Dengue Clusters Research Articles

Space-time scanning statistics in the prediction and evaluation of dengue epidemic clusters

ObjectivesTo detect clusters of dengue hemorrhagic fever in an urbanized district of Hai Phong City, Vietnam using Poisson space-time retrospective and prospective analysis. MethodsA cross-sectional and retrospective study analyzed dengue surveillance data in the period from January 01, 2018, to December 31, 2022. Spatial-temporal scanning statistics were performed using the free software SatScan v10.1.2. ResultsA total of 519 cases were recorded. The cumulative incidence per 100,000 inhabitants was 3.37, 127.36, 10.96, 0, and 296.04 in 2018, 2019, 2020, 2021, and 2022, respectively. By retrospective Poisson model-based analysis, seven clusters were detected. Six of these seven detected outbreaks occurred in November and December 2022. The largest cluster had a relative risk (RR) of 1539.5 (P <0.00001). The smallest cluster has a RR of 316.1 (P = 0.006). Prospective analysis using the Poisson model significantly detected four active case clusters at the time of the study. The largest cluster of cases with RR was 47.7 (P <0.00001) and the smallest cluster with RR was 18.2 (P <0.00001). ConclusionsThis study provides a basis for improving the effectiveness of interventions and conducting further investigations into risk factors in the study area, as well as in other urban and suburban areas nationwide.

Spatial correlation of dengue with socioeconomic status and land temperature in northwest Mexico

Objective. To characterize the geographic distribution of dengue and to evaluate the spatial autocorrelation with social and climatic determinants at the census-tract level in two medium sized cities in northwestern Mexico. Methods. In this work we apply spatial analysis ecological tools, such as the Moran’s Index and the Local Indicator of Spatial Association (LISA) method, to examine global and local spatial correlation between incidence of dengue, and socioeconomic and climatic factors at the census tract-level. For the analysis of the spatial clustering, the Getis-Ord method was used to find statistically significant hot spots in each city. Results. Overall, a global spatial autocorrelation could not be identified, although local clusters of a high dengue incidence, soil surface temperature ≤ 31 °C and high degree of social marginalization coincide. Discussion. We found that at the census-tract level in urban settings, socially disadvantaged populations showed higher clusters of dengue when compared to areas with better socioeconomic conditions. In the two study sites, a similar spatial pattern was observed when considering public health conditions and its aggregation with physical attributes using spatial analysis techniques, supporting the application of this technique for a better understanding about the dengue distribution in urban areas.

Spatio-Temporal Determinants of Dengue Epidemics in the Central Region of Burkina Faso.

The aim of this study was to analyze the spatio-temporal distribution and determinants of the 2017 dengue epidemic in Burkina Faso. A principal component analysis of meteorological and environmental factors was performed to reduce dimensions and avoid collinearities. An initial generalized additive model assessed the impact of the components derived from this analysis on dengue incidence. Dengue incidence increased mainly with relative humidity, precipitation, normalized difference vegetation index and minimum temperature with an 8-week lag. A Kulldoff Satscan scan was used to identify high-risk dengue clusters, and a second generalized additive model assessed the risk of a health area being at high risk according to land-use factors. The spatio-temporal distribution of dengue fever was heterogeneous and strongly correlated with meteorological factors. The rural communes of Sabaa and Koubri were the areas most at risk. This study provides useful information for planning targeted dengue control strategies in Burkina Faso.

A Bayesian spatiotemporal approach to modelling arboviral diseases in Mexico.

