Dengue is a viral infection spread by mosquitoes and is widespread in tropical and sub-tropical regions. Dengue epidemics in Brazil often occur without warning, and can overwhelm the public health services. Forecasts of seasonal climates combined with early data from a dengue surveillance system could help public health services anticipate dengue outbreaks several months in advance. However, this information has not been previously exploited to predict dengue epidemics in a practical real-life framework.

Recently, a group of researchers developed a prototype of a dengue early warning system based on 13 years worth of data, and used it to predict the risk of dengue three months ahead of the 2014 FIFA World Cup in Brazil. Now Lowe et al. – including most of the researchers involved in the earlier work – have evaluated the prototype against the actual reported cases of dengue during the event. Brazil is divided into over 550 'microregions', and the forecasts correctly predicted high risk of dengue for 57% of the microregions reporting high levels of dengue during the games. Forecasts based on seasonal dengue averages would have only detected high risk in 33% of these microregions. The forecasts also correctly predicted the dengue risk level in seven out of the twelve cities where the World Cup games were hosted. However, the prototype failed to predict the high risk in both São Paulo and Brasília. Lowe et al. speculate that this may have been due to changes in how water was stored in these cities (standing water is a breeding site for mosquitoes) and the circulation of a new strain of the dengue virus.

The implementation of seasonal climate forecasts and early reports of dengue cases into an early warning system is now a priority for public health authorities. This action is likely to help them to prepare for and minimize epidemics of dengue and other diseases that are spread by mosquitoes, such as chikungunya and Zika virus.

Dengue is an arboviral infection of major international public health concern (Guzman and Harris, 2015). Dengue is endemic in more than 100 countries in the tropics and sub-tropics, with Brazil reporting more cases than any other country in the world (Bhatt et al., 2013; Teixeira et al., 2009). Dengue is caused by four distinct dengue virus serotypes (DENV 1–4), which are transmitted to humans by Aedes mosquitoes (Guzman and Harris, 2015). The distribution of both Ae. aegypti and Ae. albopictus is widespread across Brazil, with Ae. aegypti found predominantly in urban settings, breeding in artificial containers, and Ae. albopictus more commonly found in rural and peri-urban settings (Kraemer et al., 2015). However, dengue incidence is unequally distributed in Brazil, with higher and sustainable incidence along the Atlantic coast and in the central region. Temperature and rainfall regimes seem to control the magnitude and seasonality of dengue transmission (Campbell et al., 2015). Large outbreaks are typically observed after rainy and warm periods, at the end of summer, particularly in densely populated urban areas (Teixeira et al., 2009). The presence and abundance of dengue mosquitoes is a necessary but not sufficient condition for dengue transmission and the occurrence of large outbreaks. Besides vector infestation, an important factor regulating transmission is the introduction of new serotypes of virus in areas with a high susceptible population. This may be facilitated by the increasing international and internal mobility across the country. Thus, large and touristic cities are prone to the introduction and maintenance of virus circulation. International mass gathering events have become an important health issue in the recent years (Abubakar et al., 2012) as they create the opportunity for the introduction of new pathogens in a susceptible population, as well as exposing visitors to new and unknown local risks (Matos and Barcellos, 2010).

Early warning systems, which take into account multiple dengue risk factors, can assist public health authorities to implement timely control measures ahead of imminent dengue outbreaks. Seasonal climate forecasts combined with early dengue surveillance system data provide an opportunity to anticipate dengue outbreaks several months in advance (Lowe et al., 2014). To date, several studies have assessed the use of climate information in early warning systems for diseases, such as malaria and Rift Valley fever (Anyamba et al., 2009; Thomson et al., 2006). The incorporation of climate information for dengue early warning systems has also been explored (Degallier et al., 2010; Lowe et al., 2011; Stewart-Ibarra and Lowe, 2013). However, to our knowledge, real-time climate forecasts have not been previously applied to predict dengue epidemics in a practical real-life framework.

