Finding hotspots: development of an adaptive spatial sampling approach

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.

Sampling for malaria molecular surveillance

Strategic use of Plasmodium falciparum genetic variation has great potential to inform public health actions for malaria control and elimination. Malaria molecular surveillance (MMS) begins with a strategy to identify and collect parasite samples, guided by public-health priorities. In this review we discuss sampling design practices for MMS and point out epidemiological, biological, and statistical factors that need to be considered. We present examples for different use cases, including detecting emergence and spread of rare variants, establishing transmission sources and inferring changes in malaria transmission intensity. This review will potentially guide the collection of samples and data, serve as a starting point for further methodological innovation, and enhance utilization of MMS to support malaria elimination.

Implications for selecting persistent hot spots of schistosomiasis from community- and school-based surveys in Blue Nile, North Kordofan, and Sennar States, Sudan

In several schistosomiasis-endemic countries, the prevalence has remained high in some areas owing to reinfection despite repeated mass drug administration (MDA) interventions; these areas are referred to as persistent hot spots. Identifying hotspots is critical for interrupting transmission. This study aimed to determine an effective means of identifying persistent hot spots. First, we investigated the differences between Schistosoma haematobium and Schistosoma mansoni prevalence among school-aged children (SAC) estimated by a community-based survey, for which local key informants purposively selected communities, and a randomly sampled school-based survey. A total of 6,225 individuals residing in 60 villages in 8 districts of North Kordofan, Blue Nile, or Sennar States, Sudan participated in a community-based survey in March 2018. Additionally, the data of 3,959 students attending 71 schools in the same 8 districts were extracted from a nationwide school-based survey conducted in January 2017. The community-based survey identified 3 districts wherein the prevalence of S. haematobium or S. mansoni infection among SAC was significantly higher than that determined by the randomly sampled school survey (e.g., S. haematobium in the Sennar district: 10.8% vs. 1.1%, P<0.001). At the state level, the prevalence of schistosomiasis among SAC, as determined by the community-based survey, was consistently significantly higher than that determined by the school-based survey. Purposeful selection of villages or schools based on a history of MDA, latrine coverage, open defecation, and the prevalence of bloody urine improved the ability for identifying persistent hot spots.

First bovine vaccine to prevent human schistosomiasis - a cluster randomised Phase 3 clinical trial

OBJECTIVE

Schistosomiasis is a neglected tropical parasitic disease caused by blood flukes of the genus Schistosoma. Schistosoma japonicum is zoonotic in China, the Philippines, and Indonesia, with bovines acting as major reservoirs of human infection. The primary objective of the trial was to examine the impact of a combination of human mass chemotherapy, snail control through mollusciciding, and SjCTPI bovine vaccination on the rate of human infection.

METHODS

A 5-year phase IIIa cluster randomized control trial was conducted among 18 schistosomiasis-endemic villages comprising 18,221 residents in Northern Samar, The Philippines.

RESULTS

Overall, bovine vaccination resulted in a statistically significant decrease in human infection (relative risk [RR] = 0.75; 95% confidence interval [CI] = 0.69 to 0.82) across all trial follow-ups. The best outcome of the trial was when bovine vaccination was combined with snail mollusciciding. This combination resulted in a 31% reduction (RR = 0.69; 95% CI = 0.61 to 0.78) in human infection.

CONCLUSION

This is the first trial to demonstrate the effectiveness of a bovine vaccine for schistosomiasis in reducing human schistosome infection. The trial is registered with Australian New Zealand Clinical Trials Registry (ACTRN12619001048178).

Spatial epidemiology and adaptive targeted sampling to manage the Chagas disease vector Triatoma dimidiata

Widespread application of insecticide remains the primary form of control for Chagas disease in Central America, despite only temporarily reducing domestic levels of the endemic vector Triatoma dimidiata and having little long-term impact. Recently, an approach emphasizing community feedback and housing improvements has been shown to yield lasting results. However, the additional resources and personnel required by such an intervention likely hinders its widespread adoption. One solution to this problem would be to target only a subset of houses in a community while still eliminating enough infestations to interrupt disease transfer. Here we develop a sequential sampling framework that adapts to information specific to a community as more houses are visited, thereby allowing us to efficiently find homes with domiciliary vectors while minimizing sampling bias. The method fits Bayesian geostatistical models to make spatially informed predictions, while gradually transitioning from prioritizing houses based on prediction uncertainty to targeting houses with a high risk of infestation. A key feature of the method is the use of a single exploration parameter, α, to control the rate of transition between these two design targets. In a simulation study using empirical data from five villages in southeastern Guatemala, we test our method using a range of values for α, and find it can consistently select fewer homes than random sampling, while still bringing the village infestation rate below a given threshold. We further find that when additional socioeconomic information is available, much larger savings are possible, but that meeting the target infestation rate is less consistent, particularly among the less exploratory strategies. Our results suggest new options for implementing long-term T. dimidiata control.

