Predicting lymphatic filariasis transmission and elimination dynamics using a multi-model ensemble framework

Heterogeneity in elimination efforts could increase the risk of resurgence of lymphatic filariasis in Madagascar

BACKGROUND

Progress in lymphatic filariasis (LF) elimination is spatially heterogeneous in many endemic countries, which may lead to resurgence in areas that have achieved elimination. Understanding the drivers and consequences of such heterogeneity could help inform strategies to reach global LF elimination goals by 2030. This study assesses whether differences in age-specific compliance with mass drug administration (MDA) could explain LF prevalence patterns in southeastern Madagascar and explores how spatial heterogeneity in prevalence and age-specific MDA compliance may affect the risk of LF resurgence after transmission interruption.

METHODOLOGY

We used LYMFASIM model with parameters in line with the context of southeastern Madagascar and explored a wide range of scenarios with different MDA compliance for adults and children (40-100%) to estimate the proportion of elimination, non-elimination and resurgence events associated with each scenario. Finally, we evaluated the risk of resurgence associated with different levels of migration (2-6%) from surrounding districts combined with varying levels of LF microfilaria (mf) prevalence (0-24%) during that same study period.

RESULTS

Differences in MDA compliance between adults and children better explained the observed heterogeneity in LF prevalence for these age groups than differences in exposure alone. The risk of resurgence associated with differences in MDA compliance scenarios ranged from 0 to 19% and was highest when compliance was high for children (e.g. 90%) and low for adults (e.g. 50%). The risk of resurgence associated with migration was generally higher, exceeding 60% risk for all the migration levels explored (2-6% per year) when mf prevalence in the source districts was between 9% and 20%.

CONCLUSION

Gaps in the implementation of LF elimination programme can increase the risk of resurgence and undermine elimination efforts. In Madagascar, districts that have not attained elimination pose a significant risk for those that have achieved it. More research is needed to help guide LF elimination programme on the optimal strategies for surveillance and control that maximize the chances to sustain elimination and avoid resurgence.

Subnational Projections of Lymphatic Filariasis Elimination Targets in Ethiopia to Support National Level Policy

BACKGROUND

Lymphatic filariasis (LF) is a debilitating, poverty-promoting, neglected tropical disease (NTD) targeted for worldwide elimination as a public health problem (EPHP) by 2030. Evaluating progress towards this target for national programmes is challenging, due to differences in disease transmission and interventions at the subnational level. Mathematical models can help address these challenges by capturing spatial heterogeneities and evaluating progress towards LF elimination and how different interventions could be leveraged to achieve elimination by 2030.

METHODS

Here we used a novel approach to combine historical geo-spatial disease prevalence maps of LF in Ethiopia with 3 contemporary disease transmission models to project trends in infection under different intervention scenarios at subnational level.

RESULTS

Our findings show that local context, particularly the coverage of interventions, is an important determinant for the success of control and elimination programmes. Furthermore, although current strategies seem sufficient to achieve LF elimination by 2030, some areas may benefit from the implementation of alternative strategies, such as using enhanced coverage or increased frequency, to accelerate progress towards the 2030 targets.

CONCLUSIONS

The combination of geospatial disease prevalence maps of LF with transmission models and intervention histories enables the projection of trends in infection at the subnational level under different control scenarios in Ethiopia. This approach, which adapts transmission models to local settings, may be useful to inform the design of optimal interventions at the subnational level in other LF endemic regions.

Lymphatic filariases and soil-transmitted helminthiases in Sri Lanka: the challenge of eliminating residual pockets of transmission

Sri Lanka has successfully met the challenge of controlling both lymphatic filariasis (LF) and soil-transmitted helminthiases (STH) as public health problems. The primary public health strategy for combatting both conditions has been preventive chemotherapy. The national programme for the elimination of LF implemented five annual rounds of mass chemotherapy in the endemic districts from 2002 to 2006 using a combination of diethylcarbamazine and albendazole. The overall microfilaria rate declined from 0.21% in 2001 before the mass chemotherapy, to 0.06% in 2016, at declaration of elimination of LF as a public health problem by the World Health Organization. Currently Sri Lanka is in the phase of post-validation surveillance. Achieving control of STH has been more difficult. Mass deworming programmes have been implemented for nearly a century, and national-level surveys reported prevalence rates declining from 6.9% in 2003 to 1% in 2017. However, neither of these infections has been completely eliminated. A situation analysis indicates continued transmission of both among high-risk communities. This paper explores the reasons for persistence of transmission of both LF and STH in residual pockets and the measures that are required to achieve long-term control, or perhaps even interrupt transmission in Sri Lanka. This article is part of the theme issue 'Challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs'.

