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Next generation sequencing technologies have revolutionized the field of environmental science. Widely used short-read sequencing enables accurate microbial identification but is often slow, requires large centralised equipment, and does not allow to distinguish highly homologous genomic regions, making the taxonomic classification and genome assembly of closely-related species complicated. Those challenges may be resolved by the implementation of real-time long-read sequencing technologies such as Nanopore sequencing. Nanopore sequencing is a rapid technology that can be employed in situ through portable devices. The long sequencing reads can further improve precision of species identification in mixed microbial communities, and can provide more detailed characterisations of individual microorganisms. Despite its promising application to other environmental samples, nanopore sequencing has not yet been implemented to study bioaerosols and detect pathogens in air samples.Here, we used nanopore sequencing to analyse the environmental DNA extracted from air samples collected in Barcelona, Spain, as an example of a highly urbanised area. As the total amount of DNA found in the air is significantly lower compared to other typical environmental material such as soil or water, we first optimised DNA extraction in combination with the newest nanopore rapid sequencing protocols to achieve a highly accurate genome assembly-based description of the air microbiome. We identified the presence of potentially pathogenic organisms, and annotated the genome assemblies with respect to phenotypic read-outs such as increased virulence and antimicrobial resistance. We hereby compared the air microbiome assessed through a variety of air sampling methods, including high-volume air samplers, liquid impingers, and standard air filtering approaches. We further assessed if our optimized DNA extraction methods introduced a bias into the described microbiome composition by including positive controls. We were able to demonstrate that it is possible to identify airborne pathogens even when the amount of DNA is low, by leveraging cutting-edge nanopore sequencing technology without requiring cultivation or amplification. This method has the potential to enhance and speed up the surveillance of airborne diseases such as pneumonia, measles, and COVID-19. In upcoming research, we plan to utilise this framework to study the microorganisms present in the air in different settings in order to detect the potential emergence of antimicrobial resistance or highly virulent pathogens in real-time.

In this Comment, we show for the first time the contribution of previously unobserved extreme heat to the trends and seasonality changes of temperature-attributable mortality (TAM) projections in 147 contiguous regions in 16 European countries.

BACKGROUND AND AIM: The Barcelona’s Climate Shelters pilot project has converted eleven primary schools, considered vulnerable to heat and air pollution, into urban climate shelters by implementing...

Significance Forecasts routinely provide critical information for dangerous weather events but not yet for epidemics. Researchers develop computational models that can be used for infectious disease forecasting, but forecasts have not been broadly compared or tested. We collaboratively compared forecasts from 16 teams for 8 y of dengue epidemics in Peru and Puerto Rico. The comparison highlighted components that forecasts did well (e.g., situational awareness late in the season) and those that need more work (e.g., early season forecasts). It also identified key facets to improve forecasts, including using multiple model ensemble approaches to improve overall forecast skill. Future infectious disease forecasting work can build on these findings and this framework to improve the skill and utility of forecasts., 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.

SummaryExplanations for the genesis and propagation of recurrent cholera pandemics since 1817 have remained elusive. Evolutionary change of the pathogen is presumed to have been a dominant factor behind the 7th “El Tor” pandemic, but little is known to support this hypothesis for preceding pandemics. We investigate the concomitant roles of climate and putative strain variation for the 6th cholera pandemic (1899-1923; the one with the highest ever associated mortality in India), using newly assembled historical records for climate variables and cholera deaths for Bengal, Assam, Bihar and Bombay provinces in former British India. We compare this historical pandemic with the 7th (El Tor) one and with the temporary emergence of the O139 strain in Bangladesh and globally. Finally, we ran multi-model climate simulations to infer past and future long-term means of rainfall distributions on the basis of 39 models for 1861-2100, and for different periods of 50 years (1875-1925; 1975-2025 and 2050-2100).The 6th cholera pandemic featured a large scale synchronisation with a delay of a few years in both seasonal and interannual cholera variability over the endemic Bengal region during the El Niño event of 1904-07. Additional evidence supporting the establishment of a new strain includes a shift of cholera incidence to older age groups, an increase in the case fatality rate and the suppression of the spring cholera peak.The 6th cholera pandemic of Indian origin was associated with a novel and particularly invasive strain of new territory, and also with some delay, of endemic parts of India that act as a genetic regional reservoir of the disease. Climate anomalies appear to have played an important role in facilitating the establishment of this invasive strain, with environmental conditions similar to those underlying strain changes associated with ENSO in today’s Bangladesh. The evolutionary change of pathogens can act synergistically with climatic conditions in the replacement and propagation of emerging strains, as was the case in cholera’s 7th pandemic. Increased climate variability and extremes under global warming would thus provide windows of opportunity for emerging new pathogens.

