Syndromic Surveillance System Research Articles

Epidemiology of Coronavirus Disease Outbreak among Crewmembers on Cruise Ship, Nagasaki City, Japan, April 2020.

In April 2020, a coronavirus disease (COVID-19) outbreak occurred on the cruise ship Costa Atlantica in Nagasaki, Japan. Our outbreak investigation included 623 multinational crewmembers onboard on April 20. Median age was 31 years; 84% were men. Each crewmember was isolated or quarantined in a single room inside the ship, and monitoring of health status was supported by a remote health monitoring system. Crewmembers with more severe illness were hospitalized. The investigation found that the outbreak started in late March and peaked in late April, resulting in 149 laboratory-confirmed and 107 probable cases of infection with severe acute respiratory syndrome coronavirus 2. Six case-patients were hospitalized for COVID-19 pneumonia, including 1 in severe condition and 2 who required oxygen administration, but no deaths occurred. Although the virus can spread rapidly on a cruise ship, we describe how prompt isolation and quarantine combined with a sensitive syndromic surveillance system can control a COVID-19 outbreak.

Evaluation of Data Sources for Carbon Monoxide Poisoning Surveillance in New York City.

Carbon monoxide (CO) exposure can be life-threatening. Suspected and confirmed cases of CO poisoning warranting health care in New York City (NYC) are reportable to the NYC Poison Control Center (PCC). We evaluated 4 hospital-based sources of CO surveillance data to identify ways to improve data capture and reporting. Suspected and confirmed CO poisoning records from October 2015 through December 2016 were collected from the NYC emergency department (ED) syndromic surveillance system, New York State Statewide Planning and Research Cooperative System (SPARCS) ED billing data, NYC PCC calls made from hospitals, and the Electronic Clinical Laboratory Reporting System (ECLRS). Syndromic and SPARCS records were person- and visit-matched. SPARCS and ECLRS records were also matched to PCC records on combinations of name, demographic characteristics, and visit information. Hospitals in NYC. Individuals who visited NYC hospitals for CO-related health effects. We assessed the validity of syndromic data, with SPARCS records as the gold standard. We matched SPARCS and ECLRS records to PCC records to analyze reporting rates by case characteristics. The sensitivity of syndromic surveillance was 60% (225 true-positives detected among 372 visit-matched SPARCS cases), and positive predictive value was 46%. Syndromic records often missed CO flags because of a nonspecific or absent International Classification of Diseases code in the diagnosis field. Only 15% of 428 SPARCS records (total includes 56 records not visit-matched to syndromic) and 16% of 199 ECLRS records were reported to PCC, with male sex and younger age associated with higher reporting. Mandatory reporting makes PCC useful for tracking CO poisoning in NYC, but incomplete reporting and challenges in distinguishing between confirmed and suspected cases limit its utility. Simultaneous tracking of the systems we evaluated can best reveal surveillance patterns.

Proximity to Industrial Poultry and Hog Production and Emergency Department Visits for Urinary Tract Infection in North Carolina, USA

