The El Niño southern oscillation and malaria epidemics in South America

A better understanding of the relationship between the El Ni?o Southern Oscillation (ENSO), the climatic anomalies it engenders, and malaria epidemics could help mitigate the world-wide increase in incidence of this mosquito-transmitted disease. The purpose of this paper is to assess the possibility of using ENSO forecasts for improving malaria control. This paper analyses the relationship between ENSO events and malaria epidemics in a number of South American countries (Colombia, Ecuador, French Guiana, Guyana, Peru, Suriname, and Venezuela). A statistically significant relationship was found between El Ni?o and malaria epidemics in Colombia, Guyana, Peru, and Venezuela. We demonstrate that flooding engenders malaria epidemics in the dry coastal region of northern Peru, while droughts favor the development of epidemics in Colombia and Guyana, and epidemics lag a drought by 1 year in Venezuela. In Brazil, French Guiana, and Ecuador, where we did not detect an ENSO/malaria signal, non-climatic factors such as insecticide sprayings, variation in availability of anti-malaria drugs, and population migration are likely to play a stronger role in malaria epidemics than ENSO-generated climatic anomalies. In some South American countries, El Ni?o forecasts show strong potential for informing public health efforts to control malaria.     

Forcing Versus Feedback: Epidemic Malaria and Monsoon Rains in Northwest India

Malaria epidemics in regions with seasonal windows of transmission can vary greatly in size from year to year. A central question has been whether these interannual cycles are driven by climate, are instead generated by the intrinsic dynamics of the disease, or result from the resonance of these two mechanisms. This corresponds to the more general inverse problem of identifying the respective roles of external forcings vs. internal feedbacks from time series for nonlinear and noisy systems. We propose here a quantitative approach to formally compare rival hypotheses on climate vs. disease dynamics, or external forcings vs. internal feedbacks, that combines dynamical models with recently developed, computational inference methods. The interannual patterns of epidemic malaria are investigated here for desert regions of northwest India, with extensive epidemiological records for Plasmodium falciparum malaria for the past two decades. We formulate a dynamical model of malaria transmission that explicitly incorporates rainfall, and we rely on recent advances on parameter estimation for nonlinear and stochastic dynamical systems based on sequential Monte Carlo methods. Results show a significant effect of rainfall in the inter-annual variability of epidemic malaria that involves a threshold in the disease response. The model exhibits high prediction skill for yearly cases in the malaria transmission season following the monsoonal rains. Consideration of a more complex model with clinical immunity demonstrates the robustness of the findings and suggests a role of infected individuals that lack clinical symptoms as a reservoir for transmission. Our results indicate that the nonlinear dynamics of the disease itself play a role at the seasonal, but not the interannual, time scales. They illustrate the feasibility of forecasting malaria epidemics in desert and semi-arid regions of India based on climate variability. This approach should be applicable to malaria in other locations, to other infectious diseases, and to other nonlinear systems under forcing.     

An accurate mathematical model predicting number of dengue cases in tropics

Dengue fever is a systemic viral infection of epidemic proportions in tropical countries. The incidence of dengue fever is ever increasing and has doubled over the last few decades. Estimated 50million new cases are detected each year and close to 10000 deaths occur each year. Epidemics are unpredictable and unprecedented. When epidemics occur, health services are over whelmed leading to overcrowding of hospitals. At present there is no evidence that dengue epidemics can be predicted. Since the breeding of the dengue mosquito is directly influenced by environmental factors, it is plausible that epidemics could be predicted using weather data. We hypothesized that there is a mathematical relationship between incidence of dengue fever and environmental factors and if such relationship exists, new cases of dengue fever in the succeeding months can be predicted using weather data of the current month. We developed a mathematical model using machine learning technique. We used Island wide dengue epidemiology data, weather data and population density in developing the model. We used incidence of dengue fever, average rain fall, humidity, wind speed, temperature and population density of each district in the model. We found that the model is able to predict the incidence of dengue fever of a given month in a given district with precision (RMSE between 18- 35.3). Further, using weather data of a given month, the number of cases of dengue in succeeding months too can be predicted with precision (RMSE 10.4—30). Health authorities can use existing weather data in predicting epidemics in the immediate future and therefore measures to prevent new cases can be taken and more importantly the authorities can prepare local authorities for outbreaks.     

Ranking Malaria Risk Factors to Guide Malaria Control Efforts in African Highlands

Introduction

Malaria is re-emerging in most of the African highlands exposing the non immune population to deadly epidemics. A better understanding of the factors impacting transmission in the highlands is crucial to improve well targeted malaria control strategies.

