The Indian Ocean Dipole and malaria risk in the highlands of western Kenya

Preconception and Prenatal Environment and Growth Faltering Among Children in Uganda

Importance

Children with growth faltering are more susceptible to infections and may experience cognitive, physical, and metabolic developmental impairments.

Objective

To assess whether prenatal and preconception meteorological and environmental factors are associated with village-level rates of childhood growth outcomes in Uganda.

Design, Setting, and Participants

This cross-sectional study used data collected between June 20, 2015, and December 16, 2016, from the 2016 Ugandan Demographic and Health Survey for individuals aged 0 to 59 months with available anthropometric measures (weight and length or height). Data analysis was conducted from October 2020 to April 2024.

Exposures

Factors assessed included meteorological information, such as drought index (Standardized Precipitation-Evapotranspiration Index [SPEI]), Aridity Index, rainfall, temperature, and vegetation indices; demographic and economic development factors (nighttime light emissions, driving time to the nearest city); and land topography (slope angle, elevation above sea level).

Main Outcomes and Measures

The main outcomes were height-for-age z score (HAZ), weight-for-age z score (WAZ), and weight-for-height z score (WHZ). Spatial resolution estimates, at 1 km × 1 km of childhood growth faltering indicators, were created.

Results

Of the 5219 individuals aged 0 to 59 months included in the analysis, 2633 (50%) were female; mean (SD) age was 29 (17) months. Of these individuals, 30.22% (95% CI, 29.36%-30.98%) had stunting, 12.23% (95% CI, 11.55%-12.91%) had underweight, and 3.63% (95% CI, 3.46%-3.80%) had wasting. Large disparities in the burden of childhood growth faltering existed within Uganda at smaller and larger spatial scales; villages in the northeastern and southwestern areas of the country had the highest prevalence of all forms of growth faltering (stunting, >40%; underweight, >16%; and wasting, >6%). Higher SPEI at 3 months before birth was positively associated with all childhood growth outcomes: HAZ (β, 0.06; 95% CI, 0.02-0.10), WAZ (β, 0.04; 95% CI, 0.01-0.07), and WHZ (β, 0.03; 95% CI, 0.001-0.06). Higher location mean rainfall 11 months before birth was also positively associated with HAZ (β, 0.06; 95% CI, 0.01-0.10). Aridity Index associations with WAZ (β, 0.09; 95% CI, 0.04-0.13) and WHZ (β, 0.09; 95% CI, 0.02-0.16) were consistent with findings for SPEI.

Conclusions and Relevance

In this study of 5219 individuals 0 to 59 months of age in Uganda, rainfall and long-term availability of water at preconception and during gestation were positively associated with nutritional child growth outcomes. Understanding the relative contributions of meteorological environment factors on the spatial distribution of undernutrition at various spatial scales within Uganda (from the village to the district level) may help in the design of more cost-effective delivery of precision public health programs.

Causal inference concepts can guide research into the effects of climate on infectious diseases

A pressing question resulting from global warming is how climate change will affect infectious diseases. Answering this question requires research into the effects of weather on the population dynamics of transmission and infection; elucidating these effects, however, has proved difficult due to the challenges of assessing causality from the predominantly observational data available in epidemiological research. Here we show how concepts from causal inference-the sub-field of statistics aiming at inferring causality from data-can guide that research. Through a series of case studies, we illustrate how such concepts can help assess study design and strategically choose a study's location, evaluate and reduce the risk of bias, and interpret the multifaceted effects of meteorological variables on transmission. More broadly, we argue that interdisciplinary approaches based on explicit causal frameworks are crucial for reliably estimating the effect of weather and accurately predicting the consequences of climate change.

Deciphering the climate-malaria nexus: A machine learning approach in rural southeastern Tanzania

OBJECTIVES

Malaria remains a critical public health challenge, especially in regions like southeastern Tanzania. Understanding the intricate relationship between environmental factors and malaria incidence is essential for effective control and elimination strategies.

STUDY DESIGN

Cohort study.

METHODS

This cohort study, conducted between Jan 2016 and October 2021 across three districts in southeastern Tanzania, utilized advanced machine learning techniques, specifically the Extreme Gradient Boosting (XGBoost) model, to examine the impact of climate factors on malaria incidence. SHapley Additive exPlanations (SHAP) values were applied to interpret model predictions, highlighting the roles of normalized difference vegetation index (NDVI), temperature, and rainfall in shaping malaria transmission dynamics.

RESULTS

Analysis revealed considerable heterogeneity in malaria incidence across southeastern Tanzania, with Kibiti experiencing the highest number of cases (15,308) over the study period. Seasonal peaks corresponded with rainy periods, though incidence rates varied by district. Incorporating lagged climate variables and seasonal trends significantly improved forecast accuracy, with the one-month lag model achieving the lowest mean absolute error (MAE = 175.46) and root mean squared error (RMSE = 228.24). SHAP analysis identified seasonality (mean SHAP 29.6), followed by lagged temperature (13.8), rainfall (12.4), and NDVI (5.96), as the most influential factors, reflecting the biological underpinnings of malaria transmission.

CONCLUSIONS

This study demonstrates the utility of machine learning and explainable SHAP in malaria epidemiology, providing a data-driven framework to guide targeted, climate-informed malaria control strategies. By capturing seasonal and climate-linked risks, these methods hold promise for enhancing public health planning and adaptive response in malaria-endemic regions.

New directions for malaria vector control using geography and geospatial analysis

As we strive towards the ambitious goal of malaria elimination, we must embrace integrated strategies and interventions. Like many diseases, malaria is heterogeneously distributed. This inherent spatial component means that geography and geospatial data is likely to have an important role in malaria control strategies. For instance, focussing interventions in areas where malaria risk is highest is likely to provide more cost-effective malaria control programmes. Equally, many malaria vector control strategies, particularly interventions like larval source management, would benefit from accurate maps of malaria vector habitats - sources of water that are used for malarial mosquito oviposition and larval development. In many landscapes, particularly in rural areas, the formation and persistence of these habitats is controlled by geographical factors, notably those related to hydrology. This is especially true for malaria vector species like Anopheles funestsus that show a preference for more permanent, often naturally occurring water sources like small rivers and spring-fed ponds. Previous work has embraced geographical concepts, techniques, and geospatial data for studying malaria risk and vector habitats. But there is much to be learnt if we are to fully exploit what the broader geographical discipline can offer in terms of operational malaria control, particularly in the face of a changing climate. This chapter outlines potential new directions related to several geographical concepts, data sources and analytical approaches, including terrain analysis, satellite imagery, drone technology and field-based observations. These directions are discussed within the context of designing new protocols and procedures that could be readily deployed within malaria control programmes, particularly those within sub-Saharan Africa, with a particular focus on experiences in the Kilombero Valley and the Zanzibar Archipelago, United Republic of Tanzania.