Evaluating elimination thresholds and stopping criteria for interventions against the vector-borne macroparasitic disease, lymphatic filariasis, using mathematical modelling

We leveraged the ability of EPIFIL transmission models fit to field data to evaluate the use of the WHO Transmission Assessment Survey (TAS) for supporting Lymphatic Filariasis (LF) intervention stopping decisions. Our results indicate that understanding the underlying parasite extinction dynamics, particularly the protracted transient dynamics involved in shifts to the extinct state, is crucial for understanding the impacts of using TAS for determining the achievement of LF elimination. These findings warn that employing stopping criteria set for operational purposes, as employed in the TAS strategy, without a full consideration of the dynamics of extinction could seriously undermine the goal of achieving global LF elimination.

COVID-19: Forecasting confirmed cases and deaths with a simple time series model

Forecasting the outcome of outbreaks as early and as accurately as possible is crucial for decision-making and policy implementations. A significant challenge faced by forecasters is that not all outbreaks and epidemics turn into pandemics, making the prediction of their severity difficult. At the same time, the decisions made to enforce lockdowns and other mitigating interventions versus their socioeconomic consequences are not only hard to make, but also highly uncertain. The majority of modeling approaches to outbreaks, epidemics, and pandemics take an epidemiological approach that considers biological and disease processes. In this paper, we accept the limitations of forecasting to predict the long-term trajectory of an outbreak, and instead, we propose a statistical, time series approach to modelling and predicting the short-term behavior of COVID-19. Our model assumes a multiplicative trend, aiming to capture the continuation of the two variables we predict (global confirmed cases and deaths) as well as their uncertainty. We present the timeline of producing and evaluating 10-day-ahead forecasts over a period of four months. Our simple model offers competitive forecast accuracy and estimates of uncertainty that are useful and practically relevant.

Modelling the Impact of Vector Control on Lymphatic Filariasis Programs: Current Approaches and Limitations

Vector control is widely considered an important tool for lymphatic filariasis (LF) elimination but is not usually included in program budgets and has often been secondary to other policy questions in modelling studies. Evidence from the field demonstrates that vector control can have a large impact on program outcomes and even halt transmission entirely, but implementation is expensive. Models of LF have the potential to inform where and when resources should be focused, but often simplify vector dynamics and focus on capturing human prevalence trends, making them comparatively ill-designed for direct analysis of vector control measures. We review the recent modelling literature and present additional results using a well-established model, highlighting areas of agreement between model predictions and field evidence, and discussing the possible determinants of existing disagreements. We conclude that there are likely to be long-term benefits of vector control, both on accelerating programs and preventing resurgence.

A mathematical model of the dynamics of lymphatic filariasis in Caraga Region, the Philippines

Despite being one of the first countries to implement mass drug administration (MDA) for elimination of lymphatic filariasis (LF) in 2001 after a pilot study in 2000, the Philippines is yet to eliminate the disease as a public health problem with 6 out of the 46 endemic provinces still implementing MDA for LF as of 2018. In this work, we propose a mathematical model of the transmission dynamics of LF in the Philippines and a control strategy for its elimination using MDA. Sensitivity analysis using the Latin hypercube sampling and partial rank correlation coefficient methods suggests that the infected human population is most sensitive to the treatment parameters. Using the available LF data in Caraga Region from the Philippine Department of Health, we estimate the treatment rates r ₁ and r ₂ using the least-squares parameter estimation technique. Parameter bootstrapping showed small variability in the parameter estimates. Finally, we apply optimal control theory with the objective of minimizing the infected human population and the corresponding implementation cost of MDA, using the treatment coverage γ as the control parameter. Simulation results highlight the importance of maintaining a high MDA coverage per year to effectively minimize the infected population by the year 2030.

A triple-drug treatment regimen to accelerate elimination of lymphatic filariasis: From conception to delivery

The Global Programme to Eliminate Lymphatic Filariasis (LF) is using mass drug administration (MDA) of antifilarial medications to treat filarial infections, prevent disease and interrupt transmission. Almost 500 million people receive these medications each year. Clinical trials have recently shown that a single dose of a triple-drug combination comprised of ivermectin, diethylcarbamazine and albendazole (IDA) is dramatically superior to widely used two-drug combinations for clearing larval filarial parasites from the blood of infected persons. A large multicenter community study showed that IDA was well-tolerated when it was provided as MDA. IDA was rapidly advanced from clinical trial to policy and implementation; it has the potential to accelerate LF elimination in many endemic countries.

Design and Analysis of Elimination Surveys for Neglected Tropical Diseases

As neglected tropical diseases approach elimination status, there is a need to develop efficient sampling strategies for confirmation (or not) that elimination criteria have been met. This is an inherently difficult task because the relative precision of a prevalence estimate deteriorates as prevalence decreases, and classic survey sampling strategies based on random sampling therefore require increasingly large sample sizes. More efficient strategies for survey design and analysis can be obtained by exploiting any spatial correlation in prevalence within a model-based geostatistics framework. This framework can be used for constructing predictive probability maps that can inform in-country decision makers of the likelihood that their elimination target has been met, and where to invest in additional sampling. We evaluated our methodology using a case study of lymphatic filariasis in Ghana, demonstrating that a geostatistical approach outperforms approaches currently used to determine an evaluation unit’s elimination status.