BACKGROUND AND AIM: An increasing share of urinary tract infections (UTIs) are caused by uropathogenic {Escherichia coli} lineages that have also been identified in poultry and hogs with high genetic similarity to human clinical isolates. We investigated industrial food animal production as a potential source of community uropathogen exposure by examining relationships of poultry and hog density with emergency department (ED) visits for suspected UTI in North Carolina (NC). METHODS: ED visits for UTI in 2016-2019 were identified by ICD-10 code from NC's ZIP Code-level syndromic surveillance system and livestock counts were obtained from permit and agricultural census data. We calculated separate poultry and hog densities (animals/km²) by census block with a 5km buffer on the block perimeter and weighted by block population to estimate mean ZIP Code densities. Associations between livestock density and UTI incidence were estimated using a reparametrized Besag-York-Mollié (BYM2) model with Poisson likelihood and ZIP Code population offset to account for spatial autocorrelation. We assessed effect measure modification by ZIP Code rurality and patient sex, age, and race/ethnicity. RESULTS:Complete covariate data were available for 726 contiguous NC ZIP Codes; 233 had zero density of both poultry and hogs. UTI incidence was positively associated with a log10 density increase of both poultry (rate ratio [RR]: 1.05, 95% confidence interval [CI]: 0.99-1.11) and hogs (RR: 1.05, 95%CI: 0.98-1.14). Compared with hog-free ZIP Codes, all hog density tertiles were associated with increased UTI incidence (highest-density tertile RR: 1.21, 95%CI: 1.06-1.40); associations with poultry density tertiles were similar but weaker. The strongest associations were observed for ZIP Codes with hogs but no poultry (n=39) or with the highest-density tertiles of both animals (n=52; RR: 1.35, 95%CI: 1.11-1.62). Estimates were consistently elevated among females, young adults, and American Indian and Asian individuals. CONCLUSIONS:Industrial livestock production may be associated with increased UTI incidence in neighboring communities. KEYWORDS: Confined animal feeding operation (CAFO), syndromic surveillance, areal data, Bayesian hierarchical spatial model, intrinsic conditional autoregressive (ICAR) prior

Exposure to Industrial Hog Operations and Gastrointestinal Illness in North Carolina, USA

BACKGROUND AND AIM: With 9 million hogs, North Carolina (NC) is the second leading hog producer in the United States. Most hogs are housed at concentrated animal feeding operations (CAFOs), where millions of tons of hog waste can pollute the air and water with fecal pathogens that can cause diarrhea, vomiting, and/or nausea (known as acute gastrointestinal illness (AGI)). METHODS: We used NC’s ZIP code-level syndromic surveillance system to calculate rates of AGI emergency department (ED) visits during 2016-2019 and swine permit data to estimate hog exposure. Exposure was estimated as the inverse distances from each hog CAFO to census block centroids, weighting with Gaussian decay and by hogs per CAFO, then aggregated to ZIP codes using population weights. We compared ZIP codes in the upper quartile of hog exposure to those without hog exposure. Using inverse probability of treatment weighting, we created a control with similar demographics to the high hog exposed population and calculated rate ratios (RR) using quasi-Poisson models. We examined effect measure modification of rurality and race using adjusted models. RESULTS:Among the 111 high hog exposed ZIP codes, the median number of hogs per ZIP code was 50,022. In high hog exposed areas compared to areas without hog exposure, we observed a 17% increase in AGI rate (95% CI: 1.08, 1.26) and a 24% increase in rural areas (95% CI: 1.04, 1.48). The association was stronger during the weeks after heavy rain (RR=1.54, 95% CI: 1.25, 1.83). When restricted to rural areas, we found an increased AGI rate among American Indian (RR=5.26, 95% CI: 4.60, 5.91) and Black (RR=1.47, 95% CI: 1.04, 1.89) patients. CONCLUSIONS:Residing near hog CAFOs may increase rates of AGI ED visits. Hog CAFOs are disproportionally built near rural Black and American Indian communities in NC and are associated most strongly with increased AGI in these populations. KEYWORDS: Concentrated animal feeding operation (CAFO), animal production, swine, environmental pollution, gastrointestinal illness, waterborne pathogens

Consequences of the COVID-19 Outbreak Lockdown on Non-Viral Infectious Agents as Reported by a Laboratory-Based Surveillance System at the IHU Méditerranée Infection, Marseille, France.

