Identifying malaria vector breeding habitats with remote sensing data and terrain-based landscape indices in Zambia

Driving down malaria transmission with engineered gene drives

The last century has witnessed the introduction, establishment and expansion of mosquito-borne diseases into diverse new geographic ranges. Malaria is transmitted by female Anopheles mosquitoes. Despite making great strides over the past few decades in reducing the burden of malaria, transmission is now on the rise again, in part owing to the emergence of mosquito resistance to insecticides, antimalarial drug resistance and, more recently, the challenges of the COVID-19 pandemic, which resulted in the reduced implementation efficiency of various control programs. The utility of genetically engineered gene drive mosquitoes as tools to decrease the burden of malaria by controlling the disease-transmitting mosquitoes is being evaluated. To date, there has been remarkable progress in the development of CRISPR/Cas9-based homing endonuclease designs in malaria mosquitoes due to successful proof-of-principle and multigenerational experiments. In this review, we examine the lessons learnt from the development of current CRISPR/Cas9-based homing endonuclease gene drives, providing a framework for the development of gene drive systems for the targeted control of wild malaria-transmitting mosquito populations that overcome challenges such as with evolving drive-resistance. We also discuss the additional substantial works required to progress the development of gene drive systems from scientific discovery to further study and subsequent field application in endemic settings.

Six decades of malaria vector control in southern Africa: a review of the entomological evidence-base

BACKGROUND

Countries in the southern Africa region have set targets for malaria elimination between 2020 and 2030. Malaria vector control is among the key strategies being implemented to achieve this goal. This paper critically reviews published entomological research over the past six decades in three frontline malaria elimination countries namely, Botswana Eswatini and Namibia, and three second-line malaria elimination countries including Mozambique, Zambia, and Zimbabwe. The objective of the review is to assess the current knowledge and highlight gaps that need further research attention to strengthen evidence-based decision-making toward malaria elimination.

METHODS

Publications were searched on the PubMed engine using search terms: "(malaria vector control OR vector control OR malaria vector*) AND (Botswana OR Swaziland OR Eswatini OR Zambia OR Zimbabwe OR Mozambique)". Opinions, perspectives, reports, commentaries, retrospective analysis on secondary data protocols, policy briefs, and reviews were excluded.

RESULTS

The search resulted in 718 publications with 145 eligible and included in this review for the six countries generated over six decades. The majority (139) were from three countries, namely Zambia (59) and Mozambique (48), and Zimbabwe (32) whilst scientific publications were relatively scanty from front-line malaria elimination countries, such as Namibia (2), Botswana (10) and Eswatini (4). Most of the research reported in the publications focused on vector bionomics generated mostly from Mozambique and Zambia, while information on insecticide resistance was mostly available from Mozambique. Extreme gaps were identified in reporting the impact of vector control interventions, both on vectors and disease outcomes. The literature is particularly scanty on important issues such as change of vector ecology over time and space, intervention costs, and uptake of control interventions as well as insecticide resistance.

CONCLUSIONS

The review reveals a dearth of information about malaria vectors and their control, most noticeable among the frontline elimination countries: Namibia, Eswatini and Botswana. It is of paramount importance that malaria vector research capacity and routine entomological monitoring and evaluation are strengthened to enhance decision-making, considering changing vector bionomics and insecticide resistance, among other determinants of malaria vector control.

Malaria Exposure in Ann Township, Myanmar, as a Function of Land Cover and Land Use: Combining Satellite Earth Observations and Field Surveys

Despite progress toward malaria elimination in the Greater Mekong Subregion, challenges remain owing to the emergence of drug resistance and the persistence of focal transmission reservoirs. Malaria transmission foci in Myanmar are heterogeneous and complex, and many remaining infections are clinically silent, rendering them invisible to routine monitoring. The goal of this research is to define criteria for easy-to-implement methodologies, not reliant on routine monitoring, that can increase the efficiency of targeted malaria elimination strategies. Studies have shown relationships between malaria risk and land cover and land use (LCLU), which can be mapped using remote sensing methodologies. Here we aim to explain malaria risk as a function of LCLU for five rural villages in Myanmar's Rakhine State. Malaria prevalence and incidence data were analyzed through logistic regression with a land use survey of ~1,000 participants and a 30-m land cover map. Malaria prevalence per village ranged from 5% to 20% with the overwhelming majority of cases being subclinical. Villages with high forest cover were associated with increased risk of malaria, even for villagers who did not report visits to forests. Villagers living near croplands experienced decreased malaria risk unless they were directly engaged in farm work. Finally, land cover change (specifically, natural forest loss) appeared to be a substantial contributor to malaria risk in the region, although this was not confirmed through sensitivity analyses. Overall, this study demonstrates that remotely sensed data contextualized with field survey data can be used to inform critical targeting strategies in support of malaria elimination.

Rapid identification of shallow inundation for mosquito disease mitigation using drone-derived multispectral imagery

Mosquito breeding habitat identification often relies on slow, labour-intensive and expensive ground surveys. With advances in remote sensing and autonomous flight technologies, we endeavoured to accelerate this detection by assessing the effectiveness of a drone multispectral imaging system to determine areas of shallow inundation in an intertidal saltmarsh in South Australia. Through laboratory experiments, we characterised Near-Infrared (NIR) reflectance responses to water depth and vegetation cover, and established a reflectance threshold for mapping water sufficiently deep for potential mosquito breeding. We then applied this threshold to field-acquired drone imagery and used simultaneous in-situ observations to assess its mapping accuracy. A NIR reflectance threshold of 0.2 combined with a vegetation mask derived from Normalised Difference Vegetation Index (NDVI) resulted in a mapping accuracy of 80.3% with a Cohen's Kappa of 0.5, with confusion between vegetation and shallow water depths (< 10 cm) appearing to be major causes of error. This high degree of mapping accuracy was achieved with affordable drone equipment, and commercially available sensors and Geographic Information Systems (GIS) software, demonstrating the efficiency of such an approach to identify shallow inundation likely to be suitable for mosquito breeding.

