Dataset Information

Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm.

ABSTRACT:

Background

Mathematical and computational models for infectious diseases are increasingly used to support public-health decisions; however, their reliability is currently under debate. Real-time forecasts of epidemic spread using data-driven models have been hindered by the technical challenges posed by parameter estimation and validation. Data gathered for the 2009 H1N1 influenza crisis represent an unprecedented opportunity to validate real-time model predictions and define the main success criteria for different approaches.

Methods

We used the Global Epidemic and Mobility Model to generate stochastic simulations of epidemic spread worldwide, yielding (among other measures) the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. Using a Monte Carlo Maximum Likelihood analysis, the model provided an estimate of the seasonal transmission potential during the early phase of the H1N1 pandemic and generated ensemble forecasts for the activity peaks in the northern hemisphere in the fall/winter wave. These results were validated against the real-life surveillance data collected in 48 countries, and their robustness assessed by focusing on 1) the peak timing of the pandemic; 2) the level of spatial resolution allowed by the model; and 3) the clinical attack rate and the effectiveness of the vaccine. In addition, we studied the effect of data incompleteness on the prediction reliability.

Results

Real-time predictions of the peak timing are found to be in good agreement with the empirical data, showing strong robustness to data that may not be accessible in real time (such as pre-exposure immunity and adherence to vaccination campaigns), but that affect the predictions for the attack rates. The timing and spatial unfolding of the pandemic are critically sensitive to the level of mobility data integrated into the model.

Conclusions

Our results show that large-scale models can be used to provide valuable real-time forecasts of influenza spreading, but they require high-performance computing. The quality of the forecast depends on the level of data integration, thus stressing the need for high-quality data in population-based models, and of progressive updates of validated available empirical knowledge to inform these models.

SUBMITTER: Tizzoni M

PROVIDER: S-EPMC3585792 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

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Publications

Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm.

Tizzoni Michele M Bajardi Paolo P Poletto Chiara C Ramasco José J JJ Balcan Duygu D Gonçalves Bruno B Perra Nicola N Colizza Vittoria V Vespignani Alessandro A

BMC medicine 20121213

<h4>Background</h4>Mathematical and computational models for infectious diseases are increasingly used to support public-health decisions; however, their reliability is currently under debate. Real-time forecasts of epidemic spread using data-driven models have been hindered by the technical challenges posed by parameter estimation and validation. Data gathered for the 2009 H1N1 influenza crisis represent an unprecedented opportunity to validate real-time model predictions and define the main su ...[more]

PMID: 23237460

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Project description:ImportanceThe COVID-19 pandemic has disrupted global surgical capacity. The impact of the pandemic in low and middle income countries has the potential to worsen already strained access to surgical care. Timely assessment of surgical volumes in these countries remains challenging.ObjectiveTo determine whether usage data from a globally used anesthesiology calculator mobile application can serve as a proxy for global surgical case volume and contribute to monitoring of the impact of the COVID-19 pandemic, particularly in World Bank low income countries where official data collection is not currently practical.DesignSubset of data from an ongoing observational cohort study of users of the application collected from October 1, 2018 to April 18, 2020.SettingThe mobile application is available from public sources; users download and use the application per their own clinical needs on personal mobile devices.ParticipantsNo user data was excluded from the study. Exposure(s): Events with impacts on surgical case volumes, including weekends, holidays, and the COVID-19 pandemic. Main Outcome(s) and Measure(s): It was previously noted that application usage was decreased on weekends and during winter holidays. We subsequently hypothesized that more detailed analysis would reveal impacts of country-specific or region-specific holidays on the volume of app use.Results4,300,975 data points from 92,878 unique users were analyzed. Physicians and other anesthesia providers comprised 85.8% of the study population. Application use was reduced on holidays and weekends and correlated with fluctuations in surgical volume. The COVID-19 pandemic was associated with substantial reductions in app use globally and regionally. There was strong cross correlation between COVID-19 case count and reductions in app use. By country, there was a median global reduction in app use to 58% of baseline (interquartile range, 46%-75%). Application use in low-income continues to decline but in high-income countries has stabilized.Conclusions and relevanceApplication usage metadata provides a real-time indicator of surgical volume. This data may be used to identify impacted regions where disruptions to surgical care are disproportionate or prolonged. A dashboard for continuous visualization of these data has been deployed.

Dataset Information

Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm.

Background

Methods

Results

Conclusions

Publications

Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm.

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