Association among house infestation index, dengue incidence, and sociodemographic indicators: surveillance using geographic information system

Overlap between dengue, Zika and chikungunya hotspots in the city of Rio de Janeiro

Background

Arboviruses represent a threat to global public health. In the Americas, the dengue fever is endemic. This situation worsens with the introduction of emerging, Zika fever and chikungunya fever, causing epidemics in several countries within the last decade. Hotspot analysis contributes to understanding the spatial and temporal dynamics in the context of co-circulation of these three arboviral diseases, which have the same vector: Aedes aegypti.

Objective

To analyze the spatial distribution and agreement between the hotspots of the historical series of reported dengue cases from 2000 to 2014 and the Zika, chikungunya and dengue cases hotspots from 2015 to 2019 in the city of Rio de Janeiro.

Methods

To identify hotspots, Gi* statistics were calculated for the annual incidence rates of reported cases of dengue, Zika, and chikungunya by neighborhood. Kendall’s W statistic was used to analyze the agreement between diseases hotspots.

Results

There was no agreement between the hotspots of the dengue fever historical series (2000–2014) and those of the emerging Zika fever and chikungunya fever (2015–2019). However, there was agreement between hotspots of the three arboviral diseases between 2015 and 2019.

Conclusion

The results of this study show the existence of persistent hotspots that need to be prioritized in public policies for the prevention and control of these diseases. The techniques used with data from epidemiological surveillance services can help in better understanding of the dynamics of these diseases wherever they circulate in the world.

Data-driven computational intelligence applied to dengue outbreak forecasting: a case study at the scale of the city of Natal, RN-Brazil

Dengue is recognized as a health problem that causes significant socioeconomic impacts throughout the world, affecting millions of people each year. A commonly used method for monitoring the dengue vector is to count the eggs that Aedes aegypti mosquitoes have laid in spatially distributed ovitraps. Given this approach, the present study uses a database collected from 397 ovitraps allocated across the city of Natal, RN—Brazil. The Egg Density Index for each neighborhood was computed weekly, over four complete years (from 2016 to 2019), and simultaneously analyzed with the dengue case incidence. Our results illustrate that the incidence of dengue is related to the socioeconomic level of the neighborhoods in the city of Natal. A deep learning algorithm was used to predict future dengue case incidence, either based on the previous weeks of dengue incidence or the number of eggs present in the ovitraps. The analysis reveals that ovitrap data allows earlier prediction (four to six weeks) compared to dengue incidence itself (one week). Therefore, the results validate that the quantification of Aedes aegypti eggs can be valuable for the early planning of public health interventions.

Disentangling associations between vegetation greenness and dengue in a Latin American city: Findings and challenges

Being a Re-Emerging Infectious Disease, dengue causes 390 million cases globally and is prevalent in many urban areas in South America. Understanding the fine-scale relationships between dengue incidence and environmental and socioeconomic factors can guide improved disease prevention strategies. This ecological study examines the association between dengue incidence and satellite-based vegetation greenness in 3826 census tracts nested in 474 neighborhoods in Belo Horizonte, Brazil, during the 2010 dengue epidemic. To reduce potential bias in the estimated dengue-greenness association, we adjusted for socioeconomic vulnerability, population density, building height and density, land cover composition, elevation, weather patterns, and neighborhood random effects. We found that vegetation greenness was negatively associated with dengue incidence in a univariate model, and this association attenuated after controlling for additional covariates. The dengue-greenness association was modified by socioeconomic vulnerability: while a positive association was observed in the least vulnerable census tracts, the association was negative in the most vulnerable areas. Using greenness as a proxy for vegetation quality, our results show the potential of vegetation management in reducing dengue incidence, particularly in socioeconomically vulnerable areas. We also discuss the role of water infrastructure, sanitation services, and tree cover in lowering dengue risk.

Factors associated with the occurrence of dengue epidemics in Brazil: a systematic review

OBJECTIVE

To identify and describe broadly the factors related to the occurrence of dengue epidemics in Brazil.

