Re-Emergence of Crimean-Congo Hemorrhagic Fever Virus in Central Africa

Crimean-Congo hemorrhagic fever virus (CCHFV) is transmitted to humans through tick-bite or contact with infected blood or tissues from livestock, the main vertebrate hosts in a peri-domestic natural cycle. With numerous outbreaks, a high case fatality rate (3%–30%) and a high risk for nosocomial transmission, CCHFV became a public health concern in Europe and Asia. However virus surveillance in Africa is difficult due to the limited sanitary facilities. Especially, CCHFV occurrence in Central Africa is very poorly described and seems highly in contrast with the temperate to dry environments to which the virus is usually associated with. We described a single human infection that occurred in Democratic Republic of the Congo after nearly 50 years of absence. The phylogenetic analysis suggests that CCHFV enzootic circulation in the area is still ongoing despite the absence of notification, and thus reinforces the need for the medical workers and authorities to be aware of the outbreak risk. The source of infection seemed associated with a forest environment while no link with the usual agro-pastoral risk factors could be identified. More accurate ecological data about CCHFV enzootic cycle are required to assess the risk of emergence in developing countries subjected to deforestation

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

Climate-Based Models for Understanding and Forecasting Dengue Epidemics

Dengue fever is a major public health problem in the tropics and subtropics. Since no vaccine exists, understanding and predicting outbreaks remain of crucial interest. Climate influences the mosquito-vector biology and the viral transmission cycle. Its impact on dengue dynamics is of growing interest. We analyzed the epidemiology of dengue in Noumea (New Caledonia) from 1971 to 2010 and its relationships with local and remote climate conditions using an original approach combining a comparison of epidemic and non epidemic years, bivariate and multivariate analyses. We found that the occurrence of outbreaks in Noumea was strongly influenced by climate during the last forty years. Efficient models were developed to estimate the yearly risk of outbreak as a function of two meteorological variables that were contemporaneous (explicative model) or prior (predictive model) to the outbreak onset. Local threshold values of maximal temperature and relative humidity were identified. Our results provide new insights to understand the link between climate and dengue outbreaks, and have a substantial impact on dengue management in New Caledonia since the health authorities have integrated these models into their decision making process and vector control policies. This raises the possibility to provide similar early warning systems in other countries