Climate determines transmission hotspots of Polycystic Echinococcosis, a life-threatening zoonotic disease, across Pan-Amazonia.

Polycystic Echinococcosis (PE), a neglected life-threatening zoonotic disease caused by the cestode Echinococcus vogeli, is endemic in the Amazon. Despite being treatable, PE reaches a case fatality rate of around 29% due to late or missed diagnosis. PE is sustained in Pan-Amazonia by a complex sylvatic cycle. The hunting of its infected intermediate hosts (especially the lowland paca Cuniculus paca) enables the disease to further transmit to humans, when their viscera are improperly handled. In this study, we compiled a unique dataset of host occurrences (~86000 records) and disease infections (~400 cases) covering the entire Pan-Amazonia and employed different modeling and statistical tools to unveil the spatial distribution of PE's key animal hosts. Subsequently, we derived a set of ecological, environmental, climatic, and hunting covariates that potentially act as transmission risk factors and used them as predictors of two independent Maximum Entropy models, one for animal infections and one for human infections. Our findings indicate that temperature stability promotes the sylvatic circulation of the disease. Additionally, we show how El Niño-Southern Oscillation (ENSO) extreme events disrupt hunting patterns throughout Pan-Amazonia, ultimately affecting the probability of spillover. In a scenario where climate extremes are projected to intensify, climate change at regional level appears to be indirectly driving the spillover of E. vogeli. These results hold substantial implications for a wide range of zoonoses acquired at the wildlife-human interface for which transmission is related to the manipulation and consumption of wild meat, underscoring the pressing need for enhanced awareness and intervention strategies.

Climatic signatures in the different COVID-19 pandemic waves across both hemispheres.

The roles of climate and true seasonal signatures in the epidemiology of emergent pathogens, and that of SARS-CoV-2 in particular, remain poorly understood. With a statistical method designed to detect transitory associations, we show, for COVID-19 cases, strong consistent negative effects of both temperature and absolute humidity at large spatial scales. At finer spatial resolutions, we substantiate these connections during the seasonal rise and fall of COVID-19. Strong disease responses are identified in the first two waves, suggesting clear ranges for temperature and absolute humidity that are similar to those formerly described for seasonal influenza. For COVID-19, in all studied regions and pandemic waves, a process-based model that incorporates a temperature-dependent transmission rate outperforms baseline formulations with no driver or a sinusoidal seasonality. Our results, so far, classify COVID-19 as a seasonal low-temperature infection and suggest an important contribution of the airborne pathway in the transmission of SARS-CoV-2, with implications for the control measures we discuss.