Climate change can both facilitate zoonotic spillovers and have an effect on transmission chains. These effects, alongside human behavior and awareness, need to be integrated in pandemic forecasting models.

Sea surface temperature conditions in the central-eastern tropical Pacific indicated a mild El Niño event in October 2018, which continued throughout the spring of 2019. The global El Niño Southern Oscillation (ENSO) forecast consensus was that these generally weak warm patterns would persist at least until the end of the summer. El Niño and its impact on local climatic conditions in southern coastal Ecuador influence the inter-annual transmission of dengue fever in the region. In this study, we use an ENSO model to issue forecasts of El Niño for the year 2019, which are then used to predict local climate variables, precipitation and minimum temperature, in the city of Machala, Ecuador. All these forecasts are incorporated in a dengue transmission model, specifically developed and tested for this area, to produce out-of-sample predictions of dengue risk. Predictions are issued at the beginning of January 2019 for the whole year, thus providing the longest forecast lead time of 12 months. Preliminary results indicated that the mild El Niño event did not provide the optimum climate conditions for dengue transmission, with the model predicting a very low probability of a dengue outbreak during the typical peak season in Machala in 2019. This is contrary to 2016, when a large El Niño event resulted in excess rainfall and warmer temperatures in the region, and a dengue outbreak occurred 3 months earlier than expected. This event was successfully predicted using a similar prediction framework to the one applied here. With the present study, we continue our efforts to build and test a climate service tool to issue early warnings of dengue outbreaks in the region.

Implementing adequate health preventing measures is essential for public health decision making, particularly in the current context of rising temperatures. Most of the early warning systems are only based on climate data, and in very few cases they truly model the actual impact of the climate phenomena.Here we establish, for the first-time, the theoretical basis for the development of operational heat-health early warning systems that combine climate and health data. We studied the predictability of Temperature Attributable Mortality (TAM) at lead times of up to 15 days for a very large ensemble of European regions. To achieve this goal, we analysed daily counts of all-cause mortality for the period 1998-2012 in 147 NUTS2 regions in 16 European countries, representing more than 400 million people, and daily high-resolution weather forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). We applied epidemiological models for the fitting of the temperature-mortality relationship in each of the regions, accounting for the different vulnerabilities and socio-demographic characteristics existing in Europe. We compared the predictive skill of the temperature and health forecasts on seasons and days with higher mortality risk. We conclude that the predictability of temperature can be used to issue skilful forecasts of TAM. In general, the predictability limit of temperature is similar to the one of TAM, which implies that the use of epidemiological models to transform the climate variables into health information does not reduce the lead time limit with significant forecast skill. Nonetheless, the spatial heterogeneity of the predictability lead time for TAM is higher than for temperature, especially in summer, where the complex shape of the temperature-mortality association amplifies the forecast errors. Overall, we find a nearly-linear relationship between the predictability of temperature and TAM for different seasons and regions, suggesting that future improvements in the predictability of temperature could automatically lead to improvements in the predictability of TAM.

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]vs0·65% [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 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. Using a statistical method for transient correlations, the waxing and waning in levels of population infection by SARS-CoV-2 are shown to respond to temperature and absolute humidity, across geographical locations and for different temporal and spatial resolutions.

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. Supplementary Information The online version of this article (10.1007/s10654-020-00711-7).

When COVID-19 began to spread, environmental scientists recognized that the world faced a dangerous upper respiratory viral disease that might exhibit sensitivity to seasonal weather conditions. Many of these scientists have sought to aid COVID-19 response by studying the potential to monitor, forecast, or project disease transmission rates or symptom severity as a function of climate zone, season, meteorological variability, air quality, and other environmental parameters1,2,3,4. The rapid pace of COVID-19 research has meant that studies on this topic appeared on pre-print servers and then on news and social media outlets faster than the information could be cross-checked and peer-reviewed. As many such studies accumulated, it became clear that reported evidence was often contradictory, and in some cases studies were being selected subjectively in a manner that seemed intended to support political agendas. Carlson et al.5 recently provided a cogent assessment of the policy-relevant challenges associated with studies that have attempted to quantify meteorological sensitivities of the virus and the disease. We appreciate this perspective. Here we argue that the research community must act to ensure that work on this topic meets its potential to contribute to pandemic understanding and response, and that fears of inappropriate data analysis or miscommunication do not dampen innovation or the effective use of research results. Por pares