The objective of this paper is to describe the surveillance system MIDaS and to show how this system has been used for evaluating the consequences of the French COVID-19 lockdown on the bacterial mix of AP-HM and the antibiotic resistance. MIDas is a kind of surveillance activity hub, allowing the automatic construction of surveillance control boards. We investigated the diversity and resistance of bacterial agents from respiratory, blood, and urine samples during the lockdown period (from week 12 to 35 of 2020), using the same period of years from 2017 to 2019 as control. Taking into account the drop in patient recruitment, several species have exhibited significant changes in their relative abundance (either increasing or decreasing) with changes up to 9%. The changes were more important for respiratory and urine samples than for blood samples. The relative abundance in respiratory samples for the whole studied period was higher during the lockdown. A significant increase in the percentage of wild phenotypes during the lockdown was observed for several species. The use of the MIDaS syndromic collection and surveillance system made it possible to efficiently detect, analyze, and follow changes of the microbiological population as during the lockdown period.

COVID-19-related deaths among women of reproductive age in Brazil: The burden of postpartum.

ObjectiveTo compare risk of death due to COVID‐19 among pregnant, postpartum, and non‐pregnant women of reproductive age in Brazil, using the severe acute respiratory syndrome surveillance system (SARS‐SS).MethodsA secondary analysis was performed of the Brazilian official SARS‐SS, with data retrieved up to August 17, 2020. Cases were stratified by pregnancy status, risk factors or co‐morbidities, and outcome (death or recovery). Multiple logistic regression was employed to examine associations between independent variables and risk of death.ResultsA total of 24 805 cases were included, with 3129 deaths (12.6%), including 271 maternal deaths. Postpartum was associated with increased risk of death, admission to the intensive care unit (ICU), and mechanical ventilation. Co‐morbidities with higher impact on case fatality rate among non‐obstetric cases were cancer and neurological and kidney diseases. Among pregnant women, cancer, diabetes mellitus, obesity, and rheumatology diseases were associated with risk of death. In the postpartum subgroup, age over 35 years and diabetes mellitus were independently associated with higher chance of death.ConclusionPostpartum was associated with worse outcomes among the obstetric population, despite lower risk of dying without accessing ICU care. Non‐pregnant women with cancer, neurological diseases, and kidney diseases have a higher risk of death due to COVID‐19.

Development and validation of an automated emergency department-based syndromic surveillance system to enhance public health surveillance in Yukon: a lower-resourced and remote setting

BackgroundAutomated Emergency Department syndromic surveillance systems (ED-SyS) are useful tools in routine surveillance activities and during mass gathering events to rapidly detect public health threats. To improve the existing surveillance infrastructure in a lower-resourced rural/remote setting and enhance monitoring during an upcoming mass gathering event, an automated low-cost and low-resources ED-SyS was developed and validated in Yukon, Canada.MethodsSyndromes of interest were identified in consultation with the local public health authorities. For each syndrome, case definitions were developed using published resources and expert elicitation. Natural language processing algorithms were then written using Stata LP 15.1 (Texas, USA) to detect syndromic cases from three different fields (e.g., triage notes; chief complaint; discharge diagnosis), comprising of free-text and standardized codes. Validation was conducted using data from 19,082 visits between October 1, 2018 to April 30, 2019. The National Ambulatory Care Reporting System (NACRS) records were used as a reference for the inclusion of International Classification of Disease, 10th edition (ICD-10) diagnosis codes. The automatic identification of cases was then manually validated by two raters and results were used to calculate positive predicted values for each syndrome and identify improvements to the detection algorithms.ResultsA daily secure file transfer of Yukon’s Meditech ED-Tracker system data and an aberration detection plan was set up. A total of six syndromes were originally identified for the syndromic surveillance system (e.g., Gastrointestinal, Influenza-like-Illness, Mumps, Neurological Infections, Rash, Respiratory), with an additional syndrome added to assist in detecting potential cases of COVID-19. The positive predictive value for the automated detection of each syndrome ranged from 48.8–89.5% to 62.5–94.1% after implementing improvements identified during validation. As expected, no records were flagged for COVID-19 from our validation dataset.ConclusionsThe development and validation of automated ED-SyS in lower-resourced settings can be achieved without sophisticated platforms, intensive resources, time or costs. Validation is an important step for measuring the accuracy of syndromic surveillance, and ensuring it performs adequately in a local context. The use of three different fields and integration of both free-text and structured fields improved case detection.