A Heterogeneous Ensemble Forecasting Model for Disease Prediction

The manuscript presents a bragging-based ensemble forecasting model for predicting the number of incidences of a disease based on past occurrences. The objectives of this research work are to enhance accuracy, reduce overfitting, and handle overdrift; the proposed model has shown promising results in terms of error metrics. The collated dataset of the diseases is collected from the official government site of Hong Kong from the year 2010 to 2019. The preprocessing is done using log transformation and z score transformation. The proposed ensemble model is applied, and its applicability to a specific disease dataset is presented. The proposed ensemble model is compared against the ensemble models, namely dynamic ensemble for time series, arbitrated dynamic ensemble, and random forest using different error metrics. The proposed model shows the reduced value of MAE (mean average error) by 27.18%, 3.07%, 11.58%, 13.46% for tuberculosis, dengue, food poisoning, and chickenpox, respectively. The comparison drawn between the proposed model and the existing models shows that the proposed ensemble model gives better accuracy in the case of all the four-disease datasets.

Statistical methods for linking geostatistical maps and transmission models: Application to lymphatic filariasis in East Africa

•

Novel methodology for combining geostatistical mapping and transmission modelling.

•

Guide the planning of spatial control programmes by identifying affected areas.

•

Current intervention strategy will not be sufficient to eliminate LF in most areas.

•

Alternative strategies will be required to accelerate LF elimination in East Africa.

Infectious diseases remain one of the major causes of human mortality and suffering. Mathematical models have been established as an important tool for capturing the features that drive the spread of the disease, predicting the progression of an epidemic and hence guiding the development of strategies to control it. Another important area of epidemiological interest is the development of geostatistical methods for the analysis of data from spatially referenced prevalence surveys. Maps of prevalence are useful, not only for enabling a more precise disease risk stratification, but also for guiding the planning of more reliable spatial control programmes by identifying affected areas. Despite the methodological advances that have been made in each area independently, efforts to link transmission models and geostatistical maps have been limited. Motivated by this fact, we developed a Bayesian approach that combines fine-scale geostatistical maps of disease prevalence with transmission models to provide quantitative, spatially-explicit projections of the current and future impact of control programs against a disease. These estimates can then be used at a local level to identify the effectiveness of suggested intervention schemes and allow investigation of alternative strategies. The methodology has been applied to lymphatic filariasis in East Africa to provide estimates of the impact of different intervention strategies against the disease.

Defining a prevalence level to describe the elimination of Lymphatic Filariasis (LF) transmission and designing monitoring & evaluating (M&E) programmes post the cessation of mass drug administration (MDA)

The global decline in prevalence of lymphatic filariasis has been one of the major successes of the WHO's NTD programme. The recommended strategy of intensive, community-wide mass drug administration, aims to break localised transmission by either reducing the prevalence of microfilaria positive infections to below 1%, or antigen positive infections to below 2%. After the threshold is reached, and mass drug administration is stopped, geographically defined evaluation units must pass Transmission Assessment Surveys to demonstrate that transmission has been interrupted. In this study, we use an empirically parameterised stochastic transmission model to investigate the appropriateness of 1% microfilaria-positive prevalence as a stopping threshold, and statistically evaluate how well various monitoring prevalence-thresholds predict elimination or disease resurgence in the future by calculating their predictive value. Our results support the 1% filaremia prevalence target as appropriate stopping criteria. However, because at low prevalence-levels random events dominate the transmission dynamics, we find single prevalence measurements have poor predictive power for predicting resurgence, which suggests alternative criteria for restarting MDA may be beneficial.

Elimination or Resurgence: Modelling Lymphatic Filariasis After Reaching the 1% Microfilaremia Prevalence Threshold

The low prevalence levels associated with lymphatic filariasis elimination pose a challenge for effective disease surveillance. As more countries achieve the World Health Organization criteria for halting mass treatment and move on to surveillance, there is increasing reliance on the utility of transmission assessment surveys (TAS) to measure success. However, the long-term disease outcomes after passing TAS are largely untested. Using 3 well-established mathematical models, we show that low-level prevalence can be maintained for a long period after halting mass treatment and that true elimination (0% prevalence) is usually slow to achieve. The risk of resurgence after achieving current targets is low and is hard to predict using just current prevalence. Although resurgence is often quick (<5 years), it can still occur outside of the currently recommended postintervention surveillance period of 4-6 years. Our results highlight the need for ongoing and enhanced postintervention monitoring, beyond the scope of TAS, to ensure sustained success.

