Stephen J. Connor

Malaria early warnings based on seasonal climate forecasts from multi-model ensembles.

The control of epidemic malaria is a priority for the international health community and specific targets for the early detection and effective control of epidemics have been agreed. Interannual climate variability is an important determinant of epidemics in parts of Africa where climate drives both mosquito vector dynamics and parasite development rates. Hence, skilful seasonal climate forecasts may provide early warning of changes of risk in epidemic-prone regions. Here we discuss the development of a system to forecast probabilities of anomalously high and low malaria incidence with dynamically based, seasonal-timescale, multi-model ensemble predictions of climate, using leading global coupled ocean–atmosphere climate models developed in Europe. This forecast system is successfully applied to the prediction of malaria risk in Botswana, where links between malaria and climate variability are well established, adding up to four months lead time over malaria warnings issued with observed precipitation and having a comparably high level of probabilistic prediction skill. In years in which the forecast probability distribution is different from that of climatology, malaria decision-makers can use this information for improved resource allocation.

Validation of satellite rainfall products over East Africa's complex topography

An extensive evaluation of 10 different satellite rainfall products was performed using station network over a complex topography, where elevation varies from below sea level to 4620 m. Evaluation was for two groups of products. The first group had low spatial (2.5°) and temporal (monthly) resolution and included the Global Precipitation Climatology Project (GPCP), the National Oceanographic and Atmospheric Administration Climate Prediction Center (NOAA‐CPC) merged analysis (CMAP), and the Tropical Rainfall Measuring Mission (TRMM‐3B43). The second group comprised products with relatively high spatial (0.1° to 1°) and temporal (3‐hourly to 10‐daily) resolution. These included the NOAA‐CPC African rainfall estimation algorithm, GPCP one‐degree‐daily (1DD), TRMM‐3B42, Tropical Applications of Meteorology using SATellite and other data (TAMSAT) estimates, and the CPC morphing technique (CMORPH). These products were aggregated to a 10‐day total and remapped to spatial resolutions of 1°, 0.5° and 0.25°. TRMM‐3B43 and CMAP from the first group and CMORPH, TAMSAT and TRMM‐3B42 from the second group performed reasonably well.

Validation of high-resolution satellite rainfall products over complex terrain

High‐resolution satellite rainfall products, at daily accumulation and 0.25° spatial resolution, are evaluated using station networks located over two different parts of Africa. The first site is located over Ethiopia with a very complex terrain. The second site, located over Zimbabwe, has a less rugged topography. The evaluated satellite rainfall products are the NOAA‐CPC African rainfall estimation algorithm (RFE), TRMM‐3B42, the CPC morphing technique (CMORPH), PERSIANN, and the Naval Research Laboratorys blended product. These products perform reasonably well over both regions in detecting the occurrence of rainfall. However, performances are poor in estimating the amount of rainfall in each pixel. The correlation coefficients are low and random errors high. The performance was better over Zimbabwe as compared with Ethiopia. Comparing the different products, CMORPH and TRMM‐3B42 showed a better performance over Ethiopia, while RFE, CMORPH, and TRMM‐3B42 preformed relatively better over Zimbabwe.

Environmental Risk and Meningitis Epidemics in Africa

Epidemics of meningococcal meningitis occur in areas with particular environmental characteristics. We present evidence that the relationship between the environment and the location of these epidemics is quantifiable and propose a model based on environmental variables to identify regions at risk for meningitis epidemics. These findings, which have substantial implications for directing surveillance activities and health policy, provide a basis for monitoring the impact of climate variability and environmental change on epidemic occurrence in Africa.

A vectorial capacity product to monitor changing malaria transmission potential in epidemic regions of Africa.

Rainfall and temperature are two of the major factors triggering malaria epidemics in warm semi-arid (desert-fringe) and high altitude (highland-fringe) epidemic risk areas. The ability of the mosquitoes to transmit Plasmodium spp. is dependent upon a series of biological features generally referred to as vectorial capacity. In this study, the vectorial capacity model (VCAP) was expanded to include the influence of rainfall and temperature variables on malaria transmission potential. Data from two remote sensing products were used to monitor rainfall and temperature and were integrated into the VCAP model. The expanded model was tested in Eritrea and Madagascar to check the viability of the approach. The analysis of VCAP in relation to rainfall, temperature and malaria incidence data in these regions shows that the expanded VCAP correctly tracks the risk of malaria both in regions where rainfall is the limiting factor and in regions where temperature is the limiting factor. The VCAP maps are currently offered as an experimental resource for testing within Malaria Early Warning applications in epidemic prone regions of sub-Saharan Africa. User feedback is currently being collected in preparation for further evaluation and refinement of the VCAP model.

