Dataset: 11.1K articles from the COVID-19 Open Research Dataset (PMC Open Access subset)
All articles are made available under a Creative Commons or similar license. Specific licensing information for individual articles can be found in the PMC source and CORD-19 metadata
More datasets: Wikipedia | CORD-19

Logo Beuth University of Applied Sciences Berlin

Made by DATEXIS (Data Science and Text-based Information Systems) at Beuth University of Applied Sciences Berlin

Deep Learning Technology: Sebastian Arnold, Betty van Aken, Paul Grundmann, Felix A. Gers and Alexander Löser. Learning Contextualized Document Representations for Healthcare Answer Retrieval. The Web Conference 2020 (WWW'20)

Funded by The Federal Ministry for Economic Affairs and Energy; Grant: 01MD19013D, Smart-MD Project, Digital Technologies

Imprint / Contact

Highlight for Query ‹Bocavirus infection symptoms

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

Conceptual framework

The successful invasion of a parasite into a new place is the result of the interaction between two players: the invading species and the invaded community. In the context of dengue, the invaded community is composed of two main species: human hosts and mosquitoes of the species Ae. aegypti whose interaction is further affected by environmental factors (climate, urbanization, human behavior). The invading species under consideration, dengue viruses, cannot establish itself in new areas unless the receiving community presents certain conditions to sustain autochthonous virus transmission. More formally, the emergence of dengue fever in a new region depends on the occurrence of two major events: (i) the arrival of at least one infected individual in the city and (ii) the subsequent positive growth of autochthonous dengue cases (i.e., disease establishment).

The sustained growth of autochthonous cases depends on “the abundant presence of vectors and the existence of other ecological and climatic factors favoring transmission”, which is the concept of receptivity found in the malaria literature. If an area is receptive, then the arrival of the virus by imported cases or vectors has a probability of triggering sustained local transmission. Transmission is then measured by the effective reproductive number, Rt, defined as the expected number of secondary human cases that would result from the arrival of a single infected individual in a susceptible population. A reproductive number greater than one suggests epidemic growth.

The larger the influx of imported cases the higher the probability of establishment given the necessary environmental conditions. In the malaria literature, this exposure to imported cases is referred to as “Vulnerability” defined as “either proximity to endemic areas or…the frequent influx of infected individuals or groups and/or infective anopheles”. The same concept can be applied to dengue epidemiology. Dengue influx might be measured directly from the number of cases that can be traced to outside areas (imported cases). In situations where the origin of the cases is difficult to ascertain, one can estimate the number of imported cases from statistics of transportation and prevalence in passengers’ origins.

Study area

The state of Acre (AC) is located in the north region of Brazil, bordering the countries of Peru and Bolivia, and the Amazonas and Rondônia states in Brazil (see Fig 1 for geographical reference). The total area is 164,122 km2 with a population of 733,559 inhabitants and a population density of 4.47 hab/km2. The state has 22 municipalities, the most populous being the capital Rio Branco and Cruzeiro do Sul, with 336,038 and 78,507 inhabitants according to 2010 Census, respectively. The Acrean climate is warm and humid equatorial, which is characterized by high temperatures with small annual fluctuations, high pluviometric precipitation indices and high relative humidity. The temperature is approximately uniform throughout the state, with an annual average around 24,5°C, and maximum around 32°C.

Dengue national data

Monthly counts of reported cases of dengue per Brazilian state, from 2001 to 2012, were obtained from Departamento de Informática do Sistema Único de Saúde (DataSUS, Department of Informatics of Universal Health System, The accumulated number of cases in each state during the period of study is shown in Fig 1.

Dengue municipal data

Weekly counts of reported cases of dengue per Acrean municipality, from 2000 to 2015, were provided by the Brazilian Ministry of Health. Dengue reporting is mandatory in Brazil, but most cases are defined using clinical-epidemiological criteria only.

Definition of dengue epidemiological year

Since dengue reaches higher activity levels during the summer (Dec to March), it is more convenient to define the epidemiological year as the period between July of a year and June of the next year, as opposed to the standard calendar year.

Aedes aegypti data

Available data regarding the presence of Ae. aegypti in the 22 municipalities of Acre was obtained from the Ministry of Health (MH) and internet bulletins. Although MH has implemented the household larval survey (LI) and the Rapid Assessment of Infestation by Ae. aegypti (LIRAa) since 1996, not all municipalities adhered to the protocol at the same time. We searched the available information to identify the year in which the presence of the mosquito was confirmed for the first time in each municipality. It is important to note, however, that this information can be incomplete for some cities, and the first detection does not necessarily imply that the mosquito was not present before.

Population and demographic data

The population size per Brazilian state from 2001 to 2012 was obtained from Instituto Brasileiro de Geografia e Estatística (IBGE, Brazilian Institute of Geography and Statistics) [25, 29]. The population count in rural and urban areas per municipality in Acre, in 2000 and 2010, as well as information on the proportion of households with access to public services and other demographic variables were obtained from.

