Meteorologic influences on Plasmodium falciparum malaria in the Highland Tea Estates of Kericho, Western Kenya

Malaria trends in Ethiopian highlands track the 2000 'slowdown' in global warming

A counterargument to the importance of climate change for malaria transmission has been that regions where an effect of warmer temperatures is expected, have experienced a marked decrease in seasonal epidemic size since the turn of the new century. This decline has been observed in the densely populated highlands of East Africa at the center of the earlier debate on causes of the pronounced increase in epidemic size from the 1970s to the 1990s. The turnaround of the incidence trend around 2000 is documented here with an extensive temporal record for malaria cases for both Plasmodium falciparum and Plasmodium vivax in an Ethiopian highland. With statistical analyses and a process-based transmission model, we show that this decline was driven by the transient slowdown in global warming and associated changes in climate variability, especially ENSO. Decadal changes in temperature and concurrent climate variability facilitated rather than opposed the effect of interventions.

Evaluating effectiveness of screening house eaves as a potential intervention for reducing indoor vector densities and malaria prevalence in Nyabondo, western Kenya

Background

Mosquito-proofing of houses using wire mesh screens is gaining greater recognition as a practical intervention for reducing exposure to malaria transmitting mosquitoes. Screening potentially protects all persons sleeping inside the house against transmission of mosquito-borne diseases indoors. The study assessed the effectiveness of house eaves screening in reducing indoor vector densities and malaria prevalence in Nyabondo, western Kenya.

Methods

160 houses were selected for the study, with half of them randomly chosen for eaves screening with fibre-glass coated wire mesh (experimental group) and the other half left without screening (control group). Randomization was carried out by use of computer-generated list in permuted blocks of ten houses and 16 village blocks, with half of them allocated treatment in a ratio of 1:1. Cross-sectional baseline entomological and parasitological data were collected before eave screening. After baseline data collection, series of sampling of indoor adult mosquitoes were conducted once a month in each village using CDC light traps. Three cross-sectional malaria parasitological surveys were conducted at three month intervals after installation of the screens. The primary outcome measures were indoor Anopheles mosquito density and malaria parasite prevalence.

Results

A total of 15,286 mosquitoes were collected over the two year period using CDC light traps in 160 houses distributed over 16 study villages (mean mosquitoes = 4.35, SD = 11.48). Of all mosquitoes collected, 2,872 (18.8%) were anophelines (2,869 Anopheles gambiae sensu lato, 1 Anopheles funestus and 2 other Anopheles spp). Overall, among An. gambiae collected, 92.6% were non-blood fed, 3.57% were blood fed and the remaining 0.47% were composed of gravid and half gravid females. More indoor adult mosquitoes were collected in the control than experimental arms of the study. Results from cross-sectional parasitological surveys showed that screened houses recorded relatively low malaria parasite prevalence rates compared to the control houses. Overall, malaria prevalence was 5.6% (95% CI: 4.2–7.5) n = 1,918, with baseline prevalence rate of 6.1% (95% CI: 3.9–9.4), n = 481 and 3^rd follow-up survey prevalence of 3.6% (95% CI: 2.0–6.8) n = 494. At all the three parasitological follow-up survey points, house screening significantly reduced the malaria prevalence by 100% (p < 0.001), 63.6% (p = 0.026), and 100% (p < 0.001) in the 1st, 2nd and 3rd follow-up surveys respectively.

Conclusions

The study demonstrated that house eave screening has potential to reduce indoor vector densities and malaria prevalence in high transmission areas.

Malaria epidemics in India: Role of climatic condition and control measures

Malaria is a major public health problem in India, which is the second most populous country in the world. This study aimed to investigate the impact of climatic parameters and malaria control efforts implemented by the Indian national malaria control program on malaria epidemics between January of 2009 and December of 2015. A chi-squared test was used to study the correlation of all implemented control methods with occurrence of epidemics within 30, 45, 60 and 90 days and in the same district, 50, 100 and 200 km distance radiuses. The effect of each control method on probability of epidemics was also measured, and the effects of district population, season, and incidence of malaria parasite types were evaluated using logistic regression models. Fever survey was found to be effective for decreasing the odds of epidemics within 45, 60 and 90 days in 100 km. Anti-larval activity was also effective within 30, 45 and 60 days in 200 km. Winter had negative effects on odds ratio while summer and fall were more likely to trigger epidemics. These results contribute to understanding the role of climate variability and control efforts performed in India.

Risk factors for Plasmodium falciparum infection in the Kenyan Highlands: a cohort study

BACKGROUND

Malaria transmission in African highland areas can be prone to epidemics, with minor fluctuations in temperature or altitude resulting in highly heterogeneous transmission. In the Kenyan Highlands, where malaria prevalence has been increasing, characterising malaria incidence and identifying risk factors for infection is complicated by asymptomatic infection.

METHODS

This all-age cohort study, one element of the Malaria Transmission Consortium, involved monthly follow-up of 3155 residents of the Kisii and Rachuonyo South districts during June 2009-June 2010. Participants were tested for malaria using rapid diagnostic testing at every visit, regardless of symptoms.

RESULTS

The incidence of Plasmodium falciparum infection was 0.2 cases per person, although infections were clustered within individuals and over time, with the majority of infections detected in the last month of the cohort study. Overall, incidence was higher in the Rachuonyo district and infections were detected most frequently in 5-10-year-olds. The majority of infections were asymptomatic (58%). Travel away from the study area was a notable risk factor for infection.

CONCLUSIONS

Identifying risk factors for malaria infection can help to guide targeting of interventions to populations most likely to be exposed to malaria.

Early warning signals of malaria resurgence in Kericho, Kenya

Campaigns to eliminate infectious diseases could be greatly aided by methods for providing early warning signals of resurgence. Theory predicts that as a disease transmission system undergoes a transition from stability at the disease-free equilibrium to sustained transmission, it will exhibit characteristic behaviours known as critical slowing down, referring to the speed at which fluctuations in the number of cases are dampened, for instance the extinction of a local transmission chain after infection from an imported case. These phenomena include increases in several summary statistics, including lag-1 autocorrelation, variance and the first difference of variance. Here, we report the first empirical test of this prediction during the resurgence of malaria in Kericho, Kenya. For 10 summary statistics, we measured the approach to criticality in a rolling window to quantify the size of effect and directions. Nine of the statistics increased as predicted and variance, the first difference of variance, autocovariance, lag-1 autocorrelation and decay time returned early warning signals of critical slowing down based on permutation tests. These results show that time series of disease incidence collected through ordinary surveillance activities may exhibit characteristic signatures prior to an outbreak, a phenomenon that may be quite general among infectious disease systems.

Aggravation of Human Diseases and Climate Change Nexus

For decades, researchers have debated whether climate change has an adverse impact on diseases, especially infectious diseases. They have identified a strong relationship between climate variables and vector's growth, mortality rate, reproduction, and spatiotemporal distribution. Epidemiological data further indicates the emergence and re-emergence of infectious diseases post every single extreme weather event. Based on studies conducted mostly between 1990-2018, three aspects that resemble the impact of climate change impact on diseases are: (a) emergence and re-emergence of vector-borne diseases, (b) impact of extreme weather events, and (c) social upliftment with education and adaptation. This review mainly examines and discusses the impact of climate change based on scientific evidences in published literature. Humans are highly vulnerable to diseases and other post-catastrophic effects of extreme events, as evidenced in literature. It is high time that human beings understand the adverse impacts of climate change and take proper and sustainable control measures. There is also the important requirement for allocation of effective technologies, maintenance of healthy lifestyles, and public education.

