Computers and epidemiology

Kephart, Jeffrey O.; White, Steve R.; Chess, David M.

doi:10.1109/6.275061

Cited by 228 publications

(110 citation statements)

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“…Figure 1 shows that the surviving probability suffers a sharp drop in the first two months of a virus' life. This is a well-known feature [10,13] indicating that statistically only a small percentage of viruses gives rise to a significant outbreak in the computer community. Figure 1, on the other hand, shows for larger times a clean exponential tail, P s ͑t͒ ϳ exp͑2t͞t͒, where t represents the characteristic lifetime of the virus strain [18].…”

Section: (Received 20 October 2000)mentioning

confidence: 99%

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Epidemic Spreading in Scale-Free Networks

2001

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The Internet has a very complex connectivity recently modeled by the class of scale-free networks. This feature, which appears to be very efficient for a communications network, favors at the same time the spreading of computer viruses. We analyze real data from computer virus infections and find the average lifetime and persistence of viral strains on the Internet. We define a dynamical model for the spreading of infections on scale-free networks, finding the absence of an epidemic threshold and its associated critical behavior. This new epidemiological framework rationalizes data of computer viruses and could help in the understanding of other spreading phenomena on communication and social networks. These studies have revealed, among other facts, that the probability that a node of these networks has k connections follows a scale-free distribution P͑k͒ ϳ k 2g , with an exponent g that ranges between 2 and 3. The presence of nodes with a very large number of connections (local clustering) is indeed the key ingredient in the modeling of these networks with the recent introduction of scale-free (SF) graphs [6].In view of the wide occurrence of complex networks in nature it is of great interest to inspect the effect of their features on epidemic and disease spreading [7], and more in general in the context of the nonequilibrium phase transitions typical of these phenomena [8]. The study of epidemics on these networks finds an immediate practical application in the understanding of computer virus spreading [9,10], and could also be relevant to the fields of epidemiology [11] and pollution control [12].In this Letter, we analyze data from real computer virus epidemics, providing a statistical characterization that points out the importance of incorporating the peculiar topology of scale-free networks in the theoretical description of these infections. With this aim, we study by large scale simulations and analytical methods the susceptibleinfected-susceptible (SIS) [11] model on SF graphs. We find the absence of an epidemic threshold and its associated critical behavior, which implies that SF networks are prone to the spreading and the persistence of infections at whatever spreading rate the epidemic agents possess. The absence of the epidemic threshold -a standard element in mathematical epidemiology [11]-radically changes many of the standard conclusions drawn in epidemic modeling. The present results are also relevant in the field of absorbing-state phase transitions and catalytic reactions [8].

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Section: (Received 20 October 2000)mentioning

confidence: 99%

“…The analysis of computer viruses has been the subject of a continuous interest in the computer science community [10,[13][14][15], mainly following approaches borrowed from biological epidemiology [11]. The standard model used in the study of computer virus infections is the SIS epidemiological model.…”

Section: (Received 20 October 2000)mentioning

confidence: 99%

Epidemic Spreading in Scale-Free Networks

2001

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“…Today Bernoulli's paper is still quoted frequently and is praised even for results which are not contained in it. For instance in [24] it is claimed that the first dynamic model of epidemics is due to Bernoulli and the authors then apply epidemic modeling to the spread of 'viruses' in computer networks. If they had read the paper carefully, they would have found out that the paper is only concerned with a static state, i.e.…”

Section: Bernoulli's Paper -Its Origins and Impactmentioning

confidence: 99%

Daniel Bernoulli’s epidemiological model revisited

Dietz

Heesterbeek

2002

Mathematical Biosciences

278

144

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The seminal paper by Daniel Bernoulli published in 1766 is put into a new perspective. After a short account of smallpox inoculation and of Bernoulli's life, the motivation for that paper and its impact are described. It determines the age-specific equilibrium prevalence of immune individuals in an endemic potentially lethal infectious disease. The gain in life expectancy after elimination of this cause of death can be explicitly expressed in terms of the case fatality and the endemic prevalence of susceptibles. D'Alembert developed in 1761 an alternative method for dealing with competing risks of death, which is also applicable to non-infectious diseases. Bernoulli's formula for the endemic prevalence of susceptibles has so far escaped attention. It involves the lifetime risk of the infection, the force of infection and the life expectancy at birth. A new formula for the basic reproduction number is derived which involves the average force of infection, the average case fatality and the life expectancy at the time of infection. One can use this estimate to assess the gain in life expectancy if only a fraction of the population is immunized.

