{"paper_id":"334463dc-65ec-4df0-86a0-2f5b0696f5ff","body_text":"Real-time sentinel syndromic surveillance for infectious disease detection: lessons from the 4S network in Senegal, 2015–2023 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Real-time sentinel syndromic surveillance for infectious disease detection: lessons from the 4S network in Senegal, 2015–2023 Mamadou Aliou Barry, Abdourahmane Sow, Cheikh Talla, Samba Niang Sagne, and 26 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8355367/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Sub-Saharan Africa faces persistent challenges in the timely detection of infectious disease outbreaks due to inadequate early warning and response systems. To address this gap, Senegal's Ministry of Health partnered with the Institut Pasteur de Dakar to establish the Senegalese Syndromic Sentinel Surveillance Network (4S Network) in 2012 a comprehensive surveillance system designed to identify epidemic-prone syndromes and enable rapid public health interventions. Methods We analysed data from the Senegalese 4S real-time sentinel syndromic surveillance network collected between 2015 (15 sites) and 2023 (27 sites). The network monitored four key febrile syndromes, including malaria, dengue-like syndromes, diarrheal syndrome, and influenza-like illness (ILI), via standardized WHO case definitions. Laboratory confirmation was achieved through molecular and serological testing of biological samples. Sentinel general practitioners submitted daily reports via a digital platform that facilitated real-time reporting and automated alert generation. We evaluated system performance through completeness, timeliness, temporal patterns, geographical distribution, and alert validation rates. Results During the nine-year surveillance period, the network documented 1,816,340 outpatient consultations, with febrile syndromes accounting for 11.7% of all visits and demonstrating notable annual fluctuations. Distinct regional patterns of infectious disease events were observed: ILI predominated in western regions, dengue-like syndromes were clustered in north-central areas, and malaria cases were concentrated in southeastern zones. The system demonstrated robust performance metrics, achieving 94.5% data completeness and 80.0% reporting timeliness. Of the 202 alerts generated, 51.0% received laboratory confirmation. Dengue virus circulation was documented in 2017, 2018, 2021, 2022, and 2023. Despite these successes, 37.1% of febrile cases remained etiologically unclassified. The system's early multidisciplinary investigation capabilities enabled swift outbreak containment and transmission control. Conclusion The 4S network validates the effectiveness and practical implementation of digital, real-time syndromic surveillance in Senegal. It successfully facilitated early outbreak detection and supported prompt public health responses. Although the system has significant potential for resource-constrained environments, addressing current operational limitations remains crucial for maximizing public health impacts. These findings provide strong evidence supporting the regional expansion of similar surveillance frameworks to enhance health security and epidemic preparedness throughout West Africa. Infectious disease Syndromic surveillance Early warning system Influenza-like illness Dengue-like syndromes Outbreak detection Senegal Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Owing to major public health threats from emerging infectious diseases and potential bioterrorism attacks, widespread syndromic surveillance systems have been established to enable early identification of epidemic patterns. [ 1 , 2 ]. This surveillance approach prioritizes the identification of syndromes (e.g., influenza-like or dengue-like illnesses) over the collection of confirmatory diagnostic data. The system demonstrates notable sensitivity to fluctuations in disease trends [ 3 ] and has proven effective in the early detection of outbreaks [ 2 ]. The implementation of real-time syndromic surveillance has emerged as a promising strategy to increase the timeliness and completeness of disease outbreak detection. The process involves the capture of preliminary information regarding health events that occur prior to formal biological confirmation. It involves the rapid collection of data from existing electronic sources and the subsequent daily analysis of the data to identify potential outbreak signals to be investigated [ 1 ]. Recently, numerous sentinel surveillance systems from Africa have been considered in the context of influenza surveillance and, more recently, the recent COVID-19 pandemic [ 4 ]. These surveillance systems, however, still face significant limitations in coverage and available resources, and delays associated with diagnostic capacity and reporting. Across Africa, the future sustainability and continued financing of such programmes depend on providing clear evidence to health leaders that surveillance information effectively supports disease prevention, risk control, patient management, economic prosperity, and successful intervention in other critical health areas. [ 5 ]. In Senegal, teams from the Institut Pasteur de Dakar (IPD), in collaboration with and for the Senegalese Ministry of Health (MoH), established the Senegalese Syndromic Sentinel Surveillance Network (4S network) in 2012. This network is based on daily data and was developed to reinforce the previous influenza surveillance network and strengthen routine monitoring based on weekly data [ 6 ]. As Senegal's first surveillance system of this nature, this innovative network consolidates various public health syndromes, enhanced by laboratory confirmation and an early warning system (EWS) platform. This comprehensive approach allows swift detection and intervention regarding unusual health events. This study aimed to evaluate the performance of the 4S real-time sentinel syndromic surveillance network from January 2015 to December 2023 by analysing its key components and achievements and assessing its contribution to early infectious disease outbreak detection and preparedness. Methods Presentation of the Senegalese Sentinel Syndromic Surveillance Network (4S Network) In Senegal, an influenza surveillance system was established in 1996 through the National Influenza Reference Centre based at IPD, with the primary objective of monitoring circulating virological viral strains [ 7 ]. In 2012, the institution made a strategic decision to expand its operations into the U.S. market. The surveillance system employed an early warning objective, utilizing a syndromic approach, as previously delineated [ 6 ]. A national staff member was appointed as the Sentinel focal point (SFP) at each selected field site to manage site operations, supported by additional personnel serving as backup. The network underwent substantial geographical expansion, moving from 15 sentinel sites in 2015 to 27 sites in 2023 (see Table 1 ), thereby encompassing all 14 regions of Senegal. The network encompasses community-based sites situated within primary or district-level health facilities, which cater to ambulatory patients. The geographical distribution of the sentinel sites is shown in Fig. 1 . Syndromic case definitions To ensure global compatibility, the study employed WHO case definitions from 2012, relying on clinical data obtained before diagnostic confirmation. Previous publications have documented these case definitions [ 6 ]. Given that fever represents the primary surveillance indicator, accurate temperature measurement is critical. Thermometers are provided to sites and replaced when necessary due to technical issues or loss. Data collection In 2015, the 4S network was based on aggregated data collected daily by the SFPs through an Android application linked to a web platform specifically developed for the 4S network. The collected data included the total number of visits and their stratification by age groups (< 1 year, 1–4 years, 5–14 years, 15–24 years, and ≥ 25 years), the total number of fever cases and the total number of each syndromic case. If data from a designated sentinel site are not received by 10:00, an IPD staff member contacts the health facility to retrieve the missing information. The financial burden of data transmission is minimal, with an estimated cost of less than USD 10 per month per centre, inclusive of mobile internet connection expenses. Furthermore, SFPs meticulously documented the demographic data and clinical and epidemiological details of each new febrile case on paper forms via identification numbers. This case investigation form was disseminated, along with the biological sample, on a weekly basis by the postal service to the IPD staff, who were responsible for requesting any missing or unclear information via mobile phone. Individual case data, along with laboratory analysis results for associated samples, were entered into an access form linked to the MongoDB® database server. Timeliness and completeness Timeliness was gauged as the proportion of reporting sites that submitted daily reports to the IPD staff through the EWS platform prior to 10:00. on the subsequent day. The calculation was derived via the following formula: the percentage is determined