Air temperature suitability for Plasmodium falciparum malaria transmission in Africa 2000-2012: a high-resolution spatiotemporal prediction

Geospatial joint modeling of vector and parasite serology to microstratify malaria transmission

The World Health Organization identifies a strong surveillance system for malaria and its mosquito vector as an essential pillar of the malaria elimination agenda. Anopheles salivary antibodies are emerging biomarkers of exposure to mosquito bites that potentially overcome sensitivity and logistical constraints of traditional entomological surveys. Using samples collected by a village health volunteer network in 104 villages in Southeast Myanmar during routine surveillance, the present study employs a Bayesian geostatistical modeling framework, incorporating climatic and environmental variables together with Anopheles salivary antigen serology, to generate spatially continuous predictive maps of Anopheles biting exposure. Our maps quantify fine-scale spatial and temporal heterogeneity in Anopheles salivary antibody seroprevalence (ranging from 9 to 99%) that serves as a proxy of exposure to Anopheles bites and advances current static maps of only Anopheles occurrence. We also developed an innovative framework to perform surveillance of malaria transmission. By incorporating antibodies against the vector and the transmissible form of malaria (sporozoite) in a joint Bayesian geostatistical model, we predict several foci of ongoing transmission. In our study, we demonstrate that antibodies specific for Anopheles salivary and sporozoite antigens are a logistically feasible metric with which to quantify and characterize heterogeneity in exposure to vector bites and malaria transmission. These approaches could readily be scaled up into existing village health volunteer surveillance networks to identify foci of residual malaria transmission, which could be targeted with supplementary interventions to accelerate progress toward elimination.

Estimating the effects of temperature on transmission of the human malaria parasite, Plasmodium falciparum

Despite concern that climate change could increase the human risk to malaria in certain areas, the temperature dependency of malaria transmission is poorly characterized. Here, we use a mechanistic model fitted to experimental data to describe how Plasmodium falciparum infection of the African malaria vector, Anopheles gambiae, is modulated by temperature, including its influences on parasite establishment, conversion efficiency through parasite developmental stages, parasite development rate, and overall vector competence. We use these data, together with estimates of the survival of infected blood-fed mosquitoes, to explore the theoretical influence of temperature on transmission in four locations in Kenya, considering recent conditions and future climate change. Results provide insights into factors limiting transmission in cooler environments and indicate that increases in malaria transmission due to climate warming in areas like the Kenyan Highlands, might be less than previously predicted.

Two decades of malaria control in Malawi: Geostatistical Analysis of the changing malaria prevalence from 2000-2022

Background

Malaria remains a public health problem in Malawi and has a serious socio-economic impact on the population. In the past two decades, available malaria control measures have been substantially scaled up, such as insecticide-treated bed nets, artemisinin-based combination therapies, and, more recently, the introduction of the malaria vaccine, the RTS,S/AS01. In this paper, we describe the epidemiology of malaria for the last two decades to understand the past transmission and set the scene for the elimination agenda.

Methods

A collation of parasite prevalence surveys conducted between the years 2000 and 2022 was done. A spatio-temporal geostatistical model was fitted to predict the yearly malaria risk for children aged 2-10 years (PfPR 2-10) at 1×1 km spatial resolutions. Parameter estimation was done using the Monte Carlo maximum likelihood method. District-level prevalence estimates adjusted for population are calculated for the years 2000 to 2022.

Results

A total of 2,595 sampled unique locations from 2000 to 2022 were identified through the data collation exercise. This represents 70,565 individuals that were sampled in the period. In general, the PfPR2_10 declined over the 22 years. The mean modelled national PfPR2_10 in 2000 was 43.93 % (95% CI:17.9 to 73.8%) and declined to 19.2% (95%CI 7.49 to 37.0%) in 2022. The smoothened estimates of PfPR2_10 indicate that malaria prevalence is very heterogeneous with hotspot areas concentrated on the southern shores of Lake Malawi and the country's central region.

Conclusions

The last two decades are associated with a decline in malaria prevalence, highly likely associated with the scale-up of control interventions. The country should move towards targeted malaria control approaches informed by surveillance data.

Application of mathematical modelling to inform national malaria intervention planning in Nigeria

BACKGROUND

For their 2021-2025 National Malaria Strategic Plan (NMSP), Nigeria's National Malaria Elimination Programme (NMEP), in partnership with the World Health Organization (WHO), developed a targeted approach to intervention deployment at the local government area (LGA) level as part of the High Burden to High Impact response. Mathematical models of malaria transmission were used to predict the impact of proposed intervention strategies on malaria burden.

METHODS

An agent-based model of Plasmodium falciparum transmission was used to simulate malaria morbidity and mortality in Nigeria's 774 LGAs under four possible intervention strategies from 2020 to 2030. The scenarios represented the previously implemented plan (business-as-usual), the NMSP at an 80% or higher coverage level and two prioritized plans according to the resources available to Nigeria. LGAs were clustered into 22 epidemiological archetypes using monthly rainfall, temperature suitability index, vector abundance, pre-2010 parasite prevalence, and pre-2010 vector control coverage. Routine incidence data were used to parameterize seasonality in each archetype. Each LGA's baseline malaria transmission intensity was calibrated to parasite prevalence in children under the age of five years measured in the 2010 Malaria Indicator Survey (MIS). Intervention coverage in the 2010-2019 period was obtained from the Demographic and Health Survey, MIS, the NMEP, and post-campaign surveys.

RESULTS

Pursuing a business-as-usual strategy was projected to result in a 5% and 9% increase in malaria incidence in 2025 and 2030 compared with 2020, while deaths were projected to remain unchanged by 2030. The greatest intervention impact was associated with the NMSP scenario with 80% or greater coverage of standard interventions coupled with intermittent preventive treatment in infants and extension of seasonal malaria chemoprevention (SMC) to 404 LGAs, compared to 80 LGAs in 2019. The budget-prioritized scenario with SMC expansion to 310 LGAs, high bed net coverage with new formulations, and increase in effective case management rate at the same pace as historical levels was adopted as an adequate alternative for the resources available.

CONCLUSIONS

Dynamical models can be applied for relative assessment of the impact of intervention scenarios but improved subnational data collection systems are required to allow increased confidence in predictions at sub-national level.

Mapping current and future thermal limits to suitability for malaria transmission by the invasive mosquito Anopheles stephensi

BACKGROUND

Anopheles stephensi is a malaria-transmitting mosquito that has recently expanded from its primary range in Asia and the Middle East, to locations in Africa. This species is a competent vector of both Plasmodium falciparum and Plasmodium vivax malaria. Perhaps most alarming, the characteristics of An. stephensi, such as container breeding and anthropophily, make it particularly adept at exploiting built environments in areas with no prior history of malaria risk.

METHODS

In this paper, global maps of thermal transmission suitability and people at risk (PAR) for malaria transmission by An. stephensi were created, under current and future climate. Temperature-dependent transmission suitability thresholds derived from recently published species-specific thermal curves were used to threshold gridded, monthly mean temperatures under current and future climatic conditions. These temperature driven transmission models were coupled with gridded population data for 2020 and 2050, under climate-matched scenarios for future outcomes, to compare with baseline predictions for 2020 populations.

RESULTS

Using the Global Burden of Disease regions approach revealed that heterogenous regional increases and decreases in risk did not mask the overall pattern of massive increases of PAR for malaria transmission suitability with An. stephensi presence. General patterns of poleward expansion for thermal suitability were seen for both P. falciparum and P. vivax transmission potential.

CONCLUSIONS

Understanding the potential suitability for An. stephensi transmission in a changing climate provides a key tool for planning, given an ongoing invasion and expansion of the vector. Anticipating the potential impact of onward expansion to transmission suitable areas, and the size of population at risk under future climate scenarios, and where they occur, can serve as a large-scale call for attention, planning, and monitoring.

