Malaria Early Warning Research Articles

Predicting malaria hyper endemic zones in West Africa using a regional scale dynamical malaria model

Due to the continuing large number of malaria-related deaths in tropical Africa, the need to develop a robust Malaria Early Warning System (MEWS) for effective action is growing to guide cost-effective implementation of interventions. This study employs a two-stage hierarchical evaluation technique to evaluate the ability of the VECTRI malaria model to simulate malaria dynamics at seasonal time scale (1 - 7 months) over Nigeria and West Africa. Two sets of malaria simulations are considered. The first set is based on VECTRI simulations driven by observed rainfall and temperature datasets (hereafter referred to as control run). The second is based on malaria simulations driven by the European Centre for Medium-Range Weather Forecasting (ECMWF) System5 ensemble seasonal forecasting system (hereafter referred to as Forecast run). Different metrics are employed to assess the skill of the VECTRI malaria model. Results based on the control run indicate that the model can reproduce hyper-endemic zones and the evolution of malaria cases, particularly the observed increase in cases with decreasing population density. Despite having significant biases and low correlation, the model successfully predicts annual anomalies in malaria cases across Nigeria, particularly in the savannah region that experience large malaria burden. Annual correlations between the VECTRI Forecast run and the VECTRI Control run are relatively low at all lead times (LT) and for each start date (SD) across West Africa, although correlation generally increases from the Gulf of Guinea to the Sahel. Despite low correlations, the Rank Probability Skill Score (RPSS) reveals that the model has a statistically significant skill in predicting malaria occurrences across all categories of malaria cases, regardless of start date or lead time. While the Guinea Forest has the strongest RPSS, the increase or decrease in skill from the first to seventh lead time varies significantly across the region. In addition, the VECTRI malaria model has a good ability to discriminate variability in malaria cases across all regions, with an average Area Under the Relative Operating Characteristics (ROC) Curve (AUC) of approximately 0.62. Our findings suggest that the VECTRI malaria model could be used as a reliable Malaria Early Warning System (MEWS), particularly for identifying malaria hyper-endemic zones in West Africa at seasonal time scale.

Cloud-based applications for accessing satellite Earth observations to support malaria early warning

Malaria epidemics can be triggered by fluctuations in temperature and precipitation that influence vector mosquitoes and the malaria parasite. Identifying and monitoring environmental risk factors can thus provide early warning of future outbreaks. Satellite Earth observations provide relevant measurements, but obtaining these data requires substantial expertise, computational resources, and internet bandwidth. To support malaria forecasting in Ethiopia, we developed software for Retrieving Environmental Analytics for Climate and Health (REACH). REACH is a cloud-based application for accessing data on land surface temperature, spectral indices, and precipitation using the Google Earth Engine (GEE) platform. REACH can be implemented using the GEE code editor and JavaScript API, as a standalone web app, or as package with the Python API. Users provide a date range and data for 852 districts in Ethiopia are automatically summarized and downloaded as tables. REACH was successfully used in Ethiopia to support a pilot malaria early warning project in the Amhara region. The software can be extended to new locations and modified to access other environmental datasets through GEE.

Interactions between climatic changes and intervention effects on malaria spatio-temporal dynamics in Uganda

