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.

The role of outdoor and indoor air quality in the spread of SARS-CoV-2: Overview and recommendations by the research group on COVID-19 and particulate matter (RESCOP commission).

There are important questions surrounding the potential contribution of outdoor and indoor air quality in the transmission of SARS-CoV-2 and perpetuation of COVID-19 epidemic waves. Environmental health may be a critical component of COVID-19 prevention. The public health community and health agencies should consider the evolving evidence in their recommendations and statements, and work to issue occupational guidelines. Evidence coming from the current epidemiological and experimental research is expected to add knowledge about virus diffusion, COVID-19 severity in most polluted areas, inter-personal distance requirements and need for wearing face masks in indoor or outdoor environments. The COVID-19 pandemic has highlighted the need for maintaining particulate matter concentrations at low levels for multiple health-related reasons, which may also include the spread of SARS-CoV-2. Indoor environments represent even a more crucial challenge to cope with, as it is easier for the SARS-COV2 to spread, remain vital and infect other subjects in closed spaces in the presence of already infected asymptomatic or mildly symptomatic people. The potential merits of preventive measures, such as CO2 monitoring associated with natural or controlled mechanical ventilation and air purification, for schools, indoor public places (restaurants, offices, hotels, museums, theatres/cinemas etc.) and transportations need to be carefully considered. Hospital settings and nursing/retirement homes as well as emergency rooms, infectious diseases divisions and ambulances represent higher risk indoor environments and may require additional monitoring and specific decontamination strategies based on mechanical ventilation or air purification.

An open challenge to advance probabilistic forecasting for dengue epidemics.

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue.

Does death from Covid-19 arise from a multi-step process?

The Covid-19 death rate increases exponentially with age, and the main risk factors are having underlying conditions such as hypertension, diabetes, cardiovascular disease, severe chronic respiratory disease and cancer. These characteristics are consistent with the multi-step model of disease. We applied this model to Covid-19 case fatality rates (CFRs) from China, South Korea, Italy, Spain and Japan. In all countries we found that a plot of log(CFR) against log(age) was approximately linear with a slope of about 5. We also conducted similar analyses for selected other respiratory diseases. SARS showed a similar log-log age-pattern to that of Covid-19, albeit with a lower slope, whereas seasonal and pandemic influenza showed quite different age-patterns. Thus, death from Covid-19 and SARS appears to follow a distinct age-pattern, consistent with a multi-step model of disease that in the case of Covid-19 is probably defined by comorbidities and age producing immune-related susceptibility.

Dynamical malaria models reveal how immunity buffers effect of climate variability.

Assessing the influence of climate on the incidence of Plasmodium falciparum malaria worldwide and how it might impact local malaria dynamics is complex and extrapolation to other settings or future times is controversial. This is especially true in the light of the particularities of the short- and long-term immune responses to infection. In sites of epidemic malaria transmission, it is widely accepted that climate plays an important role in driving malaria outbreaks. However, little is known about the role of climate in endemic settings where clinical immunity develops early in life. To disentangle these differences among high- and low-transmission settings we applied a dynamical model to two unique adjacent cohorts of mesoendemic seasonal and holoendemic perennial malaria transmission in Senegal followed for two decades, recording daily P. falciparum cases. As both cohorts are subject to similar meteorological conditions, we were able to analyze the relevance of different immunological mechanisms compared with climatic forcing in malaria transmission. Transmission was first modeled by using similarly unique datasets of entomological inoculation rate. A stochastic nonlinear human-mosquito model that includes rainfall and temperature covariates, drug treatment periods, and population variability is capable of simulating the complete dynamics of reported malaria cases for both villages. We found that under moderate transmission intensity climate is crucial; however, under high endemicity the development of clinical immunity buffers any effect of climate. Our models open the possibility of forecasting malaria from climate in endemic regions but only after accounting for the interaction between climate and immunity.

Tropospheric winds from northeastern China carry the etiologic agent of Kawasaki disease from its source to Japan.

