A global model for predicting the arrival of imported dengue infections

With approximately half of the world's population at risk of contracting dengue, this mosquito-borne disease is of global concern. International travellers significantly contribute to dengue's rapid and large-scale spread by importing the disease from endemic into non-endemic countries. To prevent future outbreaks and dengue from establishing in non-endemic countries, knowledge about the arrival time and location of infected travellers is crucial. We propose a network model that predicts the monthly number of dengue-infected air passengers arriving at any given airport. We consider international air travel volumes to construct weighted networks, representing passenger flows between airports. We further calculate the probability of passengers, who travel through the international air transport network, being infected with dengue. The probability of being infected depends on the destination, duration and timing of travel. Our findings shed light onto dengue importation routes and reveal country-specific reporting rates that have been until now largely unknown. This paper provides important new knowledge about the spreading dynamics of dengue that is highly beneficial for public health authorities to strategically allocate the often limited resources to more efficiently prevent the spread of dengue.Comment: 32 pages, 20 figure

a network connectivity-based approach

The spread of dengue through global human mobility is a major public health concern. A key challenge is understanding the transmission pathways and mediating factors that characterized the patterns of dengue importation into non-endemic areas. Utilizing a network connectivity-based approach, we analyze the importation patterns of dengue fever into European countries. Seven connectivity indices were developed to characterize the role of the air passenger traffic, seasonality, incidence rate, geographical proximity, epidemic vulnerability, and wealth of a source country, in facilitating the transport and importation of dengue fever. We used generalized linear mixed models (GLMMs) to examine the relationship between dengue importation and the connectivity indices while accounting for the air transport network structure. We also incorporated network autocorrelation within a GLMM framework to investigate the propensity of a European country to receive an imported case, by virtue of its position within the air transport network. The connectivity indices and dynamical processes of the air transport network were strong predictors of dengue importation in Europe. With more than 70% of the variation in dengue importation patterns explained. We found that transportation potential was higher for source countries with seasonal dengue activity, high passenger traffic, high incidence rates, high epidemic vulnerability, and in geographical proximity to a destination country in Europe. We also found that position of a European country within the air transport network was a strong predictor of the country's propensity to receive an imported case. Our findings provide evidence that the importation patterns of dengue into Europe can be largely explained by appropriately characterizing the heterogeneities of the source, and topology of the air transport network. This contributes to the foundational framework for building integrated predictive models for bio-surveillance of dengue importation.publishersversionpublishe

Modeling of the spatiotemporal distribution patterns and transmission dynamics of dengue, for an early warning surveillance system

