The novel settings provided by social media facilitate users to seek and share information on a wide array of subjects, including healthcare and wellness. Analyzing health-related opinions and discussions on these platforms complement traditional public health surveillance systems to support timely and effective interventions. This study aims to characterize the HIV-related conversations on Twitter by identifying the prevalent topics and the key events and actors involved in these discussions. Through Twitter API, we collected tweets containing the hashtag #HIV for a one-year period. After pre-processing the collected data, we conducted engagement analysis, temporal analysis, and topic modeling algorithm on the analytical sample (n = 122,807). Tweets by HIV/AIDS/LGBTQ activists and physicians received the highest level of engagement. An upsurge in tweet volume and engagement was observed during global and local events such as World Aids Day and HIV/AIDS awareness and testing days for trans-genders, blacks, women, and the aged population. Eight topics were identified that include “stigma”, “prevention”, “epidemic in the developing countries”, “World Aids Day”, “treatment”, “events”, “PrEP”, and “testing”. Social media discussions offer a nuanced understanding of public opinions, beliefs, and sentiments about numerous health-related issues. The current study reports various dimensions of HIV-related posts on Twitter. Based on the findings, public health agencies and pertinent entities need to proactively use Twitter and other social media by engaging the public through involving influencers. The undertaken methodological choices may be applied to further assess HIV discourse on other popular social media platforms.

The effectiveness of syndromic surveillance for the early detection of waterborne outbreaks: a systematic review

BackgroundWaterborne outbreaks are still a risk in high-income countries, and their early detection is crucial to limit their societal consequences. Although syndromic surveillance is widely used for the purpose of detecting outbreaks days earlier than traditional surveillance systems, evidence of the effectiveness of such systems is lacking. Thus, our objective was to conduct a systematic review of the effectiveness of syndromic surveillance to detect waterborne outbreaks.MethodWe searched the Cochrane Library, Medline/PubMed, EMBASE, Scopus, and Web of Science for relevant published articles using a combination of the keywords ‘drinking water’, ‘surveillance’, and ‘waterborne disease’ for the period of 1990 to 2018. The references lists of the identified articles for full-text record assessment were screened, and searches in Google Scholar using the same key words were conducted. We assessed the risk of bias in the included articles using the ROBINS-I tool and PRECEPT for the cumulative body of evidence.ResultsFrom the 1959 articles identified, we reviewed 52 articles, of which 18 met the eligibility criteria. Twelve were descriptive/analytical studies, whereas six were simulation studies. There is no clear evidence for syndromic surveillance in terms of the ability to detect waterborne outbreaks (low sensitivity and high specificity). However, one simulation study implied that multiple sources of signals combined with spatial information may increase the timeliness in detecting a waterborne outbreak and reduce false alarms.ConclusionThis review demonstrates that there is no conclusive evidence on the effectiveness of syndromic surveillance for the detection of waterborne outbreaks, thus suggesting the need to focus on primary prevention measures to reduce the risk of waterborne outbreaks. Future studies should investigate methods for combining health and environmental data with an assessment of needed financial and human resources for implementing such surveillance systems. In addition, a more critical thematic narrative synthesis on the most promising sources of data, and an assessment of the basis for arguments that joint analysis of different data or dimensions of data (e.g. spatial and temporal) might perform better, should be carried out.Trial registrationPROSPERO: International prospective register of systematic reviews. 2019. CRD42019122332.