Systematic reduction of natural enemies and competition across variable precipitation approximates buffelgrass invasiveness (Cenchrus ciliaris) in its native range

Invasive grasses cause devastating losses to biodiversity and ecosystem function directly and indirectly by altering ecosystem processes. Escape from natural enemies, plant-plant competition, and variable resource availability provide frameworks for understanding invasion. However, we lack a clear understanding of how natural stressors interact in their native range to regulate invasiveness. In this study, we reduced diverse guilds of natural enemies and plant competitors of the highly invasive buffelgrass across a precipitation gradient throughout major climatic shifts in Laikipia, Kenya. To do this, we used a long-term ungulate exclosure experiment design across a precipitation gradient with nested treatments that (1) reduced plant competition through clipping, (2) reduced insects through systemic insecticide, and (3) reduced fungal associates through fungicide application. Additionally, we measured the interaction of ungulates on two stem-boring insect species feeding on buffelgrass. Finally, we measured a multiyear smut fungus outbreak. Our findings suggest that buffelgrass exhibits invasive qualities when released from a diverse group of natural stressors in its native range. We show natural enemies interact with precipitation to alter buffelgrass productivity patterns. In addition, interspecific plant competition decreased the basal area of buffelgrass, suggesting that biotic resistance mediates buffelgrass dominance in the home range. Surprisingly, systemic insecticides and fungicides did not impact buffelgrass production or reproduction, perhaps because other guilds filled the niche space in these highly diverse systems. For example, in the absence of ungulates, we showed an increase in host-specific stem-galling insects, where these insects compensated for reduced ungulate use. Finally, we documented a smut outbreak in 2020 and 2021, corresponding to highly variable precipitation patterns caused by a shifting Indian Ocean Dipole. In conclusion, we observed how reducing natural enemies and competitors and certain interactions increased properties related to buffelgrass invasiveness.

Decreased Indian Ocean Dipole variability under prolonged greenhouse warming

The Indian Ocean Dipole (IOD) is a major climate variability mode that substantially influences weather extremes and climate patterns worldwide. However, the response of IOD variability to anthropogenic global warming remains highly uncertain. The latest IPCC Sixth Assessment Report concluded that human influences on IOD variability are not robustly detected in observations and twenty-first century climate-model projections. Here, using millennial-length climate simulations, we disentangle forced response and internal variability in IOD change and show that greenhouse warming robustly suppresses IOD variability. On a century time scale, internal variability overwhelms the forced change in IOD, leading to a widespread response in IOD variability. This masking effect is mainly caused by a remote influence of the El Niño-Southern Oscillation. However, on a millennial time scale, nearly all climate models show a long-term weakening trend in IOD variability by greenhouse warming. Our results provide compelling evidence for a human influence on the IOD.

A negative biological Indian Ocean dipole event in 2022

The biological dipole mode index (BDMI) showed a negative biological Indian Ocean dipole (BIOD) event occurred in the Equatorial Indian Ocean with the corresponding BIOD index BDMI(Ratio) at - 0.31 in October 2022. The chlorophyll-a (Chl-a) ratio (or Chl-a anomaly) between Chl-a in October 2022 and October Chl-a climatology from the Visible Infrared Imaging Radiometer Suite (VIIRS) showed negative dipolar features with the depressed and enhanced Chl-a in the east and west IOD zones, respectively. During this negative BIOD event, Chl-a ratio dropped to ~ 0.4-0.5 in the offshore region of the west Sumatra Coast in the east IOD zone, while it increased to ~ 1.5-1.6 in the northern west IOD zone. Temporal variations of the longitudinal averaged Chl-a ratio and the 20 °C isothermal (ISO20) depth anomaly generally coincided and collocated with each other. The positive and negative BIOD events in 2019 and 2022, respectively, were attributed to the nutrient dynamics driven by the physical dynamics in these two phases of IOD events. In the negative BIOD event in 2022, the depressed Chl-a in the east IOD zone was attributed to low sea surface nutrient levels due to dampened upwelling and deepened thermocline, while anomalously high Chl-a in the west IOD zone were driven by higher sea surface nutrient concentrations caused by the surface water divergence and shoaling thermocline.

Utilizing a novel high-resolution malaria dataset for climate-informed predictions with a deep learning transformer model

Climatic factors influence malaria transmission via the effect on the Anopheles vector and Plasmodium parasite. Modelling and understanding the complex effects that climate has on malaria incidence can enable important early warning capabilities. Deep learning applications across fields are proving valuable, however the field of epidemiological forecasting is still in its infancy with a lack of applied deep learning studies for malaria in southern Africa which leverage quality datasets. Using a novel high resolution malaria incidence dataset containing 23 years of daily data from 1998 to 2021, a statistical model and XGBOOST machine learning model were compared to a deep learning Transformer model by assessing the accuracy of their numerical predictions. A novel loss function, used to account for the variable nature of the data yielded performance around + 20% compared to the standard MSE loss. When numerical predictions were converted to alert thresholds to mimic use in a real-world setting, the Transformer's performance of 80% according to AUROC was 20-40% higher than the statistical and XGBOOST models and it had the highest overall accuracy of 98%. The Transformer performed consistently with increased accuracy as more climate variables were used, indicating further potential for this prediction framework to predict malaria incidence at a daily level using climate data for southern Africa.

Disentangling local and global climate drivers in the population dynamics of mosquito-borne infections

Identifying climate drivers is essential to understand and predict epidemics of mosquito-borne infections whose population dynamics typically exhibit seasonality and multiannual cycles. Which climate covariates to consider varies across studies, from local factors such as temperature to remote drivers such as the El Niño-Southern Oscillation. With partial wavelet coherence, we present a systematic investigation of nonstationary associations between mosquito-borne disease incidence and a given climate factor while controlling for another. Analysis of almost 200 time series of dengue and malaria around the globe at different geographical scales shows a systematic effect of global climate drivers on interannual variability and of local ones on seasonality. This clear separation of time scales of action enhances detection of climate drivers and indicates those best suited for building early-warning systems.

Predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in Limpopo, South Africa, using machine learning

Malaria is the cause of nearly half a million deaths worldwide each year, posing a great socioeconomic burden. Despite recent progress in understanding the influence of climate on malaria infection rates, climatic sources of predictability remain poorly understood and underexploited. Local weather variability alone provides predictive power at short lead times of 1-2 months, too short to adequately plan intervention measures. Here, we show that tropical climatic variability and associated sea surface temperature over the Pacific and Indian Oceans are valuable for predicting malaria in Limpopo, South Africa, up to three seasons ahead. Climatic precursors of malaria outbreaks are first identified via lag-regression analysis of climate data obtained from reanalysis and observational datasets with respect to the monthly malaria case count data provided from 1998-2020 by the Malaria Institute in Tzaneen, South Africa. Out of 11 sea surface temperature sectors analyzed, two regions, the Indian Ocean and western Pacific Ocean regions, emerge as the most robust precursors. The predictive value of these precursors is demonstrated by training a suite of machine-learning classification models to predict whether malaria case counts are above or below the median historical levels and assessing their skills in providing early warning predictions of malaria incidence with lead times ranging from 1 month to a year. Through the development of this prediction system, we find that past information about SST over the western Pacific Ocean offers impressive prediction skills (~80% accuracy) for up to three seasons (9 months) ahead. SST variability over the tropical Indian Ocean is also found to provide good skills up to two seasons (6 months) ahead. This outcome represents an extension of the effective prediction lead time by about one to two seasons compared to previous prediction systems that were more computationally costly compared to the machine learning techniques used in the current study. It also demonstrates the value of climatic information and the prediction framework developed herein for the early planning of interventions against malaria outbreaks.

Impact of 1.5 oC and 2 oC global warming scenarios on malaria transmission in East Africa

Background: Malaria remains a global challenge with approximately 228 million cases and 405,000 malaria-related deaths reported in 2018 alone; 93% of which were in sub-Saharan Africa. Aware of the critical role than environmental factors play in malaria transmission, this study aimed at assessing the relationship between precipitation, temperature, and clinical malaria cases in East Africa and how the relationship may change under 1.5 ^oC and 2.0 ^oC global warming levels (hereinafter GWL1.5 and GWL2.0, respectively).

Methods: A correlation analysis was done to establish the current relationship between annual precipitation, mean temperature, and clinical malaria cases. Differences between annual precipitation and mean temperature value projections for periods 2008-2037 and 2023-2052 (corresponding to GWL1.5 and GWL2.0, respectively), relative to the control period (1977-2005), were computed to determine how malaria transmission may change under the two global warming scenarios.

