Predicting future community-level ocular Chlamydia trachomatis infection prevalence using serological, clinical, molecular, and geospatial data.

Trachoma is an infectious disease characterized by repeated exposures to Chlamydia trachomatis (Ct) that may ultimately lead to blindness. Efficient identification of communities with high infection burden could help target more intensive control efforts. We hypothesized that IgG seroprevalence in combination with geospatial layers, machine learning, and model-based geostatistics would be able to accurately predict future community-level ocular Ct infections detected by PCR. We used measurements from 40 communities in the hyperendemic Amhara region of Ethiopia to assess this hypothesis. Median Ct infection prevalence among children 0-5 years old increased from 6% at enrollment, in the context of recent mass drug administration (MDA), to 29% by month 36, following three years without MDA. At baseline, correlation between seroprevalence and Ct infection was stronger among children 0-5 years old (ρ = 0.77) than children 6-9 years old (ρ = 0.48), and stronger than the correlation between active trachoma and Ct infection (0-5y ρ = 0.56; 6-9y ρ = 0.40). Seroprevalence was the strongest concurrent predictor of infection prevalence at month 36 among children 0-5 years old (cross-validated R2 = 0.75, 95% CI: 0.58-0.85), though predictive performance declined substantially with increasing temporal lag between predictor and outcome measurements. Geospatial variables, a spatial Gaussian process, and stacked ensemble machine learning did not meaningfully improve predictions. Serological markers among children 0-5 years old may be an objective tool for identifying communities with high levels of ocular Ct infections, but accurate, future prediction in the context of changing transmission remains an open challenge.

Association of Travel Distance to Nearest Abortion Facility With Rates of Abortion.

Importance: Travel distance to abortion services varies widely in the US. Some evidence shows travel distance affects use of abortion care, but there is no national analysis of how abortion rate changes with travel distance. Objective: To examine the association between travel distance to the nearest abortion care facility and the abortion rate and to model the effect of reduced travel distance. Design, Setting, and Participants: This cross-sectional geographic analysis used 2015 data on abortions by county of residence from 1948 counties in 27 states. Abortion rates were modeled using a spatial Poisson model adjusted for age, race/ethnicity, marital status, educational attainment, household poverty, nativity, and state abortion policies. Abortion rates for 3107 counties in the 48 contiguous states that were home to 62.5 million female residents of reproductive age (15-44 years) and changes under travel distance scenarios, including integration into primary care (<30 miles) and availability of telemedicine care (<5 miles), were estimated. Data were collected from April 2018 to October 2019 and analyzed from December 2019 to July 2020. Exposures: Median travel distance by car to the nearest abortion facility. Main Outcomes and Measures: US county abortion rate per 1000 female residents of reproductive age. Results: Among the 1948 counties included in the analysis, greater travel distances were associated with lower abortion rates in a dose-response manner. Compared with a median travel distance of less than 5 miles (median rate, 21.1 [range, 1.2-63.6] per 1000 female residents of reproductive age), distances of 5 to 15 miles (median rate, 12.2 [range, 0.5-23.4] per 1000 female residents of reproductive age; adjusted coefficient, -0.05 [95% CI, -0.07 to -0.03]) and 120 miles or more (median rate, 3.9 [range, 0-12.9] per 1000 female residents of reproductive age; coefficient, -0.73 [95% CI, -0.80 to -0.65]) were associated with lower rates. In a model of 3107 counties with 62.5 million female residents of reproductive age, 696 760 abortions were estimated (mean rate, 11.1 [range, 1.0-45.5] per 1000 female residents of reproductive age). If abortion were integrated into primary care, an additional 18 190 abortions (mean rate, 11.4 [range, 1.1-45.5] per 1000 female residents of reproductive age) were estimated. If telemedicine were widely available, an additional 70 920 abortions were estimated (mean rate, 12.3 [range, 1.4-45.5] per 1000 female residents of reproductive age). Conclusions and Relevance: These findings suggest that greater travel distances to abortion services are associated with lower abortion rates. The results indicate which geographic areas have insufficient access to abortion care. Modeling suggests that integrating abortion into primary care or making medication abortion care available by telemedicine may decrease unmet need.