Field evaluation of DNA detection of human filarial and malaria parasites using mosquito excreta/feces

We recently developed a superhydrophobic cone-based method for the collection of mosquito excreta/feces (E/F) for the molecular xenomonitoring of vector-borne parasites showing higher throughput compared to the traditional approach. To test its field applicability, we used this platform to detect the presence of filarial and malaria parasites in two villages of Ghana and compared results to those for detection in mosquito carcasses and human blood. We compared the molecular detection of three parasites (Wuchereria bancrofti, Plasmodium falciparum and Mansonella perstans) in mosquito E/F, mosquito carcasses and human blood collected from the same households in two villages in the Savannah Region of the country. We successfully detected the parasite DNA in mosquito E/F from indoor resting mosquitoes, including W. bancrofti which had a very low community prevalence (2.5-3.8%). Detection in the E/F samples was concordant with detection in insect whole carcasses and human blood, and a parasite not vectored by mosquitoes was detected as well.Our approach to collect and test mosquito E/F successfully detected a variety of parasites at varying prevalence in the human population under field conditions, including a pathogen (M. perstans) which is not transmitted by mosquitoes. The method shows promise for further development and applicability for the early detection and surveillance of a variety of pathogens carried in human blood.

Data-driven modelling and spatial complexity supports heterogeneity-based integrative management for eliminating Simulium neavei-transmitted river blindness

Concern is emerging regarding the challenges posed by spatial complexity for modelling and managing the area-wide elimination of parasitic infections. While this has led to calls for applying heterogeneity-based approaches for addressing this complexity, questions related to spatial scale, the discovery of locally-relevant models, and its interaction with options for interrupting parasite transmission remain to be resolved. We used a data-driven modelling framework applied to infection data gathered from different monitoring sites to investigate these questions in the context of understanding the transmission dynamics and efforts to eliminate Simulium neavei- transmitted onchocerciasis, a macroparasitic disease that causes river blindness in Western Uganda and other regions of Africa. We demonstrate that our Bayesian-based data-model assimilation technique is able to discover onchocerciasis models that reflect local transmission conditions reliably. Key management variables such as infection breakpoints and required durations of drug interventions for achieving elimination varied spatially due to site-specific parameter constraining; however, this spatial effect was found to operate at the larger focus level, although intriguingly including vector control overcame this variability. These results show that data-driven modelling based on spatial datasets and model-data fusing methodologies will be critical to identifying both the scale-dependent models and heterogeneity-based options required for supporting the successful elimination of S. neavei-borne onchocerciasis.

Systematic selection between age and household structure for models aimed at emerging epidemic predictions

Numerous epidemic models have been developed to capture aspects of human contact patterns, making model selection challenging when they fit (often-scarce) early epidemic data equally well but differ in predictions. Here we consider the invasion of a novel directly transmissible infection and perform an extensive, systematic and transparent comparison of models with explicit age and/or household structure, to determine the accuracy loss in predictions in the absence of interventions when ignoring either or both social components. We conclude that, with heterogeneous and assortative contact patterns relevant to respiratory infections, the model’s age stratification is crucial for accurate predictions. Conversely, the household structure is only needed if transmission is highly concentrated in households, as suggested by an empirical but robust rule of thumb based on household secondary attack rate. This work serves as a template to guide the simplicity/accuracy trade-off in designing models aimed at initial, rapid assessment of potential epidemic severity.

Models of emerging epidemics can be exceedingly helpful in planning the response, but early on model selection is a difficult task. Here, the authors explore the joint contribution of age stratification and household structure on epidemic spread, and provides a rule of thumb to guide model choice.

Genomic Epidemiology in Filarial Nematodes: Transforming the Basis for Elimination Program Decisions

Onchocerciasis and lymphatic filariasis are targeted for elimination, primarily using mass drug administration at the country and community levels. Elimination of transmission is the onchocerciasis target and global elimination as a public health problem is the end point for lymphatic filariasis. Where program duration, treatment coverage, and compliance are sufficiently high, elimination is achievable for both parasites within defined geographic areas. However, transmission has re-emerged after apparent elimination in some areas, and in others has continued despite years of mass drug treatment. A critical question is whether this re-emergence and/or persistence of transmission is due to persistence of local parasites-i.e., the result of insufficient duration or drug coverage, poor parasite response to the drugs, or inadequate methods of assessment and/or criteria for determining when to stop treatment-or due to re-introduction of parasites via human or vector movement from another endemic area. We review recent genetics-based research exploring these questions in Onchocerca volvulus, the filarial nematode that causes onchocerciasis, and Wuchereria bancrofti, the major pathogen for lymphatic filariasis. We focus in particular on the combination of genomic epidemiology and genome-wide associations to delineate transmission zones and distinguish between local and introduced parasites as the source of resurgence or continuing transmission, and to identify genetic markers associated with parasite response to chemotherapy. Our ultimate goal is to assist elimination efforts by developing easy-to-use tools that incorporate genetic information about transmission and drug response for more effective mass drug distribution, surveillance strategies, and decisions on when to stop interventions to improve sustainability of elimination.