Impact of the 2013 Floods on the Incidence of Malaria in Almanagil Locality, Gezira State, Sudan

Background: Heavy rain hit Sudan in August 2013 with subsequent flash floods in different parts of the country. This study investigated the impact of the flooding on incidence of malaria in Almanagil Locality in central Sudan.

Methods: This observational retrospective study compared malaria data sets during rainfall seasons in the Almanagil Locality in the year of flooding (2013) with those of corresponding rainfall seasons of previous two non-flood years (2011 and 2012).

Results: A marked increase of new malaria cases and incidence rate was observed in the 13 sentinel malaria notification sites in the locality  (IR increased from 6.09 per 100,000 person­days in 2011 [95 % CI: 5.93-6.26] and 6.48 in 2012 [95 % CI: 6.31-6.65] to 8.24 in 2013 [95 % CI: 8.05-8.43] ; P< 0.0001), with a peaking of the incidence rate in the under-5-years age group (IR for this age group jumped from 9.80 per 100,000 person­days in 2011 [95 % CI: 9.29­10.32] and 10.00 in 2012 [95 % CI: 9.52­10.49] to 15.02 in 2013 [95 % CI: 14.41­15.64]). A noticeable increase in the slide positivity rate (P< 0.0001) was observed in the 12-week period of 2013 (SPR = 20.86% [95 % CI: 20.40 ­21.32%]) compared with the same periods in 2011 (SPR = 8.72% [95 % CI: 8.36 ­9.08%]) and 2012 (SPR = 12.62% [95 % CI: 12.24 ­13.01%]), with a more marked rise of the SPR in the under-5-year age group. Hospital data showed increase in both the inpatient and outpatient incidence proportions in the study period of 2013 compared to those of the years 2011 and 2012. Hospital OPD incidence proportion in 2013 was 19.7% (95% CI: 19.24­20.18%) compared to 12.85% (95% CI: 12.48­13.23%) in 2011, and 12.16% (95% CI: 11.82­12.51%) in 2012. The < 5 year old groups were responsible for the overall rise in the proportion of malaria cases in 2013 , particularly the < 1 year old group which more than doubled in the 2013 period compared to both 2011 and 2012 periods (Age­specific proportion of the outpatient malaria cases of the < 1 year old group in 2013 was19.5% [95% CI: 18.5­20.6%]  compared to 7.7% [95% CI: 6.9­8.6%] in 2011 and 8.1% [95% CI: 7.3­8.9%] in 2012. Incidence proportion of severe malaria cases (inpatients) increased to 22.5 % (95 % CI: 21.5 to 23.6 %) in the study period of 2013 compared to 19.8 % (95 % CI: 18.6 to 21.0 %) in 2011 and 18.4 % (95 % CI: 17.4 to 19.5) in 2012. The increase in the proportion of severe malaria cases was mainly due to a higher proportion of children < 5 years of age and especially to a higher proportion of children < 1 year of age.

Conclusion: The study revealed a significant increase in the incidence rate of malaria in Almanagil Locality following the flash flood of August 2013. The flooding had the highest impact on the malaria incidence of the under-5-years age group, and particularly of the under-1-year age group.

Keywords: Flood, Flooding, Malaria, Disaster, Sudan, Gezira, Almanagil

Impact of climate change on human infectious diseases: Empirical evidence and human adaptation

Climate change refers to long-term shifts in weather conditions and patterns of extreme weather events. It may lead to changes in health threat to human beings, multiplying existing health problems. This review examines the scientific evidences on the impact of climate change on human infectious diseases. It identifies research progress and gaps on how human society may respond to, adapt to, and prepare for the related changes. Based on a survey of related publications between 1990 and 2015, the terms used for literature selection reflect three aspects--the components of infectious diseases, climate variables, and selected infectious diseases. Humans' vulnerability to the potential health impacts by climate change is evident in literature. As an active agent, human beings may control the related health effects that may be effectively controlled through adopting proactive measures, including better understanding of the climate change patterns and of the compound disease-specific health effects, and effective allocation of technologies and resources to promote healthy lifestyles and public awareness. The following adaptation measures are recommended: 1) to go beyond empirical observations of the association between climate change and infectious diseases and develop more scientific explanations, 2) to improve the prediction of spatial-temporal process of climate change and the associated shifts in infectious diseases at various spatial and temporal scales, and 3) to establish locally effective early warning systems for the health effects of predicated climate change.

Association of Climatic Variability, Vector Population and Malarial Disease in District of Visakhapatnam, India: A Modeling and Prediction Analysis

Background

Malarial incidence, severity, dynamics and distribution of malaria are strongly determined by climatic factors, i.e., temperature, precipitation, and relative humidity. The objectives of the current study were to analyse and model the relationships among climate, vector and malaria disease in district of Visakhapatnam, India to understand malaria transmission mechanism (MTM).

Methodology

Epidemiological, vector and climate data were analysed for the years 2005 to 2011 in Visakhapatnam to understand the magnitude, trends and seasonal patterns of the malarial disease. Statistical software MINITAB ver. 14 was used for performing correlation, linear and multiple regression analysis.

Results/Findings

Perennial malaria disease incidence and mosquito population was observed in the district of Visakhapatnam with peaks in seasons. All the climatic variables have a significant influence on disease incidence as well as on mosquito populations. Correlation coefficient analysis, seasonal index and seasonal analysis demonstrated significant relationships among climatic factors, mosquito population and malaria disease incidence in the district of Visakhapatnam, India. Multiple regression and ARIMA (I) models are best suited models for modeling and prediction of disease incidences and mosquito population. Predicted values of average temperature, mosquito population and malarial cases increased along with the year. Developed MTM algorithm observed a major MTM cycle following the June to August rains and occurring between June to September and minor MTM cycles following March to April rains and occurring between March to April in the district of Visakhapatnam. Fluctuations in climatic factors favored an increase in mosquito populations and thereby increasing the number of malarial cases. Rainfall, temperatures (20°C to 33°C) and humidity (66% to 81%) maintained a warmer, wetter climate for mosquito growth, parasite development and malaria transmission.

Conclusions/Significance

Changes in climatic factors influence malaria directly by modifying the behaviour and geographical distribution of vectors and by changing the length of the life cycle of the parasite.

Testing a multi-malaria-model ensemble against 30 years of data in the Kenyan highlands

Background

Multi-model ensembles could overcome challenges resulting from uncertainties in models’ initial conditions, parameterization and structural imperfections. They could also quantify in a probabilistic way uncertainties in future climatic conditions and their impacts.

Methods

A four-malaria-model ensemble was implemented to assess the impact of long-term changes in climatic conditions on Plasmodium falciparum malaria morbidity observed in Kericho, in the highlands of Western Kenya, over the period 1979–2009. Input data included quality controlled temperature and rainfall records gathered at a nearby weather station over the historical periods 1979–2009 and 1980–2009, respectively. Simulations included models’ sensitivities to changes in sets of parameters and analysis of non-linear changes in the mean duration of host’s infectivity to vectors due to increased resistance to anti-malarial drugs.