Evidence indicates that the densely cultivated region of northeastern China acts as a source for the wind-borne agent of Kawasaki disease (KD). KD is an acute, coronary artery vasculitis of young children, and still a medical mystery after more than 40 y. We used residence times from simulations with the flexible particle dispersion model to pinpoint the source region for KD. Simulations were generated from locations spanning Japan from days with either high or low KD incidence. The postepidemic interval (1987-2010) and the extreme epidemics (1979, 1982, and 1986) pointed to the same source region. Results suggest a very short incubation period (<24 h) from exposure, thus making an infectious agent unlikely. Sampling campaigns over Japan during the KD season detected major differences in the microbiota of the tropospheric aerosols compared with ground aerosols, with the unexpected finding of the Candida species as the dominant fungus from aloft samples (54% of all fungal strains). These results, consistent with the Candida animal model for KD, provide support for the concept and feasibility of a windborne pathogen. A fungal toxin could be pursued as a possible etiologic agent of KD, consistent with an agricultural source, a short incubation time and synchronized outbreaks. Our study suggests that the causative agent of KD is a preformed toxin or environmental agent rather than an organism requiring replication. We propose a new paradigm whereby an idiosyncratic immune response, influenced by host genetics triggered by an environmental exposure carried on winds, results in the clinical syndrome known as acute KD.

Evidence of viral dissemination and seasonality in a Mediterranean river catchment: Implications for water pollution management.

Conventional wastewater treatment does not completely remove and/or inactive viruses; consequently, viruses excreted by the population can be detected in the environment. This study was undertaken to investigate the distribution and seasonality of human viruses and faecal indicator bacteria (FIB) in a river catchment located in a typical Mediterranean climate region and to discuss future trends in relation to climate change. Sample matrices included river water, untreated and treated wastewater from a wastewater treatment plant within the catchment area, and seawater from potentially impacted bathing water. Five viruses were analysed in the study. Human adenovirus (HAdV) and JC polyomavirus (JCPyV) were analysed as indicators of human faecal contamination of human pathogens; both were reported in urban wastewater (mean values of 10(6) and 10(5) GC/L, respectively), river water (10(3) and 10(2) GC/L) and seawater (10(2) and 10(1) GC/L). Human Merkel Cell polyomavirus (MCPyV), which is associated with Merkel Cell carcinoma, was detected in 75% of the raw wastewater samples (31/37) and quantified by a newly developed quantitative polymerase chain reaction (qPCR) assay with mean concentrations of 10(4) GC/L. This virus is related to skin cancer in susceptible individuals and was found in 29% and 18% of river water and seawater samples, respectively. Seasonality was only observed for norovirus genogroup II (NoV GGII), which was more abundant in cold months with levels up to 10(4) GC/L in river water. Human hepatitis E virus (HEV) was detected in 13.5% of the wastewater samples when analysed by nested PCR (nPCR). Secondary biological treatment (i.e., activated sludge) and tertiary sewage disinfection including chlorination, flocculation and UV radiation removed between 2.22 and 4.52 log10 of the viral concentrations. Climate projections for the Mediterranean climate areas and the selected river catchment estimate general warming and changes in precipitation distribution. Persistent decreases in precipitation during summer can lead to a higher presence of human viruses because river and sea water present the highest viral concentrations during warmer months. In a global context, wastewater management will be the key to preventing environmental dispersion of human faecal pathogens in future climate change scenarios.

Resistomes in freshwater bioaerosols and their impact on drinking and recreational water safety: A perspective.

Antibiotic resistance genes (ARGs) are widespread environmental pollutants of biological origin that pose a significant threat to human, animal, and plant health, as well as to ecosystems. ARGs are found in soil, water, air, and waste, and several pathways for global dissemination in the environment have been described. However, studies on airborne ARG transport through atmospheric particles are limited. The ARGs in microorganisms inhabiting an environment are referred to as the "resistome". A global search was conducted of air-resistome studies by retrieving bioaerosol ARG-related papers published in the last 30 years from PubMed. We found that there is no dedicated methodology for isolating ARGs in bioaerosols; instead, conventional methods for microbial culture and metagenomic analysis are used in combination with standard aerosol sampling techniques. There is a dearth of information on the bioaerosol resistomes of freshwater environments and their impact on freshwater sources used for drinking and recreational activities. More studies of aerobiome freshwater environments are needed to ensure the safe use of water and sanitation. In this review we outline and synthesize the few studies that address the freshwater air microbiome (from tap water, bathroom showers, rivers, lakes, and swimming pools) and their resistomes, as well as the likely impacts on drinking and recreational waters. We also discuss current knowledge gaps for the freshwater airborne resistome. This review will stimulate new investigations of the atmospheric microbiome, particularly in areas where both air and water quality are of public health concern.