Real-time forecasting of epidemic trajectories using computational dynamic ensembles

Forecasting the trajectory of social dynamic processes, such as the spread of infectious diseases, poses significant challenges that call for methods that account for data and model uncertainty. Here we introduce an ensemble model for sequential forecasting that weights a set of plausible models and use a frequentist computational bootstrap approach to evaluate its uncertainty. We demonstrate the feasibility of our approach using simple dynamic differential-equation models and the trajectory of outbreak scenarios of the Ebola Forecasting Challenge. Specifically, we generate sequential short-term forecasts of epidemic outbreaks by combining phenomenological models that incorporate flexible epidemic growth scaling, namely the Generalized-Growth Model (GGM) and the Generalized Logistic Model (GLM). We rely on the root-mean-square error (RMSE) to quantify the quality of the models' fits during the calibration periods for weighting their contribution to the ensemble model while forecasting performance was evaluated using the RMSE of the forecasts. For a given forecasting horizon (1-4 weeks), we report the performance for each model as the percentage of the number of times each model outperforms the other models. The overall mean RMSE performance of the GLM and the GGM-GLM ensemble models outcompeted that of participant models of the Ebola Forecasting Challenge. We also found that the ensemble model provided more accurate forecasts with higher frequency than the GGM and GLM models, but its performance varied across forecasting horizons. For instance, across all of the Ebola Challenge Scenarios, the ensemble model outperformed the other models at horizons of 2 and 3 weeks while the GLM outperformed other models at horizons of 1 and 4 weeks.

Are Alternative Strategies Required to Accelerate the Global Elimination of Lymphatic Filariasis? Insights From Mathematical Models

Background

With the 2020 target year for elimination of lymphatic filariasis (LF) approaching, there is an urgent need to assess how long mass drug administration (MDA) programs with annual ivermectin + albendazole (IA) or diethylcarbamazine + albendazole (DA) would still have to be continued, and how elimination can be accelerated. We addressed this using mathematical modeling.

Methods

We used 3 structurally different mathematical models for LF transmission (EPIFIL, LYMFASIM, TRANSFIL) to simulate trends in microfilariae (mf) prevalence for a range of endemic settings, both for the current annual MDA strategy and alternative strategies, assessing the required duration to bring mf prevalence below the critical threshold of 1%.

Results

Three annual MDA rounds with IA or DA and good coverage (≥65%) are sufficient to reach the threshold in settings that are currently at mf prevalence <4%, but the required duration increases with increasing mf prevalence. Switching to biannual MDA or employing triple-drug therapy (ivermectin, diethylcarbamazine, and albendazole [IDA]) could reduce program duration by about one-third. Optimization of coverage reduces the time to elimination and is particularly important for settings with a history of poorly implemented MDA (low coverage, high systematic noncompliance).

Conclusions

Modeling suggests that, in several settings, current annual MDA strategies will be insufficient to achieve the 2020 LF elimination targets, and programs could consider policy adjustment to accelerate, guided by recent monitoring and evaluation data. Biannual treatment and IDA hold promise in reducing program duration, provided that coverage is good, but their efficacy remains to be confirmed by more extensive field studies.

Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S

Seasonal influenza results in substantial annual morbidity and mortality in the United States and worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. As part of ongoing efforts to incorporate data and advanced analytical methods into public health decision-making, the United States Centers for Disease Control and Prevention (CDC) has organized seasonal influenza forecasting challenges since the 2013/2014 season. In the 2017/2018 season, 22 teams participated. A subset of four teams created a research consortium called the FluSight Network in early 2017. During the 2017/2018 season they worked together to produce a collaborative multi-model ensemble that combined 21 separate component models into a single model using a machine learning technique called stacking. This approach creates a weighted average of predictive densities where the weight for each component is determined by maximizing overall ensemble accuracy over past seasons. In the 2017/2018 influenza season, one of the largest seasonal outbreaks in the last 15 years, this multi-model ensemble performed better on average than all individual component models and placed second overall in the CDC challenge. It also outperformed the baseline multi-model ensemble created by the CDC that took a simple average of all models submitted to the forecasting challenge. This project shows that collaborative efforts between research teams to develop ensemble forecasting approaches can bring measurable improvements in forecast accuracy and important reductions in the variability of performance from year to year. Efforts such as this, that emphasize real-time testing and evaluation of forecasting models and facilitate the close collaboration between public health officials and modeling researchers, are essential to improving our understanding of how best to use forecasts to improve public health response to seasonal and emerging epidemic threats.

Mapping the baseline prevalence of lymphatic filariasis across Nigeria

Introduction

The baseline endemicity profile of lymphatic filariasis (LF) is a key benchmark for planning control programmes, monitoring their impact on transmission and assessing the feasibility of achieving elimination. Presented in this work is the modelled serological and parasitological prevalence of LF prior to the scale-up of mass drug administration (MDA) in Nigeria using a machine learning based approach.

