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The B.1.1.529 (Omicron) variant of SARS-CoV-2, the virus that causes COVID-19, was first clinically identified in the United States on December 1, 2021, and spread rapidly. By late December, it became the predominant strain, and by January 15, 2022, it represented 99.5% of sequenced specimens in the United States* (1). The Omicron variant has been shown to be more transmissible and less virulent than previously circulating variants (2,3). To better understand the severity of disease and health care utilization associated with the emergence of the Omicron variant in the United States, CDC examined data from three surveillance systems and a large health care database to assess multiple indicators across three high-COVID-19 transmission periods: December 1, 2020-February 28, 2021 (winter 2020-21); July 15-October 31, 2021 (SARS-CoV-2 B.1.617.2 [Delta] predominance); and December 19, 2021-January 15, 2022 (Omicron predominance). The highest daily 7-day moving average to date of cases (798,976 daily cases during January 9-15, 2022), emergency department (ED) visits (48,238), and admissions (21,586) were reported during the Omicron period, however, the highest daily 7-day moving average of deaths (1,854) was lower than during previous periods. During the Omicron period, a maximum of 20.6% of staffed inpatient beds were in use for COVID-19 patients, 3.4 and 7.2 percentage points higher than during the winter 2020-21 and Delta periods, respectively. However, intensive care unit (ICU) bed use did not increase to the same degree: 30.4% of staffed ICU beds were in use for COVID-19 patients during the Omicron period, 0.5 percentage points lower than during the winter 2020-21 period and 1.2 percentage points higher than during the Delta period. The ratio of peak ED visits to cases (event-to-case ratios) (87 per 1,000 cases), hospital admissions (27 per 1,000 cases), and deaths (nine per 1,000 cases [lagged by 3 weeks]) during the Omicron period were lower than those observed during the winter 2020-21 (92, 68, and 16 respectively) and Delta (167, 78, and 13, respectively) periods. Further, among hospitalized COVID-19 patients from 199 U.S. hospitals, the mean length of stay and percentages who were admitted to an ICU, received invasive mechanical ventilation (IMV), and died while in the hospital were lower during the Omicron period than during previous periods. COVID-19 disease severity appears to be lower during the Omicron period than during previous periods of high transmission, likely related to higher vaccination coverage

Although COVID-19 generally results in milder disease in children and adolescents than in adults, severe illness from COVID-19 can occur in children and adolescents and might require hospitalization and intensive care unit (ICU) support (1-3). It is not known whether the B.1.617.2 (Delta) variant,* which has been the predominant variant of SARS-CoV-2 (the virus that causes COVID-19) in the United States since late June 2021,† causes different clinical outcomes in children and adolescents compared with variants that circulated earlier. To assess trends among children and adolescents, CDC analyzed new COVID-19 cases, emergency department (ED) visits with a COVID-19 diagnosis code, and hospital admissions of patients with confirmed COVID-19 among persons aged 0-17 years during August 1, 2020-August 27, 2021. Since July 2021, after Delta had become the predominant circulating variant, the rate of new COVID-19 cases and COVID-19-related ED visits increased for persons aged 0-4, 5-11, and 12-17 years, and hospital admissions of patients with confirmed COVID-19 increased for persons aged 0-17 years. Among persons aged 0-17 years during the most recent 2-week period (August 14-27, 2021), COVID-19-related ED visits and hospital admissions in the states with the lowest vaccination coverage were 3.4 and 3.7 times that in the states with the highest vaccination coverage, respectively. At selected hospitals, the proportion of COVID-19 patients aged 0-17 years who were admitted to an ICU ranged from 10% to 25% during August 2020-June 2021 and was 20% and 18% during July and August 2021, respectively. Broad, community-wide vaccination of all eligible persons is a critical component of mitigation strategies to protect pediatric populations from SARS-CoV-2 infection and severe COVID-19 illness.