From 12 June to 13 July 2014, Brazil hosted the 2014 Fédération Internationale de Football Association (FIFA) World Cup, a mass gathering of more than 3 million Brazilian and international spectators, travelling between 12 different host cities. Before the event, the potential risk of transmission of several communicable diseases, including dengue fever, was highlighted (Gallego et al., 2014; Wilson and Chen, 2014). Several research groups published dengue outlooks ahead of the World Cup. Approaches included analysing historical time series distributions of city or state level data (Hay, 2013) and mapping of historical averages, while accounting for seasonality and areas of permanent transmission (Barcellos and Lowe, 2014a). Some groups formulated deterministic (Massad et al., 2014) and statistical (van Panhuis et al., 2014) models to estimate the number of tourists expected to contract dengue fever. Another study (Lowe et al., 2014) assessed the potential for dengue epidemics during the tournament by providing probabilistic forecasts of dengue risk for the 553 microregions of Brazil with risk-level warnings issued for the 12 cities where the matches were played. The dengue early warning system, formulated using a Bayesian spatio-temporal model framework (Lowe et al., 2011, 2013), was driven by real-time seasonal climate forecasts for the period March-April-May and the dengue cases reported to the Brazilian Ministry of Health in February 2014. This information was combined to produce a dengue forecast at the start of March 2014. Predicted probability distributions of dengue incidence rates (DIR) were summarised and translated into risk warnings, using a two-tier threshold approach. First, the probability of DIR falling into categories of low, medium and high risk was determined using dengue risk thresholds of 100 and 300 cases per 100,000 inhabitants, defined by the National Dengue Control Programme of the Brazilian Ministry of Health (Ministério da Saúde, 2008). Second, probability trigger thresholds were calculated by selecting optimal cut-off values that maximised sensitivity and specificity, for each dengue risk threshold (medium and high). Using criteria related to the probability trigger thresholds, forecast warnings of low, medium or high dengue risk were determined for the 12 microregions hosting World Cup matches (see Materials and methods for further details). The forecasts were produced and made available three months ahead of the event (see Lowe et al., 2014). In this article, we provide an evaluation of the forecast model predictions by using the observed dengue incidence rates for June 2014 to assess the ability of the model framework to successfully assign dengue risk warning categories for the host microregions and all microregions in Brazil. We also compare the forecast model framework to a null model, based on seasonal averages of previously observed dengue incidence. We then discuss the challenges and limitations of producing disease risk forecasts in a real-time setting, such as the use of incomplete surveillance data to drive the model, the coarse spatial resolution of the forecasts, the definition of risk and alarm trigger thresholds, the lack of information regarding the (re)introduction of different serotypes or vector control activities, and the difficulties in communicating probabilistic forecasts. Finally, we suggest future model developments and advocate a multi-model approach to dengue prediction in the future.

In this study, we have evaluated the ability of the dengue early warnings produced three months ahead of the 2014 World Cup to anticipate dengue risk categories, using pre-determined risk and alarm trigger thresholds. The forecasts correctly predicted dengue risk categories for seven of the twelve host microregions: Fortaleza and Natal (high), Belo Horizonte, Manaus and Salvador (medium) and Curitiba and Porto Alegre (low). The model was able to detect medium risk for Recife (posterior mean DIR=142, observed DIR=161, see Figure 1), although the definition of the alarm trigger thresholds placed this microregion in a higher risk category (the probability of high risk just exceeded 18%). The model missed the higher incidence that was observed in Brasília and São Paulo. The observed dengue incidence rate for Rio de Janeiro was lower than usual for this time of year. When comparing the ability of the forecast model to the null model to predict high dengue risk across Brazil, the forecast model produced more hits and fewer missed events than the null model, with hit rates of 57% for the forecast model compared to 33% for the null model. The hit rate was almost always greater for the forecast model during previous years, 2000–2013. Despite the tendency of the forecast model to over predict dengue risk compared to the null model, we generally found the forecast model better able to detect extreme dengue incidence rates. For catastrophic events, such as epidemics, failing to predict an epidemic (e.g. forecast warning is for low risk but observed incidence is high) is considered much more damaging than announcing that an epidemic is about to occur and then does not happen (e.g. forecast warning is for high but observed incidence is low). Besides, if the dengue control programme intervene based on the forecast warnings, this could result in a decrease in cases compared to what would have happened otherwise.

The month of June is the transition in the southern hemisphere between autumn and winter, with the intensification of lower temperatures in central and southern Brazil (from Rio Grande do Sul to São Paulo and high plateaus of Minas Gerais), leading to areas with lower incidence rates. The model could, in general, distinguish climate-limiting areas for dengue fever (Barcellos and Lowe, 2014b). However, the model was unable to predict the high risk category for Brasília and São Paulo, which experienced unprecedented dengue incidence for the month of June. As the model is formulated using past data, and driven by climate forecasts and dengue data with a 3–4 month lead time, such irregularities and sudden shifts in transmission patterns can be difficult to capture. However, potential variability is accounted for in the random component of the model and correctly reflected in the probabilistic nature of our predictions.

The areas with greater model uncertainty (see pale areas in Figure 2a) are located along the fringes of climatic zones: the southern limits between the tropical warm and temperate climates, near the tropic of Capricorn; the Amazon rainforest contact with the tropical savanna vegetation (Cerrado); and the semiarid boundaries in the Northeast region. In fact, these fringes were the areas where there was a low probability of observing the correct category (see Figure 3). From the meteorological perspective, these zones should not be assumed as fixed boundaries, as they may vary over the years under the influence of the El Niño-Southern Oscillation, dominant air masses and other transient drivers. Important dengue outbreaks have been observed along these fringes that may be seen as a prelude of the expansion of dengue transmission area (Barcellos and Lowe, 2014b).

