Continental-scale, data-driven predictive assessment of eliminating the vector-borne disease, lymphatic filariasis, in sub-Saharan Africa by 2020

Michael, Edwin; Singh, Bhavna; Mayala, Benjamin K.; Smith, Morgan E.; Hampton, Scott; Nabrzyski, Jarosław

doi:10.1186/s12916-017-0933-2

Cited by 30 publications

(43 citation statements)

References 68 publications

(108 reference statements)

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“…Although our principal focus in this study was in investigating the value of longitudinal data for informing the predictive performance of the current LF models, the results presented here have also underscored the existence of significant spatial heterogeneity in the dynamics of parasite extinction between the present sites ( Table 2 , Fig 3 ). In line with our previous findings, this observed conditional dependency of systems dynamics on local transmission conditions means that timelines or durations of interventions required to break LF transmission (as depicted in Table 2 ) will also vary from site to site even under similar control conditions [ 3 – 5 , 21 ]. As we indicated before, this outcome implies that we vitally require the application of models to detailed spatio-temporal infection surveillance data, such as that exemplified by the data collected by countries in sentinel sites as part of their WHO-directed monitoring and evaluation activities, if we are to use the present models to make more reliable intervention predictions to drive policy and management decisions (particularly with respect to the durations of interventions required, need for switching to more intensified or new MDA regimens, and need for enhanced supplementary vector control) in a given endemic setting [ 64 ].…”

Section: Discussionsupporting

confidence: 88%

“…As we have previously pointed out, the development of such spatially adaptive intervention plans will require the development and use of spatially-explicit data assimilation modelling platforms that can couple geostatistical interpolation of model inputs (eg. ABR and/or sentinel site mf/antigen prevalence data) with discovery of localized models from such data in order to produce the required regional or national intervention forecasts [ 5 ].…”

Section: Discussionmentioning

confidence: 99%

“…Any misspecification of these system attributes will lead to failure in accurately forecasting the future behaviour of a system, with predictions of actual future states becoming highly uncertain even when the exact representation of the underlying deterministic process is well established but precise specification of initial conditions or forcing and/or parameter values is difficult to achieve [ 17 , 18 ]. This problem becomes even more intractable when theoretical models depend on parameter estimates taken from other studies [ 5 , 17 , 19 ]. Both these challenges, viz.…”

Section: Introductionmentioning

confidence: 99%

“…sensitivity to forcing conditions and use of parameter estimates from settings that are different from the dynamical environment in which a model will be used for simulation, imply that strong limits will be imposed on the realized predictability of any given model for an application [ 9 , 10 , 20 ]. As we have shown recently, if such uncertainties are ignored, the ability of parasite transmission models to form the scientific basis for management decisions can be severely undermined, especially when predictions are required over long time frames and across heterogeneous geographic locations [ 4 , 5 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

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Quantifying the value of surveillance data for improving model predictions of lymphatic filariasis elimination

et al. 2018

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BackgroundMathematical models are increasingly being used to evaluate strategies aiming to achieve the control or elimination of parasitic diseases. Recently, owing to growing realization that process-oriented models are useful for ecological forecasts only if the biological processes are well defined, attention has focused on data assimilation as a means to improve the predictive performance of these models.Methodology and principal findingsWe report on the development of an analytical framework to quantify the relative values of various longitudinal infection surveillance data collected in field sites undergoing mass drug administrations (MDAs) for calibrating three lymphatic filariasis (LF) models (EPIFIL, LYMFASIM, and TRANSFIL), and for improving their predictions of the required durations of drug interventions to achieve parasite elimination in endemic populations. The relative information contribution of site-specific data collected at the time points proposed by the WHO monitoring framework was evaluated using model-data updating procedures, and via calculations of the Shannon information index and weighted variances from the probability distributions of the estimated timelines to parasite extinction made by each model. Results show that data-informed models provided more precise forecasts of elimination timelines in each site compared to model-only simulations. Data streams that included year 5 post-MDA microfilariae (mf) survey data, however, reduced each model’s uncertainty most compared to data streams containing only baseline and/or post-MDA 3 or longer-term mf survey data irrespective of MDA coverage, suggesting that data up to this monitoring point may be optimal for informing the present LF models. We show that the improvements observed in the predictive performance of the best data-informed models may be a function of temporal changes in inter-parameter interactions. Such best data-informed models may also produce more accurate predictions of the durations of drug interventions required to achieve parasite elimination.SignificanceKnowledge of relative information contributions of model only versus data-informed models is valuable for improving the usefulness of LF model predictions in management decision making, learning system dynamics, and for supporting the design of parasite monitoring programmes. The present results further pinpoint the crucial need for longitudinal infection surveillance data for enhancing the precision and accuracy of model predictions of the intervention durations required to achieve parasite elimination in an endemic location.