Methods

LF prevalence data generated by the Nigeria Lymphatic Filariasis Control Programme during country-wide mapping surveys conducted between 2000 and 2013 were used to build the models. The dataset comprised of 1103 community-level surveys based on the detection of filarial antigenemia using rapid immunochromatographic card tests (ICT) and 184 prevalence surveys testing for the presence of microfilaria (Mf) in blood. Using a suite of climate and environmental continuous gridded variables and compiled site-level prevalence data, a quantile regression forest (QRF) model was fitted for both antigenemia and microfilaraemia LF prevalence. Model predictions were projected across a continuous 5 × 5 km gridded map of Nigeria. The number of individuals potentially infected by LF prior to MDA interventions was subsequently estimated.

Results

Maps presented predict a heterogeneous distribution of LF antigenemia and microfilaraemia in Nigeria. The North-Central, North-West, and South-East regions displayed the highest predicted LF seroprevalence, whereas predicted Mf prevalence was highest in the southern regions. Overall, 8.7 million and 3.3 million infections were predicted for ICT and Mf, respectively.

Conclusions

QRF is a machine learning-based algorithm capable of handling high-dimensional data and fitting complex relationships between response and predictor variables. Our models provide a benchmark through which the progress of ongoing LF control efforts can be monitored.

The roadmap towards elimination of lymphatic filariasis by 2030: insights from quantitative and mathematical modelling

The Global Programme to Eliminate Lymphatic Filariasis was launched in 2000 to eliminate lymphatic filariasis (LF) as a public health problem by 1) interrupting transmission through mass drug administration (MDA) and 2) offering basic care to those suffering from lymphoedema or hydrocele due to the infection. Although impressive progress has been made, the initial target year of 2020 will not be met everywhere. The World Health Organization recently proposed 2030 as the new target year for elimination of lymphatic filariasis (LF) as a public health problem. In this letter, LF modelers of the Neglected Tropical Diseases (NTDs) Modelling Consortium reflect on the proposed targets for 2030 from a quantitative perspective. While elimination as a public health problem seems technically and operationally feasible, it is uncertain whether this will eventually also lead to complete elimination of transmission. The risk of resurgence needs to be mitigated by strong surveillance after stopping interventions and sometimes perhaps additional interventions.

Guidelines for multi-model comparisons of the impact of infectious disease interventions

BACKGROUND

Despite the increasing popularity of multi-model comparison studies and their ability to inform policy recommendations, clear guidance on how to conduct multi-model comparisons is not available. Herein, we present guidelines to provide a structured approach to comparisons of multiple models of interventions against infectious diseases. The primary target audience for these guidelines are researchers carrying out model comparison studies and policy-makers using model comparison studies to inform policy decisions.

METHODS

The consensus process used for the development of the guidelines included a systematic review of existing model comparison studies on effectiveness and cost-effectiveness of vaccination, a 2-day meeting and guideline development workshop during which mathematical modellers from different disease areas critically discussed and debated the guideline content and wording, and several rounds of comments on sequential versions of the guidelines by all authors.

RESULTS

The guidelines provide principles for multi-model comparisons, with specific practice statements on what modellers should do for six domains. The guidelines provide explanation and elaboration of the principles and practice statements as well as some examples to illustrate these. The principles are (1) the policy and research question - the model comparison should address a relevant, clearly defined policy question; (2) model identification and selection - the identification and selection of models for inclusion in the model comparison should be transparent and minimise selection bias; (3) harmonisation - standardisation of input data and outputs should be determined by the research question and value of the effort needed for this step; (4) exploring variability - between- and within-model variability and uncertainty should be explored; (5) presenting and pooling results - results should be presented in an appropriate way to support decision-making; and (6) interpretation - results should be interpreted to inform the policy question.

CONCLUSION

These guidelines should help researchers plan, conduct and report model comparisons of infectious diseases and related interventions in a systematic and structured manner for the purpose of supporting health policy decisions. Adherence to these guidelines will contribute to greater consistency and objectivity in the approach and methods used in multi-model comparisons, and as such improve the quality of modelled evidence for policy.

A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States

Accurate prediction of the size and timing of infectious disease outbreaks could help public health officials in planning an appropriate response. This paper compares approaches developed by five different research groups to forecast seasonal influenza outbreaks in real time in the United States. Many of the models show more accurate forecasts than a historical baseline. A major impediment to predictive ability was the real-time accuracy of available data. The field of infectious disease forecasting is in its infancy and we expect that innovation will spur improvements in forecasting in the coming years.

Influenza infects an estimated 9–35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Real-time forecasts of influenza transmission can inform public health response to outbreaks. We present the results of a multiinstitution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the United States for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of seven targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the United States, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1 wk, 2 wk, and 3 wk ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision making.