The dengue early warning model takes into account seasonal climate forecasts and epidemiological data for the months preceding the event, which create the conditions (environmental and epidemiological) for vector density and distribution, as well virus circulation. These conditions largely influence the development of an epidemic. Ideally, data about health care provision and vector control intensity between regions and over time would be included in the model. However, in the absence of such data, spatial and seasonal heterogeneity and dependency structures are accounted for in the model via random effects at the scale of calendar month and microregion, to try and ensure that the variance in the predictions includes uncertainty that could be generated by these unknown features of the disease system. Temporal random effects were not included in the model. Such effects are useful to help describe variations in temporal patterns and understand the relative contribution of various temporal explanatory variables. However, when it comes to predicting into the future, these effects become obsolete. In reality, dengue outbreaks are also modulated by factors that occur in more restricted temporal and spatial scales, which are not accounted for in the model framework, thus limiting the results of this study (Lowe, 2015). Such risk factors include the implementation and efficiency of vector control activities, circulating serotypes and herd immunity, along with other complex socio-economic factors, such as water storage practices.

From the start of 2014, a high pressure system over Southeast Brazil prevented the entrance of cold frontal systems from the south and the transport of humid air from the Amazonian Region in northern Brazil towards the southeast region of Brazil (Coelho et al., 2015b). This led to severe drought conditions in the South East region, which particularly affected the states of São Paulo and Minas Gerais. This situation led to a water supply system crises that began in the first semester of 2014 (Coelho et al., 2015a). This likely resulted in an increase of artificial water storage containers, increasing mosquito breeding sites and thus intensifying dengue transmission in this region (Otto et al., 2015). The combination of introducing new serotypes of dengue virus in a large city, with a huge number of susceptible inhabitants, with high mobility and circulation of tourists, during a water crisis may have had an explosive effect on dengue transmission in this region. The unforeseen dengue outbreak in the microregion of Brasília may have resulted from a change in the dominant serotype to DENV1 (Ministério da Saúde, 2014) and the immunological status of the population. However, this requires further investigation. The four month lag between the target forecast date (June 2014) and the dengue cases observed before the forecast issue date (February 2014) was perhaps too long to capture these unprecedented changes in the dengue dynamics. In an ideal situation, the forecast would be updated each month leading up to the forecast target date, to incorporate the latest dengue and climate information available.

The objective of the model framework was to predict the probability of exceeding pre-defined 'epidemic' thresholds (see Materials and methods). The idea of a forecast that provides the probability of exceeding an epidemic threshold is to allow decision makers to quantify the level of certainty of the model predictions. For example, farmers often consult seasonal climate forecasts for their region of interest to decide when, how and what to plant their crops (Meinke and Stone, 2005). The information provided is given in a probabilistic context, e.g. “there is a 70% chance of observing temperatures above normal next season” rather than a specific value, e.g. “the temperature next season will be 27°C”. Considering a dengue forecast, if the probability of exceeding the high dengue risk threshold for June in a given location is 90%, public health officials may be inclined to increase dengue control measures in that area (Lowe et al., 2015a). This information is likely to be more informative and beneficial to decision makers than a deterministic forecast indicating that “there will be a DIR of 182 in June”.

The advantage of issuing probabilistic forecasts is that we can more easily quantify prediction uncertainty. However, effectively communicating probabilities to decision makers, the general public and even other scientists can be challenging. The use of set incidence (risk) thresholds and static probability decision (i.e. alarm trigger) thresholds means that the translation of probabilities into discrete warnings (low, medium and high) might not always reflect the predictive power of the model. For example, even though the mean estimate of the predictive distribution might be close to the observations (e.g. for the case of Recife), the predetermined cut-off alarm trigger thresholds might result in the designation of an incorrect categorical warning. Risk and alarm trigger thresholds need to be carefully chosen, as this influences the outcome of the 'translation' of probabilistic information into categorical risk warnings for decision makers.

