Early detection of a new outbreak or new information about a public health issue could prevent morbidity and mortality and reduce healthcare expenditures for the economy. Syndromic surveillance is a subset of public health surveillance practice that uses pre-diagnostic data to monitor public health threats. The syndromic surveillance approach posits that patients first interface with the healthcare system in non-traditional ways (e.g., buying over-the-counter medications, calling healthcare hotlines) before seeking traditional healthcare avenues such as emergency rooms and outpatient clinics. Thus detection of public health issues may be more timely using syndromic surveillance data sources compared to diagnosis-based surveillance systems. One source of information not yet fully integrated in syndromic surveillance is calls to poison centers. United States poison centers offer free, confidential medical advice through a national help line to assist in poison exposures. Call data are transmitted and stored in an electronic database within minutes to the National Poison Data System (NPDS), which can be used for near-real-time surveillance for disease conditions or exposures. The studies presented in the dissertation explore new ways for poison center records to be used for early identification of public health threats and for evaluating policy and program impact by identifying changing trends in poison center records. The approach and findings from these three studies expand upon current knowledge of how poison center records can be used for syndromic surveillance and provide evidence that justifies expansion of poison center surveillance into avenues not yet explored by local, state, and federal public health.

Doctor of Philosophy / All surveillance systems are based on an effective general surveillance system because this is the system that detects emerging diseases and the re-introduction of disease to a previously disease free area. General surveillance requires comprehensive coverage of the population through an extensive network of relationships between animal producers and observers and surveillance system officers. This system is under increasing threat in Australia (and many other countries) due to the increased biomass, animal movements, rate of disease emergence, and the decline in resource allocation for surveillance activities. The Australian surveillance system is state-based and has a complex management structure that includes State and Commonwealth government representatives, industry stakeholders (such as producer bodies) and private organisations. A developing problem is the decline in the effectiveness of the general surveillance system in the extensive (remote) cattle producing regions of northern Australia. The complex organisational structure of surveillance in Australia contributes to this, and is complicated by the incomplete capture of data (as demonstrated by slow uptake of electronic individual animal identification systems), poorly developed and integrated national animal health information systems, and declining funding streams for field and laboratory personnel and infrastructure. Of major concern is the reduction in contact between animal observers and surveillance personnel arising from the decline in resource allocation for surveillance. Fewer veterinarians are working in remote areas, fewer producers use veterinarians, and, as a result, fewer sick animals are being investigated by the general surveillance system. A syndrome is a collection of signs that occur in a sick individual. Syndromic surveillance is an emerging approach to monitoring populations for change in disease levels and is based on statistical monitoring of the distribution of signs, syndromes and associations between health variables in a population. Often, diseases will have syndromes that are characteristic and the monitoring of these syndromes may provide for early detection of outbreaks. Because the process uses general signs, this method may support the existing (struggling) general surveillance system for the extensive cattle producing regions of northern Australia. Syndromic surveillance systems offer many potential advantages. First, the signs that are monitored can be general and include any health-related variable. This generality provides potential as a detector of emerging diseases. Second, many of the data types used occur early in a disease process and therefore efficient syndromic surveillance systems can detect disease events in a timely manner. There are many hurdles to the successful deployment of a syndromic surveillance system and most relate to data. An effective system will ideally obtain data from multiple sources, all data will conform to a standard (therefore each data source can be validly combined), data coverage will be extensive (across the population) and data capture will be in real time (allowing early detection). This picture is one of a functional electronic data world and unfortunately this is not the norm for either human or animal heath. Less than optimal data, lack of data standards, incomplete coverage of the population and delayed data transmission result in a loss of sensitivity, specificity and timeliness of detection. In human syndromic surveillance, most focus has been placed on earlier detection of mass bioterrorism events and this has concentrated research on the problems of electronic data. Given the current state of animal health data, the development of efficient detection algorithms represents the least of the hurdles. However, the world is moving towards increased automation and therefore the problems with current data can be expected to be resolved in the next decade. Despite the lack of large scale deployment of these systems, the question is becoming when, not whether these system will contribute. The