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To assess the effectiveness of community-wide deployment of insecticide-impregnated collars for dogs- the reservoir of Leishmania infantum-to reduce infantile clinical visceral leishmaniasis (VL). A pair matched-cluster randomised controlled trial involving 40 collared and 40 uncollared control villages (161 [95% C.L.s: 136, 187] children per cluster), was designed to detect a 55% reduction in 48 month confirmed VL case incidence. The intervention study was designed by the authors, but implemented by the Leishmaniasis Control Program in NW Iran, from 2002 to 2006. The collars provided 50% (95% C.I. 17·8%-70·0%) protection against infantile VL incidence (0·95/1000/yr compared to 1·75/1000/yr). Reductions in incidence were observed across 76% (22/29) of collared villages compared to pair-matched control villages, with 31 fewer cases by the end of the trial period. In 11 paired villages, no further cases were recorded post-intervention, whereas in 7 collared villages there were 9 new clinical cases relative to controls. Over the trial period, 6,835 collars were fitted at the beginning of the 4 month sand fly season, of which 6.9% (95% C.I. 6.25%, 7.56%) were lost but rapidly replaced. Collar coverage (percent dogs collared) per village varied between 66% and 100%, with a mean annual coverage of 87% (95% C.I. 84·2, 89·0%). The variation in post-intervention clinical VL incidence was not associated with collar coverage, dog population size, implementation logistics, dog owner compliance, or other demographic variables tested. Larger reductions and greater persistence in incident case numbers (indicative of transmission) were observed in villages with higher pre-existing VL case incidence. Community-wide deployment of collars can provide a significant level of protection against infantile clinical VL, achieved in this study by the local VL Control Program, demonstrating attributes desirable of a sustainable public health program. The effectiveness is not dissimilar to the community-level protection provided against human and canine infection with L. infantum.

The kala-azar elimination programme has resulted in a significant reduction in visceral leishmaniasis (VL) cases across the Indian Subcontinent. To detect any resurgence of transmission, a sensitive cost-effective surveillance system is required. Molecular xenomonitoring (MX), detection of pathogen DNA/RNA in vectors, provides a proxy of human infection in the lymphatic filariasis elimination programme. To determine whether MX can be used for VL surveillance in a low transmission setting, large numbers of the sand fly vector Phlebotomus argentipes are required. This study will determine the best method for capturing P. argentipes females for MX. The field study was performed in two programmatic and two non-programmatic villages in Bihar, India. A total of 48 households (12/village) were recruited. Centers for Disease Control and Prevention light traps (CDC-LTs) were compared with Improved Prokopack (PKP) and mechanical vacuum aspirators (MVA) using standardised methods. Four 12x12 Latin squares, 576 collections, were attempted (12/house, 144/village,192/method). Molecular analyses of collections were conducted to confirm identification of P. argentipes and to detect human and Leishmania DNA. Operational factors, such as time burden, acceptance to householders and RNA preservation, were also considered. A total of 562 collections (97.7%) were completed with 6,809 sand flies captured. Females comprised 49.0% of captures, of which 1,934 (57.9%) were identified as P. argentipes. CDC-LTs collected 4.04 times more P. argentipes females than MVA and 3.62 times more than PKP (p<0.0001 for each). Of 21,735 mosquitoes in the same collections, no significant differences between collection methods were observed. CDC-LTs took less time to install and collect than to perform aspirations and their greater yield compensated for increased sorting time. No significant differences in Leishmania RNA detection and quantitation between methods were observed in experimentally infected sand flies maintained in conditions simulating field conditions. CDC-LTs were favoured by householders. CDC-LTs are the most useful collection tool of those tested for MX surveillance since they collected higher numbers of P. argentipes females without compromising mosquito captures or the preservation of RNA. However, capture rates are still low.

As of 2021, the National Kala-azar Elimination Programme (NKAEP) in India has achieved visceral leishmaniasis (VL) elimination (<1 case / 10,000 population/year per block) in 625 of the 633 endemic blocks (subdistricts) in four states. The programme needs to sustain this achievement and target interventions in the remaining blocks to achieve the WHO 2030 target of VL elimination as a public health problem. An effective tool to analyse programme data and predict/ forecast the spatial and temporal trends of VL incidence, elimination threshold, and risk of resurgence will be of use to the programme management at this juncture. We employed spatiotemporal models incorporating environment, climatic and demographic factors as covariates to describe monthly VL cases for 8-years (2013-2020) in 491 and 27 endemic and non-endemic blocks of Bihar and Jharkhand states. We fitted 37 models of spatial, temporal, and spatiotemporal interaction random effects with covariates to monthly VL cases for 6-years (2013-2018, training data) using Bayesian inference via Integrated Nested Laplace Approximation (INLA) approach. The best-fitting model was selected based on deviance information criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC) and was validated with monthly cases for 2019-2020 (test data). The model could describe observed spatial and temporal patterns of VL incidence in the two states having widely differing incidence trajectories, with >93% and 99% coverage probability (proportion of observations falling inside 95% Bayesian credible interval for the predicted number of VL cases per month) during the training and testing periods. PIT (probability integral transform) histograms confirmed consistency between prediction and observation for the test period. Forecasting for 2021-2023 showed that the annual VL incidence is likely to exceed elimination threshold in 16-18 blocks in 4 districts of Jharkhand and 33-38 blocks in 10 districts of Bihar. The risk of VL in non-endemic neighbouring blocks of both Bihar and Jharkhand are less than 0.5 during the training and test periods, and for 2021-2023, the probability that the risk greater than 1 is negligible (P<0.1). Fitted model showed that VL occurrence was positively associated with mean temperature, minimum temperature, enhanced vegetation index, precipitation, and isothermality, and negatively with maximum temperature, land surface temperature, soil moisture and population density. The spatiotemporal model incorporating environmental, bioclimatic, and demographic factors demonstrated that the KAMIS database of the national programmme can be used for block level predictions of long-term spatial and temporal trends in VL incidence and risk of outbreak / resurgence in endemic and non-endemic settings. The database integrated with the modelling framework and a dashboard facility can facilitate such analysis and predictions. This could aid the programme to monitor progress of VL elimination at least one-year ahead, assess risk of resurgence or outbreak in post-elimination settings, and implement timely and targeted interventions or preventive measures so that the NKAEP meet the target of achieving elimination by 2030.