METHODS

Systematic review of studies published in Medline, Lilacs, PubMed, Cochrane, BVS, Web of Science, Scopus, and thesis and dissertations databases using descriptors cataloged in DeCs and MeSH on dengue and factors associated with the occurrence of epidemics, published from 2008 to 2018.

RESULTS

Thirty-five studies carried out in the country were selected. The epidemics recorded in Brazil were associated and/or correlated with multiple factors such as environment, socioeconomic conditions, climate, and aspects related to the vector, among others.

CONCLUSIONS

Dengue epidemics are complex and multifactorial. The continuity of the vector control actions was found to be relevant to the reduction of Aedes aegypti and for disease control. To contain the spread of the disease, effective measures are needed in all sectors, including health, education, economy, population, business, and government. Actions for the early detection of cases of the disease can prevent new outbreaks of epidemics.

Spatial analysis of the incidence of Dengue, Zika and Chikungunya and socioeconomic determinants in the city of Rio de Janeiro, Brazil

In 2015–2016, simultaneous circulation of dengue, Zika and chikungunya in the municipality of Rio de Janeiro (Brazil) was reported. We conducted an ecological study to analyse the spatial distribution of dengue, Zika and chikungunya cases and to investigate socioeconomic factors associated with individual and combined disease incidence in 2015–2016. We then constructed thematic maps and analysed the bivariate global Moran indices. Classical and spatial models were used. A distinct spatial distribution pattern for dengue, Zika and chikungunya was identified in the municipality of Rio de Janeiro. The bivariate global Moran indices (P < 0.05) revealed negative spatial correlations between rates of dengue, Zika, chikungunya and combined arboviruses incidence and social development index and mean income. The regression models (P < 0.05) identified a negative relationship between mean income and each of these rates and between sewage and Zika incidence rates, as well as a positive relationship between urban areas and chikungunya incidence rates. In our study, spatial analysis techniques helped to identify high-risk and social determinants at the local level for the three arboviruses. Our findings may aid in backing effective interventions for the prevention and control of epidemics of these diseases.

Voltage-Gated Sodium Channel (Vgsc) Mutation-Based Pyrethroid Resistance in Aedes aegypti Populations of Three Endemic Dengue Risk Areas of Sri Lanka

Background

Pyrethroid insecticides are widely used in many countries for chemical-based control of Ae. aegypti. Regardless of their efficacy, the constant use of insecticides has induced insecticide resistance mechanisms, such as knockdown resistance (kdr) in mosquitoes. Sri Lankan Vector Controlling Entities (VCE) have been using a variety of pyrethroid insecticides as the primary approach for dengue control. However, development of any resistance among the Aedes mosquitoes has been limitedly studied in the country. Therefore, the current study was conducted to evaluate the prevalence of F1534C, V1016G, and S989P mutations among Ae. aegypti mosquito populations in three dengue endemic high-risk regions of Sri Lanka. Methodology. Immature (both pupae and larvae) stages of Ae. aegypti mosquitoes were collected from Colombo, Gampaha, and Kandy districts of Sri Lanka from February 2018 to December 2019. Polymerase Chain Reaction- (PCR-) based assay for molecular genotyping of mutations was performed to identify the prevalence of kdr mutations in collected Ae. aegypti populations, separately. The frequencies of the resistant and susceptible kdr alleles were determined by using the Hardy–Weinberg equilibrium.

Results

The Ae. aegypti populations from Colombo, Gampaha, and Kandy districts showed 46%, 42%, and 22% of F1534C mutation allele frequencies, along with 15%, 12%, and 6% of V1016G mutation allele frequencies, respectively. The mutation allele frequencies of S989 in Colombo, Gampaha, and Kandy districts were 9.5%, 8.5%, and 4.5%, respectively. The wild-type (PP) genotype remained predominant within all the three districts, whereas the homogenous (QQ) mutation genotype occurred only in minority. The abundance of Q allele frequency in Ae. aegypti mosquitoes was relatively higher for all the three mutations in Colombo.