Methods and Findings

A conceptual model of potential malaria risk factors in the highlands was built based on the available literature. Furthermore, the relative importance of these factors on malaria can be estimated through “classification and regression trees”, an unexploited statistical method in the malaria field. This CART method was used to analyse the malaria risk factors in the Burundi highlands. The results showed that Anopheles density was the best predictor for high malaria prevalence. Then lower rainfall, no vector control, higher minimum temperature and houses near breeding sites were associated by order of importance to higher Anopheles density.

Conclusions

In Burundi highlands monitoring Anopheles densities when rainfall is low may be able to predict epidemics. The conceptual model combined with the CART analysis is a decision support tool that could provide an important contribution toward the prevention and control of malaria by identifying major risk factors.     

Regime shifts and heterogeneous trends in malaria time series from Western Kenya Highlands

Large malaria epidemics in the East African highlands during the mid and late 1990s kindled a stream of research on the role that global warming might have on malaria transmission. Most of the inferences using temporal information have been derived from a malaria incidence time series from Kericho. Here, we report a detailed analysis of 5 monthly time series, between 15 and 41 years long, from West Kenya encompassing an altitudinal gradient along Lake Victoria basin. We found decreasing, but heterogeneous, malaria trends since the late 1980s at low altitudes (<1600 m), and the early 2000s at high altitudes (>1600 m). Regime shifts were present in 3 of the series and were synchronous in the 2 time series from high altitudes. At low altitude, regime shifts were associated with a shift from increasing to decreasing malaria transmission, as well as a decrease in variability. At higher altitudes, regime shifts reflected an increase in malaria transmission variability. The heterogeneity in malaria trends probably reflects the multitude of factors that can drive malaria transmission and highlights the need for both spatially and temporally fine-grained data to make sound inferences about the impacts of climate change and control/elimination interventions on malaria transmission.     

Understanding and improving access to prompt and effective malaria treatment and care in rural Tanzania: the ACCESS Programme

Background

Malaria is a significant public health problem in Tanzania. Approximately 16 million malaria cases are reported every year and 100,000 to 125,000 deaths occur. Although most of Tanzania is endemic to malaria, epidemics occur in the highlands, notably in Kagera, a region that was subject to widespread malaria epidemics in 1997 and 1998. This study examined the relationship between climate and malaria incidence in Kagera with the aim of determining whether seasonal forecasts may assist in predicting malaria epidemics.

Methods

A regression analysis was performed on retrospective malaria and climatic data during each of the two annual malaria seasons to determine the climatic factors influencing malaria incidence. The ability of the DEMETER seasonal forecasting system in predicting the climatic anomalies associated with malaria epidemics was then assessed for each malaria season.

Results

It was found that malaria incidence is positively correlated with rainfall during the first season (Oct-Mar) (R-squared = 0.73, p < 0.01). For the second season (Apr-Sep), high malaria incidence was associated with increased rainfall, but also with high maximum temperature during the first rainy season (multiple R-squared = 0.79, p < 0.01). The robustness of these statistical models was tested by excluding the two epidemic years from the regression analysis. DEMETER would have been unable to predict the heavy El Niño rains associated with the 1998 epidemic. Nevertheless, this epidemic could still have been predicted using the temperature forecasts alone. The 1997 epidemic could have been predicted from observed temperatures in the preceding season, but the consideration of the rainfall forecasts would have improved the temperature-only forecasts over the remaining years.

Conclusion

These results demonstrate the potential of a seasonal forecasting system in the development of a malaria early warning system in Kagera region.     

Mosquitoes and transmission of malaria parasites – not just vectors

The regional malaria epidemics of the early 1900s provided the basis for much of our current understanding of malaria epidemiology. Colonel Gill, an eminent malariologist of that time, suggested that the explosive nature of the regional epidemics was due to a sudden increased infectiousness of the adult population. His pertinent observations underlying this suggestion have, however, gone unheeded. Here, the literature on Plasmodium seasonal behaviour is reviewed and three historical data sets, concerning seasonal transmission of Plasmodium falciparum, are examined. It is proposed that the dramatic seasonal increase in the density of uninfected mosquito bites results in an increased infectiousness of the human reservoir of infection and, therefore, plays a key role in "kick-starting" malaria parasite transmission.     

The Role of Climate Variability in the Spread of Malaria in Bangladeshi Highlands

Background

Malaria is a major public health problem in Bangladesh, frequently occurring as epidemics since the 1990s. Many factors affect increases in malaria cases, including changes in land use, drug resistance, malaria control programs, socioeconomic issues, and climatic factors. No study has examined the relationship between malaria epidemics and climatic factors in Bangladesh. Here, we investigate the relationship between climatic parameters [rainfall, temperature, humidity, sea surface temperature (SST), El Niño-Southern Oscillation (ENSO), the normalized difference vegetation index (NDVI)], and malaria cases over the last 20 years in the malaria endemic district of Chittagong Hill Tracts (CHT).