Transportation network

To describe the Acrean transportation system, data regarding the availability and status of roads, waterways, and airways was obtained from the Ministry of Transport (, Agência Nacional de Aviação Civil (ANAC, National Civil Aviation Agency, and Associação dos Municípios do Acreanos (AMAC, Association of Acrean Municipalities, Description of road status was obtained by request to the Departamento de Estradas e Rodagens do Acre (DERACRE, Department of Roads and Roadways of Acre) and by consulting the website of the Programa de Aceleração do Crescimento (PAC, Growth Acceleration Program, The connections between municipalities—i.e., the network edges—were classified according to the presence and status of roads, waterways, and airways. In the cases where municipalities were connected by more than one modal, this was taken into account by including all corresponding edges. Road status was tentatively classified year by year. However, since not all information was recorded every year, it is only an approximation. The combination of modal type and road condition resulted in the following categories: (1) unpaved road, (2) paved road and (3) road under maintenance; (4) waterway; and (5) airway. The third category encompassed roads under restoration, under construction and mixed conditions.

Flight data

To describe the flow of individuals to Acre, airline data from 2001 to 2012 was provided by ANAC. This dataset contains information on the origin-destination for passengers on direct flights (with or without stoppage). For those on connecting flights, ANAC provides the number of travelers in transit per airport pair. We developed a methodology to combine the data regarding direct flights and passengers in transit to estimate the total number of passengers that travel between all possible airport pairs (see S1 Appendix). The total number of passengers to Acre by state of origin during the period of study is depicted in Fig 1.

Commutation data (Mobility)

To obtain an estimate of the flow of individuals between municipalities in Acre, we used data from the 2010 Brazilian Census. In this dataset, respondents were asked to provide the country, state, and municipality of work/study. A fraction of them did not provide full information, that is, they mentioned working outside their municipality of residence, but with no further specification of state or municipality; some only provided destination state, but no specification of the municipality. To compensate for that, for each city of residence we aggregated all entries with the same level of information, (i) displacement only, (ii) destination state only, and (iii) complete information. With that, we proportionally assigned a destination municipality based on the distribution for the particular subset of the entries with full information.

The mobility network is built from the combination of commutation and flight data. In this network, each municipality is represented by a node while edges represent the average daily flow of individuals between pairs. To estimate this quantity, we summed the estimated daily number of residents moving from one municipality to another for work and/or study over the year, given by the commutation data, with the average daily number of flight passengers in 2010.

Assessing how Acre’s vulnerability to dengue importation changed from 2001 to 2012

Within our conceptual framework, vulnerability was defined in terms of the exposure of the focal population to imported dengue cases. Here, we measured vulnerability as the expected probability of dengue importation from other Brazilian states into Acre, by air transportation.

The usage of national airline passengers database is of great interest for the study of human interaction dynamics within a country, particularly for the study of potential disease transmission routes. The flow of individuals between different regions allows agents that became infected in one area to carry the pathogen to another area within his/her path [8, 11, 17, 18, 26, 31]. This mechanism serves as a driver for the (re-)introduction of pathogens throughout the national territory. In this context, we analyzed Brazilian airline grid to estimate passenger flow between Brazilian states and how this potential risk for the spreading of dengue to Acre varied since the year 2000.

From the Brazilian airline database collected from 2001 to 2012, we estimated the daily probability of an individual from state i traveling to state j for each month m, πij,m (see S1 Appendix). Coupling this probability with the reported number of dengue cases in each state in the corresponding time window, we estimated the risk of each one state being the source of an imported dengue case to any other in the Brazilian territory. This framework allows us to estimate the most probable routes of case importation in each year to Acre.

Say we have ki,m dengue cases reported in state i in month m. Each k of the ki,m infected individuals stays τk days as infectious, with an average of 4.5 [1.9–7.9] days. Given the daily probability of travel πij,m, τk is also the number of monthly trials for the probability of an infected individual boarding a flight during his infectious period. Combining this information, the monthly case importation probability pij,m can be estimated as


Following the same rationale, the yearly probability of dengue importation, Pij,s, at dengue epidemiological year s, can be estimated by aggregating over all months of the corresponding epidemiological year, that is


This construction allows us to provide a ranking of states by their probability of exporting a dengue-infected individual to Acre, that is, the most probable sources of case importation at any given epidemiological year. It also allows us to estimate the source-independent yearly dengue importation probability, that is, the probability of at least one importation to Acre in a given year regardless of its state of origin, which can be written as:


This quantity describes the temporal dynamics of the Acre’s overall exposure to dengue-infected individuals. To take into account the uncertainty regarding the length of the infectious period, the results shown correspond to the average over 1000 simulations of this process using a gamma distribution with mean of 4.5 and 95% confidence interval of [1.9–7.9] for the infectious period of each reported case.