A world free of malaria: It is time for Africa to actively champion and take leadership of elimination and eradication strategies

The global burden of malaria seems unabated. Africa carries the greatest burden accounting for over 95% of the annual cases of malaria. For the vision of a world free of malaria by Global Technical Strategy to be achieved, Africa must take up the stakeholder's role. It is therefore imperative that Africa rises up to the challenge of malaria and champion the fight against it. The fight against malaria may just be a futile or mere academic venture if Africans are not directly and fully involved. This work reviews the roles playable by Africans in order to curb the malaria in Africa and the world at large.

The ecology and epidemiology of malaria parasitism in wild chimpanzee reservoirs

Chimpanzees (Pan troglodytes) harbor rich assemblages of malaria parasites, including three species closely related to P. falciparum (sub-genus Laverania), the most malignant human malaria parasite. Here, we characterize the ecology and epidemiology of malaria infection in wild chimpanzee reservoirs. We used molecular assays to screen chimpanzee fecal samples, collected longitudinally and cross-sectionally from wild populations, for malaria parasite mitochondrial DNA. We found that chimpanzee malaria parasitism has an early age of onset and varies seasonally in prevalence. A subset of samples revealed Hepatocystis mitochondrial DNA, with phylogenetic analyses suggesting that Hepatocystis appears to cross species barriers more easily than Laverania. Longitudinal and cross-sectional sampling independently support the hypothesis that mean ambient temperature drives spatiotemporal variation in chimpanzee Laverania infection. Infection probability peaked at ~24.5 °C, consistent with the empirical transmission optimum of P. falciparum in humans. Forest cover was also positively correlated with spatial variation in Laverania prevalence, consistent with the observation that forest-dwelling Anophelines are the primary vectors. Extrapolating these relationships across equatorial Africa, we map spatiotemporal variation in the suitability of chimpanzee habitat for Laverania transmission, offering a hypothetical baseline indicator of human exposure risk.

A method for improving the estimation of extreme air temperature by satellite

Air temperature (Ta) data obtained from meteorological stations were spatially discontinuous. Some satellite data have complete spatial coverage and strong relationships with Ta (e.g., elevation and land surface temperature). Therefore, Ta can be mapped using in situ Ta and satellite data. However, this method may have a large bias when estimating the extreme Ta. In this study, the error prediction and correction (EPC) method, incorporating Cubist machine learning algorithm, was proposed to improve the estimation of extreme Ta. The accuracy of the EPC method was compared with that of the widely used method in previous studies in east China from 2003 to 2012. The mean absolute errors (MAEs) of the estimated daily Ta using the EPC method ranged from 0.75-1.01 °C, which were 0.57-0.96 °C lower than that of the method in the literature. The biases of the estimated Ta obtained using the two methods were close to zero. However, the biases can be as high as 7.10 °C when Ta is extremely low and as low as -3.09 °C when Ta is extremely high. Compared with the method in the literature, the EPC method can reduce the MAE by 1.41 °C, root mean square error by 1.49 °C, and bias by 1.61 °C of the estimated extreme Ta. Additionally, the EPC method produced satisfactory accuracy (MAEs <0.9 °C) of the estimated heat and cold wave magnitudes. Finally, a 1 km resolution daily Ta map in east China from 2003 to 2012 was developed, which will be useful data in multiple research fields.

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.

Exploring agricultural land-use and childhood malaria associations in sub-Saharan Africa

Agriculture in Africa is rapidly expanding but with this comes potential disbenefits for the environment and human health. Here, we retrospectively assess whether childhood malaria in sub-Saharan Africa varies across differing agricultural land uses after controlling for socio-economic and environmental confounders. Using a multi-model inference hierarchical modelling framework, we found that rainfed cropland was associated with increased malaria in rural (OR 1.10, CI 1.03-1.18) but not urban areas, while irrigated or post flooding cropland was associated with malaria in urban (OR 1.09, CI 1.00-1.18) but not rural areas. In contrast, although malaria was associated with complete forest cover (OR 1.35, CI 1.24-1.47), the presence of natural vegetation in agricultural lands potentially reduces the odds of malaria depending on rural-urban context. In contrast, no associations with malaria were observed for natural vegetation interspersed with cropland (veg-dominant mosaic). Agricultural expansion through rainfed or irrigated cropland may increase childhood malaria in rural or urban contexts in sub-Saharan Africa but retaining some natural vegetation within croplands could help mitigate this risk and provide environmental co-benefits.

Five-Year Trend Analysis of Malaria Cases in East Shawa Zone, Ethiopia

BACKGROUND

Malaria is an infectious disease caused by Plasmodium parasites. Of the five human malaria parasites Plasmodium falciparum and Plasmodium vivax are the two co-endemic predominant and widely distributed species in Ethiopia, with major public health importance. Even though enormous effort has been made countrywide to reduce the disease burden little was reported about trends of malaria transmission in the several localities of malarious areas like East Shawa Zone, Ethiopia. Thus, the present study was aimed at assessing fiveyear (2016-2020) trends of malaria transmission at Adama, Boset and Lume districts of East Shawa Zone of Oromia Regional State, Ethiopia.

METHODS

Retrospective data was extracted from the central surveillance database of East Shawa Zone Health Office. The data collected was analyzed from September 2020 to December 2020 to examine trends of malaria epidemiology in three malarious districts in the Zone.

RESULTS

The results of the present study showed a remarkable decrease in slide positivity rate (SPR) from 16.3 to 1.4% from 2016 to 2018 in the areas. However, a recent slight increase of malaria SPR was observed. On the other hand, as age increases more male individuals were infected with malaria compared to female of similar age groups. Falciparum, vivax and mixed malaria infection accounted for 53%, 41% and 6% respectively.

CONCLUSIONS

Even though, an overall reduction of malaria incidence was revealed in the study areas, an increase in malaria SPR was observed in 2019 and 2020. Such inconsistency in reduction of malaria cases in the study area demands due attention of health planners.

A simulation study of disaggregation regression for spatial disease mapping

Disaggregation regression has become an important tool in spatial disease mapping for making fine-scale predictions of disease risk from aggregated response data. By including high resolution covariate information and modeling the data generating process on a fine scale, it is hoped that these models can accurately learn the relationships between covariates and response at a fine spatial scale. However, validating these high resolution predictions can be a challenge, as often there is no data observed at this spatial scale. In this study, disaggregation regression was performed on simulated data in various settings and the resulting fine-scale predictions are compared to the simulated ground truth. Performance was investigated with varying numbers of data points, sizes of aggregated areas and levels of model misspecification. The effectiveness of cross validation on the aggregate level as a measure of fine-scale predictive performance was also investigated. Predictive performance improved as the number of observations increased and as the size of the aggregated areas decreased. When the model was well-specified, fine-scale predictions were accurate even with small numbers of observations and large aggregated areas. Under model misspecification predictive performance was significantly worse for large aggregated areas but remained high when response data was aggregated over smaller regions. Cross-validation correlation on the aggregate level was a moderately good predictor of fine-scale predictive performance. While these simulations are unlikely to capture the nuances of real-life response data, this study gives insight into the effectiveness of disaggregation regression in different contexts.

Major climate change-induced risks to human health in South Africa

There are many climatic changes facing South Africa which already have, or are projected to have, a detrimental impact on human health. Here the risks to health due to several alterations in the climate of South Africa are considered in turn. These include an increase in ambient temperature, causing, for example, a significant rise in morbidity and mortality; heavy rainfall leading to changes in the prevalence and occurrence of vector-borne diseases; drought-associated malnutrition; and exposure to dust storms and air pollution leading to the potential exacerbation of respiratory diseases. Existing initiatives and strategies to prevent or reduce these adverse health impacts are outlined, together with suggestions of what might be required in the future to safeguard the health of the nation. Potential roles for the health and non-health sectors as well as preparedness and capacity development with respect to climate change and health adaptation are considered.

Indirect effects of the COVID-19 pandemic on malaria intervention coverage, morbidity, and mortality in Africa: a geospatial modelling analysis

BACKGROUND

Substantial progress has been made in reducing the burden of malaria in Africa since 2000, but those gains could be jeopardised if the COVID-19 pandemic affects the availability of key malaria control interventions. The aim of this study was to evaluate plausible effects on malaria incidence and mortality under different levels of disruption to malaria control.