by dividing the number of sites that met the specified deadline by the total number of reporting sites and then multiplying the result by 100. Completeness was gauged by the proportion of reporting sites that submitted complete datasets. The calculation was derived via the following formula: the index is calculated by dividing the number of sites that have produced a complete dataset by the total number of reporting sites and then multiplying the result by 100. Biological surveillance Biological samples were collected alongside clinical data from patients who provided voluntary consent for sampling. Informed consent was obtained at designated sentinel sites, with the basis of the consent being the suspected syndrome. For the purpose of suspected malaria, rapid diagnostic tests were administered as part of the national malaria programme. The protocol for ILI has been described previously [ 6 ]. For dengue-like syndromes, blood samples were tested by RT‒PCR and IgM ELISA for the following pathogens: chikungunya virus (CHIKV), dengue virus (DENV), West Nile virus (WNV), yellow fever (YF), Zika virus (ZIKV), Rift Valley fever (RVFV), and Crimean Congo hemorrhagic fever virus (CCHFV). The utilisation of stool samples was exclusively recommended during the course of investigations pertaining to febrile diarrhea alerts. Early warning system platform Components: The EWS platform was constructed with the R Shiny application programme interface (API) as the underlying programme language and MongoDB as the database management system. The model is composed of four interconnected components. The administrative web application enables the IPD team to perform the following functions: define user access rights, manage applications remotely on Android systems, add or remove sites from the network, define diseases to be monitored, and enable or disable associated forms. The Android application allows trained SFPs to submit daily aggregated data and visualize it through a large range of downloadable graphical representations. The EWS platform provides three main functions: data processing and analysis, data visualization in multiple ways (spatially or temporally), and alert generation. The routine outputs are as follows: In addition to the electronic alerts disseminated to the IPD epidemiology coordination staff, weekly epidemiological reports were prepared and transmitted to the MoH. The MoH was responsible for disseminating these reports to regional and district public health workers, the stakeholders of the Sentinel Centres and the national and international partners of the MoH (Fig. 2 ). The integration of daily aggregated data into the MongoDB server housed at the IPD was executed with meticulous adherence to security protocols. The transmission of data is facilitated through the use of a virtual private network, ensuring the security of the transmitted information. In the event that data collectors lack internet access, alternative methods must be employed. The option of sending data via SMS is available to the user. Validity check The MongoDB database server was integrated with a web-based EWS platform located at the IPD to monitor the number of notified clinical cases and detect any abnormal events. Data entry forms were meticulously engineered with internal consistency checks and logical validations to ensure data quality prior to export for analysis. Data analysis Descriptive analysis The statistical software programme R (version 4.0.4) was used to present the data in graphical and tabular formats. The data include the number and proportion of cases related to each syndrome over time, as well as the place and person. The time series data were examined on an annual basis, with a moving average from a three-week period utilised for the analysis. Pearson's chi-square test was used to compare categorical variables. The statistical significance was set at p < 0.05. Temporal trends in febrile syndromes were assessed via a Poisson regression model, which compared monthly consultation averages per sentinel site to those in the reference year of 2015. Relative risks (RRs) and corresponding 95% confidence intervals (CIs) were estimated for key syndromes to quantify year-to-year variations. Alert thresholds Since 2015, the EWS platform has facilitated real-time monitoring of fluctuations in values from each sentinel site. Daily and weekly baselines were calculated for each syndrome and sentinel site via a moving average algorithm, as previously defined in reference [ 8 ]. The means from the past three weeks were compared with the means from the same three-week period in the preceding three years. The definition of an alert was modified to include a two-week consecutive threshold exceedance as a condition for triggering the alert, thereby increasing its specificity. In the event that an alert was triggered, the system was programmed to automatically transmit email notifications to the IPD Epidemiology staff. Immediate telephone communication was initiated with district health officials and sentinel site staff to facilitate interpretation and subsequent follow-up. This was done prior to disseminating the findings to public health officials at the MoH and conducting further investigations at the same site. Ethical considerations The principles of the 4S network have been formally endorsed by the Senegalese Ministry of Health. The 4S network was implemented as part of routine public health surveillance, building on the long-standing national influenza sentinel surveillance system previously described and recognized as public health practice in Senegal [ 6 , 7 ]. The protocol was initially reviewed and approved by the Senegalese National Ethics Committee. However, since the activities of the 4S network were fully integrated into the routine operations of the Ministry of Health, they were considered routine public health activities and therefore did not require formal ethical renewal. All surveillance procedures adhered to the ethical principles of the Declaration of Helsinki, and data were collected solely for surveillance purposes and fully anonymized. Informed consent for adults: Adult participants were provided with a concise explanation of the study and gave oral informed consent directly to trained healthcare workers prior to specimen collection. Informed consent for minors: For minor participants, oral informed consent was obtained from a parent or legally authorized representative before any specimen was collected. The oral consent process included two questions: one regarding the information provided and the other confirming the participant's oral consent. Participation was voluntary, and patients had the right to refuse participation; no specimens were collected if consent was not granted. Results The data collected on a daily basis from January 2015 to December 2023 corresponded to 1,816,340 outpatient visits. The mean overall timeliness of reporting was 94.5%, whereas the mean completeness of reporting was 80.0%. A total of 51.0% of the patients were under the age of 15. The total number of visits, stratified by age group, is presented in Table 1 . A statistically significant discrepancy was identified in the consultation distribution according to age group (p < 0.001). A total of 212,792 patients (11.7%) presented with fever (see Table 2 ). Among these, 81,711 (38.4%) were classified as influenza-like illnesses, 22,429 (10.5%) as dengue-like syndromes, 19,924 (9.4%) as malaria cases, 9,775 (4.6%) as febrile diarrhea, and 78,953 (37.1%) as febrile symptoms. The distribution of syndromes exhibited significant disparities according to the sentinel site (p < 0.001). Fever syndrome (Table 3 ) accounted for 16.3% of the visits in 2015, 12.8% in 2016, 17.7% in 2017, 13.8% in 2018, 10.7% in 2019, 8.9% in 2020, 9.8% in 2021, 9.2% in 2022 and 7.5% in 2023 (p < 0.001). Among patients who presented with fever, the sex ratio (male/female) was 1.1, and the mean age was 12.0 ± 13.8 years. Regardless of the syndrome, the differences were statistically significant according to the year (p < 0.01). The mean number of visits or syndromes reported per month*site (see Fig. 3 ) by year exhibited a decline over time for these indicators of visits and fever syndromes. The peak observed in 2017 was associated not only with influenza-like illnesses but also with other unclassified fever syndromes, as confirmed by a Poisson regression model with a relative risk compared with 2015: RR = 1.44 (95% CI: [1.41–1.48] and RR = 1.17 (95% CI: 1.14–1.19). In 2021, the observed increase in visits was associated with fever syndrome, including malaria, as indicated by the Poisson regression model (RR = 1.18, 95% CI: [1.06–1.17]). Compared with 2015, the RRs for dengue-like syndrome were significantly greater in 2019 (RR = 1.09, 95% CI: [1.04–1.16]), 2022 (RR = 1.29, 95% CI [1.22–1.36]), and 2023 (RR = 1.39, 95% CI [1.32–1.46]). The distribution of consultations (N = 1,816,340) according to age group (Table 1 ) was significantly different depending on the year (p < 0.001) (Fig. 4 ). As illustrated in Fig. 5 , which presents syndromic surveillance data by site and year, the proportion of fever among all consultations and the proportion of febrile syndromes varied according to year and site. ILI