Establishment of dengue transmission in the Acrean cities

The earliest evidence of the onset of disease establishment is the occurrence of autochthonous cases, which are ascertained based on investigations of the travel history of the patients. Secondly, the presence of clusters or transmission chains is indicative of further transmission. Thirdly, if the transmission is sustained, the incidence curve will increase at an exponential (or subexponential) rate, characterizing an epidemic. The available dengue notification data do not have information on travel history or clustering of the reported cases. Here, evidence of transmission is indirectly obtained from the notification data via the calculation of the effective reproductive number, Rt. This index is interpreted as the average number of secondary cases generated by a primary case at time t, calculated as the ratio of secondary to primary cases. A value of Rt > 1 indicates a sustained growth of incidence. Since no information is available on who infected whom, the primary cases are inferred from the generation interval of dengue, that is, primary cases are those that could be the source of transmission for the current cases given the time between the onset of their disease symptoms. This approach formally described by Wallinga & Lipsitch results in the following expression for Rt:


where b(t) is the number of new cases reported at week t and g(a) is the probability distribution of the dengue generation interval (time between onset of symptoms in a primary case and onset of symptoms in a secondary case) taken as a Normal distribution N (μ = 3weeks, σ = 1week). Confidence intervals for the ratio of two Poisson counts were calculated using the method described in and Rt > 1 was ascertained if p(Rt > 1) > 0.95.

The effective reproductive number Rt was calculated weekly for each city, from 2000 to 2015. For each city, an epidemiological year was classified as epidemic if Rt > 1 for at least three consecutive weeks. A period of 3 weeks was chosen because it represents one generation of dengue transmission. We further defined T3 as the time in weeks from July 2000 to the first observation of three consecutive weeks with Rt > 1 in that city.

Assessing how vulnerability to dengue importation changed from 2000 to 2015 at the Acrean municipalities in response to changes in the transportation network

In 2001, Acre witnessed its first dengue epidemic, in Rio Branco. Since then, dengue cases have been recorded almost continuously and with great intensity in this municipality. Here we investigate if the spread of dengue from Rio Branco to other municipalities in Acre was associated with the transportation network. First, we investigate if more central cities were invaded earlier than peripheral ones, where centrality is defined as a property of the transportation network (defined below). Secondly, we assessed whether cities closer to Rio Branco were invaded before distant ones.

A structural transportation network was constructed as a graph with 22 nodes representing the municipalities and 29 edges representing the connections between them by (paved, unpaved or under maintenance) roads, waterways or airways, as described in the data subsection. In this network, the edges were considered as unweighted for most of the centrality metrics defined below. The only exception made was for the distance in kilometers from Rio Branco, in which case each edge received a weight equal to the corresponding distance between the connected nodes. We also constructed a mobility network with the same 22 nodes but with a total of 87 edges. As described in the mobility data subsection, in this network the edges are given weights equal to the sum of residents moving from one municipality to another for work and/or study in both directions.

There are several measures of node centrality, the literature being rich with proposals for both weighted and unweighted networks. Each of those proposals focuses on a different aspect of information flow and node properties, therefore producing potentially different node ranks for each measure [37, 38]. Here, we considered the following centrality measures that provide different interpretations in relation to dengue exposure:

To access how vulnerability to dengue importation changed in the study time at the Acrean municipalities in response to changes in the transportation network, we measured, using Spearman correlations, the intensity of the relationship between T3 to the network descriptors and compare to the structural state of the transportation network during the period of study. While the descriptors for the mobility network are static since we only have the flow of individuals for the year of 2010, the analysis of the transportation network status is dynamic since there were considerable interventions such as construction and paving of roadways during the period of study. We hypothesize that T3 and the descriptors should be correlated since the road changes in the Acrean network is very evident, and the invasion of dengue in the state occurred first in the nearest municipalities in terms of distance and with better road structure.

The analysis were performed in software R 3.3.2 (R Core Team, 2016, and in Python 3.5 (Python Software Foundation, using the libraries Networkx, Pandas, Numpy, and igraph.

Receptivity of the Acrean cities to dengue transmission

Receptivity depends on the presence, abundance and vectorial capacity of the local population of Ae. aegypti. Moreover, dengue receptivity has been linked to unplanned urbanization, fast population growth, poor infrastructure as lack of urban services and effective mosquito control, and globalization. Here, we review the available information on these topics in order to provide the best description possible for the evolution of dengue receptivity in the study period. First, we investigated the available records of Ae. aegypti presence in the region using LI/LIRAa. From this data, we computed the year when Ae. aegypti was first recorded in each municipality.

Secondly, we computed how much each municipality changed from 2000 to 2010, in terms of population growth, urbanization, garbage collection, water supply and sanitary sewage.

