Probabilistic seasonal dengue forecasting in Vietnam: A modelling study using superensembles.

BACKGROUND: With enough advanced notice, dengue outbreaks can be mitigated. As a climate-sensitive disease, environmental conditions and past patterns of dengue can be used to make predictions about future outbreak risk. These predictions improve public health planning and decision-making to ultimately reduce the burden of disease. Past approaches to dengue forecasting have used seasonal climate forecasts, but the predictive ability of a system using different lead times in a year-round prediction system has been seldom explored. Moreover, the transition from theoretical to operational systems integrated with disease control activities is rare. METHODS AND FINDINGS: We introduce an operational seasonal dengue forecasting system for Vietnam where Earth observations, seasonal climate forecasts, and lagged dengue cases are used to drive a superensemble of probabilistic dengue models to predict dengue risk up to 6 months ahead. Bayesian spatiotemporal models were fit to 19 years (2002-2020) of dengue data at the province level across Vietnam. A superensemble of these models then makes probabilistic predictions of dengue incidence at various future time points aligned with key Vietnamese decision and planning deadlines. We demonstrate that the superensemble generates more accurate predictions of dengue incidence than the individual models it incorporates across a suite of time horizons and transmission settings. Using historical data, the superensemble made slightly more accurate predictions (continuous rank probability score [CRPS] = 66.8, 95% CI 60.6-148.0) than a baseline model which forecasts the same incidence rate every month (CRPS = 79.4, 95% CI 78.5-80.5) at lead times of 1 to 3 months, albeit with larger uncertainty. The outbreak detection capability of the superensemble was considerably larger (69%) than that of the baseline model (54.5%). Predictions were most accurate in southern Vietnam, an area that experiences semi-regular seasonal dengue transmission. The system also demonstrated added value across multiple areas compared to previous practice of not using a forecast. We use the system to make a prospective prediction for dengue incidence in Vietnam for the period May to October 2020. Prospective predictions made with the superensemble were slightly more accurate (CRPS = 110, 95% CI 102-575) than those made with the baseline model (CRPS = 125, 95% CI 120-168) but had larger uncertainty. Finally, we propose a framework for the evaluation of probabilistic predictions. Despite the demonstrated value of our forecasting system, the approach is limited by the consistency of the dengue case data, as well as the lack of publicly available, continuous, and long-term data sets on mosquito control efforts and serotype-specific case data. CONCLUSIONS: This study shows that by combining detailed Earth observation data, seasonal climate forecasts, and state-of-the-art models, dengue outbreaks can be predicted across a broad range of settings, with enough lead time to meaningfully inform dengue control. While our system omits some important variables not currently available at a subnational scale, the majority of past outbreaks could be predicted up to 3 months ahead. Over the next 2 years, the system will be prospectively evaluated and, if successful, potentially extended to other areas and other climate-sensitive disease systems.

Electrografting of Carbon Surfaces with Aliphatic Chains and its Effect on the Rectification of Ferrocene as Redox Probe in Solution.

The mediated oxidation of acetate and octanoate ions in acetonitrile was used to covalently modify carbon surfaces with films bearing saturated aliphatic chains of different length. Film thickness increases proportionally with the length of the aliphatic chain within the carboxylate precursor. The thickest film was obtained from octanoate oxidation and rectification occurs when ferrocene is used as redox probe in acetonitrile solution. This effect increases with the bulky and hydrophobic nature of the supporting electrolyte cations; n-Hx4 N+ >n-Bu4 N+ >Me4 N+ . The combination of the bulky and hydrophobic properties of the supporting electrolyte ions as well as the hydrophobic properties of the electrografted films is the basis of rectification of ferrocene in cyclic voltammetry experiments. This phenomenon was simulated through a CEC mechanism in solution, where the mass transport inside the film channels was emulated through single chemical equilibria.

Limiting global-mean temperature increase to 1.5-2 °C could reduce the incidence and spatial spread of dengue fever in Latin America.