The objective of this study was to evaluate the spatial and temporal patterns of disease prevalence clusters of dengue (DENV), chikungunya (CHIKV) and Zika (ZIKV) virus and how socio-economic and climatic variables simultaneously influence the risk and rate of occurrence of infection in Mexico. To determine the spatiotemporal clustering and the effect of climatic and socio-economic covariates on the rate of occurrence of disease and risk in Mexico, we applied correlation methods, seasonal and trend decomposition using locally estimated scatterplot smoothing, hotspot analysis and conditional autoregressive Bayesian models. We found cases of the disease are decreasing and a significant association between DENV, CHIKV and ZIKV cases and climatic and socio-economic variables. An increment of cases was identified in the northeastern, central west and southeastern regions of Mexico. Climatic and socio-economic covariates were significantly associated with the rate of occurrence and risk of the three arboviral disease cases. The association of climatic and socio-economic factors is predominant in the northeastern, central west and southeastern regions of Mexico. DENV, CHIKV and ZIKV cases showed an increased risk in several states in these regions and need urgent attention to allocate public health resources to the most vulnerable regions in Mexico.

Direct and indirect effects of age on dengue severity: The mediating role of secondary infection.

Severe dengue occurrence has been attributed to increasing age and different dengue virus (DENV) serotypes that cause secondary infections and immune-enhancing phenomena. Therefore, we examined if the effect of age on dengue severity was mediated by infectivity status while controlling for sex and region. Further, we assessed the spatial clustering of dengue severity for individuals with primary and secondary infection across Mexican municipalities. Health data from 2012 to 2017 was retrieved from Mexico's Ministry of Health. A mediation analysis was performed using multiple logistic regression models based on a directed acyclic graph. The models were explored for the direct effect of age on dengue severity and its indirect impact through secondary infection. In addition, severe dengue clusters were determined in some Northeastern and Southeastern municipalities through spatial analysis. We observed a nonlinear trend between age and severe dengue. There was a downward trend of severe dengue for individuals between 0 and 10 years and an upward trend above 10 years. The effect of age on dengue severity was no longer significant for individuals between 10 and 60 years after introducing infectivity status into the model. The mediating role of infectivity status in the causal model was 17%. Clustering of severe dengue among individuals with primary infection in the Northeastern region may point to the high prevalence of DENV-3 in the region. Public health efforts may prevent secondary infection among infants and the aged. In addition, there should be a further investigation into the effect of DENV-3 in individuals with primary disease.

Predicting the dengue cluster outbreak dynamics in Yogyakarta, Indonesia: a modelling study.

Human mobility and climate conditions are recognised key drivers of dengue transmission, but their combined and individual role in the local spatiotemporal clustering of dengue cases is not well understood. This study investigated the effects of human mobility and weather conditions on dengue risk in an urban area in Yogyakarta, Indonesia. We established a Bayesian spatiotemporal model for neighbourhood outbreak prediction and evaluated the performances of two different approaches for constructing an adjacency matrix: one based on geographical proximity and the other based on human mobility patterns. We used population, weather conditions, and past dengue cases as predictors using a flexible distributed lag approach. The human mobility data were estimated based on proxies from social media. Unseen data from February 2017 to January 2020 were used to estimate the one-month ahead prediction accuracy of the model. When human mobility proxies were included in the spatial covariance structure, the model fit improved in terms of the log score (from 1.748 to 1.561) and the mean absolute error (from 0.676 to 0.522) based on the validation data. Additionally, showed only few observations outside the credible interval of predictions (1.48%) and weather conditions were not found to contribute additionally to the clustering of cases at this scale. The study shows that it is possible to make highly accurate predictions of the within-city cluster dynamics of dengue using mobility proxies from social media combined with disease surveillance data. These insights are important for proactive and timely outbreak management of dengue. Swedish Research Council Formas, Umeå Centre for Global Health Research, Swedish Council for Working Life and Social Research, Swedish research council VINNOVA and Alexander von Humboldt Foundation (Germany).

High-risk spatial clusters for Zika, dengue, and chikungunya in Rio de Janeiro, Brazil.