Anthropogenic pollution has frequently been linked to myriad human ailments despite clear mechanistic links are yet lacking, a fact that severely downgraded its actual relevance. Now a prominent unnoticed sub-weekly cycle (SWC) of 3.5 days is uncovered in the long-term epidemiological records of Kawasaki disease (KD) in Japan, a mysterious vasculitis of yet unknown origin. After ruling out the effect of reporting biases, the analysis of Light Detection and Ranging (LIDAR) atmospheric profiles further confirms that this variability is linked to atmospheric particles with an aerodynamic diameter less than 1 µm. SWC accounts for 20% of the variance in KD and its contribution is stable throughout the entire epidemiological record dating back to 1970, both at the prefecture level and for entire Japan. KD maxima in 2010-2016 always occur in full synchrony with LIDAR particle arrival in diverse locations such as Tokyo, Toyama and Tsukuba as well as for the entire of Japan. Rapid intrusion of aerosols from heights up to 6km to the surface is observed with KD admissions co-varying with their metal chemical composition. While regional intensity of winds has not changed in the interval 1979-2015, our study instead points for the first time to increased anthropogenic pollution as a necessary co-factor in the occurrence of KD and sets the field to associate other similar human vasculitis.

Covid-19 death has a different relationship with age than is the case for other severe respiratory pathogens. The Covid-19 death rate increases exponentially with age, and the main risk factors are age itself, as well as having underlying conditions such as hypertension, diabetes, cardiovascular disease, severe chronic respiratory disease and cancer. Furthermore, the almost complete lack of deaths in children suggests that infection alone is not sufficient to cause death; rather, one must have gone through a number of changes, either as a result of undefined aspects of aging, or as a result of chronic disease. These characteristics of Covid-19 death are consistent with the multistep model of disease, a model which has primarily been used for cancer, and more recently for amyotrophic lateral sclerosis (ALS). We applied the multi-step model to data on Covid-19 case fatality rates (CFRs) from China, South Korea, Italy, Spain and Japan. In all countries we found that a plot of ln (CFR) against ln (age) was approximately linear with a slope of about 5. As a comparison, 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 multistep model of disease that in the case of Covid-19 is probably defined by comorbidities and age producing immune-related susceptibility. Identification of these steps would be potentially important for prevention and therapy for SARS-COV-2 infection.

Understanding the transition of epidemic to endemic dengue transmission remains a challenge in regions where serotypes co-circulate and there is extensive human mobility. French Polynesia, an isolated group of 117 islands of which 72 are inhabited, distributed among five geographically separated subdivisions, has recorded mono-serotype epidemics since 1944, with long inter-epidemic periods of circulation. Laboratory confirmed cases have been recorded since 1978, enabling exploration of dengue epidemiology under monotypic conditions in an isolated, spatially structured geographical location. A database was constructed of confirmed dengue cases, geolocated to island for a 35-year period. Statistical analyses of viral establishment, persistence and fade-out as well as synchrony among subdivisions were performed. Seven monotypic and one heterotypic dengue epidemic occurred, followed by low-level viral circulation with a recrudescent epidemic occurring on one occasion. Incidence was asynchronous among the subdivisions. Complete viral die-out occurred on several occasions with invasion of a new serotype. Competitive serotype replacement has been observed previously and seems to be characteristic of the South Pacific. Island population size had a strong impact on the establishment, persistence and fade-out of dengue cases and endemicity was estimated achievable only at a population size in excess of 175 000. Despite island remoteness and low population size, dengue cases were observed somewhere in French Polynesia almost constantly, in part due to the spatial structuration generating asynchrony among subdivisions. Long-term persistence of dengue virus in this group of island populations may be enabled by island hopping, although could equally be explained by a reservoir of sub-clinical infections on the most populated island, Tahiti., Author summary Dengue virus is the most significant arthropod-borne virus infecting man. Understanding how long dengue virus can persist in populations of varying size is key to understanding its epidemiology. This is, however, impossible to achieve in settings where dengue is endemic, because of continued human movement and is further complexified by the occurrence of several co-circulating serotypes. By contrast, French Polynesia, an isolated group of 72 inhabited islands in the South Pacific, has had intermittent majoritarily monotypic dengue epidemics since the 1940s and offers a unique opportunity to address questions of viral persistence, turnover and the importance of spatial sub-structure in determining dengue epidemiology. Collating and analyzing a database of laboratory-confirmed dengue cases from across French Polynesia over a 35 year period we were able to show that dengue virus die-out can occur with or without replacement by a new serotype, monotypic transmission of dengue viruses fails to be maintained within small island populations but can persist for years among isolated islands connected via air and sea links. This remarkable long-term persistence of dengue virus in French Polynesia could be maintained by asynchronous viral transmission among connected islands and/or by repeated seeding from a reservoir of sub-clinical infections in the most populated island, Tahiti.