In 2021, a national emergency* for children's mental health was declared by several pediatric health organizations, and the U.S. Surgeon General released an advisory

Objective Describe how the Georgia Department of Public Health (DPH) used data from its State Electronic Notifiable Disease Surveillance System (SendSS) Syndromic Surveillance (SS) module for early detection of an outbreak of salmonellosis in Camden County, Georgia. Introduction Evidence about the value of syndromic surveillance data for outbreak detection is limited (1). In July 2018, a salmonellosis outbreak occurred following a family reunion of 300 persons held in Camden County, Georgia, where one meal was served on 7/27/2018 and on 7/28/2018. Methods SendSS-SS and SAS were used for cluster detection of Emergency Department (ED) patients with similar Chief Complaint (CC), Triage Notes (TN), or Discharge Diagnoses (DDx) by facility, time of ED visit, and zip code / county of residence. A SAS-based free-text query related to food poisoning in the CC and DDx fields was also performed on a daily basis. County- and hospital-specific charting of the Diarrhea syndrome was also conducted in SendSS-SS, whereas county- and zip code-specific charting of the same syndrome were done in both SendSS-SS and SAS (2). Results On Sunday July 29 th , 2018, three children and three adults were seen within 18 hours at the ED of Hospital A in Camden County, Georgia. All patients complained of diarrhea, vomiting, and food poisoning, after a large family reunion that had been held the day before. This early cluster was detected by the SAS-based free-text query of ‘ food poisoning ’ and the SAS-based cluster detection tool for patients with Diarrhea syndrome. The District Epidemiologists (DE) in the Coastal Health District were notified on Monday, July 30 th , 2018. One-year high daily spikes of the Diarrhea syndrome occurred from July 29 th to July 31 st , 2018 in a local hospital ED (Fig 1), Camden County, and zip code 31548. Two HIPAA-compliant line lists with a total of 27 patients seen at EDs were emailed to the DEs to support active case finding. No further spikes of the Diarrhea syndrome were detected in Camden County during the 2-week period after the 3-day spike. Conclusions Syndromic surveillance was a useful surveillance tool for early detection of a salmonellosis outbreak, helping with the active search for outbreak cases, tracking the peak of the outbreak, and assuring that no further spikes were occurring. References 1.R Hopkins, C Tong, H Burkom, et al . A Practitioner-Driven Research Agenda for Syndromic Surveillance. Public Health Reports 2017; 132(Supplement1): 116S-126S. 2. G Zhang, A Llau, J Suarez, E O'Connell, E Rico, R Borroto, F Leguen. Using ESSENCE to Track a Gastrointestinal Outbreak in a Homeless Shelter in Miami-Dade County, 2008. Advances in Disease Surveillance. 2008; 5:139.

On October 7, 2016, Hurricane Matthew traveled along the coasts of Florida, Georgia, and South Carolina causing flooding and power outages. The Georgia Department of Public Health (DPH) developed the Web-based Responder Safety, Tracking, and Resilience (R-STaR) system to monitor the health and safety of public health responders and to inform disaster response planning for Hurricane Matthew. Using R-STaR, responders (n = 126) were e-mailed a daily survey while deployed to document injuries or harmful exposures and a post-deployment survey on their post-deployment health and satisfaction with using R-STaR. DPH epidemiologists contacted responders reporting injuries or exposures to determine the need for medical care. Frequencies were tabulated for quantitative survey responses, and qualitative data were summarized into key themes. Five percent (6/126) of responders reported injuries, and 81% (43/53) found R-STaR easy to use. Suggestions for R-STaR improvement included improving accessibility using mobile platforms and conducting pre-event training of responders on R-STaR. Lessons learned from R-STaR development and evaluation can inform the development and improvement of responder health surveillance systems at other local and state health departments and disaster and emergency response agencies. (Disaster Med Public Health Preparedness. 2019;13:74–81).

This paper describes a continuing initiative of the International Society for Disease Surveillance designed to bring together public health practitioners and analytics solution developers from both academia and industry. Funded by the Defense Threat Reduction Agency, a series of consultancies have been conducted on a range of topics of pressing concern to public health (e.g. developing methods to enhance prediction of asthma exacerbation, developing tools for asyndromic surveillance from chief complaints). The topic of this final consultancy, conducted at the University of Utah in January 2017, is focused on defining a roadmap for the development of algorithms, tools, and datasets for improving the capabilities of text processing algorithms to identify negated terms (i.e. negation detection) in free-text chief complaints and triage reports.

