Statistical Inference for Multi-Pathogen Systems

There is growing interest in understanding the nature and consequences of interactions among infectious agents. Pathogen interactions can be operational at different scales, either within a co-infected host or in host populations where they co-circulate, and can be either cooperative or competitive. The detection of interactions among pathogens has typically involved the study of synchrony in the oscillations of the protagonists, but as we show here, phase association provides an unreliable dynamical fingerprint for this task. We assess the capacity of a likelihood-based inference framework to accurately detect and quantify the presence and nature of pathogen interactions on the basis of realistic amounts and kinds of simulated data. We show that when epidemiological and demographic processes are well understood, noisy time series data can contain sufficient information to allow correct inference of interactions in multi-pathogen systems. The inference power is dependent on the strength and time-course of the underlying mechanism: stronger and longer-lasting interactions are more easily and more precisely quantified. We examine the limitations of our approach to stochastic temporal variation, under-reporting, and over-aggregation of data. We propose that likelihood shows promise as a basis for detection and quantification of the effects of pathogen interactions and the determination of their (competitive or cooperative) nature on the basis of population-level time-series data

Adaptation of sea turtles to climate warming: Will phenological responses be sufficient to counteract changes in reproductive output?

© 2023 The Authors. Global Change Biology published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.Sea turtles are vulnerable to climate change since their reproductive output is influenced by incubating temperatures, with warmer temperatures causing lower hatching success and increased feminization of embryos. Their ability to cope with projected increases in ambient temperatures will depend on their capacity to adapt to shifts in climatic regimes. Here, we assessed the extent to which phenological shifts could mitigate impacts from increases in ambient temperatures (from 1.5 to 3°C in air temperatures and from 1.4 to 2.3°C in sea surface temperatures by 2100 at our sites) on four species of sea turtles, under a “middle of the road” scenario (SSP2-4.5). Sand temperatures at sea turtle nesting sites are projected to increase from 0.58 to 4.17°C by 2100 and expected shifts in nesting of 26–43 days earlier will not be sufficient to maintain current incubation temperatures at 7 (29%) of our sites, hatching success rates at 10 (42%) of our sites, with current trends in hatchling sex ratio being able to be maintained at half of the sites. We also calculated the phenological shifts that would be required (both backward for an earlier shift in nesting and forward for a later shift) to keep up with present-day incubation temperatures, hatching success rates, and sex ratios. The required shifts backward in nesting for incubation temperatures ranged from −20 to −191 days, whereas the required shifts forward ranged from +54 to +180 days. However, for half of the sites, no matter the shift the median incubation temperature will always be warmer than the 75th percentile of current ranges. Given that phenological shifts will not be able to ameliorate predicted changes in temperature, hatching success and sex ratio at most sites, turtles may need to use other adaptive responses and/or there is the need to enhance sea turtle resilience to climate warming.Peer reviewe

Chronic coronary syndromes without standard modifiable cardiovascular risk factors and outcomes: the CLARIFY registry

Background and Aims: It has been reported that patients without standard modifiable cardiovascular (CV) risk factors (SMuRFs—diabetes, dyslipidaemia, hypertension, and smoking) presenting with first myocardial infarction (MI), especially women, have a higher in-hospital mortality than patients with risk factors, and possibly a lower long-term risk provided they survive the post-infarct period. This study aims to explore the long-term outcomes of SMuRF-less patients with stable coronary artery disease (CAD). Methods: CLARIFY is an observational cohort of 32 703 outpatients with stable CAD enrolled between 2009 and 2010 in 45 countries. The baseline characteristics and clinical outcomes of patients with and without SMuRFs were compared. The primary outcome was a composite of 5-year CV death or non-fatal MI. Secondary outcomes were 5-year all-cause mortality and major adverse cardiovascular events (MACE—CV death, non-fatal MI, or non-fatal stroke). Results: Among 22 132 patients with complete risk factor and outcome information, 977 (4.4%) were SMuRF-less. Age, sex, and time since CAD diagnosis were similar across groups. SMuRF-less patients had a lower 5-year rate of CV death or non-fatal MI (5.43% [95% CI 4.08–7.19] vs. 7.68% [95% CI 7.30–8.08], P = 0.012), all-cause mortality, and MACE. Similar results were found after adjustments. Clinical event rates increased steadily with the number of SMuRFs. The benefit of SMuRF-less status was particularly pronounced in women. Conclusions: SMuRF-less patients with stable CAD have a substantial but significantly lower 5-year rate of CV death or non-fatal MI than patients with risk factors. The risk of CV outcomes increases steadily with the number of risk factors