To analyze the spatial distribution and identify high-risk spatial clusters of Zika, dengue, and chikungunya (ZDC), in the city of Rio de Janeiro, Brazil, and their socioeconomic status. An ecological study based on data from a seroprevalence survey. Using a rapid diagnostic test to detect the arboviruses, 2,114 individuals were tested in 2018. The spatial distribution was analyzed using kernel estimation. To detect high-risk spatial clusters of arboviruses, we used multivariate scan statistics. The Social Development Index (SDI) was considered in the analysis of socioeconomic status. Among the 2,114 individuals, 1,714 (81.1%) were positive for at least one arbovirus investigated. The kernel estimation showed positive individuals for at least one arbovirus in all regions of the city, with hot spots in the North, coincident with regions with very low or low SDI. The scan statistic detected three significant (p<0.05) high-risk spatial clusters for Zika, dengue, and chikungunya viruses. These clusters correspond to 35.7% (n=613) of all positive individuals of the sample. The most likely cluster was in the North (cluster 1) and overlapped regions with very low and low SDI. Clusters 2 and 3 were in the West and overlapping regions with low and very low SDI, respectively. The highest values of relative risks were in cluster 1 for CHIKV (1.97), in cluster 2 for ZIKV (1.58), and in cluster 3 for CHIKV (1.44). Regarding outcomes in the clusters, the Flavivirus had the highest frequency in clusters 1, 2, and 3 (42.83%, 54.46%, and 52.08%, respectively). We found an over-risk for arboviruses in areas with the worst socioeconomic conditions in Rio de Janeiro. Moreover, the highest concentration of people negative for arboviruses occurred in areas considered to have better living conditions.

Spatial Analysis of Dengue Clusters at Department, Municipality and Local Scales in the Southwest of Colombia, 2014-2019.

Dengue is an arbovirus transmitted by mosquitoes of the genus Aedes and is one of the 15 main public health problems in the world, including Colombia. Where limited financial resources create a problem for management, there is a need for the department to prioritize target areas for public health implementation. This study focuses on a spatio-temporal analysis to determine the targeted area to manage the public health problems related to dengue cases. To this end, three phases at three different scales were carried out. First, for the departmental scale, four risk clusters were identified in Cauca (RR ≥ 1.49) using the Poisson model, and three clusters were identified through Getis-Ord Gi* hotspots analysis; among them, Patía municipality presented significantly high incidence rates in the time window (2014-2018). Second, on the municipality scale, altitude and minimum temperature were observed to be more relevant than precipitation; considering posterior means, no spatial autocorrelation for the Markov Chain Monte Carlo was found (Moran test ˂ 1.0), and convergence was reached for b1-b105 with 20,000 iterations. Finally, on the local scale, a clustered pattern was observed for dengue cases distribution (nearest neighbour index, NNI = 0.202819) and the accumulated number of pupae (G = 0.70007). Two neighbourhoods showed higher concentrations of both epidemiological and entomological hotspots. In conclusion, the municipality of Patía is in an operational scenario of a high transmission of dengue.

Spatial epidemiology and the detection of spatial clusters of dengue in Paraíba: a comparison between the flexible Scan and circular Scan methods

Resumo Introdução Estatísticas espaciais são usadas para auxiliar gestores de saúde na tomada de decisão, informando a taxa de ocorrência de agravos na população e destacando quando estas alcançam valores além do esperado. Objetivo Compreender o funcionamento e aplicabilidade das Estatísticas Espaciais Scan flexível e Scan circular, comparando seus resultados na detecção de aglomerados espaciais usando dados epidemiológicos reais do dengue no estado da Paraíba – Brasil. Método Descreveu-se o processo detalhado da aplicação das estatísticas Scan flexível e Scan circular para a detecção de áreas significativas de risco (aglomerados) do dengue na Paraíba, nos anos de 2009 a 20013, por meio dos software FLeXScan e SaTScan. Resultados Ambos os métodos revelaram o oeste do estado como a região com maior frequência de aglomerados detectados com alto risco, em todos os anos analisados, levando-se em consideração os mapas de risco de incidência do dengue na Paraíba, nos anos de 2009 a 2013. Conclusão As estatísticas Scan flexível e Scan circular são praticamente similares quanto à eficiência na detecção de aglomerados do dengue. Entretanto, verificaram-se problemas de superestimação no método Scan circular e subestimação no método Scan flexível na detecção dos aglomerados. Destacou-se ainda o auxílio destas estatísticas espaciais aos gestores de saúde quanto à localização das regiões de agravo da doença, tornando mais efetivo o direcionamento das ações de combate de forma politicamente correta.