Results

The ensemble explained from 32 to 38% of the variance of the observed P. falciparum malaria incidence. Obtained R²-values were above the results achieved with individual model simulation outputs. Up to 18.6% of the variance of malaria incidence could be attributed to the +0.19 to +0.25°C per decade significant long-term linear trend in near-surface air temperatures. On top of this 18.6%, at least 6% of the variance of malaria incidence could be related to the increased resistance to anti-malarial drugs. Ensemble simulations also suggest that climatic conditions have likely been less favourable to malaria transmission in Kericho in recent years.

Conclusions

Long-term changes in climatic conditions and non-linear changes in the mean duration of host’s infectivity are synergistically driving the increasing incidence of P. falciparum malaria in the Kenyan highlands. User-friendly, online-downloadable, open source mathematical tools, such as the one presented here, could improve decision-making processes of local and regional health authorities.

Evidence of man-vector contact in torn long-lasting insecticide-treated nets

BACKGROUND

Studies indicate that physical damage to long-lasting insecticide-treated nets (LLINs) occurs at a surprisingly rapid rate following net distribution. To what extent does such damage affect the impact of LLINs? Can vectors pass a compromised LLIN barrier to bite? Do more resistant vectors enter the insecticide-treated nets (ITNs) through holes?

METHODS

The study was carried out in three geo-locations. Two types of LLINs (polyester and polyethylene) with 'standardized' physical damage were compared with similarly damaged, but non-insecticidal (control) nets. The proportionate Holes Index (pHI) of each net was 276. Mosquitoes were captured inside the nets, identified taxonomically, and subjected to molecular analysis to estimate Knock-down resistance (Kdr) frequency.

RESULTS

The most commonly observed species was Anopheles gambiae, accounting for approximately 70% (1,076/1,550) of the total mosquitoes collected both in LLINs and non-insecticidal nets. When compared with controls, number of vectors captured in torn LLINs was significantly reduced. Nonetheless in a night, an average of 5 An. gambiae s.l could enter the damaged LLINs to bite. Similar numbers of resistant mosquitoes were collected in both LLINs and non-insecticidal (control) nets (p > 0.05).

CONCLUSIONS

At a pHI of 276, man-vector contact was observed in torn LLINs. The insecticide at the surface of LLINs could only reduce the number of vectors. Resistant mosquitoes have opportunity to enter both non-insecticidal (control) nets and LLINs to bite.

Estimating air temperature and its influence on malaria transmission across Africa

Malaria transmission is strongly influenced by climatic conditions which determine the abundance and seasonal dynamics of the Anopheles vector. In particular, water temperature influences larval development rates whereas air temperature determines adult longevity as well as the rate of parasite development within the adult mosquito. Although data on land surface temperature exist at a spatial resolution of approximately 1 km globally with four time steps per day, comparable data are not currently available for air temperature. In order to address this gap and demonstrate the importance of using the right type of temperature data, we fitted simple models of the relationship between land-surface and air temperature at lower resolution to obtain a high resolution estimate of air temperature across Africa. We then used these estimates to calculate some crucial malaria transmission parameters that strongly depend on air temperatures. Our results demonstrate substantial differences between air and surface temperatures that impact temperature-based maps of areas suitable for transmission. We present high resolution maps of the malaria transmission parameters driven by air temperature and their seasonal variation. The fitted air temperature datasets are made publicly available alongside this publication.

The use of census migration data to approximate human movement patterns across temporal scales

Human movement plays a key role in economies and development, the delivery of services, and the spread of infectious diseases. However, it remains poorly quantified partly because reliable data are often lacking, particularly for low-income countries. The most widely available are migration data from human population censuses, which provide valuable information on relatively long timescale relocations across countries, but do not capture the shorter-scale patterns, trips less than a year, that make up the bulk of human movement. Census-derived migration data may provide valuable proxies for shorter-term movements however, as substantial migration between regions can be indicative of well connected places exhibiting high levels of movement at finer time scales, but this has never been examined in detail. Here, an extensive mobile phone usage data set for Kenya was processed to extract movements between counties in 2009 on weekly, monthly, and annual time scales and compared to data on change in residence from the national census conducted during the same time period. We find that the relative ordering across Kenyan counties for incoming, outgoing and between-county movements shows strong correlations. Moreover, the distributions of trip durations from both sources of data are similar, and a spatial interaction model fit to the data reveals the relationships of different parameters over a range of movement time scales. Significant relationships between census migration data and fine temporal scale movement patterns exist, and results suggest that census data can be used to approximate certain features of movement patterns across multiple temporal scales, extending the utility of census-derived migration data.

Critical review of research literature on climate-driven malaria epidemics in sub-Saharan Africa

OBJECTIVES

To obtain a better understanding of existing research evidence towards the development of climate-driven malaria early warning systems (MEWS) through critical review of published literature in order to identify challenges and opportunities for future research.

STUDY DESIGN

Literature review.

METHODS

A comprehensive search of English literature published between 1990 and 2009 was conducted using the electronic bibliographic database, PubMed. Only studies that explored the associations between environmental and meteorological covariates, El Nino Southern Oscillation (ENSO) and malaria as the basis for developing, testing or implementing MEWS were considered.

RESULTS

In total, 35 relevant studies revealed that the development of functional climate-based MEWS remains a challenge, partly due to the complex web of causality and partly due to the use of imprecise malaria data, spatially and temporally varying covariate data, and different analytical approaches with divergent underlying assumptions. Nevertheless, high resolution spatial and temporal data, innovative analytical tools, and new and automated approaches for early warning and the development of operational MEWS.

CONCLUSIONS

Future research should exploit these opportunities and incorporate the various aspects of MEWS for functional epidemic forecasting systems to be realized.

Malaria pattern observed in the highland fringe of Butajira, Southern Ethiopia: a ten-year retrospective analysis from parasitological and metrological data

Background

Studying the magnitude of highland malaria is necessary to implement effective control measures in highland fringes of Ethiopia. Since repeated epidemics were reported in Butajira, this study hypothesized autochthonous transmission in the highland fringes of Butajira. Thus we aimed to determine the malaria occurrence and its association with meteorological variables in the highland fringe of Butajira, Southern Ethiopia from parasitological and metrological data.

Methods

Retrospective monthly malaria case data was collected from monthly outpatient morbidity reports of the Butajira Health Center, for January 2000 to December 2009. Monthly total rainfall and average temperature (maximum and minimum), which was recorded in Butajira weather station, was obtained from National Meteorological Agency, for the same period. Spearman correlation coefficient was used to quantify the strength of linear relationships between meteorological variables and malaria cases. The effect of each independent variable on malaria cases was assessed using multiple linear regression.

Results

During the study period, a fluctuating trend of malaria cases was observed with Plasmodium vivax (62.5%) dominancy. The highest occurrence of malaria was recorded in 2003, 2004, 2008 and 2009 and the least was recorded in 2000-2002. None of the meteorological variables was positively correlated with monthly malaria cases at zero months lag. However, minimum temperature was positively correlated with total malaria cases, P. vivax and P. falciparum with one month lag. Correlation analysis showed that all of the meteorological variables, except maximum temperature at one month lag, were not significant with total monthly malaria cases and each species of malaria (P-value >0.05) at zero and one month lag effect. Nevertheless, after adjusting for the effect of other variables the linear regression analysis indicated that cumulative monthly rainfall (Beta= -0.24; 95% CI: -0.47, -0.02) at zero months lag and maximum temperature (Beta= -12.13; 95% CI: -23.43, -0.82) at one month lag had a significant negative effect on the total malaria count.