Development and assessment of a hospital admissions-based syndromic surveillance system for COVID-19 in Ontario, Canada: ACES Pandemic Tracker

BackgroundThe COVID-19 pandemic has continued to pose a major global public health risk. The importance of public health surveillance systems to monitor the spread and impact of COVID-19 has been well demonstrated. The purpose of this study was to describe the development and effectiveness of a real-time public health syndromic surveillance system (ACES Pandemic Tracker) as an early warning system and to provide situational awareness in response to the COVID-19 pandemic in Ontario, Canada.MethodsWe used hospital admissions data from the Acute Care Enhanced Surveillance (ACES) system to collect data on pre-defined groupings of symptoms (syndromes of interest; SOI) that may be related to COVID-19 from 131 hospitals across Ontario. To evaluate which SOI for suspected COVID-19 admissions were best correlated with laboratory confirmed admissions, laboratory confirmed COVID-19 hospital admissions data were collected from the Ontario Ministry of Health. Correlations and time-series lag analysis between suspected and confirmed COVID-19 hospital admissions were calculated. Data used for analyses covered the period between March 1, 2020 and September 21, 2020.ResultsBetween March 1, 2020 and September 21, 2020, ACES Pandemic Tracker identified 22,075 suspected COVID-19 hospital admissions (150 per 100,000 population) in Ontario. After correlation analysis, we found laboratory-confirmed hospital admissions for COVID-19 were strongly and significantly correlated with suspected COVID-19 hospital admissions when SOI were included (Spearman’s rho = 0.617) and suspected COVID-19 admissions when SOI were excluded (Spearman’s rho = 0.867). Weak to moderate significant correlations were found among individual SOI. Laboratory confirmed COVID-19 hospital admissions lagged in reporting by 3 days compared with suspected COVID-19 admissions when SOI were excluded.ConclusionsOur results demonstrate the utility of a hospital admissions syndromic surveillance system to monitor and identify potential surges in severe COVID-19 infection within the community in a timely manner and provide situational awareness to inform preventive and preparatory health interventions.

Abstract 045: Declines In Acute Myocardial Infarction And Stroke Emergency Department Visits Observed During Covid-19 Restrictions In North Carolina

Introduction: Efforts to control the COVID-19 pandemic brought sweeping social change, with stay-at-home orders and physical distancing mandates in 43 of 50 states by April 2020. Early on, isolated studies around the world described reduced hospital admissions. Reports from some US hospitals also described declines in catheterization laboratory activations, and acute myocardial infarction (AMI) and stroke admissions. However, there have been few population-based analyses of emergency department (ED) visits to verify these initial reports and describe longer term impacts of the pandemic on care seeking behavior. Hypothesis: We hypothesized that AMI and stroke ED visits in North Carolina (NC) would decrease substantially after a statewide stay-at-home order was announced on March 27, 2020. Methods: We analyzed all ED visits from January 5 to August 28, 2020 using data collected by the NC Disease Event Tracking and Epidemiologic Collection Tool, a syndromic surveillance system that automatically gathers ED data in near-real time for all EDs in NC. Counts of AMI and stroke/transient ischemic attack (TIA) were ascertained using ICD-10-CM diagnosis codes. We compared weekly 2020 ED visit data before and after NC’s stay-at-home order, and to 2019 ED visit data. Results: Overall ED volume declined by 44% in the weeks before and after the stay-at-home order ( Figure ) while the prior year’s ED volume stayed steady at ~100,000 visits per week. From January 5 to March 28, there were 593 AMI and 791 stroke/TIA visits per week on average. By April 11, ED visits reached a nadir at 426 AMI and 543 stroke/TIA visits per week, representing a 28% and 31% decrease, respectively. Since June, AMI and stroke/TIA ED visits have rebounded slightly but have yet to reach pre-pandemic levels. Conclusions: We observed swift declines in AMI and stroke/TIA ED visits following NC’s stay-at-home order. These findings potentially reflect the avoidance of medical care due to fears of COVID-19 exposure and may eventually result in higher associated case fatality.

