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BACKGROUND: In May 2023, Romagna, Italy, faced a devastating flood resulting in 16 fatalities, forced displacement of 26,000 citizens, and significant economic losses. Due to potential water contamination, implementing public health strategies became imperative for the Local Health Authority to mitigate the health consequences, analyze the flood's impact on the local population's health, and detect early anomalies requiring timely public health interventions. METHODS: Between June and July 2023, general practitioners who were part of the RespiVirNet surveillance network completed weekly structured forms. These forms collected data on individuals exposed or not to floodwaters and clinical syndromes. Rates per 1000 resident population aged > 14 were stratified by district, week of observation, and symptomatology. Missing data were addressed by imputation using second-order autoregressive modeling. RESULTS: An incidence of 3.52 syndromes potentially related to flood water exposure per 1000 individuals (95% CI 2.82-4.35) was estimated. Ravenna, the city most affected by the flood, recorded the highest rate (6.05 per 1000, 95% CI 4.59-7.82). Incidence decreased in the weeks post-event. Anxiety, or trauma and stress symptoms, exhibited higher rates among the exposed, diminishing over weeks. The incidence for the non-exposed (12.76 per 1000, 95% CI 10.55-15.29) showed no significant territorial differences compared to the exposed ones. CONCLUSIONS: Syndromic surveillance provided timely information on the flood's health impact, revealing a higher incidence of individual syndromes among the non-exposed. This study contributes to guiding the implementation of future public health preparedness and response strategies for populations facing similar natural disasters.

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BACKGROUND: Information on worker occupation and industry is critical to understanding the occupational risks of heat-related illness (HRI), yet few syndromic surveillance systems capture these key data elements. This study evaluates the work data reported through Washington syndromic surveillance for its utility in characterizing HRI ED visits by industry and occupation. METHODS: Standard industry and occupation codes were assigned to employer name and occupation descriptions reported in Washington ED visit records maintained within the state's syndromic surveillance system, for visits involving HRI in 2020-2022. HRI ED visits involving workplace heat exposure were identified based on discharge diagnoses or on keywords in the triage note or chief complaint fields. HRI ED visits were summarized by patient characteristics, and visit rates were calculated by industry and occupation. RESULTS: Employer name or occupation descriptions were reported in 21.5% of HRI ED records among patients age 16 and older, and in 41.2% of records with mention of heat exposure at work. Twice as many records were classified for industry as for occupation. Agriculture, forestry, fishing, and hunting and transportation and warehousing had the highest rates of HRI ED visits. Specific industries with the highest rates included support activities for agriculture and forestry, the postal service, and fruit and vegetable preserving and specialty food manufacturing. CONCLUSION: Syndromic surveillance data are a valuable source of occupational health surveillance information when work characteristics are reported, enhancing our understanding of the occupational risks of injuries and illnesses.

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Background: The dynamicity and mobility of the population in a mass gathering setting pose a challenge to traditional disease surveillance methods and strain the local health services. Velankanni is one of the most sacred Christian pilgrimage places located in Nagapattinam, Tamil Nadu, India. We participated in the Velankanni festival to describe the public health preparedness, surveillance, and response activities carried out during the festival. Methods: This was a cross-sectional study. We reviewed the national and international guidelines and published literature and discussed with the key stakeholders. We developed a checklist to observe public health preparedness activities. We facilitated the staff and monitored the activities by the implementers. We established the syndromic surveillance in the designated locations of the event and used tracker software to capture the data. Emergency medical teams were formed with trained health personnel to respond to medical emergencies. Results: The team monitored all the public health activities. There are 59 primary care public health facilities and nine ambulatory Mobile Medical Units, with 160 medical officers available at the site. Of the 16,169 persons who attended the medical camps, 9863 (61%) were males and 8408 (52%) were aged 15-44. Acute diarrheal disease was the most frequent of the reported syndromes, followed by injuries, acute febrile illness, and animal bites. Conclusions: There was no outbreak of any disease either identified or reported. Our findings suggest that risk assessments should be used, and establishing an Incident Command Center is vital for executing command and control mechanisms during mass gatherings.

