Migration statistics relevant for malaria transmission in Senegal derived from mobile phone data and used in an agent-based migration model.

One year of mobile phone location data from Senegal is analysed to determine the characteristics of journeys that result in an overnight stay, and are thus relevant for malaria transmission. Defining the home location of each person as the place of most frequent calls, it is found that approximately 60% of people who spend nights away from home have regular destinations that are repeatedly visited, although only 10% have 3 or more regular destinations. The number of journeys involving overnight stays peaks at a distance of 50 km, although roughly half of such journeys exceed 100 km. Most visits only involve a stay of one or two nights away from home, with just 4% exceeding one week. A new agent-based migration model is introduced, based on a gravity model adapted to represent overnight journeys. Each agent makes journeys involving overnight stays to either regular or random locations, with journey and destination probabilities taken from the mobile phone dataset. Preliminary simulations show that the agent-based model can approximately reproduce the patterns of migration involving overnight stays

Assessment of malaria transmission changes in Africa, due to the climate impact of land use change using Coupled Model Intercomparison Project Phase 5 earth system models

Using mathematical modelling tools, we assessed the potential for land use change (LUC) associated with the Intergovernmental Panel on Climate Change low- and high-end emission scenarios (RCP2.6 and RCP8.5) to impact malaria transmission in Africa. To drive a spatially explicit, dynamical malaria model, data from the four available earth system models (ESMs) that contributed to the LUC experiment of the Fifth Climate Model Intercomparison Project are used. Despite the limited size of the ESM ensemble, stark differences in the assessment of how LUC can impact climate are revealed. In three out of four ESMs, the impact of LUC on precipitation and temperature over the next century is limited, resulting in no significant change in malaria transmission. However, in one ESM, LUC leads to increases in precipitation under scenario RCP2.6, and increases in temperature in areas of land use conversion to farmland under both scenarios. The result is a more intense transmission and longer transmission seasons in the southeast of the continent, most notably in Mozambique and southern Tanzania. In contrast, warming associated with LUC in the Sahel region reduces risk in this model, as temperatures are already above the 25-30°C threshold at which transmission peaks. The differences between the ESMs emphasise the uncertainty in such assessments. It is also recalled that the modelling framework is unable to adequately represent local-scale changes in climate due to LUC, which some field studies indicate could be significant

Seasonal Ensemble Predictions of West African Monsoon Precipitation in the ECMWF System 3 with a Focus on the AMMA Special Observing Period in 2006

Abstract The West Africa monsoon precipitation of the ECMWF operational Seasonal Forecast System (SYS3) is evaluated at a lead time of 2–4 months in a 49-yr hindcast dataset, with special attention paid to the African Monsoon Multidisciplinary Analysis (AMMA) special observation period during 2006. In both the climatology and the year 2006 the SYS3 reproduces the progression of the West Africa monsoon but with a number of differences, most notably a southerly shift of the precipitation in the main monsoon months of July and August and the lack of preonset rainfall suppression and sudden onset jump. The model skill at predicting summer monsoon rainfall anomalies has increased in recent years indicating improvements in the ocean analysis since the 1990s. Examination of other model fields shows a widespread warm sea surface temperature (SST) bias exceeding 1.5 K in the Gulf of Guinea throughout the monsoon months in addition to a cold bias in the North Atlantic, which would both tend to enhance rainfall over the Gulf of Guinea coast at the expense of the monsoon rainfall over the Sahel. Seasonal forecasts were repeated for 2006 using the same release of the atmospheric forecast model forced by observed SSTs, and the monsoon rainfall reverts to its observed position, indicating the importance of the SST biases. A lack of stratocumulus off the west coast of Africa in SYS3 was hypothesized as a possible cause of the systematic rain and SST biases. Two more sets of ensembles were thus conducted with atmospheric model upgrades designed to tackle radiation, deep convection, and turbulence deficiencies. While these enhancements improve the simulation of stratocumulus significantly, it is found that the improvement in the warm SST bias is limited in scope to the southern cold tongue region. In contrast, the changes to the representation of convection cause an increase in surface downwelling shortwave radiation that, combined with latent heat flux changes associated with the wind stress field, increases the SST warm bias on and to the north of the equator. Thus, while the precipitation shortfall in the Sahel is reduced with the new physics, the overestimated rainfall of SYS3 in the coastal region is further enhanced, degrading the model systematic errors overall in the West Africa region. Finally, the difference in the systematic biases between the coupled and uncoupled systems was noted to be an impediment to the development of seamless forecasting systems

