Dengue Importation Research Articles

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

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

Global dengue importation: a systematic review

BackgroundImportation of dengue following globalization presents an emerging threat to global health. However, evidence on global geographical sources and the potential of dengue importation globally are lacking. This study aims to systematically review the sources of dengue importation globally and the risk of dengue outbreaks globally.MethodsThis systematic review was conducted in accordance to Cochrane’s PRISMA guidelines. Articles published through 31 December 2019 with laboratory-confirmed dengue imported cases were consolidated from PubMed, EMBASE and Scopus. Sources of dengue importation reported worldwide were analysed by country and geographical regions. Sources of dengue importation into United States of America and Europe specifically were also analysed.ResultsA total of 3762 articles were found. Among which, 210 articles—documenting 14,972 imported dengue cases with reported sources—were eligible. 76.3% of imported cases worldwide were from Asia. 15.7%, 5.6%, 2.0% and 0.1% were imported from the Americas, Africa, Oceania and Europe regions respectively. Imported dengue cases in the U.S. were from Americas (55.3%), Asia (34.7%), Africa (6.7%) and Oceania (3.3%). Imported dengue cases in Europe were from Asia (66.0%), Americas (21.9%), Africa (10.8%) and Oceania (1.1%).ConclusionThe potential of dengue outbreaks occurring globally, especially among non-endemic regions with dengue-susceptible populations is high. With the expansion of Aedes mosquito population globally due to global warming and globalisation, dengue importation constitutes an emerging global health security threat.

Dengue in parts of the Guinea Savannah region of Nigeria and the risk of increased transmission.

BackgroundDengue virus (DENV) is spreading to parts of the world where it had not been previously reported. Increased international travel has led to intercontinental importation of dengue by visitors returning from countries in sub-Saharan Africa. Although dengue is well documented in the rain forest region of Nigeria, there is a dearth of information in the Guinea Savannah region, which is a major transit point for local and international visitors in the most populous nation in Africa. We provide preliminary evidence of dengue activity in the Guinea Savannah and highlight the risk factors.MethodsBlood was collected from a cross-section of 200 patients attending four hospitals in Anyigba, Kogi State. Anti-dengue antibody was identified using DENV immunoglobulin G (IgG) immunoassays. Questionnaires were used to obtain sociodemographic variables and risk factors. Data were analysed with SPSS version 16.0 for Windows.ResultsForty-two (20.5%) participants had anti-DENV IgG antibodies. Persons within the 45–59 y age group were more seropositive, with a rate of 35%. Males were more seropositive compared with females. Marriage, formal education, involvement in business activities and the presence of grasses around homes were associated with higher IgG seropositivity. The presence of open water containers around human dwellings and a lack of mosquito net use are predisposing factors.ConclusionsThis study identified past exposure to DENV among people in Anyigba, located in the Guinea Savannah region. Proper diagnosis of febrile episodes is required to improve case management and curtail off-target treatment. The high rate of previous exposure of patients to dengue indicates the need to strengthen vector control and dengue surveillance programs.

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.

Epidemiological analysis of the Kaohsiung city strategy for dengue fever quarantine and epidemic prevention

BackgroundDengue is endemic in over 100 countries and is an important public health problem worldwide. Dengue fever is not endemic in Taiwan; the importation of dengue viruses from neighboring countries via close commercial links and air travel is considered to be the cause of local outbreaks. Therefore, efforts toward disease control have focused on preventing the importation of dengue into Taiwan. In this study, we investigated the relationships between the numbers of imported and indigenous dengue cases to test the validity of this strategy.MethodsData on cases of dengue fever that occurred between 2013 and 2018 were obtained from the surveillance systems of the Taiwan Center for Disease Control and Kaohsiung City Health Department. Standard epidemiological data, including the monthly numbers of indigenous and imported cases of dengue, were calculated. Potential associations between the numbers of indigenous and imported cases were investigated using correlation analyses.ResultsWe identified a possible relationship between the period of disease concealment and the number of imported dengue cases, which resulted in epidemics of indigenous dengue fever within local communities. Further analysis of confirmed cases during previous epidemics in Kaohsiung City found that the risk of indigenous dengue fever may be related to the likelihood that patients with imported dengue fever will stay within local communities.ConclusionGiven the correlations found between imported and indigenous cases of dengue fever, as well as the relationship between the disease concealment period and the risk of indigenous dengue fever, prevention of disease importation and efficient identification of dengue cases within high-risk communities remain the major priorities for disease control.