The Paris Climate Agreement aims to hold global-mean temperature well below 2 °C and to pursue efforts to limit it to 1.5 °C above preindustrial levels. While it is recognized that there are benefits for human health in limiting global warming to 1.5 °C, the magnitude with which those societal benefits will be accrued remains unquantified. Crucial to public health preparedness and response is the understanding and quantification of such impacts at different levels of warming. Using dengue in Latin America as a study case, a climate-driven dengue generalized additive mixed model was developed to predict global warming impacts using five different global circulation models, all scaled to represent multiple global-mean temperature assumptions. We show that policies to limit global warming to 2 °C could reduce dengue cases by about 2.8 (0.8-7.4) million cases per year by the end of the century compared with a no-policy scenario that warms by 3.7 °C. Limiting warming further to 1.5 °C produces an additional drop in cases of about 0.5 (0.2-1.1) million per year. Furthermore, we found that by limiting global warming we can limit the expansion of the disease toward areas where incidence is currently low. We anticipate our study to be a starting point for more comprehensive studies incorporating socioeconomic scenarios and how they may further impact dengue incidence. Our results demonstrate that although future climate change may amplify dengue transmission in the region, impacts may be avoided by constraining the level of warming.

Impact of non-pharmaceutical interventions against COVID-19 in Europe in 2020: a quasi-experimental non-equivalent group and time series design study.

IntroductionThe current pandemic of coronavirus disease (COVID-19) is unparalleled in recent history as are the social distancing interventions that have led to a considerable halt on the economic and social life of so many countries.AimWe aimed to generate empirical evidence about which social distancing measures had the most impact in reducing case counts and mortality.MethodsWe report a quasi-experimental (observational) study of the impact of various interventions for control of the outbreak through 24 April 2020. Chronological data on case numbers and deaths were taken from the daily published figures by the European Centre for Disease Prevention and Control and dates of initiation of various control strategies from the Institute of Health Metrics and Evaluation website and published sources. Our complementary analyses were modelled in R using Bayesian generalised additive mixed models and in STATA using multilevel mixed-effects regression models.ResultsFrom both sets of modelling, we found that closure of education facilities, prohibiting mass gatherings and closure of some non-essential businesses were associated with reduced incidence whereas stay-at-home orders and closure of additional non-essential businesses was not associated with any independent additional impact.ConclusionsOur findings are that schools and some non-essential businesses operating 'as normal' as well as allowing mass gatherings were incompatible with suppressing disease spread. Closure of all businesses and stay at home orders are less likely to be required to keep disease incidence low. Our results help identify what were the most effective non-pharmaceutical interventions in this period.

Comparison of statistical algorithms for daily syndromic surveillance aberration detection.

MOTIVATION: Public health authorities can provide more effective and timely interventions to protect populations during health events if they have effective multi-purpose surveillance systems. These systems rely on aberration detection algorithms to identify potential threats within large datasets. Ensuring the algorithms are sensitive, specific and timely is crucial for protecting public health. Here, we evaluate the performance of three detection algorithms extensively used for syndromic surveillance: the 'rising activity, multilevel mixed effects, indicator emphasis' (RAMMIE) method and the improved quasi-Poisson regression-based method known as 'Farrington Flexible' both currently used at Public Health England, and the 'Early Aberration Reporting System' (EARS) method used at the US Centre for Disease Control and Prevention. We model the wide range of data structures encountered within the daily syndromic surveillance systems used by PHE. We undertake extensive simulations to identify which algorithms work best across different types of syndromes and different outbreak sizes. We evaluate RAMMIE for the first time since its introduction. Performance metrics were computed and compared in the presence of a range of simulated outbreak types that were added to baseline data. RESULTS: We conclude that amongst the algorithm variants that have a high specificity (i.e. >90%), Farrington Flexible has the highest sensitivity and specificity, whereas RAMMIE has the highest probability of outbreak detection and is the most timely, typically detecting outbreaks 2-3 days earlier. AVAILABILITY AND IMPLEMENTATION: R codes developed for this project are available through https://github.com/FelipeJColon/AlgorithmComparison. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Linkage between Fluorescence and Electrochemical Properties of Imidazolium Compounds in Acetonitrile Solution.