Conclusions

The findings clearly indicate that long-term insecticide applications and multiple use of pyrethroids have led to the acquisition of kdr mutations, leading to the development of insecticide resistance among local Ae. aegypti populations, especially in the Colombo and Gampaha districts. Therefore, evaluation of the prevalence levels of these kdr mutations highlights the necessity for shifting towards novel vector control strategies.

Climate variability, socio-ecological factors and dengue transmission in tropical Queensland, Australia: A Bayesian spatial analysis

BACKGROUND

Dengue is a wide-spread mosquito-borne disease globally with a likelihood of becoming endemic in tropical Queensland, Australia. The aim of this study was to analyse the spatial variation of dengue notifications in relation to climate variability and socio-ecological factors in the tropical climate zone of Queensland, Australia.

METHODS

Data on the number of dengue cases and climate variables including minimum temperature, maximum temperature and rainfall for the period of January 1^st, 2010 to December 31^st, 2015 were obtained for each Statistical Local Area (SLA) from Queensland Health and Australian Bureau of Meteorology, respectively. Socio-ecological data including estimated resident population, percentage of Indigenous population, housing structure (specifically terrace house), socio-economic index and land use types for each SLA were obtained from Australian Bureau of Statistics, and Australian Bureau of Agricultural and Resource Economics and Sciences, respectively. To quantify the relationship between dengue, climate and socio-ecological factors, multivariate Poisson regression models in a Bayesian framework were developed with a conditional autoregressive prior structure. Posterior parameters were estimated using Bayesian Markov Chain Monte Carlo simulation with Gibbs sampling.

RESULTS

In the tropical climate zone of Queensland, the estimated number of dengue cases was predicted to increase by 85% [95% Credible Interval (CrI): 25%, 186%] and 7% (95% CrI: 0.1%, 14%) for a 1-mm increase in average annual rainfall and 1% increase in the proportion of terrace houses, respectively. The estimated spatial variation (structured random effects) was small compared to the remaining unstructured variation, suggesting that the inclusion of covariates resulted in no residual spatial autocorrelation in dengue data.

CONCLUSIONS

Climate and socio-ecological factors explained much of the heterogeneity of dengue transmission dynamics in the tropical climate zone of Queensland. Results will help to further understand the risk factors of dengue transmission and will provide scientific evidence in designing effective local dengue control programs in the most needed areas.

Spatial analysis of dengue incidence and Aedes aegypti ovitrap surveillance in Belo Horizonte, Brazil

OBJECTIVES

Understanding the intra-urban spatial dynamics of Aedes aegypti and dengue transmission is important to effectively guide vector control. Ovitraps are a sensitive, cost-effective vector surveillance tool, yet few longitudinal studies have evaluated ovitrap indices and dengue occurrence. We aimed to assess the spatial patterns of dengue incidence and Ae. aegypti ovitrap positivity index (OPI) over time and to examine the spatial relationship between these two variables.

METHODS

This study used 12 years (2007-2018) of dengue case records and biweekly Ae. aegypti ovitrap data in Belo Horizonte, Brazil. We aggregated data by year and health centre catchment area (n = 152) and used both univariate and bivariate global Moran's I statistic and LISA to evaluate spatial clustering.

RESULTS

Annual dengue incidence ranged from 18 to 6262/100 000 residents and displayed spatial autocorrelation in 10/12 years, with shifting areas of high incidence. Annual OPI ranged from 35.7 to 47.6% and was clustered in all study years, but unlike dengue had consistent spatial patterns over time. Bivariate analysis found both positive (6/12 years) and negative (1/12 years) spatial associations between the two variables.

CONCLUSIONS

Low detected presence of Ae. aegypti was not a limiting factor in dengue transmission. However, stable spatial distribution of OPI suggests that certain areas may have persistent breeding sites. Future research should identify factors related to persistent Ae. aegypti hotspots to better guide vector management. Vector control efforts should be paired with additional data on population immunity, circulating serotypes and urban factors to better predict and control outbreaks.