Population size estimation of seasonal forest-going populations in southern Lao PDR.

Forest-going populations are key to malaria transmission in the Greater Mekong Sub-region (GMS) and are therefore targeted for elimination efforts. Estimating the size of this population is essential for programs to assess, track and achieve their elimination goals. Leveraging data from three cross-sectional household surveys and one survey among forest-goers, the size of this high-risk population in a southern province of Lao PDR between December 2017 and November 2018 was estimated by two methods: population-based household surveys and capture-recapture. During the first month of the dry season, the first month of the rainy season, and the last month of the rainy season, respectively, 16.2% [14.7; 17.7], 9.3% [7.2; 11.3], and 5.3% [4.4; 6.1] of the adult population were estimated to have engaged in forest-going activities. The capture-recapture method estimated a total population size of 18,426 [16,529; 20,669] forest-goers, meaning 61.0% [54.2; 67.9] of the adult population had engaged in forest-going activities over the 12-month study period. This study demonstrates two methods for population size estimation to inform malaria research and programming. The seasonality and turnover within this forest-going population provide unique opportunities and challenges for control programs across the GMS as they work towards malaria elimination.

Finding hotspots: development of an adaptive spatial sampling approach.

The identification of disease hotspots is an increasingly important public health problem. While geospatial modeling offers an opportunity to predict the locations of hotspots using suitable environmental and climatological data, little attention has been paid to optimizing the design of surveys used to inform such models. Here we introduce an adaptive sampling scheme optimized to identify hotspot locations where prevalence exceeds a relevant threshold. Our approach incorporates ideas from Bayesian optimization theory to adaptively select sample batches. We present an experimental simulation study based on survey data of schistosomiasis and lymphatic filariasis across four countries. Results across all scenarios explored show that adaptive sampling produces superior results and suggest that similar performance to random sampling can be achieved with a fraction of the sample size.

Effectiveness of reactive focal mass drug administration and reactive focal vector control to reduce malaria transmission in the low malaria-endemic setting of Namibia: a cluster-randomised controlled, open-label, two-by-two factorial design trial.

BACKGROUND: In low malaria-endemic settings, screening and treatment of individuals in close proximity to index cases, also known as reactive case detection (RACD), is practised for surveillance and response. However, other approaches could be more effective for reducing transmission. We aimed to evaluate the effectiveness of reactive focal mass drug administration (rfMDA) and reactive focal vector control (RAVC) in the low malaria-endemic setting of Zambezi (Namibia). METHODS: We did a cluster-randomised controlled, open-label trial using a two-by-two factorial design of 56 enumeration area clusters in the low malaria-endemic setting of Zambezi (Namibia). We randomly assigned these clusters using restricted randomisation to four groups: RACD only, rfMDA only, RAVC plus RACD, or rfMDA plus RAVC. RACD involved rapid diagnostic testing and treatment with artemether-lumefantrine and single-dose primaquine, rfMDA involved presumptive treatment with artemether-lumefantrine, and RAVC involved indoor residual spraying with pirimiphos-methyl. Interventions were administered within 500 m of index cases. To evaluate the effectiveness of interventions targeting the parasite reservoir in humans (rfMDA vs RACD), in mosquitoes (RAVC vs no RAVC), and in both humans and mosquitoes (rfMDA plus RAVC vs RACD only), an intention-to-treat analysis was done. For each of the three comparisons, the primary outcome was the cumulative incidence of locally acquired malaria cases. This trial is registered with ClinicalTrials.gov, number NCT02610400. FINDINGS: Between Jan 1, 2017, and Dec 31, 2017, 55 enumeration area clusters had 1118 eligible index cases that led to 342 interventions covering 8948 individuals. The cumulative incidence of locally acquired malaria was 30·8 per 1000 person-years (95% CI 12·8-48·7) in the clusters that received rfMDA versus 38·3 per 1000 person-years (23·0-53·6) in the clusters that received RACD; 30·2 per 1000 person-years (15·0-45·5) in the clusters that received RAVC versus 38·9 per 1000 person-years (20·7-57·1) in the clusters that did not receive RAVC; and 25·0 per 1000 person-years (5·2-44·7) in the clusters that received rfMDA plus RAVC versus 41·4 per 1000 person-years (21·5-61·2) in the clusters that received RACD only. After adjusting for imbalances in baseline and implementation factors, the incidence of malaria was lower in clusters receiving rfMDA than in those receiving RACD (adjusted incidence rate ratio 0·52 [95% CI 0·16-0·88], p=0·009), lower in clusters receiving RAVC than in those that did not (0·48 [0·16-0·80], p=0·002), and lower in clusters that received rfMDA plus RAVC than in those receiving RACD only (0·26 [0·10-0·68], p=0·006). No serious adverse events were reported. INTERPRETATION: In a low malaria-endemic setting, rfMDA and RAVC, implemented alone and in combination, reduced malaria transmission and should be considered as alternatives to RACD for elimination of malaria. FUNDING: Novartis Foundation, Bill & Melinda Gates Foundation, and Horchow Family Fund.

