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In 2012, the World Health Organization (WHO) set the elimination of Chagas disease intradomiciliary vectorial transmission as a goal by 2020. After a decade, some progress has been made, but the new 2021-2030 WHO roadmap has set even more ambitious targets. Innovative and robust modelling methods are required to monitor progress towards these goals. We present a modelling pipeline using local seroprevalence data to obtain national disease burden estimates by disease stage. Firstly, local seroprevalence information is used to estimate spatio-temporal trends in the Force-of-Infection (FoI). FoI estimates are then used to predict such trends across larger and fine-scale geographical areas. Finally, predicted FoI values are used to estimate disease burden based on a disease progression model. Using Colombia as a case study, we estimated that the number of infected people would reach 506 000 (95% credible interval (CrI) = 395 000-648 000) in 2020 with a 1.0% (95%CrI = 0.8-1.3%) prevalence in the general population and 2400 (95%CrI = 1900-3400) deaths (approx. 0.5% of those infected). The interplay between a decrease in infection exposure (FoI and relative proportion of acute cases) was overcompensated by a large increase in population size and gradual population ageing, leading to an increase in the absolute number of Chagas disease cases over time. 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'.

Background: Chagas disease is a long-lasting disease with a prolonged asymptomatic period. Cumulative indices of infection such as prevalence do not shed light on the current epidemiological situation, as they integrate infection over long periods. Instead, metrics such as the Force-of-Infection (FoI) provide information about the rate at which susceptible people become infected and permit sharper inference about temporal changes in infection rates. FoI is estimated by fitting (catalytic) models to available age-stratified serological (ground-truth) data. Predictive FoI modelling frameworks are then used to understand spatial and temporal trends indicative of heterogeneity in transmission and changes effected by control interventions. Ideally, these frameworks should be able to propagate uncertainty and handle spatiotemporal issues., Methodology/principal Findings: We compare three methods in their ability to propagate uncertainty and provide reliable estimates of FoI for Chagas disease in Colombia as a case study: two Machine Learning (ML) methods (Boosted Regression Trees (BRT) and Random Forest (RF)), and a Linear Model (LM) framework that we had developed previously. Our analyses show consistent results between the three modelling methods under scrutiny. The predictors (explanatory variables) selected, as well as the location of the most uncertain FoI values, were coherent across frameworks. RF was faster than BRT and LM, and provided estimates with fewer extreme values when extrapolating to areas where no ground-truth data were available. However, BRT and RF were less efficient at propagating uncertainty., Conclusions/significance: The choice of FoI predictive models will depend on the objectives of the analysis. ML methods will help characterise the mean behaviour of the estimates, while LM will provide insight into the uncertainty surrounding such estimates. Our approach can be extended to the modelling of FoI patterns in other Chagas disease-endemic countries and to other infectious diseases for which serosurveys are regularly conducted for surveillance., Competing Interests: The authors have declared that no competing interests exist.

Age-stratified serosurvey data are often used to understand spatiotemporal trends in disease incidence and exposure through estimating the Force-of-Infection (FoI). Typically, median or mean FoI estimates are used as the response variable in predictive models, often overlooking the uncertainty in estimated FoI values when fitting models and evaluating their predictive ability. To assess how this uncertainty impact predictions, we compared three approaches with three levels of uncertainty integration. We propose a performance indicator to assess how predictions reflect initial uncertainty.In Colombia, 76 serosurveys (1980-2014) conducted at municipality level provided age-stratified Chagas disease prevalence data. The yearly FoI was estimated at the serosurvey level using a time-varying catalytic model. Environmental, demographic and entomological predictors were used to fit and predict the FoI at municipality level from 1980 to 2010 across Colombia.A stratified bootstrap method was used to fit the models without temporal autocorrelation at the serosurvey level. The predictive ability of each model was evaluated to select the best-fit models within urban, rural and (Amerindian) indigenous settings. Model averaging, with the 10 best-fit models identified, was used to generate predictions.Our analysis shows a risk of overconfidence in model predictions when median estimates of FoI alone are used to fit and evaluate models, failing to account for uncertainty in FoI estimates. Our proposed methodology fully propagates uncertainty in the estimated FoI onto the generated predictions, providing realistic assessments of both central tendency and current uncertainty surrounding exposure to Chagas disease., (© 2022. The Author(s).)