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Open surveys complementing surveillance programs often yield opportunistically sampled data characterised by spatio-temporal imbalance. We set up our study to understand to what extent spatio-temporal statistical models using such data achieve in describing epidemiological trends. We used self-reported symptomatic COVID-19 data from two Belgian regions, Flanders and the Brussels-Capital Region. These data were collected in a large-scale open survey with spatio-temporally imbalanced participation rates. We compared incidence estimates of both self-reported symptoms and test-confirmed COVID-19 cases obtained through generalised linear mixed models correcting for spatio-temporal correlation. We additionally simulated symptom incidences under different sampling strategies to explore the impact of sample imbalance, sample size and disease incidence, on trend detection. Our study shows that spatio-temporal sample imbalance generally does not lead to bad model performances in spatio-temporal trend estimation and high-risk area detection. Except for low-incidence diseases, collecting large samples will often be more essential than ensuring spatio-temporally sample balance.

Asunto(s)

COVID-19 , SARS-CoV-2 , Autoinforme , Análisis Espacio-Temporal , Humanos , COVID-19/epidemiología , Bélgica/epidemiología , Incidencia , Masculino , Femenino , Adulto , Persona de Mediana Edad , Monitoreo Epidemiológico , Vigilancia de la Población/métodos

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OBJECTIVE: Digital technologies have improved the performance of surveillance systems through early detection of outbreaks and epidemic control. The aim of this study is to introduce an outbreak detection web application called OBDETECTOR (Outbreak Detector), which as a professional web application has the ability to process weekly or daily reported data from disease surveillance systems and facilitates the early detection of disease outbreaks. RESULTS: OBDETECTOR generates a histogram that exhibits the trend of infection within a time range selected by the user. The output comprises red triangles and plus signs, where the former denotes outbreak days determined by the algorithm applied to the data, and the latter represents days identified as outbreaks by the researcher. The graph also displays threshold values and its symbols enable researchers to compute evaluation criteria for outbreak detection algorithms, including sensitivity and specificity. OBDETECTOR allows users to modify algorithm parameters based on their research objectives immediately after loading data. The implementation of automatic web applications results in immediate reporting, precise analysis, and prompt alert notification. Moreover, Public Health authorities and other stakeholders of surveillance can benefit from the widespread accessibility and user-friendliness of these tools, enhancing their knowledge and skills for better engagement in surveillance programs.

Asunto(s)

Algoritmos , Brotes de Enfermedades , Internet , Vigilancia de la Población , Humanos , Brotes de Enfermedades/prevención & control , Vigilancia de la Población/métodos , Epidemias/prevención & control , Enfermedades Transmisibles/epidemiología , Enfermedades Transmisibles/diagnóstico , Programas Informáticos

RESUMEN

Since November 2023, the absolute number of attendances at emergency departments for pneumonia among children aged 5-14 years in England have been above expected levels for the time of year. This increased signal peaked during March 2024 but then persisted into early summer 2024 despite decreases in prevalence of seasonal respiratory pathogens. Record linkage between emergency department and laboratory databases points to this unusual activity being driven largely by Mycoplasma pneumoniae.

Asunto(s)

Servicio de Urgencia en Hospital , Mycoplasma pneumoniae , Neumonía , Humanos , Niño , Inglaterra/epidemiología , Preescolar , Adolescente , Incidencia , Neumonía/epidemiología , Masculino , Femenino , Mycoplasma pneumoniae/aislamiento & purificación , Servicio de Urgencia en Hospital/estadística & datos numéricos , Prevalencia , Neumonía por Mycoplasma/epidemiología , Neumonía por Mycoplasma/diagnóstico , Estaciones del Año , Vigilancia de la Población

RESUMEN

We investigated a fatal case of primary amoebic meningoencephalitis from an indoor surfing center in Taiwan. The case was detected through encephalitis syndromic surveillance. Of 56 environmental specimens, 1 was positive for Naegleria fowleri ameba. This report emphasizes the risk for N. fowleri infection from inadequately disinfected recreational waters, even indoors.