Middle East Respiratory Syndrome Coronavirus (MERS-CoV) causes severe acute respiratory illness with a case fatality rate (CFR) of 35,5%. The highest number of MERS-CoV cases are from Saudi-Arabia, the major worldwide hotspot for this disease. In the absence of neither effective treatment nor a ready-to-use vaccine and with yet an incomplete understanding of its epidemiological cycle, prevention and containment measures can be derived from mathematical models of disease epidemiology. We constructed 2-strain models to predict past outbreaks in the interval 2012–2016 and derive key epidemiological information for Macca, Madina and Riyadh. We approached variability in infection through three different disease incidence functions capturing social behavior in response to an epidemic (e.g. Bilinear, BL; Non-monotone, NM; and Saturated, SAT models). The best model combination successfully anticipated the total number of MERS-CoV clinical cases for the 2015–2016 season and accurately predicted both the number of cases at the peak of seasonal incidence and the overall shape of the epidemic cycle. The evolution in the basic reproduction number (R0) warns that MERS-CoV may easily take an epidemic form. The best model correctly captures this feature, indicating a high epidemic risk (1≤R0≤2,5) in Riyadh and Macca and confirming the alleged co-circulation of more than one strain. Accurate predictions of the future MERS-CoV peak week, as well as the number of cases at the peak are now possible. These results indicate public health agencies should be aware that measures for strict containment are urgently needed before new epidemics take off in the region., Author summary There is currently no way to anticipate MERS-CoV epidemic outbreaks and strategies for disease prediction and containment are largely undermined by the limited knowledge of its epidemiological cycle. Not an effective treatment nor a vaccine for MERS-CoV exist to date. Instead, using three two-strain mathematical models that incorporate human social behavior as different disease incidence functions (e.g. bilinear, non-monotone and saturated), the best model combinations successfully anticipate the occurrence of the peak week in the season and the incidence at the peak. Our results confirm there are currently 2 strains co-circulating in the most populated regions in Saudi Arabia and highlight the high risk for large epidemic outbreaks, while the role of super-spreaders appears irrelevant for disease spread.

The theoretical predictability limit of El Niño–Southern Oscillation has been shown to be on the order of years, but long-lead predictions of El Niño (EN) and La Niña (LN) are still lacking. State-of-the-art forecasting schemes traditionally do not predict beyond the spring barrier. Recent efforts have been dedicated to the improvement of dynamical models, while statistical schemes still need to take full advantage of the availability of ocean subsurface variables, provided regularly for the last few decades as a result of the Tropical Ocean–Global Atmosphere Program (TOGA). Here we use a number of predictor variables, including temperature at different depths and regions of the equatorial ocean, in a flexible statistical dynamic components model to make skillful long-lead retrospective predictions (hindcasts) of the Niño-3.4 index in the period 1970–2016. The model hindcasts the major EN episodes up to 2.5 years in advance, including the recent extreme 2015/16 EN. The analysis demonstrates that events are predicted more accurately after the completion of the observational array in the tropical Pacific in 1994, as a result of the improved data quality and coverage achieved by TOGA. Therefore, there is potential to issue long-lead predictions of this climatic phenomenon at a low computational cost.

Between October 2018 - May 2019, sea surface temperature conditions in the central-eastern tropical Pacific indicated a mild El Niño event. In May 2019, the global El Niño Southern Oscillation (ENSO) forecast consensus was that these generally weak warm patterns will persist at least until the end of the northern hemisphere summer. El Niño and its impact on local climatic conditions in southern coastal Ecuador influence the inter-annual transmission of dengue fever in the region. In this study, we use an ENSO model to issue forecasts of El Niño for the year 2019, which are then used to predict local climate variables, precipitation and minimum temperature, in the city of Machala, Ecuador. All these forecasts are incorporated in a dengue transmission model, specifically developed and tested for this area, to produce out-of-sample predictions of dengue risk. Predictions are issued at the beginning of January 2019 for the whole year, thus providing the longest forecast lead time of 12 months. Preliminary results indicate that the mild and ongoing El Niño event did not provide the optimum climate conditions for dengue transmission, with the model predicting a very low probability of a dengue outbreak during the typical peak season in Machala in 2019. This is contrary to 2016, when a large El Niño event resulted in excess rainfall and warmer temperatures in the region, and a dengue outbreak occurred 3 months earlier than expected. This event was successfully predicted using a similar prediction framework to the one applied here. With the present study, we continue our efforts to build and test a climate service tool to issue early warnings of dengue outbreaks in the region.