cases were more prevalent in the western regions, while dengue-like cases were observed in the central and northeastern regions, and malaria cases were identified in the southeastern region. Unclassified febrile syndromes were observed to be prevalent across a significant proportion of sites and over the course of a substantial number of years. Alert event detection From January 2015 to December 2023, the EWS platform identified 202 alert events, of which 103 (51.0%) were laboratory confirmed (Table 4 ). ILI was the most frequent cause of alerts (n = 64), accounting for 31.7% of all alerts and 52.4% of confirmed events. This was followed by dengue-like syndromes (48 alerts, 23.8% of alerts and 24.3% of confirmed events), malaria (45 alerts, 22.3% and 21.4%, respectively), and diarrheal syndromes (45 alerts, 22.3% and 2.0%, respectively). No statistically significant differences were observed between years (p = 0.38) or regions (p = 0.14) regarding the number of alerts or confirmations. Table 4 alerts events and confirmed events by year, region and syndrome, 4S Network, Senegal, 2015–2023. Large table placed at the end of the manuscript due to its size. Discussion The present study delineates the fundamental components of the 4S network and expounds upon its primary evolutionary trajectory and the outcomes emanating from 2015 to 2023. These outcomes encompass the annual and geographical distribution, the identification of events of interest, and case identifications. The study demonstrated a high degree of timeliness (94.5%) and completeness (80.0%) in reporting. The enhanced completeness and timeliness of reporting are probably attributable to internet-based reporting and regular mobile phone reminders, as evidenced in other African IDSR programmes [ 9 , 10 ]. Adherence to these indicators remains imperative; however, it is insufficient in and of itself. In fact, the quality of the systems appears to be deteriorating, as evidenced by the annual decline in the monthly mean number of visits and other febrile syndromes (Fig. 3 ). This decline may be associated with the increasing number of sentinel sites that national health workers hit in 2018 and between 2022 and 2023 and the impact of the COVID-19 pandemic in 2020, which redirected sentinel site personnel to outbreak response activities. The 4S network should undergo periodic evaluation by external experts. The extension to all districts must be implemented in accordance with the same process initiated at the outset, and the human resources responsible for coordination and sentinel site supervision must be augmented. The selection of a sentinel site is contingent upon a multifaceted set of criteria, including but not limited to geographical considerations, the frequency of visits, the demographic composition of the health centre's patient population, and the degree of health staff participation. This meticulous approach is undertaken to ensure the integrity of the sentinel data and to preserve the stability of the 4S network. Regardless of the circumstances, the evaluation of surveillance systems should be a task systematically incorporated into the design of every surveillance system [ 11 , 12 ]. This evaluation should entail the monitoring of indicators specific to the evaluation process in a manner analogous to the monitoring of epidemiological indicators. The objective is to ensure that the results associated with the data are able to support decision-making on clearly identified risks. One of the aims of surveillance systems is to detect abnormal events and potential risks of outbreaks with respect to International Health Regulation [ 13 ]. Our data revealed that the 4S network's EWS, which is based on a real-time-like process, detected earlier alert events than did the routine surveillance system. Over a 9-year period, the system identified 202 early warning events, 103 of which were confirmed, resulting in a positive predictive value of 51.0%. Each alert triggered a response involving multidisciplinary epidemiological investigations tailored to the specific pathogen. This process was facilitated through collaboration with the MoH and chief medical officers of the affected regions and districts, enabling faster response and reduced morbidity and mortality. All syndromes except febrile diarrhea produced confirmed warning events in each region, suggesting that influenza viruses, arboviruses, and malaria parasites warrant focused attention in public health strategies. Malaria transmission in Senegal is highly seasonal, peaking between October and November, with most cases occurring during the rainy season. The incidence is highest in the wetter southeastern regions and decreases toward the north due to environmental and biological factors that favor Anopheles mosquito breeding, as shown by several studies [ 14 ]. These factors drive the seasonal pattern of malaria cases. These findings are similar to those of previous studies showing that dengue fever is endemic. In Senegal, the 4S network reported confirmed cases of dengue fever in all regions of Senegal [ 15 , 16 ]. Multiple dengue outbreaks involving different serotypes have been reported [ 15 , 17 ]. The increase in cases between 2022 and 2023 in the Matam and Dakar regions may be due to the introduction of a new serotype to the population that is naïve to it. Dengue serotypes do not persist long-term in specific areas because of lineage shifts, introductions, or replacements [ 18 ]. The introduction of new serotypes may create bottlenecks and displace previously dominant serotypes; however, cocirculation with existing serotypes can still occur [ 19 ]. These shifts or replacements can impact immunity and lead to more severe cases. These findings underscore the urgent need for enhanced disease surveillance and vector control policies to mitigate transmission intensity and manage outbreaks effectively. Prior to the advent of SARS-CoV-2, the epidemiology of influenza in Senegal exhibited characteristics consistent with the tropical climate, with persistent circulation throughout the year and heightened transmission during the rainy season [ 20 ] attributable to increased indoor contact and the resumption of school activities. Despite the impact of SARS-CoV-2 on influenza dynamics, there was no disruption to the typical seasonal peaks observed during the rainy season. However, a notable surge in influenza cases from May to July 2022 suggests the potential for viral interference or competition between influenza and SARS-CoV-2, as suggested by experimental and epidemiological studies [ 21 ]. The years 2017 and 2021 were marked by elevated levels of febrile syndrome indicators (see Fig. 3 ), which coincided with an increase in ILI in 2027 and malaria cases in 2021. These periods also included undetermined febrile illnesses. The prevalence of undetermined febrile illnesses (37.1%) is noteworthy across all years (see Table 3 ) and sentinel sites (see Table 2 ). This observation underscores the necessity for additional research investigating the etiology of febrile illnesses in Senegal. Such research is crucial for the development and implementation of more precise surveillance tools and rapid diagnostic tests. Conclusion The 4S Network in Senegal has proven highly effective as a syndromic surveillance model for resource-limited settings, successfully enabling early outbreak detection and rapid response capabilities. To maximize its public health impact and establish it as a replicable framework for other African nations, the programme requires ongoing evaluation and systematic addressing of current operational limitations. Expanding this proven surveillance model across the African continent represents a strategic opportunity to strengthen regional health security. However, successful continental implementation will depend on ensuring seamless interoperability with existing surveillance systems and fostering coordination among diverse national health programmes throughout Africa. Abbreviations 4S Network Senegal Syndromic Sentinel Surveillance Network MoH Ministry of Health IPD Institut Pasteur de Dakar SFP Sentinel Focal Point EWS Early Warning System ILI Influenza-Like Illness RT-PCR Reverse Transcription Polymerase Chain Reaction CHIKV Chikungunya Virus DENV Dengue Virus WNV West Nile Virus YFV Yellow Fever Virus ZIKV Zika Virus RVFV Rift Valley Fever Virus CCHFV Crimean-Congo Hemorrhagic Fever Virus RR Relative Risk CI Confidence Interval SMS Short Message Service Declarations Ethics approval and consent to participate The 4S network was formally endorsed by the Ministry of Health and classified as routine public health surveillance rather than research by the Senegalese National Ethics Committee. Data were collected anonymously for surveillance purposes. Participants (or parents for minors) received a brief explanation and gave informed oral consent, documented on the patient form. Specimens were collected only after consent, and patients could refuse participation without consequence. Consent for publication Not applicable. Data availability The data used for the analyses in this study are property of the Senegalese Ministry of Health and the Institut Pasteur de Dakar. Access to the dataset can be requested from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. Funding The study was initially funded by the U.S. Department of Health and Human Services (grant IDSEP140020–01-00) through the International Network Division of the Pasteur Institute in Paris. Additional support from the Bill & Melinda Gates Foundation and the Africa CDC enabled the development, expansion, and sustainability of the 4S Network as a real-time digital sentinel syndromic surveillance system in Senegal and its integration into regional epidemic preparedness initiatives. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors' contributions M.A.B. coordinated the 4S Network, conceived the study, extracted and analysed the data, wrote the original draft. C. T. coordinate the 4S network platform; participated in the study design and revised the original draft. A. S. participated in the study design and revised the original draft. S. S. contributed to data collection, system supervision and field activities. A. Gaye. contributed to data management and analysis. B.S. & N.K. N. contributed to data collection and system supervision. M.A. & V. R participated in the study design, supervised and contributed to the review and editing of the paper. D.M participated in the study design and contributed to the review and editing of the paper. M. M. D, M.F., M. M. D, F.D.S., O.F., G. F., C. T. D, Y. S., C. F., O. F., I.O.B., B. D., C.L., B. D. and A.A.S. contributed either to laboratory analysis, management of the surveillance system and edited the paper. All authors read and approved the final manuscript. Acknowledgments We thank all the focal points and staff at the 4S Network sentinel sites, as well as the teams from the Virology and Microbiology Departments of the Institut Pasteur de Dakar, the field investigators, and the community workers for their active participation in the surveillance system. We are also grateful to Marie Louise Senghor, Mbaye Diop, Moussa Dieng, Joseph Faye, Debora Goudiaby, Amary, Davy Kiori, Diogop Camara, Makhfouz Traoré, Mame Astou Gassama, Mamadou Cissé and all others who contributed directly or indirectly to diagnostics, data management and field coordination. We would like to express our sincere thanks to our colleagues from the International Department of the Pasteur Institute in Paris, particularly Dr. Muriel Vray, former head of the IPD Epidemiology Unit, Dr. Kathleen Victoir, Nicole Prada, and Sarah Respaut; the WHO Country Office in Senegal; Africa CDC; WAHO; and the Bill & Melinda Gates Foundation for their unwavering support throughout the implementation and expansion of the 4S network. We particularly acknowledge the Senegalese Ministry of Health for its leadership and continued support of the 4S Network. References Henning KJ. What is syndromic surveillance? MMWR Suppl. 2004;53:5–11. Rajatonirina S, Rakotomanana F, Randrianasolo L, Razanajatovo NH, Andriamandimby SF, Ravolomanana L, et al. Early-warning health and process indicators for sentinel surveillance in Madagascar 2007–2011. Online J Public Health Inf. 2014;6:e197. https://doi.org/10.5210/ojphi.v6i3.5400 . 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Supplementary Files Tables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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2023.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8355367/v1/d86458b630ed9b64d44d9fa3.png\"},{\"id\":100400279,\"identity\":\"bde24d53-7341-475e-8d1f-dcd30e00b476\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 11:58:02\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":214160,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRepresentation of the 4S network architecture. Senegal. 2023\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8355367/v1/ca397f1195c69d4d747956cf.png\"},{\"id\":100399451,\"identity\":\"1dda821f-0586-4b46-8b08-fb7bd52a2256\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 11:56:59\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":74826,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCurves of the monthly site mean number of consultations and syndromes in the 4S network, Senegal, 2015–2023.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8355367/v1/1669d8cef41eed4bdc937a1b.png\"},{\"id\":100399766,\"identity\":\"5f864b33-dfa0-45a1-8a3d-e406731b6369\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 11:57:34\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":31775,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDistribution of consultations by age group and year within the 4S Network, Senegal, 2015–2023.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8355367/v1/33e05ce8afe3a9afe879c4cf.png\"},{\"id\":100400207,\"identity\":\"8ca79c62-5ed9-4ce2-8c12-3b6ae790242b\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 11:58:01\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":225679,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFever among all visits and distribution of febrile syndromes by year and sentinel site, 4S Network, Senegal, 2015–2023 (the height of the bars varies according to the percentage of fever in all consultations—max: 33%; min: 1%). The internal color segments show the distribution of key febrile syndromes, including ILI, dengue-like, malaria, diarrheal and other unclassified syndromes.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8355367/v1/b4b433f291fff78dbe26e97c.png\"},{\"id\":104808473,\"identity\":\"e1ad539e-41ff-433a-ae87-9d1ea6b1ebfa\",\"added_by\":\"auto\",\"created_at\":\"2026-03-17 12:37:52\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1396753,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8355367/v1/f1ecbfbe-13c0-4fe7-b9d9-2f6b8d45cdd6.pdf\"},{\"id\":100399417,\"identity\":\"a1db8815-cec4-49e6-b9eb-38ec5e65724c\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 11:56:56\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":55626,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Tables.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8355367/v1/3b6acd3c61903f110e3ccf69.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Real-time sentinel syndromic surveillance for infectious disease detection: lessons from the 4S network in Senegal, 2015–2023\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eOwing to major public health threats from emerging infectious diseases and potential bioterrorism attacks, widespread syndromic surveillance systems have been established to enable early identification of epidemic patterns. [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. This surveillance approach prioritizes the identification of syndromes (e.g., influenza-like or dengue-like illnesses) over the collection of confirmatory diagnostic data. The system demonstrates notable sensitivity to fluctuations in disease trends [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e] and has proven effective in the early detection of outbreaks [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. The implementation of real-time syndromic surveillance has emerged as a promising strategy to increase the timeliness and completeness of disease outbreak detection. The process involves the capture of preliminary information regarding health events that occur prior to formal biological confirmation. It involves the rapid collection of data from existing electronic sources and the subsequent daily analysis of the data to identify potential outbreak signals to be investigated [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eRecently, numerous sentinel surveillance systems from Africa have been considered in the context of influenza surveillance and, more recently, the recent COVID-19 pandemic [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. These surveillance systems, however, still face significant limitations in coverage and available resources, and delays associated with diagnostic capacity and reporting. Across Africa, the future sustainability and continued financing of such programmes depend on providing clear evidence to health leaders that surveillance information effectively supports disease prevention, risk control, patient management, economic prosperity, and successful intervention in other critical health areas. [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn Senegal, teams from the Institut Pasteur de Dakar (IPD), in collaboration with and for the Senegalese Ministry of Health (MoH), established the Senegalese Syndromic Sentinel Surveillance Network (4S network) in 2012. This network is based on daily data and was developed to reinforce the previous influenza surveillance network and strengthen routine monitoring based on weekly data [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eAs Senegal's first surveillance system of this nature, this innovative network consolidates various public health syndromes, enhanced by laboratory confirmation and an early warning system (EWS) platform. This comprehensive approach allows swift detection and intervention regarding unusual health events. This study aimed to evaluate the performance of the 4S real-time sentinel syndromic surveillance network from January 2015 to December 2023 by analysing its key components and achievements and assessing its contribution to early infectious disease outbreak detection and preparedness.