The fluorescence properties of some imidazolium derivatives are relevant in photosensing and therefore, the structural analysis of them is a key point for its rational design, which would be useful to prepare new systems with novel applications. Herein we report a multidisciplinary study of the fluorescence and voltammetric properties of three imidazolium compounds {1,3-bis[(R,R)-1'-chloro-1'-phenylpropan-2'-yl]-imidazolium chloride (1), 1,3-bis[(Z)-1'-phenylprop-1'-en-2'-yl]imidazolium chloride (2) 1,3-bis[(R)-1'-chlorobutan-2'-yl]-imidazolium chloride (3)}. Electronic structure calculations and Bader analyses were used to correlate both fluorescence and the capability of the molecules to be reduced through a heterogeneous electron transfer process. Both properties are strongly dependent on the proton in position two of the imidazolium ring, where the electron transfer as well as the excitation of the electrons are carried out. The reactivity in this position is controlled by the N-substituents on the imidazolium ring and is due to single contacts H⋅⋅⋅Cl- , tricentric contacts Cl⋅⋅⋅Cl- ⋅⋅⋅Cl, π-electronic delocalization and π-stacking interactions.

Can economic indicators predict infectious disease spread? A cross-country panel analysis of 13 European countries.

Aims: It is unclear how economic factors impact on the epidemiology of infectious disease. We evaluated the relationship between incidence of selected infectious diseases and economic factors, including economic downturn, in 13 European countries between 1970 and 2010. Methods: Data were obtained from national communicable disease surveillance centres. Negative binomial forms of the generalised additive model (GAM) and the generalised linear model were tested to see which best reflected transmission dynamics of: diphtheria, pertussis, measles, meningococcal disease, hepatitis B, gonorrhoea, syphilis, hepatitis A and salmonella. Economic indicators were gross domestic product per capita (GDPpc), unemployment rates and (economic) downturn. Results: GAM models produced the best goodness-of-fit results. The relationship between GDPpc and disease incidence was often non-linear. Strength and directions of association between population age, tertiary education levels, GDPpc and unemployment were disease dependent. Overdispersion for almost all diseases validated the assumption of a negative binomial relationship. Downturns were not independently linked to disease incidence. Conclusions: Social and economic factors can be correlated with many infections. However, the trend is not always in the same direction, and these associations are often non-linear. Economic downturn or recessions as indicators of increased disease risk may be better replaced by GDPpc or unemployment measures.

Seasonality and the effects of weather on Campylobacter infections.

BACKGROUND: Campylobacteriosis is a major public health concern. The weather factors that influence spatial and seasonal distributions are not fully understood. METHODS: To investigate the impacts of temperature and rainfall on Campylobacter infections in England and Wales, cases of Campylobacter were linked to local temperature and rainfall at laboratory postcodes in the 30 days before the specimen date. Methods for investigation included a comparative conditional incidence, wavelet, clustering, and time series analyses. RESULTS: The increase of Campylobacter infections in the late spring was significantly linked to temperature two weeks before, with an increase in conditional incidence of 0.175 cases per 100,000 per week for weeks 17 to 24; the relationship to temperature was not linear. Generalized structural time series model revealed that changes in temperature accounted for 33.3% of the expected cases of Campylobacteriosis, with an indication of the direction and relevant temperature range. Wavelet analysis showed a strong annual cycle with additional harmonics at four and six months. Cluster analysis showed three clusters of seasonality with geographic similarities representing metropolitan, rural, and other areas. CONCLUSIONS: The association of Campylobacteriosis with temperature is likely to be indirect. High-resolution spatial temporal linkage of weather parameters and cases is important in improving weather associations with infectious diseases. The primary driver of Campylobacter incidence remains to be determined; other avenues, such as insect contamination of chicken flocks through poor biosecurity should be explored.

A methodological framework for the evaluation of syndromic surveillance systems: a case study of England.