We also reviewed the scientific literature searching any biological information regarding the vector populations found in Acre. The literature is scarce, and a single study was found on the competence of Acrean Ae. aegypti to DENV-2.

Vulnerability to dengue importation to Acre

From 2001 to 2012, the overall flow of passengers to Acre increased from about 50,000 to more than 150,000 passengers per year. During this period, the main states of origin for travelers to Acre were Distrito Federal (DF), Rondônia (RO), Amazonas (AM) and São Paulo (SP) (Fig 1). Meanwhile, the states of Rio de Janeiro (RJ), São Paulo, Minas Gerais (MG) and Bahia (BA) had the highest dengue cases recorded. As discussed in the methods section, the probability of case importation to Acre is a combination of both the number of travelers and dengue activity. Fig 2 shows the probability of dengue case importation into Acre from 2001/2002 to 2011/2012. It reached ca. 70% in 2001/2002, reducing to its lowest level (38%) in 2003/2004, steadily increasing to ca. 100% from 2004/2005 to 2007/2008, remaining so until the end of the study period. The north region was found as the main source of dengue cases to Acre, in particular, the neighboring state of Rondônia (RO), followed by the center-west (Distrito Federal-DF and Mato Grosso-MT), southeast (Rio de Janeiro-RJ and São Paulo-SP), and northeast (Ceará-CE). The contribution of Rondônia, Distrito Federal and Mato Grosso to the exportation of dengue to Acre was mainly associated with the flow intensity between these states while that of Rio de Janeiro and São Paulo was a result of the combination of moderate flow intensity and high rates of dengue activity. Others states such as Tocantins (TO), Roraima (RR) and Amapá (AP) in the north, Maranhão (MA), Piauí (PI), Paraíba (PB) and Alagoas (AL) in the east and all states in the south region were unlikely exporters of dengue cases to Acre due to the combination of low flow and low dengue activity.

Dengue establishment in the Acrean municipalities

The first autochthonous dengue case was reported in Acre in 2000. From this year until 2008, the annual incidence did not exceed 900 cases per 100,000 inhabitants, a level of activity similar to those found in the other northern states of Brazil, except for Roraima. However, in 2009 there was a significant increase in dengue incidence in Acre when it tripled to an alarming level of 2,800 cases per 100,000. In 2010, dengue incidence was even higher: 4,793.3 cases per 100,000. According to the Ministry of Health, in 2011, Acre was among the states classified as at moderate risk, and within the state, the capital Rio Branco was classified as at the highest risk. On the other hand, the Alto Juruá region, in the northwest of Acre, which until 2014 had not yet registered autochthonous cases of dengue, witnessed its first dengue epidemic in Cruzeiro do Sul. In 2014, Cruzeiro do Sul was among the municipalities with the highest number of dengue cases between the epidemiological weeks 01 and 47 of 2014. Compared to 2013, the number of cases jumped from 30 cases to 23,130 [53, 54].

In Fig 3 we show the weeks with Rt > 1, from 2000 to 2015 at each Acrean municipality. A sequence of weeks with Rt > 1 is an indication of epidemic growth. All municipalities presented this event at least once during the study period. The municipalities that presented the lowest percentages of weeks with Rt > 1 were Porto Walter (PW) and Jordão (Jrd) (1.15% for both) throughout the study period. These are the two most peripheral in the state, and whose access includes a stretch traveled by river. The ones with the highest percentages of “epidemic weeks” were Rio Branco (RB, 9.32%), Senador Guiomard (SG, 6.39%), Epitaciolândia (Epcd), and Brasiléia (Brl) (6.00% for both).

In Fig 3, colors indicate the main type of access to each city. There are two main types of access, those cities in which access is by road (black and orange) and those in which the final access is by waterway (blue). Before 2008/2009, only cities in which access is by road registered Rt > 1. However, these records were mostly intermittent, as can be seen for example in Bujari (Bjr), Capixaba (Cpb), Assis Brasil (AB), Porto Acre (PA), Acrelândia (Acld), Plácido de Castro (PC) and Xapuri (Xpr). Cruzeiro do Sul (CZS) in gray, which is the only city served by an airline to Rio Branco, also registered few weeks with Rt > 1 before 2008. After this year, when several paving works and road maintenance were concluded, the frequency increased eventually becoming more intense culminating with CZS recording the first sustained epidemic in 2014. The time frame of infrastructural changes and its potential impact on dengue cases notification is discussed in more detail in the section Dengue spread in Acre. The municipalities in orange, Mâncio Lima (ML), Rodrigues Alves (RA), Tarauacá (Trc) and Manoel Urbano (MU), also have access by road, but part of the final access was under construction or maintenance during all or almost all the period of study. In these municipalities, as well as in those with final access by waterway, the first records of Rt > 1 occurred in the beginning of 2008, in a slow way, while in those cities with access by waterway, the records were even rarer than those accessible by roads.