An Interpretation of Cloud Overlap Statistics

Abstract Observational studies have shown that the vertical overlap of cloudy layers separated by clear sky can exceed that of the random overlap assumption, suggesting a tendency toward minimum overlap. In addition, the rate of decorrelation of vertically continuous clouds with increasing layer separation is sensitive to the horizontal scale of the cloud scenes used. The authors give a heuristic argument that these phenomena result from data truncation, where overcast or single cloud layers are removed from the analysis. This occurs more frequently as the cloud sampling scale falls progressively below the typical cloud system scale. The postulate is supported by sampling artificial cyclic and subsequently more realistic fractal cloud scenes at various length scales. The fractal clouds indicate that the degree of minimal overlap diagnosed in previous studies for discontinuous clouds could result from sampling randomly overlapped clouds at spatial scales that are 30%–80% of the cloud system scale. Removing scenes with cloud cover exceeding 50% from the analysis reduces the impact of data truncation, with discontinuous clouds not minimally overlapped and the decorrelation of continuous clouds less sensitive to the sampling scale. Using CloudSat–CALIPSO data, a decorrelation length scale of approximately 4.0 km is found. In light of these results, the previously documented dependence of overlap decorrelation length scale on latitude is not entirely a physical phenomenon but can be reinterpreted as resulting from sampling cloud systems that increase significantly in size from the tropics to midlatitudes using a fixed sampling scale

Generalizing Cloud Overlap Treatment to Include the Effect of Wind Shear

Abstract Six months of CloudSat and CALIPSO observations have been divided into over 8 million cloud scenes and collocated with ECMWF wind analyses to identify an empirical relationship between cloud overlap and wind shear for use in atmospheric models. For vertically continuous cloudy layers, cloud decorrelates from maximum toward random overlap as the layer separation distance increases, and the authors demonstrate a systematic impact of wind shear on the resulting decorrelation length scale. As expected, cloud decorrelates over smaller distances as wind shear increases. A simple, empirical linear fit parameterization is suggested that is straightforward to add to existing radiation schemes, although it is shown that the parameters are quite sensitive to the processing details of the cloud mask data and also to the fitting method used. The wind shear–overlap dependency is implemented in the radiation scheme of the ECMWF Integrated Forecast System. It has a similar-magnitude impact on the radiative budget as that of switching from a fixed decorrelation length scale to the latitude-dependent length scale presently used in the operational model, altering the zonal-mean, top-of-atmosphere, equator-to-midlatitude gradient of shortwave radiation by approximately 2 W m−2

Relative importance of climatic, geographic and socio-economic determinants of malaria in Malawi.

BACKGROUND: Malaria transmission is influenced by variations in meteorological conditions, which impact the biology of the parasite and its vector, but also socio-economic conditions, such as levels of urbanization, poverty and education, which impact human vulnerability and vector habitat. The many potential drivers of malaria, both extrinsic, such as climate, and intrinsic, such as population immunity are often difficult to disentangle. This presents a challenge for the modelling of malaria risk in space and time. METHODS: A statistical mixed model framework is proposed to model malaria risk at the district level in Malawi, using an age-stratified spatio-temporal dataset of malaria cases from July 2004 to June 2011. Several climatic, geographic and socio-economic factors thought to influence malaria incidence were tested in an exploratory model. In order to account for the unobserved confounding factors that influence malaria, which are not accounted for using measured covariates, a generalized linear mixed model was adopted, which included structured and unstructured spatial and temporal random effects. A hierarchical Bayesian framework using Markov chain Monte Carlo simulation was used for model fitting and prediction. RESULTS: Using a stepwise model selection procedure, several explanatory variables were identified to have significant associations with malaria including climatic, cartographic and socio-economic data. Once intervention variations, unobserved confounding factors and spatial correlation were considered in a Bayesian framework, a final model emerged with statistically significant predictor variables limited to average precipitation (quadratic relation) and average temperature during the three months previous to the month of interest. CONCLUSIONS: When modelling malaria risk in Malawi it is important to account for spatial and temporal heterogeneity and correlation between districts. Once observed and unobserved confounding factors are allowed for, precipitation and temperature in the months prior to the malaria season of interest are found to significantly determine spatial and temporal variations of malaria incidence. Climate information was found to improve the estimation of malaria relative risk in 41% of the districts in Malawi, particularly at higher altitudes where transmission is irregular. This highlights the potential value of climate-driven seasonal malaria forecasts