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePresentation of the Senegalese Sentinel Syndromic Surveillance Network (4S Network)\\u003c/h2\\u003e \\u003cp\\u003eIn Senegal, an influenza surveillance system was established in 1996 through the National Influenza Reference Centre based at IPD, with the primary objective of monitoring circulating virological viral strains [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. In 2012, the institution made a strategic decision to expand its operations into the U.S. market. The surveillance system employed an early warning objective, utilizing a syndromic approach, as previously delineated [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eA national staff member was appointed as the Sentinel focal point (SFP) at each selected field site to manage site operations, supported by additional personnel serving as backup. The network underwent substantial geographical expansion, moving from 15 sentinel sites in 2015 to 27 sites in 2023 (see Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e), thereby encompassing all 14 regions of Senegal. The network encompasses community-based sites situated within primary or district-level health facilities, which cater to ambulatory patients. The geographical distribution of the sentinel sites is shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eSyndromic case definitions\\u003c/h3\\u003e\\n\\u003cp\\u003eTo ensure global compatibility, the study employed WHO case definitions from 2012, relying on clinical data obtained before diagnostic confirmation. Previous publications have documented these case definitions [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. Given that fever represents the primary surveillance indicator, accurate temperature measurement is critical. Thermometers are provided to sites and replaced when necessary due to technical issues or loss.\\u003c/p\\u003e\\n\\u003ch3\\u003eData collection\\u003c/h3\\u003e\\n\\u003cp\\u003eIn 2015, the 4S network was based on aggregated data collected daily by the SFPs through an Android application linked to a web platform specifically developed for the 4S network. The collected data included the total number of visits and their stratification by age groups (\\u0026lt;\\u0026thinsp;1 year, 1\\u0026ndash;4 years, 5\\u0026ndash;14 years, 15\\u0026ndash;24 years, and \\u0026ge;\\u0026thinsp;25 years), the total number of fever cases and the total number of each syndromic case. If data from a designated sentinel site are not received by 10:00, an IPD staff member contacts the health facility to retrieve the missing information. The financial burden of data transmission is minimal, with an estimated cost of less than USD 10 per month per centre, inclusive of mobile internet connection expenses. Furthermore, SFPs meticulously documented the demographic data and clinical and epidemiological details of each new febrile case on paper forms via identification numbers. This case investigation form was disseminated, along with the biological sample, on a weekly basis by the postal service to the IPD staff, who were responsible for requesting any missing or unclear information via mobile phone. Individual case data, along with laboratory analysis results for associated samples, were entered into an access form linked to the MongoDB\\u0026reg; database server.\\u003c/p\\u003e\\n\\u003ch3\\u003eTimeliness and completeness\\u003c/h3\\u003e\\n\\u003cp\\u003eTimeliness was gauged as the proportion of reporting sites that submitted daily reports to the IPD staff through the EWS platform prior to 10:00. on the subsequent day. The calculation was derived via the following formula: the percentage is determined by dividing the number of sites that met the specified deadline by the total number of reporting sites and then multiplying the result by 100. Completeness was gauged by the proportion of reporting sites that submitted complete datasets. The calculation was derived via the following formula: the index is calculated by dividing the number of sites that have produced a complete dataset by the total number of reporting sites and then multiplying the result by 100.\\u003c/p\\u003e\\n\\u003ch3\\u003eBiological surveillance\\u003c/h3\\u003e\\n\\u003cp\\u003eBiological samples were collected alongside clinical data from patients who provided voluntary consent for sampling. Informed consent was obtained at designated sentinel sites, with the basis of the consent being the suspected syndrome. For the purpose of suspected malaria, rapid diagnostic tests were administered as part of the national malaria programme. The protocol for ILI has been described previously [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. For dengue-like syndromes, blood samples were tested by RT‒PCR and IgM ELISA for the following pathogens: chikungunya virus (CHIKV), dengue virus (DENV), West Nile virus (WNV), yellow fever (YF), Zika virus (ZIKV), Rift Valley fever (RVFV), and Crimean Congo hemorrhagic fever virus (CCHFV). The utilisation of stool samples was exclusively recommended during the course of investigations pertaining to febrile diarrhea alerts.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eEarly warning system platform\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eComponents:\\u003c/h2\\u003e \\u003cp\\u003eThe EWS platform was constructed with the R Shiny application programme interface (API) as the underlying programme language and MongoDB as the database management system. The model is composed of four interconnected components. The administrative web application enables the IPD team to perform the following functions: define user access rights, manage applications remotely on Android systems, add or remove sites from the network, define diseases to be monitored, and enable or disable associated forms. The Android application allows trained SFPs to submit daily aggregated data and visualize it through a large range of downloadable graphical representations. The EWS platform provides three main functions: data processing and analysis, data visualization in multiple ways (spatially or temporally), and alert generation. The routine outputs are as follows: In addition to the electronic alerts disseminated to the IPD epidemiology coordination staff, weekly epidemiological reports were prepared and transmitted to the MoH. The MoH was responsible for disseminating these reports to regional and district public health workers, the stakeholders of the Sentinel Centres and the national and international partners of the MoH (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The integration of daily aggregated data into the MongoDB server housed at the IPD was executed with meticulous adherence to security protocols. The transmission of data is facilitated through the use of a virtual private network, ensuring the security of the transmitted information. In the event that data collectors lack internet access, alternative methods must be employed. The option of sending data via SMS is available to the user.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eValidity check\\u003c/h3\\u003e\\n\\u003cp\\u003eThe MongoDB database server was integrated with a web-based EWS platform located at the IPD to monitor the number of notified clinical cases and detect any abnormal events. Data entry forms were meticulously engineered with internal consistency checks and logical validations to ensure data quality prior to export for analysis.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData analysis\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eDescriptive analysis\\u003c/h2\\u003e \\u003cp\\u003eThe statistical software programme R (version 4.0.4) was used to present the data in graphical and tabular formats. The data include the number and proportion of cases related to each syndrome over time, as well as the place and person. The time series data were examined on an annual basis, with a moving average from a three-week period utilised for the analysis. Pearson's chi-square test was used to compare categorical variables. The statistical significance was set at p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05.\\u003c/p\\u003e \\u003cp\\u003eTemporal trends in febrile syndromes were assessed via a Poisson regression model, which compared monthly consultation averages per sentinel site to those in the reference year of 2015. Relative risks (RRs) and corresponding 95% confidence intervals (CIs) were estimated for key syndromes to quantify year-to-year variations.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAlert thresholds\\u003c/h2\\u003e \\u003cp\\u003eSince 2015, the EWS platform has facilitated real-time monitoring of fluctuations in values from each sentinel site. Daily and weekly baselines were calculated for each syndrome and sentinel site via a moving average algorithm, as previously defined in reference [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. The means from the past three weeks were compared with the means from the same three-week period in the preceding three years. The definition of an alert was modified to include a two-week consecutive threshold exceedance as a condition for triggering the alert, thereby increasing its specificity. In the event that an alert was triggered, the system was programmed to automatically transmit email notifications to the IPD Epidemiology staff. Immediate telephone communication was initiated with district health officials and sentinel site staff to facilitate interpretation and subsequent follow-up. This was done prior to disseminating the findings to public health officials at the MoH and conducting further investigations at the same site.