Asunto(s)

Infecciones Protozoarias del Sistema Nervioso Central , Naegleria fowleri , Humanos , Naegleria fowleri/aislamiento & purificación , Naegleria fowleri/genética , Taiwán/epidemiología , Infecciones Protozoarias del Sistema Nervioso Central/parasitología , Infecciones Protozoarias del Sistema Nervioso Central/diagnóstico , Infecciones Protozoarias del Sistema Nervioso Central/epidemiología , Resultado Fatal , Masculino , Meningoencefalitis/parasitología , Meningoencefalitis/diagnóstico , Amebiasis/diagnóstico , Amebiasis/parasitología , Adulto

RESUMEN

BACKGROUND: Collecting information on adverse events following immunization from as many sources as possible is critical for promptly identifying potential safety concerns and taking appropriate actions. Febrile convulsions are recognized as an important potential reaction to vaccination in children aged <6 years. OBJECTIVE: The primary aim of this study was to evaluate the performance of natural language processing techniques and machine learning (ML) models for the rapid detection of febrile convulsion presentations in emergency departments (EDs), especially with respect to the minimum training data requirements to obtain optimum model performance. In addition, we examined the deployment requirements for a ML model to perform real-time monitoring of ED triage notes. METHODS: We developed a pattern matching approach as a baseline and evaluated ML models for the classification of febrile convulsions in ED triage notes to determine both their training requirements and their effectiveness in detecting febrile convulsions. We measured their performance during training and then compared the deployed models' result on new incoming ED data. RESULTS: Although the best standard neural networks had acceptable performance and were low-resource models, transformer-based models outperformed them substantially, justifying their ongoing deployment. CONCLUSIONS: Using natural language processing, particularly with the use of large language models, offers significant advantages in syndromic surveillance. Large language models make highly effective classifiers, and their text generation capacity can be used to enhance the quality and diversity of training data.

RESUMEN

Influenza-like illness (ILI) patients co-detected with respiratory pathogens exhibit poorer health outcomes than those with single infections. To address the paucity of knowledge concerning the incidence of concurrent respiratory pathogens, their relationships, and the clinical differences between patients detected with single and multiple pathogens, we performed an in-depth characterization of the oropharyngeal samples of primary care patients collected in Genoa (Northwest Italy), during winter seasons 2018/19-2019/20.The apriori algorithm was employed to evaluate the incidence of viral, bacterial, and viral-bacterial pairs during the study period. The grade of correlation between pathogens was investigated using the Phi coefficient. Factors associated with viral, bacterial or viral-bacterial co-detection were assessed using logistic regression.The most frequently identified pathogens included influenza A, rhinovirus, Haemophilus influenzae and Streptococcus pneumoniae. The highest correlations were found between bacterial-bacterial and viral-bacterial pairs, such as Haemophilus influenzae-Streptococcus pneumoniae, adenovirus-Haemophilus influenzae, adenovirus-Streptococcus pneumoniae, RSV-A-Bordetella pertussis, and influenza B Victoria-Bordetella parapertussis. Viruses were detected together at significantly lower rates. Notably, rhinovirus, influenza, and RSV exhibited significant negative correlations with each other. Co-detection was more prevalent in children aged < 4, and cough was shown to be a reliable indicator of viral co-detection.Given the evolving epidemiological landscape following the COVID-19 pandemic, future research utilizing the methodology described here, while considering the circulation of SARS-CoV-2, could further enrich the understanding of concurrent respiratory pathogens.

Asunto(s)

Coinfección , Infecciones del Sistema Respiratorio , Humanos , Coinfección/epidemiología , Coinfección/virología , Coinfección/microbiología , Masculino , Femenino , Persona de Mediana Edad , Adulto , Italia/epidemiología , Infecciones del Sistema Respiratorio/epidemiología , Infecciones del Sistema Respiratorio/virología , Infecciones del Sistema Respiratorio/microbiología , Infecciones del Sistema Respiratorio/diagnóstico , Adolescente , Anciano , Preescolar , Niño , Adulto Joven , Lactante , Gripe Humana/epidemiología , Gripe Humana/virología , Estaciones del Año , Bacterias/aislamiento & purificación , Bacterias/clasificación , Bacterias/genética , Orofaringe/microbiología , Orofaringe/virología , Virus/aislamiento & purificación , Virus/clasificación , Virus/genética , Anciano de 80 o más Años , Infecciones Bacterianas/epidemiología , Infecciones Bacterianas/microbiología , Infecciones Bacterianas/diagnóstico , Recién Nacido

