Dengue importation into Europe: A network connectivity-based approach

Emerging and re-emerging infectious diseases in pregnant women in an amazonian region: a large retrospective study from French Guiana

PURPOSE

Over the past decade, the Amazon basin has faced numerous infectious epidemics. Our comprehension of the actual extent of these infections during pregnancy remains limited. This study aimed to clarify the clinical and epidemiological features of emerging and re-emerging infectious diseases during pregnancy in western French Guiana and along the Maroni River over the previous nine years.

METHODS

This retrospective cohort study enrolled pregnant women living in west French Guiana territory and giving birth in the only local referral center after 22 weeks of gestation between 2013 and 2021. Data on symptomatic or asymptomatic biologically confirmed emerging or re-emerging diseases during pregnancy was collected.

RESULTS

Six epidemic waves were experienced during the study period, including 498 confirmed Zika virus infections (2016), 363 SARS-CoV-2 infections (2020-2021), 87 chikungunya virus infections (2014), 76 syphilis infections (2013-2021), and 60 dengue virus infections (2013-2021) at different gestational ages. Furthermore, 1.1% (n = 287) and 1.4% (n = 350) of pregnant women in west French Guiana were living with HIV and HTLV, respectively. During the study period, at least 5.5% (n = 1,371) faced an emerging or re-emerging infection during pregnancy.

CONCLUSION

These results highlight the diversity, abundance, and dynamism of emerging and re-emerging infectious agents faced by pregnant women in the Amazon basin. Considering the maternal and neonatal adverse outcomes associated with these infections, increased efforts are required to enhance diagnosis, reporting, and treatment of these conditions.

Modeling the Regional Distribution of International Travelers in Spain to Estimate Imported Cases of Dengue and Malaria: Statistical Inference and Validation Study

BACKGROUND

Understanding the patterns of disease importation through international travel is paramount for effective public health interventions and global disease surveillance. While global airline network data have been used to assist in outbreak prevention and effective preparedness, accurately estimating how these imported cases disseminate locally in receiving countries remains a challenge.

OBJECTIVE

This study aimed to describe and understand the regional distribution of imported cases of dengue and malaria upon arrival in Spain via air travel.

METHODS

We have proposed a method to describe the regional distribution of imported cases of dengue and malaria based on the computation of the "travelers' index" from readily available socioeconomic data. We combined indicators representing the main drivers for international travel, including tourism, economy, and visits to friends and relatives, to measure the relative appeal of each region in the importing country for travelers. We validated the resulting estimates by comparing them with the reported cases of malaria and dengue in Spain from 2015 to 2019. We also assessed which motivation provided more accurate estimates for imported cases of both diseases.

RESULTS

The estimates provided by the best fitted model showed high correlation with notified cases of malaria (0.94) and dengue (0.87), with economic motivation being the most relevant for imported cases of malaria and visits to friends and relatives being the most relevant for imported cases of dengue.

CONCLUSIONS

Factual descriptions of the local movement of international travelers may substantially enhance the design of cost-effective prevention policies and control strategies, and essentially contribute to decision-support systems. Our approach contributes in this direction by providing a reliable estimate of the number of imported cases of nonendemic diseases, which could be generalized to other applications. Realistic risk assessments will be obtained by combining this regional predictor with the observed local distribution of vectors.

The potential habitat and environmental fitness change of Aedes albopictus in Western Eurasia for 2081-2100

BACKGROUND OBJECTIVES

The range of Aedes albopictus, the most important vector mosquito in Western Eurasia is growing due to climate change. However, it is not known how it will influence the habitats occupied by the species and its environmental fitness within its future range.

METHODS

To study this question, the habitat characteristic of the mosquito was investigated for 2081-2100.

RESULTS

The models suggest a notable future spread of the mosquito in the direction of Northern Europe and the parallel northward and westward shift of the southern and eastern potential occurrences of the mosquito. The models suggest a notable increase in generation numbers in the warmest quarter, which can reach 4-5 generations in the peri-Mediterranean region. However, both the joint survival rate of larvae and pupae and the number of survival days of adults in the warmest quarter exhibit decreasing values, as does the potential disappearance of the mosquito in the southern regions of Europe and Asia Minor, along with the growing atmospheric CO2 concentration-based scenarios.