As doenças emergentes transmitidas por vetores representam um desafio significativo para a saúde pública global. Nos últimos tempos, os surtos de doenças como a dengue e a febre de chikungunya, aumentaram em frequência. Tal é facilitado pela globalização, pelo aumento do comércio e das viagens, e pela dispersão para novas áreas dos seus vetores invasores. Na Europa, este facto é exemplificado pela recente introdução e estabelecimento de espécies de mosquitos do género Aedes com a subsequente ocorrência de surtos de doenças como a dengue. Com a crescente disseminação da dengue em todo o mundo, a região europeia também tem vindo a registar um aumento de casos - a maioria destes relacionados com viagens. Da mesma forma, tem havido um aumento de eventos esporádicos de transmissão autóctone de dengue em áreas onde ocorre o vetor sob condições ambientais favoráveis. Assim, atualmente, a Europa enfrenta o desafio de avaliar o risco de importação de casos virémicos de dengue e a probabilidade de ocorrência de transmissão local deste vírus. Esta tese visa contribuir para a compreensão dos fatores relacionados com a importação do vírus da dengue na Europa e a sua transmissão neste território, nomeadamente na ilha da Madeira. Para tal foi implementado uma estrutura integrada de modelos computacionais da importação e transmissão da doença. A estrutura combina três submodelos: (i) um modelo explicativo de importação da doença assente em teoria de redes (ii) um modelo preditivo de aprendizagem automática e, (iii) um modelo compartimental de transmissão vetor-hospedeiro. Os modelos de teoria de redes e de aprendizagem automática foram parametrizados com recurso a dados históricos referentes a estimativas de casos importados de dengue em 21 países na Europa e índices que caracterizam parâmetros com relevância na importação da dengue: (i) tráfego de passageiros aéreos, (ii) atividade e sazonalidade da dengue, (iii) taxa de incidência, (iv) proximidade geográfica, (v) vulnerabilidade à epidemia, e, (vi) contexto económico do país de origem. O modelo compartimental de transmissão foi calibrado com parâmetros empíricos referentes ao ciclo de vida do mosquito, à transmissão viral e à variação anual de temperatura do Funchal, na ilha da Madeira. Os resultados dos modelos de teoria de redes e aprendizagem automática demonstram um maior risco de importação de casos virémicos de países com elevado tráfego de passageiros, elevadas taxas de incidência, situação económica débil e com maior proximidade geográfica em relação ao país de destino. O modelo de aprendizagem automática alcançou elevada performance preditiva, com uma pontuação AUC de 0,94. O modelo compartimental de transmissão demonstra a existência de um potencial de transmissão da dengue no Funchal nos períodos de verão e outono, com a data de chegada da pessoa infeciosa a afetar significativamente a distribuição no tempo e tamanho do pico da epidemia. Da mesma forma, a variação sazonal da temperatura afeta dramaticamente a dinâmica da epidemia, em que temperaturas iniciais mais quentes levam a surtos de maiores proporções, com o pico de casos a ocorrer mais cedo. A estrutura de modelação descrita nesta tese tem o potencial de servir como uma ferramenta integrada de vigilância de alerta precoce para a ocorrência de surtos de dengue na Europa. Este trabalho fornece orientação prática para auxiliar as autoridades de saúde pública na prevenção de surtos de dengue e na redução do risco de transmissão local, em áreas onde ocorrem os vetores. Essa estrutura, com os devidos reajustamentos, pode ser aplicada a outras doenças transmitidas por Aedes, como chikungunya e febre amarela.Emerging vector-borne diseases pose a significant global public health challenge. In recent times, outbreaks of diseases, such as dengue and chikungunya fever, have increased in frequency. This is facilitated by globalization, increase in trade and travel, and the spread of invasive vectors into new areas. In Europe, this is exemplified by the recent introduction and establishment of Aedes mosquito species and subsequent outbreaks of diseases like dengue. With the increasing spread of dengue worldwide, the European region has also experienced increase in reported cases - majority being travel related. Likewise, there has been an increase in sporadic events of autochthonous dengue transmission, in areas with established vector presence and favourable environmental conditions. Europe is currently faced with the challenge of assessing its importation risk of viraemic cases of dengue, and the probability of local transmission. This thesis aims to study the dynamics of viraemic cases importation and virus transmission of dengue fever in Europe, namely in Madeira Island. This is achieved by establishing an importation and transmission modelling framework. The framework combines three sub-models: (i) a network connectivity importation model (ii) a machine learning predictive model and, (iii) a compartmental vector-host transmission model. The network connectivity and machine learning model were both parameterized using a historical dengue importation data for 21 countries in Europe, and indices that characterize important parameters for dengue importation: (i) the air passenger traffic, (ii) dengue activity and seasonality, (iii) incidence rate, (iv) geographical proximity, (v) epidemic vulnerability, and (vi) wealth of a source country. The transmission model was calibrated using empirical parameters for the mosquito life history traits, viral transmission, and temperature seasonality of Funchal, Madeira Island. The results of the network connectivity and machine learning models demonstrate a higher importation risk of a viraemic case from source countries with high passenger traffic, high incidence rates, lower economic status, and geographical proximity to a destination country. The machine learning model achieved high predictive accuracy with an AUC score of 0.94. The transmission model demonstrates the potential for summer and autumn season transmission of dengue in Funchal, with the arrival date of the infectious person significantly affecting the distribution of the timing and peak size of the epidemic. Likewise, seasonal temperature variation dramatically affects the epidemic dynamics, with warmer starting temperatures producing large epidemics with peaks occurring more rapidly. The modelling framework described in this thesis has the potential to serve as an integrated early warning surveillance tool for dengue in Europe. This work provides practical guidance to assist public health officials in preventing outbreaks of dengue and reducing the risk of local transmission in areas with vectors presence. This framework could be applied to other Aedes-borne diseases such as chikungunya and yellow fever