Geographical clusters of dengue outbreak in Singapore during the Covid-19 nationwide lockdown of 2020

Dengue, a mosquito-transmitted viral disease, has posed a public health challenge to Singaporean residents over the years. In 2020, Singapore experienced an unprecedented dengue outbreak. We collected a dataset of geographical dengue clusters reported by the National Environment Agency (NEA) from 15 February to 9 July in 2020, covering the nationwide lockdown associated with Covid-19 during the period from 7 April to 1 June. NEA regularly updates the dengue clusters during which an infected person may be tagged to one cluster based on the most probable infection location (residential apartment or workplace address), which is further matched to fine-grained spatial units with an average coverage of about 1.35 km2. Such dengue cluster dataset helps not only reveal the dengue transmission patterns, but also reflect the effects of lockdown on dengue spreading dynamics. The resulting data records are released in simple formats for easy access to facilitate studies on dengue epidemics.

Spatio-temporal clusters and patterns of spread of dengue, chikungunya, and Zika in Colombia.

Colombia has one of the highest burdens of arboviruses in South America. The country was in a state of hyperendemicity between 2014 and 2016, with co-circulation of several Aedes-borne viruses, including a syndemic of dengue, chikungunya, and Zika in 2015. We analyzed the cases of dengue, chikungunya, and Zika notified in Colombia from January 2014 to December 2018 by municipality and week. The trajectory and velocity of spread was studied using trend surface analysis, and spatio-temporal high-risk clusters for each disease in separate and for the three diseases simultaneously (multivariate) were identified using Kulldorff's scan statistics. During the study period, there were 366,628, 77,345 and 74,793 cases of dengue, chikungunya, and Zika, respectively, in Colombia. The spread patterns for chikungunya and Zika were similar, although Zika's spread was accelerated. Both chikungunya and Zika mainly spread from the regions on the Atlantic coast and the south-west to the rest of the country. We identified 21, 16, and 13 spatio-temporal clusters of dengue, chikungunya and Zika, respectively, and, from the multivariate analysis, 20 spatio-temporal clusters, among which 7 were simultaneous for the three diseases. For all disease-specific analyses and the multivariate analysis, the most-likely cluster was identified in the south-western region of Colombia, including the Valle del Cauca department. The results further our understanding of emerging Aedes-borne diseases in Colombia by providing useful evidence on their potential site of entry and spread trajectory within the country, and identifying spatio-temporal disease-specific and multivariate high-risk clusters of dengue, chikungunya, and Zika, information that can be used to target interventions.

Spatiotemporal patterns of dengue and Zika incidence during the 2015-2018 outbreak of Zika in Mexico.

Evaluate spatially and temporally simultaneous presence of clusters of dengue and Zika clinical cases and their relationship with expected dengue transmission risk. A classification of dengue risk transmission was carried out for whole country, and spatial autocorrelation analyses to identify clusters of confirmed clinical cases of dengue and Zika from 2015 to 2018 was conducted using Moran's Index statistics. Clusters of both diseases were identified in dengue-high risk munici-palities at the beginning of the outbreak, but, at the end of the outbreak, Zika clusters occurred in dengue low-risk mu-nicipalities. This study identified Zika clusters in low-risk dengue areas suggesting participation of several factors that favor virus introduction and dissemination, such as differences in entomological and control interventions, and the possibility of cross-immunity in the population.

Temporal Variations and Spatial Clusters of Dengue in Thailand: Longitudinal Study before and during the Coronavirus Disease (COVID-19) Pandemic.