Phylogenetic Characterization of Arboviruses in Patients Suffering from Acute Fever in Rondônia, Brazil

The purpose of the study was to classify, through phylogenetic analyses, the main arboviruses that have been isolated in the metropolitan region of Porto Velho, Rondônia, Brazil. Serum samples from patients with symptoms suggesting arboviruses were collected and tested by One Step RT-qPCR for Zika, Dengue (serotypes 1–4), Chikungunya, Mayaro and Oropouche viruses. Positive samples were amplified by conventional PCR and sequenced utilizing the Sanger method. The obtained sequences were aligned, and an evolutionary analysis was carried out using Bayesian inference. A total of 308 samples were tested. Of this total, 20 had a detectable viral load for Dengue, being detected DENV1 (18/20), co-infection DENV1 and DENV2 (1/20) and DENV4 (1/20). For Dengue serotype 3 and for the CHIKV, ZIKV, MAYV and OROV viruses, no individuals with a detectable viral load were found. A total of 9 of these samples were magnified by conventional PCR for sequencing. Of these, 6 were successfully sequenced and, according to the evolutionary profile, 5 corresponded to serotype DENV-1 genotype V, and 1 to serotype DENV-4 genotype II. In the study, we demonstrate co-circulation of the DENV-1 genotype V and the DENV-4 genotype II. Co-circulation of several DENV serotypes in the same city poses a risk to the population and is correlated with the increase of the most severe forms of the disease. Similarly, co-circulation of genetically distinct DENV and the occurrence of simultaneous infections can affect recombination events and lead to the emergence of more virulent isolates.

The Mosquito, the Virus, the Climate: An Unforeseen Réunion in 2018

The 2018 outbreak of dengue in the French overseas department of Réunion was unprecedented in size and spread across the island. This research focuses on the cause of the outbreak, asserting that climate played a large role in the proliferation of the Aedes albopictus mosquitoes, which transmitted the disease, and led to the dengue outbreak in early 2018. A stage-structured model was run using observed temperature and rainfall data to simulate the life cycle and abundance of the Ae. albopictus mosquito. Further, the model was forced with bias-corrected subseasonal forecasts to determine if the event could have been forecast up to 4 weeks in advance. With unseasonably warm temperatures remaining above 25°C, along with large tropical-cyclone-related rainfall events accumulating 10-15 mm per event, the modeled Ae. albopictus mosquito abundance did not decrease during the second half of 2017, contrary to the normal behavior, likely contributing to the large dengue outbreak in early 2018. Although subseasonal forecasts of rainfall for the December-January period in Réunion are skillful up to 4 weeks in advance, the outbreak could only have been forecast 2 weeks in advance, which along with seasonal forecast information could have provided enough time to enhance preparedness measures. Our research demonstrates the potential of using state-of-the-art subseasonal climate forecasts to produce actionable subseasonal dengue predictions. To the best of the authors' knowledge, this is the first time subseasonal forecasts have been used this way.

Using the intrinsic growth rate of the mosquito population improves spatio-temporal dengue risk estimation

Understanding geographic population dynamics of mosquitoes is an essential requirement for estimating the risk of mosquito-borne disease transmission and geographically targeted interventions. However, the use of population dynamics measures, such as the intrinsic growth rate, as predictors in spatio-temporal point processes has not been investigated before. In this work we compared the predictive accuracy of four spatio-temporal log-Gaussian Cox models: (i) With no predictors; (ii) mosquito abundance as predictor; (iii) intrinsic growth rate as predictor; (iv) intrinsic growth rate and mosquito abundance as predictors. This analysis is based on Aedes aegypti mosquito surveillance and human dengue data obtained from the urban area of Caratinga, Brazil. We used a statistical Moran Curve approach to estimate the intrinsic growth rate and a zero inflated Poisson kriging model for estimating mosquito abundance at locations of dengue cases. The incidence of dengue cases was positively associated with mosquito intrinsic growth rate and this model outperformed, in terms of predictive accuracy, the abundance and the null models. The latter includes only the spatio-temporal random effect but no predictors. In the light of these results we suggest that the intrinsic growth rate should be investigated further as a potential tool for predicting the risk of dengue transmission and targeting health interventions for vector-borne diseases.

Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review

Dengue fever (DF) is one of the world's most disabling mosquito-borne diseases, with a variety of approaches available to model its spatial and temporal dynamics. This paper aims to identify and compare the different spatial and spatio-temporal Bayesian modelling methods that have been applied to DF and examine influential covariates that have been reportedly associated with the risk of DF. A systematic search was performed in December 2017, using Web of Science, Scopus, ScienceDirect, PubMed, ProQuest and Medline (via Ebscohost) electronic databases. The search was restricted to refereed journal articles published in English from January 2000 to November 2017. Thirty-one articles met the inclusion criteria. Using a modified quality assessment tool, the median quality score across studies was 14/16. The most popular Bayesian statistical approach to dengue modelling was a generalised linear mixed model with spatial random effects described by a conditional autoregressive prior. A limited number of studies included spatio-temporal random effects. Temperature and precipitation were shown to often influence the risk of dengue. Developing spatio-temporal random-effect models, considering other priors, using a dataset that covers an extended time period, and investigating other covariates would help to better understand and control DF transmission.

Geography of Microcephaly in the Zika Era: A Study of Newborn Distribution and Socio-environmental Indicators in Recife, Brazil, 2015-2016

OBJECTIVES

We assessed sociodemographic and health care factors of mothers and newborns during a 2015-2016 outbreak of microcephaly in Recife, Brazil, and we analyzed the spatial distribution and incidence risk of newborns with microcephaly in relation to socio-environmental indicators.

METHODS

We collected data from August 2015 through May 2016 from Brazil's Live Birth Information System and Bulletin of Microcephaly Notification, and we geocoded the data by maternal residence. We constructed thematic maps of districts, according to socio-environmental and vector indicators. We identified spatial aggregates of newborns with microcephaly by using the Bernoulli model. We performed logistic regression analyses to compare the incidence risk of microcephaly within socio-environmental indicator groups.

RESULTS

We geocoded 17 990 of 19 554 (92.0%) live births in Recife, of which 202 (1.1%) newborns were classified as having microcephaly, based on a head circumference of ≥2 standard deviations below the mean. Larger proportions of newborns with microcephaly (compared with newborns without microcephaly) were born to mothers who delivered in a public hospital, did not attend college, were aged ≤19, or were black or mixed race. A higher risk of microcephaly (incidence rate ratio [IRR] = 3.90; 95% confidence interval [CI], 1.88-8.06) occurred in districts with the lowest (vs highest) Municipal Human Development Index (ie, an index that assesses longevity, education, and income). The risk of microcephaly was significantly higher where rates of larvae density (IRR = 2.31; 95% CI, 1.19-4.50) and larvae detection (IRR = 2.04; 95% CI, 1.05-4.00) were higher and rates of sewage system (IRR = 2.20; 95% CI, 1.16-4.18) and garbage collection (IRR = 1.96; 95% CI, 0.99-3.88) were lower. Newborns with microcephaly lived predominantly in the poorest areas and in a high-risk cluster (relative risk = 1.89, P = .01) in the north.

CONCLUSIONS

The disproportionate incidence of microcephaly in newborns in poor areas of Recife reinforces the need for government and public health authorities to formulate policies that promote social equity and support for families and their children with microcephaly.