Real-Time Surveillance of Infectious Diseases, Injuries, and Chronic Conditions During the 2018 Iraq Arba'een Mass Gathering.

The Arba'een pilgrimage in Iraq is now the largest annual gathering in a single place worldwide. To monitor and address the health needs of pilgrims in field clinics near the pilgrimage route, a syndromic surveillance system was designed and implemented by Global Health Development/Eastern Mediterranean Public Health Network in collaboration with Iraq Ministry of Health. This study aimed to analyze the effectiveness of the surveillance system and the data it gathered in evaluating the burden of common acute and infectious conditions, chronic conditions, and trauma and injuries during the Arba'een pilgrimage in 2018. Data were collected at 152 field clinics located across 11 governorates in Iraq at strategic points along the Arba'een pilgrimage route from all governorates to Karbala between October 8 and November 3, 2018. A team of 24 surveillance supervisors trained, oversaw, and provided technical support for 304 data collectors. The data collectors recorded data from 338,399 patients (42.5% female and 57.5% male) in the span of 26 days. The vast majority of patients were from Iraq (n = 294,260, 87.6%) and Iran (n = 34,691, 10.3%). Of the 338,399 patients whose data were recorded by the surveillance system, 246,469 (72.8%) reported acute and infectious conditions, 202,032 (59.70%) reported chronic conditions, and 6,737 (2.0%) reported traumas and injuries. Many patients reported several conditions in multiple categories. The most prevalent acute condition treated was influenza-like illness, identified through patients exhibiting a combination of fever and cough symptoms. Findings from this study will help inform future planning efforts so healthcare workers can be better prepared for treating such cases at mass gatherings. With the latest challenges posed by the COVID-19 pandemic, preparations for a possible future outbreak of the novel coronavirus are also discussed. The information from this study serves as a foundation to inform and optimize future planning of wide-scale surveillance efforts and address challenges in health service delivery and health security.

The impact of the COVID-19 pandemic on the utilization of emergency department services for the treatment of injuries.

ContextThe global COVID-19 pandemic has had a major impact on the utilization of healthcare services; however, the impact on population-level emergency department (ED) utilization patterns for the treatment of acute injuries has not been fully characterized.ObjectiveThis study examined the frequency of North Carolina (NC) EDs visits for selected injury mechanisms during the first eleven months of the COVID-19 pandemic.MethodsData were obtained from the NC Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT), NC's legislatively mandated statewide syndromic surveillance system for the years 2019 and 2020. Frequencies of January – November 2020 NC ED visits were compared to frequencies of 2019 visits for selected injury mechanisms, classified according to International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) injury diagnosis and mechanism codes.ResultsIn 2020, the total number of injury-related visits declined by 19.5% (N = 651,158) as compared to 2019 (N = 809,095). Visits related to motor vehicle traffic crashes declined by a greater percentage (29%) and falls (19%) declined by a comparable percentage to total injury-related visits. Visits related to assault (15%) and self-harm (10%) declined by smaller percentages. Medication/drug overdose visits increased (10%), the only injury mechanism studied to increase during this period.ConclusionBoth ED avoidance and decreased exposures may have contributed to these declines, creating implications for injury morbidity and mortality. Injury outcomes exacerbated by the pandemic should be addressed by timely public health responses.