Conclusion

There was no significant association between malaria occurrences and meteorological variables between January 2000 and December 2009; therefore non-climatic factors together with climatic variables should be assessed to know the spread and intensity of malaria in the highland fringe of Butajira. This report also warrants the Ministry of Health to include highland areas in its current malaria controlling campaign so as to address those non-endemic foci of the country.

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

BACKGROUND

Malaria 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.

METHODS

In 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.

RESULTS

Malaria 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.

CONCLUSIONS

Malaria 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.

The health effects of climate change: a survey of recent quantitative research

In recent years there has been a large scientific and public debate on climate change and its direct as well as indirect effects on human health. In particular, a large amount of research on the effects of climate changes on human health has addressed two fundamental questions. First, can historical data be of some help in revealing how short-run or long-run climate variations affect the occurrence of infectious diseases? Second, is it possible to build more accurate quantitative models which are capable of predicting the future effects of different climate conditions on the transmissibility of particularly dangerous infectious diseases? The primary goal of this paper is to review the most relevant contributions which have directly tackled those questions, both with respect to the effects of climate changes on the diffusion of non-infectious and infectious diseases, with malaria as a case study. Specific attention will be drawn on the methodological aspects of each study, which will be classified according to the type of quantitative model considered, namely time series models, panel data and spatial models, and non-statistical approaches. Since many different disciplines and approaches are involved, a broader view is necessary in order to provide a better understanding of the interactions between climate and health. In this respect, our paper also presents a critical summary of the recent literature related to more general aspects of the impacts of climate changes on human health, such as: the economics of climate change; how to manage the health effects of climate change; the establishment of Early Warning Systems for infectious diseases.

The changing limits and incidence of malaria in Africa: 1939-2009

Understanding the historical, temporal changes of malaria risk following control efforts in Africa provides a unique insight into what has been and might be archived towards a long-term ambition of elimination on the continent. Here, we use archived published and unpublished material combined with biological constraints on transmission accompanied by a narrative on malaria control to document the changing incidence of malaria in Africa since earliest reports pre-second World War. One result is a more informed mapped definition of the changing margins of transmission in 1939, 1959, 1979, 1999 and 2009.

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.

Large-scale use of mosquito larval source management for malaria control in Africa: a cost analysis

Background

At present, large-scale use of two malaria vector control methods, long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS) is being scaled up in Africa with substantial funding from donors. A third vector control method, larval source management (LSM), has been historically very successful and is today widely used for mosquito control globally, except in Africa. With increasing risk of insecticide resistance and a shift to more exophilic vectors, LSM is now under re-evaluation for use against afro-tropical vector species. Here the costs of this intervention were evaluated.

Methods

The 'ingredients approach' was used to estimate the economic and financial costs per person protected per year (pppy) for large-scale LSM using microbial larvicides in three ecologically diverse settings: (1) the coastal metropolitan area of Dar es Salaam in Tanzania, (2) a highly populated Kenyan highland area (Vihiga District), and (3) a lakeside setting in rural western Kenya (Mbita Division). Two scenarios were examined to investigate the cost implications of using alternative product formulations. Sensitivity analyses on product prices were carried out.

Results

The results show that for programmes using the same granular formulation larviciding costs the least pppy in Dar es Salaam (US$0.94), approximately 60% more in Vihiga District (US$1.50) and the most in Mbita Division (US$2.50). However, these costs are reduced substantially if an alternative water-dispensable formulation is used; in Vihiga, this would reduce costs to US$0.79 and, in Mbita Division, to US$1.94. Larvicide and staff salary costs each accounted for approximately a third of the total economic costs per year. The cost pppy depends mainly on: (1) the type of formulation required for treating different aquatic habitats, (2) the human population density relative to the density of aquatic habitats and (3) the potential to target the intervention in space and/or time.

Conclusion

Costs for LSM compare favourably with costs for IRS and LLINs, especially in areas with moderate and focal malaria transmission where mosquito larval habitats are accessible and well defined. LSM presents an attractive tool to be integrated in ongoing malaria control effort in such settings. Further data on the epidemiological health impact of larviciding is required to establish cost effectiveness.

The influence of mosquito resting behaviour and associated microclimate for malaria risk

BACKGROUND

The majority of the mosquito and parasite life-history traits that combine to determine malaria transmission intensity are temperature sensitive. In most cases, the process-based models used to estimate malaria risk and inform control and prevention strategies utilize measures of mean outdoor temperature. Evidence suggests, however, that certain malaria vectors can spend large parts of their adult life resting indoors.

PRESENTATION OF HYPOTHESIS

If significant proportions of mosquitoes are resting indoors and indoor conditions differ markedly from ambient conditions, simple use of outdoor temperatures will not provide reliable estimates of malaria transmission intensity. To date, few studies have quantified the differential effects of indoor vs outdoor temperatures explicitly, reflecting a lack of proper understanding of mosquito resting behaviour and associated microclimate.

TESTING THE HYPOTHESIS

Published records from 8 village sites in East Africa revealed temperatures to be warmer indoors than outdoors and to generally show less daily variation. Exploring the effects of these temperatures on malaria parasite development rate suggested indoor-resting mosquitoes could transmit malaria between 0.3 and 22.5 days earlier than outdoor-resting mosquitoes. These differences translate to increases in transmission risk ranging from 5 to approaching 3,000%, relative to predictions based on outdoor temperatures. The pattern appears robust for low- and highland areas, with differences increasing with altitude.

IMPLICATIONS OF THE HYPOTHESIS

Differences in indoor vs outdoor environments lead to large differences in the limits and the intensity of malaria transmission. This finding highlights a need to better understand mosquito resting behaviour and the associated microclimate, and to broaden assessments of transmission ecology and risk to consider the potentially important role of endophily.

Climatic variables and malaria transmission dynamics in Jimma town, South West Ethiopia

BACKGROUND

In Ethiopia, malaria is seasonal and unstable, causing frequent epidemics. It usually occurs at altitudes < 2,000 m above sea level. Occasionally, transmission of malaria occurs in areas previously free of malaria, including areas > 2,000 m above sea level. For transmission of malaria parasite, climatic factors are important determinants as well as non-climatic factors that can negate climatic influences. Indeed, there is a scarcity of information on the correlation between climatic variability and malaria transmission risk in Ethiopia in general and in the study area in particular. Therefore, the aim of this study was to determine the level of correlation between meteorological variables and malaria cases.

METHODS

Time-series analysis was conducted using data on monthly meteorological variables and monthly total malaria in Jimma town, south west Ethiopia, for the period 2000-2009. All the data were entered and analyzed using SPSS-15 database program. Spearman correlation and linear regression analysis were used to asses association between the variables.

RESULTS

During last ten years (2000-2009), a fluctuating trend of malaria transmission was observed with P.vivax becoming predominant species. Spearman correlation analysis showed that monthly minimum temperature, total rainfall and two measures of relative humidity were positively related with malaria but monthly maximum temperature negatively related. Also regression analysis suggested that monthly minimum (p = 0.008), monthly maximum temperature (p = 0.013) and monthly total rainfall (p = 0.040), at one month lagged effect, were significant meteorological factors for transmission of malaria in the study area.