The dengue incidence rate baseline has been increasing over the years. Since 1998, the overall incidence rate reached a plateau, situated between 100 and 300 cases per 100,000 inhabitants (Barreto et al., 2011). However, this seems to be a consequence of the spread of the disease in the country rather than an increase of incidence rates in cities (Barcellos and Lowe, 2014b). For example, the number of cities reporting dengue cases increased since the late 1980's from a few hundred to almost 4000, reaching 72% of the Brazilian territory (Barreto et al., 2011). The three class dengue risk levels (low<100, 100<medium<300 and high>300 cases per 100,000 inhabitants per year) used by the National Dengue Control Programme have been shown to appropriately capture the occurrence of large epidemics for certain cities, such as Rio de Janeiro (Lowe et al., 2013). For this city, the thresholds of 100 and 300 cases per 100,000 inhabitants has been adequate for distinguishing the recent outbreaks during the peak season (February-April) in the city since the year 2000. Using these thresholds, three large outbreaks in 2002, 2008 and 2012 (DIR>300), and smaller outbreaks in 2011 and 2013 (DIR>100) would have been detected (data not shown). However, these thresholds may not be appropriate for other cities, regions or seasons. This could be revised by taking into account population variability between microregions. For example, in the state of São Paulo, the decision to clinically or epidemiologically confirm dengue cases (see Materials and methods) is based on dengue incidence rates exceeding the high risk category threshold in small cities and the medium risk category threshold for large cities (SES, 2010). Recently, several different outbreak definitions have been tested using dengue data for Brazil, including moving averages, cumulative mean and fixed thresholds (Brady et al., 2015). However, there is still no clear recommendation as to the most appropriate threshold(s) to apply for a nationwide dengue early warning system in Brazil. The current dengue risk thresholds are defined using yearly dengue incidence rates and applied to the whole country. These could be refined by considering seasonal dengue variations in different regions across Brazil to create location specific thresholds, better able to detect the onset of dengue outbreaks at different times throughout the year.

The model framework could be improved by revising the definition of the alarm trigger thresholds, which are currently determined by assessing the ability of the model to distinguish medium and high risk categories in the past. These could be developed by stochastically simulating the optimum threshold for each category boundary multiple times, to produce a probability distribution of alarm trigger thresholds. Therefore, credible intervals could be assigned to the alarm trigger thresholds. This could help to flag warnings that might border two categories, e.g. low-medium or medium-high. For example, for Recife, the probability of high risk was 19% and the high risk alarm trigger threshold was a point estimate of 18%, which placed Recife into the high category, even though the predicted probability was just 1% greater than the trigger threshold. Ideally, a cost-benefit analysis should be performed by the public health services to assess the economic cost of false positives versus false negatives, to define optimum alert trigger thresholds. Another point to consider is the extent to which forecast warnings, issued several months in advance, influence vector control and personal protection activities. If a dengue forecast warning provokes more intense interventions or increased education and public awareness, this could potentially change the course of the dengue transmission patterns expected due to the environmental conditions and epidemiological trends.

Despite these limitations, the analyses performed in this study revealed several areas across Brazil with successful predictions. These results are promising for the future development of a routine dengue early warning system for Brazil. This will be conducted in consultation with the Brazilian Ministry of Health to align model capabilities with decision-maker requirements and data availability. In practice, early warning systems for such a complex disease should include several surveillance activities. Our model provides advance warning of the likely dengue risk three months later, based on climate forecasts and the current dengue risk situation at the time of forecast. This information should be updated and complimented with surveillance on the (re)introduction of new serotypes and notification tracking to ensure the ability of local health services to stay alert for dengue diagnosis. More widespread and better designed surveys to track changes in dengue seroprevalence should be routinely conducted. Establishing a new seroprevalence surveillance system at the scale of the whole country is a challenge. A national system could be initiated in key capital cities, to capture the entrance of new pathogens from other states or countries. The probability of introducing new serotypes could be determined by the number of people entering local airports and bus terminals from endemic regions. Given prior knowledge of seroprevalence, an estimate of the population at risk stratified by serotype could be incorporated in the model offset. By integrating data from the sentinel dengue seroprevalence surveillance systems at the time of forecast, predictions for each serotype could then be produced, to assess the epidemic potential generated by a re-emerging or dominant stereotype.

The model framework could also be downscaled and refined to account for within city disease dynamics and consider the impact of climate of other mosquito-transmitted viruses, such as chikungunya and Zika, which have recently emerged in Brazil (Nunes et al., 2015; Zanluca et al., 2015). This could help to assess the risk of arboviral disease importation and transmission in Rio de Janeiro ahead of the 2016 Olympics.

Several research groups developed dengue risk maps and forecasts ahead of the World Cup (Barcellos and Lowe, 2014a; Hay, 2013; Lowe et al., 2014; Massad et al., 2014; van Panhuis et al., 2014). While results varied between studies, all agreed that the northeast cities of Fortaleza and Natal would be at greater risk of dengue transmission and that the risk for the southern cities of Porto Alegre and Curitiba would be very low. Massad et al. (2014) also forecast a greater risk to tourists in the city of Rio de Janeiro. Only one study corrected predicted elevated dengue risk for teams and tourists in Brasília, using an Empirical Bayes model and weekly dengue incidence data up to May 2014 (i.e., one month lead time) (van Panhuis et al., 2014). Given the different research questions, methods, data and underlying populations that were used for each study, the direct comparison of these model results is not feasible in the absence of a consensus to translate results into equivalent quantities (Lowe et al., 2015). For example, the system evaluated here generated probabilistic distributions of yearly equivalent dengue incidence rates in the Brazilian population, at the monthly time scale and microregion spatial scale with 3–4 months lead-time, while other studies estimated the number of visitors at risk of dengue infection in specific cities. This emphasises the importance of dengue model inter-comparison projects, such as the 'Epidemic Prediction Initiative' launched by the White House Office of Science and Technology Policy (http://dengueforecasting.noaa.gov/). The comparison of different models, forecasting common targets, will identify strengths and weakness in model formulation and gaps in data and methods.

