Susceptible host availability modulates climate effects on dengue dynamics

Abstract

Experiments and models suggest that climate affects mosquito‐borne disease transmission. However, disease transmission involves complex nonlinear interactions between climate and population dynamics, which makes detecting climate drivers at the population level challenging. By analysing incidence data, estimated susceptible population size, and climate data with methods based on nonlinear time series analysis (collectively referred to as empirical dynamic modelling), we identified drivers and their interactive effects on dengue dynamics in San Juan, Puerto Rico. Climatic forcing arose only when susceptible availability was high: temperature and rainfall had net positive and negative effects respectively. By capturing mechanistic, nonlinear and context‐dependent effects of population susceptibility, temperature and rainfall on dengue transmission empirically, our model improves forecast skill over recent, state‐of‐the‐art models for dengue incidence. Together, these results provide empirical evidence that the interdependence of host population susceptibility and climate drives dengue dynamics in a nonlinear and complex, yet predictable way.

Environmental drivers of biseasonal anthrax outbreak dynamics in two multihost savanna systems

“…The concept of a conjectural causal relationship in CCM is that if A causes B, the shadow attractor constructed by B can have dynamic features of A and potentially predict the dynamics of A (Sugihara et al, 2012). Though the use of EDM is still relatively uncommon, it has been used for studying diseases or vectors and climatic/weather drivers (Deyle, Maher, et al, 2016;Grziwotz et al, 2018;Nova et al, 2021).…”

“…We standardized variables of anthrax cases, rainfall, and NDVI, with center at zero and standard deviations equal to one. For the EDM approaches, we followed and modified the procedures from Chang et al (2017) and Nova et al (2021). We first decided on an optimal embedding dimension between 1 and 10, for anthrax mortality by species and park using simplex projections, based on the highest Pearson's correlation coefficient with out-of-sample predictions using one time lag (Chang et al, 2017;Ye, Deyle, et al, 2015).…”

“…Using anthrax mortalities to reconstruct NDVI and rainfall dynamics with time-lags from one to six intervals (2 weeks to 3 months), we repeatedly conducted CCM to evaluate relationships and the most important time lags for the environmental variable (Wang et al, 2018;Ye, Deyle, et al, 2015). We used the Pearson's correlation coefficient ρ (cross-mapping skill) between predicted and observed environmental values as the index to evaluate the relationships (Nova et al, 2021;Sugihara et al, 2012;Ye, Deyle, et al, 2015). To calculate ρ, 500 samples were randomly selected for each library size (i.e., number of total intervals calculated) in CCM.…”

Short-term effects of tropical cyclones on the incidence of dengue: a time-series study in Guangzhou, China

“…Natural cubic spline was used for time and week of year (woy) to control long-term trend and seasonality. Previous studies have discovered that meteorological variables influenced dengue transmission [ 27 – 29 ]; hence, we included weekly average temperature, cumulative precipitation, and average relative humidity in DLNM to adjust for potential effects of these meteorological variables. Considering the delayed transmission of dengue virus in mosquito and human hosts and the incubation period of dengue, we explored a 4-week lag for tropical cyclones when establishing cross-basis functions in the model.…”

Generalism drives abundance: A computational causal discovery approach

“…In ecology, the most adopted tool of causal discovery is convergent cross mapping [75]. While this method has been widely applied to many ecological questions [76][77][78][79][80], this method only works for time series data. The field of causal discovery has recently developed a line of methods that work with cross-sectional data, such as the additive noise model and the method of information-geometric inference.…”