BACKGROUND: Syndromic surveillance complements traditional public health surveillance by collecting and analysing health indicators in near real time. The rationale of syndromic surveillance is that it may detect health threats faster than traditional surveillance systems permitting more timely, and hence potentially more effective public health action. The effectiveness of syndromic surveillance largely relies on the methods used to detect aberrations. Very few studies have evaluated the performance of syndromic surveillance systems and consequently little is known about the types of events that such systems can and cannot detect. METHODS: We introduce a framework for the evaluation of syndromic surveillance systems that can be used in any setting based upon the use of simulated scenarios. For a range of scenarios this allows the time and probability of detection to be determined and uncertainty is fully incorporated. In addition, we demonstrate how such a framework can model the benefits of increases in the number of centres reporting syndromic data and also determine the minimum size of outbreaks that can or cannot be detected. Here, we demonstrate its utility using simulations of national influenza outbreaks and localised outbreaks of cryptosporidiosis. RESULTS: Influenza outbreaks are consistently detected with larger outbreaks being detected in a more timely manner. Small cryptosporidiosis outbreaks (<1000 symptomatic individuals) are unlikely to be detected. We also demonstrate the advantages of having multiple syndromic data streams (e.g. emergency attendance data, telephone helpline data, general practice consultation data) as different streams are able to detect different outbreak types with different efficacy (e.g. emergency attendance data are useful for the detection of pandemic influenza but not for outbreaks of cryptosporidiosis). We also highlight that for any one disease, the utility of data streams may vary geographically, and that the detection ability of syndromic surveillance varies seasonally (e.g. an influenza outbreak starting in July is detected sooner than one starting later in the year). We argue that our framework constitutes a useful tool for public health emergency preparedness in multiple settings. CONCLUSIONS: The proposed framework allows the exhaustive evaluation of any syndromic surveillance system and constitutes a useful tool for emergency preparedness and response.

Impact of climate change on global malaria distribution.

Malaria is an important disease that has a global distribution and significant health burden. The spatial limits of its distribution and seasonal activity are sensitive to climate factors, as well as the local capacity to control the disease. Malaria is also one of the few health outcomes that has been modeled by more than one research group and can therefore facilitate the first model intercomparison for health impacts under a future with climate change. We used bias-corrected temperature and rainfall simulations from the Coupled Model Intercomparison Project Phase 5 climate models to compare the metrics of five statistical and dynamical malaria impact models for three future time periods (2030s, 2050s, and 2080s). We evaluated three malaria outcome metrics at global and regional levels: climate suitability, additional population at risk and additional person-months at risk across the model outputs. The malaria projections were based on five different global climate models, each run under four emission scenarios (Representative Concentration Pathways, RCPs) and a single population projection. We also investigated the modeling uncertainty associated with future projections of populations at risk for malaria owing to climate change. Our findings show an overall global net increase in climate suitability and a net increase in the population at risk, but with large uncertainties. The model outputs indicate a net increase in the annual person-months at risk when comparing from RCP2.6 to RCP8.5 from the 2050s to the 2080s. The malaria outcome metrics were highly sensitive to the choice of malaria impact model, especially over the epidemic fringes of the malaria distribution.

Multimodel assessment of water scarcity under climate change.

Water scarcity severely impairs food security and economic prosperity in many countries today. Expected future population changes will, in many countries as well as globally, increase the pressure on available water resources. On the supply side, renewable water resources will be affected by projected changes in precipitation patterns, temperature, and other climate variables. Here we use a large ensemble of global hydrological models (GHMs) forced by five global climate models and the latest greenhouse-gas concentration scenarios (Representative Concentration Pathways) to synthesize the current knowledge about climate change impacts on water resources. We show that climate change is likely to exacerbate regional and global water scarcity considerably. In particular, the ensemble average projects that a global warming of 2 °C above present (approximately 2.7 °C above preindustrial) will confront an additional approximate 15% of the global population with a severe decrease in water resources and will increase the number of people living under absolute water scarcity (<500 m(3) per capita per year) by another 40% (according to some models, more than 100%) compared with the effect of population growth alone. For some indicators of moderate impacts, the steepest increase is seen between the present day and 2 °C, whereas indicators of very severe impacts increase unabated beyond 2 °C. At the same time, the study highlights large uncertainties associated with these estimates, with both global climate models and GHMs contributing to the spread. GHM uncertainty is particularly dominant in many regions affected by declining water resources, suggesting a high potential for improved water resource projections through hydrological model development.