Importation of dengue, Zika and chikungunya infections in Europe: the current situation in Greece.

Although several arboviruses—such as dengue (DENV), Zika (ZIKV) and chikungunya (CHIKV) viruses—are not endemic in Europe, they have the potential to emerge following importation of the virus, taking advantage of the favourable climate and ecosystem. DENV, ZIKV and CHIKV are transmitted by Aedes species mosquitoes and are amongst the most common travel-associated arboviruses. Furthermore, they are linked to sporadic, local outbreaks, especially in the southern parts of Europe. In this review we present in brief the DENV, ZIKV and CHIKV cases imported to Greece during the last 6 years (2013–2018), and we describe the recent laboratory data obtained from the Hellenic Pasteur Institute and the National Reference Centre for Arboviruses. We report 21 imported cases of DENV, ZIKV and CHIKV infections in travellers arriving in Greece. The probable origins were south-eastern Asian (71%) and north-central American (29%) countries. Furthermore, we stress the importance of early and accurate diagnosis in spite of a plethora of diagnostic challenges that clinicians and virologists have to face. Altogether, with the authorities' awareness and the preventive measures to be applied, local transmission events can be successfully avoided, especially in summer when the temperature is favourable for mosquito-borne infections.

Dengue importation into Europe: 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.

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.

The use of air travel data for predicting dengue importation to China: A modelling study

Dengue virus importation from abroad is still the main driver of dengue incidence in China. Using global flight data to model importation may improve our understanding and prediction of dengue virus importation and onward transmission. A retrospective analysis was performed of surveillance cases of dengue infections imported to China and volume of air traffic to China for the years 2005 through 2014, inclusive. The data were aggregated by year, destination province, and source country. Descriptive statistics were calculated, and a random effects negative binomial model was created to predict the number of imported cases based on the volume of travelers from dengue-endemic countries. There were 1,822 cases of imported dengue infections over the study period. Most imported cases are from a small number of high-incidence countries with a large volume of travel to China, most notably Myanmar (22% of cases). The number of imported cases of dengue infections increased by 5.9% for every 10% increase in travel volume from dengue-endemic countries. Patterns of air travel have a measurable impact on the importation of dengue to China. Modelling dengue importation risk may be a useful strategy to direct public health surveillance and interventions.

Development of a Recurrent Neural Network Model for Prediction of Dengue Importation

Development of a Recurrent Neural Network Model for Prediction of Dengue Importation

Uncertainty analysis of species distribution models.

The maximum entropy model, a commonly used species distribution model (SDM) normally combines observations of the species occurrence with environmental information to predict the geographic distributions of animal or plant species. However, it only produces point estimates for the probability of species existence. To understand the uncertainty of the point estimates, we analytically derived the variance of the outputs of the maximum entropy model from the variance of the input. We applied the analytic method to obtain the standard deviation of dengue importation probability and Aedes aegypti suitability. Dengue occurrence data and Aedes aegypti mosquito abundance data, combined with demographic and environmental data, were applied to obtain point estimates and the corresponding variance. To address the issue of not having the true distributions for comparison, we compared and contrasted the performance of the analytical expression with the bootstrap method and Poisson point process model which proved of equivalence of maximum entropy model with the assumption of independent point locations. Both Dengue importation probability and Aedes aegypti mosquito suitability examples show that the methods generate comparatively the same results and the analytic method we introduced is dramatically faster than the bootstrap method and directly apply to maximum entropy model.

A combination of incidence data and mobility proxies from social media predicts the intra-urban spread of dengue in Yogyakarta, Indonesia.