observations of a stock worker are always the start of the surveillance pathway in animal health. Traditionally this required the worker to contact a veterinarian who would investigate unusual cases with the pathway ending in laboratory samples and specific diagnostic tests. The process is inefficient as only a fraction of cases observed by stock workers end in diagnostic samples. These observations themselves are most likely to be amenable to capture and monitoring using syndromic surveillance techniques. A pilot study of stock workers in the extensive cattle producing Lower Gulf region of Queensland demonstrated that experienced non-veterinary observers of cattle can describe the signs that they see in sick cattle in an effective manner. Lay observers do not posses a veterinary vocabulary, but the provision of a system to facilitate effective description of signs resulted in effective and standardised description of disease. However, most producers did not see personal benefit from providing this information and worried that they might be exposing themselves to regulatory impost if they described suspicious signs. Therefore the pilot study encouraged the development of a syndromic surveillance system that provides a vocabulary (a template) for lay observers to describe disease and a reason for them to contribute their data. The most important disease related drivers for producers relate to what impact the disease may have in their herd. For this reason, the Bovine Syndromic Surveillance System (BOSSS) was developed incorporating the Bayesian cattle disease diagnostic program BOVID. This allowed the observer to receive immediate information from interpretation of their observation providing a differential list of diseases, a list of questions that may help further differentiate cause, access to information and other expertise, and opportunity to benchmark disease performance. BOSSS was developed as a web-based reporting system and used a novel graphical user interface that interlinked with an interrogation module to enable lay observers to accurately and fully describe disease. BOSSS used a hierarchical reporting system that linked individual users with other users along natural reporting pathways and this encouraged the seamless and rapid transmission of information between users while respecting confidentiality. The system was made available for testing at the state level in early 2006, and recruitment of producers is proceeding. There is a dearth of performance data from operational syndromic surveillance systems. This is due, in part, to the short period that these systems have been operational and the lack of major human health outbreaks in areas with operational systems. The likely performance of a syndromic surveillance system is difficult to theorise. Outbreaks vary in size and distribution, and quality of outbreak data capture is not constant. The combined effect of a lack of track record and the many permutations of outbreak and data characteristics make computer simulation the most suitable method to evaluate likely performance. A stochastic simulation model of disease spread and disease reporting by lay observers throughout a grid of farms was modelled. The reporting characteristics of lay observers were extrapolated from the pilot study and theoretical disease was modelled (as a representation of newly emergent disease). All diseases were described by their baseline prevalence and by conditional sign probabilities (obtained from BOVID and from a survey of veterinarians in Queensland). The theoretical disease conditional sign probabilities were defined by the user. Their spread through the grid of farms followed Susceptible-Infected-Removed (SIR) principles (in herd) and by mass action between herds. Reporting of disease events and signs in events was modelled as a probabilistic event using sampling from distributions. A non-descript disease characterised by gastrointestinal signs and a visually spectacular disease characterised by neurological signs were modelled, each over three outbreak scenarios (least, moderately and most contagious). Reports were examined using two algorithms. These were the cumulative sum (CuSum) technique of adding excess of cases (above a maximum limit) for individual signs and the generic detector What’s Strange About Recent Events (WSARE) that identifies change to variable counts or variable combination counts between time periods. Both algorithms detected disease for all disease and outbreak characteristics combinations. WSARE was the most efficient algorithm, detecting disease on average earlier than CuSum. Both algorithms had high sensitivity and excellent specificity. The timeliness of detection was satisfactory for the insidious gastrointestinal disease (approximately 24 months after introduction), but not sufficient for the visually spectacular neurological disease (approximately 20 months) as the traditional surveillance system can be expected to detect visually spectacular diseases in reasonable time. Detection efficiency was not influenced greatly by the proportion of producers that report or by the proportion of cases or the number of signs per case that are reported. The modelling process demonstrated that a syndromic surveillance system in this remote region is likely to be a useful addition to the existing system. Improvements that are planned include development of a hand-held computer version and enhanced disease and syndrome mapping capability. The increased use of electronic recording systems, including livestock identification, will facilitate the deployment of BOSSS. Long term sustainability will require that producers receive sufficient reward from BOSSS to continue to provide reports over time. This question can only be answered by field deployment and this work is currently proceeding.