Objective: To describe how the Georgia Department of Public Health (DPH) uses ICD-9 and ICD-10-based discharge diagnoses (DDx) codes assigned to Emergency Department (ED) patients to support the early detection and investigation of outbreaks, clusters, and individual cases of reportable diseases. Introduction: The Georgia DPH has used its State Electronic Notifiable Disease Surveillance System (SendSS) Syndromic Surveillance (SS) module to collect, analyze and display analyses of ED patient visits, including DDx data from hospitals throughout Georgia for early detection and investigation of cases of reportable diseases before laboratory test results are available. Evidence on the value of syndromic surveillance approaches for outbreak or event detection is limited (1, 2). Use of the DDx field within datasets, specifically as it might be used for investigation of outbreaks, clusters, and / or individual cases of reportable diseases, has not been widely discussed. Methods: The DDx field of the ED data set sent to DPH by 120 facilities was queried for diseases that are immediately-reportable, as well as those reportable within 7 days of diagnosis. The query was performed twice a day using a combination of SAS 9.4 and the internal query capabilities of SendSS. District Epidemiologists (DE) were notified by email, with an Excel file attached that includes the details of the patient’s visit. DEs contacted Infection Control Practitioners (ICPs) of the facilities where the patients had received a discharge diagnosis of a reportable disease. True or false positives were determined after the outcome of the follow-up with the ICP had been known and after the patient had been entered as a case of reportable disease in SendSS by the DE. Hence, if the patient was a confirmed or probable case of a reportable disease, it was recorded as a True Positive, and True Negative otherwise. This led to the calculation of Predictive Value Positive (PVP) by reportable disease. Results: Table 1 shows the number of notifications sent to DEs that were later demonstrated to be True Positives and False Positives. It also shows the PVP by diseases being reported, for the period spanning from 05/01/2016 to 08/31/2017. Use of these notifications has allowed early investigation and identification of 158 cases of notifiable diseases by DEs. This includes 25 epi-linked cases (varicella=12, pertussis=4, cryptosporidiosis=3, shigellosis=2, malaria=2, and viral meningitis=2), as well as two clusters of varicella, one cluster of pertussis, and one outbreak of varicella in an elementary school that had not been reported to the local health department. A notable limitation of this study is that no systematic and exhaustive tracking is done of all notifications, as DEs have latitude to decide whether to follow up on a specific notification, depending on other local data that could be related to the event. Therefore, the PPVs may be biased due to over- / under-estimation of unknown size and direction. One exception to this is varicella notifications, whose outcomes have been exhaustively tracked by the DPH surveillance coordinator of this disease. Conclusions: The use of ED discharge diagnoses to examine potential cases of reportable diseases can help improve detection and early response by local health departments to outbreaks, clusters, and individual cases of reportable diseases. Exhaustive tracking of all the notifications by specific diseases may improve the estimation of the PPVs of the notifications sent to DEs.