Only a few studies have investigated the potential of using geotagged social media data for predicting the patterns of spatio-temporal spread of vector-borne diseases. We herein demonstrated the role of human mobility in the intra-urban spread of dengue by weighting local incidence data with geo-tagged Twitter data as a proxy for human mobility across 45 neighborhoods in Yogyakarta city, Indonesia. To estimate the dengue virus importation pressure in each study neighborhood monthly, we developed an algorithm to estimate a dynamic mobility-weighted incidence index (MI), which quantifies the level of exposure to virus importation in any given neighborhood. Using a Bayesian spatio-temporal regression model, we estimated the coefficients and predictiveness of the MI index for lags up to 6 months. Specifically, we used a Poisson regression model with an unstructured spatial covariance matrix. We compared the predictability of the MI index to that of the dengue incidence rate over the preceding months in the same neighborhood (autocorrelation) and that of the mobility information alone. We based our estimates on a volume of 1·302·405 geotagged tweets (from 118·114 unique users) and monthly dengue incidence data for the 45 study neighborhoods in Yogyakarta city over the period from August 2016 to June 2018. The MI index, as a standalone variable, had the highest explanatory power for predicting dengue transmission risk in the study neighborhoods, with the greatest predictive ability at a 3-months lead time. The MI index was a better predictor of the dengue risk in a neighborhood than the recent transmission patterns in the same neighborhood, or just the mobility patterns between neighborhoods. Our results suggest that human mobility is an important driver of the spread of dengue within cities when combined with information on local circulation of the dengue virus. The geotagged Twitter data can provide important information on human mobility patterns to improve our understanding of the direction and the risk of spread of diseases, such as dengue. The proposed MI index together with traditional data sources can provide useful information for the development of more accurate and efficient early warning and response systems.

Seasonal and interannual risks of dengue introduction from South-East Asia into China, 2005-2015.

Due to worldwide increased human mobility, air-transportation data and mathematical models have been widely used to measure risks of global dispersal of pathogens. However, the seasonal and interannual risks of pathogens importation and onward transmission from endemic countries have rarely been quantified and validated. We constructed a modelling framework, integrating air travel, epidemiological, demographical, entomological and meteorological data, to measure the seasonal probability of dengue introduction from endemic countries. This framework has been applied retrospectively to elucidate spatiotemporal patterns and increasing seasonal risk of dengue importation from South-East Asia into China via air travel in multiple populations, Chinese travelers and local residents, over a decade of 2005–15. We found that the volume of airline travelers from South-East Asia into China has quadrupled from 2005 to 2015 with Chinese travelers increased rapidly. Following the growth of air traffic, the probability of dengue importation from South-East Asia into China has increased dramatically from 2005 to 2015. This study also revealed seasonal asymmetries of transmission routes: Sri Lanka and Maldives have emerged as origins; neglected cities at central and coastal China have been increasingly vulnerable to dengue importation and onward transmission. Compared to the monthly occurrence of dengue reported in China, our model performed robustly for importation and onward transmission risk estimates. The approach and evidence could facilitate to understand and mitigate the changing seasonal threat of arbovirus from endemic regions.

The introduction of dengue follows transportation infrastructure changes in the state of Acre, Brazil: A network-based analysis.

Human mobility, presence and passive transportation of Aedes aegypti mosquito, and environmental characteristics are a group of factors which contribute to the success of dengue spread and establishment. To understand this process, we assess data from dengue national and municipal basins regarding population and demographics, transportation network, human mobility, and Ae. aegypti monitoring for the Brazilian state of Acre since the first recorded dengue case in the year 2000 to the year 2015. During this period, several changes in Acre’s transport infrastructure and urbanization have been started. To reconstruct the process of dengue introduction in Acre, we propose an analytic framework based on concepts used in malaria literature, namely vulnerability and receptivity, to inform risk assessments in dengue-free regions as well as network theory concepts for disease invasion and propagation. We calculate the probability of dengue importation to Acre from other Brazilian states, the evolution of dengue spread between Acrean municipalities and dengue establishment in the state. Our findings suggest that the landscape changes associated with human mobility have created favorable conditions for the establishment of dengue virus transmission in Acre. The revitalization of its major roads, as well as the increased accessibility by air to and within the state, have increased dengue vulnerability. Unplanned urbanization and population growth, as observed in Acre during the period of study, contribute to ideal conditions for Ae. aegypti mosquito establishment, increase the difficulty in mosquito control and consequently its local receptivity.

