Fasting Blood Glucose in a Ghanaian Adult is Causally Linked with Malaria Parasite Count: A Mechanistic Case Study Using Convergent Cross Mapping

Background: Adults with diabetes mellitus (DM) in malaria-endemic areas might be more susceptible to Plasmodium infection than healthy individuals. Herein, we aimed at verifying the hypothesis that increased fasting blood glucose (FBG) promotes parasite growth as reflected by increased parasite density. Methods: Seven adults without DM were recruited in rural Ghana to determine the relationships between FBG and malaria parasite load. Socio-economic data were recorded in questionnaire-based interviews. Over a period of 6 weeks, we measured FBG and Plasmodium spc. infection in peripheral blood samples photometrically and by polymerase chain reaction (PCR)-assays, respectively. Daily physical activity and weather data were documented via smartphone recording. For the complex natural systems of homeostatic glucose control and Plasmodium spc. lifecycle, empirical dynamic modelling was applied. Results: At baseline, four men and three women (median age, 33 years; interquartile range, 30-48) showed a median FBG of 5.5 (5.1-6.0 mmol/L); one participant had an asymptomatic Plasmodium spc. infection (parasite density: 240 /µL). In this participant, convergent cross mapping (CCM) for 34 consecutive days, showed that FBG was causally affected by parasite density (p <0.02), while the reciprocal relationship was not discernible (p >0.05). Additionally, daily ambient temperature affected parasite density (p<0.01).Conclusion: In this study population living in a malaria-endemic area, we successfully piloted time series analyses for the relationships between FBG and Plasmodium spc. density. Longer observation periods and larger samples are required to confirm these findings and determine the direction of causality.

A potential feedback loop underlying glacial-interglacial cycles

The sawtooth-patterned glacial-interglacial cycles in the Earth’s atmospheric temperature are a well-known, though poorly understood phenomenon. Pinpointing the relevant mechanisms behind these cycles will not only provide insights into past climate dynamics, but also help predict possible future responses of the Earth system to changing CO$$_2$$ 2 levels. Previous work on this phenomenon suggests that the most important underlying mechanisms are interactions between marine biological production, ocean circulation, temperature and dust. So far, interaction directions (i.e., what causes what) have remained elusive. In this paper, we apply Convergent Cross-Mapping (CCM) to analyze paleoclimatic and paleoceanographic records to elucidate which mechanisms proposed in the literature play an important role in glacial-interglacial cycles, and to test the directionality of interactions. We find causal links between ocean ventilation, biological productivity, benthic $$\delta ^{18}$$ δ 18 O and dust, consistent with some but not all of the mechanisms proposed in the literature. Most importantly, we find evidence for a potential feedback loop from ocean ventilation to biological productivity to climate back to ocean ventilation. Here, we propose the hypothesis that this feedback loop of connected mechanisms could be the main driver for the glacial-interglacial cycles.

Beyond linearity, stability, and equilibrium: The edm package for empirical dynamic modeling and convergent cross-mapping in Stata

How can social and health researchers study complex dynamic systems that function in nonlinear and even chaotic ways? Common methods, such as experiments and equation-based models, may be ill-suited to this task. To address the limitations of existing methods and offer nonparametric tools for characterizing and testing causality in nonlinear dynamic systems, we introduce the edm command in Stata. This command implements three key empirical dynamic modeling (EDM) methods for time series and panel data: 1) simplex projection, which characterizes the dimensionality of a system and the degree to which it appears to function deterministically; 2) S-maps, which quantify the degree of nonlinearity in a system; and 3) convergent cross-mapping, which offers a nonparametric approach to modeling causal effects. We illustrate these methods using simulated data on daily Chicago temperature and crime, showing an effect of temperature on crime but not the reverse. We conclude by discussing how EDM allows checking the assumptions of traditional model-based methods, such as residual autocorrelation tests, and we advocate for EDM because it does not assume linearity, stability, or equilibrium.

Causality analysis in process control based on denoising and periodicity-removing CCM

Purpose1. To improve the causality analysis performance, a novel causality detector based on time-delayed convergent cross mapping (TD-CCM) is proposed in this work. 2. Identify the root cause of plant-wide oscillations in process control system.Design/methodology/approachA novel causality analysis framework is proposed based on denoising and periodicity-removing TD-CCM (time-delayed convergent cross mapping). We first point out that noise and periodicity have adverse effects on causality detection. Then, the empirical mode decomposition (EMD) and detrended fluctuation analysis (FDA) are combined to achieve denoising. The periodicities are effectively removed through singular spectrum analysis (SSA). Following, the TD-CCM can accurately capture the causalities and locate the root cause by analyzing the filtered signals.Findings1. A novel causality detector based on denoising and periodicity-removing time-delayed convergent cross mapping (TD-CCM) is proposed. 2. Simulation studies show that the proposed method is able to improve the causality analysis performance. 3. Industrial case study shows the proposed method can be used to analyze the root cause of plant-wide oscillations in process control system.Originality/value1. A novel causality detector based on denoising and periodicity-removing time-delayed convergent cross mapping (TD-CCM) is proposed. 2. The influences of noise and periodicity on causality analysis are investigated. 3. Simulations and industrial case shows that the proposed method can improve the causality analysis performance and can be used to identify the root cause of plant-wide oscillations in process control system.