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eEthical considerations\\u003c/h2\\u003e \\u003cp\\u003eThe principles of the 4S network have been formally endorsed by the Senegalese Ministry of Health. The 4S network was implemented as part of routine public health surveillance, building on the long-standing national influenza sentinel surveillance system previously described and recognized as public health practice in Senegal [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. The protocol was initially reviewed and approved by the Senegalese National Ethics Committee. However, since the activities of the 4S network were fully integrated into the routine operations of the Ministry of Health, they were considered routine public health activities and therefore did not require formal ethical renewal. All surveillance procedures adhered to the ethical principles of the Declaration of Helsinki, and data were collected solely for surveillance purposes and fully anonymized.\\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eInformed consent\\u003c/strong\\u003e \\u003cp\\u003efor adults: Adult participants were provided with a concise explanation of the study and gave oral informed consent directly to trained healthcare workers prior to specimen collection.\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eInformed consent\\u003c/strong\\u003e \\u003cp\\u003efor minors: For minor participants, oral informed consent was obtained from a parent or legally authorized representative before any specimen was collected.\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e The oral consent process included two questions: one regarding the information provided and the other confirming the participant's oral consent. Participation was voluntary, and patients had the right to refuse participation; no specimens were collected if consent was not granted.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eThe data collected on a daily basis from January 2015 to December 2023 corresponded to 1,816,340 outpatient visits. The mean overall timeliness of reporting was 94.5%, whereas the mean completeness of reporting was 80.0%. A total of 51.0% of the patients were under the age of 15. The total number of visits, stratified by age group, is presented in Table \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. A statistically significant discrepancy was identified in the consultation distribution according to age group (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).\\u003c/p\\u003e\\n\\u003cp\\u003eA total of 212,792 patients (11.7%) presented with fever (see Table \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Among these, 81,711 (38.4%) were classified as influenza-like illnesses, 22,429 (10.5%) as dengue-like syndromes, 19,924 (9.4%) as malaria cases, 9,775 (4.6%) as febrile diarrhea, and 78,953 (37.1%) as febrile symptoms. The distribution of syndromes exhibited significant disparities according to the sentinel site (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).\\u003c/p\\u003e\\n\\u003cp\\u003eFever syndrome (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e) accounted for 16.3% of the visits in 2015, 12.8% in 2016, 17.7% in 2017, 13.8% in 2018, 10.7% in 2019, 8.9% in 2020, 9.8% in 2021, 9.2% in 2022 and 7.5% in 2023 (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Among patients who presented with fever, the sex ratio (male/female) was 1.1, and the mean age was 12.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;13.8 years.\\u003c/p\\u003e\\n\\u003cp\\u003eRegardless of the syndrome, the differences were statistically significant according to the year (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01).\\u003c/p\\u003e\\n\\u003cp\\u003eThe mean number of visits or syndromes reported per month*site (see Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e) by year exhibited a decline over time for these indicators of visits and fever syndromes. The peak observed in 2017 was associated not only with influenza-like illnesses but also with other unclassified fever syndromes, as confirmed by a Poisson regression model with a relative risk compared with 2015: RR\\u0026thinsp;=\\u0026thinsp;1.44 (95% CI: [1.41\\u0026ndash;1.48] and RR\\u0026thinsp;=\\u0026thinsp;1.17 (95% CI: 1.14\\u0026ndash;1.19). In 2021, the observed increase in visits was associated with fever syndrome, including malaria, as indicated by the Poisson regression model (RR\\u0026thinsp;=\\u0026thinsp;1.18, 95% CI: [1.06\\u0026ndash;1.17]). Compared with 2015, the RRs for dengue-like syndrome were significantly greater in 2019 (RR\\u0026thinsp;=\\u0026thinsp;1.09, 95% CI: [1.04\\u0026ndash;1.16]), 2022 (RR\\u0026thinsp;=\\u0026thinsp;1.29, 95% CI [1.22\\u0026ndash;1.36]), and 2023 (RR\\u0026thinsp;=\\u0026thinsp;1.39, 95% CI [1.32\\u0026ndash;1.46]).\\u003c/p\\u003e\\n\\u003cp\\u003eThe distribution of consultations (N\\u0026thinsp;=\\u0026thinsp;1,816,340) according to age group (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e) was significantly different depending on the year (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003cp\\u003eAs illustrated in Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e, which presents syndromic surveillance data by site and year, the proportion of fever among all consultations and the proportion of febrile syndromes varied according to year and site. ILI cases were more prevalent in the western regions, while dengue-like cases were observed in the central and northeastern regions, and malaria cases were identified in the southeastern region. Unclassified febrile syndromes were observed to be prevalent across a significant proportion of sites and over the course of a substantial number of years.\\u003c/p\\u003e\\n\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eAlert event detection\\u003c/h2\\u003e\\n \\u003cp\\u003eFrom January 2015 to December 2023, the EWS platform identified 202 alert events, of which 103 (51.0%) were laboratory confirmed (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). ILI was the most frequent cause of alerts (n\\u0026thinsp;=\\u0026thinsp;64), accounting for 31.7% of all alerts and 52.4% of confirmed events. This was followed by dengue-like syndromes (48 alerts, 23.8% of alerts and 24.3% of confirmed events), malaria (45 alerts, 22.3% and 21.4%, respectively), and diarrheal syndromes (45 alerts, 22.3% and 2.0%, respectively). No statistically significant differences were observed between years (p\\u0026thinsp;=\\u0026thinsp;0.38) or regions (p\\u0026thinsp;=\\u0026thinsp;0.14) regarding the number of alerts or confirmations.\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTable \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u0026nbsp;\\u003c/span\\u003e\\u003c/strong\\u003ealerts events and confirmed events by year, region and syndrome, 4S Network, Senegal, 2015\\u0026ndash;2023. Large table placed at the end of the manuscript due to its size.\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThe present study delineates the fundamental components of the 4S network and expounds upon its primary evolutionary trajectory and the outcomes emanating from 2015 to 2023. These outcomes encompass the annual and geographical distribution, the identification of events of interest, and case identifications.\\u003c/p\\u003e \\u003cp\\u003eThe study demonstrated a high degree of timeliness (94.5%) and completeness (80.0%) in reporting. The enhanced completeness and timeliness of reporting are probably attributable to internet-based reporting and regular mobile phone reminders, as evidenced in other African IDSR programmes [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. Adherence to these indicators remains imperative; however, it is insufficient in and of itself. In fact, the quality of the systems appears to be deteriorating, as evidenced by the annual decline in the monthly mean number of visits and other febrile syndromes (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). This decline may be associated with the increasing number of sentinel sites that national health workers hit in 2018 and between 2022 and 2023 and the impact\\u003c/p\\u003e \\u003cp\\u003eof the COVID-19 pandemic in 2020, which redirected sentinel site personnel to outbreak response activities. The 4S network should undergo periodic evaluation by external experts. The extension to all districts must be implemented in accordance with the same process initiated at the outset, and the human resources responsible for coordination and sentinel site supervision must be augmented. The selection of a sentinel site is contingent upon a multifaceted set of criteria, including but not limited to geographical considerations, the frequency of visits, the demographic composition of the health centre's patient population, and the degree of health staff participation. This meticulous approach is undertaken to ensure the integrity of the sentinel data and to preserve the stability of the 4S network. Regardless of the circumstances, the evaluation of surveillance systems should be a task systematically incorporated into the design of every surveillance system [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. This evaluation should entail the monitoring of indicators specific to the evaluation process in a manner analogous to the monitoring of epidemiological indicators. The objective is to ensure that the results associated with the data are able to support decision-making on clearly identified risks.