Improving the efficiency of reactive case detection for malaria elimination in southern Zambia: a cross-sectional study

Background

Reactive case detection (RCD) seeks to enhance malaria surveillance and control by identifying and treating parasitaemic individuals residing near index cases. In Zambia, this strategy starts with passive detection of symptomatic incident malaria cases at local health facilities or by community health workers, with subsequent home visits to screen-and-treat residents in the index case and neighbouring (secondary) households within a 140-m radius using rapid diagnostic tests (RDTs). However, a small circular radius may not be the most efficient strategy to identify parasitaemic individuals in low-endemic areas with hotspots of malaria transmission. To evaluate if RCD efficiency could be improved by increasing the probability of identifying parasitaemic residents, environmental risk factors and a larger screening radius (250 m) were assessed in a region of low malaria endemicity.

Methods

Between January 12, 2015 and July 26, 2017, 4170 individuals residing in 158 index and 531 secondary households were enrolled and completed a baseline questionnaire in the catchment area of Macha Hospital in Choma District, Southern Province, Zambia. Plasmodium falciparum prevalence was measured using PfHRP2 RDTs and quantitative PCR (qPCR). A Quickbird™ high-resolution satellite image of the catchment area was used to create environmental risk factors in ArcGIS, and generalized estimating equations were used to evaluate associations between risk factors and secondary households with parasitaemic individuals.

Results

The parasite prevalence in secondary (non-index case) households was 0.7% by RDT and 1.8% by qPCR. Overall, 8.5% (n = 45) of secondary households had at least one resident with parasitaemia by qPCR or RDT. The risk of a secondary household having a parasitaemic resident was significantly increased in proximity to higher order streams and marginally with increasing distance from index households. The adjusted OR for proximity to third- and fifth-order streams were 2.97 (95% CI 1.04–8.42) and 2.30 (95% CI 1.04–5.09), respectively, and that for distance to index households for each 50 m was 1.24 (95% CI 0.98–1.58).

Conclusion

Applying proximity to streams as a screening tool, 16% (n = 3) more malaria-positive secondary households were identified compared to using a 140-m circular screening radius. This analysis highlights the potential use of environmental risk factors as a screening strategy to increase RCD efficiency.

Availability, access, analysis and dissemination of small-area data

In this era of 'big data', there is growing recognition of the value of environmental, health, social and demographic data for research. Open government data initiatives are growing in number and in terms of content. Remote sensing data are finding widespread use in environmental research, including in low- and middle-income settings. While our ability to study environment and health associations across countries and continents grows, data protection rules and greater patient control over the use of their data present new challenges to using health data in research. Innovative tools that circumvent the need for the physical sharing of data by supporting non-disclosive sharing of information, or that permit spatial analysis without researchers needing access to underlying patient data can be used to support analyses while protecting data confidentiality. User-friendly visualizations, allowing small-area data to be seen and understood by non-expert audiences, are revolutionizing public and researcher interactions with data. The UK Small Area Health Statistics Unit's Environment and Health Atlas for England and Wales, and the US National Environmental Public Health Tracking Network offer good examples. Open data facilitates user-generated outputs, and 'mash-ups', and user-generated inputs from social media, mobile devices and wearable tech are new data streams that will find utility in future studies, and bring novel dimensions with respect to ethical use of small-area data.

The use of spatial and genetic tools to assess Plasmodium falciparum transmission in Lusaka, Zambia between 2011 and 2015

BACKGROUND

Zambia has set itself the ambitious target of eliminating malaria by 2021. To continue tracking transmission to zero, new interventions, tools and approaches are required.

METHODS

Urban reactive case detection (RCD) was performed in Lusaka city from 2011 to 2015 to better understand the location and drivers of malaria transmission. Briefly, index cases were followed to their home and all consenting individuals living in the index house and nine proximal houses were tested with a malaria rapid diagnostic test and treated if positive. A brief survey was performed and for certain responses, a dried blood spot sample collected for genetic analysis. Aggregate health facility data, individual RCD response data and genetic results were analysed spatially and against environmental correlates.

RESULTS

Total number of malaria cases remained relatively constant, while the average age of incident cases and the proportion of incident cases reporting recent travel both increased. The estimated R₀ in Lusaka was < 1 throughout the study period. RCD responses performed within 250 m of uninhabited/vacant land were associated with a higher probability of identifying additional infections.

CONCLUSIONS

Evidence suggests that the majority of malaria infections are imported from outside Lusaka. However there remains some level of local transmission occurring on the periphery of urban settlements, namely in the wet season. Unfortunately, due to the higher-than-expected complexity of infections and the small number of samples tested, genetic analysis was unable to identify any meaningful trends in the data.

Applications of Space Technologies to Global Health: Scoping Review

Background

Space technology has an impact on many domains of activity on earth, including in the field of global health. With the recent adoption of the United Nations’ Sustainable Development Goals that highlight the need for strengthening partnerships in different domains, it is useful to better characterize the relationship between space technology and global health.

