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Providing an overview of disease surveillance, this text frames a roadmap of how newer technologies may allow all countries of the world to reach compliance with the IHR (International Health Regulations) established by the World Health Organization as it pertains to disease detection.

Bioterrorism is not a new threat, but in an increasingly interconnected world, the potential for catastrophic outcomes is greater today than ever. The medical and public health communities are establishing biosurveillance systems designed to proactively monitor populations for possible disease outbreaks as a first line of defense. The ideal biosurveillance system should identify trends not visible to individual physicians and clinicians in near-real time. Many of these systems use statistical algorithms to look for anomalies and to trigger epidemiologic investigation, quantification, localization and outbreak management. This book discusses the design and evaluation of statistical methods for effective biosurveillance for readers with minimal statistical training. Weaving public health and statistics together, it presents basic and more advanced methods, with a focus on empirically demonstrating added value. Although the emphasis is on epidemiologic and syndromic surveillance, the statistical methods can be applied to a broad class of public health surveillance problems.

Becoming a father impacts men's health and wellbeing, while also contributing to the health and wellbeing of mothers and children. There is no large-scale, public health surveillance system aimed at understanding the health and behaviors of men transitioning into fatherhood. The purpose of this study was to describe piloted randomized approaches of a state-based surveillance system examining paternal behaviors before and after their infant's birth to better understand the health needs of men and their families during the transition to parenthood. During October 2018-July 2019, 857 fathers in Georgia were sampled 2-6 months after their infant's birth from birth certificates files and surveyed via mail, online or telephone, in English or Spanish, using two randomized approaches: Indirect-to-Dads and Direct-to-Dads. Survey topics included mental and physical health, healthcare, substance use, and contraceptive use. Weighted response rates (Indirect-to-Dads, 33%; Direct-to-Dads, 31%) and population demographics did not differ by approach. Respondents completed the survey by mail (58%), online (28%) or telephone (14%). Among 266 fathers completing the survey, 55% had a primary care physician, and 49% attended a healthcare visit for themselves during their infant's mother's pregnancy or since their infant's birth. Most fathers were overweight or had obesity (70%) while fewer reported smoking cigarettes (19%), binge drinking (13%) or depressive symptoms (10%) since their infant's birth. This study tests a novel approach for obtaining population-based estimates of fathers' perinatal health behaviors, with comparable response rates from two pragmatic approaches. The pilot study results quantify a number of public health needs related to fathers' health and healthcare access.

Background Public health agencies require valid, timely and complete health information for early detection of outbreaks. Towards the end of the Ebola Virus Disease (EVD) outbreak in 2015, the Ministry of Health and Sanitation (MoHS), Sierra Leone revitalized the Integrated Disease Surveillance and Response System (IDSR). Data quality assessments were conducted to monitor accuracy of IDSR data. Methods Starting 2016, data quality assessments (DQA) were conducted in randomly selected health facilities. Structured electronic checklist was used to interview district health management teams (DHMT) and health facility staff. We used malaria data, to assess data accuracy, as malaria was endemic in Sierra Leone. Verification factors (VF) calculated as the ratio of confirmed malaria cases recorded in health facility registers to the number of malaria cases in the national health information database, were used to assess data accuracy. Allowing a 5% margin of error, VF < 95% were considered over reporting while VF > 105 was underreporting. Differences in the proportion of accurate reports at baseline and subsequent assessments were compared using Z-test for two proportions. Results Between 2016 and 2018, four DQA were conducted in 444 health facilities where 1729 IDSR reports were reviewed. Registers and IDSR technical guidelines were available in health facilities and health care workers were conversant with reporting requirements. Overall data accuracy improved from over- reporting of 4.7% (VF 95.3%) in 2016 to under-reporting of 0.2% (VF 100.2%) in 2018. Compared to 2016, proportion of accurate IDSR reports increased by 14.8% (95% CI 7.2, 22.3%) in May 2017 and 19.5% (95% CI 12.5–26.5%) by 2018. Over reporting was more common in private clinics and not- for profit facilities while under-reporting was more common in lower level government health facilities. Leading reasons for data discrepancies included counting errors in 358 (80.6%) health facilities and missing source documents in 47 (10.6%) health facilities. Conclusion This is the first attempt to institutionalize routine monitoring of IDSR data quality in Sierra Leone. Regular data quality assessments may have contributed to improved data accuracy over time. Data compilation errors accounted for most discrepancies and should be minimized to improve accuracy of IDSR data.