Unnatural selection of salmon life histories in a modified riverscape.

Altered river flows and fragmented habitats often simplify riverine communities and favor non-native fishes, but their influence on life-history expression and survival is less clear. Here, we quantified the expression and ultimate success of diverse salmon emigration behaviors in an anthropogenically altered California river system. We analyzed two decades of Chinook salmon monitoring data to explore the influence of regulated flows on juvenile emigration phenology, abundance, and recruitment. We then followed seven cohorts into adulthood using otolith (ear stone) chemical archives to identify patterns in time- and size-selective mortality along the migratory corridor. Suppressed winter flow cues were associated with delayed emigration timing, particularly in warm, dry years, which was also when selection against late migrants was the most extreme. Lower, less variable flows were also associated with reduced juvenile and adult production, highlighting the importance of streamflow for cohort success in these southernmost populations. While most juveniles emigrated from the natal stream as fry or smolts, the survivors were dominated by the rare few that left at intermediate sizes and times, coinciding with managed flows released before extreme summer temperatures. The consistent selection against early (small) and late (large) migrants counters prevailing ecological theory that predicts different traits to be favored under varying environmental conditions. Yet, even with this weakened portfolio, maintaining a broad distribution in migration traits still increased adult production and reduced variance. In years exhibiting large fry pulses, even marginal increases in their survival would have significantly boosted recruitment. However, management actions favoring any single phenotype could have negative evolutionary and demographic consequences, potentially reducing adaptability and population stability. To recover fish populations and support viable fisheries in a warming and increasingly unpredictable climate, coordinating flow and habitat management within and among watersheds will be critical to balance trait optimization versus diversification.

Household electricity access in Africa (2000-2013): Closing information gaps with model-based geostatistics.

Household electricity access data in Africa are scarce, particularly at the subnational level. We followed a model-based Geostatistics approach to produce maps of electricity access between 2000 and 2013 at a 5 km resolution. We collated data from 69 nationally representative household surveys conducted in Africa and incorporated nighttime lights imagery as well as land use and land cover data to produce maps of electricity access between 2000 and 2013. The information produced here can be an aid for understanding of how electricity access has changed in the region during this 14 year period. The resolution and the continental scale makes it possible to combine these data with other sources in applications in the socio-economic field, both at a local or regional level.

Predicting residential structures from open source remotely enumerated data using machine learning.

Having accurate maps depicting the locations of residential buildings across a region benefits a range of sectors. This is particularly true for public health programs focused on delivering services at the household level, such as indoor residual spraying with insecticide to help prevent malaria. While open source data from OpenStreetMap (OSM) depicting the locations and shapes of buildings is rapidly improving in terms of quality and completeness globally, even in settings where all buildings have been mapped, information on whether these buildings are residential, commercial or another type is often only available for a small subset. Using OSM building data from Botswana and Swaziland, we identified buildings for which 'type' was indicated, generated via on the ground observations, and classified these into two classes, "sprayable" and "not-sprayable". Ensemble machine learning, using building characteristics such as size, shape and proximity to neighbouring features, was then used to form a model to predict which of these 2 classes every building in these two countries fell into. Results show that an ensemble machine learning approach performed marginally, but statistically, better than the best individual model and that using this ensemble model we were able to correctly classify >86% (using independent test data) of structures correctly as sprayable and not-sprayable across both countries.

Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing.

Quantifying and monitoring the spatial and temporal dynamics of the global land cover is critical for better understanding many of the Earth's land surface processes. However, the lack of regularly updated, continental-scale, and high spatial resolution (30 m) land cover data limit our ability to better understand the spatial extent and the temporal dynamics of land surface changes. Despite the free availability of high spatial resolution Landsat satellite data, continental-scale land cover mapping using high resolution Landsat satellite data was not feasible until now due to the need for high-performance computing to store, process, and analyze this large volume of high resolution satellite data. In this study, we present an approach to quantify continental land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources.

Spatial clustering of patent and sub-patent malaria infections in northern Namibia: Implications for surveillance and response strategies for elimination.

BACKGROUND: Reactive case detection (RACD) around passively detected malaria cases is a strategy to identify and treat hotspots of malaria transmission. This study investigated the unproven assumption on which this approach is based, that in low transmission settings, infections cluster over small scales. METHODS: A prospective case-control study was conducted between January 2013 and August 2014 in Ohangwena and Omusati regions in north central Namibia. Patients attending health facilities who tested positive by malaria rapid diagnostic test (RDT) (index cases) were traced back to their home. All occupants of index case households (n = 116 households) and surrounding households (n = 225) were screened for Plasmodium infection with a rapid diagnostic test (RDT) and loop mediated isothermal amplification (LAMP) and interviewed to identify risk factors. A comparison group of 286 randomly-selected control households was also screened, to compare infection levels of RACD and non-RACD households and their neighbours. Logistic regression was used to investigate spatial clustering of patent and sub-patent infections around index cases and to identify potential risk factors that would inform screening approaches and identify risk groups. Estimates of the impact of RACD on onward transmission to mosquitoes was made using previously published figures of infection rates. RESULTS: Prevalence of Plasmodium falciparum infection by LAMP was 3.4%, 1.4% and 0.4% in index-case households, neighbors of index case households and control households respectively; adjusted odds ratio 6.1 [95%CI 1.9-19.5] comparing case households versus control households. Using data from Engela, neighbors of cases had higher odds of infection [adjusted OR 5.0 95%CI 1.3-18.9] compared to control households. All infections identified by RDTs were afebrile and RDTs identified only a small proportion of infections in case (n = 7; 17%) and control (0%) neighborhoods. Based on published estimates of patent and sub-patent infectiousness, these results suggest that infections missed by RDTs during RACD would allow 50-71% of infections to mosquitoes to occur in this setting. CONCLUSION: Malaria infections cluster around passively detected cases. The majority of infections are asymptomatic and of densities below the limit of detection of current RDTs. RACD using standard RDTs are unlikely to detect enough malaria infections to dramatically reduce transmission. In low transmission settings such as Namibia more sensitive field diagnostics or forms of focal presumptive treatment should be tested as strategies to reduce malaria transmission.

Optimising cluster survey design for planning schistosomiasis preventive chemotherapy.

BACKGROUND: The cornerstone of current schistosomiasis control programmes is delivery of praziquantel to at-risk populations. Such preventive chemotherapy requires accurate information on the geographic distribution of infection, yet the performance of alternative survey designs for estimating prevalence and converting this into treatment decisions has not been thoroughly evaluated. METHODOLOGY/PRINCIPAL FINDINGS: We used baseline schistosomiasis mapping surveys from three countries (Malawi, Côte d'Ivoire and Liberia) to generate spatially realistic gold standard datasets, against which we tested alternative two-stage cluster survey designs. We assessed how sampling different numbers of schools per district (2-20) and children per school (10-50) influences the accuracy of prevalence estimates and treatment class assignment, and we compared survey cost-efficiency using data from Malawi. Due to the focal nature of schistosomiasis, up to 53% simulated surveys involving 2-5 schools per district failed to detect schistosomiasis in low endemicity areas (1-10% prevalence). Increasing the number of schools surveyed per district improved treatment class assignment far more than increasing the number of children sampled per school. For Malawi, surveys of 15 schools per district and 20-30 children per school reliably detected endemic schistosomiasis and maximised cost-efficiency. In sensitivity analyses where treatment costs and the country considered were varied, optimal survey size was remarkably consistent, with cost-efficiency maximised at 15-20 schools per district. CONCLUSIONS/SIGNIFICANCE: Among two-stage cluster surveys for schistosomiasis, our simulations indicated that surveying 15-20 schools per district and 20-30 children per school optimised cost-efficiency and minimised the risk of under-treatment, with surveys involving more schools of greater cost-efficiency as treatment costs rose.

