Syndromic surveillance of population-level COVID-19 burden with cough monitoring in a hospital emergency waiting room.

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.

Utilizing Wearable Device Data for Syndromic Surveillance: A Fever Detection Approach.

Commercially available wearable devices (wearables) show promise for continuous physiological monitoring. Previous works have demonstrated that wearables can be used to detect the onset of acute infectious diseases, particularly those characterized by fever. We aimed to evaluate whether these devices could be used for the more general task of syndromic surveillance. We obtained wearable device data (Oura Ring) from 63,153 participants. We constructed a dataset using participants' wearable device data and participants' responses to daily online questionnaires. We included days from the participants if they (1) completed the questionnaire, (2) reported not experiencing fever and reported a self-collected body temperature below 38 °C (negative class), or reported experiencing fever and reported a self-collected body temperature at or above 38 °C (positive class), and (3) wore the wearable device the nights before and after that day. We used wearable device data (i.e., skin temperature, heart rate, and sleep) from the nights before and after participants' fever day to train a tree-based classifier to detect self-reported fevers. We evaluated the performance of our model using a five-fold cross-validation scheme. Sixteen thousand, seven hundred, and ninety-four participants provided at least one valid ground truth day; there were a total of 724 fever days (positive class examples) from 463 participants and 342,430 non-fever days (negative class examples) from 16,687 participants. Our model exhibited an area under the receiver operating characteristic curve (AUROC) of 0.85 and an average precision (AP) of 0.25. At a sensitivity of 0.50, our calibrated model had a false positive rate of 0.8%. Our results suggest that it might be possible to leverage data from these devices at a public health level for live fever surveillance. Implementing these models could increase our ability to detect disease prevalence and spread in real-time during infectious disease outbreaks.

Community-based symptom reporting among agro-pastoralists and their livestock in Chad in a One Health approach.

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.

Data Sharing in a Decentralized Public Health System: Lessons From COVID-19 Syndromic Surveillance.

The COVID-19 pandemic revealed that data sharing challenges persist across public health information systems. We examine the specific challenges in sharing syndromic surveillance data between state, local, and federal partners. These challenges are complicated by US federalism, which decentralizes public health response and creates friction between different government units. The current policies restrict federal access to state and local syndromic surveillance data without each jurisdiction's consent. These policies frustrate legitimate federal governmental interests and are contrary to ethical guidelines for public health data sharing. Nevertheless, state and local public health agencies must continue to play a central role as there are important risks in interpreting syndromic surveillance data without understanding local contexts. Policies establishing a collaborative framework will be needed to support data sharing between federal, state, and local partners. A collaborative framework would be enhanced by a governance group with robust state and local involvement and policy guardrails to ensure the use of data is appropriate. These policy and relational challenges must be addressed to actualize a truly national public health information system.

Monitoring COVID-19 in Belgian general practice: A tool for syndromic surveillance based on electronic health records.

BACKGROUND: COVID-19 may initially manifest as flu-like symptoms. As such, general practitioners (GPs) will likely to play an important role in monitoring the pandemic through syndromic surveillance. OBJECTIVES: To present a COVID-19 syndromic surveillance tool in Belgian general practices. METHODS: We performed a nationwide observational prospective study in Belgian general practices. The surveillance tool extracted the daily entries of diagnostic codes for COVID-19 and associated conditions (suspected or confirmed COVID-19, acute respiratory infection and influenza-like illness) from electronic medical records. We calculated the 7-day rolling average for these diagnoses and compared them with data from two other Belgian population-based sources (laboratory-confirmed new COVID-19 cases and hospital admissions for COVID-19), using time series analysis. We also collected data from users and stakeholders about the syndromic surveillance tool and performed a thematic analysis. RESULTS: 4773 out of 11,935 practising GPs in Belgium participated in the study. The curve of contacts for suspected COVID-19 followed a similar trend compared with the curves of the official data sources: laboratory-confirmed COVID-19 cases and hospital admissions but with a 10-day delay for the latter. Data were quickly available and useful for decision making, but some technical and methodological components can be improved, such as a greater standardisation between EMR software developers. CONCLUSION: The syndromic surveillance tool for COVID-19 in primary care provides rapidly available data useful in all phases of the COVID-19 pandemic to support data-driven decision-making. Potential enhancements were identified for a prospective surveillance tool.