Doctor of Philosophy / All surveillance systems are based on an effective general surveillance system because this is the system that detects emerging diseases and the re-introduction of disease to a previously disease free area. General surveillance requires comprehensive coverage of the population through an extensive network of relationships between animal producers and observers and surveillance system officers. This system is under increasing threat in Australia (and many other countries) due to the increased biomass, animal movements, rate of disease emergence, and the decline in resource allocation for surveillance activities. The Australian surveillance system is state-based and has a complex management structure that includes State and Commonwealth government representatives, industry stakeholders (such as producer bodies) and private organisations. A developing problem is the decline in the effectiveness of the general surveillance system in the extensive (remote) cattle producing regions of northern Australia. The complex organisational structure of surveillance in Australia contributes to this, and is complicated by the incomplete capture of data (as demonstrated by slow uptake of electronic individual animal identification systems), poorly developed and integrated national animal health information systems, and declining funding streams for field and laboratory personnel and infrastructure. Of major concern is the reduction in contact between animal observers and surveillance personnel arising from the decline in resource allocation for surveillance. Fewer veterinarians are working in remote areas, fewer producers use veterinarians, and, as a result, fewer sick animals are being investigated by the general surveillance system. A syndrome is a collection of signs that occur in a sick individual. Syndromic surveillance is an emerging approach to monitoring populations for change in disease levels and is based on statistical monitoring of the distribution of signs, syndromes and associations between health variables in a population. Often, diseases will have syndromes that are characteristic and the monitoring of these syndromes may provide for early detection of outbreaks. Because the process uses general signs, this method may support the existing (struggling) general surveillance system for the extensive cattle producing regions of northern Australia. Syndromic surveillance systems offer many potential advantages. First, the signs that are monitored can be general and include any health-related variable. This generality provides potential as a detector of emerging diseases. Second, many of the data types used occur early in a disease process and therefore efficient syndromic surveillance systems can detect disease events in a timely manner. There are many hurdles to the successful deployment of a syndromic surveillance system and most relate to data. An effective system will ideally obtain data from multiple sources, all data will conform to a standard (therefore each data source can be validly combined), data coverage will be extensive (across the population) and data capture will be in real time (allowing early detection). This picture is one of a functional electronic data world and unfortunately this is not the norm for either human or animal heath. Less than optimal data, lack of data standards, incomplete coverage of the population and delayed data transmission result in a loss of sensitivity, specificity and timeliness of detection. In human syndromic surveillance, most focus has been placed on earlier detection of mass bioterrorism events and this has concentrated research on the problems of electronic data. Given the current state of animal health data, the development of efficient detection algorithms represents the least of the hurdles. However, the world is moving towards increased automation and therefore the problems with current data can be expected to be resolved in the next decade. Despite the lack of large scale deployment of these systems, the question is becoming when, not whether these system will contribute. The observations of a stock worker are always the start of the surveillance pathway in animal health. Traditionally this required the worker to contact a veterinarian who would investigate unusual cases with the pathway ending in laboratory samples and specific diagnostic tests. The process is inefficient as only a fraction of cases observed by stock workers end in diagnostic samples. These observations themselves are most likely to be amenable to capture and monitoring using syndromic surveillance techniques. A pilot study of stock workers in the extensive cattle producing Lower Gulf region of Queensland demonstrated that experienced non-veterinary observers of cattle can describe the signs that they see in sick cattle in an effective manner. Lay observers do not posses a veterinary vocabulary, but the provision of a system to facilitate effective description