Two-level resolution of relative risk of dengue disease in a hyperendemic city of Colombia

Zika emergence, persistence, and transmission rate in Colombia: a nationwide application of a space-time Markov switching model

Zika, a viral disease transmitted to humans by Aedes mosquitoes, emerged in the Americas in 2015, causing large-scale epidemics. Colombia alone reported over 72,000 Zika cases between 2015 and 2016. Using national surveillance data from 1121 municipalities over 70 weeks, we identified sociodemographic and environmental factors associated with Zika's emergence, re-emergence, persistence, and transmission intensity in Colombia. We fitted a zero-state Markov-switching model under the Bayesian framework, assuming Zika switched between periods of presence and absence according to spatially and temporally varying probabilities of emergence/re-emergence (from absence to presence) and persistence (from presence to presence). These probabilities were assumed to follow a series of mixed multiple logistic regressions. When Zika was present, assuming that the cases follow a negative binomial distribution, we estimated the transmission intensity rate. Our results indicate that Zika emerged/re-emerged sooner and that transmission was intensified in municipalities that were more densely populated, at lower altitudes and/or with less vegetation cover. Warmer temperatures and less weekly-accumulated rain were also associated with Zika emergence. Zika cases persisted for longer in more densely populated areas with more cases reported in the previous week. Overall, population density, elevation, and temperature were identified as the main contributors to the first Zika epidemic in Colombia. We also estimated the probability of Zika presence by municipality and week, and the results suggest that the disease circulated undetected by the surveillance system on many occasions. Our results offer insights into priority areas for public health interventions against emerging and re-emerging Aedes-borne diseases.

Spatio-temporal models of bovine tuberculosis in the Irish cattle population, 2012-2019

INTRODUCTION

Bovine tuberculosis (bTB) is an important zoonotic disease which has serious and sometimes fatal effects on both human and non-human animals. In many countries it is endemic in the cattle population and has a considerable economic impact through losses in productivity and impacts on trade. The incidence rate in Ireland varies by herd and location and it is hoped that statistical disease-mapping models accounting for both spatio-temporal correlation and covariates might contribute towards explaining this variation.

METHODS

Ireland was divided into equally sized hexagons for computational efficiency (n = 997). Different spatio-temporal random-effects models (e.g. negative binomial Besag-York-Mollié) were explored, using comprehensive data from the national bTB eradication programme to examine the association between covariates and the number of bTB cattle. Leveraging a Bayesian framework, model parameter estimates were obtained using the integrated nested Laplace approximation (INLA) approach. Exceedance probabilities were calculated to identify spatial clusters of cases.

RESULTS

Models accounting for spatial correlation significantly improved model fit in comparison to non-spatial versions where independence between regions was assumed. In our final model at hexagon level, the number of cattle (IR = 1.142, CrI: 1.108 - 1.177 per 1000), the capture of badgers (IR = 5.951, CrI: 4.482 - 7.912), percentage of forest cover (IR = 1.031, CrI: 1.020 - 1.042) and number of farm fragments (IR = 1.012, CrI: 1.009 - 1.015 per 10 fragments) were all associated with an increased incidence of bTB. Habitat suitability for badgers, percentage of dairy herds and the number of cattle movements into the herd were not. As an epidemiological tool and to suggest future work, an interactive online dashboard was developed to monitor disease progression and disseminate results to the general public.

CONCLUSION

Accounting for spatial correlation is an important consideration in disease mapping applications and is often ignored in statistical models examining bTB risk factors. Over time, the same regions in Ireland generally show highest incidences of bTB and allocation of more resources to these areas may be needed to combat the disease. This study highlights national bTB incidence rates. Shifting from national level analysis to smaller geographical regions may help identify localised high-risk areas.

Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions

Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an Aedes mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.

Exploring the utility of social-ecological and entomological risk factors for dengue infection as surveillance indicators in the dengue hyper-endemic city of Machala, Ecuador

The management of mosquito-borne diseases is a challenge in southern coastal Ecuador, where dengue is hyper-endemic and co-circulates with other arboviral diseases. Prior work in the region has explored social-ecological factors, dengue case data, and entomological indices. In this study, we bring together entomological and epidemiological data to describe links between social-ecological factors associated with risk of dengue transmission at the household level in Machala, Ecuador. Households surveys were conducted from 2014-2017 to assess the presence of adult Aedes aegypti (collected via aspiration) and to enumerate housing conditions, demographics, and mosquito prevention behaviors. Household-level dengue infection status was determined by laboratory diagnostics in 2014-2015. Bivariate analyses and multivariate logistic regression models were used to identify social-ecological variables associated with household presence of female Ae. aegypti and household dengue infection status, respectively. Aedes aegypti presence was associated with interruptions in water service and weekly trash collection, and household air conditioning was protective against mosquito presence. Presence of female Ae. aegypti was not associated with household dengue infections. We identified shaded patios and head of household employment status as risk factors for household-level dengue infection, while window screening in good condition was identified as protective against dengue infection. These findings add to our understanding of the systems of mosquito-borne disease transmission in Machala, and in the larger region of southern Ecuador, aiding in the development of improved vector surveillance efforts, and targeted interventions.