Data extracted daily from electronic medical records can be used to monitor the COVID-19 pandemic in general practice.The Barometer provided rapidly available data to support data-driven decision-making.Improvements such as a greater standardisation were identified for a potential future tool using the same technology.

Early warning of potential epidemics: A pilot application of an early warning tool to data from the pulmonary clinic of the university hospital of Thessaly, Greece.

BACKGROUND & METHODS: This paper describes a pilot application of the Epidemic Volatility Index (EVI) to data from the pulmonary clinic of the University Hospital of Thessaly, Greece, for monitoring respiratory infections, COVID-19, and flu cases. EVI, a simple and easily implemented early warning method based on the volatility of newly reported cases, exhibited consistent and stable performance in detecting new waves of epidemics. The study highlights the importance of implementing early warning tools to address the effects of epidemics, including containment of outbreaks, timely intervention strategies, and resource allocation within real-world clinical settings as part of a broader public health strategy. RESULTS: The results presented in the figures demonstrate the association between successive early warnings and the onset of new waves, providing valuable insights for proactive decision-making. A web-based application enabling real-time monitoring and informed decision-making by healthcare professionals, public health officials, and policymakers was developed. CONCLUSIONS: This study emphasizes the significant role of early warning methods in managing epidemics and safeguarding public health. Future research may explore extensions and combinations of multiple warning systems for optimal outbreak interventions and application of the methods in the context of personalized medicine.

Enhanced measures, including PCR-based screening and syndromic surveillance for nosocomial outbreaks of the COVID-19 Omicron variant, using descriptive epidemiology and whole-genome sequencing in a Japanese tertiary care hospital.

INTRODUCTION: In this study, we aimed to analyze the effectiveness of enhanced preventive measures against nosocomial COVID-19 Omicron outbreaks based on those encountered. METHODS: We introduced PCR-based screening and syndromic surveillance, in addition to standard and transmission-based precautions, during a COVID-19 outbreak in three wards of Kagoshima University Hospital, a Japanese tertiary care hospital, in February 2022, amid the Omicron variant endemic. Furthermore, we analyzed the descriptive epidemiology and whole-genome sequencing (WGS) of positive SARS-CoV-2 PCR samples from this outbreak. RESULTS: PCR-based screening tests were conducted following the identification of three cases through syndromic surveillance. As a result, 30 individuals tested positive for SARS-CoV-2, including 13 inpatients, five attendant family members, and 12 healthcare workers across the three wards. Notably, no new infections were observed within eight days following the implementation of preventive measures. Among the SARS-CoV-2 genomes analyzed (n = 16; 53.3%), all strains were identified as belonged to BA.1.1 variant. Detailed analysis of descriptive and molecular epidemiology, incorporating single-nucleotide polymorphism analysis of WGS and clarification of transmission links, considering two potential entry routes to the hospital. CONCLUSIONS: Introduction of additional preventive measures, including PCR-based screening and syndromic surveillance, in addition to WGS and descriptive epidemiology, is useful for the early intervention of nosocomial outbreaks and for revealing the transmission route of the COVID-19 Omicron variant.

Safety of Janssen Ad26.COV.S and Astra Zeneca AZD1222 COVID-19 Vaccines among Mobile Phone Users in Malawi: Findings from a National Mobile-Based Syndromic Surveillance Survey, July 2021 to December 2021.

