Modeling and Predicting Human Infectious Diseases

Perra, Nicola; Gonçalves, Bruno

doi:10.1007/978-3-319-14011-7_4

Cited by 19 publications

(12 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the predictions for SARS failed to match the data 3,5 . Over the subsequent 15 years, the scientific community developed a rich understanding for how social contact networks, variation in health-care infrastructure, the spatial distribution of prior immunity, etc., drive complex patterns of disease transmission [6][7][8][9][10][11] , and demonstrated that data-driven, dynamic, and or agentbased models can produce actionable forecasts [12][13][14][15][16][17] . Additionally, studies have demonstrated that predicting different components of outbreaks-e.g., the expected number of cases, pace, and tempo of cases needing treatment, demand for prophylactic equipment, importation probability, etc.-is feasible 3,13,[18][19][20][21][22][23][24] .…”

mentioning

confidence: 99%

See 1 more Smart Citation

On the predictability of infectious disease outbreaks

2019

View full text Add to dashboard Cite

Infectious disease outbreaks recapitulate biology: they emerge from the multi-level interaction of hosts, pathogens, and environment. Therefore, outbreak forecasting requires an integrative approach to modeling. While specific components of outbreaks are predictable, it remains unclear whether fundamental limits to outbreak prediction exist. Here, adopting permutation entropy as a model independent measure of predictability, we study the predictability of a diverse collection of outbreaks and identify a fundamental entropy barrier for disease time series forecasting. However, this barrier is often beyond the time scale of single outbreaks, implying prediction is likely to succeed. We show that forecast horizons vary by disease and that both shifting model structures and social network heterogeneity are likely mechanisms for differences in predictability. Our results highlight the importance of embracing dynamic modeling approaches, suggest challenges for performing model selection across long time series, and may relate more broadly to the predictability of complex adaptive systems.

show abstract

mentioning

confidence: 99%

“…What remains an open question is whether the existing barriers to forecasting stem from gaps in our mechanistic understanding of disease transmission and low-quality data or from fundamental limits to the predictability of complex, sociobiological systems, i.e. outbreaks 4,6,7,[27][28][29][30] .…”

mentioning

confidence: 99%

On the predictability of infectious disease outbreaks

2019

View full text Add to dashboard Cite

show abstract

“…So far, many efforts have been recently made to model virus spreading among computer network. Scholars mainly reference traditional infectious disease models (e.g., SI, SIS, SIR) in biological engineering [13]. And then they combine with the characteristics of computer virus to form computer virus spreading model [14,15].…”

Section: Related Workmentioning

confidence: 99%

Influence of node mobility on virus spreading behaviors in multi-hop network

Liu

Jin

Wang

2016

J Wireless Com Network

View full text Add to dashboard Cite

Multi-hop network has received growing attention recently with the widely use of wireless network communication technology. At the same time, the security of multi-hop network is facing more serve challenges. Unfortunately, classic techniques for computer virus spreading model cannot be applied to multi-hop network because of ignoring dynamic topology of network. In this paper, classic susceptible-infected (SI) model, susceptible-infectedsusceptible (SIS) model and susceptible-infected-removed (SIR) model are applied to multi-hop network based on random way-point (RWP) model, and contact duration of virus is introduced. Virus spreading behaviors are examined through contact duration of virus, communication radius of node, distribution density of node, and the number of initial infected nodes. Simulation results show that node mobility has significant effect on virus spreading behaviors. In particular, a special node speed that can lead network to appear the fastest-spreading virus phenomenon is found. The special speed is approximately equal to the ratio of communication radius of node to contact duration of virus. Distribution density of node and the number of initial infected nodes almost do not affect the special speed.

show abstract

“…Use of machine learning methods in understanding influenza dynamics are discussed in [19], [20], [21]. Additionally, review of existing influenza forecasting methods is discussed in [22], [23], [24].…”

Section: Introductionmentioning

confidence: 99%

A novel data-driven model for real-time influenza forecasting

Venna

Tavanaei

Gottumukkala

et al. 2017

Preprint

View full text Add to dashboard Cite

Abstract-We provide data-driven machine learning methods that are capable of making real-time influenza forecasts that integrate the impacts of climatic factors and geographical proximity to achieve better forecasting performance. The key contributions of our approach are both applying deep learning methods and incorporation of environmental and spatio-temporal factors to improve the performance of the influenza forecasting models. We evaluate the method on Influenza Like Illness (ILI) counts and climatic data, both publicly available data sets. Our proposed method outperforms existing known influenza forecasting methods in terms of their Mean Absolute Percentage Error and Root Mean Square Error. The key advantages of the proposed data-driven methods are as following: (1) The deep-learning model was able to effectively capture the temporal dynamics of flu spread in different geographical regions, (2) The extensions to the deep-learning model capture the influence of external variables that include the geographical proximity and climatic variables such as humidity, temperature, precipitation and sun exposure in future stages, (3) The model consistently performs well for both the city scale and the regional scale on the Google Flu Trends (GFT) and Center for Disease Control (CDC) flu counts. The results offer a promising direction in terms of both datadriven forecasting methods and capturing the influence of spatio-temporal and environmental factors for influenza forecasting methods.

show abstract

Modeling and Predicting Human Infectious Diseases

Abstract: International audienc

Cited by 19 publications

References 64 publications

On the predictability of infectious disease outbreaks

On the predictability of infectious disease outbreaks

Influence of node mobility on virus spreading behaviors in multi-hop network

A novel data-driven model for real-time influenza forecasting

Contact Info

Product

Resources

About