The ClimaDat station at Gredos (GIC3) has been continuously measuring atmospheric (dry air) mixing ratios of carbon dioxide (CO2) and methane (CH4), as well as meteorological parameters, since November 2012. In this study we investigate the atmospheric variability of CH4 mixing ratios between 2013 and 2015 at GIC3 with the help of co-located observations of 222Rn concentrations, modelled 222Rn fluxes and modelled planetary boundary layer heights (PBLHs). Both daily and seasonal changes in atmospheric CH4 can be better understood with the help of atmospheric concentrations of 222Rn (and the corresponding fluxes). On a daily timescale, the variation in the PBLH is the main driver for 222Rn and CH4 variability while, on monthly timescales, their atmospheric variability seems to depend on emission changes. To understand (changing) CH4 emissions, nocturnal fluxes of CH4 were estimated using two methods: the radon tracer method (RTM) and a method based on the EDGARv4.2 bottom-up emission inventory, both using FLEXPARTv9.0.2 footprints. The mean value of RTM-based methane fluxes (FR_CH4) is 0.11 mg CH4 m−2 h−1 with a standard deviation of 0.09 or 0.29 mg CH4 m−2 h−1 with a standard deviation of 0.23 mg CH4 m−2 h−1 when using a rescaled 222Rn map (FR_CH4_rescale). For our observational period, the mean value of methane fluxes based on the bottom-up inventory (FE_CH4) is 0.33 mg CH4 m−2 h−1 with a standard deviation of 0.08 mg CH4 m−2 h−1. Monthly CH4 fluxes based on RTM (both FR_CH4 and FR_CH4_rescale) show a seasonality which is not observed for monthly FE_CH4 fluxes. During January–May, RTM-based CH4 fluxes present mean values 25 % lower than during June–December. This seasonal increase in methane fluxes calculated by RTM for the GIC3 area appears to coincide with the arrival of transhumant livestock at GIC3 in the second half of the year.

Introduction: Kawasaki disease (KD) is an acute self-limited systemic vasculitis relatively common in childhood. The etiology of KD is still unknown, although clinical, laboratory and epidemiological features suggest an infectious origin or trigger. Differences on incidence between countries have been related to specific genetic factors, ethnicity, country of birth and some other sociocultural and environmental factors. We present a population-based study on incidence of KD in Catalonia (Spain), focusing on differences between patients in rural and non-rural areas of the region. Methods: Observational population-based study including all Pediatric Units in Catalan hospitals, between 2004 and 2014. A 12-month (March 2013–March 2014) prospective collection of new cases of KD was carried out to determine the incidence of KD. The rest of the data was retrieved retrospectively. Results: Data from 399 patients over the 10-year study period was analyzed. Among the total KD patients, 353 (88.5%) lived in non-rural areas and 46 (11.5%) in rural areas. It was found that there is a significant difference (p

The subsurface heat buildup in the western tropical Pacific and the recharge phase in equatorial heat content are intrinsic elements of El Nino–Southern Oscillation, leading to changes in zonal wind stress, sea surface temperature, and thermocline tilt that characterize the growing and mature phases of El Nino (EN) events. Here we use numerical simulations to study the impact on subsequent EN episodes of a sudden increase or decrease in ocean heat content during the recharge phase and compare results with previous studies in which this perturbation is prescribed earlier during the tilting mode. We found that while not substantially affected by the phase at which a sudden rise in heat content is prescribed, the timing and magnitude of the events are very sensitive to the phase at which a major decrease is imposed. The different response to the phase of increases and decreases substantiates the importance of nonlinear subsurface ocean dynamics to the onset and growth of EN episodes and provides insight into the irreversibility of the events at different stages of the oscillation.

