ABSTRACT
The possibility to anticipate critical transitions through detecting loss of resilience has attracted attention in many fields. Resilience indicators rely on the mathematical concept of critical slowing down, which means that a system recovers more slowly from external perturbations when it gets closer to tipping point. This decrease in recovery rate can be reflected in rising autocorrelation and variance in data. To test whether resilience is changing, resilience indicators are often calculated using a moving window in long, continuous time series of the system. However, for some systems, it may be more feasible to collect several high-resolution time series in short periods of time, i.e., in bursts. Resilience indicators can then be calculated to detect a change of resilience between such bursts. Here, we compare the performance of both methods using simulated data and showcase the possible use of bursts in a case study using mood data to anticipate depression in a patient. With the same number of data points, the burst approach outperformed the moving window method, suggesting that it is possible to downsample the continuous time series and still signal an upcoming transition. We suggest guidelines to design an optimal sampling strategy. Our results imply that using bursts of data instead of continuous time series may improve the capacity to detect changes in resilience. This method is promising for a variety of fields, such as human health, epidemiology, or ecology, where continuous monitoring can be costly or unfeasible.
ABSTRACT
BACKGROUND: In recent years the Asian bush mosquito Aedes japonicus has invaded Europe, including the Netherlands. This species is a known vector for a range of arboviruses, possibly including West Nile virus (WNV). As WNV emerged in the Netherlands in 2020, it is important to investigate the vectorial capacity of mosquito species present in the Netherlands to estimate the risk of future outbreaks and further spread of the virus. Therefore, this study evaluates the potential role of Ae. japonicus in WNV transmission and spillover from birds to dead-end hosts in the Netherlands. METHODS: We conducted human landing collections in allotment gardens (Lelystad, the Netherlands) in June, August and September 2021 to study the diurnal and seasonal host-seeking behaviour of Ae. japonicus. Furthermore, their host preference in relation to birds using live chicken-baited traps was investigated. Vector competence of field-collected Ae. japonicus mosquitoes for two isolates of WNV at two different temperatures was determined. Based on the data generated from these studies, we developed a Susceptible-Exposed-Infectious-Recovered (SEIR) model to calculate the risk of WNV spillover from birds to humans via Ae. japonicus, under the condition that the virus is introduced and circulates in an enzootic cycle in a given area. RESULTS: Our results show that Ae. japonicus mosquitoes are actively host seeking throughout the day, with peaks in activity in the morning and evening. Their abundance in August was higher than in June and September. For the host-preference experiment, we documented a small number of mosquitoes feeding on birds: only six blood-fed females were caught over 4 full days of sampling. Finally, our vector competence experiments with Ae. japonicus compared to its natural vector Culex pipiens showed a higher infection and transmission rate when infected with a local, Dutch, WNV isolate compared to a Greek isolate of the virus. Interestingly, we also found a small number of infected Cx. pipiens males with virus-positive leg and saliva samples. CONCLUSIONS: Combining the field and laboratory derived data, our model predicts that Ae. japonicus could act as a spillover vector for WNV and could be responsible for a high initial invasion risk of WNV when present in large numbers.
Subject(s)
Aedes , Mosquito Vectors , West Nile Fever , West Nile virus , Animals , Aedes/virology , Aedes/physiology , Netherlands/epidemiology , Mosquito Vectors/virology , Mosquito Vectors/physiology , West Nile virus/physiology , West Nile Fever/transmission , West Nile Fever/virology , Humans , Female , Birds/virology , Chickens/virology , Host-Seeking Behavior , SeasonsABSTRACT
Importance: Psychiatric disorders may come and go with symptoms changing over a lifetime. This suggests the need for a paradigm shift in diagnosis and treatment. Here we present a fresh look inspired by dynamical systems theory. This theory is used widely to explain tipping points, cycles, and chaos in complex systems ranging from the climate to ecosystems. Observations: In the dynamical systems view, we propose the healthy state has a basin of attraction representing its resilience, while disorders are alternative attractors in which the system can become trapped. Rather than an immutable trait, resilience in this approach is a dynamical property. Recent work has demonstrated the universality of generic dynamical indicators of resilience that are now employed globally to monitor the risks of collapse of complex systems, such as tropical rainforests and tipping elements of the climate system. Other dynamical systems tools are used in ecology and climate science to infer causality from time series. Moreover, experiences in ecological restoration confirm the theoretical prediction that under some conditions, short interventions may invoke long-term success when they flip the system into an alternative basin of attraction. All this implies practical applications for psychiatry, as are discussed in part 2 of this article. Conclusions and Relevance: Work in the field of dynamical systems points to novel ways of inferring causality and quantifying resilience from time series. Those approaches have now been tried and tested in a range of complex systems. The same tools may help monitoring and managing resilience of the healthy state as well as psychiatric disorders.
Subject(s)
Mental Disorders , Humans , Mental Disorders/psychology , Resilience, Psychological , Systems TheoryABSTRACT
Importance: Dynamical systems theory is widely used to explain tipping points, cycles, and chaos in complex systems ranging from the climate to ecosystems. It has been suggested that the same theory may be used to explain the nature and dynamics of psychiatric disorders, which may come and go with symptoms changing over a lifetime. Here we review evidence for the practical applicability of this theory and its quantitative tools in psychiatry. Observations: Emerging results suggest that time series of mood and behavior may be used to monitor the resilience of patients using the same generic dynamical indicators that are now employed globally to monitor the risks of collapse of complex systems, such as tropical rainforest and tipping elements of the climate system. Other dynamical systems tools used in ecology and climate science open ways to infer personalized webs of causality for patients that may be used to identify targets for intervention. Meanwhile, experiences in ecological restoration help make sense of the occasional long-term success of short interventions. Conclusions and Relevance: Those observations, while promising, evoke follow-up questions on how best to collect dynamic data, infer informative timescales, construct mechanistic models, and measure the effect of interventions on resilience. Done well, monitoring resilience to inform well-timed interventions may be integrated into approaches that give patients an active role in the lifelong challenge of managing their resilience and knowing when to seek professional help.
Subject(s)
Mental Disorders , Humans , Mental Disorders/psychology , Mental Disorders/therapy , Resilience, Psychological , Systems TheoryABSTRACT
Mathematical models within the Ross-Macdonald framework increasingly play a role in our understanding of vector-borne disease dynamics and as tools for assessing scenarios to respond to emerging threats. These threats are typically characterized by a high degree of heterogeneity, introducing a range of possible complexities in models and challenges to maintain the link with empirical evidence. We systematically identified and analysed a total of 77 published papers presenting compartmental West Nile virus (WNV) models that use parameter values derived from empirical studies. Using a set of 15 criteria, we measured the dissimilarity compared with the Ross-Macdonald framework. We also retrieved the purpose and type of models and traced the empirical sources of their parameters. Our review highlights the increasing refinements in WNV models. Models for prediction included the highest number of refinements. We found uneven distributions of refinements and of evidence for parameter values. We identified several challenges in parametrizing such increasingly complex models. For parameters common to most models, we also synthesize the empirical evidence for their values and ranges. The study highlights the potential to improve the quality of WNV models and their applicability for policy by establishing closer collaboration between mathematical modelling and empirical work.