METHODS

Using an established set of spatiotemporal Bayesian geostatistical models, we generated geospatial estimates across malaria-endemic African countries of the clinical case incidence and mortality of malaria, incorporating an updated database of parasite rate surveys, insecticide-treated net (ITN) coverage, and effective treatment rates. We established a baseline estimate for the anticipated malaria burden in Africa in the absence of COVID-19-related disruptions, and repeated the analysis for nine hypothetical scenarios in which effective treatment with an antimalarial drug and distribution of ITNs (both through routine channels and mass campaigns) were reduced to varying extents.

FINDINGS

We estimated 215·2 (95% uncertainty interval 143·7-311·6) million cases and 386·4 (307·8-497·8) thousand deaths across malaria-endemic African countries in 2020 in our baseline scenario of undisrupted intervention coverage. With greater reductions in access to effective antimalarial drug treatment, our model predicted increasing numbers of cases and deaths: 224·1 (148·7-326·8) million cases and 487·9 (385·3-634·6) thousand deaths with a 25% reduction in antimalarial drug coverage; 233·1 (153·7-342·5) million cases and 597·4 (468·0-784·4) thousand deaths with a 50% reduction; and 242·3 (158·7-358·8) million cases and 715·2 (556·4-947·9) thousand deaths with a 75% reduction. Halting planned 2020 ITN mass distribution campaigns and reducing routine ITN distributions by 25%-75% also increased malaria burden to a total of 230·5 (151·6-343·3) million cases and 411·7 (322·8-545·5) thousand deaths with a 25% reduction; 232·8 (152·3-345·9) million cases and 415·5 (324·3-549·4) thousand deaths with a 50% reduction; and 234·0 (152·9-348·4) million cases and 417·6 (325·5-553·1) thousand deaths with a 75% reduction. When ITN coverage and antimalarial drug coverage were synchronously reduced, malaria burden increased to 240·5 (156·5-358·2) million cases and 520·9 (404·1-691·9) thousand deaths with a 25% reduction; 251·0 (162·2-377·0) million cases and 640·2 (492·0-856·7) thousand deaths with a 50% reduction; and 261·6 (167·7-396·8) million cases and 768·6 (586·1-1038·7) thousand deaths with a 75% reduction.

INTERPRETATION

Under pessimistic scenarios, COVID-19-related disruption to malaria control in Africa could almost double malaria mortality in 2020, and potentially lead to even greater increases in subsequent years. To avoid a reversal of two decades of progress against malaria, averting this public health disaster must remain an integrated priority alongside the response to COVID-19.

FUNDING

Bill and Melinda Gates Foundation; Channel 7 Telethon Trust, Western Australia.

Spatiotemporal mapping of malaria prevalence in Madagascar using routine surveillance and health survey data

Malaria transmission in Madagascar is highly heterogeneous, exhibiting spatial, seasonal and long-term trends. Previous efforts to map malaria risk in Madagascar used prevalence data from Malaria Indicator Surveys. These cross-sectional surveys, conducted during the high transmission season most recently in 2013 and 2016, provide nationally representative prevalence data but cover relatively short time frames. Conversely, monthly case data are collected at health facilities but suffer from biases, including incomplete reporting and low rates of treatment seeking. We combined survey and case data to make monthly maps of prevalence between 2013 and 2016. Health facility catchment populations were estimated to produce incidence rates from the case data. Smoothed incidence surfaces, environmental and socioeconomic covariates, and survey data informed a Bayesian prevalence model, in which a flexible incidence-to-prevalence relationship was learned. Modelled spatial trends were consistent over time, with highest prevalence in the coastal regions and low prevalence in the highlands and desert south. Prevalence was lowest in 2014 and peaked in 2015 and seasonality was widely observed, including in some lower transmission regions. These trends highlight the utility of monthly prevalence estimates over the four year period. By combining survey and case data using this two-step modelling approach, we were able to take advantage of the relative strengths of each metric while accounting for potential bias in the case data. Similar modelling approaches combining large datasets of different malaria metrics may be applicable across sub-Saharan Africa.

Global estimation of anti-malarial drug effectiveness for the treatment of uncomplicated Plasmodium falciparum malaria 1991-2019

BACKGROUND

Anti-malarial drugs play a critical role in reducing malaria morbidity and mortality, but their role is mediated by their effectiveness. Effectiveness is defined as the probability that an anti-malarial drug will successfully treat an individual infected with malaria parasites under routine health care delivery system. Anti-malarial drug effectiveness (AmE) is influenced by drug resistance, drug quality, health system quality, and patient adherence to drug use; its influence on malaria burden varies through space and time.

METHODS

This study uses data from 232 efficacy trials comprised of 86,776 infected individuals to estimate the artemisinin-based and non-artemisinin-based AmE for treating falciparum malaria between 1991 and 2019. Bayesian spatiotemporal models were fitted and used to predict effectiveness at the pixel-level (5 km × 5 km). The median and interquartile ranges (IQR) of AmE are presented for all malaria-endemic countries.

RESULTS

The global effectiveness of artemisinin-based drugs was 67.4% (IQR: 33.3-75.8), 70.1% (43.6-76.0) and 71.8% (46.9-76.4) for the 1991-2000, 2006-2010, and 2016-2019 periods, respectively. Countries in central Africa, a few in South America, and in the Asian region faced the challenge of lower effectiveness of artemisinin-based anti-malarials. However, improvements were seen after 2016, leaving only a few hotspots in Southeast Asia where resistance to artemisinin and partner drugs is currently problematic and in the central Africa where socio-demographic challenges limit effectiveness. The use of artemisinin-based combination therapy (ACT) with a competent partner drug and having multiple ACT as first-line treatment choice sustained high levels of effectiveness. High levels of access to healthcare, human resource capacity, education, and proximity to cities were associated with increased effectiveness. Effectiveness of non-artemisinin-based drugs was much lower than that of artemisinin-based with no improvement over time: 52.3% (17.9-74.9) for 1991-2000 and 55.5% (27.1-73.4) for 2011-2015. Overall, AmE for artemisinin-based and non-artemisinin-based drugs were, respectively, 29.6 and 36% below clinical efficacy as measured in anti-malarial drug trials.

CONCLUSIONS

This study provides evidence that health system performance, drug quality and patient adherence influence the effectiveness of anti-malarials used in treating uncomplicated falciparum malaria. These results provide guidance to countries' treatment practises and are critical inputs for malaria prevalence and incidence models used to estimate national level malaria burden.

Modelling the impact of climate change on the distribution and abundance of tsetse in Northern Zimbabwe

Background

Climate change is predicted to impact the transmission dynamics of vector-borne diseases. Tsetse flies (Glossina) transmit species of Trypanosoma that cause human and animal African trypanosomiasis. A previous modelling study showed that temperature increases between 1990 and 2017 can explain the observed decline in abundance of tsetse at a single site in the Mana Pools National Park of Zimbabwe. Here, we apply a mechanistic model of tsetse population dynamics to predict how increases in temperature may have changed the distribution and relative abundance of Glossina pallidipes across northern Zimbabwe.

Methods

Local weather station temperature measurements were previously used to fit the mechanistic model to longitudinal G. pallidipes catch data. To extend the use of the model, we converted MODIS land surface temperature to air temperature, compared the converted temperatures with available weather station data to confirm they aligned, and then re-fitted the mechanistic model using G. pallidipes catch data and air temperature estimates. We projected this fitted model across northern Zimbabwe, using simulations at a 1 km × 1 km spatial resolution, between 2000 to 2016.

Results

We produced estimates of relative changes in G. pallidipes mortality, larviposition, emergence rates and abundance, for northern Zimbabwe. Our model predicts decreasing tsetse populations within low elevation areas in response to increasing temperature trends during 2000–2016. Conversely, we show that high elevation areas (> 1000 m above sea level), previously considered too cold to sustain tsetse, may now be climatically suitable.