The 2013 Chikungunya outbreak in the Caribbean was structured by the network of cultural relationships among islands.

In 2013, the Caribbean underwent an unprecedented epidemic of Chikungunya that affected 29 islands and mainland territories throughout the Caribbean in the first six months. Analysing the spread of the epidemic among the Caribbean islands, we show that the initial patterns of the epidemic can be explained by a network model based on the flight connections among islands. The network does not follow a random graph model and its topology is likely the product of geo-political relationships that generate increased connectedness among locations sharing the same language. Therefore, the infection propagated preferentially among islands that belong to the same cultural domain, irrespective of their human and vector population densities. Importantly, the flight network topology was also a more important determinant of the disease dynamics than the actual volume of traffic. Finally, the severity of the epidemic was found to depend, in the first instance, on which island was initially infected. This investigation shows how a simple epidemic model coupled with an appropriate human mobility model can reproduce the observed epidemiological dynamics. Also, it sheds light on the design of interventions in the face of the emergence of infections in similar settings of naive subpopulations loosely interconnected by host movement. This study delves into the feasibility of developing models to anticipate the emergence of vector-borne infections, showing the importance of network topology, bringing valuable methods for public health officials when planning control policies. Significance statement: The study shows how a simple epidemic model associated with an appropriate human mobility model can reproduce the observed epidemiological dynamics of the 2014 Chikungunya epidemic in the Caribbean region. This model sheds light on the design of interventions in the face of the emergence of infections in similar settings of naive subpopulations loosely interconnected by the host.

Importance of humidity for characterization and communication of dangerous heatwave conditions.

Heatwaves are one of the leading causes of climate-induced mortality. Using the examples of recent heatwaves in Europe, the United States and Asia, we illustrate how the communication of dangerous conditions based on temperature maps alone can lead to insufficient societal perception of health risks. Comparison of maximum daily values of temperature with physiological heat stress indices accounting for impacts of both temperature and humidity, illustrates substantial differences in geographical extent and timing of their respective peak values during these recent events. This signals the need to revisit how meteorological heatwaves and their expected impacts are communicated. Close collaboration between climate and medical communities is needed to select the best heat stress indicators, establish them operationally, and introduce them to the public. npj Climate and Atmospheric Science (2023) 6:33.

Considering waning immunity to better explain dengue dynamics.

Life-long serotype-specific immunity following dengue virus infection may not always occur, but the true extent of this effect is unknown. Analysis of more than 20 years of monotypic epidemics in the isolated French Polynesian islands revealed that whilst the risk of symptomatic dengue infection did conform to the classical paradigms of homotypic immunity and increased disease risk in heterotypic secondary infections, incorporation of waning immunity improved the ability of epidemiological models to capture the observed epidemic dynamics. Not only does this show how inclusion of waning immunity into classical models can reveal important facets of the immune response to natural dengue virus infection, it also has significant ramifications for vaccine development and implementation in dengue endemic areas.

Daylight saving time affects European mortality patterns.

Daylight saving time (DST) consists in a one-hour advancement of legal time in spring offset by a backward transition of the same magnitude in fall. It creates a minimal circadian misalignment that could disrupt sleep and homoeostasis in susceptible individuals and lead to an increased incidence of pathologies and accidents during the weeks immediately following both transitions. How this shift affects mortality dynamics on a large population scale remains, however, unknown. This study examines the impact of DST on all-cause mortality in 16 European countries for the period 1998-2012. It shows that mortality decreases in spring and increases in fall during the first two weeks following each DST transition. Moreover, the alignment of time data around DST transition dates revealed a septadian mortality pattern (lowest on Sundays, highest on Mondays) that persists all-year round, irrespective of seasonal variations, in men and women aged above 40.

A history of the MetaSUB consortium: Tracking urban microbes around the globe.

