Vector Surveillance Data Research Articles

Are they there, how many, and how big? Investigating potential trap biases in the surveillance of La Crosse virus vectors.

Several methods of mosquito collection are used for the surveillance of the primary La Crosse virus (LACV) vectors, Aedes triseriatus (Say, 1823), Ae. albopictus (Skuse, 1895), and Ae. japonicus (Theobald, 1901). However, little is known about how the choice of collection method may confound inferences made from LACV vector surveillance data. Therefore, the objective of this study was to investigate potential biases in the surveillance of LACV vectors using the Biogents BG-Sentinel 2 (BGS), CDC-Light Trap (CDC-LT), Biogents Gravid Aedes Trap (BG-GAT), and standard oviposition cup (ovicup). The traps were deployed simultaneously at 10 sites in Knovxille, Tennessee, USA for 20 consecutive weeks. Surveillance results differed widely among the traps, demonstrating a strong potential for trap biases in LACV vector surveillance. The BGS and CDC-LT were effective for collecting Ae. albopictus but were not sensitive to the presence of Ae. triseriatus or Ae. japonicus. The ovicup was the best trap for detecting Ae. triseriatus, while the BG-GAT was the only trap that regularly collected Ae. japonicus. Surveillance conducted with the CDC-LT or BGS indicated that Ae. albopictus was dominant at all sites, but the ovicup and BG-GAT suggested a much larger relative abundance of Ae. triseriatus and Ae. japonicus, respectively. Aedes albopictus and Ae. triseriatus collected in the BG-GAT were significantly larger than those collected from the BGS and CDC-LT, indicating that the traps sampled different sub-populations. A multi-method surveillance approach is recommended to reduce potential biases when conducting surveillance of LACV vectors.

Ecologic, Geoclimatic, and Genomic Factors Modulating Plague Epidemics in Primary Natural Focus, Brazil.

Plague is a deadly zoonosis that still poses a threat in many regions of the world. We combined epidemiologic, host, and vector surveillance data collected during 1961-1980 from the Araripe Plateau focus in northeastern Brazil with ecologic, geoclimatic, and Yersinia pestis genomic information to elucidate how these factors interplay in plague activity. We identified well-delimited plague hotspots showing elevated plague risk in low-altitude areas near the foothills of the plateau's concave sectors. Those locations exhibited distinct precipitation and vegetation coverage patterns compared with the surrounding areas. We noted a seasonal effect on plague activity, and human cases linearly correlated with precipitation and rodent and flea Y. pestis positivity rates. Genomic characterization of Y. pestis strains revealed a foundational strain capable of evolving into distinct genetic variants, each linked to temporally and spatially constrained plague outbreaks. These data could identify risk areas and improve surveillance in other plague foci within the Caatinga biome.

Machine Learning Modeling of Aedes albopictus Habitat Suitability in the 21st Century

The Asian tiger mosquito, Aedes albopictus, is an important vector of arboviruses that cause diseases such as dengue, chikungunya, and zika. The vector is highly invasive and adapted to survive in temperate northern territories outside its native tropical and sub-tropical range. Climate and socio-economic change are expected to facilitate its range expansion and exacerbate the global vector-borne disease burden. To project shifts in the global habitat suitability of the vector, we developed an ensemble machine learning model, incorporating a combination of a Random Forest and XGBoost binary classifiers, trained with a global collection of vector surveillance data and an extensive set of climate and environmental constraints. We demonstrate the reliable performance and wide applicability of the ensemble model in comparison to the known global presence of the vector, and project that suitable habitats will expand globally, most significantly in the northern hemisphere, putting at least an additional billion people at risk of vector-borne diseases by the middle of the 21st century. We project several highly populated areas of the world will be suitable for Ae. albopictus populations, such as the northern parts of the USA, Europe, and India by the end of the century, which highlights the need for coordinated preventive surveillance efforts of potential entry points by local authorities and stakeholders.