Conclusions

To our knowledge, the results of this research represent the first regional-scale assessment of temperature related tsetse population dynamics, and the first high spatial-resolution estimates of this metric for northern Zimbabwe. Our results suggest that tsetse abundance may have declined across much of the Zambezi Valley in response to changing climatic conditions during the study period. Future research including empirical studies is planned to improve model accuracy and validate predictions for other field sites in Zimbabwe.

Environmental temperature and growth faltering in African children: a cross-sectional study

BACKGROUND

Child growth faltering persists in sub-Saharan Africa despite the scale-up of nutrition, water, and sanitation interventions over the past 2 decades. High temperatures have been hypothesised to contribute to child growth faltering via an adaptive response to heat, reduced appetite, and the energetic cost of thermoregulation. We did a cross-sectional study to assess whether child growth faltering is related to environmental temperature in sub-Saharan Africa.

METHODS

Data were extracted from 52 Demographic and Heath Surveys, dating from 2003 to 2016, that recorded anthropometric data in children aged 0-5 years, and were linked with remotely sensed monthly mean daytime land surface temperature for 2000-16. The odds of stunting (low height-for-age), wasting (low weight-for-height), and underweight (low weight-for-age) relative to monthly mean daytime land surface temperature were determined using multivariable logistic regression.

FINDINGS

The study population comprised 656 107 children resident in 373 012 households. Monthly mean daytime land surface temperature above 35°C was associated with increases in the odds of wasting (odds ratio 1·27, 95% CI 1·16-1·38; p<0·0001), underweight (1·09, 1·02-1·16; p=0·0073), and concurrent stunting with wasting (1·23, 1·07-1·41; p=0·0037), but a reduction in stunting (0·90, 0·85-0·96; p=0·00047) compared with a monthly mean daytime land surface temperature of less than 30°C.

INTERPRETATION

Children living in hotter parts of sub-Saharan Africa are more likely to be wasted, underweight, and concurrently stunted and wasted, but less likely to be stunted, than in cooler areas. Studies are needed to further investigate the relationship between temperature and child growth, including whether there is a direct effect not mediated by food security, regional wealth, and other environmental variables. Rising temperature, linked to anthropogenic climate change, might increase child growth faltering in sub-Saharan Africa.

FUNDING

UK Medical Research Council and UK Global Challenges Research Fund.

Rainfall Trends and Malaria Occurrences in Limpopo Province, South Africa

This contribution aims to investigate the influence of monthly total rainfall variations on malaria transmission in the Limpopo Province. For this purpose, monthly total rainfall was interpolated from daily rainfall data from weather stations. Annual and seasonal trends, as well as cross-correlation analyses, were performed on time series of monthly total rainfall and monthly malaria cases in five districts of Limpopo Province for the period of 1998 to 2017. The time series analysis indicated that an average of 629.5 mm of rainfall was received over the period of study. The rainfall has an annual variation of about 0.46%. Rainfall amount varied within the five districts, with the northeastern part receiving more rainfall. Spearman's correlation analysis indicated that the total monthly rainfall with one to two months lagged effect is significant in malaria transmission across all the districts. The strongest correlation was noticed in Vhembe (r = 0.54; p-value = <0.001), Mopani (r = 0.53; p-value = <0.001), Waterberg (r = 0.40; p-value =< 0.001), Capricorn (r = 0.37; p-value = <0.001) and lowest in Sekhukhune (r = 0.36; p-value = <0.001). Seasonally, the results indicated that about 68% variation in malaria cases in summer-December, January, and February (DJF)-can be explained by spring-September, October, and November (SON)-rainfall in Vhembe district. Both annual and seasonal analyses indicated that there is variation in the effect of rainfall on malaria across the districts and it is seasonally dependent. Understanding the dynamics of climatic variables annually and seasonally is essential in providing answers to malaria transmission among other factors, particularly with respect to the abrupt spikes of the disease in the province.

Satellite Earth Observation Data in Epidemiological Modeling of Malaria, Dengue and West Nile Virus: A Scoping Review

Earth Observation (EO) data can be leveraged to estimate environmental variables that influence the transmission cycle of the pathogens that lead to mosquito-borne diseases (MBDs). The aim of this scoping review is to examine the state-of-the-art and identify knowledge gaps on the latest methods that used satellite EO data in their epidemiological models focusing on malaria, dengue and West Nile Virus (WNV). In total, 43 scientific papers met the inclusion criteria and were considered in this review. Researchers have examined a wide variety of methodologies ranging from statistical to machine learning algorithms. A number of studies used models and EO data that seemed promising and claimed to be easily replicated in different geographic contexts, enabling the realization of systems on regional and national scales. The need has emerged to leverage furthermore new powerful modeling approaches, like artificial intelligence and ensemble modeling and explore new and enhanced EO sensors towards the analysis of big satellite data, in order to develop accurate epidemiological models and contribute to the reduction of the burden of MBDs.

Mapping the global prevalence, incidence, and mortality of Plasmodium falciparum, 2000-17: a spatial and temporal modelling study

BACKGROUND

Since 2000, the scale-up of malaria control interventions has substantially reduced morbidity and mortality caused by the disease globally, fuelling bold aims for disease elimination. In tandem with increased availability of geospatially resolved data, malaria control programmes increasingly use high-resolution maps to characterise spatially heterogeneous patterns of disease risk and thus efficiently target areas of high burden.

METHODS

We updated and refined the Plasmodium falciparum parasite rate and clinical incidence models for sub-Saharan Africa, which rely on cross-sectional survey data for parasite rate and intervention coverage. For malaria endemic countries outside of sub-Saharan Africa, we produced estimates of parasite rate and incidence by applying an ecological downscaling approach to malaria incidence data acquired via routine surveillance. Mortality estimates were derived by linking incidence to systematically derived vital registration and verbal autopsy data. Informed by high-resolution covariate surfaces, we estimated P falciparum parasite rate, clinical incidence, and mortality at national, subnational, and 5 × 5 km pixel scales with corresponding uncertainty metrics.

FINDINGS

We present the first global, high-resolution map of P falciparum malaria mortality and the first global prevalence and incidence maps since 2010. These results are combined with those for Plasmodium vivax (published separately) to form the malaria estimates for the Global Burden of Disease 2017 study. The P falciparum estimates span the period 2000-17, and illustrate the rapid decline in burden between 2005 and 2017, with incidence declining by 27·9% and mortality declining by 42·5%. Despite a growing population in endemic regions, P falciparum cases declined between 2005 and 2017, from 232·3 million (95% uncertainty interval 198·8-277·7) to 193·9 million (156·6-240·2) and deaths declined from 925 800 (596 900-1 341 100) to 618 700 (368 600-952 200). Despite the declines in burden, 90·1% of people within sub-Saharan Africa continue to reside in endemic areas, and this region accounted for 79·4% of cases and 87·6% of deaths in 2017.

INTERPRETATION

High-resolution maps of P falciparum provide a contemporary resource for informing global policy and malaria control planning, programme implementation, and monitoring initiatives. Amid progress in reducing global malaria burden, areas where incidence trends have plateaued or increased in the past 5 years underscore the fragility of hard-won gains against malaria. Efforts towards elimination should be strengthened in such areas, and those where burden remained high throughout the study period.

FUNDING

Bill & Melinda Gates Foundation.

Malaria smear positivity among Kenyan children peaks at intermediate temperatures as predicted by ecological models

Background

Ambient temperature is an important determinant of malaria transmission and suitability, affecting the life-cycle of the Plasmodium parasite and Anopheles vector. Early models predicted a thermal malaria transmission optimum of 31 °C, later revised to 25 °C using experimental data from mosquito and parasite biology. However, the link between ambient temperature and human malaria incidence remains poorly resolved.