\\u003c/p\\u003e \\u003cp\\u003eOne of the aims of surveillance systems is to detect abnormal events and potential risks of outbreaks with respect to International Health Regulation [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. Our data revealed that the 4S network's EWS, which is based on a real-time-like process, detected earlier alert events than did the routine surveillance system. Over a 9-year period, the system identified 202 early warning events, 103 of which were confirmed, resulting in a positive predictive value of 51.0%. Each alert triggered a response involving multidisciplinary epidemiological investigations tailored to the specific pathogen. This process was facilitated through collaboration with the MoH and chief medical officers of the affected regions and districts, enabling faster response and reduced morbidity and mortality.\\u003c/p\\u003e \\u003cp\\u003eAll syndromes except febrile diarrhea produced confirmed warning events in each region, suggesting that influenza viruses, arboviruses, and malaria parasites warrant focused attention in public health strategies.\\u003c/p\\u003e \\u003cp\\u003eMalaria transmission in Senegal is highly seasonal, peaking between October and November, with most cases occurring during the rainy season. The incidence is highest in the wetter southeastern regions and decreases toward the north due to environmental and biological factors that favor Anopheles mosquito breeding, as shown by several studies [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. These factors drive the seasonal pattern of malaria cases.\\u003c/p\\u003e \\u003cp\\u003eThese findings are similar to those of previous studies showing that dengue fever is endemic. In Senegal, the 4S network reported confirmed cases of dengue fever in all regions of Senegal [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. Multiple dengue outbreaks involving different serotypes have been reported [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. The increase in cases between 2022 and 2023 in the Matam and Dakar regions may be due to the introduction of a new serotype to the population that is na\\u0026iuml;ve to it. Dengue serotypes do not persist long-term in specific areas because of lineage shifts, introductions, or replacements [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. The introduction of new serotypes may create bottlenecks and displace previously dominant serotypes; however, cocirculation with existing serotypes can still occur [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. These shifts or replacements can impact immunity and lead to more severe cases. These findings underscore the urgent need for enhanced disease surveillance and vector control policies to mitigate transmission intensity and manage outbreaks effectively.\\u003c/p\\u003e \\u003cp\\u003ePrior to the advent of SARS-CoV-2, the epidemiology of influenza in Senegal exhibited characteristics consistent with the tropical climate, with persistent circulation throughout the year and heightened transmission during the rainy season [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e] attributable to increased indoor contact and the resumption of school activities. Despite the impact of SARS-CoV-2 on influenza dynamics, there was no disruption to the typical seasonal peaks observed during the rainy season. However, a notable surge in influenza cases from May to July 2022 suggests the potential for viral interference or competition between influenza and SARS-CoV-2, as suggested by experimental and epidemiological studies [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe years 2017 and 2021 were marked by elevated levels of febrile syndrome indicators (see Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e), which coincided with an increase in ILI in 2027 and malaria cases in 2021. These periods also included undetermined febrile illnesses. The prevalence of undetermined febrile illnesses (37.1%) is noteworthy across all years (see Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e) and sentinel sites (see Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). This observation underscores the necessity for additional research investigating the etiology of febrile illnesses in Senegal. Such research is crucial for the development and implementation of more precise surveillance tools and rapid diagnostic tests.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThe 4S Network in Senegal has proven highly effective as a syndromic surveillance model for resource-limited settings, successfully enabling early outbreak detection and rapid response capabilities. To maximize its public health impact and establish it as a replicable framework for other African nations, the programme requires ongoing evaluation and systematic addressing of current operational limitations.\\u003c/p\\u003e \\u003cp\\u003eExpanding this proven surveillance model across the African continent represents a strategic opportunity to strengthen regional health security. However, successful continental implementation will depend on ensuring seamless interoperability with existing surveillance systems and fostering coordination among diverse national health programmes throughout Africa.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cdiv class=\\\"DefinitionList\\\"\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u003cb\\u003e4S Network\\u003c/b\\u003e\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eSenegal Syndromic Sentinel Surveillance Network\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u003cb\\u003eMoH\\u003c/b\\u003e\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eMinistry of Health\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u003cb\\u003eIPD\\u003c/b\\u003e\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eInstitut Pasteur de Dakar\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u003cb\\u003eSFP\\u003c/b\\u003e\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eSentinel Focal Point\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u003cb\\u003eEWS\\u003c/b\\u003e\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eEarly Warning System\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u003cb\\u003eILI\\u003c/b\\u003e\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eInfluenza-Like Illness\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u003cb\\u003eRT-PCR\\u003c/b\\u003e\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eReverse Transcription Polymerase Chain Reaction\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u003cb\\u003eCHIKV\\u003c/b\\u003e\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eChikungunya Virus\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u003cb\\u003eDENV\\u003c/b\\u003e\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eDengue Virus\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u003cb\\u003eWNV\\u003c/b\\u003e\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eWest Nile Virus\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u003cb\\u003eYFV\\u003c/b\\u003e\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eYellow Fever Virus\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u003cb\\u003eZIKV\\u003c/b\\u003e\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eZika Virus\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u003cb\\u003eRVFV\\u003c/b\\u003e\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eRift Valley Fever Virus\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u003cb\\u003eCCHFV\\u003c/b\\u003e\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eCrimean-Congo Hemorrhagic Fever Virus\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u003cb\\u003eRR\\u003c/b\\u003e\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eRelative Risk\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u003cb\\u003eCI\\u003c/b\\u003e\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eConfidence Interval\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u003cb\\u003eSMS\\u003c/b\\u003e\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eShort Message Service\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe 4S network was formally endorsed by the Ministry of Health and classified as routine public health surveillance rather than research by the Senegalese National Ethics Committee. Data were collected anonymously for surveillance purposes. Participants (or parents for minors) received a brief explanation and gave informed oral consent, documented on the patient form. Specimens were collected only after consent, and patients could refuse participation without consequence.