Objective

The aim of this study was to identify the applications of space technologies to global health, the key stakeholders in the field, as well as gaps and challenges.

Methods

We used a scoping review methodology, including a literature review and the involvement of stakeholders, via a brief self-administered, open-response questionnaire. A distinct search on several search engines was conducted for each of the four key technological domains that were previously identified by the UN Office for Outer Space Affairs’ Expert Group on Space and Global Health (Domain A: remote sensing; Domain B: global navigation satellite systems; Domain C: satellite communication; and Domain D: human space flight). Themes in which space technologies are of benefit to global health were extracted. Key stakeholders, as well as gaps, challenges, and perspectives were identified.

Results

A total of 222 sources were included for Domain A, 82 sources for Domain B, 144 sources for Domain C, and 31 sources for Domain D. A total of 3 questionnaires out of 16 sent were answered. Global navigation satellite systems and geographic information systems are used for the study and forecasting of communicable and noncommunicable diseases; satellite communication and global navigation satellite systems for disaster response; satellite communication for telemedicine and tele-education; and global navigation satellite systems for autonomy improvement, access to health care, as well as for safe and efficient transportation. Various health research and technologies developed for inhabited space flights have been adapted for terrestrial use.

Conclusions

Although numerous examples of space technology applications to global health exist, improved awareness, training, and collaboration of the research community is needed.

Evaluating Efficacy of Landsat-Derived Environmental Covariates for Predicting Malaria Distribution in Rural Villages of Vhembe District, South Africa

Malaria in South Africa is still a problem despite existing efforts to eradicate the disease. In the Vhembe District Municipality, malaria prevalence is still high, with a mean incidence rate of 328.2 per 100,0000 persons/year. This study aimed at evaluating environmental covariates, such as vegetation moisture and vegetation greenness, associated with malaria vector distribution for better predictability towards rapid and efficient disease management and control. The 2005 malaria incidence data combined with Landsat 5 ETM were used in this study. A total of nine remotely sensed covariates were derived, while pseudo-absences in the ratio of 1:2 (presence/absence) were generated at buffer distances of 0.5-20 km from known presence locations. A stepwise logistic regression model was applied to analyse the spatial distribution of malaria in the area. A buffer distance of 10 km yielded the highest classification accuracy of 82% at a threshold of 0.9. This model was significant (ρ < 0.05) and yielded a deviance (D²) of 36%. The significantly positive relationship (ρ < 0.05) between the soil-adjusted vegetation index and malaria distribution at all buffer distances suggests that malaria vector (Anopheles arabiensis) prefer productive and greener vegetation. The significant negative relationship between water/moisture index (a₁ index) and malaria distribution in buffer distances of 0.5, 10, and 20 km suggest that malaria distribution increases with a decrease in shortwave reflectance signal. The study has shown that suitable habitats of malaria vectors are generally found within a radius of 10 km in semi-arid environments, and this insight can be useful to aid efforts aimed at putting in place evidence-based preventative measures against malaria infections. Furthermore, this result is important in understanding malaria dynamics under the current climate and environmental changes. The study has also demonstrated the use of Landsat data and the ability to extract environmental conditions which favour the distribution of malaria vector (An. arabiensis) such as the canopy moisture content in vegetation, which serves as a surrogate for rainfall.

Community-led Responses for Elimination (CoRE): a study protocol for a community randomized controlled trial assessing the effectiveness of community-level, reactive focal drug administration for reducing Plasmodium falciparum infection prevalence and incidence in Southern Province, Zambia

Background

Zambia is pushing for, and has made great strides towards, the elimination of malaria transmission in Southern Province. Reactive focal test and treat (RFTAT) using rapid diagnostic tests and artemether-lumefantrine (AL) has been key in making this progress. Reactive focal drug administration (RFDA) using dihydroartemisinin-piperaquine (DHAP), may be superior in accelerating clearance of the parasite reservoir in humans due to the provision of enhanced chemoprophylactic protection of at-risk populations against new infections. The primary aim of this study is to quantify the relative effectiveness of RFDA with DHAP against RFTAT with AL (standard of care) for reducing Plasmodium falciparum prevalence and incidence.

Methods/design

The study will be conducted in four districts in Southern Province, Zambia; an area of low malaria transmission and high coverage of vector control. A community randomized controlled trial of 16 health facility catchment areas will be used to evaluate the impact of sustained year-round routine RFDA for 2 years, relative to a control of year-round routine RFTAT. Reactive case detection will be triggered by a confirmed malaria case, e.g., by microscopy or rapid diagnostic test at any government health facility. Reactive responses will be performed by community health workers (CHW) within 7 days of the index case confirmation date. Responses will be performed out to a radius of 140 m from the index case household. A subset of responses will be followed longitudinally for 90 days to examine reinfection rates. Primary outcomes include a post-intervention survey of malaria seropositivity (n = 4800 children aged 1 month to under 5 years old) and a difference-in-differences analysis of malaria parasite incidence, as measured through routine passive case detection at health facilities enrolled in the study. The study is powered to detect approximately a 65% relative reduction in these outcomes between the intervention versus the control.

Discussion

Strengths of this trial include a robust study design and an endline cross-sectional parasite survey as well as a longitudinal sample. Primary limitations include statistical power to detect only a 65% reduction in primary outcomes, and the potential for contamination to dilute the effects of the intervention.

Trial registration

ClinicalTrials.gov, ID: NCT02654912. Registered on 12 November 2015.