Methods

To evaluate the relationship between ambient temperature and malaria risk, 5833 febrile children (<18 years-old) with an acute, non-localizing febrile illness were enrolled from four heterogenous outpatient clinic sites in Kenya (Chulaimbo, Kisumu, Msambweni and Ukunda). Thick and thin blood smears were evaluated for the presence of malaria parasites. Daily temperature estimates were obtained from land logger data, and rainfall from National Oceanic and Atmospheric Administration (NOAA)’s Africa Rainfall Climatology (ARC) data. Thirty-day mean temperature and 30-day cumulative rainfall were estimated and each lagged by 30 days, relative to the febrile visit. A generalized linear mixed model was used to assess relationships between malaria smear positivity and predictors including temperature, rainfall, age, sex, mosquito exposure and socioeconomic status.

Results

Malaria smear positivity varied between 42–83% across four clinic sites in western and coastal Kenya, with highest smear positivity in the rural, western site. The temperature ranges were cooler in the western sites and warmer in the coastal sites. In multivariate analysis controlling for socioeconomic status, age, sex, rainfall and bednet use, malaria smear positivity peaked near 25 °C at all four sites, as predicted a priori from an ecological model.

Conclusions

This study provides direct field evidence of a unimodal relationship between ambient temperature and human malaria incidence with a peak in malaria transmission occurring at lower temperatures than previously recognized clinically. This nonlinear relationship with an intermediate optimal temperature implies that future climate warming could expand malaria incidence in cooler, highland regions while decreasing incidence in already warm regions with average temperatures above 25 °C. These findings support efforts to further understand the nonlinear association between ambient temperature and vector-borne diseases to better allocate resources and respond to disease threats in a future, warmer world.

Electronic supplementary material

The online version of this article (10.1186/s13071-019-3547-z) contains supplementary material, which is available to authorized users.

A Dynamical and Zero-Inflated Negative Binomial Regression Modelling of Malaria Incidence in Limpopo Province, South Africa

Recent studies have considered the connections between malaria incidence and climate variables using mathematical and statistical models. Some of the statistical models focused on time series approach based on Box-Jenkins methodology or on dynamic model. The latter approach allows for covariates different from its original lagged values, while the Box-Jenkins does not. In real situations, malaria incidence counts may turn up with many zero terms in the time series. Fitting time series model based on the Box-Jenkins approach and ARIMA may be spurious. In this study, a zero-inflated negative binomial regression model was formulated for fitting malaria incidence in Mopani and Vhembe-two of the epidemic district municipalities in Limpopo, South Africa. In particular, a zero-inflated negative binomial regression model was formulated for daily malaria counts as a function of some climate variables, with the aim of identifying the model that best predicts reported malaria cases. Results from this study show that daily rainfall amount and the average temperature at various lags have a significant influence on malaria incidence in the study areas. The significance of zero inflation on the malaria count was examined using the Vuong test and the result shows that zero-inflated negative binomial regression model fits the data better. A dynamical climate-based model was further used to investigate the population dynamics of mosquitoes over the two regions. Findings highlight the significant roles of Anopheles arabiensis on malaria transmission over the regions and suggest that vector control activities should be intense to eradicate malaria in Mopani and Vhembe districts. Although An. arabiensis has been identified as the major vector over these regions, our findings further suggest the presence of additional vectors transmitting malaria in the study regions. The findings from this study offer insight into climate-malaria incidence linkages over Limpopo province of South Africa.

Human mobility patterns and malaria importation on Bioko Island

Malaria burden on Bioko Island has decreased significantly over the past 15 years. The impact of interventions on malaria prevalence, however, has recently stalled. Here, we use data from island-wide, annual malaria indicator surveys to investigate human movement patterns and their relationship to Plasmodium falciparum prevalence. Using geostatistical and mathematical modelling, we find that off-island travel is more prevalent in and around the capital, Malabo. The odds of malaria infection among off-island travelers are significantly higher than the rest of the population. We estimate that malaria importation rates are high enough to explain malaria prevalence in much of Malabo and its surroundings, and that local transmission is highest along the West Coast of the island. Despite uncertainty, these estimates of residual transmission and importation serve as a basis for evaluating progress towards elimination and for efficiently allocating resources as Bioko makes the transition from control to elimination.

Use of earth observations for temperature exposure assessment in epidemiological studies

PURPOSE OF REVIEW

Low, high, extreme, and variable temperatures have been linked to multiple adverse health outcomes, particularly among the elderly and children. Recent models incorporating satellite remote sensing data have mitigated several limitations of previous studies, improving exposure assessment. This review focuses on these new temperature exposure models and their application in epidemiological studies.

RECENT FINDINGS

Satellite observations of land surface temperature have been used to model air temperature across large spatial areas at high spatiotemporal resolutions. These models enable exposure assessment of entire populations and have been shown to reduce error in exposure estimates, thus mitigating downward bias in health effect estimates.

SUMMARY

Satellite-based models improve our understanding of spatiotemporal variation in temperature and the associated health effects. Further research should focus on improving the resolution of these models, especially in urban areas, and increasing their use in epidemiological studies of direct temperature exposure and vector-borne diseases.

Predictive analysis across spatial scales links zoonotic malaria to deforestation

The complex transmission ecologies of vector-borne and zoonotic diseases pose challenges to their control, especially in changing landscapes. Human incidence of zoonotic malaria ( Plasmodium knowlesi) is associated with deforestation although mechanisms are unknown. Here, a novel application of a method for predicting disease occurrence that combines machine learning and statistics is used to identify the key spatial scales that define the relationship between zoonotic malaria cases and environmental change. Using data from satellite imagery, a case-control study, and a cross-sectional survey, predictive models of household-level occurrence of P. knowlesi were fitted with 16 variables summarized at 11 spatial scales simultaneously. The method identified a strong and well-defined peak of predictive influence of the proportion of cleared land within 1 km of households on P. knowlesi occurrence. Aspect (1 and 2 km), slope (0.5 km) and canopy regrowth (0.5 km) were important at small scales. By contrast, fragmentation of deforested areas influenced P. knowlesi occurrence probability most strongly at large scales (4 and 5 km). The identification of these spatial scales narrows the field of plausible mechanisms that connect land use change and P. knowlesi, allowing for the refinement of disease occurrence predictions and the design of spatially-targeted interventions.

Spatial mapping with Gaussian processes and nonstationary Fourier features

The use of covariance kernels is ubiquitous in the field of spatial statistics. Kernels allow data to be mapped into high-dimensional feature spaces and can thus extend simple linear additive methods to nonlinear methods with higher order interactions. However, until recently, there has been a strong reliance on a limited class of stationary kernels such as the Matérn or squared exponential, limiting the expressiveness of these modelling approaches. Recent machine learning research has focused on spectral representations to model arbitrary stationary kernels and introduced more general representations that include classes of nonstationary kernels. In this paper, we exploit the connections between Fourier feature representations, Gaussian processes and neural networks to generalise previous approaches and develop a simple and efficient framework to learn arbitrarily complex nonstationary kernel functions directly from the data, while taking care to avoid overfitting using state-of-the-art methods from deep learning. We highlight the very broad array of kernel classes that could be created within this framework. We apply this to a time series dataset and a remote sensing problem involving land surface temperature in Eastern Africa. We show that without increasing the computational or storage complexity, nonstationary kernels can be used to improve generalisation performance and provide more interpretable results.

Spatio-temporal mapping of Madagascar's Malaria Indicator Survey results to assess Plasmodium falciparum endemicity trends between 2011 and 2016

BACKGROUND

Reliable measures of disease burden over time are necessary to evaluate the impact of interventions and assess sub-national trends in the distribution of infection. Three Malaria Indicator Surveys (MISs) have been conducted in Madagascar since 2011. They provide a valuable resource to assess changes in burden that is complementary to the country's routine case reporting system.

METHODS

A Bayesian geostatistical spatio-temporal model was developed in an integrated nested Laplace approximation framework to map the prevalence of Plasmodium falciparum malaria infection among children from 6 to 59 months in age across Madagascar for 2011, 2013 and 2016 based on the MIS datasets. The model was informed by a suite of environmental and socio-demographic covariates known to influence infection prevalence. Spatio-temporal trends were quantified across the country.