Vulnerability to dengue importation within Acre

Public investments since 2000 in the development of the Acrean transportation infrastructure, including the construction of the Pacific Highway and the pavement of intermunicipal roads, have brought more connectivity between the municipalities, in particular within southeast Acre, composed by Rio Branco, Senador Guiomard, Capixaba, Xapuri, Epitaciolândia, Plácido de Castro, Brasiléia, and Acrelândia. In contrast, transportation and mobility between the southeast and other regions of Acre (Alto Juruá) is still very difficult due to road conditions, although improvements have occurred during this period. The accessibility to the northwest area of Acre began to improve after the year 2008, when paving of the highway BR-364 in the region of Manuel Urbano (MU) and Feijó (Fj), in the central area of Acre. Highway BR-364 is the only one that crosses the state from Acrelândia (Acl, in the southeast) to Mâncio Lima (ML, in the northwest) and connects Acre to the rest of Brazil via Rondônia state.

Highway BR-364 has been under construction since the beginning of the last decade. In southeast Acre the paving and restoration of this highway were completed quickly, and only the stretch from Acrelândia to Rio Branco was still under construction until 2007/2008. The stretch from the capital to Cruzeiro do Sul, at the northwest part of the state, passes through very unstable soil, making it difficult to maintain during the monsoon season. Only in 2012/2013, this stretch was completely paved, but in the following years repairment works were needed. Besides this highway, an extremely important link between the southeast and northwest Acre is the daily flight between Rio Branco and Cruzeiro do Sul, which increased by 18% from 2001 to 2012. The most remote municipalities are Santa Rosa do Purus, Jordão, Marechal Thaumaturgo and Porto Walter with very limited access and only by river, most of them only by small and medium-sized vessels depending on the time of year. On the other hand, air travel alone was responsible for an average of 134 passengers to Acre per day during the epidemiological year 2001/2002, steadily increasing to almost 500 per day in 2013/2014, most of them to Rio Branco, responsible for approximately 90% of all air travel to Acre from other Brazilian states. To illustrate the impact of interstate airflow in this population, the 2010 Census registered 3.6 thousand commuters to Rio Branco, so that the average daily out-of-state air passengers corresponded to almost 4% of daily commuters in 2001/2002, up to almost 14% in that of 2013/2014. This data contrasts with the common misconception regarding air travel to Acre and its impact to information inflow, which is believed to be negligible due to its relatively low volume with respect to the rest of the Country.

The connectivity between municipalities is shown in Fig 4. We used Cytoscape software to build a representation of the Acrean municipalities’ mobility network, with edge width and color proportional to the natural logarithm of the daily number of travelers and node size proportional to the natural logarithm of each municipality’s population (node color indicate access mode, as in Fig 3). Nodes position are defined based on the Fruchterman–Reingold algorithm, which is a force-directed layout widely used in network visualization. In this layout, all nodes have a repulsion force between them, while edges weight are used as an attractive force between connected nodes. Nodes that share stronger connections, i.e., with more travelers, are placed closer to each other than those that share weaker or no connection. Therefore, groups of nodes sharing stronger bonds among them form regional clusters in this abstract space of connectivity. This particular visualization facilitates the detection of municipalities that share relatively strong bonds even when geographically far apart. On the other hand, nodes that are placed on the periphery with this algorithm are those that have relatively few and/or weak connections. We can see that the two most populous municipalities, the capital Rio Branco (RB) and Cruzeiro do Sul (CZS), are important hubs in this network. The access mode (waterway, paved and unpaved roads, airway) confers a natural clustering on the mobility network, suggesting an intuitive relationship between ease of access (transportation network) and the number of travelers (mobility network). Municipalities accessed by paved roads are more densely connected with each other, while the strong connection between Rio Branco and Cruzeiro do Sul, which are connected by airports, considerably shortens the effective distance between east and west side of the state. Another example of this relationship is the fact that municipalities which are mainly accessed via waterways are peripheral in the mobility network. In fact, Marechal Thaumaturgo (MT), Jordão (Jrd) and Santa Rosa dos Purus (SRP) have the lowest Ew, Cw and Sw centralities, with Porto Walter (PW) also among the five municipalities with lowest values for those centralities.

The capital Rio Branco is the most central node in the Acrean network with respect to all descriptors used (see in S2 Appendix) being the most present node in all shortest paths (as measured by B and Bw); the municipality with the smallest average shortest path to all other nodes (C and Cw); the strongest attractor (Ew) in the network; and the node with most travelers (Sw). Therefore, it is both the most vulnerable to exposure from a pathogen present in the state at a random municipality, as well as the node which poses the highest impact in other municipalities’ vulnerability once infected. Porto Acre, Bujari and Senador Guiomard, all with geographical borders with Rio Branco, also present high centrality measures, therefore sharing similar relevance to disease transmission. All of those cities are located on the east side of the state, and all of them can be accessed via paved federal highway.