Enhancing Influenza Epidemics Forecasting Accuracy in China with Both Official and Unofficial Online News Articles, 2019–2020

Real-time online data sources have contributed to timely and accurate forecasting of influenza activities while also suffered from instability and linguistic noise. Few previous studies have focused on unofficial online news articles, which are abundant in their numbers, rich in information, and relatively low in noise. This study examined whether monitoring both official and unofficial online news articles can improve influenza activity forecasting accuracy during influenza outbreaks. Data were retrieved from a Chinese commercial online platform and the website of the Chinese National Influenza Center. We modeled weekly fractions of influenza-related online news articles and compared them against weekly influenza-like illness (ILI) rates using autoregression analyses. We retrieved 153,958,695 and 149,822,871 online news articles focusing on the south and north of mainland China separately from 6 October 2019 to 17 May 2020. Our model based on online news articles could significantly improve the forecasting accuracy, compared to other influenza surveillance models based on historical ILI rates (p = 0.002 in the south; p = 0.000 in the north) or adding microblog data as an exogenous input (p = 0.029 in the south; p = 0.000 in the north). Our finding also showed that influenza forecasting based on online news articles could be 1–2 weeks ahead of official ILI surveillance reports. The results revealed that monitoring online news articles could supplement traditional influenza surveillance systems, improve resource allocation, and offer models for surveillance of other emerging diseases.

Towards Efficient Video Detection Object Super-Resolution with Deep Fusion Network for Public Safety

Video surveillance plays an increasingly important role in public security and is a technical foundation for constructing safe and smart cities. The traditional video surveillance systems can only provide real-time monitoring or manually analyze cases by reviewing the surveillance video. So, it is difficult to use the data sampled from the surveillance video effectively. In this paper, we proposed an efficient video detection object super-resolution with a deep fusion network for public security. Firstly, we designed a super-resolution framework for video detection objects. By fusing object detection algorithms, video keyframe selection algorithms, and super-resolution reconstruction algorithms, we proposed a deep learning-based intelligent video detection object super-resolution (SR) method. Secondly, we designed a regression-based object detection algorithm and a key video frame selection algorithm. The object detection algorithm is used to assist police and security personnel to track suspicious objects in real time. The keyframe selection algorithm can select key information from a large amount of redundant information, which helps to improve the efficiency of video content analysis and reduce labor costs. Finally, we designed an asymmetric depth recursive back-projection network for super-resolution reconstruction. By combining the advantages of the pixel-based super-resolution algorithm and the feature space-based super-resolution algorithm, we improved the resolution and the visual perception clarity of the key objects. Extensive experimental evaluations show the efficiency and effectiveness of our method.

Navigation of a UAV Network for Optimal Surveillance of a Group of Ground Targets Moving Along a Road

With the rapid increase of vehicles in recent years, traffic surveillance becomes a crucial issue of traffic management. Since the traditional static sensor-based surveillance system can only passively monitor traffic, this paper considers the usage of unmanned aerial vehicles (UAVs), which can proactively conduct traffic surveillance thanks to the excellent mobility of UAVs. Specifically, we consider the navigation problem of a network of UAVs to effectively monitor a group of ground targets which move along a curvy road. A surveillance optimization problem is stated, and a distributed navigation algorithm for the UAV network is developed. It is proved that the proposed algorithm is locally optimal. Simulations confirm the effectiveness of the proposed navigation algorithm.

Web-Based Apps for Responding to Acute Infectious Disease Outbreaks in the Community: Systematic Review.

BackgroundWeb-based technology has dramatically improved our ability to detect communicable disease outbreaks, with the potential to reduce morbidity and mortality because of swift public health action. Apps accessible through the internet and on mobile devices create an opportunity to enhance our traditional indicator-based surveillance systems, which have high specificity but issues with timeliness.ObjectiveThe aim of this study is to describe the literature on web-based apps for indicator-based surveillance and response to acute communicable disease outbreaks in the community with regard to their design, implementation, and evaluation.MethodsWe conducted a systematic search of the published literature across four databases (MEDLINE via OVID, Web of Science Core Collection, ProQuest Science, and Google Scholar) for peer-reviewed journal papers from January 1998 to October 2019 using a keyword search. Papers with the full text available were extracted for review, and exclusion criteria were applied to identify eligible papers.ResultsOf the 6649 retrieved papers, 23 remained, describing 15 web-based apps. Apps were primarily designed to improve the early detection of disease outbreaks, targeted government settings, and comprised either complex algorithmic or statistical outbreak detection mechanisms or both. We identified a need for these apps to have more features to support secure information exchange and outbreak response actions, with a focus on outbreak verification processes and staff and resources to support app operations. Evaluation studies (6 out of 15 apps) were mostly cross-sectional, with some evidence of reduction in time to notification of outbreak; however, studies lacked user-based needs assessments and evaluation of implementation.ConclusionsPublic health officials designing new or improving existing disease outbreak web-based apps should ensure that outbreak detection is automatic and signals are verified by users, the app is easy to use, and staff and resources are available to support the operations of the app and conduct rigorous and holistic evaluations.