Objective To better understand the importance of monitoring responders during public health emergencies and to learn how the Georgia Department of Public Health (DPH) developed and deployed an electronic responder monitoring tool. Introduction During an emergency, the state of Georgia depends on public health staff and volunteers to respond. It is imperative that staff are safe before, during and after deployment. Emergency response workers must be protected from the hazardous conditions that disasters and other emergencies create 1 . In October 2016 and September 2017, Hurricanes Matthew and Irma caused widespread evacuation of Georgia residents, initiating a tremendous sheltering effort. Hundreds of public health responders were deployed to assist with sheltering and other aspects of the response. DPH rapidly developed a novel electronic Responder Safety, Tracking and Resilience module, which was used to track public health responders and monitor their health and safety while deployed. Methods DPH rapidly developed a novel electronic Responder Safety, Tracking, and Resilience module (R-STaR), within the existing State Electronic Notifiable Disease Surveillance System to monitor the health and safety of responders. R-STaR was originally used during Hurricane Matthew, where it was launched the day of the storm, and was launched again four days before Hurricane Irma made landfall. Responders were emailed a web-based link to register, indicating demographic information, contact information, work location, subject area, vaccination status, and whether they considered themselves mentally and physically fit to deploy. Responders then received a daily email with a link to document their daily deployment location, duties, and whether they had any hazardous exposures, illness, or injuries while deployed. A post-deployment survey was sent to responders after Hurricane Matthew to solicit feedback about the responder safety module. Results During Hurricane Matthew, 128 responders representing 11 Georgia Public Health Districts registered in R-STaR. Seven responders reported illness or injury and were contacted to determine if medical services were needed; all remained healthy post-deployment. During Hurricane Irma, 1240 responders representing DPH and 16 Public Health Districts, and other employers, including law enforcement, fire, and education, registered in R-STaR. Of 472 responders completing daily health checks during their Irma deployment, 48 reported an injury, illness, or exposure, and were contacted to determine if services were needed. The daily health checks led to the identification of an outbreak of influenza in one of the shelters and resulted in vaccination or antiviral prophylaxis administration to 76 responders. Fifty responders to Hurricane Matthew completed the post-deployment survey; 95% found R-STaR easy to use, and 92% indicated that they liked being monitored. Supervisors indicated that the module could be used to: 1) roster and credential responders prior to an event; 2) track where responders are, monitor their health and safety during an event, and quantify the human resources deployed during a declared emergency; and, 3) to distribute post-response responder resources, monitor responder health, and gather information for after-action reports. Conclusions R-STaR was widely used and well received despite being implemented with no prior training, with a dramatic increase in the number of responders registering from the first implementation in 2016 to the second implementation in September 2017. Monitoring responder health and safety is crucial to responding to and preventing outbreaks during a response, and ensuring responders get appropriate mental and physical support after a deployment. Lessons learned from both events will be used to create a just-in-time training curriculum, and develop a more robust R-STaR, which will enable responder rostering, credentialing, tracking and monitoring before, during, and after an event to ensure the health and safety of our responders as well as for future planning. References 1. Centers for Disease Control and Prevention (2017). EMERGENCY RESPONDER HEALTH MONITORING AND SURVEILLANCE (ERHMS) . Retrieved from Centers for Disease Control and Prevention: https://www.cdc.gov/niosh/erhms/default.html.