Elimination within reach: A cross-sectional study highlighting the factors that contribute to persistent lymphatic filariasis in eight communities in rural Ghana

Background

Despite the progress achieved in scaling-up mass drug administration (MDA) for lymphatic filariasis (LF) in Ghana, communities with persistent LF still exist even after 10 years of community treatment. To understand the reasons for persistence, we conducted a study to assess the status of disease elimination and understand the adherence to interventions including MDA and insecticide treated nets.

Methodology and principal findings

We conducted a parasitological and epidemiological cross-sectional study in adults from eight villages still under MDA in the Northern Region savannah and the coastal Western Region of the country. Prevalence of filarial antigen ranged 0 to 32.4% and in five villages the prevalence of night blood microfilaria (mf) was above 1%, ranging from 0 to 5.7%. Median mf density was 67 mf/ml (range: 10–3,560). LF antigen positivity was positively associated with male sex but negatively associated with participating in MDA the previous year. Male sex was also associated with a decreased probability of participating in MDA. A stochastic model (TRANSFIL) was used to assess the expected microfilaria prevalence under different MDA coverage scenarios using historical data on one community in the Western Region. In this example, the model simulations suggested that the slow decline in mf prevalence is what we would expect given high baseline prevalence and a high correlation between MDA adherence from year to year, despite high MDA coverage.

Conclusions

There is a need for an integrated quantitative and qualitative research approach to identify the variations in prevalence, associated risk factors and intervention coverage and use levels between and within regions and districts. Such knowledge will help target resources and enhance surveillance to the communities most at risk and to reach the 2020 LF elimination goals in Ghana.

Lymphatic filariasis (LF) is a mosquito-borne disease and a leading cause of disability and chronic morbidity worldwide. Despite the progress achieved so far in stopping LF transmission by treating the affected communities with specific drugs over several years, areas where lymphatic filariasis persists still exist. Understanding the reasons behind this is pivotal to both reach and sustain elimination. We investigated the factors associated with filariasis persistence in various communities still under drug treatment from two regions of Ghana. We reported high variability in disease burden, adherence to drug treatment and mosquito net use between regions and communities. LF infection was associated with men and not taking the drugs, and men were also less likely to take treatment. Using mathematical modelling, we showed that slight increases in treatment coverage will accelerate elimination. Our findings highlight the reasons for LF persistence and provide guidance on how to successfully achieve elimination by refining drug treatment distribution and mosquito control interventions more tailored to individuals and communities. We also demonstrated the value of using field-collected data in mathematical models to assess the current status of disease elimination and to identify the gaps in control interventions.

GEOFIL: A spatially-explicit agent-based modelling framework for predicting the long-term transmission dynamics of lymphatic filariasis in American Samoa

In this study, a spatially-explicit agent-based modelling framework GEOFIL was developed to predict lymphatic filariasis (LF) transmission dynamics in American Samoa. GEOFIL included individual-level information on age, gender, disease status, household location, household members, workplace/school location and colleagues/schoolmates at each time step during the simulation. In American Samoa, annual mass drug administration from 2000 to 2006 successfully reduced LF prevalence dramatically. However, GEOFIL predicted continual increase in microfilaraemia prevalence in the absence of further intervention. Evidence from seroprevalence and transmission assessment surveys conducted from 2010 to 2016 indicated a resurgence of LF in American Samoa, corroborating GEOFIL's predictions. The microfilaraemia and antigenaemia prevalence in 6-7-yo children were much lower than in the overall population. Mosquito biting rates were found to be a critical determinant of infection risk. Transmission hotspots are likely to disappear with lower biting rates. GEOFIL highlights current knowledge gaps, such as data on mosquito abundance, biting rates and within-host parasite dynamics, which are important for improving the accuracy of model predictions.

Kernel-density estimation and approximate Bayesian computation for flexible epidemiological model fitting in Python

Fitting complex models to epidemiological data is a challenging problem: methodologies can be inaccessible to all but specialists, there may be challenges in adequately describing uncertainty in model fitting, the complex models may take a long time to run, and it can be difficult to fully capture the heterogeneity in the data. We develop an adaptive approximate Bayesian computation scheme to fit a variety of epidemiologically relevant data with minimal hyper-parameter tuning by using an adaptive tolerance scheme. We implement a novel kernel density estimation scheme to capture both dispersed and multi-dimensional data, and directly compare this technique to standard Bayesian approaches. We then apply the procedure to a complex individual-based simulation of lymphatic filariasis, a human parasitic disease. The procedure and examples are released alongside this article as an open access library, with examples to aid researchers to rapidly fit models to data. This demonstrates that an adaptive ABC scheme with a general summary and distance metric is capable of performing model fitting for a variety of epidemiological data. It also does not require significant theoretical background to use and can be made accessible to the diverse epidemiological research community.

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

Background

Mathematical 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 findings

We 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.

Significance

Knowledge 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.