We would like to express our concern about the limited capacity of actions taken by the Spanish Government to successfully control the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak and end the exponential growth phase of new cases. The measures taken so far, consisting primarily of partial restriction mobility, are in the right direction, although some researchers have warned about the pressure placed on the building blocks of the health system.1 Given that the actual extent of an epidemic can only be assessed retrospectively, governments and policy makers are forced to make decisions on the basis of mathematical models of other diseases and previous experiences by other countries taking different actions.2 In Spain, various models simulating the spread of infection and using different assumptions converge in a landscape of a high number of new cases within the next few weeks. Simulations have been done using the URV and UNIZAR models to predict the progression of the number of patients who will require admission into hospital intensive care units because of coronavirus disease 2019 (COVID-19) in three scenarios (appendix): scenario 1, no mobility restrictions; scenario 2, partial restriction of mobility (ie, movement to the workplace allowed for 30% of people); and scenario 3, complete restriction of mobility (ie, no workplace mobility allowed except for essential services).

Long-lead forecasts of El Nino events are lacking despite their enormous societal and economic impacts. These climatic events lead to floods and droughts in many tropical regions, and damage agriculture and the economy in poor countries. Due to their impact on local climate, they can also affect human health by increasing the risk of vector-borne diseases, such as dengue fever. Physical processes at the origin of this complex coupled ocean-atmosphere phenomenon are just beginning to be better understood, with subsurface processes and stored heat as two of the main driving forces leading to the development of El Nino in a quasi-periodic manner. Taking advantage of this new knowledge, a statistical dynamic components model, using a state space approach and predictors relevant to the El Nino evolution, was specifically tailored to forecast warm events at lead times of about 2 years (well beyond the traditional spring barrier limit in El Nino predictability). This forecasting scheme provides skilful information on the amplitude of El Nino events, their duration, and the peak time of the sea surface temperature anomalies at a sufficient lead time as to efficiently serve preventive public health actions. The long-lead El Nino predictions were coupled to a statistical dengue model to estimate dengue cases during the 1998 and the 2010 epidemics in El Oro Province in Ecuador, where dengue is hyper-endemic. The dengue model correctly estimated these two largest dengue epidemics even at a 2-year simulation lead time. Thus, information is successfully passed from the El Nino forecast domain to the dengue estimation domain, and the long-lead El Nino predictions are shown to potentially anticipate the magnitude of dengue epidemics in the peak season. The results validate the sensitivity of large dengue epidemics in the region to the El Nino forecasts within the proposed model coupling set-up and imply a potential for increasing lead-time in dengue prediction. This coupled model framework and exploratory analysis, based on El Nino predictions, could be easily extended to other similarly transmitted diseases in tropical and subtropical countries, which are directly and severely affected by the large-scale temperature and precipitation teleconnections occurring before, during and after El Nino events.

Cholera, although largely underreported, is thought to be responsible for around 3 million diarrheal cases and an estimated 100,000 deaths in 69 endemic countries, predominantly in sub-Saharan Africa and Asia [1,2]. Cholera causes acute diarrheal episodes which result from the ingestion of the enterotoxin producing Vibrio cholerae, a highly motile, flagellated Gram-negative rod that exists in aquatic environments, like coastal waters and estuaries [3]. There are more than 200 serogroups based on the O antigen of its membrane's lipopolysaccharide. Only two serogroups cause epidemic cholera: O1 and O139. Since Vibrio Cholerae was first observed by Pacini more than 160 years ago, there have been several epidemics, mainly caused by serogroup O1 which may be divided into two biotypes: classical and El Tor. The latter is responsible for the current seventh pandemic and has substituted the classical biotype which is supposed to have caused the previous pandemics [3]. Each biotype is classified in three serotypes: Inaba, Ogawa (the most prevalent) and Hijokima (more rare).

Previous studies have consistently shown the recurrent relationship between macroeconomic cycles and changes in mortality trends, so that recessions are generally associated with periods of faster life expectancy rise, and periods of economic growth with slower reductions or even increases in mortality trends. Here we analyze the link between annual per capita estimates of gross domestic product and daily atmospheric temperatures and standardized death rates for a large ensemble of European regions to describe the effect of the Great Recession on annual and seasonal changes in all-cause human mortality trends. Results show that the countries and regions with the largest (smallest) economic slowdown were also those with the largest (smallest) strengthening of the declining mortality trend. This procyclical evolution of mortality rates is found to be stronger during the cold part of the year, showing that it also depends on the seasonal timing of the underlying causes of death., Country difference in macroeconomic cycles-mortality changes relationships has been rarely explored. Here the authors studied the relationship between 2008 recession and daily mortality counts for EU countries and revealed a significant relationship between macroeconomic cycles and mortality trends.