Spatial targeting of irrigation development and water resource management to mitigate vector-borne disease.

Scaling irrigated agriculture is a global strategy to mitigate food insecurity concerns. While expanding irrigated agriculture is critical to meeting food production demands, it is important to consider how these land use and land cover changes (LULCC) may alter the water resources of landscapes and impact the spatiotemporal epidemiology of disease. Here, a generalizable method is presented to inform irrigation development decision-making aimed at increasing crop production through irrigation while simultaneously mitigating malaria risk to surrounding communities. Changes to the spatiotemporal patterns of malaria vector (Anopheles gambiae s.s.) suitability, driven by irrigated agricultural expansion, are presented for Malawi's rainy and dry seasons. The methods presented may be applied to other geographical areas where sufficient irrigation and malaria prevalence data are available. Results show that approximately 8.60% and 1.78% of Malawi is maximally suitable for An. gambiae s.s. breeding in the rainy and dry seasons, respectively. However, the proposed LULCC from irrigated agriculture increases the maximally suitable land area in both seasons: 15.16% (rainy) and 2.17% (dry). Proposed irrigation development sites are analyzed and ranked according to their likelihood of increasing malaria risk for those closest to the schemes. Results illustrate how geospatial information on the anticipated change to the malaria landscape driven by increasing irrigated agricultural extent can assist in altering development plans, amending policies, or reassessing water resource management strategies to mitigate expected changes in malaria risk.

A Bayesian maximum entropy model for predicting tsetse ecological distributions.

BACKGROUND: African trypanosomiasis is a tsetse-borne parasitic infection that affects humans, wildlife, and domesticated animals. Tsetse flies are endemic to much of Sub-Saharan Africa and a spatial and temporal understanding of tsetse habitat can aid surveillance and support disease risk management. Problematically, current fine spatial resolution remote sensing data are delivered with a temporal lag and are relatively coarse temporal resolution (e.g., 16 days), which results in disease control models often targeting incorrect places. The goal of this study was to devise a heuristic for identifying tsetse habitat (at a fine spatial resolution) into the future and in the temporal gaps where remote sensing and proximal data fail to supply information. METHODS: This paper introduces a generalizable and scalable open-access version of the tsetse ecological distribution (TED) model used to predict tsetse distributions across space and time, and contributes a geospatial Bayesian Maximum Entropy (BME) prediction model trained by TED output data to forecast where, herein the Morsitans group of tsetse, persist in Kenya, a method that mitigates the temporal lag problem. This model facilitates identification of tsetse habitat and provides critical information to control tsetse, mitigate the impact of trypanosomiasis on vulnerable human and animal populations, and guide disease minimization in places with ephemeral tsetse. Moreover, this BME analysis is one of the first to utilize cluster and parallel computing along with a Monte Carlo analysis to optimize BME computations. This allows for the analysis of an exceptionally large dataset (over 2 billion data points) at a finer resolution and larger spatiotemporal scale than what had previously been possible. RESULTS: Under the most conservative assessment for Kenya, the BME kriging analysis showed an overall prediction accuracy of 74.8% (limited to the maximum suitability extent). In predicting tsetse distribution outcomes for the entire country the BME kriging analysis was 97% accurate in its forecasts. CONCLUSIONS: This work offers a solution to the persistent temporal data gap in accurate and spatially precise rainfall predictions and the delayed processing of remotely sensed data collectively in the - 45 days past to + 180 days future temporal window. As is shown here, the BME model is a reliable alternative for forecasting future tsetse distributions to allow preplanning for tsetse control. Furthermore, this model provides guidance on disease control that would otherwise not be available. These 'big data' BME methods are particularly useful for large domain studies. Considering that past BME studies required reduction of the spatiotemporal grid to facilitate analysis. Both the GEE-TED and the BME libraries have been made open source to enable reproducibility and offer continual updates into the future as new remotely sensed data become available.

Proximity of Residence to Irrigation Determines Malaria Risk and Anopheles Abundance at an Irrigated Agroecosystem in Malawi.

As countries of sub-Saharan Africa expand irrigation to improve food security and foster economic growth, it is important to quantify the malaria risk associated with this process. Irrigated ecosystems can be associated with increased malaria risk, but this relationship is not fully understood. We studied this relationship at the Bwanje Valley Irrigation Scheme (800 hectares) in Malawi. Household prevalence of malaria and indoor Anopheles density were quantified in two cross-sectional studies in 2016 and 2017 (5,829 residents of 1,091 households). Multilevel logistic regression was used to estimate the association between distance to the irrigation scheme and malaria infection and mosquito density. The prevalence of malaria infection was 50.2% (2,765/5,511) by histidine-rich protein 2-based malaria rapid diagnostic tests and 30.1% (1,626/5,403) by microscopy. Individuals residing in households within 3 km of the scheme had significantly higher prevalence of infection (adjusted odds ratio [aOR] = 1.41; 95% confidence interval [CI] 1.18, 1.68); school-aged children had the highest prevalence among age groups (aOR = 1.34; 95% CI 1.11, 1.63). Individuals who reported bed net use, and households with higher socioeconomic status and higher level of education for household head or spouse, had lower odds of malaria infection. Female Anopheles mosquitoes (2,215 total; Anopheles arabiensis, 90.5%, Anopheles funestus, 9.5%) were significantly more abundant in houses located within 1.5 km of the scheme. Proximity of human dwellings to the irrigation scheme increased malaria risk, but higher household wealth index reduced risk. Therefore, multisectoral approaches that spur economic growth while mitigating increased malaria transmission are needed for people living close to irrigated sites.