Effective public health interventions for the control and elimination of neglected tropical diseases require an efficient use of resources while still causing long-term disease reduction at the community level. To use resources to best effect, areas most in need of control efforts must be identified. However, strategies for correctly identifying these areas are rarely known due to the complex environmental, biological, and cultural factors shaping disease spread. In turn, incorrect prioritization of control targets can cause the intervention to have no lasting effect. We address this tradeoff between efficiency and efficacy by adapting control priorities throughout an intervention, targeting areas of high uncertainty during the initial stages while shifting to areas of greatest risk at later stages. In the context of controlling Triatoma dimidiata, the primary vector of Chagas disease in several countries in Latin America, our methods provide a means of targeting only a subset of homes for insecticide and housing improvements, while still reducing a village’s overall infestation rate below the critical threshold.

A Digital Twin Framework for Environmental Sensing with sUAS

This paper proposes a digital twin (DT) framework for point source applications in environmental sensing (ES). The DT concept has become quite popular among process and manufacturing industries for improving performance and estimating remaining useful life (RUL). However, environmental behavior, such as in gas dispersion, is ever changing and hard to model in real-time. The DT framework is applied to the point source environmental monitoring problem, through the use of hybrid modeling and optimization techniques. A controlled release case study is overviewed to illustrate our proposed DT framework and several spatial interpolation techniques are explored for source estimation. Future research efforts and directions are discussed.

Optimizing cluster survey designs for estimating trachomatous inflammation-follicular within trachoma control programs

OBJECTIVES

The World Health Organization recommends mass drug administration (MDA) with azithromycin to eliminate trachoma as a public health problem. MDA decisions are based on prevalence estimates from two-stage cluster surveys. Work remains to mathematically evaluate current trachoma survey designs. We aimed to characterize the effects of the number of units sampled on the precision and cost of trachomatous inflammation-follicular (TF) estimates.

METHODS

We simulated a population of 30 districts to represent the breadth of possible TF distributions in Amhara, Ethiopia. Samples of varying numbers of clusters (14-34) and households (10-60) were selected. Sampling schemes were evaluated on precision, proportion of incorrect and low MDA decisions made, and estimated cost.

RESULTS

Number of clusters sampled had a greater impact on precision than number of households. The most efficient scheme depended on the underlying TF prevalence in a district. For lower prevalence areas (<10%) the most cost efficient (providing adequate precision while minimizing cost) design was 20 clusters of 20-30 households. For higher prevalence areas (>10%), the most efficient design was 15-20 clusters of 20-30 households.

CONCLUSIONS

For longer-running programs, using context-specific survey designs would allow for practical precision while reducing survey costs. Sampling 15 clusters of 20-30 households in suspected moderate to high prevalence districts and 20 clusters of 20-30 households in districts suspected to be near the 5% threshold appears to be a balanced approach.

Technical Workflow Development for Integrating Drone Surveys and Entomological Sampling to Characterise Aquatic Larval Habitats of Anopheles funestus in Agricultural Landscapes in Côte d'Ivoire

Land-use practices such as agriculture can impact mosquito vector breeding ecology, resulting in changes in disease transmission. The typical breeding habitats of Africa's second most important malaria vector Anopheles funestus are large, semipermanent water bodies, which make them potential candidates for targeted larval source management. This is a technical workflow for the integration of drone surveys and mosquito larval sampling, designed for a case study aiming to characterise An. funestus breeding sites near two villages in an agricultural setting in Côte d'Ivoire. Using satellite remote sensing data, we developed an environmentally and spatially representative sampling frame and conducted paired mosquito larvae and drone mapping surveys from June to August 2021. To categorise the drone imagery, we also developed a land cover classification scheme with classes relative to An. funestus breeding ecology. We sampled 189 potential breeding habitats, of which 119 (63%) were positive for the Anopheles genus and nine (4.8%) were positive for An. funestus. We mapped 30.42 km2 of the region of interest including all water bodies which were sampled for larvae. These data can be used to inform targeted vector control efforts, although its generalisability over a large region is limited by the fine-scale nature of this study area. This paper develops protocols for integrating drone surveys and statistically rigorous entomological sampling, which can be adjusted to collect data on vector breeding habitats in other ecological contexts. Further research using data collected in this study can enable the development of deep-learning algorithms for identifying An. funestus breeding habitats across rural agricultural landscapes in Côte d'Ivoire and the analysis of risk factors for these sites.