Background Workplace absenteeism increases significantly during influenza epidemics. Sick leave records may facilitate more timely detection of influenza outbreaks, as trends in increased sick leave may precede alerts issued by sentinel surveillance systems by days or weeks. Sick leave data have not been comprehensively evaluated in comparison to traditional surveillance methods. The aim of this paper is to study the performance and the feasibility of using a detection system based on sick leave data to detect influenza outbreaks. Methods Sick leave records were extracted from private French health insurance data, covering on average 209,932 companies per year across a wide range of sizes and sectors. We used linear regression to estimate the weekly number of new sick leave spells between 2016 and 2017 in 12 French regions, adjusting for trend, seasonality and worker leaves on historical data from 2010 to 2015. Outbreaks were detected using a 95%-prediction interval. This method was compared to results from the French Sentinelles network, a gold-standard primary care surveillance system currently in place. Results Using sick leave data, we detected 92% of reported influenza outbreaks between 2016 and 2017, on average 5.88 weeks prior to outbreak peaks. Compared to the existing Sentinelles model, our method had high sensitivity (89%) and positive predictive value (86%), and detected outbreaks on average 2.5 weeks earlier. Conclusion Sick leave surveillance could be a sensitive, specific and timely tool for detection of influenza outbreaks.

Infectious disease surveillance has evolved at an extraordinary pace during the past several decades, and continues to do so. It is increasingly used to inform public health practice in addition to its use as a tool for early detection of epidemics. It is therefore crucial that students of public health and epidemiology have a sound understanding of the concepts and principles that underpin modern surveillance of infectious disease.  Written by leaders in the field, who have vast hands-on experience in conducting surveillance and teaching applied public health, Concepts and Methods in Infectious Disease Surveillance is comprised of four sections. The first section provides an overview, a description of systems used by public health jurisdictions in the United States and legal considerations for surveillance. The second section presents chapters on major program-area or disease-specific surveillance systems, including those that monitor bacterial infections, foodborne diseases, healthcare-associated infections, and HIV/AIDS. The following section is devoted to methods for conducting surveillance and also approaches for data analysis.  A concluding section summarizes communication of surveillance findings, including the use of traditional and social media, in addition to showcasing lessons learned from the New York City Department of Health's experience in surveillance and epidemiology training. This comprehensive new book covers major topics at an introductory to intermediate level, and will be an excellent resource for instructors. Suitable for use in graduate level courses in public health, human and veterinary medicine, and in undergraduate programs in public-health-oriented disciplines, Concepts and Methods in Infectious Disease Surveillance is also a useful primer for frontline public health practitioners, hospital epidemiologists, infection control practitioners, laboratorians in public health settings, infectious disease researchers, and medical and public health informaticians interested in a concise overview of infectious disease surveillance.

In response to the early spread of COVID-19 in Lebanon, the University of Balamand developed the HAYATI app, a community-focused, geographic information system (GIS)-based digital health platform aimed at enhancing public health surveillance. At the time, while the Lebanese Ministry of Public Health utilized centralized dashboards to report confirmed cases and monitor national trends, no interactive tool existed to engage the public directly in real-time risk assessment and surveillance, especially in underserved regions. The aim of this study was to design, implement, and evaluate the effectiveness of the HAYATI app as a GIS-integrated digital surveillance tool to identify high-risk individuals and support targeted testing and contact tracing during the early stages of the COVID-19 pandemic in Lebanon. The HAYATI app was launched in March 2020 using ArcGIS Survey123 and real-time dashboards, incorporating a risk scoring algorithm based on 21 clinical and behavioral criteria. Between April 2020 and March 2021, self-reported data were collected from 10,235 individuals across Lebanon. Participants identified as high or major risk through the automated scoring algorithm were referred for free polymerase chain reaction testing at the University of Balamand. Test results were securely communicated to local municipalities and the Ministry of Public Health. Data were analyzed for associations between symptoms and positivity rates, as well as geographic and demographic trends using spatial analysis tools. Of the 10,235 individuals who submitted data, 1782 were classified as high or major risk and referred for polymerase chain reaction testing. Among them, 394 (22.1%) tested positive for SARS-CoV-2. Loss of smell and taste was strongly associated with positive test results (P<.001). The highest positivity rates were observed among individuals aged 18-29 years and in the North Governorate. GIS mapping enabled real-time visualization of case clusters, which informed localized containment responses. The HAYATI app effectively filled a critical surveillance gap during the early pandemic phase in Lebanon. By integrating GIS technology, automated risk stratification, and community-level engagement, it provided a scalable model for public health surveillance in resource-limited settings. This approach has potential for broader applications in managing future outbreaks and endemic diseases through decentralized, real-time digital health strategies.