Although parasite transmission models offer powerful tools for predicting the impacts of interventions, there is growing realization that these models can be useful for this purpose only if their governing biological processes are well defined. Recently, model-data assimilation has been applied to address this problem and improve the performance of process-oriented models for ecological forecasting. Here, we developed an analytical framework that allowed the sequential coupling of the three existing lymphatic filariasis (LF) models with longitudinal infection monitoring data collected in field sites undergoing mass drug administrations (MDAs) to examine the relative value of such data for parameterizing these models and for improving their predictions of the required durations of drug interventions to break parasite transmission. We found that data-informed models provided more precise and reliable forecasts of elimination timelines in the study sites compared to model-only predictions, and that data collected up to 5 years post-MDA reduced each model’s predictive uncertainty most. We also found that this improved performance may be intriguingly related to temporal changes in system dynamics. Our results underscore the significance of sequential model-data fusion for enhancing the understanding of LF transmission dynamics, design of surveillance, and generation of reliable model predictions for management decision making.

Innovative Surveillance Strategies to Support the Elimination of Filariasis in Africa

Lymphatic filariasis (LF) and onchocerciasis are two neglected tropical diseases (NTDs) of public health significance targeted for global elimination. The World Health Organization (WHO) African Region is a priority region, with the highest collective burden of LF and onchocerciasis globally. Coendemic loiasis further complicates elimination due to the risk of adverse events associated with ivermectin treatment. A public health framework focusing on health-related data, systematic collection of data, and analysis and interpretation of data is used to highlight the range of innovative surveillance strategies required for filariasis elimination. The most recent and significant developments include: rapid point-of-care test (POCT) diagnostics; clinical assessment tools; new WHO guidelines; open-access online data portals; mHealth platforms; large-scale prevalence maps; and the optimisation of mathematical models.

Counting Down the 2020 Goals for 9 Neglected Tropical Diseases: What Have We Learned From Quantitative Analysis and Transmission Modeling?

The control of neglected tropical diseases (NTDs) has received huge investment in recent years, leading to large reductions in morbidity. In 2012, the World Health Organization set ambitious targets for eliminating many of these diseases as a public health problem by 2020, an aspiration that was supported by donations of treatments, intervention materials, and funding committed by a broad partnership of stakeholders in the London Declaration on NTDs. Alongside these efforts, there has been an increasing role for quantitative analysis and modeling to support the achievement of these goals through evaluation of the likely impact of interventions, the factors that could undermine these achievements, and the role of new diagnostics and treatments in reducing transmission. In this special issue, we aim to summarize those insights in an accessible way. This article acts as an introduction to the special issue, outlining key concepts in NTDs and insights from modeling as we approach 2020.

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

BACKGROUND

There are growing demands for predicting the prospects of achieving the global elimination of neglected tropical diseases as a result of the institution of large-scale nation-wide intervention programs by the WHO-set target year of 2020. Such predictions will be uncertain due to the impacts that spatial heterogeneity and scaling effects will have on parasite transmission processes, which will introduce significant aggregation errors into any attempt aiming to predict the outcomes of interventions at the broader spatial levels relevant to policy making. We describe a modeling platform that addresses this problem of upscaling from local settings to facilitate predictions at regional levels by the discovery and use of locality-specific transmission models, and we illustrate the utility of using this approach to evaluate the prospects for eliminating the vector-borne disease, lymphatic filariasis (LF), in sub-Saharan Africa by the WHO target year of 2020 using currently applied or newly proposed intervention strategies. METHODS AND RESULTS: We show how a computational platform that couples site-specific data discovery with model fitting and calibration can allow both learning of local LF transmission models and simulations of the impact of interventions that take a fuller account of the fine-scale heterogeneous transmission of this parasitic disease within endemic countries. We highlight how such a spatially hierarchical modeling tool that incorporates actual data regarding the roll-out of national drug treatment programs and spatial variability in infection patterns into the modeling process can produce more realistic predictions of timelines to LF elimination at coarse spatial scales, ranging from district to country to continental levels. Our results show that when locally applicable extinction thresholds are used, only three countries are likely to meet the goal of LF elimination by 2020 using currently applied mass drug treatments, and that switching to more intensive drug regimens, increasing the frequency of treatments, or switching to new triple drug regimens will be required if LF elimination is to be accelerated in Africa. The proportion of countries that would meet the goal of eliminating LF by 2020 may, however, reach up to 24/36 if the WHO 1% microfilaremia prevalence threshold is used and sequential mass drug deliveries are applied in countries.

CONCLUSIONS

We have developed and applied a data-driven spatially hierarchical computational platform that uses the discovery of locally applicable transmission models in order to predict the prospects for eliminating the macroparasitic disease, LF, at the coarser country level in sub-Saharan Africa. We show that fine-scale spatial heterogeneity in local parasite transmission and extinction dynamics, as well as the exact nature of intervention roll-outs in countries, will impact the timelines to achieving national LF elimination on this continent.