INTERPRETATION CONCLUSION

While in 1970-2000 Aedes albopictus mainly occupied the hot and warm summer temperate regions of Europe, the species will inhabit dominantly the cool summer temperate (oceanic) and the humid continental climate territories of North and North-Eastern Europe in 2081-2100.

Mathematical model estimation of dengue fever transmission risk from Southeast and South Asia into Japan between 2016 and 2018

BACKGROUND

Dengue fever is a viral infection transmitted to humans through the bite of a mosquito infected with the dengue virus. Dengue is one of the most common infectious diseases in the world, and its incidence is rapidly increasing. We estimated the risk of dengue importation from endemic countries to Japan and the transmission risk within Japan using data collected between 2016 and 2018.

METHODS

We conducted simulations that included the number of reported dengue infections and travelers per month in ten countries in Southeast and South Asia.

RESULTS

The estimated importation risks for Japanese returnees and international travelers from each of the ten endemic countries was approximately 1.0 every month from 2016 to 2018. The autochthonous transmission risk in Japan from any target country was 1.0 from June to September yearly. The estimated number of Japanese dengue cases returning to Japan is approximately 25 times higher than that of imported cases reported in Japan.

CONCLUSIONS

The risk of dengue importation into Japan can be sufficiently high. Attention should be paid to autochthonous transmission spread between June and September when mosquitoes are active in Japan. Estimates of seasonal risk variation from each dengue virus-endemic country can be used to inform preventive and control measures for dengue in Japan.

Spatial Analysis of Mosquito-Borne Diseases in Europe: A Scoping Review

Mosquito-borne infections are increasing in endemic areas and previously unaffected regions. In 2020, the notification rate for Dengue was 0.5 cases per 100,000 population, and for Chikungunya <0.1/100,000. In 2019, the rate for Malaria was 1.3/100,000, and for West Nile Virus, 0.1/100,000. Spatial analysis is increasingly used in surveillance and epidemiological investigation, but reviews about their use in this research topic are scarce. We identify and describe the methodological approaches used to investigate the distribution and ecological determinants of mosquito-borne infections in Europe. Relevant literature was extracted from PubMed, Scopus, and Web of Science from inception until October 2021 and analysed according to PRISMA-ScR protocol. We identified 110 studies. Most used geographical correlation analysis (n = 50), mainly applying generalised linear models, and the remaining used spatial cluster detection (n = 30) and disease mapping (n = 30), mainly conducted using frequentist approaches. The most studied infections were Dengue (n = 32), Malaria (n = 26), Chikungunya (n = 26), and West Nile Virus (n = 24), and the most studied ecological determinants were temperature (n = 39), precipitation (n = 24), water bodies (n = 14), and vegetation (n = 11). Results from this review may support public health programs for mosquito-borne disease prevention and may help guide future research, as we recommended various good practices for spatial epidemiological studies.

Environmental determinants predicting population vulnerability to high yellow fever incidence

Yellow fever (YF) is an endemic mosquito-borne disease in Brazil, though many locations have not observed cases in recent decades. Some locations with low disease burden may resemble locations with higher disease burden through environmental and ecohydrological characteristics, which are known to impact YF burden, motivating increased or continued prevention measures such as vaccination, mosquito control or surveillance. This study aimed to use environmental characteristics to estimate vulnerability to observing high YF burden among all Brazilian municipalities. Vulnerability was defined in three categories based on yearly incidence between 2000 and 2017: minimal, low and high vulnerability. A cumulative logit model was fit to these categories using environmental and ecohydrological predictors, selecting those that provided the most accurate model fit. Per cent of days with precipitation, mean temperature, biome, population density, elevation, vegetation and nearby disease occurrence were included in best-fitting models. Model results were applied to estimate vulnerability nationwide. Municipalities with highest probability of observing high vulnerability was found in the North and Central-West (2000-2016) as well as the Southeast (2017) regions. Results of this study serve to identify specific locations to prioritize new or ongoing surveillance and prevention of YF based on underlying ecohydrological conditions.

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.