De-identification of Emergency Medical Records in French: Survey and Comparison of State-of-the-Art Automated Systems

In France, structured data from emergency room (ER) visits are aggregated at the national level to build a syndromic surveillance system for several health events. For visits motivated by a traumatic event, information on the causes are stored in free-text clinical notes. To exploit these data, an automated de-identification system guaranteeing protection of privacy is required.In this study we review available de-identification tools to de-identify free-text clinical documents in French. A key point is how to overcome the resource barrier that hampers NLP applications in languages other than English. We compare rule-based, named entity recognition, new Transformer-based deep learning and hybrid systems using, when required, a fine-tuning set of 30,000 unlabeled clinical notes. The evaluation is performed on a test set of 3,000 manually annotated notes.Hybrid systems, combining capabilities in complementary tasks, show the best performance. This work is a first step in the foundation of a national surveillance system based on the exhaustive collection of ER visits reports for automated trauma monitoring.

The effect of seasonal respiratory virus transmission on syndromic surveillance for COVID-19 in Ontario, Canada

Emerging evidence suggests that syndromic surveillance systems can predict outbreaks of COVID-19 with high spatial and temporal resolution.1–3 These methods can be used as early warning systems to guide regional decisions about public health policy. Tools include passive methods (eg, tracking health-care encounters) and more active participatory surveillance, whereby individuals self-report symptoms by telephone or internet.2–4 It is unknown whether circulating seasonal respiratory viruses affect the performance of surveillance tools for COVID-19, although symptomatic overlap makes it a theoretical concern.

New Approach to the Surveillance of Pediatric Infectious Diseases From Ambulatory Pediatricians in the Digital Era.

Many ambulatory networks in several countries have established syndromic surveillance systems to detect outbreaks of different illnesses. Here, we describe a new Pediatric and Ambulatory Research in Infectious diseases network that combined automated data extraction from the computers of primary care pediatricians. Pediatricians who used the same software, AxiSanté 5-Infansoft for electronic medical records were specially trained in infectious diseases, encouraged to comply with French treatments' recommendations, use of point-of-care tests and vaccination guidelines. Infectious disease diagnoses in children <16 years old in the records triggered automatic data extraction of complete records. A quality control process and external validation were developed. From September 2017 to February 2020, 107 pediatricians enrolled 57,806 children (mean age 2.9 ± 2.6 years at diagnosis) with at least one infectious disease diagnosis among those followed by the network. Among the 118,193 diagnoses, the most frequent were acute otitis media (n = 44,924, 38.0%), tonsillopharyngitis (n = 13,334, 11.3%), gastroenteritis (n = 12,367, 10.5%), influenza (n = 11,062, 9.4%), bronchiolitis (n = 10,531, 8.9%), enteroviral infections (n = 8474, 7.2%) and chickenpox (n = 6857, 5.8%). A rapid diagnostic test was performed in 84.7% of cases of tonsillopharyngitis and was positive in 44%. The antibiotic recommendations from French guidelines were strictly followed: amoxicillin was the most prescribed antibiotic and less than 10% of presumed viral infections were treated. This "tailor-made" network set up with quality controls and external validation represents a new approach to the surveillance of pediatric infectious diseases in the digital era and could highly optimize pediatric practices.

Epidemiology of Influenza in Fars Province, Southern Iran; a Population-Based Study (2015-2019).

Influenza is one of the most important viral diseases with high mortality and morbidity that can have a great impact on public health and economy. To investigate the clinical and epidemiological features of influenza virus A/H1N1, A/H3N2, and B infection in Fars province, southern Iran, in 2015-2019. In this retrospective cross-sectional study, we assessed the archived data of Syndromic Surveillance System of Iran's Health Ministry, allowed access by Communicable Diseases' Unit of Health chancellor of Shiraz University of Medical Sciences, from December 22, 2015 to September 22, 2019. The participants included all patients whose data were recorded as influenza-like illness (ILI) and severe acute respiratory infections/illness (SARI). Influenza viral infection was confirmed by reverse-transcriptase polymerase chain reaction (RT-PCR). Totally, 1269 patients suspected of influenza were sampled. The mean ± SD of age was 29.40 ± 26.91 years. Fever was the most common symptom (68.6%). The highest incidence was in winter (55.9%). Of 928/1269 laboratory's results which were recorded in the registry, 204 (16.08%) samples were positive and 724 (57.05%) were negative. Among 204 positive results, 191 (15.05%) were influenza type A, and 8 (0.63%) influenza type B. Among patients with definite influenza type A, 34 (2.68%) had H1N1/p subtype, 58 (4.57%) H3N2, and one (0.08%) had other subtypes; however, subtypes were not identified in 7.72% of patients. Six (3.0%) patients with confirmed influenza expired. The incidence of influenza, especially type A, in Fars province, southern Iran is considerable and requires more attention for prevention in health policy programs.