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Section: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantifying the value of surveillance data for improving model predictions of lymphatic filariasis elimination

et al. 2018

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“…A complete set of pre-intervention survey data used for calibration the model includes the site location, the vector species responsible for transmission, the prevailing annual biting rate, and the baseline mf survey results (data from 6 ). The map of site-specific transmission thresholds highlights the spatial heterogeneity of breakpoints used here for defining the design prevalence in the PFFI calculations (data from 69 , 70 ) …”

Section: Resultsmentioning

confidence: 99%

Substantiating freedom from parasitic infection by combining transmission model predictions with disease surveys

et al. 2018

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Stopping interventions is a critical decision for parasite elimination programmes. Quantifying the probability that elimination has occurred due to interventions can be facilitated by combining infection status information from parasitological surveys with extinction thresholds predicted by parasite transmission models. Here we demonstrate how the integrated use of these two pieces of information derived from infection monitoring data can be used to develop an analytic framework for guiding the making of defensible decisions to stop interventions. We present a computational tool to perform these probability calculations and demonstrate its practical utility for supporting intervention cessation decisions by applying the framework to infection data from programmes aiming to eliminate onchocerciasis and lymphatic filariasis in Uganda and Nigeria, respectively. We highlight a possible method for validating the results in the field, and discuss further refinements and extensions required to deploy this predictive tool for guiding decision making by programme managers.

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Big data–model integration and AI for vector‐borne disease prediction

et al. 2020

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Predicting the drivers of incursion and expansion of vector‐borne diseases as part of early‐warning strategies (EWS) is a major challenge for geographically extensive diseases where spread is mediated by spatial heterogeneity in climate and other environmental drivers. Geospatial data on these environmental drivers are increasingly available affording opportunities for application to a predictive disease ecology paradigm provided the data can be synthesized and harmonized with fine‐scale, highly resolved data on vector and host responses to their environment. Here, we apply a multi‐scale big data–model integration approach using human‐guided machine learning to objectively evaluate the importance of a large suite of spatially distributed environmental variables (>400) to develop EWS for vesicular stomatitis (VS), a common viral vector‐borne vesicular disease affecting livestock throughout the Americas. Two temporally and phylogenetically distinct events were used to develop disease occurrence–environment relationships in incursion (2004) and expansion years (2005), and then to test those relationships (2014, 2015) at two scales: (1) local and (2) landscape to regional. Our results show that VS occurrence at a local scale of individual landowners was related to conditions that can be monitored (rainfall, temperatures, streamflow) or modified (vegetation). On‐site green vegetation during the month of occurrence and higher rainfall four months prior combined with either cool daytime (expansion) or nighttime (incursion) temperatures one month prior were indicators of VS occurrence. Distance to running water (incursion) and host density based on neighboring ranches (expansion) with infected animals were also important in individual years. At landscape‐to‐regional scales, conditions that favor specific VSV biological vectors were indicated, either black flies in incursion years or biting midges in expansion years. Changes in viral genetic lineage were less important to patterns in VS occurrence than factors affecting the host–vector–environment interactions. In combination with our onset map based on latitude, elevation, and long‐term annual precipitation, this year‐ and scale‐specific information can be used to develop strategies to minimize effects of future VS events. This big data approach coupled with expert knowledge and machine learning can be applied to other emerging diseases for improvement in understanding, prediction, and management of vector‐borne diseases.

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Continental-scale, data-driven predictive assessment of eliminating the vector-borne disease, lymphatic filariasis, in sub-Saharan Africa by 2020

Cited by 30 publications

References 68 publications

Quantifying the value of surveillance data for improving model predictions of lymphatic filariasis elimination

Quantifying the value of surveillance data for improving model predictions of lymphatic filariasis elimination

Substantiating freedom from parasitic infection by combining transmission model predictions with disease surveys

Big data–model integration and AI for vector‐borne disease prediction

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