Demonstration of successful malaria forecasts for Botswana using an operational seasonal climate model

The severity and timing of seasonal malaria epidemics is strongly linked with temperature and rainfall. Advance warning of meteorological conditions from seasonal climate models can therefore potentially anticipate unusually strong epidemic events, building resilience and adapting to possible changes in the frequency of such events. Here we present validation of a process-based, dynamic malaria model driven by hindcasts from a state-of-the-art seasonal climate model from the European Centre for Medium-Range Weather Forecasts. We validate the climate and malaria models against observed meteorological and incidence data for Botswana over the period 1982–2006; the longest record of observed incidence data which has been used to validate a modeling system of this kind. We consider the impact of climate model biases, the relationship between climate and epidemiological predictability and the potential for skillful malaria forecasts. Forecast skill is demonstrated for upper tercile malaria incidence for the Botswana malaria season (January–May), using forecasts issued at the start of November; the forecast system anticipates six out of the seven upper tercile malaria seasons in the observational period. The length of the validation time series gives confidence in the conclusion that it is possible to make reliable forecasts of seasonal malaria risk, forming a key part of a health early warning system for Botswana and contributing to efforts to adapt to climate change

Rift Valley Fever Outbreaks in Mauritania and Related Environmental Conditions

Four large outbreaks of Rift Valley Fever (RVF) occurred in Mauritania in 1998, 2003, 2010 and 2012 which caused lots of animal and several human deaths. We investigated rainfall and vegetation conditions that might have impacted on RVF transmission over the affected regions. Our results corroborate that RVF transmission generally occurs during the months of September and October in Mauritania, similarly to Senegal. The four outbreaks were preceded by a rainless period lasting at least a week followed by heavy precipitation that took place during the second half of the rainy season. First human infections were generally reported three to five weeks later. By bridging the gap between meteorological forecasting centers and veterinary services, an early warning system might be developed in Senegal and Mauritania to warn decision makers and health services about the upcoming RVF risk

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. Whilst 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 climatedriven 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 towards 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

Specific vapor sorption properties of phosphorus-containing dendrimers

Specific combination of guest sorption properties was observed for phosphorus-containing dendrimers, which distinguish them from ordinary polymers and clathrate-forming hosts. The sorption capacity for 30 volatile guests, binding reversibility, guest desorption kinetics and guest exchange, glass transition behavior and ability to be plasticized with guest were studied for phosphorus dendrimers of different generations (G1-G4 and G9) using quartz crystal microbalance sensor, FTIR microspectroscopy, atomic force microscopy, simultaneous thermogravimetry and differential scanning calorimetry combined with mass-spectrometry of evolved vapors. The dendrimers were found to have a different selectivity for different homological series of guests, high glass transition points without plasticization with guest even at high temperatures and saturation levels, moderate guest-binding irreversibility and ability both for effective guest exchange and independent guest sorption. These properties constitute an advantage of the studied dendrimers as receptor materials in various applications. © 2011 Elsevier Inc

Modelling the effects of past and future climate on the risk of bluetongue emergence in Europe

Vector-borne diseases are among those most sensitive to climate because the ecology of vectors and the development rate of pathogens within them are highly dependent on environmental conditions. Bluetongue (BT), a recently emerged arboviral disease of ruminants in Europe, is often cited as an illustration of climate's impact on disease emergence, although no study has yet tested this association. Here, we develop a framework to quantitatively evaluate the effects of climate on BT's emergence in Europe by integrating high-resolution climate observations and model simulations within a mechanistic model of BT transmission risk. We demonstrate that a climate-driven model explains, in both space and time, many aspects of BT's recent emergence and spread, including the 2006 BT outbreak in northwest Europe which occurred in the year of highest projected risk since at least 1960. Furthermore, the model provides mechanistic insight into BT's emergence, suggesting that the drivers of emergence across Europe differ between the South and the North. Driven by simulated future climate from an ensemble of 11 regional climate models, the model projects increase in the future risk of BT emergence across most of Europe with uncertainty in rate but not in trend. The framework described here is adaptable and applicable to other diseases, where the link between climate and disease transmission risk can be quantified, permitting the evaluation of scale and uncertainty in climate change's impact on the future of such diseases

Dendrimers toward translational nanotherapeutics: concise key step analysis

The goal of nanomedicine is to address specific clinical problems optimally, to fight human diseases, and to find clinical relevance to change clinical practice. Nanomedicine is poised to revolutionize medicine via the development of more precise diagnostic and therapeutic tools. The field of nanomedicine encompasses numerous features and therapeutic disciplines. A plethora of nanomolecular structures have been engineered and developed for therapeutic applications based on their multitasking abilities and the wide functionalization of their core scaffolds and surface groups. Within nanoparticles used for nanomedicine, dendrimers as well polymers have demonstrated strong potential as nanocarriers, therapeutic agents, and imaging contrast agents. In this review, we present and discuss the different criteria and parameters to be addressed to prepare and develop druggable nanoparticles in general and dendrimers in particular. We also describe the major requirements, included in the preclinical and clinical roadmap, for NPs/dendrimers for the preclinical stage to commercialization. Ultimately, we raise the clinical translation of new nanomedicine issues.info:eu-repo/semantics/publishedVersio

Drivers for Rift Valley fever emergence in Mayotte: A Bayesian modelling approach

Rift Valley fever (RVF) is a major zoonotic and arboviral hemorrhagic fever. The conditions leading to RVF epidemics are still unclear, and the relative role of climatic and anthropogenic factors may vary between ecosystems. Here, we estimate the most likely scenario that led to RVF emergence on the island of Mayotte, following the 2006–2007 African epidemic. We developed the first mathematical model for RVF that accounts for climate, animal imports and livestock susceptibility, which is fitted to a 12-years dataset. RVF emergence was found to be triggered by the import of infectious animals, whilst transmissibility was approximated as a linear or exponential function of vegetation density. Model forecasts indicated a very low probability of virus endemicity in 2017, and therefore of re-emergence in a closed system (i.e. without import of infected animals). However, the very high proportion of naive animals reached in 2016 implies that the island remains vulnerable to the import of infectious animals. We recommend reinforcing surveillance in livestock, should RVF be reported is neighbouring territories. Our model should be tested elsewhere, with ecosystem-specific data