Micro-scale urbanization-based risk factors for dengue epidemics.

Dengue is one of the world's most rapidly spreading mosquito-borne viral diseases. As it is found mostly in urban and semi-urban areas, urbanization and associated human activities that affect the environment and larval habitats could become risk factors (e.g., lane width, conditions of street ditches) for the spread of dengue. However, there are currently no systematic studies of micro-scale urbanization-based risk factors for the spread of dengue epidemics. We describe the study area, two micro-scale environmental risk factors associated with urbanization, and meteorological data. Since the observations involve spatial and temporal correlations, we also use some statistical methods for the analysis of spatial and spatial-temporal data for the relationship between urbanization and dengue. In this study, we analyzed data from Kaohsiung, a densely populated city in southern Taiwan, and found a positive correlation between environmental risk factors associated with urbanization (ditches positive for mosquito larvae and closely packed streets termed "dengue lanes") and clustering effects in dengue cases. The statistical analysis also revealed that the occurrence of positive ditches was significantly associated with that of dengue lanes in the study area. The relationship between climate variables and positive ditches was also analyzed in this paper, indicating a relationship between dengue and both rainfall and temperature, with temperature having a greater effect. Overall, this work is immediately relevant and applicable for policymakers in government, who will need to reduce these favorable habitats for vector-born disease spreaders and implement regulations for new urban constructions to thus reduce dengue spread in future outbreaks.

Use of unmanned ground vehicle systems in urbanized zones: A study of vector Mosquito surveillance in Kaohsiung.

Dengue fever is a vector-borne disease that has become a serious global public health problem over the past decade. An essential aspect of controlling and preventing mosquito-borne diseases is reduction of mosquito density. Through the process of urbanization, sewers (ditches) have become easy breeding sources of vector mosquitoes. In this study, we, for the first time, used unmanned ground vehicle systems (UGVs) to enter ditches in urban areas to observe vector mosquito ecology. We found traces of vector mosquitoes in ~20.7% of inspected ditches, suggesting that these constitute viable breeding sources of vector mosquitoes in urban areas. We also analyzed the average gravitrap catch of five administrative districts in Kaohsiung city from May to August 2018. The gravitrap indices of Nanzi and Fengshan districts were above the expected average (3.26), indicating that the vector mosquitoes density in these areas is high. Using the UGVs to detect positive ditches within the five districts followed by insecticide application generally yielded good control results. Further improving the high-resolution digital camera and spraying system of the UGVs may be able to effectively and instantly monitor vector mosquitoes and implement spraying controls. This approach may be suitable to solve the complex and difficult task of detecting mosquito breeding sources in urban ditches.

Correction: The Critical Role of Early Dengue Surveillance and Limitations of Clinical Reporting - Implications for Non-Endemic Countries.

[This corrects the article DOI: 10.1371/journal.pone.0160230.].

Time-Lagging Interplay Effect and Excess Risk of Meteorological/Mosquito Parameters and Petrochemical Gas Explosion on Dengue Incidence.

In Kaohsiung, a metropolitan city in Taiwan at high risk of dengue epidemic, weather factors combined with an accidental petrochemical gas explosion (PGE) may affect mosquito‒human dynamics in 2014. Generalized estimating equations with lagged-time Poisson regression analyses were used to evaluate the effect of meteorological/mosquito parameters and PGE on dengue incidences (2000-2014) in Kaohsiung. Increased minimum temperatures rendered a 2- and 3-month lagging interactive effect on higher dengue risks, and higher rainfall exhibited a 1- and 2-month lagging interplay effect on lower risks (interaction, P ≤ 0.001). The dengue risk was significantly higher than that in a large-scale outbreak year (2002) from week 5 after PGE accident in 2014 (2.9‒8.3-fold for weeks 5‒22). The greatest cross-correlation of dengue incidences in the PGE-affected and PGE-neighboring districts was identified at weeks 1 after the PGE (rs = 0.956, P < 0.001). Compared with the reference years, the combined effect of minimum temperature, rainfall, and PGE accounted for 75.1% of excess dengue risk in 2014. In conclusion, time-lagging interplay effects from minimum temperature and rainfall may be respectively associated with early and near environments facilitating dengue transmission. Events that interact with weather and influence mosquito‒human dynamics, such as PGEs, should not be ignored in dengue prevention and control.

The Critical Role of Early Dengue Surveillance and Limitations of Clinical Reporting - Implications for Non-Endemic Countries.