Low socioeconomic condition and the risk of dengue fever: A direct relationship

This study aimed to characterize the first dengue fever epidemic in Várzea Paulista, São Paulo, Brazil, and its spatial and spatio-temporal distribution in order to assess the association of socioeconomic factors with dengue occurrence. We used autochthonous dengue cases confirmed in a 2007 epidemic, the first reported in the city, available in the Information System on Diseases of Compulsory Declaration database. These cases where geocoded by address. We identified spatial and spatio-temporal clusters of high- and low-risk dengue areas using scan statistics. To access the risk of dengue occurrence and to evaluate its relationship with socioeconomic level we used a population-based case-control design. Firstly, we fitted a generalized additive model (GAM) to dengue cases and controls without considering the non-spatial covariates to estimate the odds ratios of the occurrence of the disease. The controls were drawn considering the spatial distribution of the household of the study area and represented the source population of the dengue cases. After that, we assessed the relationship between socioeconomic variables and dengue using the GAM and obtained the effect of these covariates in the occurrence of dengue adjusted by the spatial localization of the cases and controls. Cluster analysis and GAM indicated that northeastern area of Várzea Paulista was the most affected area during the epidemic. The study showed a positive relationship between low socioeconomic condition and increased risk of dengue. We studied the first dengue epidemic in a highly susceptible population at the beginning of the outbreak and therefore it may have allowed to identify an association between low socioeconomic conditions and increased risk of dengue. These results may be useful to predict the occurrence and to identify priority areas to develop control measures for dengue, and also for Zika and Chikungunya; diseases that recently reached Latin America, especially Brazil.

Dengue epidemic typology and risk factors for extensive epidemic in Amazonas state, Brazil, 2010-2011

BACKGROUND

Dengue is the most prevalent arboviral disease affecting humans. The frequency and magnitude of dengue epidemic have significantly increased over recent decades. This study aimed to identify dengue epidemic types and risk factors for the extensive epidemics that occurred in 2010-2011, across the municipalities of Amazonas state, Brazil.

METHODS

Using an ecological approach, secondary data were obtained from the dengue fever surveillance system. Epidemic waves were classified according to three indices: duration, intensity, and coverage. A hierarchical model of multiple logistic regression was used for the identification of risk factors, with the occurrence of extensive dengue epidemic.

RESULTS

During the study period, dengue virus affected 49 of the 62 Amazonas municipalities. In 22 of these, the epidemics were of high intensity, wide range, and long time span, and therefore categorized as "extensive epidemics". The final multivariable model revealed a significant association between extensive dengue epidemics occurrence and the average number of days with precipitation (adjusted OR = 1.40, 95% CI: 1.01-1.94) and the number of years with infestation (adjusted OR = 1.53, 95% CI: 1.18-1.98).

CONCLUSIONS

Our results indicate that it is crucial to integrate vector control, case management, epidemiological investigation, and health education, in order to respond to the growing threat of multiple mosquito-borne diseases, such as dengue, Zika and chikungunya, which are highly prevalent in the South America region.

The spatial and temporal scales of local dengue virus transmission in natural settings: a retrospective analysis

Background

Dengue is a vector-borne disease caused by the dengue virus (DENV). Despite the crucial role of Aedes mosquitoes in DENV transmission, pure vector indices poorly correlate with human infections. Therefore there is great need for a better understanding of the spatial and temporal scales of DENV transmission between mosquitoes and humans. Here, we have systematically monitored the circulation of DENV in individual Aedes spp. mosquitoes and human patients from Caratinga, a dengue endemic city in the state of Minas Gerais, in Southeast Brazil. From these data, we have developed a novel stochastic point process pattern algorithm to identify the spatial and temporal association between DENV infected mosquitoes and human patients.

Methods

The algorithm comprises of: (i) parameterization of the variogram for the incidence of each DENV serotype in mosquitoes; (ii) identification of the spatial and temporal ranges and variances of DENV incidence in mosquitoes in the proximity of humans infected with dengue; and (iii) analysis of the association between a set of environmental variables and DENV incidence in mosquitoes in the proximity of humans infected with dengue using a spatio-temporal additive, geostatistical linear model.

Results

DENV serotypes 1 and 3 were the most common virus serotypes detected in both mosquitoes and humans. Using the data on each virus serotype separately, our spatio-temporal analyses indicated that infected humans were located in areas with the highest DENV incidence in mosquitoes, when incidence is calculated within 2.5–3 km and 50 days (credible interval 30–70 days) before onset of symptoms in humans. These measurements are in agreement with expected distances covered by mosquitoes and humans and the time for virus incubation. Finally, DENV incidence in mosquitoes found in the vicinity of infected humans correlated well with the low wind speed, higher air temperature and northerly winds that were more likely to favor vector survival and dispersal in Caratinga.