1. 1. Abubakar I
   2. Gautret P
   3. Brunette GW
   4. Blumberg L
   5. Johnson D
   6. Poumerol G
   7. Memish ZA
   8. Barbeschi M
   9. Khan AS

   (2012) Global perspectives for prevention of infectious diseases associated with mass gatherings

   The Lancet Infectious Diseases 12:66–74.

   https://doi.org/10.1016/S1473-3099(11)70246-8

   • Google Scholar
2. 1. Adler RF
   2. Huffman GJ
   3. Chang A
   4. Ferraro R
   5. Xie P-P
   6. Janowiak J
   7. Rudolf B
   8. Schneider U
   9. Curtis S
   10. Bolvin D
   11. Gruber A
   12. Susskind J
   13. Arkin P
   14. Nelkin E

   (2003) The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present)

   Journal of Hydrometeorology 4:1147–1167.

   https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2

   • Google Scholar
3. 1. Anyamba A
   2. Chretien J-P
   3. Small J
   4. Tucker CJ
   5. Formenty PB
   6. Richardson JH
   7. Britch SC
   8. Schnabel DC
   9. Erickson RL
   10. Linthicum KJ

   (2009) Prediction of a rift valley fever outbreak

   Proceedings of the National Academy of Sciences of the United States of America 106:955–959.

   https://doi.org/10.1073/pnas.0806490106

   • Google Scholar
4. 1. Barbosa JR
   2. Barrado Jean Carlos dos Santos
   3. Zara Ana Laura de Sene Amâncio
   4. Siqueira JB

   (2015) Avaliação da qualidade dos dados, valor preditivo positivo, oportunidade e representatividade do sistema de vigilância epidemiológica da dengue no Brasil (2005 a 2009)

   Epidemiologia e Serviços de Saúde 24:49–58.

   https://doi.org/10.5123/S1679-49742015000100006

   • Google Scholar
5. 1. Barcellos C
   2. Lowe R

   (2014a) Dengue and the World Football Cup: a matter of timing

   PLoS Neglected Tropical Diseases 8:e3022.

   https://doi.org/10.1371/journal.pntd.0003022

   • Google Scholar
6. 1. Barcellos C
   2. Lowe R

   (2014b) Expansion of the dengue transmission area in Brazil: the role of climate and cities

   Tropical Medicine & International Health 19:159–168.

   https://doi.org/10.1111/tmi.12227

   • Google Scholar
7. 1. Barreto ML
   2. Teixeira MG
   3. Bastos FI
   4. Ximenes RAA
   5. Barata RB
   6. Rodrigues LC

   (2011) Successes and failures in the control of infectious diseases in Brazil: social and environmental context, policies, interventions, and research needs

   The Lancet 377:1877–1889.

   https://doi.org/10.1016/S0140-6736(11)60202-X

   • Google Scholar
8. 1. Bhatt S
   2. Gething PW
   3. Brady OJ
   4. Messina JP
   5. Farlow AW
   6. Moyes CL
   7. Drake JM
   8. Brownstein JS
   9. Hoen AG
   10. Sankoh O
   11. Myers MF
   12. George DB
   13. Jaenisch T
   14. Wint GR
   15. Simmons CP
   16. Scott TW
   17. Farrar JJ
   18. Hay SI

   (2013) The global distribution and burden of dengue

   Nature 496:504–507.

   https://doi.org/10.1038/nature12060

   • Google Scholar
9. 1. Brady OJ
   2. Smith DL
   3. Scott TW
   4. Hay SI

   (2015) Dengue disease outbreak definitions are implicitly variable

   Epidemics 11:92–102.

   https://doi.org/10.1016/j.epidem.2015.03.002

   • Google Scholar
10. 1. Campbell KM
    2. Haldeman K
    3. Lehnig C
    4. Munayco CV
    5. Halsey ES
    6. Laguna-Torres VA
    7. Yagui M
    8. Morrison AC
    9. Lin C-D
    10. Scott TW

    (2015) Weather regulates location, timing, and intensity of dengue virus transmission between humans and mosquitoes

    PLOS Neglected Tropical Diseases 9:e0003957.

    https://doi.org/10.1371/journal.pntd.0003957

    • Google Scholar
11. 1. Coelho CA
    2. Cardoso DH
    3. Firpo MA

    (2015a) Precipitation diagnostics of an exceptionally dry event in São Paulo, Brazil

    Theoretical and Applied Climatology pp. 1–16.