Results: A predominantly positive/negative correlation between clinical malaria cases and temperature/precipitation was observed. Relative to the control period, no major significant changes in precipitation were shown in both warming scenarios. However, an increase in temperature of between 0.5 ^oC and 1.5 ^oC and 1.0 ^oC to 2.0 ^oC under GWL1.5 and GWL2.0, respectively, was recorded. Hence, more areas in East Africa are likely to be exposed to temperature thresholds favourable for increased malaria vector abundance and, hence, potentially intensify malaria transmission in the region.

Conclusions: GWL1.5 and GWL2.0 scenarios are likely to intensify malaria transmission in East Africa. Ongoing interventions should, therefore, be intensified to sustain the gains made towards malaria elimination in East Africa in a warming climate.

Impact of 1.5 oC and 2 oC global warming scenarios on malaria transmission in East Africa

Background: Malaria remains a global challenge with approximately 228 million cases and 405,000 malaria-related deaths reported in 2018 alone; 93% of which were in sub-Saharan Africa. Aware of the critical role than environmental factors play in malaria transmission, this study aimed at assessing the relationship between precipitation, temperature, and clinical malaria cases in East Africa and how the relationship may change under 1.5 oC and 2.0 oC global warming levels (hereinafter GWL1.5 and GWL2.0, respectively). Methods: A correlation analysis was done to establish the current relationship between annual precipitation, mean temperature, and clinical malaria cases. Differences between annual precipitation and mean temperature value projections for periods 2008-2037 and 2023-2052 (corresponding to GWL1.5 and GWL2.0, respectively), relative to the control period (1977-2005), were computed to determine how malaria transmission may change under the two global warming scenarios. Results: A predominantly positive/negative correlation between clinical malaria cases and temperature/precipitation was observed. Relative to the control period, no major significant changes in precipitation were shown in both warming scenarios. However, an increase in temperature of between 0.5 oC and 1.5 oC and 1.0 oC to 2.0 oC under GWL1.5 and GWL2.0, respectively, was recorded. Hence, more areas in East Africa are likely to be exposed to temperature thresholds favourable for increased malaria vector abundance and, hence, potentially intensify malaria transmission in the region. Conclusions: GWL1.5 and GWL2.0 scenarios are likely to intensify malaria transmission in East Africa. Ongoing interventions should, therefore, be intensified to sustain the gains made towards malaria elimination in East Africa in a warming climate.

Geographical heterogeneity and socio-ecological risk profiles of dengue in Jakarta, Indonesia

The aim of this study was to assess the role of climate variability on the incidence of dengue fever (DF), an endemic arboviral infection existing in Jakarta, Indonesia. The work carried out included analysis of the spatial distribution of confirmed DF cases from January 2007 to December 2018 characterising the sociodemographical and ecological factors in DF high-risk areas. Spearman's rank correlation was used to examine the relationship between DF incidence and climatic factors. Spatial clustering and hotspots of DF were examined using global Moran's I statistic and the local indicator for spatial association analysis. Classification and regression tree (CART) analysis was performed to compare and identify demographical and socio-ecological characteristics of the identified hotspots and low-risk clusters. The seasonality of DF incidence was correlated with precipitation (r=0.254, P<0.01), humidity (r=0.340, P<0.01), dipole mode index (r= -0.459, P<0.01) and Tmin (r= -0.181, P<0.05). DF incidence was spatially clustered at the village level (I=0.294, P<0.001) and 22 hotspots were identified with a concentration in the central and eastern parts of Jakarta. CART analysis showed that age and occupation were the most important factors explaining DF clustering. Areaspecific and population-targeted interventions are needed to improve the situation among those living in the identified DF high-risk areas in Jakarta.

A biological Indian Ocean Dipole event in 2019

The 2019 positive Indian Ocean Dipole (IOD) event in the boreal autumn was the most serious IOD event of the century with reports of significant sea surface temperature (SST) changes in the east and west equatorial Indian Ocean. Observations of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) between 2012 and 2020 are used to study the significant biological dipole response that occurred in the equatorial Indian Ocean following the 2019 positive IOD event. For the first time, we propose, identify, characterize, and quantify the biological IOD. The 2019 positive IOD event led to anomalous biological activity in both the east IOD zone and west IOD zone. The average chlorophyll-a (Chl-a) concentration reached over ~ 0.5 mg m^-3 in 2019 in comparison to the climatology Chl-a of ~ 0.3 mg m^-3 in the east IOD zone. In the west IOD zone, the biological activity was significantly depressed. The depressed Chl-a lasted until May 2020. The anomalous ocean biological activity in the east IOD zone was attributed to the advection of the higher-nutrient surface water due to enhanced upwelling. On the other hand, the dampened ocean biological activity in the west IOD zone was attributed to the stronger convergence of the surface waters than that in a normal year.

Effects of El Niño Southern Oscillation and Dipole Mode Index on Chikungunya Infection in Indonesia

The aim of this study was to assess the possible association of El Niño Southern Oscillation (ENSO) and Dipole Mode Index (DMI) on chikungunya incidence overtime, including the significant reduction in cases that was observed in 2017 in Indonesia. Monthly nation-wide chikungunya case reports were obtained from the Indonesian National Disease Surveillance database, and incidence rates (IR) and case fatality rate (CFR) were calculated. Monthly data of Niño3.4 (indicator used to represent the ENSO) and DMI between 2011 and 2017 were also collected. Correlations between monthly IR and CFR and Niño3.4 and DMI were assessed using Spearman's rank correlation. We found that chikungunya case reports declined from 1972 cases in 2016 to 126 cases in 2017, a 92.6% reduction; the IR reduced from 0.67 to 0.05 cases per 100,000 population. No deaths associated with chikungunya have been recorded since its re-emergence in Indonesia in 2001. There was no significant correlation between monthly Niño3.4 and chikungunya incidence with r = -0.142 (95%CI: -0.320-0.046), p = 0.198. However, there was a significant negative correlation between monthly DMI and chikungunya incidence, r = -0.404 (95%CI: -0.229--0.554) with p < 0.001. In conclusion, our initial data suggests that the climate variable, DMI but not Niño3.4, is likely associated with changes in chikungunya incidence. Therefore, further analysis with a higher resolution of data, using the cross-wavelet coherence approach, may provide more robust evidence.

Mapping the Structure of Social Vulnerability Systems for Malaria in East Africa

Millions of people fall ill with malaria every year—most of them are located in sub-Saharan Africa. The weight of the burden of malaria on a society is determined by a complex interplay of environmental and social factors, including poverty, awareness and education, among others. A substantial share of the affected population is characterized by a general lack of anticipation and coping capacities, which renders them particularly vulnerable to the disease and its adverse side effects. This work aims at identifying interdependencies and feedback mechanisms in the malaria social vulnerability system and their variations in space by combining concepts, methods and tools from Climate Change Adaptation, Spatial Analysis, and Statistics and System Dynamics. The developed workflow is applied to a selected set of social, economic and biological vulnerability indicators covering five East-African Nations. As the study areas’ local conditions vary in a multitude of aspects, the social vulnerability system is assumed to vary accordingly throughout space. The study areas’ spatial entities were therefore aggregated into three system-regions using correlation-based clustering. Their respective correlation structures are displayed as Causal Loop Diagrams (CLDs). While the three resulting CLDs do not necessarily display causal relations (as the set of social vulnerability indicators are likely linked through third variables and parts of the data are proxies), they give a good overview of the data, can be used as basis for discussions in participatory settings and can potentially enhance the understanding the malaria vulnerability system.