of signs resulted in effective and standardised description of disease. However, most producers did not see personal benefit from providing this information and worried that they might be exposing themselves to regulatory impost if they described suspicious signs. Therefore the pilot study encouraged the development of a syndromic surveillance system that provides a vocabulary (a template) for lay observers to describe disease and a reason for them to contribute their data. The most important disease related drivers for producers relate to what impact the disease may have in their herd. For this reason, the Bovine Syndromic Surveillance System (BOSSS) was developed incorporating the Bayesian cattle disease diagnostic program BOVID. This allowed the observer to receive immediate information from interpretation of their observation providing a differential list of diseases, a list of questions that may help further differentiate cause, access to information and other expertise, and opportunity to benchmark disease performance. BOSSS was developed as a web-based reporting system and used a novel graphical user interface that interlinked with an interrogation module to enable lay observers to accurately and fully describe disease. BOSSS used a hierarchical reporting system that linked individual users with other users along natural reporting pathways and this encouraged the seamless and rapid transmission of information between users while respecting confidentiality. The system was made available for testing at the state level in early 2006, and recruitment of producers is proceeding. There is a dearth of performance data from operational syndromic surveillance systems. This is due, in part, to the short period that these systems have been operational and the lack of major human health outbreaks in areas with operational systems. The likely performance of a syndromic surveillance system is difficult to theorise. Outbreaks vary in size and distribution, and quality of outbreak data capture is not constant. The combined effect of a lack of track record and the many permutations of outbreak and data characteristics make computer simulation the most suitable method to evaluate likely performance. A stochastic simulation model of disease spread and disease reporting by lay observers throughout a grid of farms was modelled. The reporting characteristics of lay observers were extrapolated from the pilot study and theoretical disease was modelled (as a representation of newly emergent disease). All diseases were described by their baseline prevalence and by conditional sign probabilities (obtained from BOVID and from a survey of veterinarians in Queensland). The theoretical disease conditional sign probabilities were defined by the user. Their spread through the grid of farms followed Susceptible-Infected-Removed (SIR) principles (in herd) and by mass action between herds. Reporting of disease events and signs in events was modelled as a probabilistic event using sampling from distributions. A non-descript disease characterised by gastrointestinal signs and a visually spectacular disease characterised by neurological signs were modelled, each over three outbreak scenarios (least, moderately and most contagious). Reports were examined using two algorithms. These were the cumulative sum (CuSum) technique of adding excess of cases (above a maximum limit) for individual signs and the generic detector What’s Strange About Recent Events (WSARE) that identifies change to variable counts or variable combination counts between time periods. Both algorithms detected disease for all disease and outbreak characteristics combinations. WSARE was the most efficient algorithm, detecting disease on average earlier than CuSum. Both algorithms had high sensitivity and excellent specificity. The timeliness of detection was satisfactory for the insidious gastrointestinal disease (approximately 24 months after introduction), but not sufficient for the visually spectacular neurological disease (approximately 20 months) as the traditional surveillance system can be expected to detect visually spectacular diseases in reasonable time. Detection efficiency was not influenced greatly by the proportion of producers that report or by the proportion of cases or the number of signs per case that are reported. The modelling process demonstrated that a syndromic surveillance system in this remote region is likely to be a useful addition to the existing system. Improvements that are planned include development of a hand-held computer version and enhanced disease and syndrome mapping capability. The increased use of electronic recording systems, including livestock identification, will facilitate the deployment of BOSSS. Long term sustainability will require that producers receive sufficient reward from BOSSS to continue to provide reports over time. This question can only be answered by field deployment and this work is currently proceeding.