Impact of human papillomavirus vaccine in reducing genital warts: A Google Trends analysis

Impact of human papillomavirus vaccine in reducing genital warts: A Google Trends analysis

GAR: Graph adversarial representation for adverse drug event detection on Twitter

Adverse drug reaction events have become one of the main causes of patient death. Since traditional post-marketing surveillance systems based on spontaneous reports have a serious underreporting issue, in recent years research on the detection of adverse reaction events using social media such as Twitter as a data source has attracted increasing attention in recent year. Deep learning models usually rely on a large number of training samples. However, due to the characteristics of user-generated content and the time-consuming data annotation process, related research is faced with the problems caused by small-scale annotated datasets, which restricts deep learning models in achieving satisfactory results. Accordingly, we introduce two regularization methods are introduced at the representation level, i.e., graph embedding-based data augmentation and adversarial training, to improve the performance of detecting adverse events under such conditions. Besides, the applicable scope of these two methods is analyzed and discussed through experiments. Combined with the convolutional neural network, this paper proposes an adverse drug event detection framework that can make full use of the methods.

Development and Usability Assessment of Classroom Surveillance System for Campus Security and Productivity

Classroom Surveillance System is an excellent system for higher learning institutions to ensure the security of students, faculty, office personnel, and its physical facilities. This study aimed to develop a classroom surveillance system for campus security and productivity. The study utilized developmental research. There were four Deans, 24 IT professionals, and 24 students utilized as respondents of the study. The instrument used in the study included the Deans’ monitoring scheme of teaching/non-teaching personnel, school facilities, and student’s assessment on the surveillance functionality, reliability, usability, efficiency, maintainability, and accuracy of the newly developed and designed classroom surveillance system. The statistical tools used were the frequency count, ranking, and weighted mean. The study revealed that the developed Classroom Surveillance System was very much functional, very much reliable, very much usable, very much efficient, very much maintainable, and very much accurate. The university administration should adopt the Classroom Surveillance System to provide excellent surveillance and monitoring of teaching personnel, non-teaching personnel, classroom facilities, and students of the University. Hence, the university should shift from utilizing the traditional surveillance system into a technologically oriented surveillance system.

Improvement of radar detection capabilities for fluctuating targets using convolutional error control coding technique

Fluctuating targets are important phenomenons that affect detection probability. Several investigations about this topic have been carried on with good results to radar capabilities improvement. However, the increasing development of stealth technology has weakened traditional surveillance systems. This work has as main objective to improve radar detection of fluctuating targets using error control codes techniques. The purposed novel idea consists on a long pulse divided into a number of shorter sub-pulses, equally spaced in time duration. The binary phases of these sub-pulses are selected in accordance with Barker codes. Convolutional coding and Binary Phase Shift Keying(BPSK) modulation techniques are used. We determinate the probability of detection computing the bit error rate values. Matlab software is used to make several simulations that back up this theory. The results demonstrate that the probability of detection for Swerling I and Swerling III fluctuating target models is increased with the use of convolutional coding technique to detect and correct sub-pulse errors. This work offers a preliminary analysis of the detection improvement method with the use of error-control coding on radar pulsed with quite generalized assumptions thereby left a bigger space for future work with assumptions to fit practical implementation of the method.