Incidence of dengue and chikungunya viruses in mosquitoes and human patients in border provinces of Vietnam

BackgroundDengue virus remains a major threat in Vietnam, while chikungunya virus is expected to become one. Surveillance was conducted from 2012 to 2014 in Vietnam to assess the presence of dengue and chikungunya viruses in patients hospitalized with acute fever in five Vietnam provinces neighboring Lao PDR and Cambodia. Surveillance was extended to mosquitoes present in the vicinity of the patients’ households.ResultsA total 558 human serum samples were collected along with 1104 adult mosquitoes and 12,041 larvae from 2250 households. Dengue virus was found in 17 (3%) human serum samples and in 9 (0.8%) adult mosquitoes. Chikungunya virus was detected in 2 adult mosquitoes (0.18%) while no chikungunya virus was detected in humans. Differing densities of mosquito populations were found, with the highest in the Long An Province border with Cambodia. Long An Province also displayed the lowest rate of infection, despite a very high Breteau Index, high human population density and presence of the main cross border road system. The highest incidence was found in Dac Nong Province, where the Breteau and Container indices were the second lowest. Dengue virus was detected in five Aedes albopictus, three Aedes aegypti and one Culex vishnui. Chikungunya virus was detected in two Ae. aegypti. All infected mosquitoes belonged to haplotypes described in other parts of the world and a number of novel haplotypes were found among uninfected mosquitoes.ConclusionsDengue is considered to be regularly introduced to Vietnam from Cambodia, mostly through human movement. The data reported here provides a complementary picture. Due to intensive international trade, long-distance transportation of mosquito populations may play a role in the regular importation of dengue in Vietnam through Ho Chi Minh City. It is important to decipher the movement of mosquitoes in Vietnam, not only at the Lao PDR and Cambodia borders but also through international trade routes. Mosquito surveillance programs should address and follow mosquito populations instead of mosquito species.

Interaction of Flavivirus with their mosquito vectors and their impact on the human health in the Americas

Some of the major arboviruses with public health importance, such as dengue, yellow fever, Zika and West Nile virus are mosquito-borne or mosquito-transmitted Flavivirus. Their principal vectors are from the family Culicidae, Aedes aegypti and Aedes albopictus being responsible of the urban cycles of dengue, Zika and yellow fever virus. These vectors are highly competent for transmission of many arboviruses. The genetic variability of the vectors, the environment and the viral diversity modulate the vector competence, in this context, it is important to determine which vector species is responsible of an outbreak in areas where many vectors coexist. As some vectors can transmit several flaviviruses and some flaviviruses can be transmitted by different species of vectors, through this review we expose importance of yellow fever, dengue and Zika virus in the world and the Americas, as well as the updated knowledge about these flaviviruses in their interaction with their mosquito vectors, guiding us on what is probably the beginning of a new stage in which the simultaneity of outbreaks will occur more frequently.

Emergence of Epidemic Dengue-1 Virus in the Southern Province of Sri Lanka.

BackgroundDengue is a frequent cause of acute febrile illness with an expanding global distribution. Since the 1960s, dengue in Sri Lanka has been documented primarily along the heavily urbanized western coast with periodic shifting of serotypes. Outbreaks from 2005–2008 were attributed to a new clade of DENV-3 and more recently to a newly introduced genotype of DENV-1. In 2007, we conducted etiologic surveillance of acute febrile illness in the Southern Province and confirmed dengue in only 6.3% of febrile patients, with no cases of DENV-1 identified. To re-evaluate the importance of dengue as an etiology of acute febrile illness in this region, we renewed fever surveillance in the Southern Province to newly identify and characterize dengue.Methodology/Principal FindingsA cross-sectional surveillance study was conducted at the largest tertiary care hospital in the Southern Province from 2012–2013. A total of 976 patients hospitalized with acute undifferentiated fever were enrolled, with 64.3% male and 31.4% children. Convalescent blood samples were collected from 877 (89.6%). Dengue virus isolation, dengue RT-PCR, and paired IgG ELISA were performed. Acute dengue was confirmed as the etiology for 388 (39.8%) of 976 hospitalizations, with most cases (291, 75.0%) confirmed virologically and by multiple methods. Among 351 cases of virologically confirmed dengue, 320 (91.2%) were due to DENV-1. Acute dengue was associated with self-reported rural residence, travel, and months having greatest rainfall. Sequencing of selected dengue viruses revealed that sequences were most closely related to those described from China and Southeast Asia, not nearby India.Conclusions/SignificanceWe describe the first epidemic of DENV-1 in the Southern Province of Sri Lanka in a population known to be susceptible to this serotype because of prior study. Dengue accounted for 40% of acute febrile illnesses in the current study. The emergence of DENV-1 as the foremost serotype in this densely populated but agrarian population highlights the changing epidemiology of dengue and the need for continued surveillance and prevention.