Lag effect of climatic variables on dengue burden in India

Dengue is a widespread vector-borne disease believed to affect between 100 and 390 million people every year. The interaction between vector, host and pathogen is influenced by various climatic factors and the relationship between dengue and climatic conditions has been poorly explored in India. This study explores the relationship between El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD) and dengue cases in India. Additionally, distributed lag non-linear model was used to assess the delayed effects of climatic factors on dengue cases. The weekly dengue cases reported by the Integrated Disease Surveillance Program (IDSP) over India during the period 2010-2017 were analysed. The study shows that dengue cases usually follow a seasonal pattern, with most cases reported in August and September. Both temperature and rainfall were positively associated with the number of dengue cases. The precipitation shows the higher transmission risk of dengue was observed between 8 and 15 weeks of lag. The highest relative risk (RR) of dengue was observed at 60 mm rainfall with a 12-week lag period when compared with 40 and 80 mm rainfall. The RR of dengue tends to increase with increasing mean temperature above 24 °C. The largest transmission risk of dengue was observed at 30 °C with a 0-3 weeks of lag. Similarly, the transmission risk increases more than twofold when the minimum temperature reaches 26 °C with a 2-week lag period. The dengue cases and El Niño were positively correlated with a 3-6 months lag period. The significant correlation observed between the IOD and dengue cases was shown for a 0-2 months lag period.

Differences of Rainfall-Malaria Associations in Lowland and Highland in Western Kenya

Many studies have reported a relationship between climate factors and malaria. However, results were inconsistent across the areas. We examined associations between climate factors and malaria in two geographically different areas: lowland (lakeside area) and highland in Western Kenya. Associations between climate factors (rainfall, land surface temperature (LST), and lake water level (LWL)) and monthly malaria cases from 2000 to 2013 in six hospitals (two in lowland and four in highland) were analyzed using time-series regression analysis with a distributed lag nonlinear model (DLNM) and multivariate meta-analysis. We found positive rainfall–malaria overall associations in lowland with a peak at 120 mm of monthly rainfall with a relative risk (RR) of 7.32 (95% CI: 2.74, 19.56) (reference 0 mm), whereas similar associations were not found in highland. Positive associations were observed at lags of 2 to 4 months at rainfall around 100–200 mm in both lowland and highland. The RRs at 150 mm rainfall were 1.42 (95% CI: 1.18, 1.71) in lowland and 1.20 (95% CI: 1.07, 1.33) in highland (at a lag of 3 months). LST and LWL did not show significant association with malaria. The results suggest that geographical characteristics can influence climate–malaria relationships.

Sensitivity of vegetation to climate variability and its implications for malaria risk in Baringo, Kenya

The global increase in vector borne diseases has been linked to climate change. Seasonal vegetation changes are known to influence disease vector population. However, the relationship is more theoretical than quantitatively defined. There is a growing demand for understanding and prediction of climate sensitive vector borne disease risks especially in regions where meteorological data are lacking. This study aimed at analyzing and quantitatively assessing the seasonal and year-to-year association between climatic factors (rainfall and temperature) and vegetation cover, and its implications for malaria risks in Baringo County, Kenya. Remotely sensed temperature, rainfall, and vegetation data for the period 2004–2015 were used. Poisson regression was used to model the association between malaria cases and climatic and environmental factors for the period 2009–2012, this being the period for which all datasets overlapped. A strong positive relationship was observed between the Normalized Difference Vegetation Index (NDVI) and monthly total precipitation. There was a strong negative relationship between NDVI and minimum temperature. The total monthly rainfall (between 94 -181mm), average monthly minimum temperatures (between 16–21°C) and mean monthly NDVI values lower than 0.35 were significantly associated with malaria incidence rates. Results suggests that a combination of climatic and vegetation greenness thresholds need to be met for malaria incidence to be significantly increased in the county. Planning for malaria control can therefore be enhanced by incorporating these factors in malaria risk mapping.

Effects of local and regional climatic fluctuations on dengue outbreaks in southern Taiwan

Background

Southern Taiwan has been a hotspot for dengue fever transmission since 1998. During 2014 and 2015, Taiwan experienced unprecedented dengue outbreaks and the causes are poorly understood. This study aims to investigate the influence of regional and local climate conditions on the incidence of dengue fever in Taiwan, as well as to develop a climate-based model for future forecasting.

Methodology/Principle findings

Historical time-series data on dengue outbreaks in southern Taiwan from 1998 to 2015 were investigated. Local climate variables were analyzed using a distributed lag non-linear model (DLNM), and the model of best fit was used to predict dengue incidence between 2013 and 2015. The cross-wavelet coherence approach was used to evaluate the regional El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) effects on dengue incidence and local climate variables. The DLNM results highlighted the important non-linear and lag effects of minimum temperature and precipitation. Minimum temperature above 23°C or below 17°C can increase dengue incidence rate with lag effects of 10 to 15 weeks. Moderate to high precipitation can increase dengue incidence rates with a lag of 10 or 20 weeks. The model of best fit successfully predicted dengue transmission between 2013 and 2015. The prediction accuracy ranged from 0.7 to 0.9, depending on the number of weeks ahead of the prediction. ENSO and IOD were associated with nonstationary inter-annual patterns of dengue transmission. IOD had a greater impact on the seasonality of local climate conditions.

Conclusions/Significance

Our findings suggest that dengue transmission can be affected by regional and local climatic fluctuations in southern Taiwan. The climate-based model developed in this study can provide important information for dengue early warning systems in Taiwan. Local climate conditions might be influenced by ENSO and IOD, to result in unusual dengue outbreaks.

Using remote sensing environmental data to forecast malaria incidence at a rural district hospital in Western Kenya

Malaria surveillance data provide opportunity to develop forecasting models. Seasonal variability in environmental factors correlate with malaria transmission, thus the identification of transmission patterns is useful in developing prediction models. However, with changing seasonal transmission patterns, either due to interventions or shifting weather seasons, traditional modelling approaches may not yield adequate predictive skill. Two statistical models,a general additive model (GAM) and GAMBOOST model with boosted regression were contrasted by assessing their predictive accuracy in forecasting malaria admissions at lead times of one to three months. Monthly admission data for children under five years with confirmed malaria at the Siaya district hospital in Western Kenya for the period 2003 to 2013 were used together with satellite derived data on rainfall, average temperature and evapotranspiration(ET). There was a total of 8,476 confirmed malaria admissions. The peak of malaria season changed and malaria admissions reduced overtime. The GAMBOOST model at 1-month lead time had the highest predictive skill during both the training and test periods and thus can be utilized in a malaria early warning system.

Assessment of climate-driven variations in malaria incidence in Swaziland: toward malaria elimination

Background

Swaziland aims to eliminate malaria by 2020. However, imported cases from neighbouring endemic countries continue to sustain local parasite reservoirs and initiate transmission. As certain weather and climatic conditions may trigger or intensify malaria outbreaks, identification of areas prone to these conditions may aid decision-makers in deploying targeted malaria interventions more effectively.

Methods

Malaria case-surveillance data for Swaziland were provided by Swaziland’s National Malaria Control Programme. Climate data were derived from local weather stations and remote sensing images. Climate parameters and malaria cases between 2001 and 2015 were then analysed using seasonal autoregressive integrated moving average models and distributed lag non-linear models (DLNM).

Results

The incidence of malaria in Swaziland increased between 2005 and 2010, especially in the Lubombo and Hhohho regions. A time-series analysis indicated that warmer temperatures and higher precipitation in the Lubombo and Hhohho administrative regions are conducive to malaria transmission. DLNM showed that the risk of malaria increased in Lubombo when the maximum temperature was above 30 °C or monthly precipitation was above 5 in. In Hhohho, the minimum temperature remaining above 15 °C or precipitation being greater than 10 in. might be associated with malaria transmission.