Population mobility, demographic, and environmental characteristics of dengue fever epidemics in a major city in Southeastern Brazil, 2007-2015

Around 14% of world dengue virus (DENV) cases occur in the Americas, most of them in Brazil. While socioeconomic, environmental, and behavioral correlates have been analyzed thoroughly, the role played by population mobility on DENV epidemics, especially at the local level, remains scarce. This study assesses whether the daily pattern of population mobility is associated with DENV incidence in Campinas, a Brazilian major city with over 1.2 million inhabitants in São Paulo State. DENV notifications from 2007 to 2015 were geocoded at street level (n = 114,884) and combined with sociodemographic and environmental data from the 2010 population census. Population mobility was extracted from the Origin-Destination Survey (ODS), carried out in 2011, and daily precipitation was obtained from satellite imagery. Multivariate zero-inflated negative binomial regression models were applied. High population mobility presented a relevant positive effect on higher risk for DENV incidence. High income and residence in apartments were found to be protective characteristics against the disease, while unpaved streets, number of strategic points (such as scrapyards and tire repair shops), and precipitation were consistently risk factors.

Analysis of the Arbovirosis Potential Occurrence in Dobrogea, Romania

Climate change creates new challenges for preventing and protecting human health against different diseases that could appear and propagate. The Aedes albopictus mosquito species is an important vector for different diseases like dengue fever or zika. Although this species is not “indigenous” in Europe, its presence is noticed in many countries on the continent. The Ae. albopictus establishment is conditioned by the species’ characteristics and environmental factors. To assess the possible spread of Ae. albopictus in the Dobrogea region (situated in the Southeast of Romania), we conducted the following analysis: (1) Investigation of the current distribution and climatic factors favoring Ae. albopictus’ establishment in Europe; (2) Analysis of climate dynamics in Dobrogea in terms of the parameters identified at stage (1); (3) Testing the hypothesis that the climate from Dobrogea favors Ae. albopictus’ establishment in the region; (4) Building a Geographic Information System (GIS)-based model of the potential geographic distribution of Ae. albopictus in Dobrogea. Results show that the climate of Dobrogea favors the apparition of the investigated species and its proliferation.

Sociocultural, behavioural and political factors shaping the COVID-19 pandemic: the need for a biocultural approach to understanding pandemics and (re)emerging pathogens

Although there has been increasing focus in recent years on interdisciplinary approaches to health and disease, and in particular the dimension of social inequalities in epidemics, infectious diseases have been much less focused on. This is especially true in the area of cultural dynamics and their effects on pathogen behaviours, although there is evidence to suggest that this relationship is central to shaping our interactions with infectious disease agents on a variety of levels. This paper makes a case for a biocultural approach to pandemics such as COVID-19. It then uses this biocultural framework to examine the anthropogenic dynamics that influenced and continue to shape the COVID-19 pandemic, both during its initial phase and during critical intersections of the pandemic. Through this understanding of biocultural interactions between people, animals and pathogens, a broader societal and political dimension is drawn as a function of population level and international cultures, to reflect on the culturally mediated differential burden of the pandemic. Ultimately, it is argued that a biocultural perspective on infectious disease pandemics will allow for critical reflection on how culture shapes our behaviours at all levels, and how the effects of these behaviours are ultimately foundational to pathogen ecology and evolution.

Predicting dengue importation into Europe, using machine learning and model-agnostic methods

The geographical spread of dengue is a global public health concern. This is largely mediated by the importation of dengue from endemic to non-endemic areas via the increasing connectivity of the global air transport network. The dynamic nature and intrinsic heterogeneity of the air transport network make it challenging to predict dengue importation. Here, we explore the capabilities of state-of-the-art machine learning algorithms to predict dengue importation. We trained four machine learning classifiers algorithms, using a 6-year historical dengue importation data for 21 countries in Europe and connectivity indices mediating importation and air transport network centrality measures. Predictive performance for the classifiers was evaluated using the area under the receiving operating characteristic curve, sensitivity, and specificity measures. Finally, we applied practical model-agnostic methods, to provide an in-depth explanation of our optimal model's predictions on a global and local scale. Our best performing model achieved high predictive accuracy, with an area under the receiver operating characteristic score of 0.94 and a maximized sensitivity score of 0.88. The predictor variables identified as most important were the source country's dengue incidence rate, population size, and volume of air passengers. Network centrality measures, describing the positioning of European countries within the air travel network, were also influential to the predictions. We demonstrated the high predictive performance of a machine learning model in predicting dengue importation and the utility of the model-agnostic methods to offer a comprehensive understanding of the reasons behind the predictions. Similar approaches can be utilized in the development of an operational early warning surveillance system for dengue importation.