Background Despite widespread implementation of syndromic surveillance systems within public health agencies, previous studies of the implementation and use of these systems have indicated that the functions and responses taken in response to syndromic surveillance data vary widely according to local context and preferences. The objective of the Syndromic Surveillance Evaluation Study was to develop and implement standardized supports in local public health agencies in Ontario, Canada, and evaluate the ability of these supports to affect actions taken as part of public health communicable disease control programs. Methods Local public health agencies (LPHA) in Ontario, which used syndromic surveillance based on emergency department visits for respiratory disease, were recruited and randomly allocated to the study intervention or control group. The intervention group health agencies received standardized supports in terms of a standardized aberrant event detection algorithm and a response protocol dictating steps to investigate and assess the public health significance of syndromic surveillance alerts. The control group continued with their pre-existing syndromic surveillance infrastructure and processes. Outcomes were assessed using logbooks, which collected quantitative and qualitative information about alerts received, investigation steps taken, and public health responses. The study was conducted prospectively for 15 months (October 2013 to February 2015). Results Fifteen LPHAs participated in the study ( n  = 9 intervention group, n  = 6 control group). A total of 1,969 syndromic surveillance alerts were received by all LPHAs. Variations in the types and amount of responses varied by LPHA, in particularly differences were noted by the size of the health unit. Smaller health units had more challenges to both detect and mount a response to any alerts. LPHAs in the control group were more likely to declare alerts to have public health significance and to initiate any action. Regression models using repeated measures showed an interaction between the year (Year 1 versus Year 2) and the intervention as well as an interaction between year and sustained nature of the alert. Both of these were linked to the control health units reporting more “watchful waiting”. Conclusions This study raises questions about the effectiveness of using standardized protocols to improve the performance of syndromic surveillance in a decentralized public health system. Despite efforts to create standardized protocols and engage public health agencies in the process, no significant differences in the effective use of syndromic alerts were observed beyond year 1. It also raises questions about the minimum capacity of the agency and minimum population size that are required for an effective response.

Prompt investigation of food poisoning outbreak are essential, as it usually involves a short incubation period. Utilizing the advancement in mobile technology, a mobile application named MyMAFI (My Mobile Apps for Field Investigation) was developed with the aim to be an alternative and better tool for current practices of field investigation of food poisoning outbreak. A randomized cross-over trial with two arms and two treatment periods was conducted to assess the effectiveness of the newly developed mobile application as compared to the standard paper-based format approach. Thirty-six public health inspectors from all districts in Kelantan participated in this study and they were randomized into two equal sized groups. Group A started the trial as control group using the paper-format investigation form via simulated outbreaks and group B used the mobile application. After a one-month ‘washout period’, the group was crossed over. The primary outcome measured was the time taken to complete the outbreak investigation. The treatment effects, the period effects and the period-by-treatment interaction were analyzed using Pkcross command in Stata software. There was a significant treatment effect with mean square 21840.5 and its corresponding F statistic 4.47 (p-value = 0.038), which indicated that the mobile application had significantly improve the reporting timeliness. The results also showed that there was a significant period effect (p-value = 0.025); however, the treatment by period interaction was not significant (p-value = 0.830). The newly developed mobile application—MyMAFI—can improve the timeliness in reporting for investigation of food poisoning outbreak.

Sickle cell disease (SCD) is a genetic blood disorder that affects approximately 100,000 individuals in the United States, with the highest prevalence among Black or African American populations. While advances in care have improved survival, comprehensive state-level data on the prevalence of SCD remain limited, which hampers efforts to optimize health care services. To address this gap, the Colorado Sickle Cell Data Collection (CO-SCDC) program was established in 2021 as part of the Centers for Disease Control and Prevention's initiative to enhance surveillance and public health efforts for SCD. The objectives of this study were to describe the establishment of the CO-SCDC program and to provide updated estimates of the prevalence and birth prevalence of SCD in Colorado, including geographic dispersion. Additional objectives include evaluating the accuracy of case identification methods and leveraging surveillance activities to inform public health initiatives. Data were collected from Health Data Compass (a multi-institutional data warehouse) containing electronic health records from the University of Colorado Health and Children's Hospital Colorado for the years 2012-2020. Colorado newborn screening program data were included for confirmed SCD diagnoses from 2001 to 2020. Records were linked using the Colorado University Record Linkage tool and deidentified for analysis. Case definitions, adapted from the Centers for Disease Control and Prevention's Registry and Surveillance System for Hemoglobinopathies project, classified cases as possible, probable, or definite SCD. Clinical validation by hematologists was performed to ensure accuracy, and prevalence rates were calculated using 2020 US Census population estimates. In 2019, 435 individuals were identified as living with SCD in Colorado, an increase of 16%-40% over previous estimates, with the majority (n=349, 80.2%) identifying as Black or African American. The median age of individuals was 19 years. The prevalence of SCD was highest in urban counties, with concentrations in Arapahoe, Denver, and El Paso counties. Birth prevalence of SCD increased from 11.9 per 100,000 live births between 2010 and 2014 to 20.1 per 100,000 live births between 2015 and 2019 with 58.5% (n=38) of cases being hemoglobin (Hb) SS or HbSβ0 thalassemia subtypes. The study highlighted a 67% (n=26) increase in SCD births over the decade, correlating with the growth of the Black or African American population in the state. The CO-SCDC program successfully established the capacity to perform SCD surveillance and, in doing so, identified baseline prevalence estimates for SCD in Colorado. The findings highlight geographic dispersion across Colorado counties, highlighting the need for equitable access to specialty care, particularly for rural populations. The combination of automated data linkage and clinical validation improved case identification accuracy. Future efforts will expand surveillance to include claims data to better capture health care use and address potential underreporting. These results will guide public health interventions aimed at improving care for individuals with SCD in Colorado.