    https://doi.org/10.1007/s00704-015-1540-9

    • Google Scholar
12. 1. Coelho CAS
    2. de Oliveira CP
    3. Ambrizzi T
    4. Reboita MS
    5. Carpenedo CB
    6. Campos JLPS
    7. Tomaziello ACN
    8. Pampuch LA
    9. Custódio Maria de Souza
    10. Dutra LMM
    11. Da Rocha RP
    12. Rehbein A

    (2015b) The 2014 southeast Brazil austral summer drought: regional scale mechanisms and teleconnections

    Climate Dynamics pp. 1–16.

    https://doi.org/10.1007/s00382-015-2800-1

    • Google Scholar
13. 1. Coelho CAS
    2. Stephenson DB
    3. Balmaseda M
    4. Doblas-Reyes FJ
    5. van Oldenborgh GJ

    (2006) Toward an integrated seasonal forecasting system for South America

    Journal of Climate 19:3704–3721.

    https://doi.org/10.1175/JCLI3801.1

    • Google Scholar
14. 1. Degallier N
    2. Favier C
    3. Menkes C
    4. Lengaigne M
    5. Ramalho WM
    6. Souza R
    7. Servain J
    8. Boulanger J-P

    (2010) Toward an early warning system for dengue prevention: modeling climate impact on dengue transmission

    Climatic Change 98:581–592.

    https://doi.org/10.1007/s10584-009-9747-3

    • Google Scholar
15. 1. Duarte HHP
    2. França EB

    (2006) Qualidade dos dados da vigilância epidemiológica da dengue em Belo Horizonte, MG

    Revista De Saúde Pública 40:134–142.

    https://doi.org/10.1590/S0034-89102006000100021

    • Google Scholar
16. 1. ECDC

    (2014)

    Risk assessment Brazil 2014 FIFA World Cup, 12 June–13 July 2014

    • Google Scholar
17. 1. Fan Y
    2. van den Dool H

    (2008) A global monthly land surface air temperature analysis for 1948–present

    Journal of Geophysical Research 113:.

    https://doi.org/10.1029/2007JD008470

    • Google Scholar
18. 1. Gallego V
    2. Berberian G
    3. Lloveras S
    4. Verbanaz S
    5. Chaves TS
    6. Orduna T
    7. Rodriguez-Morales AJ

    (2014) The 2014 FIFA World Cup: communicable disease risks and advice for visitors to Brazil--a review from the Latin American Society for Travel Medicine (SLAMVI)

    Travel Medicine and Infectious Disease 12:208–218.

    https://doi.org/10.1016/j.tmaid.2014.04.004

    • Google Scholar
19. 1. Guzman MG
    2. Harris E

    (2015) Dengue

    The Lancet 385:453–465.

    https://doi.org/10.1016/S0140-6736(14)60572-9

    • Google Scholar
20. 1. Hay S

    (2013) Football fever could be a dose of dengue

    Nature 503:439.

    https://doi.org/10.1038/503439a

    • Google Scholar
21. 1. IBGE

    (2014)

    Population estimates for the Brazilian municipalities from July 1st 2014

    Population estimates for the Brazilian municipalities from July 1st 2014.

    • Google Scholar
22. Book

    1. Jolliffe IT
    2. Stephenson DB

    (2012)

    Forecast verification: a practitioner’s guide in atmospheric science

    John Wiley & Sons.

    • Google Scholar
23. 1. Jupp TE
    2. Lowe R
    3. Coelho CAS
    4. Stephenson DB

    (2012) On the visualization, verification and recalibration of ternary probabilistic forecasts

    Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 370:1100–1120.

    https://doi.org/10.1098/rsta.2011.0350

    • Google Scholar
24. 1. Kraemer MUG
    2. Sinka ME
    3. Duda KA
    4. Mylne AQN
    5. Shearer FM
    6. Barker CM
    7. Moore CG
    8. Carvalho RG
    9. Coelho GE
    10. Van Bortel W
    11. Hendrickx G
    12. Schaffner F
    13. Elyazar IRF
    14. Teng H-J
    15. Brady OJ
    16. Messina JP
    17. Pigott DM
    18. Scott TW
    19. Smith DL
    20. Wint GRW
    21. Golding N
    22. Hay SI

    (2015) The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus

    eLife 4:e08347.

    https://doi.org/10.7554/eLife.08347

    • Google Scholar
25. 1. Lowe R
    2. Bailey TC
    3. Stephenson DB
    4. Graham RJ
    5. Coelho CAS
    6. Carvalho MS
    7. Barcellos C

    (2011) Spatio-temporal modelling of climate-sensitive disease risk: towards an early warning system for dengue in Brazil

    Computers & Geosciences 37:371–381.

    https://doi.org/10.1016/j.cageo.2010.01.008

    • Google Scholar
26. 1. Lowe R
    2. Bailey TC
    3. Stephenson DB
    4. Jupp TE
    5. Graham RJ
    6. Barcellos C
    7. Carvalho MS

