Risk mapping

Relationships are often found between rural livelihoods and environmental variables (e.g. woodland and non-timber forest products can have a positive impact on rural incomes). In many developing countries the poorest populations are often located in marginal locations that are unsuitable for sustained habitation such as those affected by flooding and erosion. Satellite data is uniquely placed to provide fine temporal and spatial resolution data for rapid assessment of environmental conditions. Therefore, if relationships can be found between livelihoods and environmental metrics derived solely from satellite data, it may be possible to create a basic estimate of local livelihoods using remotely sensed data. Data from the Millennium Villages Project (MVP) were used to explore the relationships between socioeconomic conditions and environmental variables derived from satellite data. Statistical associations from spatial regression techniques were identified between satellite-derived environmental metrics and socioeconomic conditions which suggest that there is potential to infer basic livelihood conditions from satellite data. This could provide useful information for identifying vulnerable populations to improve geographic targeting of development assistance, monitoring progress towards the MDGs.

Malaria remains one of the world's most devastating public health threats. In Peru, 75% of malaria occurs in the northern Amazon region of Loreto where 80% of cases are concentrated in just 10 districts. Loreto is the least densely populated region of Peru and also the largest. To maintain the declining malaria rates currently seen, better knowledge of where, when and why people are infected is needed. The primary factors affecting malaria endemicity in Loreto are vector habitat expansion from land use change, and social and ecological processes that increase human exposure. To refine and focus prevention strategies, spatially explicit risk estimates are necessary. In this study, we investigate how malaria risk varies across time and space in Loreto by modeling the relationship among climate, land use, and malaria from 2009 to 2012. We incorporate satellite-derived climate and land use variables with data on monthly malaria counts at each government health post in Loreto. Initial models indicate increased malaria risk for lagged rainfall and soil moisture as well as land areas prone to flood. These models will be compared against current forecasting methods to determine if more efficient prevention and control efforts can be implemented.

This paper documents the current locations of urban-dwellers in Asia of ecologically delineated zones that are expected to experience the full force of climate change: the low-elevation coastal zones, areas susceptible to inland flooding (apart from coastal sources), and the arid regions known to ecologists as drylands. Low-lying cities and towns near the coast will most probably face increased risks from storm surges and flooding; those in drylands are expected to experience increased water stress and episodes of extreme heat, as well as flash flooding. It is especially important to quantify the exposure of urban residents in low-elevation coastal zones, and to understand the likely implications for their health. While potential coastal flooding in cities has received attention, in part because the long-term implications of rising
sea-levels and change coastal zones, increasing precipitation, in general, and more extreme weather events will also lead to greater flood risks to city-dwellers from in-land water sources. In this paper, flood exposures
from in-land water sources are also estimated, not only because some coastal cities are also at risk of flooding from in-land waterway

Context: Bangladesh is divided into seven distinct climatic zones. The effect of climate on mortality can be different in different zones. This study aims to assess the weather-mortality relationships in two climatic zones.
Methods: Health and demographic surveillance sites (HDSS) maintained by icddr,b in Matlab and Abhoynagar in two climatic zones provided daily counts of death and population during 1983-2009. Weather data on daily temperature and rainfall and cyclones for the same period were obtained from nearby weather stations of the Bangladesh Metrological Department. Time series Poisson regression with cubic spline functions, was used allowing for over dispersion and lagged effects of weather on mortality, controlling for time trends and seasonal patterns. Analysis was carried out using R statistical software.
Findings and conclusions: Both temperature and rainfall showed strong seasonal patterns, explaining a significant part of mortality in both sites. Abhoynagar with more extreme in temperature exhibited stronger cold temperature-mortality relationship than Matlab. Abhoynagar with less rainfall exhibited the rainfall-mortality association, which was not the case with Matlab. The weather-mortality relationships by age, sex and broad cause were also examined to identify vulnerable groups and areas.