Assessing the associations between Aedes larval indices and dengue risk in Kalutara district, Sri Lanka: a hierarchical time series analysis from 2010 to 2019

BackgroundDengue is a major public health problem in Sri Lanka. Aedes vector surveillance and monitoring of larval indices are routine, long-established public health practices in the country. However, the association between Aedes larval indices and dengue incidence is poorly understood. It is crucial to evaluate lagged effects and threshold values of Aedes larval indices to set pragmatic targets for sustainable vector control interventions.MethodsMonthly Aedes larval indices and dengue cases in all 10 Medical Officer of Health (MOH) divisions in Kalutara district were obtained from 2010 to 2019. Using a novel statistical approach, a distributed lag non-linear model and a two-staged hierarchical meta-analysis, we estimated the overall non-linear and delayed effects of the Premise Index (PI), Breteau Index (BI) and Container Index (CI) on dengue incidence in Kalutara district. A set of MOH division-specific variables were evaluated within the same meta-analytical framework to determine their moderator effects on dengue risk. Using generalized additive models, we assessed the utility of Aedes larval indices in predicting dengue incidence.ResultsWe found that all three larval indices were associated with dengue risk at a lag of 1 to 2 months. The relationship between PI and dengue was homogeneous across MOH divisions, whereas that with BI and CI was heterogeneous. The threshold values of BI, PI and CI associated with dengue risk were 2, 15 and 45, respectively. All three indices showed a low to moderate accuracy in predicting dengue risk in Kalutara district.ConclusionsThis study showed the potential of vector surveillance information in Kalutara district in developing a threshold-based, location-specific early warning system with a lead time of 2 months. The estimated thresholds are nonetheless time-bound and may not be universally applicable. Whenever longitudinal vector surveillance data areavailable, the methodological framework we propose here can be used to estimate location-specific Aedes larval index thresholds in any other dengue-endemic setting.Graphical

Joint spatiotemporal modelling reveals seasonally dynamic patterns of Japanese encephalitis vector abundance across India.

Predicting vector abundance and seasonality, key components of mosquito-borne disease (MBD) hazard, is essential to determine hotspots of MBD risk and target interventions effectively. Japanese encephalitis (JE), an important MBD, is a leading cause of viral encephalopathy in Asia with 100,000 cases estimated annually, but data on the principal vector Culex tritaeniorhynchus is lacking. We developed a Bayesian joint-likelihood model that combined information from available vector occurrence and abundance data to predict seasonal vector abundance for C. tritaeniorhynchus (a constituent of JE hazard) across India, as well as examining the environmental drivers of these patterns. Using data collated from 57 locations from 24 studies, we find distinct seasonal and spatial patterns of JE vector abundance influenced by climatic and land use factors. Lagged precipitation, temperature and land use intensity metrics for rice crop cultivation were the main drivers of vector abundance, independent of seasonal, or spatial variation. The inclusion of environmental factors and a seasonal term improved model prediction accuracy (mean absolute error [MAE] for random cross validation = 0.48) compared to a baseline model representative of static hazard predictions (MAE = 0.95), signalling the importance of seasonal environmental conditions in predicting JE vector abundance. Vector abundance varied widely across India with high abundance predicted in northern, north-eastern, eastern, and southern regions, although this ranged from seasonal (e.g., Uttar Pradesh, West Bengal) to perennial (e.g., Assam, Tamil Nadu). One-month lagged predicted vector abundance was a significant predictor of JE outbreaks (odds ratio 2.45, 95% confidence interval: 1.52-4.08), highlighting the possible development of vector abundance as a proxy for JE hazard. We demonstrate a novel approach that leverages information from sparse vector surveillance data to predict seasonal vector abundance-a key component of JE hazard-over large spatial scales, providing decision-makers with better guidance for targeting vector surveillance and control efforts.

