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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:We evaluated the impact of man-made conflict events and climate change impact in guiding evidence-based community "One Health" epidemiology and emergency response practice against re-/emerging epidemics. Increasing evidence of emerging and re-emerging zoonotic diseases including recent Lassa fever outbreaks in almost 20 states in Nigeria led to 101 deaths and 175 suspected and confirmed cases since August 2015. Of the 75 laboratory confirmed cases, 90 deaths occurred representing 120% laboratory-confirmed case fatality. The outbreak has been imported into neighbouring country such as Benin, where 23 deaths out of 68 cases has also been reported. This study assesses the current trends in re-emerging Lassa fever outbreak in understanding spatio-geographical reservoir(s), risk factors pattern and Lassa virus incidence mapping, inherent gaps and raising challenges in health systems. It is shown that Lassa fever peak endemicity incidence and prevalence overlap the dry season (within January to March) and reduced during the wet season (of May to November) annually in Sierra Leone, Senegal to Eastern Nigeria. We documented a scarcity of consistent data on rodent (reservoirs)-linked Lassa fever outbreak, weak culturally and socio-behavioural effective prevention and control measures integration, weak or limited community knowledge and awareness to inadequate preparedness capacity and access to affordable case management in affected countries. Hence, robust sub/regional leadership commitment and investment in Lassa fever is urgently needed in building integrated and effective community "One Health" surveillance and rapid response approach practice coupled with pest management and phytosanitation measures against Lassa fever epidemic. This offers new opportunities in understanding human-animal interactions in strengthening Lassa fever outbreak early detection and surveillance, warning alerts and rapid response implementation in vulnerable settings. Leveraging on Africa CDC centre, advances in cloud-sourcing and social media tools and solutions is core in developing and integrating evidence-based and timely risk communication, and reporting systems in improving contextual community-based immunization and control decision making policy to effectively defeat Lassa fever outbreak and other emerging pandemics public health emergencies in Africa and worldwide.

Dataset Information

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

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