Geographically targeted surveillance of livestock could help prioritize intervention against antimicrobial resistance in China

The rise of antimicrobial resistance (AMR) in animals is being fuelled by the widespread use of veterinary antimicrobials. China is the largest global consumer of veterinary antimicrobials, and improving AMR surveillance strategies in this region could help prioritize intervention and preserve antimicrobial efficacy. Here we mapped AMR rates in pigs, chickens and cattle in China using 446 surveys of event-based surveillance between 2000 and 2019 for foodborne bacteria, in combination with geospatial models to identify locations where conducting new surveys could have the highest benefits. Using maps of uncertainty, we show that eastern China currently has the highest AMR rates, and southwestern and northeastern China would benefit the most from additional surveillance efforts. Instead of distributing new surveys evenly across administrative divisions, using geographically targeted surveillance could reduce AMR prediction uncertainty by two-fold. In a context of competing disease control priorities, our findings present a feasible option for optimizing surveillance efforts-and slowing the spread of AMR.

Neglected tropical disease control in a world with COVID-19: an opportunity and a necessity for innovation

Countries have seen substantial disruptions to usual health services related to coronavirus disease 2019 and these are likely to have immediate and long-term indirect effects on many disease control programmes, including neglected tropical diseases (NTDs). The pandemic has highlighted the usefulness of mathematical modelling to understand the impacts of these disruptions and future control measures on progress towards 2030 NTD goals. The pandemic also provides an opportunity, and a practical necessity, to transform NTD programmes through innovation.

The DIOS framework for optimizing infectious disease surveillance: Numerical methods for simulation and multi-objective optimization of surveillance network architectures

Infectious disease surveillance systems provide vital data for guiding disease prevention and control policies, yet the formalization of methods to optimize surveillance networks has largely been overlooked. Decisions surrounding surveillance design parameters-such as the number and placement of surveillance sites, target populations, and case definitions-are often determined by expert opinion or deference to operational considerations, without formal analysis of the influence of design parameters on surveillance objectives. Here we propose a simulation framework to guide evidence-based surveillance network design to better achieve specific surveillance goals with limited resources. We define evidence-based surveillance design as an optimization problem, acknowledging the many operational constraints under which surveillance systems operate, the many dimensions of surveillance system design, the multiple and competing goals of surveillance, and the complex and dynamic nature of disease systems. We describe an analytical framework-the Disease Surveillance Informatics Optimization and Simulation (DIOS) framework-for the identification of optimal surveillance designs through mathematical representations of disease and surveillance processes, definition of objective functions, and numerical optimization. We then apply the framework to the problem of selecting candidate sites to expand an existing surveillance network under alternative objectives of: (1) improving spatial prediction of disease prevalence at unmonitored sites; or (2) estimating the observed effect of a risk factor on disease. Results of this demonstration illustrate how optimal designs are sensitive to both surveillance goals and the underlying spatial pattern of the target disease. The findings affirm the value of designing surveillance systems through quantitative and adaptive analysis of network characteristics and performance. The framework can be applied to the design of surveillance systems tailored to setting-specific disease transmission dynamics and surveillance needs, and can yield improved understanding of tradeoffs between network architectures.

Evaluating survey designs for targeting preventive chemotherapy against Schistosoma haematobium and Schistosoma mansoni across sub-Saharan Africa: a geostatistical analysis and modelling study

BACKGROUND

Schistosomiasis control programmes primarily use school-based surveys to identify areas for mass drug administration of preventive chemotherapy. However, as the spatial distribution of schistosomiasis can be highly focal, transmission may not be detected by surveys implemented at districts or larger spatial units. Improved mapping strategies are required to accurately and cost-effectively target preventive chemotherapy to remaining foci across all possible spatial distributions of schistosomiasis.

METHODS

Here, we use geostatistical models to quantify the spatial heterogeneity of Schistosoma haematobium and S. mansoni across sub-Saharan Africa using the most comprehensive dataset available on school-based surveys. Applying this information to parameterise simulations, we assess the accuracy and cost of targeting alternative implementation unit sizes across the range of plausible schistosomiasis distributions. We evaluate the consequences of decisions based on survey designs implemented at district and subdistrict levels sampling different numbers of schools. Cost data were obtained from field surveys conducted across multiple countries and years, with cost effectiveness evaluated as the cost per correctly identified school.

RESULTS

Models identified marked differences in prevalence and spatial distributions between countries and species; however, results suggest implementing surveys at subdistrict level increase the accuracy of treatment classifications across most scenarios. While sampling intensively at the subdistrict level resulted in the highest classification accuracy, this sampling strategy resulted in the highest costs. Alternatively, sampling the same numbers of schools currently recommended at the district level but stratifying by subdistrict increased cost effectiveness.

CONCLUSIONS

This study provides a new tool to evaluate schistosomiasis survey designs across a range of transmission settings. Results highlight the importance of considering spatial structure when designing sampling strategies, illustrating that a substantial proportion of children may be undertreated even when an implementation unit is correctly classified. Control programmes need to weigh the increased accuracy of more detailed mapping strategies against the survey costs and treatment priorities.