Background: The World Health Organization's 2020 goals for Chagas disease are (1) interrupting vector-borne intradomiciliary transmission and (2) having all infected people under care in endemic countries. Insecticide spraying has proved efficacious for reaching the first goal, but active transmission remains in several regions. For the second, treatment has mostly been restricted to recently infected patients, who comprise only a small proportion of all infected individuals., Methods: We extended our previous dynamic transmission model to simulate a domestic Chagas disease transmission cycle and examined the effects of both vector control and etiological treatment on achieving the operational criterion proposed by the Pan American Health Organization for intradomiciliary, vectorial transmission interruption (ie, <2% seroprevalence in children <5 years of age)., Results: Depending on endemicity, an antivectorial intervention that decreases vector density by 90% annually would achieve the transmission interruption criterion in 2-3 years (low endemicity) to >30 years (high endemicity). When this strategy is combined with annual etiological treatment in 10% of the infected human population, the seroprevalence criterion would be achieved, respectively, in 1 and 11 years., Conclusions: Combining highly effective vector control with etiological (trypanocidal) treatment in humans would substantially reduce time to transmission interruption as well as infection incidence and prevalence. However, the success of vector control may depend on prevailing vector species. It will be crucial to improve the coverage of screening programs, the performance of diagnostic tests, the proportion of people treated, and the efficacy of trypanocidal drugs. While screening and access can be incremented as part of strengthening the health systems response, improving diagnostics performance and drug efficacy will require further research.

Limited access to Chagas disease diagnosis and treatment is a major obstacle to reaching the 2020 World Health Organization milestones of delivering care to all infected and ill patients. Colombia has been identified as a health system in transition, reporting one of the highest levels of health insurance coverage in Latin America. We explore if and how this high level of coverage extends to those with Chagas disease, a traditionally marginalised population. Using a mixed methods approach, we calculate coverage for screening, diagnosis and treatment of Chagas. We then identify supply-side constraints both quantitatively and qualitatively. A review of official registries of tests and treatments for Chagas disease delivered between 2008 and 2014 is compared to estimates of infected people. Using the Flagship Framework, we explore barriers limiting access to care. Screening coverage is estimated at 1.2% of the population at risk. Aetiological treatment with either benznidazol or nifurtimox covered 0.3-0.4% of the infected population. Barriers to accessing screening, diagnosis and treatment are identified for each of the Flagship Framework's five dimensions of interest: financing, payment, regulation, organization and persuasion. The main challenges identified were: a lack of clarity in terms of financial responsibilities in a segmented health system, claims of limited resources for undertaking activities particularly in primary care, non-inclusion of confirmatory test(s) in the basic package of diagnosis and care, poor logistics in the distribution and supply chain of medicines, and lack of awareness of medical personnel. Very low screening coverage emerges as a key obstacle hindering access to care for Chagas disease. Findings suggest serious shortcomings in this health system for Chagas disease, despite the success of universal health insurance scale-up in Colombia. Whether these shortcomings exist in relation to other neglected tropical diseases needs investigating. We identify opportunities for improvement that can inform additional planned health reforms., (Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: The clinical outcomes associated with Chagas disease remain poorly understood. In addition to the burden of morbidity, the burden of mortality due to Trypanosoma cruzi infection can be substantial, yet its quantification has eluded rigorous scrutiny. This is partly due to considerable heterogeneity between studies, which can influence the resulting estimates. There is a pressing need for accurate estimates of mortality due to Chagas disease that can be used to improve mathematical modelling, burden of disease evaluations, and cost-effectiveness studies., Methods: A systematic literature review was conducted to select observational studies comparing mortality in populations with and without a diagnosis of Chagas disease using the PubMed, MEDLINE, EMBASE, Web of Science and LILACS databases, without restrictions on language or date of publication. The primary outcome of interest was mortality (as all-cause mortality, sudden cardiac death, heart transplant or cardiovascular deaths). Data were analysed using a random-effects model to obtain the relative risk (RR) of mortality, the attributable risk percent (ARP), and the annual mortality rates (AMR). The statistic I(2) (proportion of variance in the meta-analysis due to study heterogeneity) was calculated. Sensitivity analyses and publication bias test were also conducted., Results: Twenty five studies were selected for quantitative analysis, providing data on 10,638 patients, 53,346 patient-years of follow-up, and 2739 events. Pooled estimates revealed that Chagas disease patients have significantly higher AMR compared with non-Chagas disease patients (0.18 versus 0.10; RR = 1.74, 95% CI 1.49-2.03). Substantial heterogeneity was found among studies (I(2) = 67.3%). The ARP above background mortality was 42.5%. Through a sub-analysis patients were classified by clinical group (severe, moderate, asymptomatic). While RR did not differ significantly between clinical groups, important differences in AMR were found: AMR = 0.43 in Chagas vs. 0.29 in non-Chagas patients (RR = 1.40, 95% CI 1.21-1.62) in the severe group; AMR = 0.16 (Chagas) vs. 0.08 (non-Chagas) (RR = 2.10, 95% CI 1.52-2.91) in the moderate group, and AMR = 0.02 vs. 0.01 (RR = 1.42, 95% CI 1.14-1.77) in the asymptomatic group. Meta-regression showed no evidence of study-level covariates on the effect size. Publication bias was not statistically significant (Egger's test p=0.08)., Conclusions: The results indicate a statistically significant excess of mortality due to Chagas disease that is shared among both symptomatic and asymptomatic populations.