Conclusions

This study provides a preliminary assessment of the impact of short-term climate variations on malaria transmission in Swaziland. The geographic separation of imported and locally acquired malaria, as well as population behaviour, highlight the varying modes of transmission, part of which may be relevant to climate conditions. Thus, the impact of changing climate conditions should be noted as Swaziland moves toward malaria elimination.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-017-1874-0) contains supplementary material, which is available to authorized users.

Seasonally lagged effects of climatic factors on malaria incidence in South Africa

Globally, malaria cases have drastically dropped in recent years. However, a high incidence of malaria remains in some sub-Saharan African countries. South Africa is mostly malaria-free, but northeastern provinces continue to experience seasonal outbreaks. Here we investigate the association between malaria incidence and spatio-temporal climate variations in Limpopo. First, dominant spatial patterns in malaria incidence anomalies were identified using self-organizing maps. Composite analysis found significant associations among incidence anomalies and climate patterns. A high incidence of malaria during the pre-peak season (Sep-Nov) was associated with the climate phenomenon La Niña and cool air temperatures over southern Africa. There was also high precipitation over neighbouring countries two to six months prior to malaria incidence. During the peak season (Dec-Feb), high incidence was associated with positive phase of Indian Ocean Subtropical Dipole. Warm temperatures and high precipitation in neighbouring countries were also observed two months prior to increased malaria incidence. This lagged association between regional climate and malaria incidence suggests that in areas at high risk for malaria, such as Limpopo, management plans should consider not only local climate patterns but those of neighbouring countries as well. These findings highlight the need to strengthen cross-border control of malaria to minimize its spread.

An integrated risk and vulnerability assessment framework for climate change and malaria transmission in East Africa

BACKGROUND

Malaria is one of the key research concerns in climate change-health relationships. Numerous risk assessments and modelling studies provide evidence that the transmission range of malaria will expand with rising temperatures, adversely impacting on vulnerable communities in the East African highlands. While there exist multiple lines of evidence for the influence of climate change on malaria transmission, there is insufficient understanding of the complex and interdependent factors that determine the risk and vulnerability of human populations at the community level. Moreover, existing studies have had limited focus on the nature of the impacts on vulnerable communities or how well they are prepared to cope. In order to address these gaps, a systems approach was used to present an integrated risk and vulnerability assessment framework for studies of community level risk and vulnerability to malaria due to climate change.

RESULTS

Drawing upon published literature on existing frameworks, a systems approach was applied to characterize the factors influencing the interactions between climate change and malaria transmission. This involved structural analysis to determine influential, relay, dependent and autonomous variables in order to construct a detailed causal loop conceptual model that illustrates the relationships among key variables. An integrated assessment framework that considers indicators of both biophysical and social vulnerability was proposed based on the conceptual model.

CONCLUSIONS

A major conclusion was that this integrated assessment framework can be implemented using Bayesian Belief Networks, and applied at a community level using both quantitative and qualitative methods with stakeholder engagement. The approach enables a robust assessment of community level risk and vulnerability to malaria, along with contextually relevant and targeted adaptation strategies for dealing with malaria transmission that incorporate both scientific and community perspectives.

Meteorological influences on dengue transmission in Pakistan

OBJECTIVE

To identify the influences of local and regional climate phenomena on dengue transmission in Lahore District of Pakistan, from 2006 to 2014.

METHODS

Time-series models were applied to analyze associations between reported cases of dengue and climatic parameters. The coherence trend of regional climate phenomena (IOD and ENSO) was evaluated with wavelet analysis.

RESULTS

The minimum temperature 4 months before the dengue outbreak played the most important role in the Lahore District (P = 0.03). A NINO 3.4 index 9 months before the outbreaks exhibited a significant negative effect on dengue transmission (P = 0.02). The IOD exhibited a synchronized pattern with dengue outbreak from 2010 to 2012. The ENSO effect (NINO 3.4 index) might have played a more important role after 2012.

CONCLUSIONS

This study provides preliminary results of climate influences on dengue transmission in the Lahore District of Pakistan. An increasing dengue transmission risk accompanied by frequent climate changes should be noted. Integrating the influences of climate variability into disease prevention strategies should be considered by public health authorities.

Associations between malaria and local and global climate variability in five regions in Papua New Guinea

BACKGROUND

Malaria is a significant public health issue in Papua New Guinea (PNG) as the burden is among the highest in Asia and the Pacific region. Though PNG's vulnerability to climate change and sensitivity of malaria mosquitoes to weather are well-documented, there are few in-depth epidemiological studies conducted on the potential impacts of climate on malaria incidence in the country.

METHODS

This study explored what and how local weather and global climate variability impact on malaria incidence in five regions of PNG. Time series methods were applied to evaluate the associations of malaria incidence with weather and climate factors, respectively. Local weather factors including precipitation and temperature and global climate phenomena such as El Niño-Southern Oscillation (ENSO), the ENSO Modoki, the Southern Annular Mode, and the Indian Ocean Dipole were considered in analyses.

RESULTS

The results showed that malaria incidence was associated with local weather factors in most regions but at the different lag times and in directions. Meanwhile, there were trends in associations with global climate factors by geographical locations of study sites.

CONCLUSIONS

Overall heterogeneous associations suggest the importance of location-specific approaches in PNG not only for further investigations but also public health interventions in repose to the potential impacts arising from climate change.

Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa

Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of Knowledge(SM) databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic setting, the stage of malaria elimination continuum, the characteristics of the RS variables and the analytical approach, which in turn, would support the channeling of intervention resources sustainably.

Time trend of malaria in relation to climate variability in Papua New Guinea

OBJECTIVES

This study was conducted to describe the regional malaria incidence in relation to the geographic and climatic conditions and describe the effect of altitude on the expansion of malaria over the last decade in Papua New Guinea.

METHODS

Malaria incidence was estimated in five provinces from 1996 to 2008 using national health surveillance data. Time trend of malaria incidence was compared with rainfall and minimum/maximum temperature. In the Eastern Highland Province, time trend of malaria incidence over the study period was stratified by altitude. Spatio-temporal pattern of malaria was analyzed.

RESULTS

Nationwide, malaria incidence was stationary. Regionally, the incidence increased markedly in the highland region (292.0/100000/yr, p =0.021), and remained stationary in the other regions. Seasonality of the malaria incidence was related with rainfall. Decreasing incidence of malaria was associated with decreasing rainfall in the southern coastal region, whereas it was not evident in the northern coastal region. In the Eastern Highland Province, malaria incidence increased in areas below 1700 m, with the rate of increase being steeper at higher altitudes.

CONCLUSIONS

Increasing trend of malaria incidence was prominent in the highland region of Papua New Guinea, while long-term trend was dependent upon baseline level of rainfall in coastal regions.

Effect of Climate Factors on the Childhood Pneumonia in Papua New Guinea: A Time-Series Analysis

This study aimed to assess the association between climate factors and the incidence of childhood pneumonia in Papua New Guinea quantitatively and to evaluate the variability of the effect size according to their geographic properties. The pneumonia incidence in children under five-year and meteorological factors were obtained from six areas, including monthly rainfall and the monthly average daily maximum temperatures during the period from 1997 to 2006 from national health surveillance data. A generalized linear model was applied to measure the effect size of local and regional climate factor. The pooled risk of pneumonia in children per every 10 mm increase of rainfall was 0.24% (95% confidence interval: −0.01%–0.50%), and risk per every 1 °C increase of the monthly mean of the maximum daily temperatures was 4.88% (95% CI: 1.57–8.30). Southern oscillation index and dipole mode index showed an overall negative effect on childhood pneumonia incidence, −0.57% and −4.30%, respectively, and the risk of pneumonia was higher in the dry season than in the rainy season (pooled effect: 12.08%). There was a variability in the relationship between climate factors and pneumonia which is assumed to reflect distribution of the determinants of and vulnerability to pneumonia in the community.