Space-Time Conditional Autoregressive Modeling to Estimate Neighborhood-Level Risks for Dengue Fever in Cali, Colombia

Vector-borne diseases affect more than 1 billion people a year worldwide, causing more than 1 million deaths, and cost hundreds of billions of dollars in societal costs. Mosquitoes are the most common vectors responsible for transmitting a variety of arboviruses. Dengue fever (DENF) has been responsible for nearly 400 million infections annually. Dengue fever is primarily transmitted by female Aedes aegypti and Aedes albopictus mosquitoes. Because both Aedes species are peri-domestic and container-breeding mosquitoes, dengue surveillance should begin at the local level—where a variety of local factors may increase the risk of transmission. Dengue has been endemic in Colombia for decades and is notably hyperendemic in the city of Cali. For this study, we use weekly cases of DENF in Cali, Colombia, from 2015 to 2016 and develop space–time conditional autoregressive models to quantify how DENF risk is influenced by socioeconomic, environmental, and accessibility risk factors, and lagged weather variables. Our models identify high-risk neighborhoods for DENF throughout Cali. Statistical inference is drawn under Bayesian paradigm using Markov chain Monte Carlo techniques. The results provide detailed insight about the spatial heterogeneity of DENF risk and the associated risk factors (such as weather, proximity to Aedes habitats, and socioeconomic classification) at a fine level, informing public health officials to motivate at-risk neighborhoods to take an active role in vector surveillance and control, and improving educational and surveillance resources throughout the city of Cali.

Substantial reduction in child stunting is differentially associated to geographical and socioeconomic disparities in Misiones Province, Argentina

OBJECTIVE

To estimate trends in the prevalence of child stunting in the population of children under 5 years of age covered by public health programmes, between 2009 and 2014 in Misiones, Argentina.

METHODS

Using Bayesian model-based geostatistics, we evaluated 724 872 anthropometric measurements corresponding to 110 633 children. In order to identify disparities at local scale, we evaluated the hypotheses of a differential reduction of stunting according to the geographical location (at two-level spatial resolution) and to the socioeconomic level in a rural or urban environment.

RESULTS

The prevalence of stunting had fallen significantly in the province overall. Sex and age defined gender disparities at individual level, and there were regional disparities with higher prevalence values in the north and northeast regions. In these areas, stunting decreased to a greater degree during the studied period, although the spatial pattern remained smoother. Stunting increased in peripheral urban and dispersed rural areas that are socioeconomically vulnerable.

CONCLUSIONS

The spatial multi-level geostatistical estimates of child undernutrition provide a precision public health tool to target public policies to those populations with the greatest need, in order to reduce health disparities.

Spatiotemporal analysis of historical records (2001-2012) on dengue fever in Vietnam and development of a statistical model for forecasting risk

BACKGROUND

Dengue fever is the most widespread infectious disease of humans transmitted by Aedes mosquitoes. It is the leading cause of hospitalization and death in children in the Southeast Asia and western Pacific regions. We analyzed surveillance records from health centers in Vietnam collected between 2001-2012 to determine seasonal trends, develop risk maps and an incidence forecasting model.

METHODS

The data were analyzed using a hierarchical spatial Bayesian model that approximates its posterior parameter distributions using the integrated Laplace approximation algorithm (INLA). Meteorological, altitude and land cover (LC) data were used as predictors. The data were grouped by province (n = 63) and month (n = 144) and divided into training (2001-2009) and validation (2010-2012) sets. Thirteen meteorological variables, 7 land cover data and altitude were considered as predictors. Only significant predictors were kept in the final multivariable model. Eleven dummy variables representing month were also fitted to account for seasonal effects. Spatial and temporal effects were accounted for using Besag-York-Mollie (BYM) and autoregressive (1) models. Their levels of significance were analyzed using deviance information criterion (DIC). The model was validated based on the Theil's coefficient which compared predicted and observed incidence estimated using the validation data. Dengue incidence predictions for 2010-2012 were also used to generate risk maps.

RESULTS

The mean monthly dengue incidence during the period was 6.94 cases (SD 14.49) per 100,000 people. Analyses on the temporal trends of the disease showed regular seasonal epidemics that were interrupted every 3 years (specifically in July 2004, July 2007 and September 2010) by major fluctuations in incidence. Monthly mean minimum temperature, rainfall, area under urban settlement/build-up areas and altitude were significant in the final model. Minimum temperature and rainfall had non-linear effects and lagging them by two months provided a better fitting model compared to using unlagged variables. Forecasts for the validation period closely mirrored the observed data and accurately captured the troughs and peaks of dengue incidence trajectories. A favorable Theil's coefficient of inequality of 0.22 was generated.

CONCLUSIONS

The study identified temperature, rainfall, altitude and area under urban settlement as being significant predictors of dengue incidence. The statistical model fitted the data well based on Theil's coefficient of inequality, and risk maps generated from its predictions identified most of the high-risk provinces throughout the country.