The efforts towards effective control of the COVID-19 pandemic may affect the incidence of dengue. This study aimed to investigate temporal variations and spatial clusters of dengue in Thailand before and during the COVID-19 pandemic. Reported dengue cases before (2011–2019) and during (2020–2021) the COVID-19 pandemic were obtained from the national disease surveillance datasets. The temporal variations were analyzed using graphics, a seasonal trend decomposition procedure based on Loess, and Poisson regression. A seasonal ARIMA model was used to forecast dengue cases. Spatial clusters were investigated using the local indicators of spatial associations (LISA). The cyclic pattern showed that the greatest peak of dengue cases likely changed from every other year to every two or three years. In terms of seasonality, a notable peak was observed in June before the pandemic, which was delayed by one month (July) during the pandemic. The trend for 2011–2021 was relatively stable but dengue incidence decreased dramatically by 7.05% and 157.80% on average in 2020 and 2021, respectively. The forecasted cases in 2020 were slightly lower than the reported cases (2.63% difference), whereas the forecasted cases in 2021 were much higher than the actual cases (163.19% difference). The LISA map indicated 5 to 13 risk areas or hotspots of dengue before the COVID-19 pandemic compared to only 1 risk area during the pandemic. During the COVID-19 pandemic, dengue incidence sharply decreased and was lower than forecasted, and the spatial clusters were much lower than before the pandemic.

Spatio-Temporal Analysis of Dengue Cases in Kuningan District Since 2008-2017

Dengue has spread to over 400 of Indonesia’s 497 districts, including West Java Province in which 26 of its districts have been declared as hyper-endemic. A study was conducted to describe the spread of dengue incidences and its cluster during 2008-2017 in Kuningan District. The district is located in an important route, in migration and in the economic field, connecting the northern part of West Java to the southern part. A spatio-temporal analysis based on monthly dengue incidences from the local District Health Office was performed using SaTScan™. This study revealed there were Statistically significant high-risk dengue clusters with various RR in half of the subdistricts in Kuningan in the ten-year periods of 2008-2017 and a retrospective space-time analysis detected 17 significant clusters (P&lt;0.001). Subdistrict Kuningan is detected as a high-risk area every year except for 2008, whereas Jalaksana emerged as a high-risk cluster in six of ten-year periods. We conclude that there was a dynamic spread of dengue cases initiated from the north part of Kuningan District to western areas. This study results do not properly predict RR due to a lack of information on some significant factors, such as vector density and related environmental and socioeconomic parameters. However, this study has provided a perspective on dengue incidence that can be used by local health managers and disease surveillance personnel to monitor prospective outbreaks and make decisions about how to implement an effective response.

Spatio-temporal clustering analysis using two different scanning windows: A case study of dengue fever in Peninsular Malaysia

Dengue fever is a mosquito-borne viral infection of humans caused by a virus of the Flaviviridae family. In Malaysia the annual incidence risk of dengue fever for the period 2000 to 2019 ranged from 30 to 390 cases per 100,000. The aim of this paper was to identify spatial, temporal and spatio-temporal clusters of dengue fever in Peninsular Malaysia for the period 2015 to 2017.Counts of confirmed incident cases of dengue fever for each of the 86 districts of Peninsular Malaysia for the period 1 January 2015 to 31 December 2017 (inclusive) and district-level census data allowed us to calculate the incidence rate of dengue fever, defined as the number of confirmed cases of dengue fever per 100,000 person-years at risk. We applied Kulldorff’s cylindrical space–time scan statistic and Takahashi et al.’s prismatic space–time scan statistic to the data.We identified no major differences in the number and location of spatial clusters of dengue incidence for 2015, 2016 and 2017 using Kulldorff’s and Takahashi et al.’s method. Spatio-temporal clusters of dengue occurred at several times throughout each year in various high population dense areas. These clusters not only included high population density districts but also their adjacent district neighbours. The temporal clustering of dengue cases during the monsoon season (mid September to late December each year) implies that there is a biologically plausible causal association between rainfall and the incidence of dengue.Identification of locations and time periods when the frequency of dengue is high allows Malaysian public health authorities to be more objective in their decision making around vector control and dengue public awareness campaigns. Future research will quantify the association between population density and rainfall on dengue incidence. This will allow health authorities to take a more proactive approach for dengue control.