An online operational rainfall-monitoring resource for epidemic malaria early warning systems in Africa

Periodic epidemics of malaria are a major public health problem for many sub-Saharan African countries. Populations in epidemic prone areas have a poorly developed immunity to malaria and the disease remains life threatening to all age groups. The impact of epidemics could be minimized by prediction and improved prevention through timely vector control and deployment of appropriate drugs. Malaria Early Warning Systems are advocated as a means of improving the opportunity for preparedness and timely response.Rainfall is one of the major factors triggering epidemics in warm semi-arid and desert-fringe areas. Explosive epidemics often occur in these regions after excessive rains and, where these follow periods of drought and poor food security, can be especially severe. Consequently, rainfall monitoring forms one of the essential elements for the development of integrated Malaria Early Warning Systems for sub-Saharan Africa, as outlined by the World Health Organization.The Roll Back Malaria Technical Resource Network on Prevention and Control of Epidemics recommended that a simple indicator of changes in epidemic risk in regions of marginal transmission, consisting primarily of rainfall anomaly maps, could provide immediate benefit to early warning efforts. In response to these recommendations, the Famine Early Warning Systems Network produced maps that combine information about dekadal rainfall anomalies, and epidemic malaria risk, available via their Africa Data Dissemination Service. These maps were later made available in a format that is directly compatible with HealthMapper, the mapping and surveillance software developed by the WHOs Communicable Disease Surveillance and Response Department. A new monitoring interface has recently been developed at the International Research Institute for Climate Prediction (IRI) that enables the user to gain a more contextual perspective of the current rainfall estimates by comparing them to previous seasons and climatological averages. These resources are available at no cost to the user and are updated on a routine basis.

Validation and Intercomparison of Satellite Rainfall Estimates over Colombia

Abstract Seven different satellite rainfall estimates are evaluated at daily and 10-daily time scales and a spatial resolution of 0.25° latitude/longitude. The reference data come from a relatively dense station network of about 600 rain gauges over Colombia. This region of South America has a very complex terrain with mountain ranges that form the northern tip of the Andes Mountains, valleys between the mountain ranges, and a vast plain that is part of the Amazon. The climate is very diverse with an extremely wet Pacific coast, a dry region in the north, and different rainfall regimes between the two extremes. The evaluated satellite rainfall products are the Tropical Rainfall Measuring Mission 3B42 and 3B42RT products, the NOAA/Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN), the Naval Research Laboratory’s blended product (NRLB), and two versions of the Global Satellite Mapping of Precipitation m...

Comparison of CMORPH and TRMM-3B42 over Mountainous Regions of Africa and South America

Two satellite rainfall estimation algorithms, CMORPH and TMPA, are evaluated over two mountainous regions at daily accumulation and spatial resolution 0.25°. The evaluated TMPA products are TRMM-3B42 and TRMM-3B42RT. The first of the two validations region is located over the Ethiopian highlands in the Horn of Africa. The second is located over the highlands of Columbia in South America. Both sites are characterized by a very complex terrain. Relatively dense station networks over the two sites are used to validate the satellite products. The correlation coefficients between the reference gauge data and the satellite products were found to be low. Besides, the products underestimate both the occurrence and amount of rainfall over both validation sites. These were attributed, at least partly, to orographic warm rain process over the two regions. The performance over Colombia was better compared to that for Ethiopia. And CMORPH has exhibited better performance as compared to the two TRMM products.

Challenges of satellite rainfall estimation over mountainous and arid parts of east Africa

Different satellite rainfall products are used in different applications over different parts of the world. These products are particularly important over many parts of Africa, where they are used to augment the very sparse rain-gauge network. However, the quality of the different satellite products varies from one product to another and from one climatic region to another. The climate over eastern Africa varies from wet coastal and mountainous regions to dry arid regions. Significant variations could be observed within short distances. The different climates will pose different challenges to satellite rainfall retrieval over this region. This study explores the effect of mountainous and arid climates on four different satellite rainfall-estimation (RFE) algorithms. The mountainous climate is located over the Ethiopian highlands, while the arid region covers parts of Ethiopia, Djibouti and Somalia. One infrared-only product, African rainfall climatology (ARC), one passive-microwave-only product (the Climate Prediction Center morphing technique, CMORPH) and two products (the RFE algorithm and the tropical rainfall measuring mission (TRMM-3B42)), which combine both infrared and passive-microwave estimates, are used for this investigation. All the products exhibit moderate underestimation of rainfall over the highlands of Ethiopia, while the overestimation over the dry region is found to be very high. The underestimation over the mountainous region is ascribed to the warm orographic rain process, while the overestimation over the dry region may be because of sub-cloud evaporation. Local calibration of satellite algorithms and merging of satellite estimates with all locally available rain-gauge observations are some of the approaches that could be employed to alleviate these problems.

A model to simulate the impact of timing, coverage and transmission intensity on the effectiveness of indoor residual spraying (IRS) for malaria control

Objective  (i) To develop a temperature‐ and rainfall‐driven model of malaria transmission capable of prediction. (ii) To use the model to examine the relationship between the intervention timing and transmission intensity on the effectiveness of indoor residual spraying (IRS).