RESUMEN

Influenza is a respiratory disease that causes annual epidemics during cold seasons. These epidemics increase pressure on healthcare systems, sometimes provoking their collapse. For this reason, a tool is needed to predict when an influenza epidemic will occur so that the healthcare system has time to prepare for it. This study therefore aims to develop a statistical model capable of predicting the onset of influenza epidemics in Catalonia, Spain. Influenza seasons from 2011 to 2017 were used for model training, and those from 2017 to 2018 were used for validation. Logistic regression, Support Vector Machine, and Random Forest models were used to predict the onset of the influenza epidemic. The logistic regression model was able to predict the start of influenza epidemics at least one week in advance, based on clinical diagnosis rates of various respiratory diseases and meteorological variables. This model achieved the best punctual estimates for two of three performance metrics. The most important variables in the model were the principal components of bronchiolitis rates and mean temperature. The onset of influenza epidemics can be predicted from clinical diagnosis rates of various respiratory diseases and meteorological variables. Future research should determine whether predictive models play a key role in preventing influenza.

RESUMEN

BACKGROUND: This study compared trends in norovirus cases to determine whether chief complaint-based emergency department (ED) visit data could reflect trends of norovirus in Korea. METHODS: The ED visits from the National Emergency Department Information System database and the weekly reported number of noroviruses from the sentinel surveillance system were collected between August 2017 and December 2020. The correlation between weekly norovirus cases and weekly ED visits considering the chief complaint and discharge diagnosis code was estimated using a 3-week moving average. RESULTS: In total, 6 399 774 patients with chief complaints related to digestive system disease visited an ED. A higher correlation between reported norovirus cases and ED visit with chief complaint of vomiting and discharge diagnosis code of gastroenteritis and colitis of unspecified origin or other and unspecified gastroenteritis and colitis of infectious origin was observed (R = 0.88, P < .0001). The correlation was highest for the age group 0-4 years (R = 0.89, P < .0001). However, no correlation was observed between the reported norovirus cases and the number of ED visits with norovirus identified as a discharge diagnosis code. CONCLUSIONS: ED visit data considering a combination of chief complaints and discharged diagnosis code would be useful for early detection of infectious disease trends.

Asunto(s)

Infecciones por Caliciviridae , Servicio de Urgencia en Hospital , Gastroenteritis , Norovirus , Humanos , Infecciones por Caliciviridae/epidemiología , Infecciones por Caliciviridae/diagnóstico , Servicio de Urgencia en Hospital/estadística & datos numéricos , Gastroenteritis/epidemiología , Gastroenteritis/virología , Preescolar , Lactante , República de Corea/epidemiología , Adulto , Adolescente , Niño , Femenino , Masculino , Persona de Mediana Edad , Adulto Joven , Anciano , Vigilancia de Guardia , Recién Nacido

RESUMEN

Background: Syndromic surveillance represents a potentially inexpensive supplement to test-based COVID-19 surveillance. By strengthening surveillance of COVID-19-like illness (CLI), targeted and rapid interventions can be facilitated that prevent COVID-19 outbreaks without primary reliance on testing. Objective: This study aims to assess the temporal relationship between confirmed SARS-CoV-2 infections and self-reported and health care provider-reported CLI in university and county settings, respectively. Methods: We collected aggregated COVID-19 testing and symptom reporting surveillance data from Cornell University (2020-2021) and Tompkins County Health Department (2020-2022). We used negative binomial and linear regression models to correlate confirmed COVID-19 case counts and positive test rates with CLI rate time series, lagged COVID-19 cases or rates, and day of the week as independent variables. Optimal lag periods were identified using Granger causality and likelihood ratio tests. Results: In modeling undergraduate student cases, the CLI rate (P=.003) and rate of exposure to CLI (P<.001) were significantly correlated with the COVID-19 test positivity rate with no lag in the linear models. At the county level, the health care provider-reported CLI rate was significantly correlated with SARS-CoV-2 test positivity with a 3-day lag in both the linear (P<.001) and negative binomial model (P=.005). Conclusions: The real-time correlation between syndromic surveillance and COVID-19 cases on a university campus suggests symptom reporting is a viable alternative or supplement to COVID-19 surveillance testing. At the county level, syndromic surveillance is also a leading indicator of COVID-19 cases, enabling quick action to reduce transmission. Further research should investigate COVID-19 risk using syndromic surveillance in other settings, such as low-resource settings like low- and middle-income countries.