CONCLUSION

Malaria incidences in the last decade seem to have a significant association with meteorological variables. In future, prospective and multidisciplinary cooperative research involving researchers from the fields of parasitology, epidemiology, botany, agriculture and climatology is necessary to identify the real effect of meteorological factors on vector- borne diseases like malaria.

Raised temperatures over the Kericho tea estates: revisiting the climate in the East African highlands malaria debate

BACKGROUND

Whether or not observed increases in malaria incidence in the Kenyan Highlands during the last thirty years are associated with co-varying changes in local temperature, possibly connected to global changes in climate, has been debated for over a decade. Studies, using differing data sets and methodologies, produced conflicting results regarding the occurrence of temperature trends and their likelihood of being responsible, at least in part, for the increases in malaria incidence in the highlands of western Kenya. A time series of quality controlled daily temperature and rainfall data from Kericho, in the Kenyan Highlands, may help resolve the controversy. If significant temperature trends over the last three decades have occurred then climate should be included (along with other factors such as land use change and drug resistance) as a potential driver of the observed increases in malaria in the region.

METHODS

Over 30 years (1 January 1979 to 31 December 2009) of quality controlled daily observations ( > 97% complete) of maximum, minimum and mean temperature were used in the analysis of trends at Kericho meteorological station, sited in a tea growing area of Kenya's western highlands. Inhomogeneities in all the time series were identified and corrected. Linear trends were identified via a least-squares regression analysis with statistical significance assessed using a two-tailed t-test. These 'gold standard' meteorological observations were compared with spatially interpolated temperature datasets that have been developed for regional or global applications. The relationship of local climate processes with larger climate variations, including tropical sea surface temperatures (SST), and El Niño-Southern Oscillation (ENSO) was also assessed.

RESULTS

An upward trend of ≈0.2°C/decade was observed in all three temperature variables (P < 0.01). Mean temperature variations in Kericho were associated with large-scale climate variations including tropical SST (r = 0.50; p < 0.01). Local rainfall was found to have inverse effects on minimum and maximum temperature. Three versions of a spatially interpolated temperature data set showed markedly different trends when compared with each other and with the Kericho station observations.

CONCLUSION

This study presents evidence of a warming trend in observed maximum, minimum and mean temperatures at Kericho during the period 1979 to 2009 using gold standard meteorological observations. Although local factors may be contributing to these trends, the findings are consistent with variability and trends that have occurred in correlated global climate processes. Climate should therefore not be dismissed as a potential driver of observed increases in malaria seen in the region during recent decades, however its relative importance compared to other factors needs further elaboration. Climate services, pertinent to the achievement of development targets such as the Millennium Development Goals and the analysis of infectious disease in the context of climate variability and change are being developed and should increase the availability of relevant quality controlled climate data for improving development decisions. The malaria community should seize this opportunity to make their needs heard.

Climate change, vector-borne disease and interdisciplinary research: social science perspectives on an environment and health controversy

Over the last two decades, the science of climate change's theoretical impacts on vector-borne disease has generated controversy related to its methodological validity and relevance to disease control policy. Critical social science analysis, drawing on science and technology studies and the sociology of social movements, demonstrates consistency between this controversy and the theory that climate change is serving as a collective action frame for some health researchers. Within this frame, vector-borne disease data are interpreted as a symptom of climate change, with the need for further interdisiplinary research put forth as the logical and necessary next step. Reaction to this tendency on the part of a handful of vector-borne disease specialists exhibits characteristics of academic boundary work aimed at preserving the integrity of existing disciplinary boundaries. Possible reasons for this conflict include the leadership role for health professionals and disciplines in the envisioned interdiscipline, and disagreements over the appropriate scale of interventions to control vector-borne diseases. Analysis of the competing frames in this controversy also allows identification of excluded voices and themes, such as international political economic explanations for the health problems in question. A logical conclusion of this analysis, therefore, is the need for critical reflection on environment and health research and policy to achieve integration with considerations of global health equity.

Epidemic malaria and warmer temperatures in recent decades in an East African highland

Climate change impacts on malaria are typically assessed with scenarios for the long-term future. Here we focus instead on the recent past (1970–2003) to address whether warmer temperatures have already increased the incidence of malaria in a highland region of East Africa. Our analyses rely on a new coupled mosquito–human model of malaria, which we use to compare projected disease levels with and without the observed temperature trend. Predicted malaria cases exhibit a highly nonlinear response to warming, with a significant increase from the 1970s to the 1990s, although typical epidemic sizes are below those observed. These findings suggest that climate change has already played an important role in the exacerbation of malaria in this region. As the observed changes in malaria are even larger than those predicted by our model, other factors previously suggested to explain all of the increase in malaria may be enhancing the impact of climate change.

Potential influence of climate change on vector-borne and zoonotic diseases: a review and proposed research plan

BACKGROUND

Because of complex interactions of climate variables at the levels of the pathogen, vector, and host, the potential influence of climate change on vector-borne and zoonotic diseases (VBZDs) is poorly understood and difficult to predict. Climate effects on the nonvector-borne zoonotic diseases are especially obscure and have received scant treatment.

OBJECTIVE

We described known and potential effects of climate change on VBZDs and proposed specific studies to increase our understanding of these effects. The nonvector-borne zoonotic diseases have received scant treatment and are emphasized in this paper.

DATA SOURCES AND SYNTHESIS

We used a review of the existing literature and extrapolations from observations of short-term climate variation to suggest potential impacts of climate change on VBZDs. Using public health priorities on climate change, published by the Centers for Disease Control and Prevention, we developed six specific goals for increasing understanding of the interaction between climate and VBZDs and for improving capacity for predicting climate change effects on incidence and distribution of VBZDs.

CONCLUSIONS

Climate change may affect the incidence of VBZDs through its effect on four principal characteristics of host and vector populations that relate to pathogen transmission to humans: geographic distribution, population density, prevalence of infection by zoonotic pathogens, and the pathogen load in individual hosts and vectors. These mechanisms may interact with each other and with other factors such as anthropogenic disturbance to produce varying effects on pathogen transmission within host and vector populations and to humans. Because climate change effects on most VBZDs act through wildlife hosts and vectors, understanding these effects will require multidisciplinary teams to conduct and interpret ecosystem-based studies of VBZD pathogens in host and vector populations and to identify the hosts, vectors, and pathogens with the greatest potential to affect human populations under climate change scenarios.

Relevant microclimate for determining the development rate of malaria mosquitoes and possible implications of climate change

Background

The relationship between mosquito development and temperature is one of the keys to understanding the current and future dynamics and distribution of vector-borne diseases such as malaria. Many process-based models use mean air temperature to estimate larval development times, and hence adult vector densities and/or malaria risk.

Methods

Water temperatures in three different-sized water pools, as well as the adjacent air temperature in lowland and highland sites in western Kenya were monitored. Both air and water temperatures were fed into a widely-applied temperature-dependent development model for Anopheles gambiae immatures, and subsequently their impact on predicted vector abundance was assessed.