Methods and Principal Findings

Monthly malaria case data from January 1989 to December 2008, monthly rainfall, temperature, humidity sea surface temperature in the Bay of Bengal and ENSO index at the Niño Region 3 (NIÑO3) were used. A generalized linear negative binomial regression model was developed using the number of monthly malaria cases and each of the climatic parameters. After adjusting for potential mutual confounding between climatic factors there was no evidence for any association between the number of malaria cases and temperature, rainfall and humidity. Only a low NDVI was associated with an increase in the number of malaria cases. There was no evidence of an association between malaria cases and SST in the Bay of Bengal and NIÑO3.

Conclusion and Significance

It seems counterintuitive that a low NDVI, an indicator of low vegetation greenness, is associated with increases in malaria cases, since the primary vectors in Bangladesh, such as An. dirus, are associated with forests. This relationship can be explained by the drying up of rivers and streams creating suitable breeding sites for the vector fauna. Bangladesh has very high vector species diversity and vectors suited to these habitats may be responsible for the observed results.     

Early warning systems for malaria in Africa: from blueprint to practice

Although the development of early warning systems for malaria has been advocated by international agencies and academic researchers for many years, practical progress in this area has been relatively modest. In two recent articles, Thomson et al. provide new evidence that models of malaria incidence that incorporate monitored or predicted climate can provide early warnings of epidemics one to five months in advance in semi-arid areas. Although the potential benefits of these models in terms of improved management of epidemics are clear, several technical and practical hurdles still need to be overcome before the models can be widely integrated into routine malaria-control strategies.     

Climate variability and malaria epidemics in the highlands of East Africa

Malaria epidemics in the highlands of East Africa garner significant research attention, due, in part, to their proposed sensitivity to climate change. In a recent article, Zhou et al. claim that increases in climate variance, rather than simple increases in climate mean values, have had an important role in the resurgence of malaria epidemics in the East African highlands since the early 1980s. If proven, this would be an interesting result but we believe that the methods used do not test the hypothesis suggested.     

Etiology of lactic acidosis in malaria

Lactic acidosis and hyperlactatemia are common metabolic disturbances in patients with severe malaria. Lactic acidosis causes physiological adverse effects, which can aggravate the outcome of malaria. Despite its clear association with mortality in malaria patients, the etiology of lactic acidosis is not completely understood. In this review, the possible contributors to lactic acidosis and hyperlactatemia in patients with malaria are discussed. Both increased lactate production and impaired lactate clearance may play a role in the pathogenesis of lactic acidosis. The increased lactate production is caused by several factors, including the metabolism of intraerythrocytic Plasmodium parasites, aerobic glycolysis by activated immune cells, and an increase in anaerobic glycolysis in hypoxic cells and tissues as a consequence of parasite sequestration and anemia. Impaired hepatic and renal lactate clearance, caused by underlying liver and kidney disease, might further aggravate hyperlactatemia. Multiple factors thus participate in the etiology of lactic acidosis in malaria, and further investigations are required to fully understand their relative contributions and the consequences of this major metabolic disturbance.     

Control of epidemic malaria on the highlands of Madagascar

The Malagashy national malaria control programme ('Programme National de Lutte contre le Paludisme', PNLP) has been developing, since 1996, an epidemiological early warning system for malaria epidemics in the Central Highlands with the support of the Italian Development Cooperation. The system is based on the monitoring of malaria morbidity (clinical diagnosis) in 536 peripheral health centres (CSB) of the Highlands. The intervention area corresponds to 27 districts of the Antananarivo and Fianarantsoa provinces (4.7 million inhabitants) and spans around 100,000 square km. For each CSB a monthly warning threshold, defined as the 1993-1996 monthly mean number of malaria cases plus two standard deviations, was established. Three levels of epidemic alert have been defined according to the number of times the cases of presumptive malaria surpassed the threshold and according to the reported presence of severe malaria cases. The surveillance system relies also on the monitoring, in district hospitals of the Highlands, of the Plasmodium falciparum infection rate among clinically diagnosed malaria cases. A total of 185,589 presumptive malaria cases, corresponding to a 42/1000 malaria incidence, were recorded in 1997 by the surveillance system. During the same year 184 alerts of 2nd degree were reported. During 1998 173,632 presumptive malaria cases corresponding to a 38/1000 incidence were reported and 207 alerts of 2nd degree were detected; 75 of these alerts were investigated with ad hoc surveys and 3 initial malaria epidemics identified and controlled. Out of 6884 presumptive malaria cases diagnosed in the district hospitals during 1997-1998, only 835 (12.1%) have been confirmed by microscopy (P. falciparum 81.7%, P. vivax 15.0%, P. malariae 2.5%, P. ovale 0.2%, mixed infections 0.6%); 22.4% of these infections were imported cases from coastal endemic areas. The efficiency of the system in monitoring the trend of malaria morbidity and in the rapid detection and response to malaria epidemics is still being evaluated.     