RESULTS

Despite a relatively small decrease between 2013 and 2016, the prevalence of malaria infection has increased substantially in all areas of Madagascar since 2011. In 2011, almost half (42.3%) of the country's population lived in areas of very low malaria risk (<1% parasite prevalence), but by 2016, this had dropped to only 26.7% of the population. Meanwhile, the population in high transmission areas (prevalence >20%) increased from only 2.2% in 2011 to 9.2% in 2016. A comparison of the model-based estimates with the raw MIS results indicates there was an underestimation of the situation in 2016, since the raw figures likely associated with survey timings were delayed until after the peak transmission season.

CONCLUSIONS

Malaria remains an important health problem in Madagascar. The monthly and annual prevalence maps developed here provide a way to evaluate the magnitude of change over time, taking into account variability in survey input data. These methods can contribute to monitoring sub-national trends of malaria prevalence in Madagascar as the country aims for geographically progressive elimination.

Wild bonobos host geographically restricted malaria parasites including a putative new Laverania species

Malaria parasites, though widespread among wild chimpanzees and gorillas, have not been detected in bonobos. Here, we show that wild-living bonobos are endemically Plasmodium infected in the eastern-most part of their range. Testing 1556 faecal samples from 11 field sites, we identify high prevalence Laverania infections in the Tshuapa-Lomami-Lualaba (TL2) area, but not at other locations across the Congo. TL2 bonobos harbour P. gaboni, formerly only found in chimpanzees, as well as a potential new species, Plasmodium lomamiensis sp. nov. Rare co-infections with non-Laverania parasites were also observed. Phylogenetic relationships among Laverania species are consistent with co-divergence with their gorilla, chimpanzee and bonobo hosts, suggesting a timescale for their evolution. The absence of Plasmodium from most field sites could not be explained by parasite seasonality, nor by bonobo population structure, diet or gut microbiota. Thus, the geographic restriction of bonobo Plasmodium reflects still unidentified factors that likely influence parasite transmission.

Climatic Variables and Malaria Morbidity in Mutale Local Municipality, South Africa: A 19-Year Data Analysis

The north-eastern parts of South Africa, comprising the Limpopo Province, have recorded a sudden rise in the rate of malaria morbidity and mortality in the 2017 malaria season. The epidemiological profiles of malaria, as well as other vector-borne diseases, are strongly associated with climate and environmental conditions. A retrospective understanding of the relationship between climate and the occurrence of malaria may provide insight into the dynamics of the disease’s transmission and its persistence in the north-eastern region. In this paper, the association between climatic variables and the occurrence of malaria was studied in the Mutale local municipality in South Africa over a period of 19-year. Time series analysis was conducted on monthly climatic variables and monthly malaria cases in the Mutale municipality for the period of 1998–2017. Spearman correlation analysis was performed and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model was developed. Microsoft Excel was used for data cleaning, and statistical software R was used to analyse the data and develop the model. Results show that both climatic variables’ and malaria cases’ time series exhibited seasonal patterns, showing a number of peaks and fluctuations. Spearman correlation analysis indicated that monthly total rainfall, mean minimum temperature, mean maximum temperature, mean average temperature, and mean relative humidity were significantly and positively correlated with monthly malaria cases in the study area. Regression analysis showed that monthly total rainfall and monthly mean minimum temperature (R² = 0.65), at a two-month lagged effect, are the most significant climatic predictors of malaria transmission in Mutale local municipality. A SARIMA (2,1,2) (1,1,1) model fitted with only malaria cases has a prediction performance of about 51%, and the SARIMAX (2,1,2) (1,1,1) model with climatic variables as exogenous factors has a prediction performance of about 72% in malaria cases. The model gives a close comparison between the predicted and observed number of malaria cases, hence indicating that the model provides an acceptable fit to predict the number of malaria cases in the municipality. To sum up, the association between the climatic variables and malaria cases provides clues to better understand the dynamics of malaria transmission. The lagged effect detected in this study can help in adequate planning for malaria intervention.

Quantifying the contribution of Plasmodium falciparum malaria to febrile illness amongst African children

Suspected malaria cases in Africa increasingly receive a rapid diagnostic test (RDT) before antimalarials are prescribed. While this ensures efficient use of resources to clear parasites, the underlying cause of the individual's fever remains unknown due to potential coinfection with a non-malarial febrile illness. Widespread use of RDTs does not necessarily prevent over-estimation of clinical malaria cases or sub-optimal case management of febrile patients. We present a new approach that allows inference of the spatiotemporal prevalence of both Plasmodium falciparum malaria-attributable and non-malarial fever in sub-Saharan African children from 2006 to 2014. We estimate that 35.7% of all self-reported fevers were accompanied by a malaria infection in 2014, but that only 28.0% of those (10.0% of all fevers) were causally attributable to malaria. Most fevers among malaria-positive children are therefore caused by non-malaria illnesses. This refined understanding can help improve interpretation of the burden of febrile illness and shape policy on fever case management.

Improved prediction accuracy for disease risk mapping using Gaussian process stacked generalization

Maps of infectious disease-charting spatial variations in the force of infection, degree of endemicity and the burden on human health-provide an essential evidence base to support planning towards global health targets. Contemporary disease mapping efforts have embraced statistical modelling approaches to properly acknowledge uncertainties in both the available measurements and their spatial interpolation. The most common such approach is Gaussian process regression, a mathematical framework composed of two components: a mean function harnessing the predictive power of multiple independent variables, and a covariance function yielding spatio-temporal shrinkage against residual variation from the mean. Though many techniques have been developed to improve the flexibility and fitting of the covariance function, models for the mean function have typically been restricted to simple linear terms. For infectious diseases, known to be driven by complex interactions between environmental and socio-economic factors, improved modelling of the mean function can greatly boost predictive power. Here, we present an ensemble approach based on stacked generalization that allows for multiple nonlinear algorithmic mean functions to be jointly embedded within the Gaussian process framework. We apply this method to mapping Plasmodium falciparum prevalence data in sub-Saharan Africa and show that the generalized ensemble approach markedly outperforms any individual method.

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.

Seasonal Population Movements and the Surveillance and Control of Infectious Diseases

National policies designed to control infectious diseases should allocate resources for interventions based on regional estimates of disease burden from surveillance systems. For many infectious diseases, however, there is pronounced seasonal variation in incidence. Policy-makers must routinely manage a public health response to these seasonal fluctuations with limited understanding of their underlying causes. Two complementary and poorly described drivers of seasonal disease incidence are the mobility and aggregation of human populations, which spark outbreaks and sustain transmission, respectively, and may both exhibit distinct seasonal variations. Here we highlight the key challenges that seasonal migration creates when monitoring and controlling infectious diseases. We discuss the potential of new data sources in accounting for seasonal population movements in dynamic risk mapping strategies.

Contemporary epidemiological overview of malaria in Madagascar: operational utility of reported routine case data for malaria control planning

BACKGROUND

Malaria remains a major public health problem in Madagascar. Widespread scale-up of intervention coverage has led to substantial reductions in case numbers since 2000. However, political instability since 2009 has disrupted these efforts, and a resurgence of malaria has since followed. This paper re-visits the sub-national stratification of malaria transmission across Madagascar to propose a contemporary update, and evaluates the reported routine case data reported at this sub-national scale.

METHODS

Two independent malariometrics were evaluated to re-examine the status of malaria across Madagascar. First, modelled maps of Plasmodium falciparum infection prevalence (PfPR) from the Malaria Atlas Project were used to update the sub-national stratification into 'ecozones' based on transmission intensity. Second, routine reports of case data from health facilities were synthesized from 2010 to 2015 to compare the sub-national epidemiology across the updated ecozones over time. Proxy indicators of data completeness are investigated.