Joint Beamforming Optimization for the Intelligent Reflecting Surface Assisted Wireless Surveillance System

The intelligent reflecting surface (IRS), which consists of a large number of reflecting units, can adjust the phase shifts of its reflecting units to strengthen the desired signal and/or suppress the undesired signal. In this paper, we consider an IRS-assisted wireless surveillance system where an IRS is deployed to assist the legal surveillance receiver E to monitor the information transmission of the suspicious link from AP to the suspicious receiver B. Two communication scenarios assuming whether the suspicious link is aware of the existence of the monitor are considered. The optimization problem under the constraint that the achievable rate at the monitor E is larger than that at the suspicious receiver B is proposed to jointly optimize the beamforming vector at the AP and the phase shift matrix at the IRS to maximize the achievable eavesdropping rate. To solve this non-convex problem, we introduce the semi-definite relaxation (SDR) approach and the alternating optimization (AO) method to convert the non-convex optimization problem to a series of semi-definite programs (SDPs) and solve the SDPs iteratively. Simulation results show that the assistance of IRS can greatly improve the performance of the surveillance, and achieves significant advantages over the traditional relay-assisted wireless surveillance system.

Utilizing Google Search Data With Deep Learning, Machine Learning and Time Series Modeling to Forecast Influenza-Like Illnesses in South Africa

Influenza-like illnesses (ILI) result in deaths and hospitalizations across the globe. Traditional surveillance systems rely on data from general medical practitioners. The process is resource-intensive and plagued with delay. Although recent studies have shown the potential utility of free and fast alternatives like web and social media data, the reliability cannot be generalized due to differences in technological culture. Meanwhile, there is a scarcity of studies exploring these free online data for (sub-Saharan) African countries. In this paper, we utilize Google trends (GT) data for ILI forecasting in South Africa. We study models based on deep learning (Long short-term memory (LSTM) and feedforward neural networks (FNN)), machine learning (Multiple linear regression (MLR), elastic net (EN), support vector machine (SVM)), and statistical time series (seasonal autoregressive integrated moving average (SARIMA)) algorithms. The FNN and SVM models using GT data alone, produce forecasts close in accuracy to those fitted to actual ILI data. The algorithms rank differently across various performance measures. Generally, the deep learning techniques perform better than the other algorithms in our study. However, tuning the former is quite intricate. Combining GT and historical ILI data enhances the models. The non-deep-learning algorithms benefit more from this enhancement. Furthermore, we observe that search volume increases proportional to and timeously with reported infection rates, suggesting that South Africans search Google in the week they feel flu symptoms. Thus, monitoring Google search trends is a reliable proxy for monitoring flu spread in South Africa.

Outcomes of the Deployment of the Auto-Visual Acute Flaccid Paralysis Detection and Reporting (AVADAR) System for Strengthening Polio Surveillance in Africa From 2017 to 2018: Evaluation Study.

BackgroundAs we move toward a polio-free world, the challenge for the polio program is to create an unrelenting focus on smaller areas where the virus is still present, where children are being repeatedly missed, where immunity levels are low, and where surveillance is weak.ObjectiveThis article aimed to describe a possible solution to address weak surveillance systems and document the outcomes of the deployment of the Auto-Visual Acute Flaccid Paralysis Detection and Reporting (AVADAR) project.MethodsThis intervention was implemented in 99 targeted high-risk districts with concerns for silent polio circulation from eight countries in Africa between August 1, 2017, and July 31, 2018. A total of 6954 persons (5390 community informants and 1564 health workers) were trained and equipped with a smartphone on which the AVADAR app was configured to allow community informants to send alerts on suspected acute flaccid paralysis (AFP) and allow health worker to use electronic checklists for investigation of such alerts. The AVADAR and Open Data Kit ONA servers were at the center of the entire process. A dashboard system and coordination teams for monitoring and supervision were put in place at all levels.ResultsOverall, 96.44% (24,142/25,032) of potential AFP case alerts were investigated by surveillance personnel, yielding 1414 true AFP cases. This number (n=1414) reported through AVADAR was higher than the 238 AFP cases expected during the study period in the AVADAR districts and the 491 true AFP cases reported by the traditional surveillance system. A total of 203 out of the 1414 true AFP cases reported were from special population settings, such as refugee camps and insecure areas. There was an improvement in reporting in silent health areas in all the countries using the AVADAR system. Finally, there were 23,473 reports for other diseases, such as measles, diarrhea, and cerebrospinal meningitis, using the AVADAR platform.ConclusionsThis article demonstrates the added value of AVADAR to rapidly improve surveillance sensitivity. AVADAR is capable of supporting countries to improve surveillance sensitivity within a short interval before and beyond polio-free certification.