The safety profiles of the Ad26.COV2.S and AZD1222 COVID-19 vaccines have not been described in the general population in Malawi. We present self-reported adverse events (AE) following the receipt of these vaccines in Malawi as part of a national syndromic surveillance survey. We conducted phone-based syndromic surveillance surveys among adults (≥18 years) with verbal consent. We used secure tablets through random digit dialing to select mobile phone numbers and collected data electronically. Survey questions included whether the respondent had received the COVID-19 vaccines, whether they had experienced any AE following vaccination, and the severity of the AE. We used multivariable analysis to identify factors associated with self-reported AE post-COVID-19 vaccination. A total of 11,924 (36.0%) out of 33,150 respondents reported receiving at least one dose of either Ad26.COV2.S or AZD1222 between July-December 2021; of those, 65.1% were female. About 49.2% of the vaccine recipients reported at least one AE, 90.6% of which were mild, and 2.6% were severe. Higher education level and concern about the safety of COVID-19 vaccines were associated with AE self-report (Adjusted Odds Ratio [AOR] 2.63 [95% CI 1.96-3.53] and 1.44, [95% CI 1.30-1.61], respectively), while male gender and older age were associated with reduced likelihood of AE self-report (AORs 0.81, [95% CI 0.75-0.88], 0.62 [95% CI 0.50-0.77], respectively). Ad26.COV2.S and AZD1222 vaccines are well-tolerated, with primarily mild and few severe AE among adults living in Malawi. Self-reporting of AE following COVID-19 vaccination is associated with gender, age, education, and concern about the safety of the vaccines. Recognizing these associations is key when designing and implementing COVID-19 vaccination communication messages to increase vaccination coverage.

Epidemiological surveillance of respiratory viral infections in SARS-CoV-2-negative samples during COVID-19 pandemic in Iran.

BACKGROUND: To improve the patient care, public health surveillance, and infection control, it is crucial to identify the presence and frequency of the common respiratory infections in individuals with COVID-19 symptoms but tested negative for SARS-CoV-2. This study aimed to shed light on this during the COVID-19 pandemic in Iran. METHODS: In this cross-sectional study, a total of 1,002 patients with acute respiratory infection who had negative SARS-CoV-2 test results and referred to Valfajr Health Center, the National Collaborating Laboratory of Influenza and COVID-19 National Reference Laboratory at Pasteur Institute of Iran were recruited between January 2020 and January 2022. Nasopharyngeal and oropharyngeal swab samples were collected to detect 17 common respiratory viruses via TaqMan one-step real-time multiplex PCR. Demographic and clinical data of the participants were obtained from their electronic medical records. RESULTS: In total, 218 samples (21.8%) were tested positive for at least one respiratory virus infection. Most of the common investigated respiratory viruses belonged to the years 2020 and 2022. The number of investigated patients in 2021 was few, which highlights the impact of health measures following the COVID-19 pandemic in Iran. Influenza A was the most common virus (5.8%), while adenovirus had the lowest prevalence (0.1%). Although the rate of respiratory virus infection was higher in men (24%) compared to women (19.3%), this difference was not statistically significant (P = 0.069). The prevalence of respiratory viruses had an inverse association with increasing age, with the highest rate (55.6%) observed in the age group below 2 years and the lowest rate (12.7%) in those above 65 years. CONCLUSION: Our findings underscore the significance of adopting a comprehensive approach to respiratory infections detection and management. These results can be employed for the development of syndromic surveillance systems and implementation of the effective infection control measures. Furthermore, the results contribute to better understanding of the dynamics of respiratory viruses, both during pandemic periods and in non-pandemic contexts.

Evaluating the Effectiveness of School Closure in COVID-19-Related Syndromes From Community-Based Syndromic Surveillance: Longitudinal Observational Study.

BACKGROUND: During the COVID-19 pandemic, a school closure policy was adopted to prevent cluster transmission in schools and subsequent household transmission. However, the effectiveness of school closure is not consistent in studies conducted in different countries. OBJECTIVE: This study aimed to explore the association between school closure and the daily standardized incidence of COVID-19-related syndromes in an outpatient syndromic surveillance system. METHODS: We calculated the incidence of COVID-19-related syndromes derived from a community-based syndromic surveillance system between the first week of January and the second or fourth weeks after school closure in 2021 and 2022 in Taipei City, Taiwan. The effect of school closure on the standardized incidence of COVID-19-related syndromes was evaluated by interrupted time series analysis using an autoregressive integrated moving average with a distributed lag function. The exogenous variables were changes in human mobility measured by Google COVID-19 community mobility reports. Furthermore, the models quantified the influence of different age groups and the hierarchy of medical facilities, such as clinics or community hospitals. RESULTS: School closure was only negatively and significantly associated with the overall standardized incidence of COVID-19-related syndromes in 2021 for 2 weeks after the intervention (coefficient -1.24, 95% CI -2.40 to -0.08). However, in different age groups, school closure had a significantly negative association with the standardized incidence among people aged 13-18 years and ≥65 years for 2 weeks after the intervention in clinics in 2021. In community hospitals, school closure was significantly positively associated with the standardized incidence among people aged 19-24 years in 2021. In 2022, 2 weeks after the intervention, school closure had a significantly negative association with the standardized incidence among people aged 0-6, 7-12, and 19-24 years in community hospitals and aged >45 years in clinics. Furthermore, the standardized incidence was positively associated with movement change toward grocery and pharmacy stores in all age groups in 2022. In addition, movement changes toward residences were significantly positively associated with the standardized incidence among all age groups. CONCLUSIONS: Overall, school closure effectively suppresses COVID-19-related syndromes in students owing to the reduction of physical contact. In addition, school closure has a spillover effect on elderly people who stay at home.