Leveraging big data for public health: Mapping malaria vector suitability in Malawi with Google Earth Engine.

In an era of big data, the availability of satellite-derived global climate, terrain, and land cover imagery presents an opportunity for modeling the suitability of malaria disease vectors at fine spatial resolutions, across temporal scales, and over vast geographic extents. Leveraging cloud-based geospatial analytical tools, we present an environmental suitability model that considers water resources, flow accumulation areas, precipitation, temperature, vegetation, and land cover. In contrast to predictive models generated using spatially and temporally discontinuous mosquito presence information, this model provides continuous fine-spatial resolution information on the biophysical drivers of suitability. For the purposes of this study the model is parameterized for Anopheles gambiae s.s. in Malawi for the rainy (December-March) and dry seasons (April-November) in 2017; however, the model may be repurposed to accommodate different mosquito species, temporal periods, or geographical boundaries. Final products elucidate the drivers and potential habitat of Anopheles gambiae s.s. Rainy season results are presented by quartile of precipitation; Quartile four (Q4) identifies areas most likely to become inundated and shows 7.25% of Malawi exhibits suitable water conditions (water only) for Anopheles gambiae s.s., approximately 16% for water plus another factor, and 8.60% is maximally suitable, meeting suitability thresholds for water presence, terrain characteristics, and climatic conditions. Nearly 21% of Malawi is suitable for breeding based on land characteristics alone and 28.24% is suitable according to climate and land characteristics. Only 6.14% of the total land area is suboptimal. Dry season results show 25.07% of the total land area is suboptimal or unsuitable. Approximately 42% of Malawi is suitable based on land characteristics alone during the dry season, and 13.11% is suitable based on land plus another factor. Less than 2% meets suitability criteria for climate, water, and land criteria. Findings illustrate environmental drivers of suitability for malaria vectors, providing an opportunity for a more comprehensive approach to malaria control that includes not only modeled species distributions, but also the underlying drivers of suitability for a more effective approach to environmental management.

Estimating spatio-temporal distributions of mosquito breeding pools in irrigated agricultural schemes: a case study at the Bwanje Valley Irrigation Scheme.

BACKGROUND: The association between irrigation and the proliferation of adult mosquitoes including malaria vectors is well known; however, irrigation schemes are treated as homogenous spatio-temporal units, with little consideration for how larval breeding varies across space and time. The objective of this study was to estimate the spatio-temporal distribution of pools of water facilitating breeding at the Bwanje Valley Irrigation Scheme (BVIS) in Malawi, Africa as a function of environmental and anthropogenic characteristics. METHODS: Irrigation structure and land cover were quantified during the dry and rainy seasons of 2016 and 2017, respectively. These data were combined with soil type, irrigation scheduling, drainage, and maintenance to model suitability for mosquito breeding across the landscape under three scenarios: rainy season, dry season with limited water resources, and a dry season with abundant water resources. RESULTS: Results demonstrate seasonal, asymmetrical breeding potential and areas of maximum breeding potential as a function of environmental characteristics and anthropogenic influence in each scenario. The highest percentage of suitable area for breeding occurs during the rainy season; however, findings show that it is not merely the amount of water in an irrigated landscape, but the management of water resources that determines the aggregation of water bodies. In each scenario, timing and direction of irrigation along with inefficient drainage render the westernmost portion of BVIS the area of highest breeding opportunity, which expands and contracts seasonally in response to water resource availability and management decisions. CONCLUSIONS: Changes in the geography of breeding potential across irrigated spaces can have profound effects on the distribution of malaria risk for those living in close proximity to irrigated agricultural schemes. The methods presented are generalizable across geographies for estimating spatio-temporal distributions of breeding risk for mosquitoes in irrigated schemes, presenting an opportunity for greater geographically targeted strategies for management.

Neonatal mortality in East Africa and West Africa: a geographic analysis of district-level demographic and health survey data.

Under-five child mortality declined 47% since 2000 following the implementation of the United Nation's (UN) Millennium Development Goals. To further reduce under-five child mortality, the UN's Sustainable Development Goals (SDGs) will focus on interventions to address neonatal mortality, a major contributor of under-five mortality. The African region has the highest neonatal mortality rate (28.0 per 1000 live births), followed by that of the Eastern Mediterranean (26.6) and South-East Asia (24.3). This study used the Demographic and Health Survey Birth Recode data (http://dhsprogram.com/data/File-Types-and-Names.cfm) to identify high-risk districts and countries for neonatal mortality in two sub-regions of Africa - East Africa and West Africa. Geographically weighted Poisson regression models were estimated to capture the spatially varying relationships between neonatal mortality and dimensions of potential need i) care around the time of delivery, ii) maternal education, and iii) women's empowerment. In East Africa, neonatal mortality was significantly associated with home births, mothers without an education and mothers whose husbands decided on contraceptive practices, controlling for rural residency. In West Africa, neonatal mortality was also significantly associated with home births, mothers with a primary education and mothers who did not want or plan their last child. Importantly, neonatal mortality associated with home deliveries were explained by maternal exposure to unprotected water sources in East Africa and older maternal age and female sex of infants in West Africa. Future SDG-interventions may target these dimensions of need in priority high-risk districts and countries, to further reduce the burden of neonatal mortality in Africa.