Results

Mean water temperature in typical mosquito breeding sites was 4-6°C higher than the mean temperature of the adjacent air, resulting in larval development rates, and hence population growth rates, that are much higher than predicted based on air temperature. On the other hand, due to the non-linearities in the relationship between temperature and larval development rate, together with a marginal buffering in the increase in water temperature compared with air temperature, the relative increases in larval development rates predicted due to climate change are substantially less.

Conclusions

Existing models will tend to underestimate mosquito population growth under current conditions, and may overestimate relative increases in population growth under future climate change. These results highlight the need for better integration of biological and environmental information at the scale relevant to mosquito biology.

Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia

Background

Malaria transmission is complex and is believed to be associated with local climate changes. However, simple attempts to extrapolate malaria incidence rates from averaged regional meteorological conditions have proven unsuccessful. Therefore, the objective of this study was to determine if variations in specific meteorological factors are able to consistently predict P. falciparum malaria incidence at different locations in south Ethiopia.

Methods

Retrospective data from 42 locations were collected including P. falciparum malaria incidence for the period of 1998-2007 and meteorological variables such as monthly rainfall (all locations), temperature (17 locations), and relative humidity (three locations). Thirty-five data sets qualified for the analysis. Ljung-Box Q statistics was used for model diagnosis, and R squared or stationary R squared was taken as goodness of fit measure. Time series modelling was carried out using Transfer Function (TF) models and univariate auto-regressive integrated moving average (ARIMA) when there was no significant predictor meteorological variable.

Results

Of 35 models, five were discarded because of the significant value of Ljung-Box Q statistics. Past P. falciparum malaria incidence alone (17 locations) or when coupled with meteorological variables (four locations) was able to predict P. falciparum malaria incidence within statistical significance. All seasonal AIRMA orders were from locations at altitudes above 1742 m. Monthly rainfall, minimum and maximum temperature was able to predict incidence at four, five and two locations, respectively. In contrast, relative humidity was not able to predict P. falciparum malaria incidence. The R squared values for the models ranged from 16% to 97%, with the exception of one model which had a negative value. Models with seasonal ARIMA orders were found to perform better. However, the models for predicting P. falciparum malaria incidence varied from location to location, and among lagged effects, data transformation forms, ARIMA and TF orders.

Conclusions

This study describes P. falciparum malaria incidence models linked with meteorological data. Variability in the models was principally attributed to regional differences, and a single model was not found that fits all locations. Past P. falciparum malaria incidence appeared to be a superior predictor than meteorology. Future efforts in malaria modelling may benefit from inclusion of non-meteorological factors.

Climate change and highland malaria: fresh air for a hot debate

In recent decades, malaria has become established in zones at the margin of its previous distribution, especially in the highlands of East Africa. Studies in this region have sparked a heated debate over the importance of climate change in the territorial expansion of malaria, where positions range from its neglect to the reification of correlations as causes. Here, we review studies supporting and rebutting the role of climatic change as a driving force for highland invasion by malaria. We assessed the conclusions from both sides of the argument and found that evidence for the role of climate in these dynamics is robust. However, we also argue that over-emphasizing the importance of climate is misleading for setting a research agenda, even one which attempts to understand climate change impacts on emerging malaria patterns. We review alternative drivers for the emergence of this disease and highlight the problems still calling for research if the multidimensional nature of malaria is to be adequately tackled. We also contextualize highland malaria as an ongoing evolutionary process. Finally, we present Schmalhausen's law, which explains the lack of resilience in stressed systems, as a biological principle that unifies the importance of climatic and other environmental factors in driving malaria patterns across different spatio-temporal scales.

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.

Climate change and vector-borne diseases

In this review we examine formally the conditions under which vector-borne diseases are likely to change, and the directions of those changes, under various scenarios of climate change. We specify the criteria that must be met in order to conclude that climate change is having an effect on vector-borne diseases. We then take several examples from the literature and show how some of them meet these criteria, while others do not. For those that do not, there are alternative explanations that involve much more plausible drivers of the recorded changes in the diseases concerned.

Understanding the link between malaria risk and climate

The incubation period for malaria parasites within the mosquito is exquisitely temperature-sensitive, so that temperature is a major determinant of malaria risk. Epidemiological models are increasingly used to guide allocation of disease control resources and to assess the likely impact of climate change on global malaria burdens. Temperature-based malaria transmission is generally incorporated into these models using mean monthly temperatures, yet temperatures fluctuate throughout the diurnal cycle. Here we use a thermodynamic malaria development model to demonstrate that temperature fluctuation can substantially alter the incubation period of the parasite, and hence malaria transmission rates. We find that, in general, temperature fluctuation reduces the impact of increases in mean temperature. Diurnal temperature fluctuation around means >21 degrees C slows parasite development compared with constant temperatures, whereas fluctuation around <21 degrees C speeds development. Consequently, models which ignore diurnal variation overestimate malaria risk in warmer environments and underestimate risk in cooler environments. To illustrate the implications further, we explore the influence of diurnal temperature fluctuation on malaria transmission at a site in the Kenyan Highlands. Based on local meteorological data, we find that the annual epidemics of malaria at this site cannot be explained without invoking the influence of diurnal temperature fluctuation. Moreover, while temperature fluctuation reduces the relative influence of a subtle warming trend apparent over the last 20 years, it nonetheless makes the effects biologically more significant. Such effects of short-term temperature fluctuations have not previously been considered but are central to understanding current malaria transmission and the consequences of climate change.

Is Global Warming likely to cause an increased incidence of Malaria?

The rise in the average temperature of earth has been described as global warming which is mainly attributed to the increasing phenomenon of the greenhouse effect. It is believed that global warming can have several harmful effects on human health, both directly and indirectly. Since malaria is greatly influenced by climatic conditions because of its direct relationship with the mosquito population, it is widely assumed that its incidence is likely to increase in a future warmer world.This review article discusses the two contradictory views regarding the association of global warming with an increased incidence of malaria. On one hand, there are many who believe that there is a strong association between the recent increase in malaria incidence and global warming. They predict that as global warming continues, malaria is set to spread in locations where previously it was limited, due to cooler climate. On the other hand, several theories have been put forward which are quite contrary to this prediction. There are multiple other factors which are accountable for the recent upsurge of malaria: for example drug resistance, mosquito control programs, public health facilities, and living standards.

Climate and vectorborne diseases

Climate change could significantly affect vectorborne disease in humans. Temperature, precipitation, humidity, and other climatic factors are known to affect the reproduction, development, behavior, and population dynamics of the arthropod vectors of these diseases. Climate also can affect the development of pathogens in vectors, as well as the population dynamics and ranges of the nonhuman vertebrate reservoirs of many vectorborne diseases. Whether climate changes increase or decrease the incidence of vectorborne diseases in humans will depend not only on the actual climatic conditions but also on local nonclimatic epidemiologic and ecologic factors. Predicting the relative impact of sustained climate change on vectorborne diseases is difficult and will require long-term studies that look not only at the effects of climate change but also at the contributions of other agents of global change such as increased trade and travel, demographic shifts, civil unrest, changes in land use, water availability, and other issues. Adapting to the effects of climate change will require the development of adequate response plans, enhancement of surveillance systems, and development of effective and locally appropriate strategies to control and prevent vectorborne diseases.