Impact of the 7/14/2016 Nice terrorist attack on pediatric emergency department visits thanks to syndromic surveillance: a descriptive study.

Objective: Study the impact of 14th July 2016 Nice terrorist attack on Pediatric Emergency Department (PED) visits by youth under 18 years of age. Methods: PED visits diagnoses (ICD10) were clustered and analyzed based on retrospective data from the syndromic surveillance system of the Children's university hospital of Nice (Southern France). The studied period ranges from 2013 to 2019, i.e., 3 years before and after the terrorist attack of 14th July 2016. Results: Among 416,191 PED visits, the number of visits for stress in 4-17 years old appeared to increase in the 3 years after the attack compared to the 3 years before, particularly in September 2016 (acute effect) with 11 visits compared to an average of 2.3 visits per month from September 2013 to 2016 (p = 0.001827). In September 2017, we noticed 21 visits compared to an average of 4.8 visits per month during the following period (2013-2019). In 2017, PED visits for stress among 4-17 year olds were higher in comparison to the other years of the study: 107 visits compared to an annual average of 57. Conclusion: To our knowledge, this is the first study of the use of the pediatric care system before and after a terrorist attack involving syndromic surveillance. This suggests acute and long-term effects of the terrorist attack on PED use by youth for mental health issues. Further studies of the pediatric care system involving syndromic surveillance are needed in the context of mass violent events, such as terrorist attacks.

The Epidemiology of Chickenpox in England, 2016-2022: An Observational Study Using General Practitioner Consultations.

Chickenpox is a common childhood disease caused by varicella-zoster virus (VZV). VZV vaccination is not part of the UK childhood immunisation programme, but its potential inclusion is regularly assessed. It is therefore important to understand the ongoing burden of VZV in the community to inform vaccine policy decisions. General practitioner (GP) chickenpox consultations were studied from 1 September 2016 to 9 December 2022. Over the study period, the mean weekly chickenpox consultation rate per 100,000 population in England was 3.4, with a regular peak occurring between weeks 13 and 15. Overall, rates decreased over time, from a mean weekly rate of 5.5 in 2017 to 4.2 in 2019. The highest mean weekly rates were among children aged 1-4 years. There was no typical epidemic peak during the COVID-19 pandemic, but in 2022, rates were proportionally higher among children aged < 1 year old compared to pre-pandemic years. Chickenpox GP consultation rates decreased in England, continuing a longer-term decline in the community. The COVID-19 pandemic impacted rates, likely caused by the introduction of non-pharmaceutical interventions to prevent SARS-CoV-2 transmission. The lasting impact of the interruption of typical disease transmission remains to be seen, but it is important to monitor the chickenpox burden to inform decisions on vaccine programmes.

Syndromic Surveillance Implementation During Disaster Events.

INTRODUCTION: Disease surveillance is an integral part of public health. These systems monitor disease trends and detect outbreaks, whereas they should be evaluated for efficacy. The United States Centres for Disease Control and Prevention publish Guidelines for Evaluating Surveillance Systems to encourage efficient and effective use of public health surveillance that are accepted worldwide. OBJECTIVE: This study reviews syndromic surveillance during natural and man-made disasters internationally. It aims to (1) review the performance of syndromic surveillance via pre-specified attributes during disaster and to (2) understand its strengths and limitations. METHODS: PubMed was systematically searched for the articles assessing syndromic surveillance during a disaster. A narrative review was carried out based on those articles. Updated Guidelines for Evaluating Public Health Surveillance Systems were used to review performance of systems. RESULTS: 5,059 studies from PubMed were evaluated, and 16 met inclusion criteria. The majority of these studies considered the implementation of syndromic surveillance useable during disaster events. Studies described systems giving relevant and timely information. Simplicity and timeliness were the most highlighted attributes. CONCLUSION: Syndromic surveillance is simple, flexible, useful and usable during a disaster. Timely data can be obtained, but the quality of this type of data is sensitive to incomplete and erroneous reporting; because of this, a standardized approach is necessary to optimize these systems.