Predicting Malaria occurrence in Southwest and North central Nigeria using Meteorological parameters

Malaria is a major public health problem especially in the tropics with the potential to significantly increase in response to changing weather and climate. This study explored the impact of weather and climate and its variability on the occurrence and transmission of malaria in Akure, the tropical rain forest area of southwest and Kaduna, in the savanna area of Nigeria. We investigate this supposition by looking at the relationship between rainfall, relative humidity, minimum and maximum temperature, and malaria at the two stations. This study uses monthly data of 7 years (2001–2007) for both meteorological data and record of reported cases of malaria infection. Autoregressive integrated moving average (ARIMA) models were used to evaluate the relationship between weather factors and malaria incidence. Of all the models tested, the ARIMA (1, 0, 1) model fits the malaria incidence data best for Akure and Kaduna according to normalized Bayesian information criterion (BIC) and goodness-of-fit criteria. Humidity and rainfall have almost the same trend of association in all the stations while maximum temperature share the same negative association at southwestern stations and positive in the northern station. Rainfall and humidity have a positive association with malaria incidence at lag of 1 month. In all, we found that minimum temperature is not a limiting factor for malaria transmission in Akure but otherwise in the other stations.     

Remotely Sensed Environmental Conditions and Malaria Mortality in Three Malaria Endemic Regions in Western Kenya

BackgroundMalaria is an important cause of morbidity and mortality in malaria endemic countries. The malaria mosquito vectors depend on environmental conditions, such as temperature and rainfall, for reproduction and survival. To investigate the potential for weather driven early warning systems to prevent disease occurrence, the disease relationship to weather conditions need to be carefully investigated. Where meteorological observations are scarce, satellite derived products provide new opportunities to study the disease patterns depending on remotely sensed variables. In this study, we explored the lagged association of Normalized Difference Vegetation Index (NVDI), day Land Surface Temperature (LST) and precipitation on malaria mortality in three areas in Western Kenya.ConclusionThis study identified lag patterns and association of remote- sensing environmental factors and malaria mortality in three malaria endemic regions in Western Kenya. Our results show that rainfall has the most consistent predictive pattern to malaria transmission in the endemic study area. Results highlight a potential for development of locally based early warning forecasts that could potentially reduce the disease burden by enabling timely control actions.     

Implementation of malaria control

Global trends of infant and child mortality have decreased over the last 30 years, while the proportion of malaria deaths has progressively increased due to the deteriorating situation in sub-Saharan Africa. The Global Malaria Control Strategy promoted by WHO has encountered several obstacles to its implementation. Early diagnosis and prompt treatment can reduce malaria mortality, but there is still low investment on safe and effective modalities of care delivery at the periphery, where most of the malaria burden exists. Selective vector control (indoor residual spraying and insecticide-treated nets) plays a significant role outside Africa, but its wider use is limited by cost/affordability problems and operational issues (supply, delivery and logistics). Alternative methods such as environmental management and biological control are cost-effective only under very specific epidemiological situations. In most countries forecasting, early detection and containment of malaria epidemics is deficient, and there is separation between the research and control communities, particularly in Africa. Involvement of the internal agencies, strategic investments in capacity building and institutional networking are needed to strengthen capacity for malaria and research in the countries. The major responsibility is to guide the expenditure made by the communities (which far out-weigh the limited share of national health budgets) towards the most cost-effective approaches to reduce malaria mortality and morbidity.     

The uncertain burden of Plasmodium falciparum epidemics in Africa

Although the control of malaria epidemics has been a priority for the World Health Organization and other agencies for many years, surprisingly little is known about the public health burden of these epidemics. Here, we evaluate the available evidence of the morbidity and mortality impacts of individual epidemics in Africa and examine the problems associated with using these data to estimate the average annual burden of epidemics at national and continental scales. We argue that conventional approaches that are used to assess the burden of epidemics are inadequate, and outline the future steps that are required to produce estimates that are more accurate.     