Electronic supplementary material

The online version of this article (doi:10.1186/s13063-017-2249-0) contains supplementary material, which is available to authorized users.

Reprint of "Modelling the influence of temperature and rainfall on malaria incidence in four endemic provinces of Zambia using semiparametric Poisson regression"

Although malaria morbidity and mortality are greatly reduced globally owing to great control efforts, the disease remains the main contributor. In Zambia, all provinces are malaria endemic. However, the transmission intensities vary mainly depending on environmental factors as they interact with the vectors. Generally in Africa, possibly due to the varying perspectives and methods used, there is variation on the relative importance of malaria risk determinants. In Zambia, the role climatic factors play on malaria case rates has not been determined in combination of space and time using robust methods in modelling. This is critical considering the reversal in malaria reduction after the year 2010 and the variation by transmission zones. Using a geoadditive or structured additive semiparametric Poisson regression model, we determined the influence of climatic factors on malaria incidence in four endemic provinces of Zambia. We demonstrate a strong positive association between malaria incidence and precipitation as well as minimum temperature. The risk of malaria was 95% lower in Lusaka (ARR=0.05, 95% CI=0.04-0.06) and 68% lower in the Western Province (ARR=0.31, 95% CI=0.25-0.41) compared to Luapula Province. North-western Province did not vary from Luapula Province. The effects of geographical region are clearly demonstrated by the unique behaviour and effects of minimum and maximum temperatures in the four provinces. Environmental factors such as landscape in urbanised places may also be playing a role.

Modelling the influence of temperature and rainfall on malaria incidence in four endemic provinces of Zambia using semiparametric Poisson regression

Although malaria morbidity and mortality are greatly reduced globally owing to great control efforts, the disease remains the main contributor. In Zambia, all provinces are malaria endemic. However, the transmission intensities vary mainly depending on environmental factors as they interact with the vectors. Generally in Africa, possibly due to the varying perspectives and methods used, there is variation on the relative importance of malaria risk determinants. In Zambia, the role climatic factors play on malaria case rates has not been determined in combination of space and time using robust methods in modelling. This is critical considering the reversal in malaria reduction after the year 2010 and the variation by transmission zones. Using a geoadditive or structured additive semiparametric Poisson regression model, we determined the influence of climatic factors on malaria incidence in four endemic provinces of Zambia. We demonstrate a strong positive association between malaria incidence and precipitation as well as minimum temperature. The risk of malaria was 95% lower in Lusaka (ARR=0.05, 95% CI=0.04-0.06) and 68% lower in the Western Province (ARR=0.31, 95% CI=0.25-0.41) compared to Luapula Province. North-western Province did not vary from Luapula Province. The effects of geographical region are clearly demonstrated by the unique behaviour and effects of minimum and maximum temperatures in the four provinces. Environmental factors such as landscape in urbanised places may also be playing a role.

Location, location, location: environmental factors better predict malaria-positive individuals during reactive case detection than index case demographics in Southern Province, Zambia

Background

Decreasing malaria transmission leads to increasing heterogeneity with increased risk in both hot spots (locations) and hot pops (certain demographics). In Southern Province, Zambia, reactive case detection has formed a part of malaria surveillance and elimination efforts since 2011. Various factors may be associated with finding malaria infections during case investigations, including the demographics of the incident case and environmental characteristics of the location of the incident case.

Methods

Community health worker registries were used to determine what factors were associated with finding a malaria infection during reactive case detection.

Results

Location was a more powerful predictor of finding malaria infections during case investigations than the demographics of the incident case. After accounting for environmental characteristics, no demographics around the incident case were associated with finding malaria infections during case investigations. Various time-invariant measures of the environment, such as median enhanced vegetation index, the topographic position index, the convergence index, and the topographical wetness index, were all associated as expected with increased probability of finding a malaria infection during case investigations.

Conclusions

These results suggest that targeting the locations highly at risk of malaria transmission is of importance in elimination settings.

A novel method for mapping village-scale outdoor resting microhabitats of the primary African malaria vector, Anopheles gambiae

Background

Knowledge of Anopheles resting habitats is needed to advance outdoor malaria vector control. This study presents a technique to map locations of resting habitats using high-resolution satellite imagery (world view 2) and probabilistic Dempster-Shafer (D-S) modelling, focused on a rural village in southern Mali, West Africa where field sampling was conducted to determine outdoor habitat preferences of Anopheles gambiae, the main vector in the study area.

Methods

A combination of supervised and manual image classification was used to derive an accurate land-cover map from the satellite image that provided classes (i.e., photosynthetically active vegetation, water bodies, wetlands, and buildings) suitable for habitat assessment. Linear fuzzy functions were applied to the different image classes to scale resting habitat covariates into a common data range (0–1) with fuzzy breakpoints parameterized experimentally through comparison with mosquito outdoor resting data. Fuzzy layers were entered into a Dempster-Shafer (D-S) weight-of-evidence model that produced pixel-based probability of resting habitat locations.

Results

The D-S model provided a highly detailed suitability map of resting locations. The results indicated a significant difference (p < 0.001) between D-S values at locations positive for An. gambiae and a set of randomly sampled points. Further, a negative binomial regression indicated that although the D-S estimates did not predict abundance (p > 0.05) subsequent analysis suggested that the D-S modelling approach may provide a reasonable estimate locations of low-to-medium An. gambiae density. These results suggest that that D-S modelling performed well in identifying presence points and specifically resting habitats.

