The future of influenza forecasts

Viboud, Cécile; Vespignani, Alessandro

doi:10.1073/pnas.1822167116

Cited by 70 publications

(62 citation statements)

References 20 publications

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“…Real-time, model-based epidemic forecasts are quickly becoming an integral prognostic for resource allocation and strategic intervention planning [20]. As global infectious disease threats elevate, model-supported predictions, which can inform decision making as an outbreak progresses, have become imperative.…”

Section: Plos Computational Biologymentioning

confidence: 99%

Using information theory to optimise epidemic models for real-time prediction and estimation

2020

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The effective reproduction number, R t , is a key time-varying prognostic for the growth rate of any infectious disease epidemic. Significant changes in R t can forewarn about new transmissions within a population or predict the efficacy of interventions. Inferring R t reliably and in real-time from observed time-series of infected (demographic) data is an important problem in population dynamics. The renewal or branching process model is a popular solution that has been applied to Ebola and Zika virus disease outbreaks, among others, and is currently being used to investigate the ongoing COVID-19 pandemic. This model estimates R t using a heuristically chosen piecewise function. While this facilitates real-time detection of statistically significant R t changes, inference is highly sensitive to the function choice. Improperly chosen piecewise models might ignore meaningful changes or over-interpret noise-induced ones, yet produce visually reasonable estimates. No principled piecewise selection scheme exists. We develop a practical yet rigorous scheme using the accumulated prediction error (APE) metric from information theory, which deems the model capable of describing the observed data using the fewest bits as most justified. We derive exact posterior prediction distributions for infected population size and integrate these within an APE framework to obtain an exact and reliable method for identifying the piecewise function best supported by available epidemic data. We find that this choice optimises short-term prediction accuracy and can rapidly detect salient fluctuations in R t , and hence the infected population growth rate, in real-time over the course of an unfolding epidemic. Moreover, we emphasise the need for formal selection by exposing how common heuristic choices, which seem sensible, can be misleading. Our APE-based method is easily computed and broadly applicable to statistically similar models found in phylogenetics and macroevolution, for example. Our results explore the relationships among estimate precision, forecast reliability and model complexity.

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Section: Plos Computational Biologymentioning

confidence: 99%

Using information theory to optimise epidemic models for real-time prediction and estimation

2020

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“…For completeness, we compared the predictive strength of aggregate population outflow to certain other factors -such as the relative frequency of Baidu search for virus-related terms in each prefecture (e.g., novel coronavirus, flu, SARS, atypical pneumonia, surgical mask), [23][24][25] each prefecture's GDP and population, and also other movement variables. Each of these factors became less predictive of local outbreak size over time, either for cumulative or daily reported cases ( Fig.…”

mentioning

confidence: 99%

“…Other techniques to forecast the levels of an epidemic in defined populations in advance have, of course, been proposed -whether the use of online searching [23][24][25] or the use of network sensors (i.e., the monitoring of people who are at heightened risk for falling ill given their network position). 29 Our approach relies on data regarding population flow.…”

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confidence: 99%

Population flow drives spatio-temporal distribution of COVID-19 in China

Jia

Yuan

et al. 2020

Nature

762

637

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Background: The COVID-19 pandemic has affected all sectors of the economy and society. To understand the impact of the pandemic on rms in China and suggest responding public policies, we investigated rms in Guangdong Province (a Province with the highest Gross Domestic Product in China). Methods: The survey sample included 524 rms in 15 cities in Guangdong Province. We chose these rms from list published by the government, considering the industrial characteristics of Guangdong province and rm size. The questionnaire comprised of four categories and included 17 questions was developed based on previous studies carried out by OECD. The executives of rms were contacted by phone or WeChat, and were invited to answer self-administered questionnaires through an on-line survey platform. The data was analyzed by SPSS. Results: The following ndings are worth to be noticed: (1) 48.7% of rms maintained stability, and 35.1% of the rms experienced a halt in operation or faced closure; (2) Nearly 70%-90% of the rms are or are willing to transform to online marketing, remote o ce work, and digital operations. (3) 46% of rms believe that there will be a certain loss this year, and 83.5% expected a decreasing trend of the city's GDP growth. Conclusions: rms in Guangdong Province have faced great challenges in the epidemic. The rms' production and operation activities are limited, and risks are faced. It is necessary to effectively implement supporting policies to profoundly lower production costs for rms, and help rms survive the di cult period, and even gradually transit to normal business operation status. Background The current COVID-19 is a rapidly evolving global challenge and like any pandemic, it weakens health systems, costs lives, and also poses great risks to the global economy and security [1, 2, 3, 4]. According to the data from WHO (World Health Organization) and Johns Hopkins University, till the middle of June 2020, the global COVID-19 pandemic has con rmed more than 7.5 million cases, causing nearly 420 thousand deaths in around 215 countries (https://www.arcgis.com/apps/opsdashboard/index.html). COVID-19 pandemic is a public health emergency. It's a sudden outbreak that causes or is likely to cause serious public health damages including major infectious diseases, mass unexplained diseases, major food and occupational poisonings or other serious public health issues [5]. Global economic growth is expected to decrease continually on account of the epidemic impact throughout the world [6, 7]. According to the Organization for Economic Cooperation and Development (OECD)'s forecast, the global GDP (Gross Domestic Product) growth rate will drop to 2.4% in 2020 [3]. The current continuous worldwide spread of COVID-19 has greatly increased the risk of uncertainty and global recession [8, 9][1]. Supply chain disruption, shrinking demand for consumption and investment, signi cant weakening of economic activities, and damaged market con dence have put more severe tests on the resilience of relevant economies, the level of...

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“…The question of best modeling approach has been explored in recent years in the epidemiological community in a series of infectious disease forecasting challenges [52]. For example, in 2015, the Research and Policy for Infectious Disease Dynamics (RAPIDD) Ebola forecasting challenge compared the predictive abilities of eight Ebola epidemiological models against a synthetic set of data over four scenarios at multiple points in an outbreak [53].…”

Section: Failures Of Imaginationmentioning

confidence: 99%

A Critique of Pandemic Catastrophe Modeling

Rozell¹

2019

JRACR

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Catastrophe modeling is a popular risk assessment tool for the insurance industry and has been applied to a variety of natural disaster events. More recently, catastrophe modeling techniques have been extended to events, such as pandemics, where the range of possible scenarios is less understood due to the complexity of the hazard and the dependence of event magnitude to human response. Some general limitations of catastrophe modeling are discussed in the context of pandemics-such as the failure to distinguish natural variability from incertitude and the difficulty of ensuring a representative model-along with recommendations for minimizing surprises.

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The future of influenza forecasts

Cited by 70 publications

References 20 publications

Using information theory to optimise epidemic models for real-time prediction and estimation

Using information theory to optimise epidemic models for real-time prediction and estimation

Population flow drives spatio-temporal distribution of COVID-19 in China

A Critique of Pandemic Catastrophe Modeling

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