Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions

By Vittoria Colizza, Alain Barrat, Marc Barthelemy, A. Valleron, Alessandro Vespignani. PLoS Medicine, 2007

742 citations recorded by the source corpus.

Background The highly pathogenic H5N1 avian influenza virus, which is now widespread in Southeast Asia and which diffused recently in some areas of the Balkans region and Western Europe, has raised a public alert toward the potential occurrence of a new severe influenza pandemic. Here we study the worldwide spread of a pandemic and its possible containment at a global level taking into account all available information on air travel. Methods and Findings We studied a metapopulation stochastic epidemic model on a global scale that considers airline travel flow data among urban areas. We provided a temporal and spatial evolution of the pandemic with a sensitivity analysis of different levels of infectiousness of the virus and initial outbreak conditions (both geographical and seasonal). For each spreading scenario we provided the timeline and the geographical impact of the pandemic in 3,100 urban areas, located in 220 different countries. We compared the baseline cases with different containment strategies, including travel restrictions and the therapeutic use of antiviral (AV) drugs. We investigated the effect of the use of AV drugs in the event that therapeutic protocols can be carried out with maximal coverage for the populations in all countries. In view of the wide diversity of AV stockpiles in different regions of the world, we also studied scenarios in which only a limited number of countries are prepared (i.e., have considerable AV supplies). In particular, we compared different plans in which, on the one hand, only prepared and wealthy countries benefit from large AV resources, with, on the other hand, cooperative containment scenarios in which countries with large AV stockpiles make a small portion of their supplies available worldwide. Conclusions We show that the inclusion of air transportation is crucial in the assessment of the occurrence probability of global outbreaks. The large-scale therapeutic usage of AV drugs in all hit countries would be able to mitigate a pandemic effect with a reproductive rate as high as 1.9 during the first year; with AV supply use sufficient to treat approximately 2% to 6% of the population, in conjunction with efficient case detection and timely drug distribution. For highly contagious viruses (i.e., a reproductive rate as high as 2.3), even the unrealistic use of supplies corresponding to the treatment of approximately 20% of the population leaves 30%–50% of the population infected. In the case of limited AV supplies and pandemics with a reproductive rate as high as 1.9, we demonstrate that the more cooperative the strategy, the more effective are the containment results in all regions of the world, including those countries that made part of their resources available for global use.

Subfields: Agronomy and Crop Science, Modeling and Simulation, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/5ba320dc84993439823391507ff57859056c6dd5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Agronomy and Crop Science

In Modeling and Simulation

In Pulmonary and Respiratory Medicine

Citations in the corpus

Understanding the Spreading Patterns of Mobile Phone Viruses (2009, background): “First, a Bluetooth virus can infect all Bluetooth-activated phones within a distance from 10 to 30m, resulting in a spatially localized spreading pattern similar to the one observed in the case of influenza (3, 6, 7), SARS (8, 9) and other contact-based diseases (10) (Fig.”
Modelling dynamical processes in complex socio-technical systems (2011, background): “An example is epidemic modelling, where spatially structured and agent-based models at various granularities (country, inter-city, intra-city) have been pushed to the computational limits with the integration of huge amount of data describing the flows of people and/or animals 93–97 .”
Understanding individual human mobility patterns (2008, background): “Despite their importance for urban planning [1], traffic for ecasting [2], and the spread of biological [3, 4, 5] and mobile viruses [6], our understanding of the basic laws governing human motion remains limited thanks to the lack of tools to monitor the time resolved location of individuals.”
Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: theory and simulations. (2007, methodology): “The reaction-diﬀusion framework (Colizza et al. , 2007 b ) considers that the occupation numbers N i of each subpopulation can have any in-teger value, including N i = 0, that is, void nodes with no individuals.”
FluTE, a Publicly Available Stochastic Influenza Epidemic Simulation Model (2010, background): “The simplest of these (such as the SIR model [40]) can be used to study epidemics analytically, and more complex versions have been used to model the dynamics of epidemics on a global scale [41,42].”
Networks and the Epidemiology of Infectious Disease (2010, background): “The movement of passenger aircraft as collated by the International Air Transport Association (IATA) provides very useful information about the long-distance movement of individuals and hence how rapidly infection is likely to travel around the globe [37, 45, 46].”
Mining User Mobility Features for Next Place Prediction in Location-Based Services (2012, background): “Those range from the better grasp of human behavior and migration patterns, to the evolution of epidemics and spread of disease [5], or the understanding of the mechanisms that shape social networks [7].”
Human mobility: Models and applications (2017, background): “Indeed, the elucidation and understanding of these patterns was motivated by its relation to several real-world applications such as traffic forecasting [3, 13, 14], urban planning [15, 10], internal security [16, 17] and epidemic modeling [18, 19, 20] to name but a few.”
Modeling the spatial spread of infectious diseases: the GLobal Epidemic and Mobility computational model (2010, methodology): “The network shows a high degree of heterogeneity both in the number of destinations per airport and in the number of passengers per connection [9,3,10,11].”
Spatial Networks (2010, background): “The fact that reducing travel is not an efficient strategies was also confirmed in theoretical and numerical studies [41, 89, 92, 95].”
Critical phenomena in complex networks (2007, background): “Colizza et al. (2007) showed that ρc and the critical behavior do not depend on the degree distribution.”
Modeling human mobility responses to the large-scale spreading of infectious diseases (2011, result): “Our results confirm previous findings about the inefficacy of measures like cutting down mobility of individuals by, for instance, closing airports [16, 17, 18, 19].”