Conclusion

The use of a D-S modelling framework for predicting the outdoor resting habitat locations provided novel information on this little-known aspect of anopheline ecology. The technique used here may be applied more broadly at different geographic scales using Google Earth, Landsat or other remotely-sensed imagery to assess the malaria vector resting habitats where outdoor control measures can reduce the burden of the disease in Africa and elsewhere.

Fine-scale distribution modeling of avian malaria vectors in north-central Kansas

Infectious diseases increasingly play a role in the decline of wildlife populations. Vector-borne diseases, in particular, have been implicated in mass mortality events and localized population declines are threatening some species with extinction. Transmission patterns for vector-borne diseases are influenced by the spatial distribution of vectors and are therefore not uniform across the landscape. Avian malaria is a globally distributed vector-borne disease that has been shown to affect endemic bird populations of North America. We evaluated shared habitat use between avian malaria vectors, mosquitoes in the genus Culex and a native grassland bird, the Greater Prairie-Chicken (Tympanuchus cupido), by (1) modeling the distribution of Culex spp. occurrence across the Smoky Hills of north-central Kansas using detection data and habitat variables, (2) assessing the occurrence of these vectors at nests of female Greater Prairie-Chickens, and (3) evaluating if shared habitat use between vectors and hosts is correlated with malarial infection status of the Greater Prairie-Chicken. Our results indicate that Culex occurrence increased at nest locations compared to other available but unoccupied grassland habitats; however the shared habitat use between vectors and hosts did not result in an increased prevalence of malarial parasites in Greater Prairie-Chickens that occupied habitats with high vector occurrence. We developed a predictive map to illustrate the associations between Culex occurrence and infection status with malarial parasites in an obligate grassland bird that may be used to guide management decisions to limit the spread of vector-borne diseases.

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

Background

Malaria is an important cause of morbidity and mortality in malaria endemic countries. The malaria mosquito vectors depend on environmental conditions, such as temperature and rainfall, for reproduction and survival. To investigate the potential for weather driven early warning systems to prevent disease occurrence, the disease relationship to weather conditions need to be carefully investigated. Where meteorological observations are scarce, satellite derived products provide new opportunities to study the disease patterns depending on remotely sensed variables. In this study, we explored the lagged association of Normalized Difference Vegetation Index (NVDI), day Land Surface Temperature (LST) and precipitation on malaria mortality in three areas in Western Kenya.

Methodology and Findings

The lagged effect of each environmental variable on weekly malaria mortality was modeled using a Distributed Lag Non Linear Modeling approach. For each variable we constructed a natural spline basis with 3 degrees of freedom for both the lag dimension and the variable. Lag periods up to 12 weeks were considered. The effect of day LST varied between the areas with longer lags. In all the three areas, malaria mortality was associated with precipitation. The risk increased with increasing weekly total precipitation above 20 mm and peaking at 80 mm. The NDVI threshold for increased mortality risk was between 0.3 and 0.4 at shorter lags.

Conclusion

This study identified lag patterns and association of remote- sensing environmental factors and malaria mortality in three malaria endemic regions in Western Kenya. Our results show that rainfall has the most consistent predictive pattern to malaria transmission in the endemic study area. Results highlight a potential for development of locally based early warning forecasts that could potentially reduce the disease burden by enabling timely control actions.

Environmental factors and population at risk of malaria in Nkomazi municipality, South Africa

OBJECTIVE

Nkomazi local municipality of South Africa is a high-risk malaria region with an incidence rate of about 500 cases per 100 000. We examined the influence of environmental factors on population (age group) at risk of malaria.

METHODS

r software was used to statistically analyse data. Using remote sensing technology, a Landsat 8 image of 4th October 2015 was classified using object-based classification and a 5-m resolution. Spot height data were used to generate a digital elevation model of the area.

RESULTS

A total of 60 718 malaria cases were notified across 48 health facilities in Nkomazi municipality between January 1997 and August 2015. Malaria incidence was highly associated with irrigated land (P = 0.001), water body (P = 0.011) and altitude ≤400 m (P = 0.001). The multivariate model showed that with 10% increase in the extent of irrigated areas, malaria risk increased by almost 39% in the entire study area and by almost 44% in the 2-km buffer zone of selected villages. Malaria incidence is more pronounced in the economically active population aged 15-64 and in males. Both incidence and case fatality rate drastically declined over the study period.

CONCLUSION

A predictive model based on environmental factors would be useful in the effort towards malaria elimination by fostering appropriate targeting of control measures and allocating of resources.

Targeting indoor residual spraying for malaria using epidemiological data: a case study of the Zambia experience

Background

In Zambia and other sub-Saharan African countries affected by ongoing malaria transmission, indoor residual spraying (IRS) for malaria prevention has typically been implemented over large areas, e.g., district-wide, and targeted to peri-urban areas. However, there is a recent shift in some countries, including Zambia, towards the adoption of a more strategic and targeted IRS approach, in coordination with increased emphasis on universal coverage of long-lasting insecticidal nets (LLINs) and effective insecticide resistance management. A true targeted approach would deliver IRS to sub-district areas identified as high-risk, with the goal of maximizing the prevention of malaria cases and deaths.

Results

Together with the Government of the Republic of Zambia, a new methodology was developed applying geographic information systems and satellite imagery to support a targeted IRS campaign during the 2014 spray season using health management information system data.

Discussion/Conclusion

This case study focuses on the developed methodology while also highlighting the significant research gaps which must be filled to guide countries on the most effective strategy for IRS targeting in the context of universal LLIN coverage and evolving insecticide resistance.

