van de Leemput, I. A. | bioRxiv Science

bioRxiv2024

Multivariate resilience indicators to anticipate vector-borne disease outbreaks: a West Nile virus case-study

Background and aimTo prevent the spread of infectious diseases, successful interventions require early detection. The timing of implementation of preventive measures is crucial, but as outbreaks are hard to anticipate, control efforts often start too late. This applies to mosquito-borne diseases, for which the multifaceted nature of transmission complicates surveillance. Resilience indicators have been studied as a generic, model-free early warning method. However, the large data requirements limit their use in practice. In the present study, we compare the performance of multivariate indicators of resilience, combining the information contained in multiple data sources, to the performance of univariate ones focusing on one single time series. Additionally, by comparing various monitoring scenarios, we aim to find which data sources are the most informative as early warnings. Methods and resultsWest Nile virus was used as a case study due to its complex transmission cycle with different hosts and vectors interacting. A synthetic dataset was generated using a compartmental model under different monitoring scenarios, including data-poor scenarios. Multivariate indicators of resilience relied on different data reduction techniques such as principal component analysis (PCA) and Max Autocorrelation Factor analysis (MAF). Multivariate indicators outperformed univariate ones, especially in data-poor scenarios such as reduced resolution or observation probabilities. This finding held across the different monitoring scenarios investigated. In the explored system, species that were more involved in the transmission cycle or preferred by the mosquitoes were not more informative for early warnings. ImplicationsOverall, these results indicate that combining multiple data sources into multivariate indicators can help overcome the challenges of data requirements for resilience indicators. The final decision should be based on whether the additional effort is worth the gain in prediction performance. Future studies should confirm these findings in real-world data and estimate the sensitivity, specificity, and lead time of multivariate resilience indicators. Author summaryVector-borne diseases (VBD) represent a significant proportion of infectious diseases and are expanding their range every year because of among other things climate change and increasing urbanization. Successful interventions against the spread of VBD requires anticipation. Resilience indicators are a generic, model-free approach to anticipate critical transitions including disease outbreaks, however the large data requirements limit their use in practice. Since the transmission of VBD involves several species interacting with one another, which can be monitored as different data sources. The information contained by these different data sources can be combined to calculate multivariate indicators of resilience, allowing a reduction of the data requirements compared to univariate indicators relying solely on one data source. We found that such multivariate indicators outperformed univariate indicators in data-poor contexts. Multivariate indicators could be used to anticipate not only VBD outbreaks but also other transitions in complex systems such as ecosystems collapse or episodes of chronic diseases. Adapting the surveillance programs to collect the relevant data for multivariate indicators of resilience entails new challenges related to costs, logistic ramifications and coordination of different institutions involved in surveillance.

systems biology↗

bioRxiv2023

Monitoring Resilience in Bursts

The possibility to anticipate critical transitions through detecting loss of resilience has attracted attention in a variety of fields. Resilience indicators rely on the mathematical concept of critical slowing down, which means that a system recovers increasingly slowly from external perturbations when approaching a 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 in a system between such bursts. Here, we compare the performance of both methods using simulated data, and showcase possible use of bursts in a case-study using mood data to anticipate depression in a patient. Using the same number of data points, the burst approach outperformed the moving window method, suggesting that it is possible to down-sample the continuous time series and still signal of 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 systems resilience. This method is promising for a variety of fields, such as human health, epidemiology, or ecology, where continuous monitoring is costly or unfeasible. Significance statementGauging the risk of tipping points is of great relevance in complex systems ranging from health to climate, and ecosystems. For this purpose, dynamical indicators of resilience are being derived from long continuous time series to monitor the system and obtain early warning signals. However, gathering such data is often prohibitively expensive or practically unfeasible. Here we show that collecting data in brief, intense bursts may often solve the problem, making it possible to estimate change in resilience between the bursts withrelatively high precision. This may be particularly useful for monitoring resilience of humans or animals, where brief time series of blood pressure, balance, mood or other relevant markers may be collected relatively easily to help estimating systemic resilience.

ecology↗