    (2013) The development of an early warning system for climate-sensitive disease risk with a focus on dengue epidemics in southeast Brazil

    Statistics in Medicine 32:864–883.

    https://doi.org/10.1002/sim.5549

    • Google Scholar
27. 1. Lowe R
    2. Barcellos C
    3. Coelho CA
    4. Bailey TC
    5. Coelho GE
    6. Graham R
    7. Jupp T
    8. Ramalho WM
    9. Carvalho MS
    10. Stephenson DB
    11. Rodó X

    (2014) Dengue outlook for the World Cup in Brazil: an early warning model framework driven by real-time seasonal climate forecasts

    The Lancet. Infectious Diseases 14:619–626.

    https://doi.org/10.1016/S1473-3099(14)70781-9

    • Google Scholar
28. 1. Lowe R
    2. Carvalho MS
    3. Coelho CAS
    4. Barcellos C
    5. Bailey TC
    6. Stephenson DB
    7. Rodó X

    (2015a) Interpretation of probabilistic forecasts of epidemics

    The Lancet Infectious Diseases 15:20.

    https://doi.org/10.1016/S1473-3099(14)71031-X

    • Google Scholar
29. 1. Lowe R
    2. Cazelles B
    3. Paul R
    4. Rodó X

    (2015b) Quantifying the added value of climate information in a spatio-temporal dengue model

    Stochastic Environmental Research and Risk Assessment.

    https://doi.org/10.1007/s00477-015-1053-1

    • Google Scholar
30. 1. Lowe R

    (2015) Understanding the relative importance of global dengue risk factors

    Transactions of the Royal Society of Tropical Medicine and Hygiene 109:607–608.

    https://doi.org/10.1093/trstmh/trv068

    • Google Scholar
31. 1. Massad E
    2. Wilder-Smith A
    3. Ximenes R
    4. Amaku M
    5. Lopez LF
    6. Coutinho FAB
    7. Coelho GE
    8. Silva Jr Jarbas Barbosa da
    9. Struchiner CJ
    10. Burattini MN

    (2014) Risk of symptomatic dengue for foreign visitors to the 2014 FIFA World Cup in Brazil

    Memórias Do Instituto Oswaldo Cruz 109:394–397.

    https://doi.org/10.1590/0074-0276140133

    • Google Scholar
32. 1. Matos V
    2. Barcellos C

    (2010) Relações entre turismo e saúde: abordagens metodológicas e propostas de ação

    Revista Panamericana De Salud Pública 28:128–134.

    https://doi.org/10.1590/S1020-49892010000800009

    • Google Scholar
33. 1. Meinke H
    2. Stone RC

    (2005) Seasonal and inter-annual climate forecasting: the new tool for increasing preparedness to climate variability and change in agricultural planning and operations

    Climatic Change 70:221–253.

    https://doi.org/10.1007/s10584-005-5948-6

    • Google Scholar
34. 1. Ministério da Saúde

    (2008)

    Informe Epidemiológico da Dengue Janeiro a Junho de 2008

    Informe Epidemiológico da Dengue Janeiro a Junho de 2008.

    • Google Scholar
35. 1. Ministério da Saúde

    (2014)

    Boletim Epidemiológico

    Boletim Epidemiológico, 45.

    • Google Scholar
36. 1. Ministério da Saúde. Secretaria de vigilância em saúde

    (2005)

    Guia De Vigilância Epidemiológica

    07–21, Guia De Vigilância Epidemiológica, 6 Ed., Ministério da Saúde.

    • Google Scholar
37. 1. Nunes MRT
    2. Faria NR
    3. de Vasconcelos JM
    4. Golding N
    5. Kraemer MUG
    6. de Oliveira LF
    7. Azevedo Raimunda do Socorro da Silva
    8. da Silva DEA
    9. da Silva EVP
    10. da Silva SP
    11. Carvalho VL
    12. Coelho GE
    13. Cruz ACR
    14. Rodrigues SG
    15. da Silva Gonçalves Vianez JL
    16. Nunes BTD
    17. Cardoso JF
    18. Tesh RB
    19. Hay SI
    20. Pybus OG
    21. da Costa Vasconcelos PF

    (2015) Emergence and potential for spread of chikungunya virus in Brazil

    BMC Medicine 13:.

    https://doi.org/10.1186/s12916-015-0348-x

    • Google Scholar
38. 1. Otto FEL
    2. Haustein K
    3. Uhe P
    4. Coelho CAS
    5. Aravequia JA
    6. Almeida W
    7. King A
    8. Coughlan de Perez E
    9. Wada Y
    10. Jan van Oldenborgh G
    11. Haarsma R
    12. van Aalst M
    13. Cullen H

    (2015) Factors other than climate change, main drivers of 2014/15 water shortage in southeast Brazil

    Bulletin of the American Meteorological Society 96:S35–S40.

    https://doi.org/10.1175/BAMS-EEE_2014_ch8.1

    • Google Scholar
39. Book

    1. PAHO

    (2008)

    Indicadores básicos para a saúde no Brasil: conceitos e aplicações

    Brasil: Ministério da Saúde.