The MetaSUB Consortium, founded in 2015, is a global consortium with an interdisciplinary team of clinicians, scientists, bioinformaticians, engineers, and designers, with members from more than 100 countries across the globe. This network has continually collected samples from urban and rural sites including subways and transit systems, sewage systems, hospitals, and other environmental sampling. These collections have been ongoing since 2015 and have continued when possible, even throughout the COVID-19 pandemic. The consortium has optimized their workflow for the collection, isolation, and sequencing of DNA and RNA collected from these various sites and processing them for metagenomics analysis, including the identification of SARS-CoV-2 and its variants. Here, the Consortium describes its foundations, and its ongoing work to expand on this network and to focus its scope on the mapping, annotation, and prediction of emerging pathogens, mapping microbial evolution and antibiotic resistance, and the discovery of novel organisms and biosynthetic gene clusters.

Refractory periods and climate forcing in cholera dynamics.

Outbreaks of many infectious diseases, including cholera, malaria and dengue, vary over characteristic periods longer than 1 year. Evidence that climate variability drives these interannual cycles has been highly controversial, chiefly because it is difficult to isolate the contribution of environmental forcing while taking into account nonlinear epidemiological dynamics generated by mechanisms such as host immunity. Here we show that a critical interplay of environmental forcing, specifically climate variability, and temporary immunity explains the interannual disease cycles present in a four-decade cholera time series from Matlab, Bangladesh. We reconstruct the transmission rate, the key epidemiological parameter affected by extrinsic forcing, over time for the predominant strain (El Tor) with a nonlinear population model that permits a contributing effect of intrinsic immunity. Transmission shows clear interannual variability with a strong correspondence to climate patterns at long periods (over 7 years, for monsoon rains and Brahmaputra river discharge) and at shorter periods (under 7 years, for flood extent in Bangladesh, sea surface temperatures in the Bay of Bengal and the El Niño-Southern Oscillation). The importance of the interplay between extrinsic and intrinsic factors in determining disease dynamics is illustrated during refractory periods, when population susceptibility levels are low as the result of immunity and the size of cholera outbreaks only weakly reflects climate forcing.

A modified SEIR model to predict the COVID-19 outbreak in Spain and Italy: Simulating control scenarios and multi-scale epidemics.

After the spread of the SARS-CoV-2 epidemic out of China, evolution in the pandemic worldwide shows dramatic differences among countries. In Europe, the situation of Italy first and later Spain has generated great concen, and despite other countries show better prospects, large uncertainties yet remain on the future evolution and the efficacy of containment, mitigation, or attack strategies. This Manuscript was originally written in the last days of March as a way to report on the first current wave of the pandemic. The results were updated several times for March and also for the month of July. Here we applied a modified SEIR compartmental model accounting for the spread of infection during the latent period, in which we also incorporate effects of varying proportions of containment. We fit data to reported infected populations at the beginning of the first peak of the pandemic to account for the uncertainties in case reporting and study the scenario projections for the individual regions (CCAA). The aim of this model it's to evaluate the confinement rate at the first stages of the epidemic outbreak in order to assess the scenarios that minimize the incidence but also the mortality. Results indicate that with data for March 23, the epidemics follow an evolution similar to the isolation of 1 , 5 percent of the population, and if there were no effects of intervention actions it might reach a maximum of over 1.4 M infected around April 27. The effect on the epidemics of the ongoing partial confinement measures is yet unknown (an update of results with data until March 31st is included), but increasing the isolation around ten times more could drastically reduce the peak to over 100 k cases by early April, while each day of delay in taking this hard containment scenario represents a 90 percent increase of the infected population at the peak. Dynamics at the sub aggregated levels of CCAA show epidemics at the different levels of progression with the most worrying situation in Madrid and Catalonia. Increasing alpha values up to 10 times, in addition to a drastic reduction in clinical cases, would also more than a half the number of deaths. Updates for March 31st simulations indicate a substantial reduction in burden is underway. A similar approach conducted for Italy pre-and post-intervention also begins to suggest a substantial reduction in both infected and deaths has been achieved, showing the efficacy of drastic social distancing interventions. By last we show the real evolution of the pandemic up to the end of May and the beginning of July in order to calculate the real confinement rate from data to compare with the scenarios formulated at March.

Malaria trends in Ethiopian highlands track the 2000 'slowdown' in global warming.