A breeding site model for regional, dynamical malaria simulations evaluated using in situ temporary ponds observations

Daily observations of potential mosquito developmental habitats in a suburb of Kumasi in central Ghana reveal a strong variability in their water persistence times, which ranged between 11 and 81 days. The persistence of the ponds was strongly tied with rainfall, location and size of the puddles. A simple power-law relationship is found to fit the relationship between the average pond depth and area well. A prognostic water balance model is derived that describes the temporal evolution of the pond area and depth, incorporating the power-law geometrical relation. Pond area increases in response to rainfall, while evaporation and infiltration act as sink terms. Based on a range of evaluation metrics, the prognostic model is judged to provide a good representation of the pond coverage evolution at most sites. Finally, we demonstrate that the prognostic equation can be generalised and equally applied to a grid-cell to derive a fractional pond coverage, and thus can be implemented in spatially distributed models for relevant vector- borne diseases such as malaria

The Maintenance of the Relative Humidity of the Subtropical Free Troposphere

The relative importance of different processes in the water vapor balance of the troposphere is assessed, using high-resolution hindcast data from the ECMWF Integrated Forecast System (IFS) for December–February 1998/99 interpolated to isentropic coordinates. The focus is on elucidating the processes that maintain the relative humidity of the subtropical free troposphere. The dominant drying process in the subtropical free troposphere is cross-isentropic subsidence driven by radiative cooling. In some subtropical regions [e.g., over continents in the Southern (summer) Hemisphere and over western portions of ocean basins in the Northern (winter) Hemisphere], drying by radiative subsidence is partially offset or overcompensated by moistening by cross-isentropic dynamic transport of water vapor from the surface upward (e.g., in convection). Any resultant net drying or moistening of the subtropical free troposphere by cross-isentropic motions is regionally primarily balanced by isentropic mean and eddy transport of water vapor from moister into drier regions. Isentropic transport redistributes water vapor within the subtropics and moderates relative humidity contrasts; however, it does not consistently lead to a substantial net import or export of water vapor into or out of the subtropics

Potential Predictability of Malaria in Africa Using ECMWF Monthly and Seasonal Climate Forecasts

AbstractIdealized model experiments investigate the advance warning for malaria that may be presently possible using temperature and rainfall predictions from state-of-the-art operational monthly and seasonal weather-prediction systems. The climate forecasts drive a dynamical malaria model for all of Africa, and the predictions are evaluated using reanalysis data. The regions and months for which climate is responsible for significant interannual malaria transmission variability are first identified. In addition to epidemic-prone zones these also include hyperendemic regions subject to high variability during specific months of the year, often associated with the monsoon onset. In many of these areas, temperature anomalies are predictable from 1 to 2 months ahead, and reliable precipitation forecasts are available in eastern and southern Africa 1 month ahead. The inherent lag between the rainy seasons and malaria transmission results in potential predictability in malaria transmission 3–4 months in advance, extending the early warning available from environmental monitoring by 1–2 months, although the realizable forecast skill will be less than this because of an imperfect malaria model. A preliminary examination of the forecasts for the highlands of Uganda and Kenya shows that the system is able to predict the years during the last two decades in which documented highland outbreaks occurred, in particular the major event of 1998, but that the timing of outbreaks was often imprecise and inconsistent across lead times. In addition to country-level evaluation with district health data, issues that need addressing to integrate such a climate-based prediction system into health-decision processes are briefly discussed

Mosquito breeding site water temperature observations and simulations towards improved vector-borne disease models for Africa

An energy budget model is developed to predict the water temperature of typical mosquito larval developmental habitats. It assumes a homogeneous mixed water column driven by empirically derived fluxes. The model shows good agreement at both hourly and daily time scales with 10-min temporal resolution observed water temperatures, monitored between June and November 2013 within a peri-urban area of Kumasi, Ghana. There was a close match between larvae development times calculated using either the model-derived or observed water temperatures. The water temperature scheme represents a significant improvement over assuming the water temperature to be equal to air temperature. The energy budget model requires observed minimum and maximum temperatures, information that is generally available from weather stations. Our results show that hourly variations in water temperature are important for the simulation of aquatic-stage development times. By contrast, we found that larval development is insensitive to sub-hourly variations. Modelling suggests that in addition to water temperature, an accurate estimation of degree-day development time is very important to correctly predict the larvae development times. The results highlight the potential of the model to predict water temperature of temporary bodies of surface water. Our study represents an important contribution towards the improvement of weather-driven dynamical disease models, including those designed for malaria early forecasting systems