Asunto(s)

COVID-19 , Humanos , COVID-19/epidemiología , COVID-19/diagnóstico , COVID-19/prevención & control , Estudios Retrospectivos , Universidades/estadística & datos numéricos , Vigilancia de Guardia

RESUMEN

From April 2023 to May 2024, an unusual epidemic of parvovirus B19 (B19V) infections occurred in France. The number of B19V IgM-positive serologies was four times higher than in the previous epidemic in 2019. Clinical data from emergency networks corroborated this observation. Morbidity and mortality consequences were observed in children through all data sources. In adults, the increase was only observed in laboratory-confirmed data. Physicians and decisionmakers should be informed in order to better prevent, diagnose and manage at-risk patients.

Asunto(s)

Brotes de Enfermedades , Inmunoglobulina M , Infecciones por Parvoviridae , Parvovirus B19 Humano , Humanos , Francia/epidemiología , Parvovirus B19 Humano/aislamiento & purificación , Adulto , Femenino , Masculino , Niño , Infecciones por Parvoviridae/epidemiología , Infecciones por Parvoviridae/diagnóstico , Inmunoglobulina M/sangre , Adolescente , Preescolar , Persona de Mediana Edad , Anticuerpos Antivirales/sangre , Eritema Infeccioso/epidemiología , Eritema Infeccioso/diagnóstico , Adulto Joven , Lactante , Anciano

RESUMEN

OBJECTIVES: Syndromic surveillance supplements traditional laboratory reporting for infectious diseases monitoring. Prior to widespread COVID-19 community surveillance, syndromic surveillance was one of several systems providing real-time information on changes in healthcare-seeking behaviour. The study objective was to identify changes in healthcare utilisation during periods of high local media reporting in England using 'difference-in-differences' (DiD). STUDY DESIGN: A retrospective observational study was conducted using five media events in January-February 2020 in England on four routinely monitored syndromic surveillance indicators. METHODS: Dates 'exposed' to a media event were estimated using Google Trends internet search intensity data (terms = 'coronavirus' and local authority [LA]). We constructed a negative-binomial regression model for each indicator and event time period to estimate a direct effect. RESULTS: We estimated a four-fold increase in telehealth 'cough' calls and a 1.4-fold increase in emergency department (ED) attendances for acute respiratory illness in Brighton and Hove, when a so-called 'superspreading event' in this location was reported in local and national media. Significant decreases were observed in the Buxton (telehealth and ED attendance) and Wirral (ED attendance) areas during media reports of a returnee from an outbreak abroad and a quarantine site opening in the area respectively. CONCLUSIONS: We used a novel approach to directly estimate changes in syndromic surveillance reporting during the early phase of the COVID-19 pandemic in England, providing contextual information on the interpretation of changes in health indicators. With careful consideration of event timings, DiD is useful in producing real-time estimates on specific indicators for informing public health action.

Asunto(s)

COVID-19 , Humanos , COVID-19/epidemiología , Inglaterra/epidemiología , Estudios Retrospectivos , Aceptación de la Atención de Salud/estadística & datos numéricos , Vigilancia de Guardia , SARS-CoV-2 , Medios de Comunicación de Masas/estadística & datos numéricos , Pandemias , Servicio de Urgencia en Hospital/estadística & datos numéricos , Telemedicina/estadística & datos numéricos

RESUMEN

Preparation and response to Public Health emergencies involve efforts in developing systems for early detection, alert and response. Models for dealing with notification delay and diversification of data sources are some of the commonly used strategies for faster information and action. In this paper, we present the strategy implemented in Rio de Janeiro municipality, where data from urgency and emergency visits were acquired and modeled, in order to detect trend shifts and generate alerts. From the ICD-10 field in electronic records, time series representing events of interest were created. A GAM model was fitted for smoothing, slope determination in each point, and alert generation. The results obtained are displayed in a dashboard, monitored daily. From 2023, multiple events of interest were identified through the dashboard, some of which lead to coordinated communication and actions in the territory. We draw attention to the potentials in the use of these type of data on identifying events of interest in a timely manner, approaching the concepts of a modern surveillance.