Forecasting paediatric malaria admissions on the Kenya Coast using rainfall

BACKGROUND

Malaria is a vector-borne disease which, despite recent scaled-up efforts to achieve control in Africa, continues to pose a major threat to child survival. The disease is caused by the protozoan parasite Plasmodium and requires mosquitoes and humans for transmission. Rainfall is a major factor in seasonal and secular patterns of malaria transmission along the East African coast.

OBJECTIVE

The goal of the study was to develop a model to reliably forecast incidences of paediatric malaria admissions to Kilifi District Hospital (KDH).

DESIGN

In this article, we apply several statistical models to look at the temporal association between monthly paediatric malaria hospital admissions, rainfall, and Indian Ocean sea surface temperatures. Trend and seasonally adjusted, marginal and multivariate, time-series models for hospital admissions were applied to a unique data set to examine the role of climate, seasonality, and long-term anomalies in predicting malaria hospital admission rates and whether these might become more or less predictable with increasing vector control.

RESULTS

The proportion of paediatric admissions to KDH that have malaria as a cause of admission can be forecast by a model which depends on the proportion of malaria admissions in the previous 2 months. This model is improved by incorporating either the previous month's Indian Ocean Dipole information or the previous 2 months' rainfall.

CONCLUSIONS

Surveillance data can help build time-series prediction models which can be used to anticipate seasonal variations in clinical burdens of malaria in stable transmission areas and aid the timing of malaria vector control.

Impacts of El Niño Southern Oscillation and Indian Ocean Dipole on dengue incidence in Bangladesh

Dengue dynamics are driven by complex interactions between hosts, vectors and viruses that are influenced by environmental and climatic factors. Several studies examined the role of El Niño Southern Oscillation (ENSO) in dengue incidence. However, the role of Indian Ocean Dipole (IOD), a coupled ocean atmosphere phenomenon in the Indian Ocean, which controls the summer monsoon rainfall in the Indian region, remains unexplored. Here, we examined the effects of ENSO and IOD on dengue incidence in Bangladesh. According to the wavelet coherence analysis, there was a very weak association between ENSO, IOD and dengue incidence, but a highly significant coherence between dengue incidence and local climate variables (temperature and rainfall). However, a distributed lag nonlinear model (DLNM) revealed that the association between dengue incidence and ENSO or IOD were comparatively stronger after adjustment for local climate variables, seasonality and trend. The estimated effects were nonlinear for both ENSO and IOD with higher relative risks at higher ENSO and IOD. The weak association between ENSO, IOD and dengue incidence might be driven by the stronger effects of local climate variables such as temperature and rainfall. Further research is required to disentangle these effects.

Non-stationary dynamics of climate variability in synchronous influenza epidemics in Japan

Seasonal variation in the incidence of influenza is widely assumed. However, few studies have examined non-stationary relationships between global climate factors and influenza epidemics. We examined the monthly incidence of influenza in Fukuoka, Japan, from 2000 to 2012 using cross-wavelet coherency analysis to assess the patterns of associations between indices for the Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO). The monthly incidence of influenza showed cycles of 1 year with the IOD and 2 years with ENSO indices (Multivariate, Niño 4, and Niño 3.4). These associations were non-stationary and appeared to have major influences on the synchrony of influenza epidemics. Our study provides quantitative evidence that non-stationary associations have major influences on synchrony between the monthly incidence of influenza and the dynamics of the IOD and ENSO. Our results call for the consideration of non-stationary patterns of association between influenza cases and climatic factors in early warning systems.

Long-run relative importance of temperature as the main driver to malaria transmission in Limpopo Province, South Africa: a simple econometric approach

Malaria in Limpopo Province of South Africa is shifting and now observed in originally non-malaria districts, and it is unclear whether climate change drives this shift. This study examines the distribution of malaria at district level in the province, determines direction and strength of the linear relationship and causality between malaria with the meteorological variables (rainfall and temperature) and ascertains their short- and long-run variations. Spatio-temporal method, Correlation analysis and econometric methods are applied. Time series monthly meteorological data (1998-2007) were obtained from South Africa Weather Services, while clinical malaria data came from Malaria Control Centre in Tzaneen (Limpopo Province) and South African Department of Health. We find that malaria changes and pressures vary in different districts with a strong positive correlation between temperature with malaria, r = 0.5212, and a weak positive relationship for rainfall, r = 0.2810. Strong unidirectional causality runs from rainfall and temperature to malaria cases (and not vice versa): F (1, 117) = 3.89, ρ = 0.0232 and F (1, 117) = 20.08, P < 0.001 and between rainfall and temperature, a bi-directional causality exists: F (1, 117) = 19.80; F (1,117) = 17.14, P < 0.001, respectively, meaning that rainfall affects temperature and vice versa. Results show evidence of strong existence of a long-run relationship between climate variables and malaria, with temperature maintaining very high level of significance than rainfall. Temperature, therefore, is more important in influencing malaria transmission in Limpopo Province.

Visualizing the uncertainty in the relationship between seasonal average climate and malaria risk

Around $1.6 billion per year is spent financing anti-malaria initiatives, and though malaria morbidity is falling, the impact of annual epidemics remains significant. Whilst malaria risk may increase with climate change, projections are highly uncertain and to sidestep this intractable uncertainty, adaptation efforts should improve societal ability to anticipate and mitigate individual events. Anticipation of climate-related events is made possible by seasonal climate forecasting, from which warnings of anomalous seasonal average temperature and rainfall, months in advance are possible. Seasonal climate hindcasts have been used to drive climate-based models for malaria, showing significant skill for observed malaria incidence. However, the relationship between seasonal average climate and malaria risk remains unquantified. Here we explore this relationship, using a dynamic weather-driven malaria model. We also quantify key uncertainty in the malaria model, by introducing variability in one of the first order uncertainties in model formulation. Results are visualized as location-specific impact surfaces: easily integrated with ensemble seasonal climate forecasts, and intuitively communicating quantified uncertainty. Methods are demonstrated for two epidemic regions, and are not limited to malaria modeling; the visualization method could be applied to any climate impact.

A systematic review of methodology: time series regression analysis for environmental factors and infectious diseases

BACKGROUND

Time series analysis is suitable for investigations of relatively direct and short-term effects of exposures on outcomes. In environmental epidemiology studies, this method has been one of the standard approaches to assess impacts of environmental factors on acute non-infectious diseases (e.g. cardiovascular deaths), with conventionally generalized linear or additive models (GLM and GAM). However, the same analysis practices are often observed with infectious diseases despite of the substantial differences from non-infectious diseases that may result in analytical challenges.

METHODS

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, systematic review was conducted to elucidate important issues in assessing the associations between environmental factors and infectious diseases using time series analysis with GLM and GAM. Published studies on the associations between weather factors and malaria, cholera, dengue, and influenza were targeted.

FINDINGS

Our review raised issues regarding the estimation of susceptible population and exposure lag times, the adequacy of seasonal adjustments, the presence of strong autocorrelations, and the lack of a smaller observation time unit of outcomes (i.e. daily data). These concerns may be attributable to features specific to infectious diseases, such as transmission among individuals and complicated causal mechanisms.

CONCLUSION

The consequence of not taking adequate measures to address these issues is distortion of the appropriate risk quantifications of exposures factors. Future studies should pay careful attention to details and examine alternative models or methods that improve studies using time series regression analysis for environmental determinants of infectious diseases.