The Mountain Gorillas of Central Africa are one of the most highly endangered species in the world, with only 740 individuals surviving. One of the greatest threats to this species is disease. Health of wildlife is continually garnering more attention in the public arena due to recent outbreaks of diseases such as West Nile and High Pathogenic Avian Influenza. However, no system currently exists to facilitate the management and analysis of wildlife health data. The research conducted herein was the development and testing of a health information monitoring system for the mountain gorillas entitled Internet-supported Management Program to Assist Conservation Technologies or IMPACT?. The system functions around a species database of known or unknown individuals and provides individual-based and population-based epidemiological analysis. The system also uses spatial locations of individuals or samples to link multiple species together based on spatial proximity for inter-species comparisons. A syndromic surveillance system or clinical decision tree was developed to collect standardized data to better understand the ecology of diseases within the gorilla population. The system is hierarchical in nature, using trackers and guides to conduct daily observations while specially trained veterinarians are used to confirm and assess any abnormalities detected. Assessment of the decision tree indicated that trackers and guides did not observe gorilla groups or individuals within groups similarly. Data suggests that, to be consistent, trackers and guides need to conduct observations even on the day that veterinarians collect data. Validity and reliability remain to be tested in the observation instrument. Assessment of pathogen loads and distributions within species surrounding the gorillas indicates that humans have the greatest pathogen loads with 13 species, followed by cattle and chimpanzees (11), baboon (10), gorillas (9), and rodents (3). Spatial aggregation occurred in Cryptosporidium, Giardia, and Trichuris; however, there is reason to question the test results of the former 2 species. These data suggest that researchers need to examine the impact of local human and domestic animal populations on gorillas and other wildlife.

INTRODUCTION: Syndromic surveillance is a method of rapid disease detection based on categories of syndromes, or signs, experienced before the full onset of disease. It is increasingly being used by government agencies and health departments to identify disease outbreaks in a timely manner. Environmental exposures are known to induce respiratory and gastrointestinal symptoms, tend to have a seasonality component, and adversely affect the health of millions of people. OBJECTIVE: In this study, we assess the availability of environmental exposure data for air pollution (PM2.5, ozone, and NO2), pollen, and water contaminant exposure for use in a syndromic surveillance project. We also evaluate: 1) the general proximity of HMO populations to monitors, and 2) distribution of SES characteristics of the area populations with respect to monitor locations. METHODS: We collected exposure data, patient population data, and Census tract SES data for two metropolitan areas where Kaiser Permanente (KP) provides medical services: Atlanta, Georgia and the northern Virginia, District of Columbia (DC), and Baltimore area. Exposure data for air pollution and pollen were collected for 2013-2014. Straight-line distance from a monitor to the nearest KP clinic, and from each Census tract centroid, to the nearest air pollution or pollen monitor was computed using the Euclidean distance formula. RESULTS: Air pollution is routinely monitored by a Federal mandate, is universally available, and easily obtained. Pollen data is collected by private entities, which in some cases hinders access. Water quality data is generally publically available, but it is collected at the source and not easily traceable to water delivery endpoints. In both Atlanta and DC, Maryland, and Virginia most of the clinics (78% and 94%, respectively) are located within 10 miles of an air pollution monitor; approximately 83% and 94% of the KP populations were located within 10 miles of an air pollution monitor. SES populations differ substantially by race, age, income, and education with respect to the nearest monitor. However, the median and interquartile range of various air pollutants does not differ much across the monitors – indicating that, on average, there is little SES gradient in type of level of air pollution exposure. CONCLUSIONS: Overall, this study adds knowledge regarding future considerations about the coverage of environmental monitors and to what extent exposure measure estimates can be assigned to certain populations located near monitors.

Purpose: The purpose of our work is to develop a system for automatic contact tracing with the goal of identifying individuals who are most likely infected, even if we do not have direct diagnostic information on their health status. Control of the spread of respiratory pathogens (e.g. novel influenza viruses) in the population using vaccination is a challenging problem that requires quick identification of the infectious agent followed by large-scale production and administration of a vaccine. This takes a significant amount of time. A complementary approach to control transmission is contact tracing and quarantining, which are currently applied to sexually transmitted diseases (STDs). For STDs, identifying the contacts that might have led to disease transmission is relatively easy; however, for respiratory pathogens, the contacts that can lead to transmission include a huge number of face-to-face daily social interactions that are impossible to trace manually. Method: We developed a Bayesian network model to process context awareness proximity sensor information together with (possibly incomplete) diagnosis information to track the spread of disease in a population. Our model tracks real-time proximity contacts and can provide public health agencies with the probability of infection for each individual in the model. For testing our algorithm, we used a real-world mobile sensor dataset of 80 individuals, and we simulated an outbreak. Result: We ran several experiments where different sub-populations were ???infected??? and ???diagnosed.??? By using the contact information, our model was able to automatically identify individuals in the population who were likely to be infected even though they were not directly ???diagnosed??? with an illness. Conclusion: Automatic contact tracing for respiratory pathogens is a powerful idea, however we have identified several implementation challenges. The first challenge is scalability: we note that a contact tracing system with a hundred thousand individuals requires a Bayesian model with a billion nodes. Bayesian inference on models of this scale is an open problem and an active area of research. The second challenge is privacy protection: although the test data were collected in an academic setting, deploying any system will require appropriate safeguards for user privacy. Nonetheless, our work illustrates the potential for broader use of contact tracing for modelling and controlling disease transmission.