Transient transmission of Chikungunya virus in Singapore exemplifies successful mitigation of severe epidemics in a vulnerable population

ObjectivesSingapore experienced two major outbreaks of chikungunya in 2008-09 and 2013-14. Despite repeated virus introductions, fresh local outbreaks have not emerged after 2014. The present study reviews the success of chikungunya control in Singapore, despite repeated introduction of virus strains, presence of competent vectors and an immunologically naïve population. MethodsChikungunya virus (CHIKV) sequences (421 envelope 1 genes and 56 polyproteins) were analysed to distinguish the indigenous virus groups from 2008 to 2020. Vector surveillance data was used to incriminate the vector/s associated with local outbreaks. The population exposure to CHIKV was determined by assessing the seroprevalence status in three cohorts of sera collected in 2009 (n=2,008), 2013 (n=2,000) and 2017 (n=3,615). ResultsFour distinct groups of CHIKV of East, Central and South African genotype have mainly circulated since 2008, transmitted primarily by Aedes albopictus. The age weighted CHIKV IgG prevalence rates were low (1-5%) and showed a non-significant increase from 2009 to 2013, but a significant decrease in 2017. In contrast, the prevalence of CHIKV neutralising antibodies in the population increased significantly from 2009 to 2013, with no significant change in 2017, but the levels remained below 2%. ConclusionsThe evidence suggested that surveillance and vector control strategies implemented were robust to avert severe epidemics, despite repeated introduction of virus strains, presence of competent vectors and an immunologically naïve population.

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.

Vector Surveillance, Host Species Richness, and Demographic Factors as West Nile Disease Risk Indicators.

West Nile virus (WNV) is the most common arthropod-borne virus (arbovirus) in the United States (US) and is the leading cause of viral encephalitis in the country. The virus has affected tens of thousands of US persons total since its 1999 North America introduction, with thousands of new infections reported annually. Approximately 1% of humans infected with WNV acquire neuroinvasive West Nile Disease (WND) with severe encephalitis and risk of death. Research describing WNV ecology is needed to improve public health surveillance, monitoring, and risk assessment. We applied Bayesian joint-spatiotemporal modeling to assess the association of vector surveillance data, host species richness, and a variety of other environmental and socioeconomic disease risk factors with neuroinvasive WND throughout the conterminous US. Our research revealed that an aging human population was the strongest disease indicator, but climatic and vector-host biotic interactions were also significant in determining risk of neuroinvasive WND. Our analysis also identified a geographic region of disproportionately high neuroinvasive WND disease risk that parallels the Continental Divide, and extends southward from the US–Canada border in the states of Montana, North Dakota, and Wisconsin to the US–Mexico border in western Texas. Our results aid in unraveling complex WNV ecology and can be applied to prioritize disease surveillance locations and risk assessment.

Potential for online crowdsourced biological recording data to complement surveillance for arthropod vectors.

Voluntary contributions by citizen scientists can gather large datasets covering wide geographical areas, and are increasingly utilized by researchers for multiple applications, including arthropod vector surveillance. Online platforms such as iNaturalist accumulate crowdsourced biological observations from around the world and these data could also be useful for monitoring vectors. The aim of this study was to explore the availability of observations of important vector taxa on the iNaturalist platform and examine the utility of these data to complement existing vector surveillance activities. Of ten vector taxa investigated, records were most numerous for mosquitoes (Culicidae; 23,018 records, 222 species) and ticks (Ixodida; 16,214 records, 87 species), with most data from 2019-2020. Case studies were performed to assess whether images associated with records were of sufficient quality to identify species and compare iNaturalist observations of vector species to the known situation at the state, national and regional level based on existing published data. Firstly, tick data collected at the national (United Kingdom) or state (Minnesota, USA) level were sufficient to determine seasonal occurrence and distribution patterns of important tick species, and were able to corroborate and complement known trends in tick distribution. Importantly, tick species with expanding distributions (Haemaphysalis punctata in the UK, and Amblyomma americanum in Minnesota) were also detected. Secondly, using iNaturalist data to monitor expanding tick species in Europe (Hyalomma spp.) and the USA (Haemaphysalis longicornis), and invasive Aedes mosquitoes in Europe, showed potential for tracking these species within their known range as well as identifying possible areas of expansion. Despite known limitations associated with crowdsourced data, this study shows that iNaturalist can be a valuable source of information on vector distribution and seasonality that could be used to supplement existing vector surveillance data, especially at a time when many surveillance programs may have been interrupted by COVID-19 restrictions.