Role of N-terminal methionine residues in the redox activity of copper bound to alpha-synuclein.

Amyloid aggregation of α-synuclein (AS) is one of the hallmarks of Parkinson's disease. The interaction of copper ions with the N-terminal region of AS promotes its amyloid aggregation and metal-catalyzed oxidation has been proposed as a plausible mechanism. The AS(1-6) fragment represents the minimal sequence that models copper coordination to this intrinsically disordered protein. In this study, we evaluated the role of methionine residues Met1 and Met5 in Cu(II) coordination to the AS(1-6) fragment, and in the redox activity of the Cu-AS(1-6) complex. Spectroscopic and electronic structure calculations show that Met1 may play a role as an axial ligand in the Cu(II)-AS(1-6) complex, while Met5 does not participate in metal coordination. Cyclic voltammetry and reactivity studies demonstrate that Met residues play an important role in the reduction and reoxidation processes of this complex. However, Met1 plays a more important role than Met5, as substitution of Met1 by Ile decreases the reduction potential of the Cu-AS(1-6) complex by ~80 mV, causing a significant decrease in its rate of reduction. Reoxidation of the complex by oxygen results in oxidation of the Met residues to sulfoxide, being Met1 more susceptible to copper-catalyzed oxidation than Met5. The sulfoxide species can suffer elimination of methanesulfenic acid, rendering a peptide with no thioether moiety, which would impair the ability of AS to bind Cu(I) ions. Overall, our study underscores the important roles that Met1 plays in copper coordination and the reactivity of the Cu-AS complex.

Competition between Hydrogen Bonding and Proton Transfer during Specific Anion Recognition by Dihomooxacalix[4]arene Bidentate Ureas.

Competition between hydrogen bonding and proton transfer reactions was studied for systems composed of electrogenerated dianionic species from dinitrobenzene isomers and substituted dihomooxacalix[4]arene bidentate urea derivatives. To analyze this competition, a second-order ErCrCi mechanism was considered where the binding process is succeeded by proton transfer and the voltammetric responses depend on two dimensionless parameters: the first related to hydrogen bonding reactions, and the second one to proton transfer processes. Experimental results indicated that, upon an increase in the concentration of phenyl-substituted dihomooxacalix[4]arene bidentate urea, voltammetric responses evolve from diffusion-controlled waves (where the binding process is at chemical equilibrium) into irreversible kinetic responses associated with proton transfer. In particular, the 1,3-dinitrobenzene isomer showed a higher proton transfer rate constant (∼25 M(-1) s(-1)) compared to that of the 1,2-dinitrobenzene (∼5 M(-1) s(-1)), whereas the 1,4-dinitrobenzene did not show any proton transfer effect in the experimental conditions employed.

Fracture Resistance of Monolithic High Translucency Zirconia Implant-Supported Crowns.

AIM: To evaluate the resistance to axial forces of screw-retained monolithic high translucency zirconia (mHTZr) crowns compared with high translucency zirconia + feldspathic ceramic (HTZrC) crowns, low translucency zirconia + feldspathic ceramic (LTZrC) crowns, and metal-ceramic (MC) crowns, and also to observe the different fracture patterns between all groups. METHODS: Twenty-four crowns were fabricated (6 of each group) and loaded until failure, using a testing machine with a 5.0-kN load cell. RESULTS: Mean fracture results varied between 1092.7 N (LTZrC group) and 3439.7 N (mHTZr group). No statistically significant differences were found between the HTZrC, LTZrC, and MC groups. However, statistically significant differences (P < 0.05) were found between mHTZr and the other 3 groups. In the MC group, only chipping of the ceramic veneering occurred. In the mHTZr group, when fracturing occurred, it was of the whole structure. Finally, the LTZrC and HTZrC groups suffered both chipping and core fractures. CONCLUSION: High translucency monolithic zirconia implant-supported crowns proved to be the toughest group studied when an axial force was applied. Fracture patterns varied between different materials, chipping being the most common occurrence.

