Epidemic cycles driven by host behaviour

Althouse, Benjamin M.; Hébert‐Dufresne, Laurent

doi:10.1098/rsif.2014.0575

Cited by 26 publications

(31 citation statements)

References 34 publications

(50 reference statements)

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“…[84]— Gray et al [85]—vaccination—Milner & Zhao [86]———reduce sexual risk—Milner & Zhao [86]———social distancing—Aadland et al . [87]— Gray et al [85] Althouse & Hébert-Dufresne [88]measles and pertussisvaccinationBhattacharyya & Bauch [89], Shim et al . [90], Bauch & Bhattacharyya [91] , d'Onofrio et al [92] , Oraby et al [93]————othersocial distancingPerra et al .…”

Section: Resultsmentioning

confidence: 99%

“…[33], Fukuda et al . [39], Reniers & Armbruster [78], Althouse & Hébert-Dufresne [88], Sahneh & Scoglio [122,175], Cao [125], Zhang et al . [126], Schumm et al .…”

Section: Resultsmentioning

confidence: 99%

“…We retrieved 29 BCMs in which behaviour change is modelled conditional on exceeding a predefined information threshold [12,42,57,58,61–63,70,78,81,88,114,127,132,133,135,136,138–144,162,163,180–182]. The information the individual assesses can be obtained in a direct way (e.g.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Behavioural change models for infectious disease transmission: a systematic review (2010–2015)

Verelst

Willem

Beutels

2016

J. R. Soc. Interface.

328

311

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We review behavioural change models (BCMs) for infectious disease transmission in humans. Following the Cochrane collaboration guidelines and the PRISMA statement, our systematic search and selection yielded 178 papers covering the period 2010–2015. We observe an increasing trend in published BCMs, frequently coupled to (re)emergence events, and propose a categorization by distinguishing how information translates into preventive actions. Behaviour is usually captured by introducing information as a dynamic parameter (76/178) or by introducing an economic objective function, either with (26/178) or without (37/178) imitation. Approaches using information thresholds (29/178) and exogenous behaviour formation (16/178) are also popular. We further classify according to disease, prevention measure, transmission model (with 81/178 population, 6/178 metapopulation and 91/178 individual-level models) and the way prevention impacts transmission. We highlight the minority (15%) of studies that use any real-life data for parametrization or validation and note that BCMs increasingly use social media data and generally incorporate multiple sources of information (16/178), multiple types of information (17/178) or both (9/178). We conclude that individual-level models are increasingly used and useful to model behaviour changes. Despite recent advancements, we remain concerned that most models are purely theoretical and lack representative data and a validation process.

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Section: Resultsmentioning

confidence: 99%

“…[33], Fukuda et al . [39], Reniers & Armbruster [78], Althouse & Hébert-Dufresne [88], Sahneh & Scoglio [122,175], Cao [125], Zhang et al . [126], Schumm et al .…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Behavioural change models for infectious disease transmission: a systematic review (2010–2015)

Verelst

Willem

Beutels

2016

J. R. Soc. Interface.

328

311

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“…Repeated and intensive forest cultivation can cause local resources to become depleted and ultimately trigger the abandonment of settlements, but eventually forests and soils can recover and abandoned settlements may be reoccupied. Many types of environmental and human cycling exist that could lead to interactions and regime shifts, including environmental overexploitation and recovery (45,50), biogeochemical cycling (51), land surface change (2), climate cycling (52), epidemiology (53), human demography (13), and episodes of human violence (54,55). These dynamics are predicted to exhibit the statistical signatures of CSD (46).…”