RESULTS

The epidemiology of malaria is highly diverse across the island's ecological regions, with eight contiguous ecozones emerging from the transmission intensity PfPR map. East and west coastal areas have highest transmission year-round, contrasting with the central highlands and desert south where trends appear more closely associated with epidemic outbreak events. Ecozones have shown steady increases in reported malaria cases since 2010, with a near doubling of raw reported case numbers from 2014 to 2015. Gauges of data completeness suggest that interpretation of raw reported case numbers will underestimate true caseload as only approximately 60-75 % of health facility data are reported to the central level each month.

DISCUSSION

A sub-national perspective is essential when monitoring the epidemiology of malaria in Madagascar and assessing local control needs. A robust assessment of the status of malaria at a time when intervention coverage efforts are being scaled up provides a platform from which to guide intervention preparedness and assess change in future periods of transmission.

The relative contribution of climate variability and vector control coverage to changes in malaria parasite prevalence in Zambia 2006-2012

Background

Four malaria indicator surveys (MIS) were conducted in Zambia between 2006 and 2012 to evaluate malaria control scale-up. Nationally, coverage of insecticide-treated nets (ITNs) and indoor residual spraying (IRS) increased over this period, while parasite prevalence in children 1–59 months decreased dramatically between 2006 and 2008, but then increased from 2008 to 2010. We assessed the relative effects of vector control coverage and climate variability on malaria parasite prevalence over this period.

Methods

Nationally-representative MISs were conducted in April-June of 2006, 2008, 2010 and 2012 to collect household-level information on malaria control interventions such as IRS, ITN ownership and use, and child parasite prevalence by microscopic examination of blood smears. We fitted Bayesian geostatistical models to assess the association between IRS and ITN coverage and climate variability and malaria parasite prevalence. We created predictions of the spatial distribution of malaria prevalence at each time point and compared results of varying IRS, ITN, and climate inputs to assess their relative contributions to changes in prevalence.

Results

Nationally, the proportion of households owning an ITN increased from 37.8 % in 2006 to 64.3 % in 2010 and 68.1 % in 2012, with substantial heterogeneity sub-nationally. The population-adjusted predicted child malaria parasite prevalence decreased from 19.6 % in 2006 to 10.4 % in 2008, but rose to 15.3 % in 2010 and 13.5 % in 2012. We estimated that the majority of this prevalence increase at the national level between 2008 and 2010 was due to climate effects on transmission, although there was substantial heterogeneity at the provincial level in the relative contribution of changing climate and ITN availability. We predict that if climate factors preceding the 2010 survey were the same as in 2008, the population-adjusted prevalence would have fallen to 9.9 % nationally.

Conclusions

These results suggest that a combination of climate factors and reduced intervention coverage in parts of the country contributed to both the reduction and rebound in malaria parasite prevalence. Unusual rainfall patterns, perhaps related to moderate El Niño conditions, may have contributed to this variation. Zambia has demonstrated considerable success in scaling up vector control. This analysis highlights the importance of accounting for climate variability when using cross-sectional data for evaluation of malaria control efforts.

Electronic supplementary material

The online version of this article (doi:10.1186/s13071-016-1693-0) contains supplementary material, which is available to authorized users.

Big city, small world: density, contact rates, and transmission of dengue across Pakistan

Macroscopic descriptions of populations commonly assume that encounters between individuals are well mixed; i.e. each individual has an equal chance of coming into contact with any other individual. Relaxing this assumption can be challenging though, due to the difficulty of acquiring detailed knowledge about the non-random nature of encounters. Here, we fitted a mathematical model of dengue virus transmission to spatial time-series data from Pakistan and compared maximum-likelihood estimates of 'mixing parameters' when disaggregating data across an urban-rural gradient. We show that dynamics across this gradient are subject not only to differing transmission intensities but also to differing strengths of nonlinearity due to differences in mixing. Accounting for differences in mobility by incorporating two fine-scale, density-dependent covariate layers eliminates differences in mixing but results in a doubling of the estimated transmission potential of the large urban district of Lahore. We furthermore show that neglecting spatial variation in mixing can lead to substantial underestimates of the level of effort needed to control a pathogen with vaccines or other interventions. We complement this analysis with estimates of the relationships between dengue transmission intensity and other putative environmental drivers thereof.

Spatial distribution estimation of malaria in northern China and its scenarios in 2020, 2030, 2040 and 2050

BACKGROUND

Malaria is one of the most severe parasitic diseases in the world. Spatial distribution estimation of malaria and its future scenarios are important issues for malaria control and elimination. Furthermore, sophisticated nonlinear relationships for prediction between malaria incidence and potential variables have not been well constructed in previous research. This study aims to estimate these nonlinear relationships and predict future malaria scenarios in northern China.

METHODS

Nonlinear relationships between malaria incidence and predictor variables were constructed using a genetic programming (GP) method, to predict the spatial distributions of malaria under climate change scenarios. For this, the examples of monthly average malaria incidence were used in each county of northern China from 2004 to 2010. Among the five variables at county level, precipitation rate and temperature are used for projections, while elevation, water density index, and gross domestic product are held at their present-day values.

RESULTS

Average malaria incidence was 0.107 ‰ per annum in northern China, with incidence characteristics in significant spatial clustering. A GP-based model fit the relationships with average relative error (ARE) = 8.127 % for training data (R(2) = 0.825) and 17.102 % for test data (R(2) = 0.532). The fitness of GP results are significantly improved compared with those by generalized additive models (GAM) and linear regressions. With the future precipitation rate and temperature conditions in Special Report on Emission Scenarios (SRES) family B1, A1B and A2 scenarios, spatial distributions and changes in malaria incidences in 2020, 2030, 2040 and 2050 were predicted and mapped.

CONCLUSIONS

The GP method increases the precision of predicting the spatial distribution of malaria incidence. With the assumption of varied precipitation rate and temperature, and other variables controlled, the relationships between incidence and the varied variables appear sophisticated nonlinearity and spatially differentiation. Using the future fluctuated precipitation and the increased temperature, median malaria incidence in 2020, 2030, 2040 and 2050 would significantly increase that it might increase 19 to 29 % in 2020, but currently China is in the malaria elimination phase, indicating that the effective strategies and actions had been taken. While the mean incidences will not increase even reduce due to the incidence reduction in high-risk regions but the simultaneous expansion of the high-risk areas.

A geostatistical analysis of the association between armed conflicts and Plasmodium falciparum malaria in Africa, 1997-2010

BACKGROUND

The absence of conflict in a country has been cited as a crucial factor affecting the operational feasibility of achieving malaria control and elimination, yet mixed evidence exists on the influence that conflicts have had on malaria transmission. Over the past two decades, Africa has seen substantial numbers of armed conflicts of varying length and scale, creating conditions that can disrupt control efforts and impact malaria transmission. However, very few studies have quantitatively assessed the associations between conflicts and malaria transmission, particularly in a consistent way across multiple countries.

METHODS

In this analysis an explicit geostatistical, autoregressive, mixed model is employed to quantitatively assess the association between conflicts and variations in Plasmodium falciparum parasite prevalence across a 13-year period in sub-Saharan Africa.

RESULTS

Analyses of geolocated, malaria prevalence survey variations against armed conflict data in general showed a wide, but short-lived impact of conflict events geographically. The number of countries with decreased P. falciparum parasite prevalence (17) is larger than the number of countries with increased transmission (12), and notably, some of the countries with the highest transmission pre-conflict were still found with lower transmission post-conflict. For four countries, there were no significant changes in parasite prevalence. Finally, distance from conflicts, duration of conflicts, violence of conflict, and number of conflicts were significant components in the model explaining the changes in P. falciparum parasite rate.

CONCLUSIONS

The results suggest that the maintenance of intervention coverage and provision of healthcare in conflict situations to protect vulnerable populations can maintain gains in even the most difficult of circumstances, and that conflict does not represent a substantial barrier to elimination goals.

Spatial and socio-economic effects on malaria morbidity in children under 5 years in Malawi in 2012

BACKGROUND

Malaria is a major health challenge in sub-Saharan Africa with children under 5 being most vulnerable. Identifying regions of greater malarial burden is vital in targeting interventions.