BackgroundAlthough malaria burden in Uganda has declined since 2009 following the scale-up of interventions, the disease is still the leading cause of hospitalization and death. Transmission remains high and is driven by suitable weather conditions. There is a real concern that intervention gains may be reversed by climatic changes in the country. In this study, we investigate the effects of climate on the spatio-temporal trends of malaria incidence in Uganda during 2013–2017. MethodsBayesian spatio-temporal negative binomial models were fitted on district-aggregated monthly malaria cases, reported by two age groups, defined by a cut-off age of 5 years. Weather data was obtained from remote sensing sources including rainfall, day land surface temperature (LSTD) and night land surface temperature (LSTN), Normalized Difference Vegetation Index (NDVI), altitude, land cover, and distance to water bodies. Spatial and temporal correlations were taken into account by assuming a conditional autoregressive and a first-order autoregressive process on district and monthly specific random effects, respectively. Fourier trigonometric functions modeled seasonal fluctuations in malaria transmission. The effects of climatic changes on the malaria incidence changes between 2013 and 2017 were estimated by modeling the difference in time varying climatic conditions at the two time points and adjusting for the effects of intervention coverage, socio-economic status and health seeking behavior. ResultsMalaria incidence declined steadily from 2013 to 2015 and then increased in 2016. The decrease was by over 38% and 20% in children <5 years and individuals ≥5 years, respectively. Temporal trends depict a strong bi-annual seasonal pattern with two peaks during April–June and October–December. The annual average of rainfall, LSTD and LSTN increased by 3.7 mm, 2.2 °C and 1.0 °C, respectively, between 2013 and 2017, whereas NDVI decreased by 6.8%. On the one hand, the increase in LSTD and decrease in NDVI were associated with a reduction in the incidence decline. On the other hand, malaria interventions and treatment seeking behavior had reverse effects, that were stronger compared to the effects of climatic changes. Important interactions between interventions with NDVI and LSTD suggest a varying impact of interventions on malaria burden in different climatic conditions. ConclusionClimatic changes in Uganda during the last five years contributed to a favorable environment for malaria transmission, and had a detrimental effect on malaria reduction gains achieved through interventions scale-up efforts. The NMCP should create synergies with the National Meteorological Authority with an ultimate goal of developing a Malaria Early Warning System to mitigate adverse climatic change effects on malaria risk in the country.

The current and potential role of satellite remote sensing in the campaign against malaria

Malaria and other vector borne diseases claim lives and cause illness, especially in less developed countries. Although well understood methods, such as spraying and insecticidal nets, are identified as effective deterrents to malaria transmission by mosquitoes, the nations that have the greatest burden from the disease also struggle to deploy such measures sufficiently. More targeted and up to date information is needed to identify which regions of malaria-endemic countries are most likely to be at risk of malaria in the near future. This will allow national governments, local officials and public health workers to deploy protective equipment and personnel where they are most needed. This paper explores the role of environmental data generated via satellite remote sensing as an ingredient to a Malaria Early Warning System. Data from remote sensing satellites can cover broad geographical areas frequently and consistently. Much of the relevant data may be accessed by malaria-endemic countries at minimal cost via international data sharing polices. While previous research studies have demonstrated the potential to assign malaria risk to a geographic region based on indicators from satellites and other sources, there is still a need to deploy such tools in a broader and more operational manner to inform decision making on malaria management. This paper describes current research on the use of satellite-based environmental data to predict malaria risk and examines the barriers and opportunities for implementing Malaria Early Warning Systems enabled by satellite remote sensing. A Systems Architecture Framework analyses the components of a Malaria Early Warning System and highlights the need for effective coordination across public and private sector organizations.

Implementation of malaria dynamic models in municipality level early warning systems in Colombia. Part I: description of study sites.

As part of the Integrated National Adaptation Pilot project and the Integrated Surveillance and Control System, the Colombian National Institute of Health is working on the design and implementation of a Malaria Early Warning System framework, supported by seasonal climate forecasting capabilities, weather and environmental monitoring, and malaria statistical and dynamic models. In this report, we provide an overview of the local ecoepidemiologic settings where four malaria process-based mathematical models are currently being implemented at a municipal level. The description includes general characteristics, malaria situation (predominant type of infection, malaria-positive cases data, malaria incidence, and seasonality), entomologic conditions (primary and secondary vectors, mosquito densities, and feeding frequencies), climatic conditions (climatology and long-term trends), key drivers of epidemic outbreaks, and non-climatic factors (populations at risk, control campaigns, and socioeconomic conditions). Selected pilot sites exhibit different ecoepidemiologic settings that must be taken into account in the development of the integrated surveillance and control system.

Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia

BackgroundMalaria is one of the leading public health problems in most of sub-Saharan Africa, particularly in Ethiopia. Almost all demographic groups are at risk of malaria because of seasonal and unstable transmission of the disease. Therefore, there is a need to develop malaria early-warning systems to enhance public health decision making for control and prevention of malaria epidemics. Data from orbiting earth-observing sensors can monitor environmental risk factors that trigger malaria epidemics. Remotely sensed environmental indicators were used to examine the influences of climatic and environmental variability on temporal patterns of malaria cases in the Amhara region of Ethiopia.MethodsIn this study seasonal autoregressive integrated moving average (SARIMA) models were used to quantify the relationship between malaria cases and remotely sensed environmental variables, including rainfall, land-surface temperature (LST), vegetation indices (NDVI and EVI), and actual evapotranspiration (ETa) with lags ranging from one to three months. Predictions from the best model with environmental variables were compared to the actual observations from the last 12 months of the time series.ResultsMalaria cases exhibited positive associations with LST at a lag of one month and positive associations with indicators of moisture (rainfall, EVI and ETa) at lags from one to three months. SARIMA models that included these environmental covariates had better fits and more accurate predictions, as evidenced by lower AIC and RMSE values, than models without environmental covariates.ConclusionsMalaria risk indicators such as satellite-based rainfall estimates, LST, EVI, and ETa exhibited significant lagged associations with malaria cases in the Amhara region and improved model fit and prediction accuracy. These variables can be monitored frequently and extensively across large geographic areas using data from earth-observing sensors to support public health decisions.

Seasonal Variation of Malaria Parasite Density in Paediatric Population of North Eastern Nigeria

Malaria is a major cause of morbidity and mortality in children and adults in the Sudano Sahel of Northeastern Nigeria with the highest prevalence of malaria in pregnancy of 64.5 in Nigeria recorded in this region. This study was carried out in 2003 and 2004 to provide parasitological baseline data for the development of Malaria Early Warning System (MEWS) for the surveillance of type I epidemic caused by meteorological conditions and to provide data for timing malaria key vector control measures such as Indoor Residual Spraying (IRS) for maximum effect. Clinical information about malaria cases were used in this study. In all 692 children aged 6 to 96 months were screened for Plasmodium infection and used for the analysis. The results showed that the majority of infected children (68.06%) were aged 12-60 months and their asexual parasite density (ap/ u1) was between 100-500 ap/u1 of the whole blood. The month of September recorded the highest Geometric Mean Asexual-Densities (GMPD) of 13,655 while the lowest parasite densities were observed at the peak of the dry season, especially during the months of March and April. Significance difference (p<0.05) was observed between the sexes in infection rate. It is obvious that male children have higher infection rate (about 67.5%), than while female children (32.5%) regardless of climate seasonality. Designing a malaria early warning system and providing baseline parasitological data for timing of spraying cycles for key malaria vector control measures such as Indoor Residual Spraying (IRS) should be encouraged to complement other effective malaria control strategies. Hence the need for this investigation.

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.

Improving the cost-effectiveness of IRS with climate informed health surveillance systems.

BackgroundThis paper examines how the cost-effectiveness of IRS varies depending on the severity of transmission and level of programme coverage and how efficiency could be improved by incorporating climate information into decision making for malaria control programmes as part of an integrated Malaria Early Warning and Response System (MEWS).MethodsA climate driven model of malaria transmission was used to simulate cost-effectiveness of alternative IRS coverage levels over six epidemic and non-epidemic years. Decision rules for a potential MEWS system that triggers different IRS coverage are described. The average and marginal cost per case averted with baseline IRS coverage (24%) and under varying IRS coverage levels (50%, 75% and 100%) were calculated.ResultsAverage cost-effectiveness of 24% coverage varies dramatically between years, from US$108 per case prevented in low transmission to US$0.42 in epidemic years. Similarly for higher coverage (24–100%) cost per case prevented is far higher in low than high transmission years ($108–$267 to $0.88–$2.26).DiscussionEfficiency and health benefit gains could be achieved by implementing MEWS that provides timely, accurate information. Evidence from southern Africa, (especially Botswana) supports this.ConclusionAdvance knowledge of transmission severity can help managers make coverage decisions which optimise resource use and exploit efficiency gains if a fully integrated MEWS is in place alongside a health system with sufficient flexibility to modify control plans in response to information. More countries and programmes should be supported to use the best available evidence and science to integrate climate informed MEWS into decision making within malaria control programmes.