Emergency Department Syndromic Surveillance to Monitor Tick-Borne Diseases: A 6-Year Small-Area Analysis in Northeastern Italy.

Tick-borne diseases (TBD) are endemic in Europe. However, surveillance is currently incomplete. Alternative strategies need to be considered. The aim of this study was to test an Emergency Department Syndromic Surveillance (EDSyS) system as a complementary data source to describe the impact of tick bites and TBD using a small-area analysis approach and to monitor the risk of TBD to target prevention. ED databases in the Local Health Authority 8 District (Veneto, Italy) were queried for tick-bite and TBD-related visits between January 2017 and December 2022. Hospitalisations were also collected. Events involving the resident population were used to calculate incidence rates. A total of 4187 ED visits for tick-bite and 143 for TBD were recorded; in addition, 62 TBD-related hospitalisations (of which 72.6% in over 50 s and 22.6% in over 65 s). ED visits peaked in spring and in autumn, followed by a 4-week lag in the increase in hospital admissions. The small-area analysis identified two areas at higher risk of bites and TBD. The use of a EDSyS system allowed two natural foci to be identified. This approach proved useful in predicting temporal and geographic risk of TBD and in identifying local endemic areas, thus enabling an effective multidisciplinary prevention strategy.

Trends for syndromic surveillance of norovirus in emergency department data based on chief complaints.

BACKGROUND: This study compared the trends in norovirus cases to determine whether chief complaint-based emergency department (ED) visits data could reflect trends of norovirus in Korea. METHODS: The ED visits from the National Emergency Department Information System (NEDIS) 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 three-week moving average. RESULTS: In total, 6,399,774 patients with chief complaints of digestive system disease visited 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 0-4-years age group (R=0.89, p<.0001). However, no correlation was observed between the reported norovirus cases and the number of emergency department 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 available for early detection of infectious disease trends.

Understanding the leading indicators of hospital admissions from COVID-19 across successive waves in the UK.

Following the end of universal testing in the UK, hospital admissions are a key measure of COVID-19 pandemic pressure. Understanding leading indicators of admissions at the National Health Service (NHS) Trust, regional and national geographies help health services plan for ongoing pressures. We explored the spatio-temporal relationships of leading indicators of hospitalisations across SARS-CoV-2 waves in England. This analysis includes an evaluation of internet search volumes from Google Trends, NHS triage calls and online queries, the NHS COVID-19 app, lateral flow devices (LFDs), and the ZOE app. Data sources were analysed for their feasibility as leading indicators using Granger causality, cross-correlation, and dynamic time warping at fine spatial scales. Google Trends and NHS triages consistently temporally led admissions in most locations, with lead times ranging from 5 to 20 days, whereas an inconsistent relationship was found for the ZOE app, NHS COVID-19 app, and LFD testing, which diminished with spatial resolution, showing cross-correlation of leads between -7 and 7 days. The results indicate that novel surveillance sources can be used effectively to understand the expected healthcare burden within hospital administrative areas though the temporal and spatial heterogeneity of these relationships is a key determinant of their operational public health utility.

Comparison of statistical methods for the early detection of disease outbreaks in small population settings.

