Measuring the unknown: an estimator and simulation study for assessing case reporting during epidemics

Jarvis, CI; Gimma, Amy; Finger, Flavio; Morris, Tim P.; Thompson, JA; Waroux, OlP de; Edmunds, W. John; Funk, Sebastian; Jombart, Thibaut

doi:10.1101/2021.02.17.431606

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…This can be used to inform the design of genomic surveillance programs for different pathogens by adapting the simulation parameters and scenarios. Our findings extend existing literature on estimating the rate of under-reporting during outbreaks and the effect on estimators of outbreak parameters ( 11 , 33 , 34 ), and guidelines for the design of genomic surveillance programs ( 1 , 5 , 16 ).…”

Section: Discussionsupporting

confidence: 76%

Guiding the design of SARS-CoV-2 genomic surveillance by estimating the resolution of outbreak detection

Suster

Arnott²,

Blackwell³

et al. 2022

Front. Public Health

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Genomic surveillance of SARS-CoV-2 has been essential to inform public health response to outbreaks. The high incidence of infection has resulted in a smaller proportion of cases undergoing whole genome sequencing due to finite resources. We present a framework for estimating the impact of reduced depths of genomic surveillance on the resolution of outbreaks, based on a clustering approach using pairwise genetic and temporal distances. We apply the framework to simulated outbreak data to show that outbreaks are detected less frequently when fewer cases are subjected to whole genome sequencing. The impact of sequencing fewer cases depends on the size of the outbreaks, and on the genetic and temporal similarity of the index cases of the outbreaks. We also apply the framework to an outbreak of the SARS-CoV-2 Delta variant in New South Wales, Australia. We find that the detection of clusters in the outbreak would have been delayed if fewer cases had been sequenced. Existing recommendations for genomic surveillance estimate the minimum number of cases to sequence in order to detect and monitor new virus variants, assuming representative sampling of cases. Our method instead measures the resolution of clustering, which is important for genomic epidemiology, and accommodates sampling biases.

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Section: Discussionsupporting

confidence: 76%

Guiding the design of SARS-CoV-2 genomic surveillance by estimating the resolution of outbreak detection

Suster

Arnott²,

Blackwell³

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…Uncertainty in case data is a very general issue in developing a mechanistic understanding of infectious diseases (for example, case numbers often apparently paradoxically increase with vaccination coverage, but this is actually a result of concomitant improvements in surveillance (Prada et al, 2018)). Various approaches to correcting for biases are available (Becker and Grenfell, 2017; Jarvis et al, 2021), but transparency in data generation mechanisms is an essential component.…”

Section: Discussionmentioning

confidence: 99%

Existing human mobility data sources poorly predicted the spatial spread of SARS-CoV-2 in Madagascar

Ramiadantsoa

Metcalf

Raherinandrasana

et al. 2021

Preprint

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For emerging epidemics such as the COVID-19 pandemic, quantifying travel is a key component of developing accurate predictive models of disease spread to inform public health planning. However, in many LMICs, traditional data sets on travel such as commuting surveys as well as non-traditional sources such as mobile phone data are lacking, or, where available, have only rarely been leveraged by the public health community. Evaluating the accuracy of available data to measure transmission-relevant travel may be further hampered by limited reporting of suspected and laboratory confirmed infections. Here, we leverage case data collected as part of a COVID-19 dashboard collated via daily reports from the Malagasy authorities on reported cases of SARS-CoV-2 across the 22 regions of Madagascar. We compare the order of the timing of when cases were reported with predictions from a SARS-CoV-2 metapopulation model of Madagascar informed using various measures of connectivity including a gravity model based on different measures of distance, Internal Migration Flow data, and mobile phone data. Overall, the models based on mobile phone connectivity and the gravity-based on Euclidean distance best predicted the observed spread. The ranks of the regions most remote from the capital were more difficult to predict but interestingly, regions where the mobile phone connectivity model was more accurate differed from those where the gravity model was most accurate. This suggests that there may be additional features of mobility or connectivity that were consistently underestimated using all approaches, but are epidemiologically relevant. This work highlights the importance of data availability and strengthening collaboration among different institutions with access to critical data - models are only as good as the data that they use, so building towards effective data-sharing pipelines is essential.

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Measuring the unknown: an estimator and simulation study for assessing case reporting during epidemics

Cited by 2 publications

References 24 publications

Guiding the design of SARS-CoV-2 genomic surveillance by estimating the resolution of outbreak detection

Guiding the design of SARS-CoV-2 genomic surveillance by estimating the resolution of outbreak detection

Existing human mobility data sources poorly predicted the spatial spread of SARS-CoV-2 in Madagascar

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