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A preparação e resposta às emergências em Saúde Pública envolve o investimento em sistemas de detecção precoce, alerta e resposta. Modelos de correção de atraso de notificação e a diversificação de fontes de dados utilizadas são algumas abordagens comumente utilizadas para geração de informação e ação mais oportunos. Neste artigo é apresentada a estratégia implementada no município do Rio de Janeiro de utilização de dados de atendimentos de urgência e emergência unida à aplicação de modelos de detecção de tendências para geração automatizada de alertas. A partir de CIDs marcados nos prontuários eletrônicos de atendimentos, monitoram-se séries temporais de eventos de interesse no município. Um modelo GAM é ajustado às séries para suavização, determinação da inclinação e geração dos alertas. Os resultados são exibidos em painel e monitorados diariamente. Desde 2023, múltiplos eventos de interesse foram identificados através do painel e resultaram em comunicação coordenada e ações no território. Os resultados exaltam a potencialidade no uso desses dados na identificação de eventos de interesse em tempo oportuno, alinhando-se a conceitos de uma vigilância moderna.

RESUMEN

OBJECTIVES: To improve national drowning surveillance efforts, we developed and evaluated a definition for unintentional drowning for use in the National Syndromic Surveillance Program's ESSENCE platform (Electronic Surveillance System for the Early Notification of Community-Based Epidemics) and described drowning-related emergency department (ED) visits from 2019 through 2022 using the new definition. METHODS: We adapted an unintentional drowning definition from a previous version, which included all drowning-related ED visits regardless of intent (including drowning related to assault and suicide, as well as unintentional drowning). We reviewed a random sample of 1000 visits captured by the new definition of unintentional drowning and categorized visits as likely, possibly, and unlikely to be related to unintentional drowning. We compared monthly drowning-related ED visits from 2020, 2021, and 2022 with monthly drowning ED visits from 2019, overall and by sex and age group. RESULTS: A total of 35 431 ED visits related to unintentional drowning (10.71 per 100 000 ED visits) occurred from 2019 through 2022. Most visits (86%) captured by the new definition and manually reviewed were likely related to unintentional drowning. Rates were highest among males (14.04 per 100 000 ED visits) and children aged <1 to 4 years (65.61 per 100 000 ED visits). The number of drowning-related ED visits was higher in May and August 2020, May and June 2021, and May 2022 as compared with the same months in 2019 among people aged 18 to 44 years. CONCLUSIONS: The definition for unintentional drowning is available in the National Syndromic Surveillance Program's ESSENCE platform for state and local jurisdictions to use to monitor unintentional drowning-related ED visits in near-real time to inform prevention strategies.

RESUMEN

At present, Ontario, like most other jurisdictions in Canada, uses a syndromic-based surveillance definition for acute respiratory infection (ARI) outbreaks in institutions and public hospitals. Confirmed outbreaks are defined as either two or more ARIs in 48 hours with any common epidemiological link and at least one that is laboratory-confirmed; or three cases of ARIs occurring within 48 hours with any common epidemiological link, and not necessarily with lab confirmation. However, with the adoption of broader test-based approaches for sick patients/residents throughout the pandemic, new challenges have surfaced regarding the declaration and management of ARI outbreaks with a variety of scenarios in respiratory testing results. Decisions, including the determination of epidemiological linkage when there are discordant/negative test results, have become more complicated with the addition of virus-specific test results for every sick individual. The ARI outbreak case definition and management guidance was updated in 2018. The purpose of this commentary is to highlight epidemiological trends in ARI outbreaks in Ontario over the 2022-2023 season compared to the 2018-2019 and 2019-2020 pre-pandemic seasons. This is followed by a discussion around some of the benefits and challenges of implementing a test-based versus syndromic-based approach to ARI outbreaks.

RESUMEN

The underreporting of laboratory-reported cases of community-based gastrointestinal (GI) infections poses a challenge for epidemiologists understanding the burden and seasonal patterns of GI pathogens. Syndromic surveillance has the potential to overcome the limitations of laboratory reporting through real-time data and more representative population coverage. This systematic review summarizes the utility of syndromic surveillance for early detection and surveillance of GI infections. Relevant articles were identified using the following keyword combinations: 'early warning', 'detection', 'gastrointestinal activity', 'gastrointestinal infections', 'syndrome monitoring', 'real-time monitoring', 'syndromic surveillance'. In total, 1820 studies were identified, 126 duplicates were removed, and 1694 studies were reviewed. Data extraction focused on studies reporting the routine use and effectiveness of syndromic surveillance for GI infections using relevant GI symptoms. Eligible studies (n = 29) were included in the narrative synthesis. Syndromic surveillance for GI infections has been implemented and validated for routine use in ten countries, with emergency department attendances being the most common source. Evidence suggests that syndromic surveillance can be effective in the early detection and routine monitoring of GI infections; however, 24% of the included studies did not provide conclusive findings. Further investigation is necessary to comprehensively understand the strengths and limitations associated with each type of syndromic surveillance system.