Objective To explore the timeliness of emergency room surveillance data after the advent of federal Meaningful Use initiatives and determine potential areas for improvement. Introduction Timeliness of emergency room (ER) data is arguably its strongest attribute in terms of its contribution to disease surveillance. Timely data analyses may improve the efficacy of prevention and control measures. There are a number of studies that have looked at timeliness prior to the advent of Meaningful Use, and these studies note that ER data were not fast enough for them to be useful in real time 2,3 . However, the change in messaging practices in the Meaningful Use era potentially changes this. Other studies have shown that changes in processes and protocol can dramatically improve timeliness 1,4 and this motivates the current study of timeliness to identify processes that can be changed to improve timeliness. Methods ER data were collected from March 2017 through September 2017 from both the Georgia Department of Public Health’s (GDPH) State Electronic Notifiable Disease Surveillance System (SendSS) Syndromic Surveillance Module and the Centers for Disease Control and Prevention (CDC) National Syndromic Surveillance Program’s (NSSP) ESSENCE systems. Patients from hospitals missing 10 or more days of data, as well as patients with missing or invalid triage times, and all visits after August 1st were excluded in order to ensure data were representative of a “typical” time period and that a sufficient amount of time was given for visits to arrive from hospitals. The timeliness of individual records was determined in a number of different ways. All timeliness measurements were determined by subtracting the earlier time event from the later time of the event. The overall measure of timeliness is the time between the patient’s triage time and the data being present in the ESSENCE data system. In between, Georgia’s SendSS system receives and processes the data. This is illustrated in Figure 1. Due to the skewed nature of these measures, they were analyzed using medians and Gaussian kernel density plots. Results The study in total included records from 118 Georgia hospitals, 14,203 data files and 1,897,501 patient records. Overall median timeliness of data from Triage Time to being available in SendSS for analyses was 33.62 hours (IQR=28.5), and in ESSENCE was 45.08 hours (IQR=37.05). The distributions of Triage Time of Day, Time Available in SendSS Staging, and Time Available in ESSENCE Analysis can be seen in Figure 2. Additionally, lines were added for when SendSS makes data available for its own analyses and when it sends data to ESSENCE. These latter lines represent places where the SendSS system itself could improve, and potential improved times were noted based on the kernel densities. Peak triage times for Georgia hospitals were between 10 am to 11 pm, shown in black. This represents the ideal timeliness if Hospitals sent their data immediately. However, data was all batched by Georgia hospitals and sent at different times of the day. The distribution of the time patient records arrived at SendSS staging was indicated in blue. During the period of this study, Georgia processed data into its SendSS system at 6:30am and 11:30am every day and sent data to the ESSENCE system at 1pm each day. These times are highlighted on the plot in green, and red respectively. New potential improved times, based on the kernel density of data being available in SendSS staging, are shown in the lighter shades of these colors at 8:30am and 12pm every day, while being sent to ESSENCE at 9am and 12:30pm to ensure time for data to be properly processed. These were determined to be optimal times for reducing lag in the data, however, may not be optimal for daily analysis. The purple line on the plot represents the times that data were available in ESSENCE’s system for analysis. This was notably delayed by a median 4.15 hours after the data was sent to ESSENCE on a typical day. Conclusions A data driven approach to choosing processing times could improve timeliness of data analyses in the SendSS and ESSENCE systems. By conducting this type of analysis in an ongoing periodic basis, processing lag times can be kept at a minimum. 1. Centers for Disease Control. Progress in improving state and local disease surveillance--United States, 2000-2005. MMWR Morbidity and mortality weekly report. 2005;54(33):822-825. 2. Jajosky R, Groseclose S. Evaluation of reporting timeliness of public health surveillance systems for infectious diseases. BMC Public Health. 2004;4(1). 3. Travers D, Barnett C, Ising A, Waller A. Timeliness of emergency department diagnoses for syndromic surveillance. AMIA Annual Symposium Proceedings. 2006;Vol. 2006:769. 4. Ward M, Brandsema P, van Straten E, Bosman A. Electronic reporting improves timeliness and completeness of infectious disease notification, The Netherlands, 2003. Eurosurveillance. 2005;10(1):7-8.

Information systems can be deployed in diverse ways during an emergency response, and few emergency responses happen without them. Information systems can greatly improve the efficiency of a response, often decreasing the human resources needed to achieve surveillance goals, and facilitating the collection of accurate data and rapid information sharing. There are various information system tools that are used for routine surveillance that can be adapted during an emergency response to perform surveillance for emerging threats, and manage, analyze, and disseminate data. In this chapter, we will demonstrate how the Georgia Department of Public Health has used the State Electronic Notifiable Disease Surveillance System to improve surveillance during a range of emergency responses.