Understanding Mosquito Surveillance Data for Analytic Efforts: A Case Study

Mosquito surveillance data can be used for predicting mosquito distribution and dynamics as they relate to human disease. Often these data are collected by independent agencies and aggregated to state and national level portals to characterize broad spatial and temporal dynamics. These larger repositories may also share the data for use in mosquito and/or disease prediction and forecasting models. Assumed, but not always confirmed, is consistency of data across agencies. Subtle differences in reporting may be important for development and the eventual interpretation of predictive models. Using mosquito vector surveillance data from Arizona as a case study, we found differences among agencies in how trapping practices were reported. Inconsistencies in reporting may interfere with quantitative comparisons if the user has only cursory familiarity with mosquito surveillance data. Some inconsistencies can be overcome if they are explicit in the metadata while others may yield biased estimates if they are not changed in how data are recorded. Sharing of metadata and collaboration between modelers and vector control agencies is necessary for improving the quality of the estimations. Efforts to improve sharing, displaying, and comparing vector data from multiple agencies are underway, but existing data must be used with caution.

Incidence and epidemiological features of dengue in Sabah, Malaysia.

In South East Asia, dengue epidemics have increased in size and geographical distribution in recent years. We examined the spatiotemporal distribution and epidemiological characteristics of reported dengue cases in the predominantly rural state of Sabah, in Malaysian Borneo–an area where sylvatic and urban circulation of pathogens are known to intersect. Using a public health data set of routinely notified dengue cases in Sabah between 2010 and 2016, we described demographic and entomological risk factors, both before and after a 2014 change in the clinical case definition for the disease. Annual dengue incidence rates were spatially variable over the 7-year study period from 2010–2016 (state-wide mean annual incidence of 21 cases/100,000 people; range 5-42/100,000), but were highest in rural localities in the western districts of the state (Kuala Penyu, Nabawan, Tenom and Kota Marudu). Eastern districts exhibited lower overall dengue rates, although a high proportion of severe (haemorrhagic) dengue cases (44%) were focused in Sandakan and Tawau. Dengue incidence was highest for those aged between 10 and 29 years (24/100,000), and was slightly higher for males compared to females. Available vector surveillance data indicated that during large outbreaks in 2015 and 2016 the mosquito Aedes albopictus was more prevalent in both urban and rural households (House Index of 64%) than Ae. aegypti (15%). Demographic patterns remained unchanged both before and after the dengue case definition was changed; however, in the years following the change, reported case numbers increased substantially. Overall, these findings suggest that dengue outbreaks in Sabah are increasing in both urban and rural settings. Future studies to better understand the drivers of risk in specific age groups, genders and geographic locations, and to test the potential role of Ae. albopictus in transmission, may help target dengue prevention and control efforts.

Detecting space-time clusters of dengue fever in Panama after adjusting for vector surveillance data.

Long term surveillance of vectors and arboviruses is an integral aspect of disease prevention and control systems in countries affected by increasing risk. Yet, little effort has been made to adjust space-time risk estimation by integrating disease case counts with vector surveillance data, which may result in inaccurate risk projection when several vector species are present, and when little is known about their likely role in local transmission. Here, we integrate 13 years of dengue case surveillance and associated Aedes occurrence data across 462 localities in 63 districts to estimate the risk of infection in the Republic of Panama. Our exploratory space-time modelling approach detected the presence of five clusters, which varied by duration, relative risk, and spatial extent after incorporating vector species as covariates. The Ae. aegypti model contained the highest number of districts with more dengue cases than would be expected given baseline population levels, followed by the model accounting for both Ae. aegypti and Ae. albopictus. This implies that arbovirus case surveillance coupled with entomological surveillance can affect cluster detection and risk estimation, potentially improving efforts to understand outbreak dynamics at national scales.

