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Accuracy of State-Level Surveillance during Emerging Outbreaks of Respiratory Viruses: A Model-Based Assessment.

ABSTRACT: It is long perceived that the more data collection, the more knowledge emerges about the real disease progression. During emergencies like the H1N1 and the severe acute respiratory syndrome coronavirus 2 pandemics, public health surveillance requested increased testing to address the exacerbated demand. However, it is currently unknown how accurately surveillance portrays disease progression through incidence and confirmed case trends. State surveillance, unlike commercial testing, can process specimens based on the upcoming demand (e.g., with testing restrictions). Hence, proper assessment of accuracy may lead to improvements for a robust infrastructure. Using the H1N1 pandemic experience, we developed a simulation that models the true unobserved influenza incidence trend in the State of Michigan, as well as trends observed at different data collection points of the surveillance system. We calculated the growth rate, or speed at which each trend increases during the pandemic growth phase, and we performed statistical experiments to assess the biases (or differences) between growth rates of unobserved and observed trends. We highlight the following results: 1) emergency-driven high-risk perception increases reporting, which leads to reduction of biases in the growth rates; 2) the best predicted growth rates are those estimated from the trend of specimens submitted to the surveillance point that receives reports from a variety of health care providers; and 3) under several criteria to queue specimens for viral subtyping with limited capacity, the best-performing criterion was to queue first-come, first-serve restricted to specimens with higher hospitalization risk. Under this criterion, the lab released capacity to subtype specimens for each day in the trend, which reduced the growth rate bias the most compared to other queuing criteria. Future research should investigate additional restrictions to the queue.

SUBMITTER: Gu Y

PROVIDER: S-EPMC8488654 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAlthough syndromic surveillance is increasingly used to detect unusual illness, there is a debate whether it is useful for detecting local outbreaks. We evaluated whether syndromic surveillance detects local outbreaks of lower-respiratory infections (LRIs) without swamping true signals by false alarms.Methods and findingsUsing retrospective hospitalization data, we simulated prospective surveillance for LRI-elevations. Between 1999-2006, a total of 290762 LRIs were included by date of hospitalization and patients place of residence (>80% coverage, 16 million population). Two large outbreaks of Legionnaires disease in the Netherlands were used as positive controls to test whether these outbreaks could have been detected as local LRI elevations. We used a space-time permutation scan statistic to detect LRI clusters. We evaluated how many LRI-clusters were detected in 1999-2006 and assessed likely causes for the cluster-signals by looking for significantly higher proportions of specific hospital discharge diagnoses (e.g. Legionnaires disease) and overlap with regional influenza elevations. We also evaluated whether the number of space-time signals can be reduced by restricting the scan statistic in space or time. In 1999-2006 the scan-statistic detected 35 local LRI clusters, representing on average 5 clusters per year. The known Legionnaires' disease outbreaks in 1999 and 2006 were detected as LRI-clusters, since cluster-signals were generated with an increased proportion of Legionnaires disease patients (p:<0.0001). 21 other clusters coincided with local influenza and/or respiratory syncytial virus activity, and 1 cluster appeared to be a data artifact. For 11 clusters no likely cause was defined, some possibly representing as yet undetected LRI-outbreaks. With restrictions on time and spatial windows the scan statistic still detected the Legionnaires' disease outbreaks, without loss of timeliness and with less signals generated in time (up to 42% decline).ConclusionsTo our knowledge this is the first study that systematically evaluates the performance of space-time syndromic surveillance with nationwide high coverage data over a longer period. The results show that syndromic surveillance can detect local LRI-outbreaks in a timely manner, independent of laboratory-based outbreak detection. Furthermore, since comparatively few new clusters per year were observed that would prompt investigation, syndromic hospital-surveillance could be a valuable tool for detection of local LRI-outbreaks.

Project description:There is growing concern in Sub-Saharan Africa about the spread of the Ebola virus disease (EVD), formerly known as Ebola haemorrhagic fever, and the public health burden that it ensues. Since 1976, there have been 885,343 suspected and laboratory confirmed cases of EVD and the disease has claimed 2,512 cases and 932 fatality in West Africa. There are certain requirements that must be met when responding to EVD outbreaks and this process could incur certain challenges. For the purposes of this paper, five have been identified: (i) the deficiency in the development and implementation of surveillance response systems against Ebola and others infectious disease outbreaks in Africa; (ii) the lack of education and knowledge resulting in an EVD outbreak triggering panic, anxiety, psychosocial trauma, isolation and dignity impounding, stigmatisation, community ostracism and resistance to associated socio-ecological and public health consequences; (iii) limited financial resources, human technical capacity and weak community and national health system operational plans for prevention and control responses, practices and management; (iv) inadequate leadership and coordination; and (v) the lack of development of new strategies, tools and approaches, such as improved diagnostics and novel therapies including vaccines which can assist in preventing, controlling and containing Ebola outbreaks as well as the spread of the disease. Hence, there is an urgent need to develop and implement an active early warning alert and surveillance response system for outbreak response and control of emerging infectious diseases. Understanding the unending risks of transmission dynamics and resurgence is essential in implementing rapid effective response interventions tailored to specific local settings and contexts.(i) national and regional inter-sectorial and trans-disciplinary surveillance response systems that include early warnings, as well as critical human resources development, must be quickly adopted by allied ministries and organisations in African countries in epidemic and pandemic responses; (ii) harnessing all stakeholders commitment and advocacy in sustained funding, collaboration, communication and networking including community participation to enhance a coordinated responses, as well as tracking and prompt case management to combat challenges; (iii) more research and development in new drug discovery and vaccines; and (iv) understanding the involvement of global health to promote the establishment of public health surveillance response systems with functions of early warning, as well as monitoring and evaluation in upholding research-action programmes and innovative interventions.

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Accuracy of State-Level Surveillance during Emerging Outbreaks of Respiratory Viruses: A Model-Based Assessment.

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