Modelling of malaria temporal variations in Iran

To model the temporal variations in malaria episodes in a hypo-endemic area of Iran and to assess the feasibility of an epidemic early warning system. Malaria episode data for Kahnooj District, south-east Iran, were collected from the local health system for the period 1994-2002. Plasmodium species-specific models were generated using Poisson regression. Starting with a simple model which included only temporal effects, we iteratively added more explanatory variables to maximize goodness of fit. Of 18,268 recorded malaria episodes, more than 67% were due to P. vivax. In addition to seasonality and secular trend, we found that incorporating a 1-month time lag between key meteorological variables and the predicted number of cases maximized goodness of fit. Maximum temperature, mean relative humidity and previous numbers of malaria cases were the most important predictors. These were included in the model with lags of no less than three dekads, i.e. three 10-day periods or effectively 1 month. Simple models based on climatic factors and information on past case numbers may be useful in improving the quality of the malaria control programme in Iran, particularly in terms of assuring accurate targeting of interventions in time and space. The models developed in this study are based on explanatory data that incorporate a lag of 1 month (i.e. data that were recorded 21-50 days previously). In practice, this translates into an operational 'window' of 1 month. Provided appropriate modes of data exchange exist between key stakeholders and appropriate systems for operational response are in place, this type of early warning information has the potential to lead to significant reductions in malaria morbidity in Iran.

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

(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). A dynamic model of malaria transmission was developed from existing models of malaria transmission dynamics. The model was used to retrospectively predict actual malaria cases from Hwange district in Zimbabwe using actual meteorological and IRS timing and coverage data. Simulations of alternative intervention scenarios (timing and coverage) examined the effectiveness of earlier and later interventions, at higher and lower coverage levels in epidemic and non-epidemic years. The model was able to predict actual malaria cases in Hwange over a four-and-a-half-year period with a lead time of 4 months (e.g. January rainfall and temperature predicts April malaria) and a correlation coefficient of 0.825 (r(2) = 0.6814). The IRS simulations show that the marginal benefits of increasing IRS coverage are higher in high-transmission (HT) years relative to lower transmission years. This implies that over a period of years, maximum impact could be achieved with a given quantity of insecticide by increasing coverage in HT years. However, the model also shows that earlier spraying is more effective in all years, especially so in epidemic years, and that IRS has limited impact if it is carried out too late in relation to peak transmission. Temperature- and rainfall-driven models of malaria transmission have the potential to predict malaria epidemics. Early intervention based on prior knowledge of the magnitude of the malaria season can be more effective and efficient than carrying out routine activities every year. Malaria control planners need improved access to the technology that would allow them to better predict malaria epidemics and develop Malaria Early Warning Systems (MEWS). MEWS can then be linked to intervention planning to reduce the devastating impact of malaria epidemics on populations.

Modelling entomological-climatic interactions of Plasmodium falciparum malaria transmission in two Colombian endemic-regions: contributions to a National Malaria Early Warning System.

BackgroundMalaria has recently re-emerged as a public health burden in Colombia. Although the problem seems to be climate-driven, there remain significant gaps of knowledge in the understanding of the complexity of malaria transmission, which have motivated attempts to develop a comprehensive model.MethodsThe mathematical tool was applied to represent Plasmodium falciparum malaria transmission in two endemic-areas. Entomological exogenous variables were estimated through field campaigns and laboratory experiments. Availability of breeding places was included towards representing fluctuations in vector densities. Diverse scenarios, sensitivity analyses and instabilities cases were considered during experimentation-validation process.ResultsCorrelation coefficients and mean square errors between observed and modelled incidences reached 0.897–0.668 (P > 0.95) and 0.0002–0.0005, respectively. Temperature became the most relevant climatic parameter driving the final incidence. Accordingly, malaria outbreaks are possible during the favourable epochs following the onset of El Niño warm events. Sporogonic and gonotrophic cycles showed to be the entomological key-variables controlling the transmission potential of mosquitoes' population. Simulation results also showed that seasonality of vector density becomes an important factor towards understanding disease transmission.ConclusionThe model constitutes a promising tool to deepen the understanding of the multiple interactions related to malaria transmission conducive to outbreaks. In the foreseeable future it could be implemented as a tool to diagnose possible dynamical patterns of malaria incidence under several scenarios, as well as a decision-making tool for the early detection and control of outbreaks. The model will be also able to be merged with forecasts of El Niño events to provide a National Malaria Early Warning System.