Asunto(s)

Enfermedades Gastrointestinales , Humanos , Enfermedades Gastrointestinales/epidemiología , Enfermedades Gastrointestinales/diagnóstico , Enfermedades Gastrointestinales/microbiología , Vigilancia de la Población/métodos , Diagnóstico Precoz

RESUMEN

Syndromic surveillance is an effective tool for enabling the timely detection of infectious disease outbreaks and facilitating the implementation of effective mitigation strategies by public health authorities. While various information sources are currently utilized to collect syndromic signal data for analysis, the aggregated measurement of cough, an important symptom for many illnesses, is not widely employed as a syndromic signal. With recent advancements in ubiquitous sensing technologies, it becomes feasible to continuously measure population-level cough incidence in a contactless, unobtrusive, and automated manner. In this work, we demonstrate the utility of monitoring aggregated cough count as a syndromic indicator to estimate COVID-19 cases. In our study, we deployed a sensor-based platform (Syndromic Logger) in the emergency room of a large hospital. The platform captured syndromic signals from audio, thermal imaging, and radar, while the ground truth data were collected from the hospital's electronic health record. Our analysis revealed a significant correlation between the aggregated cough count and positive COVID-19 cases in the hospital (Pearson correlation of 0.40, p-value < 0.001). Notably, this correlation was higher than that observed with the number of individuals presenting with fever (ρ = 0.22, p = 0.04), a widely used syndromic signal and screening tool for such diseases. Furthermore, we demonstrate how the data obtained from our Syndromic Logger platform could be leveraged to estimate various COVID-19-related statistics using multiple modeling approaches. Aggregated cough counts and other data, such as people density collected from our platform, can be utilized to predict COVID-19 patient visits related metrics in a hospital waiting room, and SHAP and Gini feature importance-based metrics showed cough count as the important feature for these prediction models. Furthermore, we have shown that predictions based on cough counting outperform models based on fever detection (e.g., temperatures over 39°C), which require more intrusive engagement with the population. Our findings highlight that incorporating cough-counting based signals into syndromic surveillance systems can significantly enhance overall resilience against future public health challenges, such as emerging disease outbreaks or pandemics.

Asunto(s)

COVID-19 , Vigilancia de Guardia , Humanos , COVID-19/epidemiología , Salas de Espera , Hospitales , Brotes de Enfermedades/prevención & control , Fiebre/epidemiología

RESUMEN

One Health Syndromic Surveillance has a high potential for detecting early epidemiological events in remote and hard-to-reach populations. Chadian pastoralists living close to their animals and being socio-economically unprivileged have an increased risk for zoonosis exposure. Engaging communities in disease surveillance could also strengthen preparedness capacities for outbreaks in rural Chad. This study describes a retrospective cross-sectional survey that collected data on clinical symptoms reported in people and livestock in Chadian agro-pastoral communities. In January-February 2018, interviews were conducted in rural households living in nomadic camps or settled villages in the Yao and Danamadji health districts. The questionnaire covered demographic data and symptoms reported in humans and animals for the hot, wet, and cold seasons over the last 12 months. Incidence rates of human and animal symptoms were comparatively analyzed at the household level. Ninety-two households with a homogeneous socio-demographic distribution were included. We observed cough and diarrhea as the most frequent symptoms reported simultaneously in humans and animals. In all species, the incidence rate of cough was significantly higher during the cold season, and diarrhea tended to occur more frequently during the wet season. However, the incidence rate of cough and diarrhea in animals did not predict the incidence rate of these symptoms in humans. Overall, the variations in reported symptoms were consistent with known seasonal, regional, and sociological influences on endemic diseases. Our retrospective study demonstrated the feasibility of collecting relevant health data in humans and animals in remote regions with low access to health services by actively involving community members. This encourages establishing real-time community-based syndromic surveillance in areas such as rural Chad.

Asunto(s)

Ganado , Salud Única , Animales , Humanos , Chad/epidemiología , Estudios Retrospectivos , Estudios Transversales , Diarrea , Tos