Effect of non-stationary climate on infectious gastroenteritis transmission in Japan

Local weather factors are widely considered to influence the transmission of infectious gastroenteritis. Few studies, however, have examined the non-stationary relationships between global climatic factors and transmission of infectious gastroenteritis. We analyzed monthly data for cases of infectious gastroenteritis in Fukuoka, Japan from 2000 to 2012 using cross-wavelet coherency analysis to assess the pattern of associations between indices for the Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO). Infectious gastroenteritis cases were non-stationary and significantly associated with the IOD and ENSO (Multivariate ENSO Index [MEI], Niño 1 + 2, Niño 3, Niño 4, and Niño 3.4) for a period of approximately 1 to 2 years. This association was non-stationary and appeared to have a major influence on the synchrony of infectious gastroenteritis transmission. Our results suggest that non-stationary patterns of association between global climate factors and incidence of infectious gastroenteritis should be considered when developing early warning systems for epidemics of infectious gastroenteritis.

Climate variability and nonstationary dynamics of Mycoplasma pneumoniae pneumonia in Japan

Background

A stationary association between climate factors and epidemics of Mycoplasma pneumoniae (M. pneumoniae) pneumonia has been widely assumed. However, it is unclear whether elements of the local climate that are relevant to M. pneumoniae pneumonia transmission have stationary signatures of climate factors on their dynamics over different time scales.

Methods

We performed a cross-wavelet coherency analysis to assess the patterns of association between monthly M. pneumoniae cases in Fukuoka, Japan, from 2000 to 2012 and indices for the Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO).

Results

Monthly M. pneumoniae cases were strongly associated with the dynamics of both the IOD and ENSO for the 1–2-year periodic mode in 2005–2007 and 2010–2011. This association was non-stationary and appeared to have a major influence on the synchrony of M. pneumoniae epidemics.

Conclusions

Our results call for the consideration of non-stationary, possibly non-linear, patterns of association between M. pneumoniae cases and climatic factors in early warning systems.

Protein microarray analysis of antibody responses to Plasmodium falciparum in western Kenyan highland sites with differing transmission levels

Malaria represents a major public health problem in Africa. In the East African highlands, the high-altitude areas were previously considered too cold to support vector population and parasite transmission, rendering the region particularly prone to epidemic malaria due to the lack of protective immunity of the population. Since the 1980’s, frequent malaria epidemics have been reported and these successive outbreaks may have generated some immunity against Plasmodium falciparum amongst the highland residents. Serological studies reveal indirect evidence of human exposure to the parasite, and can reliably assess prevalence of exposure and transmission intensity in an endemic area. However, the vast majority of serological studies of malaria have been, hereto, limited to a small number of the parasite’s antigens. We surveyed and compared the antibody response profiles of age-stratified sera from residents of two endemic areas in the western Kenyan highlands with differing malaria transmission intensities, during two distinct seasons, against 854 polypeptides of P. falciparum using high-throughput proteomic microarray technology. We identified 107 proteins as serum antibody targets, which were then characterized for their gene ontology biological process and cellular component of the parasite, and showed significant enrichment for categories related to immune evasion, pathogenesis and expression on the host’s cell and parasite’s surface. Additionally, we calculated age-fitted annual seroconversion rates for the immunogenic proteins, and contrasted the age-dependent antibody acquisition for those antigens between the two sampling sites. We observed highly immunogenic antigens that produce stable antibody responses from early age in both sites, as well as less immunogenic proteins that require repeated exposure for stable responses to develop and produce different seroconversion rates between sites. We propose that a combination of highly and less immunogenic proteins could be used in serological surveys to detect differences in malaria transmission levels, distinguishing sites of unstable and stable transmission.

Improving the modeling of disease data from the government surveillance system: a case study on malaria in the Brazilian Amazon

The study of the effect of large-scale drivers (e.g., climate) of human diseases typically relies on aggregate disease data collected by the government surveillance network. The usual approach to analyze these data, however, often ignores a) changes in the total number of individuals examined, b) the bias towards symptomatic individuals in routine government surveillance, and; c) the influence that observations can have on disease dynamics. Here, we highlight the consequences of ignoring the problems listed above and develop a novel modeling framework to circumvent them, which is illustrated using simulations and real malaria data. Our simulations reveal that trends in the number of disease cases do not necessarily imply similar trends in infection prevalence or incidence, due to the strong influence of concurrent changes in sampling effort. We also show that ignoring decreases in the pool of infected individuals due to the treatment of part of these individuals can hamper reliable inference on infection incidence. We propose a model that avoids these problems, being a compromise between phenomenological statistical models and mechanistic disease dynamics models; in particular, a cross-validation exercise reveals that it has better out-of-sample predictive performance than both of these alternative models. Our case study in the Brazilian Amazon reveals that infection prevalence was high in 2004–2008 (prevalence of 4% with 95% CI of 3–5%), with outbreaks (prevalence up to 18%) occurring during the dry season of the year. After this period, infection prevalence decreased substantially (0.9% with 95% CI of 0.8–1.1%), which is due to a large reduction in infection incidence (i.e., incidence in 2008–2010 was approximately one fifth of the incidence in 2004–2008).We believe that our approach to modeling government surveillance disease data will be useful to advance current understanding of large-scale drivers of several diseases.

Disease data collected by the government surveillance system are frequently used to understand the influence of large-scale phenomena (e.g., climate) on human health because these data often have a large temporal and/or geographical span. The down side is that a) these data are often biased towards individuals that come to the health facilities (i.e., symptomatic individuals); and b) the number of individuals examined can vary substantially regardless of concurrent changes in prevalence or incidence (e.g., due to shortage of personnel or supplies in health facilities), directly impacting the number of disease cases detected. Current modeling approaches typically ignore these peculiarities of the government data. Furthermore, current approaches do not take into account that observations directly influence disease dynamics since individuals with a positive diagnosis are often subsequently treated for the disease. In this article, we develop a novel model to circumvent these shortcomings and apply it to simulated data, highlighting how inference on infection incidence and prevalence might be misleading when some of the issues mentioned above are ignored. Finally, we illustrate this model using malaria data from the Brazilian Amazon, revealing the strong role of precipitation on infection prevalence seasonality and striking patterns in infection incidence.

Environmental variability and the transmission of haemorrhagic fever with renal syndrome in Changsha, People's Republic of China

The transmission of haemorrhagic fever with renal syndrome (HFRS) is influenced by climatic, reservoir and environmental variables. The epidemiology of the disease was studied over a 6-year period in Changsha. Variables relating to climate, environment, rodent host distribution and disease occurrence were collected monthly and analysed using a time-series adjusted Poisson regression model. It was found that the density of the rodent host and multivariate El Niño Southern Oscillation index had the greatest effect on the transmission of HFRS with lags of 2–6 months. However, a number of climatic and environmental factors played important roles in affecting the density and transmission potential of the rodent host population. It was concluded that the measurement of a number of these variables could be used in disease surveillance to give useful advance warning of potential disease epidemics.

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.

Rainfall drives hydrocephalus in East Africa

OBJECT

Hydrocephalus is one of the most common brain disorders in children throughout the world. The majority of infant hydrocephalus cases in East Africa appear to be postinfectious, related to preceding neonatal infections, and are thus preventable if the microbial origins and routes of infection can be characterized. In prior microbiological work, the authors noted evidence of seasonality in postinfectious hydrocephalus (PIH) cases.

METHODS

The geographical address of 696 consecutive children with PIH who were treated over 6 years was fused with satellite rainfall data for the same time period. A comprehensive time series and spatiotemporal analysis of cases and rainfall was performed.

RESULTS

Four infection-onset peaks were found to straddle the twice-yearly rainy season peaks, demonstrating that the infections occurred at intermediate levels of rainfall.

CONCLUSIONS

The findings in this study reveal a previously unknown link between climate and a neurosurgical condition. Satellite-derived rainfall dynamics are an important factor in driving the infections that lead to PIH. Given prior microbial analysis, these findings point to the importance of environmental factors with respect to preventing the newborn infections that lead to PIH.