Objectives: This study examines the performance of 6 aberration detection algorithms for the early detection of disease outbreaks in small population settings using syndrome-based early warning surveillance data collected by the Pacific Syndromic Surveillance System (PSSS). Although previous studies have proposed statistical methods for detecting aberrations in larger datasets, there is limited knowledge about how these perform in the presence of small numbers of background cases. Methods: To address this gap a simulation model was developed to test and compare the performance of the 6 algorithms in detecting outbreaks of different magnitudes, durations, and case distributions. Results: The study found that while the Early Aberration Reporting System-C1 algorithm developed by Hutwagner et al. outperformed others, no single approach provided reliable monitoring across all outbreak types. Furthermore, aberration detection approaches could only detect very large and acute outbreaks with any reliability. Conclusion: The findings of this study suggest that algorithm-based approaches to outbreak signal detection perform poorly when applied to settings with small numbers of background cases and should not be relied upon in these contexts. This highlights the need for alternative approaches for accurate and timely outbreak detection in small population settings, particularly those that are resource-constrained.

The Association of Acute Watery Diarrhea in Iranian Pilgrims of Iraq with Cholera Cases in Iranians.

Background: Despite the advances in the control of infectious diseases like cholera, they can potentially cause epidemics, especially in mass gathering events. One of the most important countries on the walking way of the Arbaeen religious event is Iran, which requires health system preparedness. The aim of this study was to predict the cholera epidemic in Iran by using the syndromic surveillance system of Iranian pilgrims in Iraq. Methods: The data of the Iranian pilgrims with acute watery diarrhea in Iraq during the Arbaeen religious event and the confirmed cholera cases of pilgrims after returning to Iran were analyzed. We used the Poisson regression model of the relationship between the numbers of cases to evaluate acute watery diarrhea and cholera. Spatial statistics and hot spot analysis were used to identify the provinces with the highest incidence. SPSS software Version 24 was used for statistical analysis. Results: The frequency of acute watery diarrhea cases was 2232 and the frequency of cholera in pilgrims after returning to Iran was 641. The results of spatial analysis for acute watery diarrhea cases showed a high number of acute watery diarrhea cases in the Khuzestan and Isfahan provinces, located in hot spots. Using Poisson regression, the relationship between the number of acute watery diarrhea reported in the syndromic surveillance system and the number of cholera cases was confirmed. Conclusion: The syndromic surveillance system is useful to predict the outbreak of infectious diseases in large religious mass gatherings.

Passive Monitoring of Crowd-Level Cough Counts in Waiting Areas produces Reliable Syndromic Indicator for Total COVID-19 Burden in a Hospital Emergency Clinic.

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. Our findings highlight the efficacy of aggregated cough count as a valuable syndromic indicator associated with the occurrence of COVID-19 cases. Incorporating this signal into syndromic surveillance systems for such diseases can significantly enhance overall resilience against future public health challenges, such as emerging disease outbreaks or pandemics.

Syndromic Surveillance for COVID-19, Massachusetts, February 2020-November 2022: The Impact of Fever and Severity on Algorithm Performance.

OBJECTIVES: Syndromic surveillance can help identify the onset, location, affected populations, and trends in infectious diseases quickly and efficiently. We developed an electronic medical record-based surveillance algorithm for COVID-19-like illness (CLI) and assessed its performance in 5 Massachusetts medical practice groups compared with statewide counts of confirmed cases. MATERIALS AND METHODS: Using data from February 2020 through November 2022, the CLI algorithm was implemented in sites that provide ambulatory and inpatient care for about 25% of the state. The initial algorithm for CLI was modeled on influenza-like illness: an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis code for COVID-19 and an ICD-10-CM diagnosis code suggesting severe lower respiratory tract infection or ≥1 ICD-10-CM diagnosis code for upper or lower respiratory tract infection plus fever. We generated weekly counts of CLI cases and patients with ≥1 clinical encounter and visually compared trends with those of statewide laboratory-confirmed cases. RESULTS: The initial algorithm tracked well with the spring 2020 wave of COVID-19, but the components that required fever did not clearly detect the November 2020-January 2021 surge and identified <1% of weekly encounters as CLI. We revised the algorithm by adding more mild symptoms and removing the fever requirement; this revision improved alignment with statewide confirmed cases through spring 2022 and increased the proportion of encounters identified as CLI to about 2% to 6% weekly. Alignment between CLI trends and confirmed COVID-19 case counts diverged again in fall 2022, likely because of decreased COVID-19 testing and increases in other respiratory viruses. PRACTICE IMPLICATIONS: Our work highlights the importance of using a broad definition for COVID-19 syndromic surveillance and the need for surveillance systems that are flexible and adaptable to changing trends and patterns in disease or care.