Malaria risk in young male travellers but local transmission persists: a case-control study in low transmission Namibia.

BACKGROUND: A key component of malaria elimination campaigns is the identification and targeting of high risk populations. To characterize high risk populations in north central Namibia, a prospective health facility-based case-control study was conducted from December 2012-July 2014. Cases (n = 107) were all patients presenting to any of the 46 health clinics located in the study districts with a confirmed Plasmodium infection by multi-species rapid diagnostic test (RDT). Population controls (n = 679) for each district were RDT negative individuals residing within a household that was randomly selected from a census listing using a two-stage sampling procedure. Demographic, travel, socio-economic, behavioural, climate and vegetation data were also collected. Spatial patterns of malaria risk were analysed. Multivariate logistic regression was used to identify risk factors for malaria. RESULTS: Malaria risk was observed to cluster along the border with Angola, and travel patterns among cases were comparatively restricted to northern Namibia and Angola. Travel to Angola was associated with excessive risk of malaria in males (OR 43.58 95% CI 2.12-896), but there was no corresponding risk associated with travel by females. This is the first study to reveal that gender can modify the effect of travel on risk of malaria. Amongst non-travellers, male gender was also associated with a higher risk of malaria compared with females (OR 1.95 95% CI 1.25-3.04). Other strong risk factors were sleeping away from the household the previous night, lower socioeconomic status, living in an area with moderate vegetation around their house, experiencing moderate rainfall in the month prior to diagnosis and living <15 km from the Angolan border. CONCLUSIONS: These findings highlight the critical need to target malaria interventions to young male travellers, who have a disproportionate risk of malaria in northern Namibia, to coordinate cross-border regional malaria prevention initiatives and to scale up coverage of prevention measures such as indoor residual spraying and long-lasting insecticide nets in high risk areas if malaria elimination is to be realized.

The Hitchhiking Parasite: Why Human Movement Matters to Malaria Transmission and What We Can Do About It.

The failure of the Global Malaria Eradication Program (GMEP) during the 1960s highlighted the relevance of human movement to both re-introducing parasites in elimination settings and spreading drug-resistant parasites widely. Today, given the sophisticated surveillance of human movement patterns and key traveler groups, it is hoped that interventions can be implemented to protect and treat travelers, prevent onward transmission in low transmission settings, and eliminate sources of transmission, including sources of drug-resistant parasites.

Spatiotemporal Analysis of Malaria in Urban Ahmedabad (Gujarat), India: Identification of Hot Spots and Risk Factors for Targeted Intervention.

The world population, especially in developing countries, has experienced a rapid progression of urbanization over the last half century. Urbanization has been accompanied by a rise in cases of urban infectious diseases, such as malaria. The complexity and heterogeneity of the urban environment has made study of specific urban centers vital for urban malaria control programs, whereas more generalizable risk factor identification also remains essential. Ahmedabad city, India, is a large urban center located in the state of Gujarat, which has experienced a significant Plasmodium vivax and Plasmodium falciparum disease burden. Therefore, a targeted analysis of malaria in Ahmedabad city was undertaken to identify spatiotemporal patterns of malaria, risk factors, and methods of predicting future malaria cases. Malaria incidence in Ahmedabad city was found to be spatially heterogeneous, but temporally stable, with high spatial correlation between species. Because of this stability, a prediction method utilizing historic cases from prior years and seasons was used successfully to predict which areas of Ahmedabad city would experience the highest malaria burden and could be used to prospectively target interventions. Finally, spatial analysis showed that normalized difference vegetation index, proximity to water sources, and location within Ahmedabad city relative to the dense urban core were the best predictors of malaria incidence. Because of the heterogeneity of urban environments and urban malaria itself, the study of specific large urban centers is vital to assist in allocating resources and informing future urban planning.