Global climate change and its potential impact on disease transmission by salinity-tolerant mosquito vectors in coastal zones

Global climate change can potentially increase the transmission of mosquito vector-borne diseases such as malaria, lymphatic filariasis, and dengue in many parts of the world. These predictions are based on the effects of changing temperature, rainfall, and humidity on mosquito breeding and survival, the more rapid development of ingested pathogens in mosquitoes and the more frequent blood feeds at moderately higher ambient temperatures. An expansion of saline and brackish water bodies (water with <0.5 ppt or parts per thousand, 0.5–30 ppt and >30 ppt salt are termed fresh, brackish, and saline respectively) will also take place as a result of global warming causing a rise in sea levels in coastal zones. Its possible impact on the transmission of mosquito-borne diseases has, however, not been adequately appreciated. The relevant impacts of global climate change on the transmission of mosquito-borne diseases in coastal zones are discussed with reference to the Ross–McDonald equation and modeling studies. Evidence is presented to show that an expansion of brackish water bodies in coastal zones can increase the densities of salinity-tolerant mosquitoes like Anopheles sundaicus and Culex sitiens, and lead to the adaptation of fresh water mosquito vectors like Anopheles culicifacies, Anopheles stephensi, Aedes aegypti, and Aedes albopictus to salinity. Rising sea levels may therefore act synergistically with global climate change to increase the transmission of mosquito-borne diseases in coastal zones. Greater attention therefore needs to be devoted to monitoring disease incidence and preimaginal development of vector mosquitoes in artificial and natural coastal brackish/saline habitats. It is important that national and international health agencies are aware of the increased risk of mosquito-borne diseases in coastal zones and develop preventive and mitigating strategies. Application of appropriate counter measures can greatly reduce the potential for increased coastal transmission of mosquito-borne diseases consequent to climate change and a rise in sea levels. It is proposed that the Jaffna peninsula in Sri Lanka may be a useful case study for the impact of rising sea levels on mosquito vectors in tropical coasts.

Indian Ocean dipole and rainfall drive a Moran effect in East Africa malaria transmission

BACKGROUND

Patterns of concerted fluctuation in populations-synchrony-can reveal impacts of climatic variability on disease dynamics. We examined whether malaria transmission has been synchronous in an area with a common rainfall regime and sensitive to the Indian Ocean Dipole (IOD), a global climatic phenomenon affecting weather patterns in East Africa.

METHODS

We studied malaria synchrony in 5 15-year long (1984-1999) monthly time series that encompass an altitudinal gradient, approximately 1000 m to 2000 m, along Lake Victoria basin. We quantified the association patterns between rainfall and malaria time series at different altitudes and across the altitudinal gradient encompassed by the study locations.

RESULTS

We found a positive seasonal association of rainfall with malaria, which decreased with altitude. By contrast, IOD and interannual rainfall impacts on interannual disease cycles increased with altitude. Our analysis revealed a nondecaying synchrony of similar magnitude in both malaria and rainfall, as expected under a Moran effect, supporting a role for climatic variability on malaria epidemic frequency, which might reflect rainfall-mediated changes in mosquito abundance.

CONCLUSIONS

Synchronous malaria epidemics call for the integration of knowledge on the forcing of malaria transmission by environmental variability to develop robust malaria control and elimination programs.

Indian Ocean Dipole drives malaria resurgence in East African highlands

Malaria resurgence in African highlands in the 1990s has raised questions about the underlying drivers of the increase in disease incidence including the role of El-Niño-Southern Oscillation (ENSO). However, climatic anomalies other than the ENSO are clearly associated with malaria outbreaks in the highlands. Here we show that the Indian Ocean Dipole (IOD), a coupled ocean-atmosphere interaction in the Indian Ocean, affected highland malaria re-emergence. Using cross-wavelet coherence analysis, we found four-year long coherent cycles between the malaria time series and the dipole mode index (DMI) in the 1990s in three highland localities. Conversely, we found a less pronounced coherence between malaria and DMI in lowland localities. The highland/lowland contrast can be explained by the effects of mesoscale systems generated by Lake Victoria on its climate basin. Our results support the need to consider IOD as a driving force in the resurgence of malaria in the East African highlands.

Progress towards understanding the ecology and epidemiology of malaria in the western Kenya highlands: opportunities and challenges for control under climate change risk

Following severe malaria epidemics in the western Kenya highlands after the late 1980s it became imperative to undertake eco-epidemiological assessments of the disease and determine its drivers, spatial-temporal distribution and control strategies. Extensive research has indicated that the major biophysical drivers of the disease are climate change and variability, terrain, topography, hydrology and immunity. Vector distribution is focalized at valley bottoms and abundance is closely related with drainage efficiency, habitat availability, stability and productivity of the ecosystems. Early epidemic prediction models have been developed and they can be used to assess climate risks that warrant extra interventions with a lead time of 2-4 months. Targeted integrated vector management strategies can significantly reduce the cost on the indoor residual spraying by targeting the foci of transmission in transmission hotspots. Malaria control in the highlands has reduced vector population by 90%, infections by 50-90% in humans and in some cases transmission has been interrupted. Insecticide resistance is increasing and as transmission decreases so will immunity. Active surveillance will be required to monitor and contain emerging threats. More studies on eco-stratification of the disease, based on its major drivers, are required so that interventions are tailored for specific ecosystems. New and innovative control interventions such as house modification with a one-application strategy may reduce the threat from insecticide resistance and low compliance associated with the use of ITNs.

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.

Sensitivity of Anopheles gambiae population dynamics to meteo-hydrological variability: a mechanistic approach

BACKGROUND

Mechanistic models play an important role in many biological disciplines, and they can effectively contribute to evaluate the spatial-temporal evolution of mosquito populations, in the light of the increasing knowledge of the crucial driving role on vector dynamics played by meteo-climatic features as well as other physical-biological characteristics of the landscape.

METHODS

In malaria eco-epidemiology landscape components (atmosphere, water bodies, land use) interact with the epidemiological system (interacting populations of vector, human, and parasite). In the background of the eco-epidemiological approach, a mosquito population model is here proposed to evaluate the sensitivity of An. gambiae s.s. population to some peculiar thermal-pluviometric scenarios. The scenarios are obtained perturbing meteorological time series data referred to four Kenyan sites (Nairobi, Nyabondo, Kibwesi, and Malindi) representing four different eco-epidemiological settings.

RESULTS

Simulations highlight a strong dependence of mosquito population abundance on temperature variation with well-defined site-specific patterns. The upper extreme of thermal perturbation interval (+ 3°C) gives rise to an increase in adult population abundance at Nairobi (+111%) and Nyabondo (+61%), and a decrease at Kibwezi (-2%) and Malindi (-36%). At the lower extreme perturbation (-3°C) is observed a reduction in both immature and adult mosquito population in three sites (Nairobi -74%, Nyabondo -66%, Kibwezi -39%), and an increase in Malindi (+11%). A coherent non-linear pattern of population variation emerges. The maximum rate of variation is +30% population abundance for +1°C of temperature change, but also almost null and negative values are obtained. Mosquitoes are less sensitive to rainfall and both adults and immature populations display a positive quasi-linear response pattern to rainfall variation.

CONCLUSIONS

The non-linear temperature-dependent response is in agreement with the non-linear patterns of temperature-response of the basic bio-demographic processes. This non-linearity makes the hypothesized biological amplification of temperature effects valid only for a limited range of temperatures. As a consequence, no simple extrapolations can be done linking temperature rise with increase in mosquito distribution and abundance, and projections of An. gambiae s.s. populations should be produced only in the light of the local meteo-climatic features as well as other physical and biological characteristics of the landscape.

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.