Local and regional dynamics of chikungunya virus transmission in Colombia: the role of mismatched spatial heterogeneity

Toward One Health: a spatial indicator system to model the facilitation of the spread of zoonotic diseases

Recurrent outbreaks of zoonotic infectious diseases highlight the importance of considering the interconnections between human, animal, and environmental health in disease prevention and control. This has given rise to the concept of One Health, which recognizes the interconnectedness of between human and animal health within their ecosystems. As a contribution to the One Health approach, this study aims to develop an indicator system to model the facilitation of the spread of zoonotic diseases. Initially, a literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to identify relevant indicators related to One Health. The selected indicators focused on demographics, socioeconomic aspects, interactions between animal and human populations and water bodies, as well as environmental conditions related to air quality and climate. These indicators were characterized using values obtained from the literature or calculated through distance analysis, geoprocessing tasks, and other methods. Subsequently, Multi-Criteria Decision-Making (MCDM) techniques, specifically the Entropy and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods, were utilized to combine the indicators and create a composite metric for assessing the spread of zoonotic diseases. The final indicators selected were then tested against recorded zoonoses in the Valencian Community (Spain) for 2021, and a strong positive correlation was identified. Therefore, the proposed indicator system can be valuable in guiding the development of planning strategies that align with the One Health principles. Based on the results achieved, such strategies may prioritize the preservation of natural landscape features to mitigate habitat encroachment, protect land and water resources, and attenuate extreme atmospheric conditions.

Epidemics of chikungunya, Zika, and COVID-19 reveal bias in case-based mapping

Accurate tracing of epidemic spread over space enables effective control measures. We examined three metrics of infection and disease in a pediatric cohort (N≈3,000) over two chikungunya and one Zika epidemic, and in a household cohort (N=1,793) over one COVID-19 epidemic in Managua, Nicaragua. We compared spatial incidence rates (cases/total population), infection risks (infections/total population), and disease risks (cases/infected population). We used generalized additive and mixed-effects models, Kulldorf's spatial scan statistic, and intracluster correlation coefficients. Across different analyses and all epidemics, incidence rates considerably underestimated infection and disease risks, producing large and spatially non-uniform biases distinct from biases due to incomplete case ascertainment. Infection and disease risks exhibited distinct spatial patterns, and incidence clusters inconsistently identified areas of either risk. While incidence rates are commonly used to infer infection and disease risk in a population, we find that this can induce substantial biases and adversely impact policies to control epidemics.

Trade-offs between individual and ensemble forecasts of an emerging infectious disease

Probabilistic forecasts play an indispensable role in answering questions about the spread of newly emerged pathogens. However, uncertainties about the epidemiology of emerging pathogens can make it difficult to choose among alternative model structures and assumptions. To assess the potential for uncertainties about emerging pathogens to affect forecasts of their spread, we evaluated the performance 16 forecasting models in the context of the 2015-2016 Zika epidemic in Colombia. Each model featured a different combination of assumptions about human mobility, spatiotemporal variation in transmission potential, and the number of virus introductions. We found that which model assumptions had the most ensemble weight changed through time. We additionally identified a trade-off whereby some individual models outperformed ensemble models early in the epidemic, but on average the ensembles outperformed all individual models. Our results suggest that multiple models spanning uncertainty across alternative assumptions are necessary to obtain robust forecasts for emerging infectious diseases.

Mapping of Ebola virus spillover: Suitability and seasonal variability at the landscape scale

The unexpected Ebola virus outbreak in West Africa in 2014 involving the Zaire ebolavirus made clear that other regions outside Central Africa, its previously documented niche, were at risk of future epidemics. The complex transmission cycle and a lack of epidemiological data make mapping areas at risk of the disease challenging. We used a Geographic Information System-based multicriteria evaluation (GIS-MCE), a knowledge-based approach, to identify areas suitable for Ebola virus spillover to humans in regions of Guinea, Congo and Gabon where Ebola viruses already emerged. We identified environmental, climatic and anthropogenic risk factors and potential hosts from a literature review. Geographical data layers, representing risk factors, were combined to produce suitability maps of Ebola virus spillover at the landscape scale. Our maps show high spatial and temporal variability in the suitability for Ebola virus spillover at a fine regional scale. Reported spillover events fell in areas of intermediate to high suitability in our maps, and a sensitivity analysis showed that the maps produced were robust. There are still important gaps in our knowledge about what factors are associated with the risk of Ebola virus spillover. As more information becomes available, maps produced using the GIS-MCE approach can be easily updated to improve surveillance and the prevention of future outbreaks.