An investigation of the spatial distribution of dengue cases in Johor Bahru, Johor, Malaysia

In recent decades, dengue fever has spread throughout developing countries, including Malaysia. Understanding the distribution pattern is thus important for the sustainability of urbanization. The goal of this study is to examine the trend of dengue cases reported in Johor Bahru District from 2015 to 2019, as well as the spatial pattern of dengue transmission in relation to weather parameters. The distribution pattern and spatial mean centre for dengue cases were found to be localized in the centre of Johor Bahru region, according to spatial statistical analysis. The extended polygon of dengue cluster spanned from the east to the west of Johor Bahru district, according to directional distribution. The least standard distance for dengue cases was 0.104 m in 2015, and the greatest was 0.120 m in 2018, whereas dengue cases in 2016, 2017, and 2019 were measured at 0.111 m and 0.116 m, 0.106 m respectively. Dengue cases in Johor Bahru district were likewise concentrated, according to the average nearest neighbour analysis. Temperature (r = 0.222) was found to be positively connected with dengue cases, although rainfall (r = −0.124) and relative humidity (r = −0.152) were in contrast with dengue cases throughout the five years of study period. The outcomes of the these spatial statistical analyses revealed that dengue cases concentrated in the east to west of the research region from 2015 to 2019. Temperature is determined to be a substantial positive factor correlated for most study years.

Short Report: Adult Aedes abundance and risk of dengue transmission.

Dengue is transmitted mainly by the adult female Aedes aegypti mosquito. However, little is known about the impact of adult Aedes abundance on the risk of dengue transmission. Here we analysed nationally representative dengue case and vector surveillance data collected from Singapore, to determine the effect of adult Aedes abundance on the risk of dengue transmission. A case was an area with active dengue transmission as indicated by the presence of dengue cluster. A control was an area where no dengue cluster was reported. Using multivariate logistic regression, we analysed 88 cases and 602 controls and estimated the odds of dengue cluster formation at various adult Aedes abundance levels, estimated by the mean number of adult female Aedes per Gravitrap per week and categorised into Low, Moderate, High and Very High abundance level. We found that the risk of dengue cluster formation was positively associated with adult Ae. aegypti abundance. We observed a three to four-fold increase in the odds of dengue clusters forming in areas with High (AOR: 3.40, 95% CI: 2.09, 5.52) and Very High (AOR: 3.99, 95% CI: 2.46, 6.46) adult Aedes aegypti abundance level compared to those with low Ae. aegypti abundance level. Our study strengthens the evidence for the use of adult Aedes indices for dengue risk assessment and early warning for dengue outbreaks. Entomological indicators of adult Ae. aegypti could be used to anticipate and prioritize areas for dengue control.

Spatial pattern of dengue cases: An analysis in Bangi District, Selangor, Malaysia.

In recent decades, dengue outbreaks have become increasingly common around the developing countries, including Malaysia. Thus, it is essential for rural as well as urbanised livelihood to understand the distribution pattern of this infection. The objective of this study is to determine the trend of dengue cases reported from the year 2014 to 2018 and the spatial pattern for this spread. Spatial statistical analyses conducted found that the distribution pattern and spatial mean centre for dengue cases were clustered in the eastern part of the Bangi region. Directional distribution observed that the elongated polygon of dengue cluster stretched from the Northeast to the Southwest of Bangi District. The standard distance observed for dengue cases was smallest in the year 2014 (0.017 m), and largest in 2016 (0.019 m), whereas in the year 2015, 2017 and 2018, it measured 0.018 m. The average nearest neighbour analysis also displayed clustered patterns for dengue cases in the Bangi District. The three spatial statistical analyses (spatial mean centre, standard distance and directional distribution) findings illustrate that the dengue cases from the year 2014 to 2018 are clustered in the Northeast to the Southwest of the study region.