Potential Emergence of Dengue in the Phoenix Metropolitan Area: A Micro-climatic and Demographic Analysis

abstract: The spread of dengue worldwide currently places half of the world’s population at risk. In the absence of a dengue vaccine, control of the disease requires control of the mosquito species that transmit the virus. The most important of these is. Advances in research detailing the responsiveness of Aedes aegypti to small changes in climate enable the production of more sophisticated remote sensing and surveillance techniques for monitoring these populations. Close monitoring of global dengue activity and outbreaks likewise enables a greater specificity when determining to which human populations the virus is most likely to spread. There have been no locally acquired cases in Arizona to date, but the high abundance of Aedes aegypti in the Phoenix Metropolitan area raises concern within the Arizona Department of Health Services over the potential transmission of dengue in the city. This study develops a model that combines mosquito abundance, micro-climatic and demographic information to delineate regions in Phoenix that are most support transmission of dengue. The first chapter focuses on the impact that daytime high and low temperatures have on Aedes aegypti’s ability to become infectious with dengue. It argues that NDVI (normal difference vegetative index) imaging of the Phoenix area can be used to plot areas where mosquitoes are most likely to become competent vectors. The second chapter focuses on the areas in the city where mosquitoes are most likely to be exposed to the virus. Based on proximity to Phoenix and the high volume of traffic across the Arizona-Mexico border, I treat the Mexican state of Sonora as the source of infection. I combine these two analyses, micro-climatic and demographic, to produce maps of Phoenix that show the locations with the highest likelihood of transmission overall.Dissertation/ThesisMasters Thesis Biology 201

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.publishersversionpublishe

Dengue in Finnish international travelers, 2016–2019:a retrospective analysis of places of exposure and the factors associated with the infection

Abstract. As an emerging infectious disease dengue is putting a constantly growing number of international tourists at risk of the infection. To have a more complete picture of the phenomena among the Finnish travelers, the backgrounds of infections were retrospectively examined to find out the place of exposure, type of traveler and the trip, risk perceptions and protective measures taken. The study period was from January 2016 to May 2019 and reported dengue infections from this period were obtained from the National Infectious Disease Register, which is maintained by the Finnish Institute for Health and Welfare (THL). The questionnaire both in Finnish and Swedish was sent to the participants. The response rate in this study was 61.3 %. Data was analyzed spatially with QGIS 3.4.8 Madeira and statistically by using R 3.6.0. Descriptive statistics were used to analyze the demographic variables as well as answers given to the questionnaire. In addition, two binary logistic models were fitted to find out statistically significant factors for risk perception and the use of protective measures. Crude attack rates were calculated for different destinations using UNWTO travel data. Further on, the results were compared to existing literature related to this research. Thailand and Indonesia were identified as destinations with the most abundant number of infections imported to Finland. However, Maldives had the highest crude attack rate per 100,000 travelers. The type of travel during which the infections were acquired was mainly pre-booked holiday of 14 days with time spent mostly on the beach. Most of the travelers were not aware of the dengue risk before the travel and did not seek pre-travel advice. Those who sought pre-travel advice were 34.9 times more likely to use protective measures than those who did not. Moreover, the majority applied some protective measures but not during the right time of the day, and thus the measures were chosen incorrectly. Based on these results the knowledge about dengue, day-active/urban mosquito and the correct use of protective measures needs increasing. Further on, the risk within touristic destinations requires highlighting and the distinction between malaria and other mosquito-borne diseases could be made clearer. In addition, there is a need to increase the knowledge of dengue among healthcare workers

A Systematic Review of Mathematical Models of Dengue Transmission and Vector Control: 2010–2020

Vector control methods are considered effective in averting dengue transmission. However, several factors may modify their impact. Of these controls, chemical methods, in the long run, may increase mosquitoes’ resistance to chemicides, thereby decreasing control efficacy. The biological methods, which may be self-sustaining and very effective, could be hampered by seasonality or heatwaves (resulting in, e.g., loss of Wolbachia infection). The environmental methods that could be more effective than the chemical methods are under-investigated. In this study, a systematic review is conducted to explore the present understanding of the effectiveness of vector control approaches via dengue transmission models

Spatial Evaluation and Modeling of Dengue Seroprevalence and Vector Density in Rio de Janeiro, Brazil

Dengue is a major public health problem in many tropical regions of the world, including Brazil, where Aedes aegypti is the main vector. We present a household study that combines data on dengue fever seroprevalence, recent dengue infection, and vector density, in three neighborhoods of Rio de Janeiro, Brazil, during its most devastating dengue epidemic to date. This integrated entomological–serological survey showed evidence of silent transmission even during a severe epidemic. Also, past exposure to dengue virus was highly associated with age and living in areas of high movement of individuals and social/commercial activity. No association was observed between household infestation index and risk of dengue infection in these areas. Our findings are discussed in the light of current theories regarding transmission thresholds and relative role of mosquitoes and humans as vectors of dengue viruses