Another municipality with high centrality for most indicators is Cruzeiro do Sul (CZS), on the west side of Acre. Although geographically distant from Rio Branco, this municipality shares an airport connection with the capital, drawing a significant amount of travelers and therefore shortening its network distance with respect to individual flow. Being the second most populous municipality, it also shares strong connectivity with its geographical neighbors. In fact, it has the third highest strength centrality, that is, the third municipality with highest number of travelers. Due to its connectivity to Rio Branco and its role as western hub, Cruzeiro do Sul acts as an important bridge between the capital and the western region.

Dengue spread in Acre

In Fig 5 we can observe a sequence of panels showing how dengue invaded and established itself in Acre, following the transportation network. The nodes represent the cities, colored according to the number of consecutive weeks with Rt > 1 per year. In this study, we consider “at least three consecutive weeks with Rt > 1” as a marker of (at least temporary) disease establishment. Of the 22 Acre counties, nine were positive for this marker at least once during the study period despite the fact that all of them registered at least one case of dengue during this time. Until 2008/2009, only Rio Branco e Sena Madureira had evidence of disease establishment according to this criterion. The two cities are well connected in terms of human mobility, close regarding geographical distance, and have paved roads connecting them. At the end of the study period, other seven cities presented at least three consecutive weeks with Rt > 1. Of those, six are in southeast Acre, in the most connected and best-accessed region. The other city is Cruzeiro do Sul, one of the most distant cities in kilometers from Rio Branco, but populous and with an air connection to the capital.

We further investigate if there is evidence of correlation between the centrality of a municipality and the time it took for dengue to establish itself, measured by T3, as defined previously. A summary of the main results is shown in Table 1. This is an exploratory analysis only, since the sample size is small for modeling. Strength centrality was the network descriptor most negatively correlated with T3. This result suggests that municipalities in which the sharing of individuals with other municipalities was larger had a greater exposure to dengue and, consequently, smaller T3 than those with lower integration. The Eigenvector centrality of the municipalities, which is related to how strong is the connectivity of a node’s neighborhood, therefore its ability to concentrate population flow in the network, also presents negative correlation with T3. This is an intuitive result since this property enhances the probability of a node being invaded once a node in its vicinity presents an outbreak, even when not in direct contact. Finally, the distance from Rio Branco, measured by both the effective distance and in kilometers, also showed high correlation with T3. It is interesting to note that, geographical distance aside, topological properties of the unweighted structural network are not significantly correlated with T3. It is important to highlight that about half of the Acrean municipalities did not meet the criteria of dengue establishment, which has a significant impact on the correlation obtained. With that in mind, we also investigated if those network properties were correlated with the event of dengue establishment itself. In Fig 6, we show boxplots comparing the centrality of municipalities that witnessed dengue establishment during the study period and those which did not. This comparison allows for the evaluation of whether municipalities grouped by occurrence (YES/NO) are also characterized by significantly distinct network properties. It is possible to identify that the descriptors most correlated to T3 were also those that differed the most between municipalities with and without dengue establishment, which are the geographical and effective distance to Rio Branco, Eigenvector and Strength centralities.

Receptivity of the Acrean municipalities to dengue transmission

The first record of Ae. aegypti in Acre occurred in 1995 in Rio Branco (Table 2). Gradually the nearest municipalities in the southeast of the state also started recording it, and in 2001, eight municipalities had confirmed the presence of Ae. aegypti. These municipalities are the same municipalities where Rt > 1 was detected earlier in time, being municipalities along BR-317 (SG, Cpb, Xpr, Epcd and Brl) or with a direct connection to RB (PA) and two with good access to BR-364 since the beginning of the study (SM and Acld). From 2002 to 2006, another three municipalities reported the presence of Ae. aegypti: Plácido de Castro, Bujari and Assis Brasil. Bujari is the nearest neighbor of Rio Branco, but Ae. aegypti presence was not confirmed until 2006. It is difficult to ascertain the specific causes for this. On the one hand, this was a predominantly rural municipality in the early 2000’s. On the other hand, there was not a well-structured vector surveillance program implemented in small towns such as Bujari until later. Plácido de Castro and Assis Brasil are frontier municipalities, the former at the border with Rondônia state, receiving a considerable traffic of vehicles from the highly dengue endemic neighbor state. It is reasonable to infer that Ae. aegypti could be present there previously but unnoticed. However, dengue only established itself in Plácido de Castro in 2012/2013, despite the consistent detection of cases since 2001. Assis Brasil is a small town at the triple frontier Brasil-Peru-Bolivia. In 2000, it had a considerably small population, which may have contributed to the delayed invasion of Ae. aegypti.