A counterargument to the importance of climate change for malaria transmission has been that regions where an effect of warmer temperatures is expected, have experienced a marked decrease in seasonal epidemic size since the turn of the new century. This decline has been observed in the densely populated highlands of East Africa at the center of the earlier debate on causes of the pronounced increase in epidemic size from the 1970s to the 1990s. The turnaround of the incidence trend around 2000 is documented here with an extensive temporal record for malaria cases for both Plasmodium falciparum and Plasmodium vivax in an Ethiopian highland. With statistical analyses and a process-based transmission model, we show that this decline was driven by the transient slowdown in global warming and associated changes in climate variability, especially ENSO. Decadal changes in temperature and concurrent climate variability facilitated rather than opposed the effect of interventions.

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.

Projections of temperature-attributable mortality in Europe: a time series analysis of 147 contiguous regions in 16 countries.

BACKGROUND: Europe has emerged as a major climate change hotspot, both in terms of an increase in seasonal averages and climate extremes. Projections of temperature-attributable mortality, however, have not been comprehensively reported for an extensive part of the continent. Therefore, we aim to estimate the future effect of climate change on temperature-attributable mortality across Europe. METHODS: We did a time series analysis study. We derived temperature-mortality associations by collecting daily temperature and all-cause mortality records of both urban and rural areas for the observational period between 1998 and 2012 from 147 regions in 16 European countries. We estimated the location-specific temperature-mortality relationships by using standard time series quasi-Poisson regression in conjunction with a distributed lag non-linear model. These associations were used to transform the daily temperature simulations from the climate models in the historical period (1971-2005) and scenario period (2006-2099) into projections of temperature-attributable mortality. We combined the resulting risk functions with daily time series of future temperatures simulated by four climate models (ie, GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5) under three greenhouse gas emission scenarios (ie, Representative Concentration Pathway [RCP]2.6, RCP6.0, and RCP8.5), providing projections of future mortality attributable fraction due to moderate and extreme cold and heat temperatures. FINDINGS: Overall, 7·17% (95% CI 5·81-8·50) of deaths registered in the observational period were attributed to non-optimal temperatures, cold being more harmful than heat by a factor of ten (6·51% [95% CI 5·14-7·80] vs 0·65% [0·40-0·89]), and with large regional differences across countries-eg, ranging from 4·85% (95% CI 3·75-6·00) in Germany to 9·87% (8·53-11·19) in Italy. The projection of temperature anomalies by RCP scenario depicts a progressive increase in temperatures, more exacerbated in the high-emission scenario RCP8.5 (4·54°C by 2070-2099) than in RCP6.0 (2·89°C) and RCP2.6 (1·67°C). This increase in temperatures was transformed into attributable fraction. Projections consistently indicated that the increase in heat attributable fraction will start to exceed the reduction of cold attributable fraction in the second half of the 21st century, especially in the Mediterranean and in the higher emission scenarios. The comparison between scenarios highlighted the important role of mitigation, given that the total attributable fraction will only remain stable in RCP2.6, whereas the total attributable fraction will rapidly start to increase in RCP6.0 by the end of the century and in RCP8.5 already by the middle of the century. INTERPRETATION: The increase in heat attributable fraction will start to exceed the reduction of cold attributable fraction in the second half of the 21st century. This finding highlights the importance of implementing mitigation policies. These measures would be especially beneficial in the Mediterranean, where the high vulnerability to heat will lead to an imbalance between the decreasing cold and increasing heat-attributable mortality. FUNDING: None.

The end of social confinement and COVID-19 re-emergence risk.

The lack of effective pharmaceutical interventions for SARS-CoV-2 raises the possibility of COVID-19 recurrence. We explore different post-confinement scenarios by using a stochastic modified SEIR (susceptible-exposed-infectious-recovered) model that accounts for the spread of infection during the latent period and also incorporates time-decaying effects due to potential loss of acquired immunity, people's increasing awareness of social distancing and the use of non-pharmaceutical interventions. Our results suggest that lockdowns should remain in place for at least 60 days to prevent epidemic growth, as well as a potentially larger second wave of SARS-CoV-2 cases occurring within months. The best-case scenario should also gradually incorporate workers in a daily proportion at most 50% higher than during the confinement period. We show that decaying immunity and particularly awareness and behaviour have 99% significant effects on both the current wave of infection and on preventing COVID-19 re-emergence. Social distancing and individual non-pharmaceutical interventions could potentially remove the need for lockdowns.