The elimination programme for visceral leishmaniasis (VL) in India has seen great progress, with total cases decreasing by over 80% since 2010 and many blocks now reporting zero cases from year to year. Prompt diagnosis and treatment is critical to continue progress and avoid epidemics in the increasingly susceptible population. Short-term forecasts could be used to highlight anomalies in incidence and support health service logistics. The model which best fits the data is not necessarily most useful for prediction, yet little empirical work has been done to investigate the balance between fit and predictive performance. We developed statistical models of monthly VL case counts at block level. By evaluating a set of randomly-generated models, we found that fit and one-month-ahead prediction were strongly correlated and that rolling updates to model parameters as data accrued were not crucial for accurate prediction. The final model incorporated auto-regression over four months, spatial correlation between neighbouring blocks, and seasonality. Ninety-four percent of 10-90% prediction intervals from this model captured the observed count during a 24-month test period. Comparison of one-, three- and four-month-ahead predictions from the final model fit demonstrated that a longer time horizon yielded only a small sacrifice in predictive power for the vast majority of blocks. The model developed is informed by routinely-collected surveillance data as it accumulates, and predictions are sufficiently accurate and precise to be useful. Such forecasts could, for example, be used to guide stock requirements for rapid diagnostic tests and drugs. More comprehensive data on factors thought to influence geographic variation in VL burden could be incorporated, and might better explain the heterogeneity between blocks and improve uniformity of predictive performance. Integration of the approach in the management of the VL programme would be an important step to ensuring continued successful control.

Robust diagnostic tools and surveillance are crucial for malaria control and elimination efforts. Malaria caused by neglected Plasmodium parasites is often underestimated due to the lack of rapid diagnostic tools that can accurately detect these species. While nucleic-acid amplification technologies stand out as the most sensitive methods for detecting and confirming Plasmodium species, their implementation in resource-constrained settings poses significant challenges. Here, we present a Pan Plasmodium recombinase polymerase amplification lateral flow (RPA–LF) assay, capable of detecting all six human infecting Plasmodium species in low resource settings. The Pan Plasmodium RPA-LF assay successfully detected low density clinical infections with a preliminary limit of detection between 10–100 fg/µl for P. falciparum. When combined with crude nucleic acid extraction, the assay can serve as a point-of-need tool for molecular xenomonitoring. This utility was demonstrated by screening laboratory-reared Anopheles stephensi mosquitoes fed with Plasmodium -infected blood, as well as field samples of An. funestus s.l. and An. gambiae s.l. collected from central Africa. Overall, our proof-of-concept Pan Plasmodium diagnostic tool has the potential to be applied for clinical and xenomonitoring field surveillance, and after further evaluation, could become an essential tool to assist malaria control and elimination.

The scientific community recognizes that molecular xenomonitoring (MX) can allow infected mosquitoes to serve as a proxy for human infection in vector-borne disease surveillance, but developing reliable MX systems for programmatic use has been challenging. The primary aim of this article is to examine the available evidence to recommend how MX can best be used for various purposes. Although much of the literature published within the last 20 years focuses on using MX for lymphatic filariasis elimination, a growing body of evidence supports its use in early warning systems for emerging infectious diseases (EIDs). An MX system design must consider the goal and target (e.g. diseases targeted for elimination versus EIDs), mosquito and pathogen characteristics, and context (e.g. setting and health system). MX is currently used as a ‘supplement’ to human surveillance and will not be considered as a ‘replacement’ until the correlation between pathogen-infection rates in human and mosquito populations is better understood. Establishing such relationships may not be feasible in elimination scenarios, due to increasingly dwindling human infection prevalence after successful control, but may still be possible for EIDs and in integrated disease surveillance systems. This article is part of the theme issue ‘Novel control strategies for mosquito-borne diseases'.

