An Evaluation of Epidemic Intelligence Service Alumni in Public Health Leadership Roles

Background The U.S. Centers for Disease Control and Prevention (CDC) has administered the Epidemic Intelligence Service (EIS) fellowship for over 50 years, with the goal of developing scientists and leaders in applied epidemiology. Our objective was to understand the extent to which CDC EIS alumni are present in select public health leadership roles. Methods We conducted an evaluation describing EIS alumni representation in five preselected leadership positions (CDC director [1953–2016]; CDC center director, state epidemiologist, Field Epidemiology Training Program [FETP] resident advisor, and Career Epidemiology Field Officer [CEFO] [2000–2016]). We developed a dataset using multiple sources to identify staff in selected positions. We then matched these data with an internal EIS alumni dataset. Results Selected positions were staffed by 353 persons, of which 185 (52%) were EIS alumni; 10 persons served in >1 leadership position, of which 6 were EIS alumni. Among 12 CDC directors, four (33%) were EIS alumni; collectively these alumni led CDC for approximately 25 years. EIS alumni accounted for 29 (58%) of 50 CDC center directors, 61 (35%) of 175 state epidemiologists, 27 (56%) of 48 Field Epidemiology Training Program resident advisors, and 70 (90%) of 78 Career Epidemiology Field Officers. Of 185 EIS alumni in leadership positions, 136 (74%) were physicians, 22 (12%) were scientists, 21 (11%) were veterinarians, 6 (3%) were nurses; 94 (51%) were assigned to a state or local health department. Among 61 EIS alumni who served as state epidemiologists, 40 (66%) were assigned to a state or local health department during EIS. Conclusions EIS alumni accounted for between approximately one-third (CDC directors and state epidemiologists) and 90% (CEFOs) of people serving in essential leadership positions at multiple levels.

Global variation in event-based surveillance for disease outbreak detection: A time series analysis (Preprint)

BACKGROUND Robust and flexible infectious disease surveillance is crucial for public health. Event-based surveillance (EBS) was developed to allow timely detection of infectious disease outbreaks using mostly web-based data. Despite its widespread use, EBS has not been evaluated systematically on a global scale in terms of outbreak detection performance. OBJECTIVE To assess the variation in timing and frequency of EBS reports compared to true outbreaks and to identify the determinants of variability, using the example of seasonal influenza epidemics in 24 countries. METHODS We obtained influenza-related reports from two EBS systems, HealthMap and the WHO Epidemic Intelligence from Open Sources (EIOS), and weekly virologic influenza counts from FluNet as a gold standard. Epidemic influenza periods were detected based on report frequency using Bayesian change point analysis. Timely sensitivity, i.e., outbreak detection within the first two weeks before or after an outbreak onset, was calculated along with sensitivity, specificity, positive predictive value, and timeliness of detection. Linear regressions were performed to assess the influence of country-specific factors on EBS performance. RESULTS Overall, monitoring the frequency of EBS reports detected 73.5% of outbreaks, but only 9.2% within two weeks of onset; in the best case, monitoring the frequency of health-related reports identified 29% of outbreaks within two weeks of onset. We observed a large degree of variability in all evaluation metrics across countries. The number of EBS reports available within a country, the human development index, and the country’s geographical location partially explained this variability. CONCLUSIONS Monitoring the frequency of EBS reports allowed just under 10% of seasonal influenza outbreaks to be detected in a timely manner in a worldwide analysis, with a large variability in detection capabilities. This article documents the global variation of EBS performance and demonstrates that monitoring report frequency alone in EBS may be insufficient for timely detection of outbreaks. Moreover, factors such as human development index and geographical location of a country may influence the performance of EBS and should be considered in future evaluations.

The Korean 3T practice: A New Biosurveillance Model utilizing New IT and Digital Tools (Preprint)

UNSTRUCTURED South Korea COVID-19 pandemic responses, namely the 3T (testing, tracing, and treating) strategy, come to the fore as a new biosurveillance regime utilizing new IT and digital tools actively. The 3T biosurveillance system is a developed version of the traditional biosurveillance systems (indicator-based or event-based systems), which can provide epidemic intelligence capabilities for both ex ante prevention/preparedness or ex post response/recovery missions. Epidemiological investigation efforts exploiting the use of new digital and IT tools are the ground of the Korean 3T system practicing test, trace, and treatment mission, which can be referred to as ‘contact-based biosurveillance system.’ However, critics argue that the Korea’s 3T strategy may violate individuals’ privacy and human rights in addressing that the Korean biosurveillance system would strengthen the social surveillance and population control by the government as a “digital big brother” in the cyber age. However, closer scrutiny reveals that the Korea’s digital-based biosurveillance system for pandemic response has evolved since the experience of the 2015 Middle East Respiratory Syndrome (MERS) outbreak, by citizen’s requests and self-help behaviors

Reforms Reaffirmed to Improve International Surveillance and Response Mechanism in WHO in the Post-Coronavirus Situation

The emergence of COVID–19 has shown that the inability of WHO to have prompt disease surveillance could be callous to recognize and respond to the situation of Public Health Emergency of International Concern (PHEIC). While the paper traces out the timeline of WHO to perform its mandate in combating the spread of infectious diseases, coincidently it also explains the needs to improve the epidemic intelligence on the basis of coordinated international and national surveillance and response mechanisms. On the note, the post COVID – 19 situation requires WHO member states to strengthen the institution within the international order of global health governance. The only way to do so will be through effective and prompt global disease surveillance and response systems. Further, the article attempts to shed light on the current and previous reaction of WHO over the infectious diseases including COVID – 19. In the process, this article tries to suggest reforms within the IHR and WHO’s applicable effort to develop effective disease surveillance and prompt response systems.

COVID-19: How Can Doulas Support Pandemic Control?

COVID-19 represents a great threat to public health in the 21st century so far, with the maternal and child (MCH) population being particularly vulnerable. With the healthcare workforce getting overwhelmed by a resurgence of cases, task-shifting approaches are a viable option to address the acute shortage of personnel for epidemic intelligence assignments related to the COVID-19 pandemic. We propose an intensive and short-duration curriculum to train Doulas versed in MCH epidemic intelligence and capable of reducing the burden of COVID-19 and its consequences on the MCH population. The effectiveness of the proposed curriculum will be monitored using a rapid-cycle evaluative approach to ascertain progressive enhancement over time. Copyright © 2021 Maiyegun, et al. Published by Global Health and Education Projects, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in this journal, is properly cited.

COVID-19 Risk Minimization Decision Making Strategy Using Data-Driven Model

In order to establish social resilient and sustainable cities during the pandemic outbreak, it is essential to forecast the epidemic trends and trace infection by means of data-driven solution addressing the requirements of local operational defense applications and global strategies. The smartphone based Digital Proximity Tracing Technology (DPTT) has obtained a great deal of interest with the ongoing COVID-19 pandemic in terms of mitigation, containing and monitoring with the population acceptance insights and effectiveness of the function. The DPTTs and Data-Driven Epidemic Intelligence Strategies (DDEIS) are compared in this paper to identify the shortcomings and propose a novel solution to overcome them. In terms of epidemic resurgence risk minimization, guaranteeing public health safety and quick return of cities to normalcy, a social as well as technological solution may be provided by incorporating the key features of DDEIS. The role of human behavior is taken into consideration while assessing its limitations and benefits for policy making as well as individual decision making. The epidemiological model of SEIR (Susceptible–Exposed–Infectious–Recovered) provides preliminary data for the preferences of users in a DPTT. The impact of the proposed model on the spread dynamics of Covid-19 is evaluated and the results are presented.