The Spread of Dengue in an Endemic Urban Milieu--The Case of Delhi, India.

BackgroundDengue is a major international public health concern, one of the most important arthropod-borne diseases. More than 3.5 billion people are at risk of dengue infection and there are an estimated 390 million dengue infections annually. This prolific increase has been connected to societal changes such as population growth and increasing urbanization generating intense agglomeration leading to proliferation of synanthropic mosquito species. Quantifying the spatio-temporal epidemiology of dengue in large cities within the context of a Geographic Information System is a first step in the identification of socio-economic risk factors.Methodology/Principal FindingsThis Project has been approved by the ethical committee of Institut Pasteur. Data has been anonymized and de-identified prior to geolocalisation and analysis. A GIS was developed for Delhi, enabling typological characterization of the urban environment. Dengue cases identified in the Delhi surveillance system from 2008 to 2010 were collated, localised and embedded within this GIS. The spatio-temporal distribution of dengue cases and extent of clustering were analyzed. Increasing distance from the forest in Delhi reduced the risk of occurrence of a dengue case. Proximity to a hospital did not increase risk of a notified dengue case. Overall, there was high heterogeneity in incidence rate within areas with the same socio-economical profiles and substantial inter-annual variability. Dengue affected the poorest areas with high density of humans, but rich areas were also found to be infected, potentially because of their central location with respect to the daily mobility network of Delhi. Dengue cases were highly clustered in space and there was a strong relationship between the time of introduction of the virus and subsequent cluster size. At a larger scale, earlier introduction predicted the total number of cases.Conclusions/SignificanceDENV epidemiology within Delhi has a forest fire signature. The stochastic nature of this invasion process likely smothers any detectable socio-economic risk factors. However, the significant finding that the size of the dengue case cluster depends on the timing of its emergence emphasizes the need for early case detection and implementation of effective mosquito control. A better understanding of the role of population mobility in contributing to dengue risk could also help focus control on areas at particular risk of dengue virus importation.

Modeling Importations and Exportations of Infectious Diseases via Travelers.

This paper is an attempt to estimate the risk of infection importation and exportation by travelers. Two countries are considered: one disease-free country and one visited or source country with a running endemic or epidemic infectious disease. Two models are considered. In the first model (disease importation), susceptible individuals travel from their disease-free home country to the endemic country and come back after some weeks. The risk of infection spreading in their home country is then estimated supposing the visitors are submitted to the same force of infection as the local population but do not contribute to it. In the second model (disease exportation), it is calculated the probability that an individual from the endemic (or epidemic) country travels to a disease-free country in the condition of latent infected and eventually introduces the infection there. The input of both models is the force of infection at the visited/source country, assumed known. The models are deterministic, but a preliminary stochastic formulation is presented as an appendix. The models are exemplified with two distinct real situations: the risk of dengue importation from Thailand to Europe and the risk of Ebola exportation from Liberia to the USA.

Impact of human mobility on the emergence of dengue epidemics in Pakistan

The recent emergence of dengue viruses into new susceptible human populations throughout Asia and the Middle East, driven in part by human travel on both local and global scales, represents a significant global health risk, particularly in areas with changing climatic suitability for the mosquito vector. In Pakistan, dengue has been endemic for decades in the southern port city of Karachi, but large epidemics in the northeast have emerged only since 2011. Pakistan is therefore representative of many countries on the verge of countrywide endemic dengue transmission, where prevention, surveillance, and preparedness are key priorities in previously dengue-free regions. We analyze spatially explicit dengue case data from a large outbreak in Pakistan in 2013 and compare the dynamics of the epidemic to an epidemiological model of dengue virus transmission based on climate and mobility data from ∼40 million mobile phone subscribers. We find that mobile phone-based mobility estimates predict the geographic spread and timing of epidemics in both recently epidemic and emerging locations. We combine transmission suitability maps with estimates of seasonal dengue virus importation to generate fine-scale dynamic risk maps with direct application to dengue containment and epidemic preparedness.