Tropical malaria does not mean hot environments

If global warming progresses, many consider that malaria in presently malaria-endemic areas will become more serious, with increasing development rates of the vector mosquito and malaria parasites. However, the correlation coefficients between the monthly malaria cases and the monthly mean of daily maximum temperature were negative, showing that the number of malaria cases in tropical areas of Africa decreases during the season when temperature was higher than normal. Moreover, an analysis of temperature and development rate using a thermodynamic model showed that the estimated intrinsic optimum temperatures for the development of the malaria parasites, Plasmodium falciparum and P. vivax, in the adult mosquito stage and that of the vector mosquito Anopheles gambiae s.s. were all approximately 23-24 degrees C. Here, the intrinsic optimum temperature is defined in the thermodynamic model as the temperature at which it is assumed that there are no or negligible adverse effects for development. Therefore, this study indicates that the development of malaria parasites in their mosquito hosts and the development of their vector mosquitoes are inhibited at temperatures higher than 23-24 degrees C. If global warming progresses further, the present center of malarial endemicity in sub-Saharan Africa will move to an area with an optimum temperature for both the vector and the parasite, migrating to avoid the hot environment.

A study of the distribution and abundance of the adult malaria vector in western Kenya highlands

BACKGROUND

A sharp rise in the malaria mortality rate has been observed recently in western Kenya. Malaria is transmitted by mosquito vectors. Malaria control strategies can be more successful if the distribution and abundance of mosquito vectors is predicted. However, how mosquito vectors are distributed in space remain poor understood, and this question is rarely studied using spatial methods. This study aims to provide a better understanding of the distribution and abundance of mosquito vectors. To achieve this objective, spatial and non-spatial methods were employed. The data on the distribution of adult mosquitoes, and mosquito breeding habitats in a study area in western Kenya, and environmental variables were analyzed.

RESULTS

The models developed using spatial methods outperformed the models developed using non-spatial methods. Houses close to locations where mosquito breeding habitats were repeatedly observed had more abundant adult female mosquitoes. Distance to high-order streams was identified as an effective predictor for the distribution of adult mosquitoes.

CONCLUSION

The spatial method is more effective in modeling the distribution of adult mosquitoes than the non-spatial method. The results of this study can be used to facilitate decision-making related to mosquito surveillance and malaria prevention.

One-year delayed effect of fog on malaria transmission: a time-series analysis in the rain forest area of Mengla County, south-west China

BACKGROUND

Malaria is a major public health burden in the tropics with the potential to significantly increase in response to climate change. Analyses of data from the recent past can elucidate how short-term variations in weather factors affect malaria transmission. This study explored the impact of climate variability on the transmission of malaria in the tropical rain forest area of Mengla County, south-west China.

METHODS

Ecological time-series analysis was performed on data collected between 1971 and 1999. Auto-regressive integrated moving average (ARIMA) models were used to evaluate the relationship between weather factors and malaria incidence.

RESULTS

At the time scale of months, the predictors for malaria incidence included: minimum temperature, maximum temperature, and fog day frequency. The effect of minimum temperature on malaria incidence was greater in the cool months than in the hot months. The fog day frequency in October had a positive effect on malaria incidence in May of the following year. At the time scale of years, the annual fog day frequency was the only weather predictor of the annual incidence of malaria.

CONCLUSION

Fog day frequency was for the first time found to be a predictor of malaria incidence in a rain forest area. The one-year delayed effect of fog on malaria transmission may involve providing water input and maintaining aquatic breeding sites for mosquitoes in vulnerable times when there is little rainfall in the 6-month dry seasons. These findings should be considered in the prediction of future patterns of malaria for similar tropical rain forest areas worldwide.

Using hydrologic conditions to forecast the risk of focal and epidemic arboviral transmission in peninsular Florida

The accurate forecasting and tracking of arboviral transmission is becoming increasingly critical for the early recognition and management of arboviral epidemics. Meteorological factors, especially rainfall and temperature, drive arboviral epidemics, but monitoring rainfall and temperature alone is not predictive of increased levels of vector-borne disease transmission. In Florida, model simulations of water table depth (WTD) provide a measure of drought, and they have been shown to provide an accurate forecast of arboviral transmission. Here, we tracked WTD in two peninsular Florida regions where focal West Nile virus (family Flaviviridae, genus Flavivirus, WNV) transmission was reported during 2004 and 2005. We compared the resulting WTD profiles with historical WTD simulations for Indian River County (IRC), FL, where two peninsular Florida St. Louis encephalitis virus epidemics had their epicenters in 1977 and 1990. In both of the regions where focal WNV transmission was reported during 2004 and 2005, the local WTD profiles approached the 1977 and 1990 IRC WTD profiles; however, differences in the local temporal sequence of hydrologic conditions were observed. These differences seem in part to explain why the focal WNV transmission during 2004 and 2005 failed to reach epidemic levels in peninsular Florida. These findings suggest that hydrologic monitoring, specifically WTD, may help determine the geographic extent, timing, and intensity of WNV transmission. We speculate that a more precise sequence of drought and wetting, including a secondary summer drying and wetting cycle, as occurred in IRC during 1977 and 1990, may provide the optimal hydrologic conditions for the expansion of an arbovirus outbreak from focal to epidemic. This study documents that monitoring hydrologic conditions, along with vector, avian amplification host, and virus population data, increases our ability to track and predict significant levels of arboviral transmission.

Effect of meteorological factors on clinical malaria risk among children: an assessment using village-based meteorological stations and community-based parasitological survey

Background

Temperature, rainfall and humidity have been widely associated with the dynamics of malaria vector population and, therefore, with spread of the disease. However, at the local scale, there is a lack of a systematic quantification of the effect of these factors on malaria transmission. Further, most attempts to quantify this effect are based on proxy meteorological data acquired from satellites or interpolated from a different scale. This has led to controversies about the contribution of climate change to malaria transmission risk among others. Our study addresses the original question of relating meteorological factors measured at the local scale with malaria infection, using data collected at the same time and scale.

Methods

676 children (6–59 months) were selected randomly from three ecologically different sites (urban and rural). During weekly home visits between December 1, 2003, and November 30, 2004, fieldworkers tested children with fever for clinical malaria. They also collected data on possible confounders monthly. Digital meteorological stations measured ambient temperature, humidity, and rainfall in each site. Logistic regression was used to estimate the risk of clinical malaria given the previous month's meteorological conditions.

Results

The overall incidence of clinical malaria over the study period was 1.07 episodes per child. Meteorological factors were associated with clinical malaria with mean temperature having the largest effect.

Conclusion

Temperature was the best predictor for clinical malaria among children under five. A systematic measurement of local temperature through ground stations and integration of such data in the routine health information system could support assessment of malaria transmission risk at the district level for well-targeted control efforts.

Assessing the risk of self-diagnosed malaria in urban informal settlements of Nairobi using self-reported morbidity survey

BACKGROUND

Because of the belief that Nairobi is a low risk zone for malaria, little empirical data exists on malaria risk in the area. The aim of this study was to explore the risk of perceived malaria and some associated factors in Nairobi informal settlements using self-reported morbidity survey.

METHODS

The survey was conducted from May to August 2004 on 7,288 individuals in two informal settlements of Nairobi. Participants were asked to report illnesses they experienced in the past 14 days. Logistic regression was used to estimate the odds of perceived-malaria. The model included variables such as site of residence, age, ethnicity and number of reported symptoms.