Prevention and control of malaria epidemics

Malaria epidemics have recently occurred in many areas of the world, particularly in the irregular fringe, along the limits of distribution of malaria endemicity, whether the limiting factors are temperature (latitude or altitude) or relative humidity (deserts), which were the scene of the major epidemics of the past. A review is made of the current approaches to epidemic prevention and control in line with the global malaria control strategy, of which it constitutes an essential element. The different components are discussed, including the identification and study of epidemic prone areas, the search for indicators of epidemic risk, their monitoring, and the early detection and control of actual epidemics. The potential implementation of preventive and control activities depends, nevertheless, on the degree of preparedness of the health services to detect alarm signals, the real time left for action and their capacity of implementation within that time. Recent developments in geographical information systems (GIS) and satellite derived meteorological information offer most useful tools for precise and timely epidemic forecasting, although it should be recognised that such information is only useful if based on a real understanding of their local significance. There remains an urgent need to develop and support local capabilities for the ground truthing of satellite information and for translating it into preventive and control actions.     

Genetic effects of human migrations and isolation

This paper analyses how migrations, environment and epidemics interact to shape genetic variation in the moder human species. The gene mutation that makes humans resistant to malaria is a striking example of how disease can shape the human genome. In Europe malaria spread in coincidence with the arrival of populations from Asia Minor and eastern Mediterranean and was favoured by the spread of agriculture, by the sedentary conditions of life and the related demographic increase. Natural selection, generally, shape the gene pool of a population in order to fit a different environment. This is the reason because hemoglobinopathies and enzyme G6PD deficit are greatly spread in areas hit by malaria epidemic. These effects are particularly evident in isolated regions or in islands with low population density, e.g. Sardinia. Disasters such as epidemics may drastically reduced the size of a population, and the victims under such circumstances are not selected. As a result the survivors within this small population are unlikely to be representative of the original population in its genetic makeup, and this occurrence is known as “bottleneck effect”. Sardinia, for instance, was hit between 1300 and 1700 by several plague epidemics. Such events drastically reduced the total number of inhabitants; creating a local alteration in the gene frequencies, that have moulded the genetics of the population. This has brought about not only a differentiation with respect to other Mediterranean populations, but creating a variability inside the island.     

T cells as mediators of protective immunity against liver stages of Plasmodium

T cells from different subsets play a major role in protective immunity against pre-erythrocytic stages of malaria parasites. Exposure of humans and animals to malaria sporozoites induces (alphabeta CD8(+) and CD4(+) T cells specific for antigens expressed in pre-erythrocytic stages of Plasmodium. These T cells inhibit parasite development in the liver, and immunization with subunit vaccines expressing the respective antigenic moieties confers protection against sporozoite challenge. gammadelta and natural killer T cells can also play a role in protective immunity. Recent studies with mice transgenic for the alphabeta T-cell receptor have revealed the existence of complex mechanisms regulating the induction and development of these responses.     

A Mixed Method to Evaluate Burden of Malaria Due to Flooding and Waterlogging in Mengcheng County,China: A Case Study

Background

Malaria is a highly climate-sensitive vector-borne infectious disease that still represents a significant public health problem in Huaihe River Basin. However, little comprehensive information about the burden of malaria caused by flooding and waterlogging is available from this region. This study aims to quantitatively assess the impact of flooding and waterlogging on the burden of malaria in a county of Anhui Province, China.

Methods

A mixed method evaluation was conducted. A case-crossover study was firstly performed to evaluate the relationship between daily number of cases of malaria and flooding and waterlogging from May to October 2007 in Mengcheng County, China. Stratified Cox models were used to examine the lagged time and hazard ratios (HRs) of the risk of flooding and waterlogging on malaria. Years lived with disability (YLDs) of malaria attributable to flooding and waterlogging were then estimated based on the WHO framework of calculating potential impact fraction in the Global Burden of Disease study.

Results

A total of 3683 malaria were notified during the study period. The strongest effect was shown with a 25-day lag for flooding and a 7-day lag for waterlogging. Multivariable analysis showed that an increased risk of malaria was significantly associated with flooding alone [adjusted hazard ratio (AHR)  = 1.467, 95% CI = 1.257, 1.713], waterlogging alone (AHR = 1.879, 95% CI = 1.696, 2.121), and flooding and waterlogging together (AHR = 2.926, 95% CI = 2.576, 3.325). YLDs per 1000 of malaria attributable to flooding alone, waterlogging alone and flooding and waterlogging together were 0.009 per day, 0.019 per day and 0.022 per day, respectively.

Conclusion

Flooding and waterlogging can lead to higher burden of malaria in the study area. Public health action should be taken to avoid and control a potential risk of malaria epidemics after these two weather disasters.