Evidence Synthesis for Stochastic Epidemic Models

Birrell, Paul; Angelis, Daniela De; Presanis, Anne M.

doi:10.1214/17-sts631

Cited by 25 publications

(15 citation statements)

References 68 publications

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“…Evidence synthesis approaches, which fit to multiple data sources rather than a single dataset (or datapoint) can enable more robust estimation of the underlying dynamics of transmission from noisy data. 11,12 Combining a mathematical model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission with four datasets from within and outside Wuhan, we estimated how transmission in Wuhan varied between December, 2019, and February, 2020. We used these estimates to assess the potential for sustained human-to-human transmission to occur in locations outside Wuhan if cases were introduced.…”

Section: Introductionmentioning

confidence: 99%

Early dynamics of transmission and control of COVID-19: a mathematical modelling study

Kucharski

Russell

Diamond

et al. 2020

The Lancet Infectious Diseases

2,564

2,422

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Background An outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to 95 333 confirmed cases as of March 5, 2020. Understanding the early transmission dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas. Combining a mathematical model of severe SARS-CoV-2 transmission with four datasets from within and outside Wuhan, we estimated how transmission in Wuhan varied between December, 2019, and February, 2020. We used these estimates to assess the potential for sustained human-to-human transmission to occur in locations outside Wuhan if cases were introduced. Methods We combined a stochastic transmission model with data on cases of coronavirus disease 2019 (COVID-19) inWuhan and international cases that originated in Wuhan to estimate how transmission had varied over time during January, 2020, and February, 2020. Based on these estimates, we then calculated the probability that newly introduced cases might generate outbreaks in other areas. To estimate the early dynamics of transmission in Wuhan, we fitted a stochastic transmission dynamic model to multiple publicly available datasets on cases in Wuhan and internationally exported cases from Wuhan. The four datasets we fitted to were: daily number of new internationally exported cases (or lack thereof), by date of onset, as of Jan 26, 2020; daily number of new cases in Wuhan with no market exposure, by date of onset, between Dec 1, 2019, and Jan 1, 2020; daily number of new cases in China, by date of onset, between Dec 29, 2019, and Jan 23, 2020; and proportion of infected passengers on evacuation flights between Jan 29, 2020, and Feb 4, 2020. We used an additional two datasets for comparison with model outputs: daily number of new exported cases from Wuhan (or lack thereof) in countries with high connectivity to Wuhan (ie, top 20 most at-risk countries), by date of confirmation, as of Feb 10, 2020; and data on new confirmed cases reported in Wuhan between Jan 16, 2020, and Feb 11, 2020.Findings We estimated that the median daily reproduction number (R t ) in Wuhan declined from 2·35 (95% CI 1·15-4·77) 1 week before travel restrictions were introduced on Jan 23, 2020, to 1·05 (0·41-2·39) 1 week after. Based on our estimates of R t , assuming SARS-like variation, we calculated that in locations with similar transmission potential to Wuhan in early January, once there are at least four independently introduced cases, there is a more than 50% chance the infection will establish within that population.Interpretation Our results show that COVID-19 transmission probably declined in Wuhan during late January, 2020, coinciding with the introduction of travel control measures. As more cases arrive in international locations with similar transmission potential to Wuhan before these control measures, it is likely many chains of transmission will fail to establish initially, but might lead to new outbreaks eventually.

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Section: Introductionmentioning

confidence: 99%

Early dynamics of transmission and control of COVID-19: a mathematical modelling study

Kucharski

Russell

Diamond

et al. 2020

The Lancet Infectious Diseases

2,564

2,422

View full text Add to dashboard Cite

show abstract

“…In addition, individual data sources may be biased, incomplete, or only capture certain aspects of the outbreak dynamics. Evidence synthesis approaches, which fit to multiple data sources rather than a single dataset (or data point) can enable more robust estimation of the underlying dynamics of transmission from noisy data (11,12). Combining a mathematical model of SARS-CoV-2 transmission with four datasets from within and outside Wuhan, we estimated how transmission in Wuhan varied between December and February 2020.…”

Section: Introductionmentioning

confidence: 99%

Early dynamics of transmission and control of COVID-19: a mathematical modelling study

Kucharski

Russell

Diamond

et al. 2020

Preprint

385

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Background: An outbreak of the novel coronavirus SARS-CoV-2 has led to 46,997 confirmed cases as of 13 th February 2020. Understanding the early transmission dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas. Methods:We combined a stochastic transmission model with data on cases of novel coronavirus disease in Wuhan and international cases that originated in Wuhan to estimate how transmission had varied over time during January and February 2020. Based on these estimates, we then calculated the probability that newly introduced cases might generate outbreaks in other areas. Findings:We estimated that the median daily reproduction number, R t , declined from 2.35 (95% CI: 1.15-4.77) one week before travel restrictions were introduced on 23 rd January to 1.05 (95% CI: 0.413-2.39) one week after. Based on our estimates of R t ,we calculated that in locations with similar transmission potential as Wuhan in early January, once there are at least four independently introduced cases, there is a more than 50% chance the infection will establish within that population.

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“…It is intrinsically difficult to incorporate the dynamics of human behaviour into an infectious disease modelling framework, as we have attempted here, because it involves many complexities and sources of uncertainty [11,28]. Retrospective analyses of data from multiple surveillance systems have previously been used to estimate disease severity in, e.g., the 2009 H1N1 pandemic in England [29], and evidence synthesis in a modelling framework is an active area of research [30]. However, the use of one surveillance system to calibrate the interpretation of another in real-time is, to our knowledge, a novel and significant contribution of this study.…”

Section: Discussionmentioning

confidence: 99%

Accounting for Healthcare-Seeking Behaviours and Testing Practices in Real-Time Influenza Forecasts

et al. 2019

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For diseases such as influenza, where the majority of infected persons experience mild (if any) symptoms, surveillance systems are sensitive to changes in healthcare-seeking and clinical decision-making behaviours. This presents a challenge when trying to interpret surveillance data in near-real-time (e.g., to provide public health decision-support). Australia experienced a particularly large and severe influenza season in 2017, perhaps in part due to: (a) mild cases being more likely to seek healthcare; and (b) clinicians being more likely to collect specimens for reverse transcription polymerase chain reaction (RT-PCR) influenza tests. In this study, we used weekly Flutracking surveillance data to estimate the probability that a person with influenza-like illness (ILI) would seek healthcare and have a specimen collected. We then used this estimated probability to calibrate near-real-time seasonal influenza forecasts at each week of the 2017 season, to see whether predictive skill could be improved. While the number of self-reported influenza tests in the weekly surveys are typically very low, we were able to detect a substantial change in healthcare seeking behaviour and clinician testing behaviour prior to the high epidemic peak. Adjusting for these changes in behaviour in the forecasting framework improved predictive skill. Our analysis demonstrates a unique value of community-level surveillance systems, such as Flutracking, when interpreting traditional surveillance data. These methods are also applicable beyond the Australian context, as similar community-level surveillance systems operate in other countries.

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Evidence Synthesis for Stochastic Epidemic Models

Cited by 25 publications

References 68 publications

Early dynamics of transmission and control of COVID-19: a mathematical modelling study

Early dynamics of transmission and control of COVID-19: a mathematical modelling study

Early dynamics of transmission and control of COVID-19: a mathematical modelling study

Accounting for Healthcare-Seeking Behaviours and Testing Practices in Real-Time Influenza Forecasts

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