Conclusions

We have proposed a new way of modeling bivariate point pattern on the transmission of arthropod-borne pathogens between vector and host when the location of infection in the latter is known. This strategy avoids some of the strong and unrealistic assumptions made by other point-process models. Regarding virus transmission in Caratinga, our model showed a strong and significant association between high DENV incidence in mosquitoes and the onset of symptoms in humans at specific spatial and temporal windows. Together, our results indicate that vector surveillance must be a priority for dengue control. Nevertheless, localized vector control at distances lower than 2.5 km around premises with infected vectors in densely populated areas are not likely to be effective.

Electronic supplementary material

The online version of this article (10.1186/s13071-018-2662-6) contains supplementary material, which is available to authorized users.

The reintroduction of DENV-2 in 2011 in Panama and subsequent outbreak characteristic

The circulation of the South-east Asian/American (AS/AM) dengue 2 virus (DENV-2) genotype in the Americas has been associated with a high rate of severe disease. From 1993, the year DENV was reintroduced in Panama, until 2011 there were 29 dengue-associated deaths, 17 of which occurred in 2011, the most severe outbreak with a case fatality rate (CFR) of 44% (17 deaths out of 38 severe dengue cases). During this outbreak DENV-2 was reintroduced into the country, whereas over the prior five years DENV-1 and -3 were predominant. Herein, we describe the 2011 Panama outbreak and genetically characterize the Panamanian DENV-2 strains, which were associated with severe dengue disease in Panama. Our results suggest that the DENV-2 isolates from this outbreak belonged to the AS/AM genotype sub-clade 2BI and were genetically close to viruses described in the outbreaks in Nicaragua, Honduras, Guatemala and Mexico from 2006-2011. Sub-clade 2BI has previously been associated with severe disease in Nicaragua during outbreaks from 2005-2007.

Inferring the Spatio-temporal Patterns of Dengue Transmission from Surveillance Data in Guangzhou, China

Background

Dengue is a serious vector-borne disease, and incidence rates have significantly increased during the past few years, particularly in 2014 in Guangzhou. The current situation is more complicated, due to various factors such as climate warming, urbanization, population increase, and human mobility. The purpose of this study is to detect dengue transmission patterns and identify the disease dispersion dynamics in Guangzhou, China.

Methodology

We conducted surveys in 12 districts of Guangzhou, and collected daily data of Breteau index (BI) and reported cases between September and November 2014 from the public health authority reports. Based on the available data and the Ross-Macdonald theory, we propose a dengue transmission model that systematically integrates entomologic, demographic, and environmental information. In this model, we use (1) BI data and geographic variables to evaluate the spatial heterogeneities of Aedes mosquitoes, (2) a radiation model to simulate the daily mobility of humans, and (3) a Markov chain Monte Carlo (MCMC) method to estimate the model parameters.

Results/Conclusions

By implementing our proposed model, we can (1) estimate the incidence rates of dengue, and trace the infection time and locations, (2) assess risk factors and evaluate the infection threat in a city, and (3) evaluate the primary diffusion process in different districts. From the results, we can see that dengue infections exhibited a spatial and temporal variation during 2014 in Guangzhou. We find that urbanization, vector activities, and human behavior play significant roles in shaping the dengue outbreak and the patterns of its spread. This study offers useful information on dengue dynamics, which can help policy makers improve control and prevention measures.

Dengue transmission is a spatio-temporal process with interactions between hosts, vectors, and viruses. Its transmission also involves multiple complex or even hidden factors, such as climate, social environment, vector ecology, and host mobility. These complexities make the underlying process of dengue transmission difficult to clarify. We address how the patterns of dengue transmission can be inferred by investigating the 2014 dengue outbreak in the city of Guangzhou, China, taking the available surveillance data and applying mathematical models and computational methods. We can then estimate the distribution of dengue infections and identify the transmission mechanisms. In our study, we systematically investigate the critical factors, enabling us to estimate the real patterns and dynamics of dengue transmission beyond the surveillance data.