Nowcasting Sexually Transmitted Infections in Chicago: Predictive Modeling and Evaluation Study Using Google Trends.

BackgroundSexually transmitted infections (STIs) pose a significant public health challenge in the United States. Traditional surveillance systems are adversely affected by data quality issues, underreporting of cases, and reporting delays, resulting in missed prevention opportunities to respond to trends in disease prevalence. Search engine data can potentially facilitate an efficient and economical enhancement to surveillance reporting systems established for STIs.ObjectiveWe aimed to develop and train a predictive model using reported STI case data from Chicago, Illinois, and to investigate the model’s predictive capacity, timeliness, and ability to target interventions to subpopulations using Google Trends data.MethodsDeidentified STI case data for chlamydia, gonorrhea, and primary and secondary syphilis from 2011-2017 were obtained from the Chicago Department of Public Health. The data set included race/ethnicity, age, and birth sex. Google Correlate was used to identify the top 100 correlated search terms with “STD symptoms,” and an autocrawler was established using Google Health Application Programming Interface to collect the search volume for each term. Elastic net regression was used to evaluate prediction accuracy, and cross-correlation analysis was used to identify timeliness of prediction. Subgroup elastic net regression analysis was performed for race, sex, and age.ResultsFor gonorrhea and chlamydia, actual and predicted STI values correlated moderately in 2011 (chlamydia: r=0.65; gonorrhea: r=0.72) but correlated highly (chlamydia: r=0.90; gonorrhea: r=0.94) from 2012 to 2017. However, for primary and secondary syphilis, the high correlation was observed only for 2012 (r=0.79), 2013 (r=0.77), 2016 (0.80), and 2017 (r=0.84), with 2011, 2014, and 2015 showing moderate correlations (r=0.55-0.70). Model performance was the most accurate (highest correlation and lowest mean absolute error) for gonorrhea. Subgroup analyses improved model fit across disease and year. Regression models using search terms selected from the cross-correlation analysis improved the prediction accuracy and timeliness across diseases and years.ConclusionsIntegrating nowcasting with Google Trends in surveillance activities can potentially enhance the prediction and timeliness of outbreak detection and response as well as target interventions to subpopulations. Future studies should prospectively examine the utility of Google Trends applied to STI surveillance and response.

Mobile edge computing based video surveillance model for improving the performance of extended training

The extended training is a global campaign and the video surveillance plays an important role during the process of extended training since it has the considerably requirement on the real‐time benefit. The traditional video surveillance system model faces some limitations, such as high resource overhead, serious bandwidth pressure and unwarranted timeliness. Given this, this letter takes full advantages of mobile edge computing to design the video surveillance model, improving the performance of extended training, named MSET. The proposed MSET framework consists of the rich roles, that is, cloud center, wireless base station, edge computing devices and mobile wearables. Driven by the real experiments, the proposed MSET is feasible and outperforms the traditional methods. In particular, the proposed frame filtering algorithm can greatly decrease the process delay.