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe data used for the analyses in this study are property of the Senegalese Ministry of Health and the Institut Pasteur de Dakar. Access to the dataset can be requested from the corresponding author upon reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study was initially funded by the U.S. Department of Health and Human Services (grant IDSEP140020\\u0026ndash;01-00) through the International Network Division of the Pasteur Institute in Paris. Additional support from the Bill \\u0026amp; Melinda Gates Foundation and the Africa CDC enabled the development, expansion, and sustainability of the 4S Network as a real-time digital sentinel syndromic surveillance system in Senegal and its integration into regional epidemic preparedness initiatives. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026apos; contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eM.A.B. coordinated the 4S Network, conceived the study, extracted and analysed the data, wrote the original draft. C. T. coordinate the 4S network platform; participated in the study design and revised the original draft. A. S. participated in the study design and revised the original draft. S. S. contributed to data collection, system supervision and field activities. A. Gaye. contributed to data management and analysis. B.S. \\u0026amp; N.K. N. contributed to data collection and system supervision. M.A. \\u0026amp; V. R participated in the study design, supervised and contributed to the review and editing of the paper. D.M participated in the study design and contributed to the review and editing of the paper. M. M. D, M.F., M. M. D, F.D.S., O.F., G. F., C. T. D, Y. S., C. F., O. F., I.O.B., B. D., C.L., B. D. and A.A.S. contributed either to laboratory analysis, management of the surveillance system and edited the paper. All authors read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe thank all the focal points and staff at the 4S Network sentinel sites, as well as the teams from the Virology and Microbiology Departments of the Institut Pasteur de Dakar, the field investigators, and the community workers for their active participation in the surveillance system.\\u003c/p\\u003e\\n\\u003cp\\u003eWe are also grateful to Marie Louise Senghor, Mbaye Diop, Moussa Dieng, Joseph Faye, Debora Goudiaby, Amary, Davy Kiori, Diogop Camara, Makhfouz Traor\\u0026eacute;, Mame Astou Gassama, Mamadou Ciss\\u0026eacute; and all others who contributed directly or indirectly to diagnostics, data management and field coordination.\\u003c/p\\u003e\\n\\u003cp\\u003eWe would like to express our sincere thanks to our colleagues from the International Department of the Pasteur Institute in Paris, particularly Dr. Muriel Vray, former head of the IPD Epidemiology Unit, Dr. Kathleen Victoir, Nicole Prada, and Sarah Respaut; the WHO Country Office in Senegal; Africa CDC; WAHO; and the Bill \\u0026amp; Melinda Gates Foundation for their unwavering support throughout the implementation and expansion of the 4S network.\\u003c/p\\u003e\\n\\u003cp\\u003eWe particularly acknowledge the Senegalese Ministry of Health for its leadership and continued support of the 4S Network.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eHenning KJ. What is syndromic surveillance? MMWR Suppl. 2004;53:5\\u0026ndash;11.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRajatonirina S, Rakotomanana F, Randrianasolo L, Razanajatovo NH, Andriamandimby SF, Ravolomanana L, et al. Early-warning health and process indicators for sentinel surveillance in Madagascar 2007\\u0026ndash;2011. Online J Public Health Inf. 2014;6:e197. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.5210/ojphi.v6i3.5400\\u003c/span\\u003e\\u003cspan address=\\\"10.5210/ojphi.v6i3.5400\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRajatonirina S, Heraud J-M, Orelle A, Randrianasolo L, Razanajatovo N, Rajaona YR, et al. The Spread of Influenza A(H1N1)pdm09 Virus in Madagascar Described by a Sentinel Surveillance Network. 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BMC Infect Dis. 2020;20:424. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1186/s12879-020-05145-w\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s12879-020-05145-w\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDieng I, Ndione MHD, Fall C, Diagne MM, Diop M, Gaye A, Barry MA, Diop B, Ndiaye M, Bousso A, et al. Multifoci and multiserotypes circulation of dengue virus in Senegal between 2017 and 2018. BMC Infect Dis. 2021;21:867.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDieng I, Diarra M, Diagne MM, Faye M, Dior Ndione MH, Ba Y, et al. Field Deployment of a Mobile Biosafety Laboratory Reveals the Co-Circulation of Dengue Viruses Serotype 1 and Serotype 2 in Louga City, Senegal, 2017. 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Interactions between SARS-CoV-2 and influenza, and the impact of coinfection on disease severity: a test-negative design. Int J Epidemiol. 2021;50:1124\\u0026ndash;33. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/ije/dyab081\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/ije/dyab081\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"},{\"header\":\"Tables\",\"content\":\"\\u003cp\\u003eTables 1 to 4 are available in the Supplementary Files section.\\u003c/p\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Infectious disease, Syndromic surveillance, Early warning system, Influenza-like illness, Dengue-like syndromes, Outbreak detection, Senegal\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8355367/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8355367/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eSub-Saharan Africa faces persistent challenges in the timely detection of infectious disease outbreaks due to inadequate early warning and response systems. To address this gap, Senegal's Ministry of Health partnered with the Institut Pasteur de Dakar to establish the Senegalese Syndromic Sentinel Surveillance Network (4S Network) in 2012 a comprehensive surveillance system designed to identify epidemic-prone syndromes and enable rapid public health interventions.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eWe analysed data from the Senegalese 4S real-time sentinel syndromic surveillance network collected between 2015 (15 sites) and 2023 (27 sites). The network monitored four key febrile syndromes, including malaria, dengue-like syndromes, diarrheal syndrome, and influenza-like illness (ILI), via standardized WHO case definitions. Laboratory confirmation was achieved through molecular and serological testing of biological samples. Sentinel general practitioners submitted daily reports via a digital platform that facilitated real-time reporting and automated alert generation. We evaluated system performance through completeness, timeliness, temporal patterns, geographical distribution, and alert validation rates.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eDuring the nine-year surveillance period, the network documented 1,816,340 outpatient consultations, with febrile syndromes accounting for 11.7% of all visits and demonstrating notable annual fluctuations. Distinct regional patterns of infectious disease events were observed: ILI predominated in western regions, dengue-like syndromes were clustered in north-central areas, and malaria cases were concentrated in southeastern zones. The system demonstrated robust performance metrics, achieving 94.5% data completeness and 80.0% reporting timeliness. Of the 202 alerts generated, 51.0% received laboratory confirmation. Dengue virus circulation was documented in 2017, 2018, 2021, 2022, and 2023. Despite these successes, 37.1% of febrile cases remained etiologically unclassified. The system's early multidisciplinary investigation capabilities enabled swift outbreak containment and transmission control.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eThe 4S network validates the effectiveness and practical implementation of digital, real-time syndromic surveillance in Senegal. It successfully facilitated early outbreak detection and supported prompt public health responses. Although the system has significant potential for resource-constrained environments, addressing current operational limitations remains crucial for maximizing public health impacts. These findings provide strong evidence supporting the regional expansion of similar surveillance frameworks to enhance health security and epidemic preparedness throughout West Africa.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Real-time sentinel syndromic surveillance for infectious disease detection: lessons from the 4S network in Senegal, 2015–2023\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-01-16 08:48:55\",\"doi\":\"10.21203/rs.3.rs-8355367/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"83e31d45-0a7d-44ff-a03a-b81f6093e5f8\",\"owner\":[],\"postedDate\":\"January 16th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-03-13T07:12:05+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-01-16 08:48:55\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8355367\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8355367\",\"identity\":\"rs-8355367\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}