Section: A Framework For Interpreting Ewss Among Human Populations Atmentioning

confidence: 99%

European Neolithic societies showed early warning signals of population collapse

Downey

Haas

Shennan

2016

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

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Ecosystems on the verge of major reorganization-regime shiftmay exhibit declining resilience, which can be detected using a collection of generic statistical tests known as early warning signals (EWSs). This study explores whether EWSs anticipated human population collapse during the European Neolithic. It analyzes recent reconstructions of European Neolithic (8-4 kya) population trends that reveal regime shifts from a period of rapid growth following the introduction of agriculture to a period of instability and collapse. We find statistical support for EWSs in advance of population collapse. Seven of nine regional datasets exhibit increasing autocorrelation and variance leading up to collapse, suggesting that these societies began to recover from perturbation more slowly as resilience declined. We derive EWS statistics from a prehistoric population proxy based on summed archaeological radiocarbon date probability densities. We use simulation to validate our methods and show that sampling biases, atmospheric effects, radiocarbon calibration error, and taphonomic processes are unlikely to explain the observed EWS patterns. The implications of these results for understanding the dynamics of Neolithic ecosystems are discussed, and we present a general framework for analyzing societal regime shifts using EWS at large spatial and temporal scales. We suggest that our findings are consistent with an adaptive cycling model that highlights both the vulnerability and resilience of early European populations. We close by discussing the implications of the detection of EWS in human systems for archaeology and sustainability science.archaeology | early warning signs | human paleodemography | Neolithic Europe | resilience A 2012 Special Issue in PNAS debates how analysis of historical collapse in ancient societies can contribute to sustainability science (1). Key themes include accounting for complexity and multicausality in instances of collapse, modeling, and predicting both short-and long-term environmental change and the importance of historical and archaeological case studies. Although significant progress has been made in measuring ecosystem resilience and predicting collapse (2), quantifying the resilience of human societies presents a major challenge for social science research (3, 4). Further, the use of archaeological data and EWS methods to predict known periods of collapse in ancient human societies (i.e., retrodiction) (5) remains largely unexplored. Resilience as we use the concept here is defined as the ability of a system to absorb change and recover from disturbance while maintaining relationships between populations or state variables (6). Recent developments in ecology point to a promising new direction that follows from the observation that ecosystem resilience tends to decrease in advance of regime shifts-major transitions among qualitatively distinct ecosystem states (7). Theoretical and empirical studies of nonhuman systems reveal that decreasing resilience is detectable via time series statistics term...

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“…The epidemic threshold can vanish in networks with a scale-free degree distribution (2) or in growing networks (3). The phase transition can be discontinuous in the case of complex contagions with threshold exposition or reinforcement (4), interacting epidemics (5, 6), or adaptive networks (7)(8)(9).…”

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confidence: 99%

Asymmetric percolation drives a double transition in sexual contact networks

Allard

Althouse

Scarpino

2017

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

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Zika virus (ZIKV) exhibits unique transmission dynamics in that itis concurrently spread by a mosquito vector and through sexual contact. Due to the highly asymmetric durations of infectiousness between males and females-it is estimated that males are infectious for periods up to 10 times longer than females-we show that this sexual component of ZIKV transmission behaves akin to an asymmetric percolation process on the network of sexual contacts. We exactly solve the properties of this asymmetric percolation on random sexual contact networks and show that this process exhibits two epidemic transitions corresponding to a core-periphery structure. This structure is not present in the underlying contact networks, which are not distinguishable from random networks, and emerges because of the asymmetric percolation. We provide an exact analytical description of this double transition and discuss the implications of our results in the context of ZIKV epidemics. Most importantly, our study suggests a bias in our current ZIKV surveillance, because the community most at risk is also one of the least likely to get tested.A bstract modeling of epidemics on networks remains an active field, because some of the most basic features of epidemics are still misunderstood. The classic model is quite simple (1): disease spreads stochastically with a fixed transmission probability, T , through contacts around a given patient zero. The outbreak dies quickly if T is too small but spreads to a macroscopic fraction S of the entire population if T is larger than a threshold Tc. At Tc, most of the typical insights from phase transition theory are valuable. For instance, the sizes of microscopic outbreaks follow a power law distribution, such that the expected size of microscopic outbreaks, s , indicates the position of a phase transition. Indeed, as T increases, s monotonically increases, diverges exactly at Tc, and then monotonically goes down; meanwhile, the expected macroscopic epidemic size, S , starts increasing monotonically at Tc.However, simple modifications to this model can dramatically alter its phenomenology. The epidemic threshold can vanish in networks with a scale-free degree distribution (2) or in growing networks (3). The phase transition can be discontinuous in the case of complex contagions with threshold exposition or reinforcement (4), interacting epidemics (5, 6), or adaptive networks (7-9). Recently, a unique phenomenon of double-phase transitions has also been observed numerically when networks have a very heterogeneous and clustered structure (10, 11).The current Zika virus (ZIKV) epidemic exhibits two unique properties. First, while the main transmission pathway for ZIKV is through a mosquito vector [predominantly Aedes aegypti or Aedes albopictus (13, 14)], a feature which has its own type of well-studied model and behavior (14-16), it can also spread through sexual contacts (17, 18). Second, the probability of sexual transmission is highly asymmetric between males and females. Although this is also true for ot...

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Epidemic cycles driven by host behaviour

Cited by 26 publications

References 34 publications

Behavioural change models for infectious disease transmission: a systematic review (2010–2015)

Behavioural change models for infectious disease transmission: a systematic review (2010–2015)

European Neolithic societies showed early warning signals of population collapse

Asymmetric percolation drives a double transition in sexual contact networks

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