Predictive Malaria Risk and Uncertainty Mapping in Nchelenge District, Zambia: Evidence of Widespread, Persistent Risk and Implications for Targeted Interventions

Malaria risk maps may be used to guide policy decisions on whether vector control interventions should be targeted and, if so, where. Active surveillance for malaria was conducted through household surveys in Nchelenge District, Zambia from April 2012 through December 2014. Households were enumerated based on satellite imagery and randomly selected for study enrollment. At each visit, participants were administered a questionnaire and a malaria rapid diagnostic test (RDT). Logistic regression models were used to construct spatial prediction risk maps and maps of risk uncertainty. A total of 461 households were visited, comprising 1,725 participants, of whom 48% were RDT positive. Several environmental features were associated with increased household malaria risk in a multivariable logistic regression model adjusting for seasonal variation. The model was validated using both internal and external evaluation measures to generate and assess root mean square error, as well as sensitivity and specificity for predicted risk. The final, validated model was used to predict and map malaria risk including a measure of risk uncertainty. Malaria risk in a high, perennial transmission setting is widespread but heterogeneous at a local scale, with seasonal variation. Targeting malaria control interventions may not be appropriate in this epidemiological setting.

Modeling larval malaria vector habitat locations using landscape features and cumulative precipitation measures

Background

Predictive models of malaria vector larval habitat locations may provide a basis for understanding the spatial determinants of malaria transmission.

Methods

We used four landscape variables (topographic wetness index [TWI], soil type, land use-land cover, and distance to stream) and accumulated precipitation to model larval habitat locations in a region of western Kenya through two methods: logistic regression and random forest. Additionally, we used two separate data sets to account for variation in habitat locations across space and over time.

Results

Larval habitats were more likely to be present in locations with a lower slope to contributing area ratio (i.e. TWI), closer to streams, with agricultural land use relative to nonagricultural land use, and in friable clay/sandy clay loam soil and firm, silty clay/clay soil relative to friable clay soil. The probability of larval habitat presence increased with increasing accumulated precipitation. The random forest models were more accurate than the logistic regression models, especially when accumulated precipitation was included to account for seasonal differences in precipitation. The most accurate models for the two data sets had area under the curve (AUC) values of 0.864 and 0.871, respectively. TWI, distance to the nearest stream, and precipitation had the greatest mean decrease in Gini impurity criteria in these models.

Conclusions

This study demonstrates the usefulness of random forest models for larval malaria vector habitat modeling. TWI and distance to the nearest stream were the two most important landscape variables in these models. Including accumulated precipitation in our models improved the accuracy of larval habitat location predictions by accounting for seasonal variation in the precipitation. Finally, the sampling strategy employed here for model parameterization could serve as a framework for creating predictive larval habitat models to assist in larval control efforts.

High burden of malaria following scale-up of control interventions in Nchelenge District, Luapula Province, Zambia

BACKGROUND

Malaria control interventions have been scaled-up in Zambia in conjunction with a malaria surveillance system. Although substantial progress has been achieved in reducing morbidity and mortality, national and local information demonstrated marked heterogeneity in the impact of malaria control across the country. This study reports the high burden of malaria in Nchelenge District, Luapula Province, Zambia from 2006 to 2012 after seven years of control measures.

METHODS

Yearly aggregated information on cases of malaria, malaria deaths, use of malaria diagnostics, and malaria control interventions from 2006 to 2012 were obtained from the Nchelenge District Health Office. Trends in the number of malaria cases, methods of diagnosis, malaria positivity rate among pregnant women, and intervention coverage were analysed using descriptive statistics.

RESULTS

Malaria prevalence remained high, increasing from 38% in 2006 to 53% in 2012. Increasing numbers of cases of severe malaria were reported until 2010. Intense seasonal malaria transmission was observed with seasonal declines in the number of cases between April and August, although malaria transmission continued throughout the year. Clinical diagnosis without accompanying confirmation declined from 95% in 2006 to 35% in 2012. Intervention coverage with long-lasting insecticide-treated nets and indoor residual spraying increased from 2006 to 2012.

CONCLUSIONS

Despite high coverage with vector control interventions, the burden of malaria in Nchelenge District, Zambia remained high. The high parasite prevalence could accurately reflect the true burden, perhaps in part as a consequence of population movement, or improved access to care and case reporting. Quality information at fine spatial scales will be critical for targeting effective interventions and measurement of progress.

Battling Malaria in Rural Zambia with Modern Technology: A Qualitative Study on the Value of Cell Phones, Geographical Information Systems, Asymptomatic Carriers and Rapid Diagnostic Tests to Identify, Treat and Control Malaria

During the last decade much progress has been made in reducing malaria transmission in Macha, Southern Province, Zambia. Introduction of artemisinin combination therapies as well as mass screenings of asymptomatic carriers is believed to have contributed the most. When an endemic malaria situation is moving towards a non-endemic situation the resident population loses acquired immunity and therefore active case detection and efficient surveillance is crucial to prevent epidemic outbreaks. Our purpose was to evaluate the impact of cell phone surveillance and geographical information systems on malaria control in Macha. Furthermore, it evaluates what screening and treatment of asymptomatic carriers and implementation of rapid diagnostic tests in rural health care has led to. Ten in-depth semi-structured interviews, field observations and data collection were performed at the Macha Research Trust and at surrounding rural health centers. This qualitative method was inspired by rapid assessment procedure. The cell phone surveillance has been easily integrated in health care, and its integration with Geographical Information Systems has provided the ability to follow malaria transmission on a weekly basis. In addition, active case detection of asymptomatic carriers has been fruitful, which is reflected in it soon being applied nationwide. Furthermore, rapid diagnostic tests have provided rural health centers with reliable malaria diagnostics, thereby decreasing excessive malaria treatments and selection for drug resistance. This report reflects the importance of asymptomatic carriers in targeting malaria elimination, as well as development of effective surveillance systems when transmission decreases. Such an approach would be cost-efficient in the long run through positive effects in reduced child mortality and relief in health care.