Between 2007 and 2013 no other city reported the presence of Ae. aegypti. The only exception being Cruzeiro do Sul, in 2008. This invasion was interrupted, however, and the mosquito was detected again only in 2013. From the municipalities to the north of Sena Madureira along the BR-364 highway, four confirmed the presence of Ae. aegypti in 2015: Feijó, Tarauacá, Mâncio Lima and Porto Walter. The first three, together with Cruzeiro do Sul, are located along the BR-364 and are important stopping points for those traveling. For the same reason, it is not possible to affirm that the mosquito was actually absent before that, since monitoring was not implemented. Five municipalities remained Ae. aegypti free as of 2015, according to the LI/LIRAa dataset (Table 2). These are the most peripheral municipalities of Acre, and also the most rural.

Structural characteristics of Acrean municipalities can be part of the explanation for the slow invasion and establishment of Ae. aegypti in the state. According to IBGE’s urban/rural classification, Acre is mostly a rural state, but the population of all municipalities and the percentage of urbanization increased from 2000 to 2010. The exceptions are two, Rodrigues Alves and Assis Brasil, where total population increased as well but the urban population decreased. Both municipalities presented an increase in the number of rural settlements. Population growth, coupled with the increase in urbanization without improving the coverage of general services—such as garbage collection, water supply and sanitary sewage (Table 2) –, contribute to the fact that, even in a more rural state, the ideal conditions for establishing the mosquito are guaranteed. Those characteristics increase the difficulty in controlling Ae. aegypti and consequently the local receptivity.


In the process of describing the introduction of dengue in Acre, we assessed the contribution of network metrics and reproductive numbers as indicators of vulnerability, disease establishment and receptivity in the study of disease emergence. In the landscape epidemiology of infectious diseases, the description of the temporal dynamics of the populations of hosts, vectors, and pathogens, all spatially interacting in a favorable environment, contributes to the understanding of what characteristics and factors favor disease transmission an establishment. Even small differences in landscape composition, which are often unapparent, can alter the microhabitats of the vector and thus the conditions that allow the amplification of a pathogen. In addition, the dispersion of pathogens and vectors is directly linked to the development of transportation networks and increased globalization. In this context, human population expansion has significantly affected the epidemiology of vector-borne diseases, creating a large urban continuum, altering the landscape structure and providing rapid mechanisms for hosts and pathogens to disperse.

The results of this study suggest that the landscape changes that occurred in the last decade have created favorable conditions for the establishment of dengue virus transmission, bringing together all the fundamental factors for its occurrence: the man, the virus, the vector, and especially the environmental, political, economic, social and cultural conditions favorable for the establishment of the transmission chain. In Acre, the revitalization of its major roads, as well as the increased accessibility by air both to and within the state, have increased dengue vulnerability. Notice that the increase in the flow of people and importation of dengue cases to Acre coincides with when disease dispersion becomes more pronounced within the state, suggesting a synergy between increasing vulnerability of the state at a global scale and increasing local vulnerability among municipalities, fueling viral spread in the region. Human mobility is responsible for viral spreading, as both asymptomatic humans or those with very mild symptoms continue to carry out their tasks and take the virus to other regions. Humans are also responsible for possibly transporting the mosquito itself (which can be infected or not) by air, road, and waterway [2, 7, 62].

Some network descriptors used in this study were useful for characterizing the level of centrality/periphery of the Acrean municipalities and its relationship with dengue importation. The mobility network that connects the Acrean counties is small and dense—22 nodes and density 0.38 –, so centrality measures such as betweenness (B and Bw) and closeness (C and Cw) were not as relevant as they would be in larger and sparser networks. However, the strength (S) was the most relevant of the descriptors, because, in a small network, the relationship between local and global characteristics are stronger. Rio Branco is the main attractor of the state, with highest and strongest connectivity within Acre, since most of the routes connecting municipalities in the northwest to the southeast region pass by the capital. Also, it is a reference center for the entire state. A node with high connectivity, that is, a node to which many others connect to and where there are many outputs, is an essential node for the propagation of infectious diseases. Therefore, investing in dengue control and prevention in a systematic manner in Rio Branco has potentially a strong impact throughout the state, not only because of its connectivity within Acre but also because this city is the main port-of-entry from other Brazilian states.

The structure of the network, the distribution of degrees between nodes and the main routes are known to impact the spread of diseases. In fact, we can observe that dengue first spread in the vicinity of Rio Branco and then, after an extended period of time, approximately 13 years, the first event of sustained transmission (here defined as at least three consecutive weeks with Rt > 1) occurred in Cruzeiro do Sul. The flow of people by air between Rio Branco and Cruzeiro do Sul, and the improvement of the BR-364 were very significant predictors of this first epidemic in the northwest region of Acre. Although Cruzeiro do Sul confirmed the presence of the Ae. aegypti only in 2008, ever since the beginning of the study there were records of cases in the municipality, even when there were no records in intermediate connections by road between both municipalities. This last result highlights the importance of the airline connection between them for dengue case importation.