Objective To describe how flexible surveillance systems can be rapidly adapted and deployed, and increase the efficiency and accuracy of surveillance, during responses to outbreaks and all hazard emergent events. Introduction Georgia Department of Public Health (DPH) epidemiologists have responded to multiple emergent outbreaks with diverse surveillance needs. During the 2009 H1N1 influenza response, it was necessary to electronically integrate multiple reporting sources and view population-level data, while during the 2014–2015 West African Ebola epidemic, it was necessary to easily collect and view individual level data from travelers to facilitate early detection of potential imported Ebola disease. DPH in-house information technology (IT) staff work closely with epidemiologists to understand and accommodate surveillance needs. Through this collaboration, IT created a robust electronic surveillance and outbreak management system (OMS) to accommodate routine reporting of notifiable diseases and outbreak investigations, and surveillance during emergent events. Methods OMS was created within the State Electronic Notifiable Disease Surveillance System (SendSS); a secure, HIPAA-compliant, Oracle and web-based platform which collects data on all notifiable diseases in Georgia. This flexible platform has multi-functionality including dynamic web-based surveys that link to case records or outbreaks, online case reporting, electronic laboratory reporting, contact tracing, visual dashboards summarizing outbreak data, electronic alerts, and individual accounts for users with varying privileges to limit access to specific modules. These features can be customized for any emergent event. Results SendSS and OMS are widely used by state and district epidemiologists. Individual case and outbreak management activities include but are not limited to: notifiable disease and condition cases; all disease clusters; animal bites surveillance including bite investigation and laboratory results; and syndromic surveillance data automatically collected from 90 emergency facilities. OMS has been rapidly modified to facilitate efficient epidemiologic responses to emergent events such as: integrating multiple reporting sources during the H1N1 outbreak; shelter surveillance during hurricanes Katrina and Rita in 2005; active monitoring of >2,500 travelers in Georgia during the Ebola response; tracking cases investigations during the Zika response, and future monitoring of poultry workers if highly- pathogenic avian influenza occurs in Georgia. Conclusions The flexible and customizable features of SendSS and OMS accommodate the changing needs of epidemiologists to monitor a variety of diseases. Rapid implementation has enabled DPH epidemiologists to respond efficiently to emergent events using limited human resources, achieving immediate situational awareness by incorporating multiple data sources into user friendly dashboards and notifications, and easily sharing information among state and federal stakeholders to facilitate rapid risk assessment and response as needed. The success of these systems illustrates the return on DPH’s preparedness investment in retaining technical staff to work with epidemiologists to meet urgent surveillance needs.

Objective Describe how the Georgia Department of Public Health (DPH) uses syndromic surveillance to initiate review by District Epidemiologists (DEs) to events that may warrant a public health response ( 1 ). Introduction DPH uses its State Electronic Notifiable Disease Surveillance System (SendSS) Syndromic Surveillance (SS) Module to collect, analyze and display results of emergency department patient chief complaint data from hospitals throughout Georgia. Methods DPH prepares a daily SS report, based upon the analysis of daily visits to 112 Emergency Department (EDs). The visits are classified in 33 syndromes. Queries of chief complaint and discharge diagnosis are done using the internal query capability of SendSS-SS and programming in SAS/BASE. Charting of the absolute counts or percentage of ED visits by syndromes is done using the internal charting capability of SendSS-SS. A daily SS report includes the following sections: Statewide Emergency Department Visits by Priority Syndromes ( Bioterrorism, BloodyRespiratory, FeverRespiratory, FeverChest, FeverFluAdmit, FeverFluDeaths, VeryIll , and PoxRashFever, Botulism, Poison, BloodyDiarrhea, BloodyVomit, FeverGI, ILI, FeverFlu, RashFever, Diarrhea, Vomit ). Statewide Flag Analysis : Is intended to detect statewide flags, by using the Charts capability in SendSS SS. Possible cases with presumptive diagnosis of potentially notifiable diseases : Is intended to provide early-warning to the DEs of possible cases that are reportable to public health immediately or within 7 days using queries in the Chief Complaint and Preliminary Diagnosis fields of SendSS-SS. Possible clusters of illness : Since any cluster of illness must be reported immediately to DPH, this analysis is aimed at querying and identifying possible clusters of patients with similar symptoms ( 2 ). Possible travel-related illness : Is intended to identify patients with symptoms and recent travel history. Other events of interest : Exposures to ill patients in institutional settings (e.g. chief complaint indicates that other children in the daycare have similar symptoms). Trend Analysis : Weekly analysis of seasonality and trends of 14 syndromes. Finally, specific events are notified to and reviewed by the 18 DEs, who follow up by contacting the Infection Preventionists of the hospitals to identify the patients using medical records or other hospital-specific identification numbers and follow up on the laboratory test results. Results Since 05/15/2016, 12 travel-related illnesses, 29 vaccine- preventable diseases, 14 clusters, and 3 chemical exposures have been notified to DEs. For instance, a cluster of chickenpox in children was identified after the DE contacted the Infection Preventionist of a hospital, who provided the DE with the laboratory results and the physician notes about the symptoms of the patients. These actions have resulted in earlier awareness of single cases or cluster of illness, prompt reporting of notifiable diseases, and successful interaction between DEs and health care providers. In addition, SS continues to track the onset, peak, and decline of seasonal illnesses. Conclusions The implementation of SS in the State of Georgia is helping with the timely detection and early responses to disease events and could prove useful in reducing the disease burden caused by a bioterrorist attack.