Mosquito-borne diseases are an increasing global health challenge, threatening over 40% of the world's population. Despite major advances in malaria control since 2000, recent progress has stalled. Additionally, the risk of Aedes -borne arboviruses is rapidly growing, with the unprecedented spread of dengue and chikungunya viruses, outbreaks of yellow fever and the 2015 epidemic of Zika virus in Latin America. To counteract this growing problem, diverse and innovative mosquito control technologies are currently under development. Conceptually, these span an impressive spectrum of approaches, from invasive transgene cassettes with the potential to crash mosquito populations or reduce the vectorial capacity of a population, to low-cost alterations in housing design that restrict mosquito entry. This themed issue will present articles providing insight into the breadth of mosquito control research, while demonstrating the requirement for an interdisciplinary approach. The issue will highlight mosquito control technologies at varying stages of development and includes both opinion pieces and research articles with laboratory and field-based data on control strategy development. This article is part of the theme issue ‘Novel control strategies for mosquito-borne diseases'.

BackgroundVisceral leishmaniasis (VL) is a debilitating and—without treatment—fatal parasitic disease which burdens the most impoverished communities in northeastern India. Control and ultimately, elimination of VL depends heavily on prompt case detection. However, a proportion of VL cases remain undiagnosed many months after symptom onset. Delay to diagnosis increases the chance of onward transmission, and poses a risk of resurgence in populations with waning immunity. We analysed the spatial variation of delayed diagnosis of VL in Bihar, India and aimed to understand the potential driving factors of these delays.MethodsThe spatial distribution of time to diagnosis was explored using a Bayesian hierarchical model fit to 4270 geo-located cases notified between January 2018 and July 2019 through routine surveillance. Days between symptoms meeting clinical criteria (14-day fever) and diagnosis were assumed to be Poisson-distributed, adjusting for individual- and village-level characteristics. Residual variance was modelled with an explicit spatial structure. Cumulative delays were estimated under different scenarios of active case detection coverage.ResultsThe 4270 cases analysed were found to be prone to excessive delays in areas outside existing endemic ‘hot spots’. After accounting for differences associated with age, HIV status and mode of detection (active versus passive surveillance), cases diagnosed within recently affected (≥ 1 case reported in the previous year) blocks and villages experienced shorter delays on average (by 13% [2.9–21.7%] (95% credible interval) and 7% [1.3–13.1%], respectively) than those in non-recently-affected areas.ConclusionsDelays to VL diagnosis when incidence is low could influence whether transmission of the disease could be interrupted or resurges. Prioritising and narrowing surveillance to high-burden areas may increase the likelihood of excessive delays in diagnosis in peripheral areas. Active surveillance driven by observed incidence may lead to missing the risk posed by as-yet-undiagnosed cases in low-endemic areas, and such surveillance could be insufficient for achieving and sustaining elimination.

Background As cases of visceral leishmaniasis (VL) in India dwindle, there is motivation to monitor elimination progress on a finer geographic scale than sub-district (block). Low-incidence projections across geographically- and demographically- heterogeneous communities are difficult to act upon, and equitable elimination cannot be achieved if local pockets of incidence are overlooked. However, maintaining consistent surveillance at this scale is resource-intensive and not sustainable in the long-term. Methods We analysed VL incidence across 45,000 villages in Bihar state, exploring spatial autocorrelation and associations with local environmental conditions in order to assess the feasibility of inference at this scale. We evaluated a statistical disaggregation approach to infer finer spatial variation from routinely-collected, block-level data, validating against observed village-level incidence. Results This disaggregation approach does not estimate village-level incidence more accurately than a baseline assumption of block-homogeneity. Spatial auto-correlation is evident on a block-level but weak between neighbouring villages within the same block, possibly suggesting that longer-range transmission (e.g., due to population movement) may be an important contributor to village-level heterogeneity. Conclusions Increasing the range of reactive interventions to neighbouring villages may not improve their efficacy in suppressing transmission, but maintaining surveillance and diagnostic capacity in areas distant from recently observed cases - particularly along routes of population movement from endemic regions - could reduce reintroduction risk in currently unaffected villages. The reactive, spatially-targeted approach to VL surveillance limits interpretability of data observed at the village level, and hence the feasibility of routinely drawing and validating inference at this scale. Plain Language Summary Near elimination, it is important to understand how the remaining cases of disease are distributed on a local level. However, surveillance data are more easily collated according to larger administrative units. We investigated whether village-level patterns of visceral leishmaniasis (VL) incidence could be inferred from administrative-level data using a statistical modelling approach. We found strong similarity in incidence between neighbouring administrative units but not between neighbouring villages, and model predictions did not correspond well to observed village-level case data. This could suggest that longer-range transmission contributes more to the village-level pattern of incidence than short in this near-elimination context, which should be considered in intervention planning. However, increased surveillance effort in assumed high-risk villages makes interpretation of data at this level challenging. Nightingale et al. compare block-homogeneity and statistical disaggregation approaches to analyse visceral leishmaniasis incidence across 45,000 villages in Bihar state. Village-level incidence is not measured more accurately by the disaggregation approach and spatial auto-correlation is evident on a block-level but weak between neighbouring villages within the same block.