Correlation between Emergency Medical Services Suspected COVID-19 Patients and Daily Hospitalizations

Objective: We sought to determine if Emergency Medical Services (EMS) identified Persons Under Investigation (PUI) for COVID-19 are associated with hospitalizations for COVID-19 disease for the purposes of serving as a potential early indicator of hospital surge. Methods: A retrospective analysis was conducted using data from the Maryland statewide EMS electronic medical records and daily COVID-19 hospitalizations from March 13, 2020 through July 31, 2020. All unique EMS patients who were identified as COVID-19 PUIs during the study period were included. Descriptive analysis was performed. The Box-Jenkins approach was used to evaluate the relationship between EMS transports and daily new hospitalizations. Separate Auto Regressive Integrated Moving Average (ARIMA) models were constructed to transform the data into a series of independent, identically distributed random variables. Fit was measured using the Akaike Information Criterion (AIC). The Box-Ljung white noise test was utilized to ensure there was no autocorrelation in the residuals. Results: EMS units in Maryland identified a total of 26,855 COVID-19 PUIs during the 141-day study period. The median patient age was 62 years old, and 19,111 (71.3%) were 50 years and older. 6,886 (25.6%) patients had an abnormal initial pulse oximetry (<92%). A strong degree of correlation was observed between EMS PUI transports and new hospitalizations. The correlation was strongest and significant at a 9-day lag from time of EMS PUI transports to new COVID-19 hospitalizations, with a cross correlation coefficient of 0.26 (p < .01). Conclusions: A strong correlation between EMS PUIs and COVID-19 hospitalizations was noted in this state-wide analysis. These findings demonstrate the potential value of incorporating EMS clinical information into the development of a robust syndromic surveillance system for COVID-19. This correlation has important utility in the development of predictive tools and models that seek to provide indicators of an impending surge on the healthcare system at large.

Using farmer observations for animal health syndromic surveillance: Participation and performance of an online enhanced passive surveillance system

The challenge of animal health surveillance is to provide the information necessary to appropriately inform disease prevention and control activities within the constraints of available resources. Syndromic surveillance of farmers’ disease observations can improve animal health data capture from extensive livestock farming systems, especially where data are not otherwise being systematically collected or when data on confirmed aetiological diagnoses are unavailable at the disease level. As it is rarely feasible to recruit a truly random sample of farmers to provide observational reports, directing farmer sampling to align with the surveillance objectives is a reasonable and practical approach. As long as potential bias is recognised and managed, farmers who will report reliably can be desirable participants in a surveillance system. Thus, one early objective of a surveillance program should be to identify characteristics associated with reporting behaviour. Knowledge of the demographic and managerial characteristics of good reporters can inform efforts to recruit additional farms into the system or aid understanding of potential bias of system reports.We describe the operation of a farmer syndromic surveillance system in Victoria, Australia, over its first two years from 2014 to 2016. Survival analysis and classification and regression tree analysis were used to identify farm level factors associated with ‘reliable’ participation (low non-response rates in longitudinal reporting). Response rate and timeliness were not associated with whether farmers had disease to report, or with different months of the year. Farmers keeping only sheep were the most reliable and timely respondents. Farmers < 43 years of age had lower response rates than older farmers. Farmers with veterinary qualifications and those working full-time on-farm provided less timely reports than other educational backgrounds and farmers who worked part-time on-farm. These analyses provide a starting point to guide recruitment of participants for surveillance of farmers’ observations using syndromic surveillance, and provide examples of strengths and weaknesses of syndromic surveillance systems for extensively-managed livestock. Once farm characteristics associated with reliable participation are known, they can be incorporated into surveillance system design in accordance with the objectives of the system.