Recombinant osteoprotegerin effects during orthodontic movement in a rat model.

BACKGROUND AND OBJECTIVES: Anchorage is one of the most challenging sides in orthodontics. The use of biological modulators that inhibit osteoclasts could be a solution to address these problems and provide new adjunctive approaches. The aim of this study was to assess the effectiveness of recombinant osteoprotegerin fusion protein (OPG-Fc) in orthodontic anchorage. MATERIALS AND METHODS: Two groups of male Sprague-Dawley rats were utilized. The animals in the experimental group received twice-weekly injections with high dose of OPG-Fc (5.0mg/kg) in mesial and distal mucosa of the first molars, and those in the control group received no drugs. Right first maxillary molars were mesialized using a calibrated nickel-titanium spring connected to an anterior mini-screw. Tooth movement was measured by two blinded observers using scanned and magnified stone casts. Receptor activator of nuclear factor κB (RANK), run-related transcription factor 2 (Runx2), type I collagen, vimentin, matrix metalloproteinases 2 and 9, S100 protein and the putative mechanoproteins acid-sensing ion channel (ASIC2) and transient receptor potential vainilloid 4 (TRPV4) were evaluated using immunohistochemistry. RESULTS: OPG-Fc group showed an important decreased in mesial molar movement with only 52%, 31%, and 22% of the total mesial molar movement compared with control group at Days 7, 14, and 21, respectively (P < 0.001). RANK ligand and Runx2 positive cells were severely reduced after OPG-Fc treatment. Periodontal ligament architecture, cell arrangement, and immunohistochemical patter for vimentin, type I collagen and the mechanoproteins TRPV4 and ASIC2 were altered by tooth movement and all these parameters altered by the applied treatment. CONCLUSIONS: OPG-Fc effectively inhibits osteoclastogenesis resulting in improved bone quantity and orthodontic anchorage. Based on present results, OPG-Fc could have clinical utility in preventing undesired tooth movements.

Redox cycling of copper-amyloid ß 1-16 peptide complexes is highly dependent on the coordination mode.

Copper (Cu)-amyloid ß (Aß) interactions play a role in the etiology of Alzheimer's disease. This work presents a spectroscopic and electrochemical study of two physiologically relevant Aß-Cu(II) complexes, as a function of pH and relative Cu-Aß(1-16) concentrations. Our results reveal that these coordination modes display distinct redox behaviors and provide experimental evidence for the existence of an intermediate Cu(I) species. A mechanism for the redox cycling of these complexes is proposed, providing further insight into the redox relevance of Aß-Cu interactions.

Nature of electrogenerated intermediates in nitro-substituted nor-ß-lapachones: the structure of radical species during successive electron transfer in multiredox centers.

Electrochemical, spectroelectrochemical, and theoretical studies of the reduction reactions in nor-ß-lapachone derivatives including a nitro redox center showed that reduction of the compounds involves the formation of several radical intermediates, including a biradical dianion resultant from the separate reduction of the quinone and nitro groups in the molecules. Theoretical descriptions of the corresponding Fukui functions f(αα)⁺ and f(ßß)⁺(r) and LUMO densities considering finite differences and frozen core approximations for describing the changes in electron and spin densities of the system allowed us to confirm these results. A description of the potential relationship with the obtained results and biological activity selectivity indexes suggests that both the formation of stable biradical dianion species and the stability of the semiquinone intermediates during further reduction are determining factors in the description of their biological activity.

Climate change and the emergence of vector-borne diseases in Europe: case study of dengue fever.