METHODS

This study analysed malaria morbidity using data from the Malawi 2012 Malaria Indicator Survey that were obtained from Demographic and Health Survey (DHS) program website. These data captured malaria related information on children under 5. Poisson regression was done to determine associations between outcome (number of children under 5 with malaria in household) and explanatory variables. A Bayesian smoothing approach was employed to adjust for spatial random effects on child related variables.

RESULTS

There were 1878 households in 140 clusters. The number of children under five was 1900. Spatially structured effects accounted for more than 90% of random effects as these had a mean of 1.32 (95% Credible Interval (CI)=0.37, 2.50) whilst spatially unstructured had a mean of 0.10 (CI=9.0 × 10(-4), 0.38). Spatially adjusted significant variables were; type of place of residence (urban or rural) [posterior odds ratio (POR)=2.06; CI= 1.27, 3.34], not owning land [POR=1.77; CI=1.19, 2.64], not staying in a slum [POR=0.52; CI=0.33, 0.83] and enhanced vegetation index [POR=0.02; CI=0.00, 1.08]. A trend was observed on usage of insecticide treated mosquito nets [POR=0.80; CI=0.63, 1.03].

CONCLUSION

This study showed that malaria is a disease of poverty. Enhanced vegetation index was an important factor in malaria morbidity. The central region was identified as the area with greatest disease burden.

Progress and Challenges in Infectious Disease Cartography

Quantitatively mapping the spatial distributions of infectious diseases is key to both investigating their epidemiology and identifying populations at risk of infection. Important advances in data quality and methodologies have allowed for better investigation of disease risk and its association with environmental factors. However, incorporating dynamic human behavioural processes in disease mapping remains challenging. For example, connectivity among human populations, a key driver of pathogen dispersal, has increased sharply over the past century, along with the availability of data derived from mobile phones and other dynamic data sources. Future work must be targeted towards the rapid updating and dissemination of appropriately designed disease maps to guide the public health community in reducing the global burden of infectious disease.

Malaria mapping: understanding the global endemicity of falciparum and vivax malaria

The mapping of malaria risk has a history stretching back over 100 years. The last decade, however, has seen dramatic progress in the scope, rigour and sophistication of malaria mapping such that its global distribution is now probably better understood than any other infectious disease. In this minireview we consider the main factors that have facilitated the recent proliferation of malaria risk mapping efforts and describe the most prominent global-scale endemicity mapping endeavours of recent years. We describe the diversification of malaria mapping to span a wide range of related metrics of biological and public health importance and consider prospects for the future of the science including its key role in supporting elimination efforts.

Re-examining environmental correlates of Plasmodium falciparum malaria endemicity: a data-intensive variable selection approach

Background

Malaria risk maps play an increasingly important role in disease control planning, implementation, and evaluation. The construction of these maps using modern geospatial techniques relies on covariate grids: continuous surfaces quantifying environmental factors that partially explain spatial heterogeneity in malaria endemicity. Although crucial, past variable selection processes for this purpose have often been subjective and ad-hoc, with many covariates used in modeling with little quantitative justification.

Methods

This research consists of an extensive covariate construction and selection process for predicting Plasmodium falciparum parasite rates (PfPR) in Africa for years 2000-2012. First, a literature review was conducted to establish a comprehensive list of covariates used for malaria mapping. Second, a library of covariate data was assembled to reflect this list, a process that included the construction of multiple, temporally dynamic datasets. Third, the resulting set of covariates was leveraged to create more than 50 million possible covariate terms via factorial combinations of different spatial and temporal aggregations, transformations, and pairwise interactions. Fourth, the expanded set of covariates was reduced via successive selection criteria to yield a robust covariate subset that was assessed using an out-of-sample validation approach.

Results

The final covariate subset included predominately dynamic covariates and it substantially out-performed earlier sets used by the Malaria Atlas Project (MAP) for creating global malaria risk maps, with the pseudo-R² value for the out-of-sample validation increasing from 0.43 to 0.52. Dynamic covariates improved the model, with 17 of the 20 new covariates consisting of monthly or annual products, but the selected covariates were typically interaction terms that included both dynamic and synoptic datasets. Thus the interplay between normal (i.e., long-term averages) and immediate conditions may be key for characterizing environmental controls on parasite rate.

Conclusions

This analysis represents the first effort to systematically audit covariate utility for malaria mapping and then derive an objective, empirically based set of environmental covariates for modeling PfPR. The new covariates produce more reliable representations of malaria risk patterns and how they are changing through time, and these covariates will be used to characterize spatially and temporally varying environmental conditions affecting PfPR within a geostatistical-modeling framework, thus building upon previous research by MAP that produced global malaria maps for 2007 and 2010.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-015-0574-x) contains supplementary material, which is available to authorized users.

Structural and inhibitory effects of hinge loop mutagenesis in serpin-2 from the malaria vector Anopheles gambiae

Serpin-2 (SRPN2) is a key negative regulator of the melanization response in the malaria vector Anopheles gambiae. SRPN2 irreversibly inhibits clip domain serine proteinase 9 (CLIPB9), which functions in a serine proteinase cascade culminating in the activation of prophenoloxidase and melanization. Silencing of SRPN2 in A. gambiae results in spontaneous melanization and decreased life span and is therefore a promising target for vector control. The previously determined structure of SRPN2 revealed a partial insertion of the hinge region of the reactive center loop (RCL) into β sheet A. This partial hinge insertion participates in heparin-linked activation in other serpins, notably antithrombin III. SRPN2 does not contain a heparin binding site, and any possible mechanistic function of the hinge insertion was previously unknown. To investigate the function of the SRPN2 hinge insertion, we developed three SRPN2 variants in which the hinge regions are either constitutively expelled or inserted and analyzed their structure, thermostability, and inhibitory activity. We determined that constitutive hinge expulsion resulted in a 2.7-fold increase in the rate of CLIPB9Xa inhibition, which is significantly lower than previous observations of allosteric serpin activation. Furthermore, we determined that stable insertion of the hinge region did not appreciably decrease the accessibility of the RCL to CLIPB9. Together, these results indicate that the partial hinge insertion in SRPN2 does not participate in the allosteric activation observed in other serpins and instead represents a molecular trade-off between RCL accessibility and efficient formation of an inhibitory complex with the cognate proteinase.

Mapping infectious disease landscapes: unmanned aerial vehicles and epidemiology

The potential applications of unmanned aerial vehicles (UAVs), or drones, have generated intense interest across many fields. UAVs offer the potential to collect detailed spatial information in real time at relatively low cost and are being used increasingly in conservation and ecological research. Within infectious disease epidemiology and public health research, UAVs can provide spatially and temporally accurate data critical to understanding the linkages between disease transmission and environmental factors. Using UAVs avoids many of the limitations associated with satellite data (e.g., long repeat times, cloud contamination, low spatial resolution). However, the practicalities of using UAVs for field research limit their use to specific applications and settings. UAVs fill a niche but do not replace existing remote-sensing methods.

Mapping the zoonotic niche of Ebola virus disease in Africa

Ebola virus disease (EVD) is a complex zoonosis that is highly virulent in humans. The largest recorded outbreak of EVD is ongoing in West Africa, outside of its previously reported and predicted niche. We assembled location data on all recorded zoonotic transmission to humans and Ebola virus infection in bats and primates (1976-2014). Using species distribution models, these occurrence data were paired with environmental covariates to predict a zoonotic transmission niche covering 22 countries across Central and West Africa. Vegetation, elevation, temperature, evapotranspiration, and suspected reservoir bat distributions define this relationship. At-risk areas are inhabited by 22 million people; however, the rarity of human outbreaks emphasises the very low probability of transmission to humans. Increasing population sizes and international connectivity by air since the first detection of EVD in 1976 suggest that the dynamics of human-to-human secondary transmission in contemporary outbreaks will be very different to those of the past.