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“…In epidemiology and ecology, SIS infections are challenging to manage because a treated node (person or location) can be cured and reinfected (we use the term "infection" to refer to infectious disease incidence, infestation by invasive pests, and colonization by endangered species). SIS network models can represent sexually transmitted diseases (gonorrhea) (19), other infectious diseases (meningitis, plague, malaria, and sleeping sickness) (20), metapopulations of invasive or threatened (21) species, influence in a social network (22,23), and computer viruses across physical networks (24,25). Here, we build on lessons from previous empirical studies and theoretical frameworks for optimal decision making (26)(27)(28) to develop coherent guidance for allocating resources across time and space between three activities: managing, surveying, and doing nothing for networks of cryptic diseases, pests, and threatened species.…”

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confidence: 99%

General rules for managing and surveying networks of pests, diseases, and endangered species

Chadès

Martin

Nicol

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

195

223

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The efficient management of diseases, pests, or endangered species is an important global issue faced by agencies constrained by limited resources. The management challenge is even greater when organisms are difficult to detect. We show how to prioritize management and survey effort across time and space for networks of susceptible-infected-susceptible subpopulations. We present simple and robust rules of thumb for protecting desirable, or eradicating undesirable, subpopulations connected in typical network patterns (motifs). We further demonstrate that these rules can be generalized to larger networks when motifs are combined in more complex formations. Results show that the best location to manage or survey a pest or a disease on a network is also the best location to protect or survey an endangered species. The optimal starting point in a network is the fastest motif to manage, where line, star, island, and cluster motifs range from fast to slow. Managing the most connected node at the right time and maintaining the same management direction provide advantages over previously recommended outside-in strategies. When a species or disease is not detected and our belief in persistence decreases, our results recommend shifting resources toward management or surveillance of the most connected nodes. Our analytic approximation provides guidance on how long we should manage or survey networks for hard-to-detect organisms. Our rules take into account management success, dispersal, economic cost, and imperfect detection and offer managers a practical basis for managing networks relevant to many significant environmental, biosecurity, and human health issues.conservation planning | decision theory | metapopulation | optimization | Markov decision process I nfectious diseases, invasive pests, and other threats to species persistence have profound impacts on human health, agriculture, and biodiversity (1-3). Many threatened or invasive species are difficult to detect and their presence in an area can be uncertain due to the imperfect nature of most detection methods (4, 5). Even large charismatic mammals such as the Sumatran tiger (Panthera tigris sumatrae) or the Sumatran rhinoceros (Dicerorrhinus sumatrensis) can be surprisingly hard to detect. It is possible that some areas are being managed while the invasive pests, diseases, or threatened species have already disappeared. It is also likely that managers, in the absence of sighting, might stop managing too early and give up too soon on a species or disease (6, 7). Managers need to know when to stop managing or surveying for species in areas of particular interest.At present the epidemiology, ecology, and conservation literature provides little guidance on how to approach such a problem. The problem of how to allocate management and surveillance effort has been studied for a single cryptic population (8-10). For example, Regan et al. (8) determined when to stop monitoring an invasive plant with a long-lived seed bank. Chadès et al. (9) determined when to stop managing ...

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Computers and epidemiology

Cited by 228 publications

References 0 publications

Epidemic Spreading in Scale-Free Networks

Epidemic Spreading in Scale-Free Networks

Daniel Bernoulli’s epidemiological model revisited

General rules for managing and surveying networks of pests, diseases, and endangered species

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