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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. [ABSTRACT FROM AUTHOR]

Zika virus (ZIKV) and chikungunya virus (CHIKV) were recently introduced into the Americas resulting in significant disease burdens. Understanding their spatial and temporal dynamics at the subnational level is key to informing surveillance and preparedness for future epidemics. We analyzed anonymized line list data on approximately 105,000 Zika virus disease and 412,000 chikungunya fever suspected and laboratory-confirmed cases during the 2014–2017 epidemics. We first determined the week of invasion in each city. Out of 1,122, 288 cities met criteria for epidemic invasion by ZIKA and 338 cities by CHIKV. We analyzed risk factors for invasion using linear and logistic regression models. We also estimated that the geographic origin of both epidemics was located in Barranquilla, north Colombia. We assessed the spatial and temporal invasion dynamics of both viruses to analyze transmission between cities using a suite of (i) gravity models, (ii) Stouffer's rank models, and (iii) radiation models with two types of distance metrics, geographic distance and travel time between cities. Invasion risk was best captured by a gravity model when accounting for geographic distance and intermediate levels of density dependence; Stouffer's rank model with geographic distance performed similarly well. Although a few long-distance invasion events occurred at the beginning of the epidemics, an estimated distance power of 1.7 (95% CrI: 1.5–2.0) from the gravity models suggests that spatial spread was primarily driven by short-distance transmission. Similarities between the epidemics were highlighted by jointly fitted models, which were preferred over individual models when the transmission intensity was allowed to vary across arboviruses. However, ZIKV spread considerably faster than CHIKV. Author summary: Understanding the spread of infectious diseases across space and time is critical for preparedness, designing interventions, and elucidating mechanisms underlying transmission. We analyzed human case data from over 500,000 reported cases to investigate the spread of the recent Zika virus (ZIKV) and chikungunya virus (CHIKV) epidemics in Colombia. Both viruses were introduced into northern Colombia. We found that gravity models and Stouffer's rank models best described transmission and that transmission mainly occurred over short distances. Our results highlight similarities and key differences between the ZIKV and CHIKV epidemics in Colombia, which can be used to anticipate future epidemic waves and prioritize cities for active surveillance and targeted interventions. [ABSTRACT FROM AUTHOR]

Zika virus (ZIKV) is a mosquito-borne pathogen that recently caused a major epidemic in the Americas. Although the majority of ZIKV infections are asymptomatic, the virus has been associated with birth defects in fetuses and newborns of infected mothers as well as neurological complications in adults. We performed a descriptive analysis on approximately 106,000 suspected and laboratory-confirmed cases of Zika virus disease (ZVD) that were reported during the 2015–2017 epidemic in Colombia. We also analyzed a dataset containing patients with neurological complications and recent febrile illness compatible with ZVD. Females had higher cumulative incidence of ZVD than males. Compared to the general population, cases were more likely to be reported in young adults (20 to 39 years of age). We estimated the cumulative incidence of ZVD in pregnant females at 3,120 reported cases per 100,000 population (95% CI: 3,077–3,164), which was considerably higher than the incidence in both males and non-pregnant females. ZVD cases were reported in all 32 departments. Four-hundred and eighteen patients suffered from ZIKV-associated neurological complications, of which 85% were diagnosed with Guillain-Barré syndrome. The median age of ZIKV cases with neurological complications was 12 years older than that of ZVD cases. ZIKV-associated neurological complications increased with age, and the highest incidence was reported among individuals aged 75 and older. Even though neurological complications and deaths due to ZIKV were rare in this epidemic, better risk communication is needed for people living in or traveling to ZIKV-affected areas. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

Background: A significant increase in microcephaly incidence was reported in Northeast Brazil at the end of 2015, which has since been attributed to an epidemic of Zika virus (ZIKV) infections earlier that year. Further incidence of congenital Zika syndrome (CZS) was expected following waves of ZIKV infection throughout Latin America; however, only modest increases in microcephaly and CZS incidence have since been observed. The quantitative relationship between ZIKV infection, gestational age and congenital outcome remains poorly understood., Methodology/principle Findings: We characterised the gestational-age-varying risk of microcephaly given ZIKV infection using publicly available incidence data from multiple locations in Brazil and Colombia. We found that the relative timings and shapes of ZIKV infection and microcephaly incidence curves suggested different gestational risk profiles for different locations, varying in both the duration and magnitude of gestational risk. Data from Northeast Brazil suggested a narrow window of risk during the first trimester, whereas data from Colombia suggested persistent risk throughout pregnancy. We then used the model to estimate which combination of behavioural and reporting changes would have been sufficient to explain the absence of a second microcephaly incidence wave in Bahia, Brazil; a population for which we had two years of data. We found that a 18.9-fold increase in ZIKV infection reporting rate was consistent with observed patterns., Conclusions: Our study illustrates how surveillance data may be used in principle to answer key questions in the absence of directed epidemiological studies. However, in this case, we suggest that currently available surveillance data are insufficient to accurately estimate the gestational-age-varying risk of microcephaly from ZIKV infection. The methods used here may be of use in future outbreaks and may help to inform improved surveillance and interpretation in countries yet to experience an outbreak of ZIKV infection., Competing Interests: The authors have declared that no competing interests exist.