Ebola virus disease is a highly pathogenic disease transmitted from wildlife to humans. It was first described in 1976 and its distribution remained restricted to Central Africa until 2014, when an outbreak in West Africa, causing more than 28,000 cases and more than 11,000 deaths, took place. Anthropogenic factors, such as bushmeat hunting, trade and consumption, and environmental and climatic factors, may promote the contact between humans and infected animals, such as bats, primates and duikers, increasing the risk of virus transmission to the human population. In this study, we used the spatial multicriteria evaluation framework to gather all available information on risk factors and animal species susceptible to infection, and produce maps of areas suitable for Ebola virus spillover in regions in Guinea, Congo and Gabon. The resulting maps highlighted high spatial and temporal variability in the suitability for Ebola virus spillover. Data from reported cases of Ebola virus transmission from wild animals to humans were used to validate the maps. The approach developed is capable of integrating a wide diversity of risk factors, and provides a flexible and simple tool for surveillance, which can be updated as more data and knowledge on risk factors become available.

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.

Megacities as drivers of national outbreaks: The 2017 chikungunya outbreak in Dhaka, Bangladesh

BACKGROUND

Several large outbreaks of chikungunya have been reported in the Indian Ocean region in the last decade. In 2017, an outbreak occurred in Dhaka, Bangladesh, one of the largest and densest megacities in the world. Population mobility and fluctuations in population density are important drivers of epidemics. Measuring population mobility during outbreaks is challenging but is a particularly important goal in the context of rapidly growing and highly connected cities in low- and middle-income countries, which can act to amplify and spread local epidemics nationally and internationally.

METHODS

We first describe the epidemiology of the 2017 chikungunya outbreak in Dhaka and estimate incidence using a mechanistic model of chikungunya transmission parametrized with epidemiological data from a household survey. We combine the modeled dynamics of chikungunya in Dhaka, with mobility estimates derived from mobile phone data for over 4 million subscribers, to understand the role of population mobility on the spatial spread of chikungunya within and outside Dhaka during the 2017 outbreak.

RESULTS

We estimate a much higher incidence of chikungunya in Dhaka than suggested by official case counts. Vector abundance, local demographics, and population mobility were associated with spatial heterogeneities in incidence in Dhaka. The peak of the outbreak in Dhaka coincided with the annual Eid holidays, during which large numbers of people traveled from Dhaka to other parts of the country. We show that travel during Eid likely resulted in the spread of the infection to the rest of the country.

CONCLUSIONS

Our results highlight the impact of large-scale population movements, for example during holidays, on the spread of infectious diseases. These dynamics are difficult to capture using traditional approaches, and we compare our results to a standard diffusion model, to highlight the value of real-time data from mobile phones for outbreak analysis, forecasting, and surveillance.

Epidemiology and genetic diversity of circulating dengue viruses in Medellin, Colombia: a fever surveillance study

Background

Dengue fever is a major public health problem in Colombia. A fever surveillance study was conducted for evaluation of the clinical, epidemiological, and molecular patterns of dengue, prior to Chikungunya and Zika epidemics.

Methods

In November 2011–February 2014, a passive facility-based surveillance was implemented in Santa Cruz Hospital, Medellin, and enrolled eligible febrile patients between 1 and 65 years-of-age. Acute and convalescent blood samples were collected 10–21 days apart and tested for dengue using IgM/IgG ELISA. RNA was extracted for serotyping using RT-PCR on acute samples and genotyping was performed by sequencing.