Spatio-Temporal Patterns of Dengue Incidence in Medan City, North Sumatera, Indonesia

Dengue has been a perennial public health problem in Medan city, North Sumatera, despite the widespread implementation of dengue control. Understanding the spatial and temporal pattern of dengue is critical for effective implementation of dengue control strategies. This study aimed to characterize the epidemiology and spatio-temporal patterns of dengue in Medan City, Indonesia. Data on dengue incidence were obtained from January 2016 to December 2019. Kulldorff’s space-time scan statistic was used to identify dengue clusters. The Getis-Ord Gi* and Anselin Local Moran’s I statistics were used for further characterisation of dengue hotspots and cold spots. Results: A total of 5556 cases were reported from 151 villages across 21 districts in Medan City. Annual incidence in villages varied from zero to 439.32 per 100,000 inhabitants. According to Kulldorf’s space-time scan statistic, the most likely cluster was located in 27 villages in the south-west of Medan between January 2016 and February 2017, with a relative risk (RR) of 2.47. Getis-Ord Gi* and LISA statistics also identified these villages as hotpot areas. Significant space-time dengue clusters were identified during the study period. These clusters could be prioritized for resource allocation for more efficient prevention and control of dengue.

Do Aedes aegypti lead to larger-scale and more severe epidemic than Aedes albopictus?

Background: Taiwan belongs to both tropical and subtropical climate zones, which results in distinctions of spatial distribution between vectors and cases of human infection of dengue. The vectors of dengue in Taiwan comprise Aedes albopictus (distributed island-wide) and Aedes aegypti (located in southern Taiwan). The aims of this study were to (1) find out similarities and differences in epidemiologic characteristics between indigenous dengue cases in southern and other regions of Taiwan, and (2) compare case fatality rates and incidence rates of dengue cases in outbreak cities in southern and non-southern Taiwan. Methods and materials: The data was obtained from Taiwan centers for disease control. Demographic data was from statistics of Ministry of Interior. Epidemiologic characteristics, case fatality rates and incidence rates of 733 indigenous dengue cases from Aedes albopictus prevalent regions (Taipei, New Taipei, Taichung, and Taoyuan Cities) and 74309 indigenous dengue cases from southern Taiwan (Kaohsiung, Tainan, and Pingtung Cities) during 1998–2019 were analyzed by SAS 9.4 and QGIS 3.8. Results: Dengue clusters were frequently detected in southern Taiwan, while cases in other regions were more likely to be sporadic. Age distribution for cases in southern Taiwan and other regions peaked at 50–59 years (N = 13576, 18.27%) and 30–39 years (N = 159, 21.69%), respectively with statistical significance (p < 0.001). The percentage of male dengue patients in southern Taiwan was significantly lower than those in other regions [N = 36685 (N = 49.37%) vs. N = 403 (54.98%)] (p < 0.0025). Case fatality rates (CFRs) of dengue in southern Taiwan in 2002, 2014, 2015, and 2016 were 0.36%, 0.14%, 0.53%, 1.30%, much higher than CFR in Aedes albopictus prevalent area. Incidence rate of indigenous cases per 100 thousand population in southern Taiwan in 2002, 2014, 2015, and 2016 (95.57, 279.03, 778.90, 6.78) were much higher than those in other regions (0.26, 0.43, 2.55, 0.05). Conclusion: Dengue in Aedes aegypti-affected areas resulted in larger-scale and more severe epidemics involving more numbers of clusters than in areas with only Aedes albopictus. Therefore, more active surveillance and early detection of dengue cases in Aedes aegypti distributed areas is needed.