    • Google Scholar
40. Book

    1. SES

    (2010)

    Programa De Vigilância De Dengue

    Secretaria de estado de Saúde de São Paulo.

    • Google Scholar
41. 1. Stephenson DB

    (2000) Use of the “odds ratio” for diagnosing forecast skill

    Weather and Forecasting 15:221–232.

    https://doi.org/10.1175/1520-0434(2000)015<0221:UOTORF>2.0.CO;2

    • Google Scholar
42. 1. Stewart-Ibarra AM
    2. Lowe R

    (2013) Climate and non-climate drivers of dengue epidemics in southern coastal Ecuador

    The American Journal of Tropical Medicine and Hygiene 88:971–981.

    https://doi.org/10.4269/ajtmh.12-0478

    • Google Scholar
43. 1. Teixeira MG
    2. Costa Maria da Conceição N.
    3. Barreto F
    4. Barreto ML

    (2009) Dengue: twenty-five years since reemergence in Brazil

    Cadernos De Saúde Pública 25:S7–S18.

    https://doi.org/10.1590/S0102-311X2009001300002

    • Google Scholar
44. 1. Thomson MC
    2. Doblas-Reyes FJ
    3. Mason SJ
    4. Hagedorn R
    5. Connor SJ
    6. Phindela T
    7. Morse AP
    8. Palmer TN

    (2006) Malaria early warnings based on seasonal climate forecasts from multi-model ensembles

    Nature 439:576–579.

    https://doi.org/10.1038/nature04503

    • Google Scholar
45. 1. van Panhuis WG
    2. Hyun S
    3. Blaney K
    4. Marques ETA
    5. Coelho GE
    6. Siqueira JB
    7. Tibshirani R
    8. da Silva JB
    9. Rosenfeld R

    (2014) Risk of dengue for tourists and teams during the World Cup 2014 in Brazil

    PLoS Neglected Tropical Diseases 8:e3063.

    https://doi.org/10.1371/journal.pntd.0003063

    • Google Scholar
46. 1. Wilson ME
    2. Chen LH

    (2014) Health risks among travelers to Brazil: implications for the 2014 FIFA World Cup and 2016 Olympic Games

    Travel Medicine and Infectious Disease 12:205–207.

    https://doi.org/10.1016/j.tmaid.2014.04.001

    • Google Scholar
47. 1. Zanluca C
    2. Melo Vanessa Campos Andrade de
    3. Mosimann ALP
    4. Santos Glauco Igor Viana dos
    5. Santos Claudia Nunes Duarte dos
    6. Luz K

    (2015) First report of autochthonous transmission of zika virus in Brazil

    Memórias Do Instituto Oswaldo Cruz 110:569–572.

    https://doi.org/10.1590/0074-02760150192

    • Google Scholar

Author details

1. Rachel Lowe

   Climate Dynamics and Impacts Unit, Institut Català de Ciències del Clima, Barcelona, Spain

   Contribution

   RL, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   rachel.lowe@ic3.cat

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0003-3939-7343

2. Caio AS Coelho

   Centro de Previsão de Tempo e Estudos Climáticos, Instituto Nacional de Pesquisas Espaciais, Cachoeira Paulista, Brazil

   Contribution

   CASC, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

3. Christovam Barcellos

   Fundação Oswaldo Cruz, Rio de Janeiro, Brazil

   Contribution

   CB, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-1161-2753

4. Marilia Sá Carvalho

   Fundação Oswaldo Cruz, Rio de Janeiro, Brazil

   Contribution

   MSC, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

5. Rafael De Castro Catão

   1. Climate Dynamics and Impacts Unit, Institut Català de Ciències del Clima, Barcelona, Spain
   2. Faculdade de Ciências e Tecnologia, Universidade Estadual Paulista, Presidente Prudente, Brazil

   Contribution

   RDCC, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

6. Giovanini E Coelho

   Coordenação Geral do Programa Nacional de Controle da Dengue, Ministério da Saúde, Brasília, Brazil

   Contribution

   GEC, Acquisition of data, Drafting or revising the article, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

7. Walter Massa Ramalho

   Faculdade de Ceilândia, Universidade de Brasília, Brasília, Brazil

   Contribution

   WMR, Acquisition of data, Drafting or revising the article, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

8. Trevor C Bailey

   Exeter Climate Systems, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom

   Contribution

   TCB, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

9. David B Stephenson

   Exeter Climate Systems, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom

   Contribution

   DBS, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

10. Xavier Rodó

    1. Climate Dynamics and Impacts Unit, Institut Català de Ciències del Clima, Barcelona, Spain
    2. Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain

    Contribution

    XR, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

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