Testing the Utility of Electronic Health Records and Health Information Network Data to Determine Potential Health Impacts Associated with Opportunities for Exposure to Environmental Contaminants

It was hypothesized that health care claims (HCC) and electronic health records (EHRs) might provide the opportunity to access more timely and geographically refined data, and more information than is typically available from traditional public health surveillance systems; for example, measurements of risk factors (such as high cholesterol), co-diagnosis, medication prescription information and laboratory test results. We designed a pilot study in the state of Delaware (DE) to explore the utility of these data as a surveillance tool. The city of New Castle, DE was selected because: (1) it had levels of contaminants in drinking water that exceeded federal advisory levels and (2) it has universal claims and access to EHR data through the Delaware Health Information Network (DHIN).We included residents who lived in New Castle City during the period 2013 to 2018. We selected the following outcomes to focus on based on the contaminants of interest: cardiovascular disease; thyroid disease; kidney cancer; and testicular cancer. We are in the process of defining the inclusion and exclusion criteria for de identified laboratory and clinical diagnosis data on these outcomes, to identify HCC and EHR datasets for analysis. We will analyze the data by zip code and census tract, and include demographic information (age, race/ethnicity, and gender) for each outcome of interest. We will also compare rates of health outcomes obtained using HCC data to those obtained using EHR data.The overall goal is to determine the value of conducting ecologic analyses using HCC and EHR data to inform the design of larger, long term epidemiological studies evaluating exposure to environmental contaminants and potential health impacts. The need to identify such surveillance tools is critical as existing disease registries are often not timely enough due to reporting requirements and disease latency and/or are limited by small numbers of events.

An Intelligent Automatic Human Detection and Tracking System Based on Weighted Resampling Particle Filtering

At present, traditional visual-based surveillance systems are becoming impractical, inefficient, and time-consuming. Automation-based surveillance systems appeared to overcome these limitations. However, the automatic systems have some challenges such as occlusion and retaining images smoothly and continuously. This research proposes a weighted resampling particle filter approach for human tracking to handle these challenges. The primary functions of the proposed system are human detection, human monitoring, and camera control. We used the codebook matching algorithm to define the human region as a target and track it, and we used the practical filter algorithm to follow and extract the target information. Consequently, the obtained information was used to configure the camera control. The experiments were tested in various environments to prove the stability and performance of the proposed system based on the active camera.

Supporting Healthcare-Associated Infection (HAI) Surveillance in Resource-Limited Settings: Lessons Learned, 2015–2019

Background: Since 2015, the CDC has supported the development and implementation of healthcare-associated infection (HAI) surveillance in resource-limited settings through technical support of case definitions and methods that are feasible with existing surveillance capacity and integration with clinical care to maximize sustainability and data use for action. Methods: Surveillance initiatives included facility-level implementation programs in Kenya, Sierra Leone, Thailand, and Georgia; larger national or regional network-level projects in India and Vietnam were also supported. For assessment and planning, surveillance capacities were grouped into 3 domains: staff, informatics, and diagnostic capacities. Based on these capacities, simplified case definitions surveillance methodologies were devised to balance resources and effort with the anticipated value and use of findings. Results: There was broad understanding of the importance of HAI surveillance; however, the required resources and other challenges (eg, training, staffing, quality of available data) were underappreciated. Staff capacities were often influenced by a lack of dedicated surveillance staff and limited experience in systematic data collection and analysis. Informatics capacities were generally limited by the lack of digital data management, nonstandardized clinical data collection and storage, and the inability to assign and maintain unique patient identifiers. We found that capacity for diagnostics, a critical component of traditional HAI surveillance systems, was limited by its availability, frequency of use, and inconsistent rationale in clinical care. We found that successful surveillance strategies were generally simple, matched existing capacities, and targeted specific HAI priorities identified by clinical teams. For example, in Kenya and Sierra Leone, participating facilities established, with minimal external support, simplified SSI surveillance among post–caesarean-delivery patients. These initiatives improved integration of surveillance with clinical care through encouraging participation of the clinical team in surveillance and planning. Furthermore, these models directly linked surveillance activities to improved patient care (eg, combined clinical checklists with surveillance data collection forms). Discussion: In resource-limited settings, the local cost and effort required to establish and sustain the necessary infrastructure for HAI surveillance can be substantial. Establishing actionable and sustainable HAI surveillance can be achieved through simplifying HAI surveillance to match existing capacities and can result in valuable surveillance programs, even in very resource-limited settings.Funding: NoneDisclosures: None