BACKGROUND: Dengue fever is the most prevalent mosquito-borne viral disease worldwide. Dengue transmission is critically dependent on climatic factors and there is much concern as to whether climate change would spread the disease to areas currently unaffected. The occurrence of autochthonous infections in Croatia and France in 2010 has raised concerns about a potential re-emergence of dengue in Europe. The objective of this study is to estimate dengue risk in Europe under climate change scenarios. METHODS: We used a Generalized Additive Model (GAM) to estimate dengue fever risk as a function of climatic variables (maximum temperature, minimum temperature, precipitation, humidity) and socioeconomic factors (population density, urbanisation, GDP per capita and population size), under contemporary conditions (1985-2007) in Mexico. We then used our model estimates to project dengue incidence under baseline conditions (1961-1990) and three climate change scenarios: short-term 2011-2040, medium-term 2041-2070 and long-term 2071-2100 across Europe. The model was used to calculate average number of yearly dengue cases at a spatial resolution of 10 × 10 km grid covering all land surface of the currently 27 EU member states. To our knowledge, this is the first attempt to model dengue fever risk in Europe in terms of disease occurrence rather than mosquito presence. RESULTS: The results were presented using Geographical Information System (GIS) and allowed identification of areas at high risk. Dengue fever hot spots were clustered around the coastal areas of the Mediterranean and Adriatic seas and the Po Valley in northern Italy. CONCLUSIONS: This risk assessment study is likely to be a valuable tool assisting effective and targeted adaptation responses to reduce the likely increased burden of dengue fever in a warmer world.

Contribution of climate change to the spatial expansion of West Nile virus in Europe.

West Nile virus (WNV) is an emerging mosquito-borne pathogen in Europe where it represents a new public health threat. While climate change has been cited as a potential driver of its spatial expansion on the continent, a formal evaluation of this causal relationship is lacking. Here, we investigate the extent to which WNV spatial expansion in Europe can be attributed to climate change while accounting for other direct human influences such as land-use and human population changes. To this end, we trained ecological niche models to predict the risk of local WNV circulation leading to human cases to then unravel the isolated effect of climate change by comparing factual simulations to a counterfactual based on the same environmental changes but a counterfactual climate where long-term trends have been removed. Our findings demonstrate a notable increase in the area ecologically suitable for WNV circulation during the period 1901-2019, whereas this area remains largely unchanged in a no-climate-change counterfactual. We show that the drastic increase in the human population at risk of exposure is partly due to historical changes in population density, but that climate change has also been a critical driver behind the heightened risk of WNV circulation in Europe.

A reproducible ensemble machine learning approach to forecast dengue outbreaks.

Dengue fever, a prevalent and rapidly spreading arboviral disease, poses substantial public health and economic challenges in tropical and sub-tropical regions worldwide. Predicting infectious disease outbreaks on a countrywide scale is complex due to spatiotemporal variations in dengue incidence across administrative areas. To address this, we propose a machine learning ensemble model for forecasting the dengue incidence rate (DIR) in Brazil, with a focus on the population under 19 years old. The model integrates spatial and temporal information, providing one-month-ahead DIR estimates at the state level. Comparative analyses with a dummy model and ablation studies demonstrate the ensemble model's qualitative and quantitative efficacy across the 27 Brazilian Federal Units. Furthermore, we showcase the transferability of this approach to Peru, another Latin American country with differing epidemiological characteristics. This timely forecast system can aid local governments in implementing targeted control measures. The study advances climate services for health by identifying factors triggering dengue outbreaks in Brazil and Peru, emphasizing collaborative efforts with intergovernmental organizations and public health institutions. The innovation lies not only in the algorithms themselves but in their application to a domain marked by data scarcity and operational scalability challenges. We bridge the gap by integrating well-curated ground data with advanced analytical methods, addressing a significant deficiency in current practices. The successful transfer of the model to Peru and its consistent performance during the 2019 outbreak in Brazil showcase its scalability and practical application. While acknowledging limitations in handling extreme values, especially in regions with low DIR, our approach excels where accurate predictions are critical. The study not only contributes to advancing DIR forecasting but also represents a paradigm shift in integrating advanced analytics into public health operational frameworks. This work, driven by a collaborative spirit involving intergovernmental organizations and public health institutions, sets a precedent for interdisciplinary collaboration in addressing global health challenges. It not only enhances our understanding of factors triggering dengue outbreaks but also serves as a template for the effective implementation of advanced analytical methods in public health.