More than 100 years after its formal description, Chagas disease remains a major public health concern in Latin America with a yearly burden of 430,000 Disability-Adjusted Life Years (DALYs). The aetiological agent, a protozoan named Trypanosoma cruzi, is mainly transmitted to mammalian hosts by triatomine vectors. Multiple species of mammals and triatomines can harbour and transmit the parasite, and the feeding range of triatomine species typically includes many noncompetent hosts. Furthermore, the transmission of the pathogen can occur via several routes including the typical vector's faeces, but also oral, congenital and blood transfusion routes. These ecological and epidemiological complexities of the disease have hindered many control initiatives. In such a context, mathematical models provide invaluable tools to explore and understand the dynamics of T. cruzi transmission, and to design, optimize and monitor the efficacy of control interventions. We intend here to provide the first review of the mathematical models of Chagas disease, focussing on how they have contributed to our understanding of (1) the population dynamics and control of triatomine vectors, and (2) the epidemiology of T. cruzi infections. We also aim at suggesting promising lines of modelling that could further improve our understanding of the ecology, evolution, and control of the disease., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Chagas disease has a major impact on human health in Latin America and is becoming of global concern due to international migrations. Trypanosoma cruzi, the etiological agent of the disease, is one of the rare human parasites transmitted by the feces of its vector, as it is unable to reach the salivary gland of the insect. This stercorarian transmission is notoriously poorly understood, despite its crucial role in the ecology and evolution of the pathogen and the disease. The objective of this study was to quantify the probability of T. cruzi vectorial transmission to humans, and to use such an estimate to predict human prevalence from entomological data. We developed several models of T. cruzi transmission to estimate the probability of transmission from vector to host. Using datasets from the literature, we estimated the probability of transmission per contact with an infected triatomine to be 5.8 × 10(-4) (95%CI: [2.6 ; 11.0] × 10(-4)). This estimate was consistent across triatomine species, robust to variations in other parameters, and corresponded to 900-4,000 contacts per case. Our models subsequently allowed predicting human prevalence from vector abundance and infection rate in 7/10 independent datasets covering various triatomine species and epidemiological situations. This low probability of T. cruzi transmission reflected well the complex and unlikely mechanism of transmission via insect feces, and allowed predicting human prevalence from basic entomological data. Although a proof of principle study would now be valuable to validate our models' predictive ability in an even broader range of entomological and ecological settings, our quantitative estimate could allow switching the evaluation of disease risk and vector control program from purely entomological indexes to parasitological measures, as commonly done for other major vector borne diseases. This might lead to different quantitative perspectives as these indexes are well known not to be proportional one to another.

Abstract: Triatoma dimidiata is one of the main vectors of Chagas disease, and it has been shown to be a species complex. In the Yucatán peninsula, Mexico, vector populations are non-domiciliated, and the transmission of Trypanosoma cruzi thus critically relies on vector dispersal. This leads us to study the morphologic variations in T. dimidiata wings with respect to genetic factors (sex and genotype at the ITS-2 locus), geographic location, and T. cruzi-infection status. Females were found to have larger and more symmetrical wings than males. Wing shape was influenced by ITS-2 genotypes, although differences are unlikely sufficient to allow taxonomic discrimination of the sibling species. Hybrids were shown to have similar fluctuating asymmetries in wing size and shape as parental species, but the level of asymmetry in shape varied slightly between villages. The two later findings are consistent with a high level of gene flow between parental species, and the high dispersal potential of these non-domiciliated vectors. More surprisingly, individuals infected with T. cruzi were found to have larger wings than non-infected ones. This effect, which was consistently observed across sexes, genotypes and villages, is likely to be due to a direct impact of T. cruzi on insect development. Sex and infection status are thus likely to be key factors influencing vector dispersal with important impacts on disease transmission, since dispersal directly controls the domestic abundance of these vectors. These aspects should be investigated further to fully capture the ecology and evolution of Chagas disease transmission by non-domiciliated vectors. [Copyright &y& Elsevier]