Introduction: Surveillance for health outcomes is critical for rapid responses and timely prevention of disaster-related illnesses and injuries after a disaster-causing event. The Disaster Surveillance Workgroup (DSWG) of the US Centers for Disease Control and Prevention developed a standardized, single-page, morbidity surveillance form, called the Natural Disaster Morbidity Surveillance Individual Form (Morbidity Surveillance Form), to describe the distribution of injuries and illnesses, detect outbreaks, and guide timely interventions during a disaster.Problem: Traditional data sources can be used during a disaster; however, supplemental active surveillance may be required because traditional systems often are disrupted, and many persons will seek care outside of typical acute care settings. Generally, these alternative settings lack health surveillance and reporting protocols. The need for standardized data collection was demonstrated during Hurricane Katrina, as the multiple surveillance instruments that were developed and deployed led to varied and uncoordinated data collection methods, analyses, and morbidity data reporting. Active, post-event surveillance of affected populations is critical for rapid responses to minimize and prevent morbidity and mortality, allocate resources, and target public health messaging.Methods: The CDC and the Georgia Department of Public Health (GDPH) conducted a study to evaluate a Morbidity Surveillance Form to determine its ability to capture clinical presentations. The form was completed for each patient evaluated in an emergency department (ED) during triage from 01 August, 2007 through 07 August, 2007. Data from the form were compared with the ED discharge diagnoses from electronic medical records, and kappa statistics were calculated to assess agreement.Results: Nine hundred forty-nine patients were evaluated, 41% were male and 57% were Caucasian. According to the forms, the most common reasons for seeking treatment were acute illness, other (29%); pain (12%); and gastrointestinal illness (8%). The frequency of agreement between discharge diagnoses and the form ranged from 3 to 100%. Kappa values ranged from 0.23–1.0, with nine of the 12 categories having very good or good agreement.Conclusion: With modifications to increase sensitivity for capturing certain clinical presentations, the Morbidity Surveillance Form can be a useful tool for capturing data needed to guide public health interventions during a disaster. A validated collection instrument for a post-disaster event facilitates rapid and standardized comparison and aggregation of data across multiple jurisdictions, thus, improving the coordination, timeliness, and accuracy of public health responses. The DSWG revised the Morbidity Surveillance Form based on information from this study.

During August through September 2009, a surge in emergency department (ED) visits for 2009 pandemic influenza A (pH1N1) illness occurred in Georgia, particularly among children. To understand surge preparedness and capacity, we obtained influenza-like illness (ILI) ED visit data from the Georgia State Electronic Notifiable Disease Surveillance System (SendSS) and conducted a retrospective, Internet-based survey among all 26 metro Atlanta ED managers with reference to the period 1 July-1 October 2009. SendSS detected a marked and progressive increase in mean monthly ILI visits from 1 July-1 October 2009, which more than tripled (from 399 to 2196) for the 2 participating EDs that cared for pediatric patients during this time. ED managers reported patient volume surges, resulting in space and supply limitations, especially at pediatric EDs. Most (92%) of the facilities had current pandemic influenza plans. Pandemic planning can help to ensure preparedness for natural and man-made disasters and for future influenza pandemics.