Patterns of spatio-temporal distribution, abundance, and diversity in a mosquito community from the eastern Smoky Hills of Kansas

Nearly 30% of emerging infectious disease events are caused by vector-borne pathogens with wildlife origins. Their transmission involves a complex interplay among pathogens, arthropod vectors, the environment and host species, and they pose a risk for public health, livestock and wildlife species. Examining habitat associations of vector species known to transmit infectious diseases, and quantifying spatio-temporal dynamics of mosquito vector communities is one aspect of the holistic One Health approach that is necessary to develop effective control measures. A survey was conducted from May to August, 2010 of the abundance and diversity of mosquito species occurring in the mixed-grass prairie habitat of the Smoky Hills of Kansas. This region is an important breeding ground for North America's grassland nesting birds and, as such, it could represent an important habitat for the enzootic amplification cycle of avian malaria and infectious encephalitides, as well as spill-over events to humans and livestock. A total of 11 species, belonging to the three genera Aedes, Anopheles, and Culex, was collected during this study. Aedes nigromaculis, Ae. sollicitans, Ae. taeniorhynchus, Culex salinarius, and Cx. tarsalis accounted for 98% of the collected species. Multiple linear regression models suggested that mosquito abundances in the grasslands of the central Great Plains were explained by meteorological and environmental variables. Temporal dynamics in mosquito abundances were well supported by models that included maximum and minimum temperature indices (adjusted R(2) = 0.73). Spatial dynamics of mosquito abundances were best explained by a model containing the following environmental variables (adjusted R(2) =0.37): ground curvature, topographic wetness index, distance to woodland, and distance to road. The mosquito species we detected are known vectors for infectious encephalitides, including West Nile virus. Understanding the microhabitat characteristics of these mosquito species in a grassland ecosystem will aid in the control and management of these disease vectors.

Risk factors associated with clinical malaria episodes in Bangladesh: a longitudinal study

Malaria is endemic to Bangladesh. In this longitudinal study, we used hydrologic, topographic, and socioeconomic risk factors to explain single and multiple malaria infections at individual and household levels. Malaria incidence was determined for 1,634 households in 54 villages in 2009 and 2010. During the entire study period 21.8% of households accounted for all (n = 497) malaria cases detected; 15.4% of households had 1 case and 6.4% had ≥ 2 cases. The greatest risk factors for malaria infection were low bed net ratio per household, house construction materials (wall), and high density of houses. Hydrologic and topographic factors were not significantly associated with malaria risk. This study identifies stable malaria hotspots and risk factors that should be considered for cost-effective targeting of malaria interventions that may contribute to potential elimination of malaria in Bangladesh.

Community-acquired acute kidney injury in tropical countries

Community-acquired acute kidney injury (AKI) in developing tropical countries is markedly different from AKI in developed countries with a temperate climate, which exemplifies the influence that environment can have on the epidemiology of human diseases. The aetiology and presentation of AKI reflect the ethnicity, socioeconomic factors, climatic and ecological characteristics in tropical countries. Tropical zones are characterized by high year-round temperatures and the absence of frost, which supports the propagation of infections that can cause AKI, including malaria, leptospirosis, HIV and diarrhoeal diseases. Other major causes of AKI in tropical countries are envenomation; ingestion of toxic herbs or chemicals; poisoning; and obstetric complications. These factors are associated with low levels of income, poor access to treatment, and social or cultural practices (such as the use of traditional herbal medicines and treatments) that contribute to poor outcomes of patients with AKI. Most causes of AKI in developing tropical countries are preventable, but strategies to improve the outcomes and reduce the burden of tropical AKI require both improvements in basic public health, achieved through effective interventions, and increased access to effective medical care (especially for patients with established AKI).

Heterogeneity and changes in inequality of malaria risk after introduction of insecticide-treated bed nets in Macha, Zambia

In 2007, the first free mass distribution of insecticide-treated bed nets (ITNs) occurred in southern Zambia. To determine the effect of ITNs on heterogeneity in biting rates, human DNA from Anopheles arabiensis blood meals was genotyped to determine the number of hosts that had contributed to the blood meals. The multiple feeding rate decreased from 18.9% pre-ITN to 9.1% post-ITN, suggesting that mosquito biting had focused onto a smaller fraction of the population. Pre-ITN, 20% of persons in a household provided 40% of blood meals, which increased to 59% post-ITN. To measure heterogeneity over a larger scale, mosquitoes were collected in 90 households in two village areas. Of these households, 25% contributed 78.1% of An. arabiensis, and households with high frequencies of An. arabiensis were significantly spatially clustered. The results indicate that substantial heterogeneity in malaria risk exists at local and household levels, and household-level heterogeneity may be influenced by interventions, such as ITNs.

