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

Diagnosing risk factors alongside mass drug administration using serial diagnostic tests-which test first?

Background: When tests are used in series to determine individual risk factors and infection status in a mass drug administration (MDA), the diagnostics, test order and subsequent treatment decisions (the testing algorithm) affect population-level treatment coverage and cost, but there is no existing framework for evaluating which algorithm optimizes any given outcome. Methods: We present a mathematical tool (with spreadsheet implementation) to analyse the effect of test ordering, illustrated using treatment for onchocerciasis in an area where high-burden Loa loa co-infections present a known risk factor. Results: The prevalence of the infection and risk factor have a non-linear impact on the optimal ordering of tests. Testing for the MDA infection first always leaves more infected people untreated but fewer people with the risk factor being misclassified. The cost of the treatment given to infected individuals with the risk factor does not affect which algorithm is more cost effective. Conclusions: For a given test and treat algorithm and its costs, the correct strategy depends on the expected prevalence. In most cases, when the apparent prevalence of the target infection is greater than the apparent prevalence of the risk factor, it is cheaper to do the risk factor test first, and vice versa

High Transmissibility During Early HIV Infection Among Men Who Have Sex With Men-San Francisco, California.

We estimate the relative transmission rate in early versus later infection among men who have sex with men (MSM) in San Francisco, California, by studying the characteristics of a sample of transmitters, recruited through newly diagnosed, recently infected MSM between 1996 and 2009. Of 36 transmitters identified, 9 were determined on the basis of testing history and serologic testing to have been recently infected. The unadjusted odds ratio of transmitting during early infection was 15.2 (95% confidence interval [CI], 6.3–33.4; P < .001); the odds ratio was 8.9 (95% CI, 4.1–19.4) after adjustment for self-reported antiretroviral treatment. This high transmissibility could be due to both high infectiousness and high rates of sex partner change or concurrent partnerships

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

Uniting mathematics and biology for control of visceral leishmaniasis

The neglected tropical disease (NTD) visceral leishmaniasis (VL) has been targeted by the WHO for elimination as a public health problem on the Indian subcontinent by 2017 or earlier. To date there is a surprising scarcity of mathematical models capable of capturing VL disease dynamics, which are widely considered central to planning and assessing the efficacy of interventions. The few models that have been developed are examined, highlighting the necessity for better data to parameterise and fit these and future models. In particular, the characterisation and infectiousness of the different disease stages will be crucial to elimination. Modelling can then assist in establishing whether, when, and how the WHO VL elimination targets can be met

Considering equity in priority setting using transmission models: recommendations and data needs

Objectives Disease transmission models are used in impact assessment and economic evaluations of infectious disease prevention and treatment strategies, prominently so in the COVID-19 response. These models rarely consider dimensions of equity relating to the differential health burden between individuals and groups. We describe concepts and approaches which are useful when considering equity in the priority setting process, and outline the technical choices concerning model structure, outputs, and data requirements needed to use transmission models in analyses of health equity. Methods We reviewed the literature on equity concepts and approaches to their application in economic evaluation and undertook a technical consultation on how equity can be incorporated in priority setting for infectious disease control. The technical consultation brought together health economists with an interest in equity-informative economic evaluation, ethicists specialising in public health, mathematical modellers from various disease backgrounds, and representatives of global health funding and technical assistance organisations, to formulate key areas of consensus and recommendations. Results We provide a series of recommendations for applying the Reference Case for Economic Evaluation in Global Health to infectious disease interventions, comprising guidance on 1) the specification of equity concepts; 2) choice of evaluation framework; 3) model structure; and 4) data needs. We present available conceptual and analytical choices, for example how correlation between different equity- and disease-relevant strata should be considered dependent on available data, and outline how assumptions and data limitations can be reported transparently by noting key factors for consideration. Conclusions Current developments in economic evaluations in global health provide a wide range of methodologies to incorporate equity into economic evaluations. Those employing infectious disease models need to use these frameworks more in priority setting to accurately represent health inequities. We provide guidance on the technical approaches to support this goal and ultimately, to achieve more equitable health policies

Introduction to the special issue: challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs

Twenty neglected tropical diseases (NTDs) are currently prioritised by the World Health Organization for eradication, elimination as a public health problem, elimination of transmission or control by 2030. This issue celebrates progress made since the 2012 London Declaration on NTDs and discusses challenges currently faced to achieve these goals. It comprises 14 contributions spanning NTDs tackled by intensified disease management to those addressed by preventive chemotherapy. Although COVID-19 negatively affected NTD programmes, it also served to spur new multisectoral approaches to strengthen school-based health systems. The issue highlights the needs to improve impact survey design, evaluate new diagnostics, understand the consequences of heterogeneous prevalence and human movement, the potential impact of alternative treatment strategies and the importance of zoonotic transmission. 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'

Seven challenges for model-driven data collection in experimental and observational studies.

Infectious disease models are both concise statements of hypotheses and powerful techniques for creating tools from hypotheses and theories. As such, they have tremendous potential for guiding data collection in experimental and observational studies, leading to more efficient testing of hypotheses and more robust study designs. In numerous instances, infectious disease models have played a key role in informing data collection, including the Garki project studying malaria, the response to the 2009 pandemic of H1N1 influenza in the United Kingdom and studies of T-cell immunodynamics in mammals. However, such synergies remain the exception rather than the rule; and a close marriage of dynamic modeling and empirical data collection is far from the norm in infectious disease research. Overcoming the challenges to using models to inform data collection has the potential to accelerate innovation and to improve practice in how we deal with infectious disease threats

Gradual acquisition of immunity to severe malaria with increasing exposure

Previous analyses have suggested that immunity to non-cerebral severe malaria due to Plasmodium falciparum is acquired after only a few infections, whereas longitudinal studies show that some children experience multiple episodes of severe disease, suggesting that immunity may not be acquired so quickly. We fitted a mathematical model for the acquisition and loss of immunity to severe disease to the age distribution of severe malaria cases stratified by symptoms from a range of transmission settings in Tanzania, combined with data from several African countries on the age distribution and overall incidence of severe malaria. We found that immunity to severe disease was acquired more gradually with exposure than previously thought. The model also suggests that physiological changes, rather than exposure, may alter the symptoms of disease with increasing age, suggesting that a later age at infection would be associated with a higher proportion of cases presenting with cerebral malaria regardless of exposure. This has consequences for the expected pattern of severe disease as transmission changes. Careful monitoring of the decline in immunity associated with reduced transmission will therefore be needed to ensure rebound epidemics of severe and fatal malaria are avoided