Short-term temperature fluctuations increase disease in a Daphnia-parasite infectious disease system

Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations

Climate, weather and environmental change have significantly influenced patterns of infectious disease transmission, necessitating the development of early warning systems to anticipate potential impacts and respond in a timely and effective way. Statistical modelling plays a pivotal role in understanding the intricate relationships between climatic factors and infectious disease transmission. For example, time series regression modelling and spatial cluster analysis have been employed to identify risk factors and predict spatial and temporal patterns of infectious diseases. Recently advanced spatio-temporal models and machine learning offer an increasingly robust framework for modelling uncertainty, which is essential in climate-driven disease surveillance due to the dynamic and multifaceted nature of the data. Moreover, Artificial Intelligence (AI) techniques, including deep learning and neural networks, excel in capturing intricate patterns and hidden relationships within climate and environmental data sets. Web-based data has emerged as a powerful complement to other datasets encompassing climate variables and disease occurrences. However, given the complexity and non-linearity of climate-disease interactions, advanced techniques are required to integrate and analyse these diverse data to obtain more accurate predictions of impending outbreaks, epidemics or pandemics. This article presents an overview of an approach to creating climate-driven early warning systems with a focus on statistical model suitability and selection, along with recommendations for utilizing spatio-temporal and machine learning techniques. By addressing the limitations and embracing the recommendations for future research, we could enhance preparedness and response strategies, ultimately contributing to the safeguarding of public health in the face of evolving climate challenges.

Extreme heat reduces host and parasite performance in a butterfly-parasite interaction

Environmental temperature fundamentally shapes insect physiology, fitness and interactions with parasites. Differential climate warming effects on host versus parasite biology could exacerbate or inhibit parasite transmission, with far-reaching implications for pollination services, biocontrol and human health. Here, we experimentally test how controlled temperatures influence multiple components of host and parasite fitness in monarch butterflies (Danaus plexippus) and their protozoan parasites Ophryocystis elektroscirrha. Using five constant-temperature treatments spanning 18-34°C, we measured monarch development, survival, size, immune function and parasite infection status and intensity. Monarch size and survival declined sharply at the hottest temperature (34°C), as did infection probability, suggesting that extreme heat decreases both host and parasite performance. The lack of infection at 34°C was not due to greater host immunity or faster host development but could instead reflect the thermal limits of parasite invasion and within-host replication. In the context of ongoing climate change, temperature increases above current thermal maxima could reduce the fitness of both monarchs and their parasites, with lower infection rates potentially balancing negative impacts of extreme heat on future monarch abundance and distribution.

Fluctuating temperatures have a surprising effect on disease transmission

Theory predicts that temperature fluctuations should reduce performance near an organism's thermal optimum. A new study in PLOS Biology found fluctuations increased parasite transmission instead, highlighting questions about how climate change will impact infectious diseases.