Improving e-health governance through syndromic surveillance systems and data mining in KSA

Recently, the KSA has witnessed significant technical advances in the health sector, where local hospitals are using high-quality systems and technologies to serve patients. However, even with this great progress in healthcare systems, communication is still limited with other decision-makers in different sectors who need to access some health-related information to take the best decisions for serving patients. Therefore, this project aims to utilise the concept of electronic health governance (e-health governance) to build an automated system, which will help the health sector to know which common diseases are currently prevalent and facilitate the decision-making process by providing them with the necessary health information to help them provide the best service for patients in various fields. To do that this research will apply classification data mining techniques through using naïve Bayes classification algorithm; where this project aims to build a common diseases prediction system (CDPS) to work as syndromic surveillance system.

Developing a sentinel syndromic surveillance system using school-absenteeism data, example monitoring absences over the 2020 COVID-19 pandemic.

This study describes the development of a pilot sentinel school absence syndromic surveillance system. Using data from a sample of schools in England the capability of this system to monitor the impact of disease on school absences in school-aged children is shown, using the coronavirus disease 2019 (COVID-19) pandemic period as an example. Data were obtained from an online app service used by schools and parents to report their children absent, including reasons/symptoms relating to absence. For 2019 and 2020, data were aggregated into daily counts of 'total' and 'cough' absence reports. There was a large increase in the number of absence reports in March 2020 compared to March 2019, corresponding to the first wave of the COVID-19 pandemic in England. Absence numbers then fell rapidly and remained low from late March 2020 until August 2020, while lockdown was in place in England. Compared to 2019, there was a large increase in the number of absence reports in September 2020 when schools re-opened in England, although the peak number of absences was smaller than in March 2020. This information can help provide context around the absence levels in schools associated with COVID-19. Also, the system has the potential for further development to monitor the impact of other conditions on school absence, e.g. gastrointestinal infections.

Preventing Pandemics and Containing Disease: A Proposed Symptoms-Based Syndromic Surveillance System

Several disease surveillance systems are in place to track rises in disease incidence; however, the current global landscape concerning the spread of COVID-19 has demonstrated our lack of ability to stop the spread of disease efficiently, exposing inadequate local and global pandemic preparedness and placing extreme stress on healthcare facilities and medical supply demands. Current surveillance methods are insufficient. A healthcare provider must see patients before their symptoms are reported, allowing for unchecked transmission of disease. Access to quality healthcare is limited and inconceivable in many parts of the world. Current methods also lack communication and integration. Current approaches do not track disease early enough, leading to delays in outbreak detection and delays in response to these outbreaks. The current systems in place are acceptable for tracking disease but do not allow for effective prevention of disease spread. A symptoms-based surveillance system was designed that addresses current limitations and adequately detects disease. The system improves overall health surveillance with a focus on early detection of both known and unknown diseases, prevents disease transmission within the community, and can be employed globally, thus reducing the potential for future pandemics. The platform is a self-reported, symptoms-based syndromic surveillance system that is universal, interactive, integrated, and combined with artificial intelligence (AI). The platform will identify potential and confirmed outbreaks and advise individuals using AI-based pattern recognition. To determine when further action is warranted by individuals and public health officials, the AI collects, stores, and monitors symptom-based data and alerts in response to spikes in illness. The AI also tracks and predicts potential disease spread. The proposed platform provides an innovative and scalable alternative to the existing fragmented and ineffective health surveillance systems. Keywords: syndromic surveillance, artificial intelligence, pandemic, proposal, public health, organizational models