RESULTS

Participants reported 165 illnesses among which malaria was the leading cause (28.1%). The risk of perceived-malaria was significantly higher in Viwandani compared to Korogocho (OR 1.61, 95%CI: 1.10-2.26). Participants in age group 25-39 years had significantly higher odds of perceived-malaria compared to those under-five years (OR 2.07, 95%CI: 1.43-2.98). The Kikuyu had reduced odds of perceived-malaria compared to other ethnic groups. Individuals with five and more symptoms had higher odds compared to those with no symptoms (OR 23.69, 95%CI: 12.98-43.23).

CONCLUSION

Malaria was the leading cause of illness as perceived by the residents in the two informal settlements. This was rational as the number of reported symptoms was highly associated with the risk of reporting the illness. These results highlight the need for a more comprehensive assessment of malaria epidemiology in Nairobi to be able to offer evidence-based guidance to policy on malaria in Kenya and particularly in Nairobi.

Reproductive phase locking of mosquito populations in response to rainfall frequency

The frequency of moderate to heavy rainfall events is projected to change in response to global warming. Here we show that these hydrologic changes may have a profound effect on mosquito population dynamics and rates of mosquito-borne disease transmission. We develop a simple model, which treats the mosquito reproductive cycle as a phase oscillator that responds to rainfall frequency forcing. This model reproduces observed mosquito population dynamics and indicates that mosquito-borne disease transmission can be sensitive to rainfall frequency. These findings indicate that changes to the hydrologic cycle, in particular the frequency of moderate to heavy rainfall events, could have a profound effect on the transmission rates of some mosquito-borne diseases.

Malaria hotspot areas in a highland Kenya site are consistent in epidemic and non-epidemic years and are associated with ecological factors

Background

Malaria epidemics in highland areas of East Africa have caused considerable morbidity and mortality in the past two decades. Knowledge of "hotspot" areas of high malaria incidence would allow for focused preventive interventions in resource-poor areas, particularly if the hotspot areas can be discerned during non-epidemic periods and predicted by ecological factors.

Methods

To address this issue, spatial distribution of malaria incidence and the relationship of ecological factors to malaria incidence were assessed in the highland area of Kipsamoite, Kenya, from 2001–2004.

Results

Clustering of disease in a single geographic "hotspot" area occurred in epidemic and non-epidemic years, with a 2.6 to 3.2-fold increased risk of malaria inside the hotspot, as compared to outside the area (P < 0.001, all 4 years). Altitude and proximity to the forest were independently associated with increased malaria risk in all years, including epidemic and non-epidemic years.

Conclusion

In this highland area, areas of high malaria risk are consistent in epidemic and non-epidemic years and are associated with specific ecological risk factors. Ongoing interventions in areas of ecological risk factors could be a cost-effective method of significantly reducing malaria incidence and blunting or preventing epidemics, even in the absence of malaria early warning systems. Further studies should be conducted to see if these findings hold true in varied highland settings.

Low larval vector survival explains unstable malaria in the western Kenya highlands

Several highland areas in eastern Africa have recently suffered from serious malaria epidemics. Some models predict that, in the short term, these areas will experience more epidemics as a result of global warming. However, the various processes underlying these changes are poorly understood. We therefore investigated malaria prevalence, malaria vector densities and malaria vector survival in a highland area in western Kenya, ranging from approximately 1,550-1,650 m altitude. Although only five adult malaria vectors were collected during 180 light traps and 180 resting collections over a 23-month study period, malaria was prevalent among school children (average parasite prevalence: 10%). During an extensive survey of potential larval habitats, we identified only seven habitats containing Anopheles gambiae Giles s.l. larvae. Their limited number and low larval densities suggested that their contribution to the adult vector population was small. Experiments on adult and larval survival showed that at this altitude, adult mosquitoes survived inside local houses, but that larval development was severely retarded: only 2 of 500 A. gambiae s.l. larvae developed to the pupal stage, whereas all other larvae died prior to pupation. At present, high vector densities are unlikely because of unfavourable abiotic conditions in the area. However, temporary favourable conditions, such as during El Niño years, may increase larval vector survival and may lead to malaria epidemics.

Achieving operational hydrologic monitoring of mosquitoborne disease

Mosquitoes and mosquitoborne disease transmission are sensitive to hydrologic variability. If local hydrologic conditions can be monitored or modeled at the scales at which these conditions affect the population dynamics of vector mosquitoes and the diseases they transmit, a means for monitoring or modeling mosquito populations and mosquitoborne disease transmission may be realized. We review how hydrologic conditions have been associated with mosquito abundances and mosquitoborne disease transmission and discuss the advantages of different measures of hydrologic variability. We propose that the useful application of any measure of hydrologic conditions requires additional consideration of the scales for both the hydrologic measurement and the vector control interventions that will be used to mitigate an outbreak of vectorborne disease. Our efforts to establish operational monitoring of St. Louis encephalitis virus and West Nile virus transmission in Florida are also reviewed.

Malaria in Kenya's western highlands

Reemergence of epidemics in tea plantations will likely result in antimalarial-drug resistance.

Records from tea estates in the Kericho district in Kenya show that malaria reemerged in the 1980s. Renewed epidemic activity coincided with the emergence of chloroquine-resistant Plasmodium falciparum malaria and may have been triggered by the failure of antimalarial drugs. Meteorologic changes, population movements, degradation of health services, and changes in Anopheles vector populations are possible contributing factors. The highland malaria epidemics of the 1940s were stopped largely by sporontocidal drugs, and combination chemotherapy has recently limited transmission. Antimalarial drugs can limit the pool of gametocytes available to infect mosquitoes during the brief transmission season.

Malaria resurgence in the East African highlands: temperature trends revisited

The incidence of malaria in the East African highlands has increased since the end of the 1970s. The role of climate change in the exacerbation of the disease has been controversial, and the specific influence of rising temperature (warming) has been highly debated following a previous study reporting no evidence to support a trend in temperature. We revisit this result using the same temperature data, now updated to the present from 1950 to 2002 for four high-altitude sites in East Africa where malaria has become a serious public health problem. With both nonparametric and parametric statistical analyses, we find evidence for a significant warming trend at all sites. To assess the biological significance of this trend, we drive a dynamical model for the population dynamics of the mosquito vector with the temperature time series and the corresponding detrended versions. This approach suggests that the observed temperature changes would be significantly amplified by the mosquito population dynamics with a difference in the biological response at least 1 order of magnitude larger than that in the environmental variable. Our results emphasize the importance of considering not just the statistical significance of climate trends but also their biological implications with dynamical models.

Global environmental data for mapping infectious disease distribution

This contribution documents the satellite data archives, data processing methods and temporal Fourier analysis (TFA) techniques used to create the remotely sensed datasets on the DVD distributed with this volume. The aim is to provide a detailed reference guide to the genesis of the data, rather than a standard review. These remotely sensed data cover the entire globe at either 1 x 1 or 8 x 8 km spatial resolution. We briefly evaluate the relationships between the 1 x 1 and 8 x 8 km global TFA products to explore their inter-compatibility. The 8 x 8 km TFA surfaces are used in the mapping procedures detailed in the subsequent disease mapping reviews, since the 1 x 1 km products have been validated less widely. Details are also provided on additional, current and planned sensors that should be able to provide continuity with these environmental variable surfaces, as well as other sources of global data that may be used for mapping infectious disease.