Topographic models for predicting malaria vector breeding habitats: potential tools for vector control managers

BACKGROUND

Identification of malaria vector breeding sites can enhance control activities. Although associations between malaria vector breeding sites and topography are well recognized, practical models that predict breeding sites from topographic information are lacking. We used topographic variables derived from remotely sensed Digital Elevation Models (DEMs) to model the breeding sites of malaria vectors. We further compared the predictive strength of two different DEMs and evaluated the predictability of various habitat types inhabited by Anopheles larvae.

METHODS

Using GIS techniques, topographic variables were extracted from two DEMs: 1) Shuttle Radar Topography Mission 3 (SRTM3, 90-m resolution) and 2) the Advanced Spaceborne Thermal Emission Reflection Radiometer Global DEM (ASTER, 30-m resolution). We used data on breeding sites from an extensive field survey conducted on an island in western Kenya in 2006. Topographic variables were extracted for 826 breeding sites and for 4520 negative points that were randomly assigned. Logistic regression modelling was applied to characterize topographic features of the malaria vector breeding sites and predict their locations. Model accuracy was evaluated using the area under the receiver operating characteristics curve (AUC).

RESULTS

All topographic variables derived from both DEMs were significantly correlated with breeding habitats except for the aspect of SRTM. The magnitude and direction of correlation for each variable were similar in the two DEMs. Multivariate models for SRTM and ASTER showed similar levels of fit indicated by Akaike information criterion (3959.3 and 3972.7, respectively), though the former was slightly better than the latter. The accuracy of prediction indicated by AUC was also similar in SRTM (0.758) and ASTER (0.755) in the training site. In the testing site, both SRTM and ASTER models showed higher AUC in the testing sites than in the training site (0.829 and 0.799, respectively). The predictability of habitat types varied. Drains, foot-prints, puddles and swamp habitat types were most predictable.

CONCLUSIONS

Both SRTM and ASTER models had similar predictive potentials, which were sufficiently accurate to predict vector habitats. The free availability of these DEMs suggests that topographic predictive models could be widely used by vector control managers in Africa to complement malaria control strategies.

Satellite Microwave Remote Sensing for Environmental Modeling of Mosquito Population Dynamics

Environmental variability has important influences on mosquito life cycles and understanding the spatial and temporal patterns of mosquito populations is critical for mosquito control and vector-borne disease prevention. Meteorological data used for model-based predictions of mosquito abundance and life cycle dynamics are typically acquired from ground-based weather stations; however, data availability and completeness are often limited by sparse networks and resource availability. In contrast, environmental measurements from satellite remote sensing are more spatially continuous and can be retrieved automatically. This study compared environmental measurements from the NASA Advanced Microwave Scanning Radiometer on EOS (AMSR-E) and in situ weather station data to examine their ability to predict the abundance of two important mosquito species (Aedes vexans and Culex tarsalis) in Sioux Falls, South Dakota, USA from 2005 to 2010. The AMSR-E land parameters included daily surface water inundation fraction, surface air temperature, soil moisture, and microwave vegetation opacity. The AMSR-E derived models had better fits and higher forecasting accuracy than models based on weather station data despite the relatively coarse (25-km) spatial resolution of the satellite data. In the AMSR-E models, air temperature and surface water fraction were the best predictors of Aedes vexans, whereas air temperature and vegetation opacity were the best predictors of Cx. tarsalis abundance. The models were used to extrapolate spatial, seasonal, and interannual patterns of climatic suitability for mosquitoes across eastern South Dakota. Our findings demonstrate that environmental metrics derived from satellite passive microwave radiometry are suitable for predicting mosquito population dynamics and can potentially improve the effectiveness of mosquito-borne disease early warning systems.

Early detection of malaria foci for targeted interventions in endemic southern Zambia

BACKGROUND

Zambia has achieved significant reductions in the burden of malaria through a strategy of "scaling-up" effective interventions. Progress toward ultimate malaria elimination will require sustained prevention coverage and further interruption of transmission through active strategies to identify and treat asymptomatic malaria reservoirs. A surveillance system in Zambia's Southern Province has begun to implement such an approach. An early detection system could be an additional tool to identify foci of elevated incidence for targeted intervention.

METHODS

Based on surveillance data collected weekly from 13 rural health centres (RHCs) divided into three transmission zones, early warning thresholds were created following a technique successfully implemented in Thailand. Alert levels were graphed for all 52 weeks of a year using the mean and 95% confidence interval upper limit of a Poisson distribution of the weekly diagnosed malaria cases for every available week of historic data (beginning in Aug, 2008) at each of the sites within a zone. Annually adjusted population estimates for the RHC catchment areas served as person-time of weekly exposure. The zonal threshold levels were validated against the incidence data from each of the 13 respective RHCs.

RESULTS

Graphed threshold levels for the three zones generally conformed to observed seasonal incidence patterns. Comparing thresholds with historic weekly incidence values, the overall percentage of aberrant weeks ranged from 1.7% in Mbabala to 36.1% in Kamwanu. For most RHCs, the percentage of weeks above threshold was greater during the high transmission season and during the 2009 year compared to 2010. 39% of weeks breaching alert levels were part of a series of three or more consecutive aberrant weeks.

CONCLUSIONS

The inconsistent sensitivity of the zonal threshold levels impugns the reliability of the alert system. With more years of surveillance data available, individual thresholds for each RHC could be calculated and compared to the technique outlined here. Until then, "aberrant" weeks during low transmission seasons, and during high transmission seasons at sites where the threshold level is less sensitive, could feasibly be followed up for household screening. Communities with disproportionate numbers of aberrant weeks could be reviewed for defaults in the scaling-up intervention coverage.