The increasing dengue burden and epidemic severity worldwide have highlighted the need to improve surveillance. In non-endemic areas such as Taiwan, where outbreaks start mostly with imported cases from Southeast Asia, a closer examination of surveillance dynamics to detect cases early is necessary. To evaluate problems with dengue surveillance and investigate the involvement of different factors at various epidemic stages, we investigated 632 laboratory-confirmed indigenous dengue cases in Kaohsiung City, Taiwan during 2009-2010. The estimated sensitivity of clinical surveillance was 82.4% (521/632). Initially, the modified serological surveillance (targeting only the contacts of laboratory-confirmed dengue cases) identified clinically unrecognized afebrile cases in younger patients who visited private clinics and accounted for 30.4% (35/115) of the early-stage cases. Multivariate regression indicated that hospital/medical center visits [Adjusted Odds Ratio (aOR): 11.6, 95% confidence interval (CI): 6.3-21.4], middle epidemic stage [aOR: 2.4 (1.2-4.7)], fever [aOR: 2.3 (2.3-12.9)], and musculo-articular pain [aOR: 1.9 (1.05-3.3)] were significantly associated with clinical reporting. However, cases with pruritus/rash [aOR: 0.47 (0.26-0.83)] and diarrhea [aOR: 0.47 (0.26-0.85)] were underreported. In conclusion, multiple factors contributed to dengue surveillance problems. To prevent a large-scale epidemic and minimize severe dengue cases, there is a need for integrated surveillance incorporating entomological, clinical, serological, and virological surveillance systems to detect early cases, followed by immediate prevention and control measures and continuous evaluation to ensure effectiveness. This effort will be particularly important for an arbovirus, such as Zika virus, with a high asymptomatic infection ratio. For dengue- non-endemic countries, we recommend serological surveillance be implemented in areas with high Aedes mosquito indices or many breeding sites. Syndromic surveillance, spatial analysis and monitoring changes in epidemiological characteristics using a geographical information system, as well as epidemic prediction models involving epidemiological, meteorological and environmental variables will be helpful for early risk communication to increase awareness.

Re-assess Vector Indices Threshold as an Early Warning Tool for Predicting Dengue Epidemic in a Dengue Non-endemic Country.

BACKGROUND: Despite dengue dynamics being driven by complex interactions between human hosts, mosquito vectors and viruses that are influenced by climate factors, an operational model that will enable health authorities to anticipate the outbreak risk in a dengue non-endemic area has not been developed. The objectives of this study were to evaluate the temporal relationship between meteorological variables, entomological surveillance indices and confirmed dengue cases; and to establish the threshold for entomological surveillance indices including three mosquito larval indices [Breteau (BI), Container (CI) and House indices (HI)] and one adult index (AI) as an early warning tool for dengue epidemic. METHODOLOGY/PRINCIPAL FINDINGS: Epidemiological, entomological and meteorological data were analyzed from 2005 to 2012 in Kaohsiung City, Taiwan. The successive waves of dengue outbreaks with different magnitudes were recorded in Kaohsiung City, and involved a dominant serotype during each epidemic. The annual indigenous dengue cases usually started from May to June and reached a peak in October to November. Vector data from 2005-2012 showed that the peak of the adult mosquito population was followed by a peak in the corresponding dengue activity with a lag period of 1-2 months. Therefore, we focused the analysis on the data from May to December and the high risk district, where the inspection of the immature and mature mosquitoes was carried out on a weekly basis and about 97.9% dengue cases occurred. The two-stage model was utilized here to estimate the risk and time-lag effect of annual dengue outbreaks in Taiwan. First, Poisson regression was used to select the optimal subset of variables and time-lags for predicting the number of dengue cases, and the final results of the multivariate analysis were selected based on the smallest AIC value. Next, each vector index models with selected variables were subjected to multiple logistic regression models to examine the accuracy of predicting the occurrence of dengue cases. The results suggested that Model-AI, BI, CI and HI predicted the occurrence of dengue cases with 83.8, 87.8, 88.3 and 88.4% accuracy, respectively. The predicting threshold based on individual Model-AI, BI, CI and HI was 0.97, 1.16, 1.79 and 0.997, respectively. CONCLUSION/SIGNIFICANCE: There was little evidence of quantifiable association among vector indices, meteorological factors and dengue transmission that could reliably be used for outbreak prediction. Our study here provided the proof-of-concept of how to search for the optimal model and determine the threshold for dengue epidemics. Since those factors used for prediction varied, depending on the ecology and herd immunity level under different geological areas, different thresholds may be developed for different countries using a similar structure of the two-stage model.