Results

Among 537 febrile patients enrolled during the study period, 29% (n = 155) were identified to be dengue-positive. Only 7% of dengue cases were hospitalized, but dengue-positive patients were 2.6 times more likely to be hospitalized, compared to non-dengue cases, based on a logistic regression. From those tested with RT-PCR (n = 173), 17 were dengue-confirmed based on PCR and/or virus isolation showing mostly DENV-3 (n = 9) and DENV-4 (n = 7) with 1 DENV-1. Genotyping results showed that: DENV-1 isolate belongs to the genotype V or American/African genotype; DENV-3 isolates belong to genotype III; and DENV-4 isolates belong to the II genotype and specifically to the IIb sub-genotype or linage.

Conclusions

Our surveillance documented considerable dengue burden in Santa Cruz comuna during non-epidemic years, and genetic diversity of circulating DENV isolates, captured prior to Chikungunya epidemic in 2014 and Zika epidemic in 2015. Our study findings underscore the need for continued surveillance and monitoring of dengue and other arboviruses and serve as epidemiological and molecular evidence base for future studies to assess changes in DENV transmission in Medellin, given emerging and re-emerging arboviral diseases in the region.

Heterogeneous local dynamics revealed by classification analysis of spatially disaggregated time series data

Time series data provide a crucial window into infectious disease dynamics, yet their utility is often limited by the spatially aggregated form in which they are presented. When working with time series data, violating the implicit assumption of homogeneous dynamics below the scale of spatial aggregation could bias inferences about underlying processes. We tested this assumption in the context of the 2015-2016 Zika epidemic in Colombia, where time series of weekly case reports were available at national, departmental, and municipal scales. First, we performed a descriptive analysis, which showed that the timing of departmental-level epidemic peaks varied by three months and that departmental-level estimates of the time-varying reproduction number, R(t), showed patterns that were distinct from a national-level estimate. Second, we applied a classification algorithm to six features of proportional cumulative incidence curves, which showed that variability in epidemic duration, the length of the epidemic tail, and consistency with a cumulative normal density curve made the greatest contributions to distinguishing groups. Third, we applied this classification algorithm to data simulated with a stochastic transmission model, which showed that group assignments were consistent with simulated differences in the basic reproduction number, R₀. This result, along with associations between spatial drivers of transmission and group assignments based on observed data, suggests that the classification algorithm is capable of detecting differences in temporal patterns that are associated with differences in underlying drivers of incidence patterns. Overall, this diversity of temporal patterns at local scales underscores the value of spatially disaggregated time series data.

Is epidemiology ready for Big Software?

Large-scale, open-source software projects like EMOD offer a new approach to epidemiological modeling. Built by a team of professional software developers, EMOD offers significant advantages over 'single-use' models designed by individual research teams, including comprehensive documentation, automated testing and extensive support. In addition, as an individual-based model, it allows much greater complexity and flexibility than the compartmental models that are most commonly used in epidemiology. Adopting modern software development practices, as embodied by EMOD, is essential for ensuring that the best models are available to the greatest number of people.

Spatial infectious disease epidemiology: on the cusp

Infectious diseases continue to pose a significant public health burden despite the great progress achieved in their prevention and control over the last few decades. Our ability to disentangle the factors and mechanisms driving their propagation in space and time has dramatically advanced in recent years. The current era is rich in mathematical and computational tools and detailed geospatial information, including sociodemographic, geographic, and environmental data, which are essential to elucidate key drivers of infectious disease transmission from epidemiological and genetic data. Indeed, this paradigm shift was driven by dramatic advances in complex systems approaches along with substantial improvements in data availability and computational power. The burgeoning output of infectious disease spatial modeling suggests that we are close to a fully integrated approach for early epidemic detection and intervention. This special collection in BMC Medicine aims to bring together a broad range of quantitative investigations that improve our understanding of the spatiotemporal transmission dynamics of infectious diseases in order to mitigate their impact on the human population.