The mobility data used in the present work is from the 2010 Census when the structural network around Cruzeiro do Sul and Rio Branco was already stabilized regarding paving and maintenance, as shown in Fig 5. Since dengue epidemiological year of 2009/2010, all roadways directly connected to those two hubs had their maintenance concluded. Also, the unpaved connections to and from Manoel Urbano, which is structurally in between the two Acrean hubs, had also improved by that time. Most of its paving work was concluded by the end of 2010, although maintenance works continued for the rest of the study period. Despite the unavailability of detailed movement for performing a rigorous analysis, it is to be expected that during the period with unpaved and with major maintenance works in the roadways the flow of individuals was lower than when those structural bottlenecks were resolved. Therefore, we expect that the 2010 mobility flux data is more representative of the work/study-related movement from 2009 onwards. Before that, we conjecture that this particular type of human mobility was more concentrated over those connections with paved roads and without major maintenance work.

The structural network status evolution depicted in Fig 5 allows us to identify a few key factors regarding the temporal evolution of ease of access and dengue case notification. Up until epidemiological year 2009/2010, when paved roads (black edges in that network) were mainly present in the vicinity of Rio Branco, the occurrence of Rt > 1 was also mostly present in that area—which can be identified by node’s color in Fig 5 as well as by the markings in Fig 3. In the area surrounding Cruzeiro do Sul, which was mainly connected by waterways (dashed blue edges) and roads under maintenance (orange edges), before 2007/2008 the occurrence of Rt > 1 was registered only once at Mâncio Lima (ML) and a few times in Cruzeiro do Sul. These results reinforce the hypothesis that those cases notified in Cruzeiro do Sul were most likely imported cases from Rio Branco due to the airline connection. It is interesting to note that in terms of roadways, the path from Rio Branco to Cruzeiro do Sul had a structural bottleneck at Manoel Urbano (MU) since it was only accessed by means of unpaved road (red edges) up to 2007/2008.

Improvements in the structural network started in 2008/2009, which included paving and maintenance work of the roadways around Manoel Urbano, and were consolidated in 2009/2010 with the conclusion of major maintenance work in the roadways directly connected to both Rio Branco and Cruzeiro do Sul. We can see that is was precisely after those improvements that dengue notifications started to occur throughout the state of Acre, as well as the number of consecutive weeks with Rt > 1 in each municipality. The combination of low occurrence of Rt > 1 during the years with unpaved and under maintenance roadways, and the increased observation of Rt > 1 after improvements were implemented reinforces the hypothesis that the human mobility network and the structural changes were relevant to the observed geographical spread of the virus in the state of Acre.

As for demographic characteristics, Acrean municipalities are marked by having small populations (Table 2). The capital Rio Branco, the one with the largest population, has 336,038 inhabitants. The second largest municipality in population size is Cruzeiro do Sul, with 78,507. The remaining ones do not exceed 40,000 inhabitants, the majority of them having less than 20,000 inhabitants. The peripheral municipalities according to the network descriptors are also those with the smallest populations and are, mostly, more rural than urban. These municipalities are less vulnerable and less receptive to dengue, which is seen by the low occurrence of Rt > 1 and no event of sustained transmission as defined in this study. Nonetheless, in the world, there has been increased reporting of Ae. aegypti invasion and dengue introduction in less urbanized areas. In Colombia, dengue-infected Ae. aegypti was found in rural regions south of Bogotá. In Nicaragua, a study showed the unsuspected presence of dengue cases in those areas as well.

These results from the literature and the results shown in the present manuscript suggest that introduction and establishment of dengue in those peripheral areas of Acre could be just a matter of time, especially since the virus has already been able to establish itself in the state.

Overall, with this study, we understand that the determination of vulnerable and receptive localities for dengue in the state of Acre is essential for an effective action of entomological and epidemiological surveillance. It is fundamental that all municipalities, especially those with a roadway link, systematically monitor mosquito infestation, which is not currently the case. In addition, favorable conditions for mosquito development are present in Acrean municipalities, with low coverage of services, climate and increased urbanization, which probably impacts on the availability of breeding sites for Ae. aegypti and its establishment. Acre has a humid equatorial climate, which may contribute to the transmission of the dengue virus occurring throughout the year. According to, the mosquitoes of the region have the vectorial competence to transmit the DENV-2. Unfortunately, we did not find more biological information on the mosquito populations of Acre. We can conclude that Acre is a state at risk of major dengue epidemics, with entire populations susceptible to all serotypes of the disease. Due to the limited data and number of municipalities, our analysis is more descriptive/qualitative than quantitative. Nonetheless, we believe that the descriptors discussed here as well as the methodological approach has proved a valuable way to assess invasion risk which can be easily expanded to larger areas—conditioned on data availability –, allowing for a more quantitative analysis..