Taking a (mosquito) bite out of malaria

Early in the last decade, great strides were made toward eliminating malaria through the use of pest control and extermination of mosquitoes, which are responsible for transmitting the infectious Plasmodium parasites that cause the disease. Malaria was once prevalent in the US and Europe, but the draining of wetlands and improved sanitation eliminated the scourge from these affluent regions. Today, most deaths from the disease occur in poor, rural areas of Africa, where malaria incidence is on the rise, killing more than one million people each year, most of whom are young children. This statistic seems shocking considering the disease’s preventability by relatively simple means, such as mosquito netting and prophylactic drugs such as quinine and its derivatives. Unfortunately, pharmaceutical treatment is costly, in short supply, and rendered ineffective as more virile strains of the disease become resistant to the medication, so malaria prevention is widely considered a top priority. As a first line of defense, the United Nations Children’s Fund recommends the wide distribution of mosquito nets impregnated with insect repellents as the most cost-effective disease prevention method. Many international health organizations have now donated millions of dollars for the production and allocation of insecticide-treated nets. The United Kingdom’s Department for International Development’s $3 million donation will provide 400,000 of these nets to pregnant women and children less than 5 years of age in Zimbabwe, where malaria is the second-highest killer of young children. Japan, in an unprecedented contribution, has promised 10 million bed nets to Africa by 2007. The Global Fund, through US sponsorship, began a $22 million, 5-year plan to fight malaria in 2003 at the urging of Swaziland, Mozambique, and South Africa. According to the Global Fund, the efforts have “tripled the area of operations in Mozambique.” The US pledges at least $2 billion more through 2008. The WHO’s Roll Back Malaria Department has attempted to strike preemptively by initiating the Malaria Early Warning System, which began monitoring rainfall in June 2002. Rainfall in warm, semi-arid and desert-fringe regions can trigger explosive epidemics by providing fertile breeding grounds for mosquitoes. By studying rainfall data, the organization is able to identify regions at risk for a future epidemic, and assistance can arrive before the outbreak occurs. On the clinical front, technological advances have paved the way for new means of malaria prevention. Using the recently decoded genomes of both the carrier mosquito and the dangerous parasite it spreads, scientists hope to exploit sequences that might belie weaknesses in the genetic makeup of the organisms in order to disrupt the life cycle of the deadly protozoan. Researchers have already found genes that can be manipulated to help produce more effective repellents and insecticides. Theoretically, the pests themselves can be genetically altered to resist malaria, but that idea invites controversy, with the possibility of genetic mixing among mosquito species perhaps creating an even stronger malaria carrier. Controversy aside, the world is witnessing an increasing effort to eradicate the disease by boosting prevention measures and identifying new treatment options. The alarming number of child-related deaths from this wholly treatable illness has signaled the urgency to treat malaria on a global scale. The donations by world bodies, innovative research by both public and private organizations, and new advancement in technology represent fresh hope.

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 WHO's 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.

The development of Malaria Early Warning Systems for Africa.

Current efforts to predict malaria epidemics focus on the role weather anomalies can play in epidemic prediction. Alongside weather monitoring and seasonal climate forecasts, epidemiological, social and environmental factors can also play a role in predicting the timing and severity of malaria epidemics. Such factors can be incorporated into a framework for malaria early warning.

Malaria early warning in Kenya.

Kenya displays large spatiotemporal diversity in its climate and ecology. It follows that malaria transmission will reflect this environmental heterogeneity in both space and time. In this article, we discuss how such heterogeneity, and its epidemiological consequences, should be considered in the development of early warning systems for malaria epidemics.

Predicting malaria infection in Gambian children from satellite data and bed net use surveys: the importance of spatial correlation in the interpretation of results.

In line with the renewed World Health Organization Global Malaria Control Strategy, we have advocated the use of satellite imagery by control services to provide environmental information for malaria stratification, monitoring, and early warning. To achieve this operationally, appropriate methodologies must be developed for integrating environmental and epidemiologic data into models that can be used by decision-makers for improved resource allocation. Using methodologies developed for the Famine Early Warning Systems and spatial statistics, we show a significant association between age related malaria infection in Gambian children and the amount of seasonal environmental greenness as measured using the normalized difference vegetation index derived from satellite data. The resulting model is used to predict changes in malaria prevalence rates in children resulting from different bed net control scenarios.

Mapping malaria risk in Africa: What can satellite data contribute?

Recent developments in the access of remotely sensed vegetation and weather data, and their analysis along with other data sources within a geographical information system (GIS), have opened up new possibilities for African health services and research institutes in malaria stratification, monitoring and early warning. Madeleine Thomson, Stephen Connor, Paul Milligan and Stephane Flasse review the current situation and outline the way ahead.