The Ebola virus disease (Ebola) epidemic in West Africa has so far produced approximately 25,000 cases, more than 40 times the number in any previously documented Ebola outbreak. Because of the risk for imported disease from infected travelers, in October 2014 CDC recommended that all travelers to the United States from Ebola-affected countries receive enhanced entry screening and postarrival active monitoring for Ebola signs or symptoms until 21 days after their departure from an Ebola-affected country. The state of Georgia began its active monitoring program on October 25, 2014. The Georgia Department of Public Health (DPH) modified its existing, web-based electronic notifiable disease reporting system to create an Ebola Active Monitoring System (EAMS). DPH staff members developed EAMS from conceptualization to implementation in 6 days. In accordance with CDC recommendations, "low (but not zero) risk" travelers are required to report their daily health status to DPH, and the EAMS dashboard enables DPH epidemiologists to track symptoms and compliance with active monitoring. Through March 31, 2015, DPH monitored 1,070 travelers, and 699 (65%) used their EAMS traveler login instead of telephone or e-mail to report their health status. Medical evaluations were performed on 30 travelers, of whom three were tested for Ebola. EAMS has enabled two epidemiologists to monitor approximately 100 travelers daily, and to rapidly respond to travelers reporting signs and symptoms of potential Ebola virus infection. Similar electronic tracking systems might be useful for other jurisdictions.

In 2012, the Syndromic Surveillance Program (SSP) of the Georgia Department of Public Health and Effingham County Schools began collecting syndromic surveillance school nurse clinic visit data. The hypothesis was that these data could provide situational awareness during a pandemic, inform health interventions, elucidate disease burden in students, and characterize school nurse activities. Analysis of the data highlighted a significant burden of asthma and diabetes management and a disparate burden of illnesses across schools. In response to the initial findings of this project, chronic disease programs at the state health department are considering funding Effingham schools for targeted health interventions.

Through retrospectively analyzing billions of internet search queries, Ginsberg et al. identified a collection of specific searches that track the course of influenza-like illness (ILI) reported by the US Centers for Disease Control and Prevention (CDC). Prospective monitoring during 2007-2008 found high correlation between Google estimates and CDC-reported ILI, with next-day timeliness compared to the 1-2 week delay reported in traditional CDC ILI surveillance. The assertion by Ginsberg et al., however, that internet search term estimates enable public health officials to respond better to seasonal and pandemic influenza does not take into account the current practice of public health, or the state of the art in electronic disease surveillance.

Introduction The Internet has revolutionized the way public health surveillance is conducted. Georgia has used it for notifiable disease reporting, electronic outbreak management, and early event detection. We used it in our public health response to the 125,000 Hurricane Katrina evacuees who came to Georgia. Methods We developed Internet-based surveillance forms for evacuation shelters and an Internet-based death registry. District epidemiologists, hospital-based physicians, and medical examiners/coroners electronically completed the forms. We analyzed these data and data from emergency departments used by the evacuees. Results Shelter residents and patients who visited emergency departments reported primarily chronic diseases. Among 33 evacuee deaths, only 2 were from infectious diseases, and 1 was indirectly related to the hurricane. Conclusion The Internet was essential to collect health data from multiple locations, by many different people, and for multiple types of health encounters during Georgia's Hurricane Katrina public health response.

Summary Background Little is known about the extent of implementation or the effectiveness of the Centers for Disease Control and Prevention's (CDC) recommended non-pharmaceutical interventions (NPIs) in schools to control the spread of 2009 pandemic influenza A H1N1 (pH1N1). Methods A web-based, cross-sectional survey of all public K–12 schools in Georgia, USA was conducted about preparedness and response to pH1N1, and absenteeism and respiratory illness. Schools that reported ≥10% absenteeism and at least two times the normal level of respiratory illness in the same week were designated as having experienced significant respiratory illness and absenteeism (SRIA) during that week. Results Of 2248 schools surveyed, 704 (31.3%) provided sufficient data to include in our analysis. Participating schools were spread throughout Georgia, USA and were similar to non-participating schools. Of 704 schools, 160 (22.7%) reported at least 1 week of SRIA. Most schools reported implementing the CDC recommendations for the control of pH1N1, and only two schools reported canceling or postponing activities. Schools that communicated with parents about influenza in the summer, had shorter school days, and were located in urban areas were less likely to experience SRIA. Conclusions Most Georgia schools in the United States adopted the CDC recommendations for pH1N1 mitigation and few disruptions of school activities were reported. Early and timely communication with parents, as well as shorter school days, may have been effective in limiting the effect of pH1N1 on schools.