Mapping Malaria Risk in Low Transmission Settings: Challenges and Opportunities.

As malaria transmission declines, it becomes increasingly focal and prone to outbreaks. Understanding and predicting patterns of transmission risk becomes an important component of an effective elimination campaign, allowing limited resources for control and elimination to be targeted cost-effectively. Malaria risk mapping in low transmission settings is associated with some unique challenges. This article reviews the main challenges and opportunities related to risk mapping in low transmission areas including recent advancements in risk mapping low transmission malaria, relevant metrics, and statistical approaches and risk mapping in post-elimination settings.

Predicting malaria vector distribution under climate change scenarios in China: Challenges for malaria elimination.

Projecting the distribution of malaria vectors under climate change is essential for planning integrated vector control activities for sustaining elimination and preventing reintroduction of malaria. In China, however, little knowledge exists on the possible effects of climate change on malaria vectors. Here we assess the potential impact of climate change on four dominant malaria vectors (An. dirus, An. minimus, An. lesteri and An. sinensis) using species distribution models for two future decades: the 2030 s and the 2050 s. Simulation-based estimates suggest that the environmentally suitable area (ESA) for An. dirus and An. minimus would increase by an average of 49% and 16%, respectively, under all three scenarios for the 2030 s, but decrease by 11% and 16%, respectively in the 2050 s. By contrast, an increase of 36% and 11%, respectively, in ESA of An. lesteri and An. sinensis, was estimated under medium stabilizing (RCP4.5) and very heavy (RCP8.5) emission scenarios. in the 2050 s. In total, we predict a substantial net increase in the population exposed to the four dominant malaria vectors in the decades of the 2030 s and 2050 s, considering land use changes and urbanization simultaneously. Strategies to achieve and sustain malaria elimination in China will need to account for these potential changes in vector distributions and receptivity.

Tackling imported malaria: an elimination endgame.

As countries move toward malaria elimination, imported infections become increasingly significant as they often represent the majority of cases, can sustain transmission, cause resurgences, and lead to mortality. Here we review and critique current methods to prevent malaria importation in countries pursuing elimination and explore methods applied in other transmission settings and to other diseases that could be transferred to support malaria elimination. To improve intervention targeting we need a better understanding of the characteristics of populations importing infections and their patterns of migration, improved methods to reliably classify infections as imported or acquired locally, and ensure early and accurate diagnosis. The potential for onward transmission in the most receptive and vulnerable locations can be predicted through high-resolution risk mapping that can help malaria elimination or prevention of reintroduction programs target resources. Cross border and regional initiatives can be highly effective when based on an understanding of human and parasite movement. Ultimately, determining the optimal combinations of approaches to address malaria importation will require an evaluation of their impact, cost effectiveness, and operational feasibility.

Information systems to support surveillance for malaria elimination.

Robust and responsive surveillance systems are critical for malaria elimination. The ideal information system that supports malaria elimination includes: rapid and complete case reporting, incorporation of related data, such as census or health survey information, central data storage and management, automated and expert data analysis, and customized outputs and feedback that lead to timely and targeted responses. Spatial information enhances such a system, ensuring cases are tracked and mapped over time. Data sharing and coordination across borders are vital and new technologies can improve data speed, accuracy, and quality. Parts of this ideal information system exist and are in use, but have yet to be linked together coherently. Malaria elimination programs should support the implementation and refinement of information systems to support surveillance and response and ensure political and financial commitment to maintain the systems and the human resources needed to run them. National malaria programs should strive to improve the access and utility of these information systems and establish cross-border data sharing mechanisms through the use of standard indicators for malaria surveillance. Ultimately, investment in the information technologies that support a timely and targeted surveillance and response system is essential for malaria elimination.