No description available.

Foodborne illnesses continue to have a negative impact on the nation's health, accounting annually for an estimated 76 million illnesses, 325,000 hospitalizations, and 5,000 deaths in the United States. Syndromic surveillance systems that analyze pre-diagnostic data, such as pharmaceutical sales data are being used to monitor diarrheal disease. The purpose of this study is to evaluate the usefulness of a poison control center (PCC) data collection and triage system for early detection of increases in foodborne illnesses.Data on calls to the Arizona Poison and Drug Information Center (APDIC) reporting suspected foodborne illnesses, and Pima County Health Department (PCHD) enteric illness reports were obtained for July 1, 2002 - June 30, 2007. Prediction algorithms were constructed using the first two and a half years, and validated in the remaining two and a half years. Multiple outcomes were assessed using unadjusted and adjusted raw counts, five and seven day moving averages, and exponentially weighted moving averages. Sensitivity analyses were conducted to evaluate model performance. Increases in PCHD laboratory reports of enteric illnesses were used as a proxy measure for foodborne disease outbreaks.Over the five year study period there were 1,094 APDIC calls reporting suspected foodborne illnesses, and 2,433 PCHD enteric illness cases. Seventy-five percent of cases were reported to PCHD within 23 days of symptom onset. In contrast, 62% of callers contacted APDIC within 24 hours of symptom onset. Forty percent of PCHD cases were missing symptom onset dates, which necessitated constructing and validating predictive algorithms using only those PCHD cases with known symptom onset dates.None of the prediction models performed at sensitivity levels considered acceptable by public health department standards. However, it is possible that a temporal relationship actually exists, but data quality (lack of outbreak dates, and missing symptom onset dates) may have prevented its detection. The study suggests that current surveillance by PCCs is insufficient as a univariate model for syndromic surveillance of diarrheal illness because of low caller volume reporting suspected foodborne illnesses; this can be improved. Methods were discussed to utilize PCCs for active surveillance of foodborne illnesses that are of public health significance.

La surveillance syndromique est un concept récent en épidémiologie. Fondée sur le suivi automatisé d’indicateurs de santé non spécifiques, cette nouvelle approche offre des perspectives intéressantes pour la détection de phénomènes pathologiques émergents. Nous nous sommes basés sur les données actuellement collectées en France sur la mortalité bovine pour évaluer la faisabilité et la pertinence d’un système de surveillance syndromique basé sur cet indicateur. Nous avons d’abord modélisé le niveau de référence de la mortalité bovine en France puis proposé des méthodes pour identifier et quantifier d’éventuels excès de mortalité. Nous avons d’abord analysé des données réelles pour estimer rétrospectivement les conséquences sur la mortalité de l’épizootie de fièvre catarrhale ovine qui a touché le cheptel bovin français en 2007 et 2008. Nous avons ensuite proposé une méthode visant à identifier des regroupements d’unités spatiales présentant des augmentations inhabituelles de mortalité, et évalué ses performances pour détecter des foyers d’une maladie infectieuse dont nous avons simulé la propagation dans le cheptel bovin. Sur la base de ces travaux, nous discutons finalement de l’intérêt pour la protection de la santé animale d’un système de surveillance non spécifique basé sur la mortalité, et émettons des propositions pour la mise en place opérationnelle d’un tel système. / Syndromic surveillance is a recent concept in epidemiology. Based on automated monitoringof non-specific health indicators, this new approach offers interesting prospects for the detection of various health events. We analyzed data on cattle mortality routinely collected inFrance to assess the feasibility and relevance of a syndromic surveillance system based on this indicator. We modeled the baseline of cattle mortality in France and proposed methods to identify and quantify excess mortality. First we analyzed real data to retrospectively estimate the effects on mortality of the bluetongue outbreak which affected the French cattle in 2007 and 2008. We then proposed a method to detect unusual increases mortality, and evaluated its performance for the detection of outbreaks of an infectious disease of which we simulated the spread in the cattle population. We finally discuss the interest of a surveillance system based on non-specific mortality for the protection of animal health, and make proposals for the operational implementation of such a system.

Les maladies infectieuses demeurent l'une des causes majeures de décès dans le monde. Au Gabon, on estime que plus de la moitié des données disponibles ne sont pas collectées, et que plus de la moitié des données potentiellement collectées ne sont pas transmises au niveau central expliquant ainsi la lenteur à la réactivité du système de santé. Un réseau de surveillance syndromique des maladies infectieuses à potentiel épidémique (SuSyMIPE) a pu être mis en place dans quatre sites (Gamba, Koulamoutou, Libreville et Oyem) sentinelles. Pour chaque syndrome, un groupe de maladies était évoqué. Les notifications journalières de cas codifiées par "Short Message Service" (SMS) étaient transmis en fin d'après-midi. De janvier et octobre 2016, 5348 cas suspects des syndromes surveillés ont été enregistré, 28,1% (n = 1502) de Koulamoutou 24,5% (n = 1310) de Libreville, 24% (n = 1284) Gamba et 23,4% (n = 1252) d' Oyem. 71,3% (n = 3816) des cas étaient des fièvres, 19,7% (n = 1053) des syndromes respiratoires et les cas des syndromes diarrhéiques représentaient 9% (n = 479). Ce réseau nous a permis de détecter assez précocement l'épidémie de rougeole dans deux chefs-lieux de provinces (Libreville et Oyem) en 2016. Au total, entre les semaines 13 et 19, 79 cas suspects ont été notifiés, principalement 82,3% (n = 65) à Oyem et 17,7% (n = 14) à Libreville. Le sex-ratio M / F était de 0,88 (37/42), et l'âge moyen était de 49,37 ± 72.82 mois. Cependant, 53,3% (n = 16/30) seulement ont été confirmés pour la rougeole. La mise en place de ce système de surveillance syndromique nous a permis de répondre de manière plus rapide et plus efficiente aux épisodes de rougeole qui se sont manifestés. / Infectious diseases remain one of the leading causes of death in the world. In Gabon, it is estimated that more than half of the available data are not collected, and that more than half of the potentially collected data are not transmitted centrally, thus explaining the slowness of the responsiveness of the health system. A syndromic surveillance network for infectious diseases with epidemic potential (SuSyMIPE) has been set up in four sites (Gamba, Koulamoutou, Libreville and Oyem) sentinels. For each syndrome, a group of diseases was mentioned. The daily notifications of cases coded by "Short Message Service" (SMS) were transmitted at the end of the afternoon. From January to October 2016, 5348 cases of suspected syndromes were recorded, 28.1% (n = 1502) of Koulamoutou 24.5% (n = 1310) of Libreville, 24% (n = 1284) Gamba and 23, 4% (n = 1252) from Oyem. 71.3% (n = 3816) of the cases were fevers, 19.7% (n = 1053) of the respiratory syndromes and the cases of diarrheal syndromes accounted for 9% (n = 479). This network enabled us to detect the measles epidemic early in two provincial capitals (Libreville and Oyem) in 2016. In total, between weeks 13 and 19, 79 suspected cases were reported, mainly 82.3. % (n = 65) in Oyem and 17.7% (n = 14) in Libreville. The sex ratio M / F was 0.88 (37/42), and the mean age was 49.37 ± 72.82 months. However, only 53.3% (n = 16/30) were confirmed for measles. The implementation of this syndromic surveillance system allowed us to respond more quickly and more efficiently to the measles episodes that occurred.