Incidence dynamics and investigation of key interventions in a dengue outbreak in Ningbo City, China.

BackgroundThe reported incidence of dengue fever increased dramatically in recent years in China. This study aimed to investigate and to assess the effectiveness of intervention implemented in a dengue outbreak in Ningbo City, Zhejiang Province, China.MethodsData of a dengue outbreak were collected in Ningbo City in China by a field epidemiological survey according to a strict protocol and case definition. Serum specimens of all cases were collected for diagnosis and the virological characteristics were detected by using polymerase chain reaction (PCR) and gene sequencing. Vector surveillance was implemented during the outbreak to collect the larva and adult mosquito densities to calculate the Breteau Index (BI) and human biting rate (HBR), respectively. Data of monthly BI and light-trap density in 2018 were built to calculate the seasonality of the vector. A transmission mathematical model was developed to dynamic the incidence of the disease. The parameters of the model were estimated by the data of the outbreak and vector surveillance data in 2018. The effectiveness of the interventions implemented during the outbreak was assessed by the data and the modelling.ResultsFrom 11 August to 8 September, 2018, a dengue outbreak was reported with 27 confirmed cases in a population of 5536-people community (community A) of Ningbo City. Whole E gene sequences were obtained from 24 cases and were confirmed as dengue virus type 1 (DENV-1). The transmission source of the outbreak was origin from community B where a dengue case having the same E gene sequence was onset on 30 July. Aedes albopictus was the only vector species in the area. The value of BI and HBR was 57.5 and 12 per person per hour respectively on 18 August, 2018 and decreased dramatically after interventions. The transmission model fitted well (χ2 = 6.324, P = 0.388) with the reported cases data. With no intervention, the total simulated number of the cases would be 1728 with a total attack rate (TAR) of 31.21% (95%CI: 29.99%– 32.43%). Case isolation and larva control (LC) have almost the same TAR and duration of outbreak (DO) as no intervention. Different levels of reducing HBR (rHBR) had different effectiveness with TARs ranging from 1.05% to 31.21% and DOs ranging from 27 days to 102 days. Adult vector control (AVC) had a very low TAR and DO. “LC+AVC” had a similar TAR and DO as that of AVC. “rHBR100%+LC”, “rHBR100%+AVC”, “rHBR100%+LC+AVC” and “rHBR100%+LC+AVC+Iso” had the same effectiveness.ConclusionsWithout intervention, DENV-1 could be transmitted rapidly within a short period of time and leads to high attack rate in community in China. AVC or rHBR should be recommended as primary interventions to control rapid transmission of the dengue virus at the early stage of an outbreak.

Sequence-based identification of Anopheles species in eastern Ethiopia

BackgroundThe recent finding of a typically non-African Anopheles species in eastern Ethiopia emphasizes the need for detailed species identification and characterization for effective malaria vector surveillance. Molecular approaches increase the accuracy and interoperability of vector surveillance data. To develop effective molecular assays for Anopheles identification, it is important to evaluate different genetic loci for the ability to characterize species and population level variation. Here the utility of the internal transcribed spacer 2 (ITS2) and cytochrome oxidase I (COI) loci for detection of Anopheles species from understudied regions of eastern Ethiopia was investigated.MethodsAdult mosquitoes were collected from the Harewe locality (east) and Meki (east central) Ethiopia. PCR and Sanger sequencing were performed for portions of the ITS2 and COI loci. Both NCBI’s Basic Local Alignment Search tool (BLAST) and phylogenetic analysis using a maximum-likelihood approach were performed to identify species of Anopheles specimens.ResultsTwo species from the east Ethiopian collection, Anopheles arabiensis and Anopheles pretoriensis were identified. Analyses of ITS2 locus resulted in delineation of both species. In contrast, analysis of COI locus could not be used to delineate An. arabiensis from other taxa in Anopheles gambiae complex, but could distinguish An. pretoriensis sequences from sister taxa.ConclusionThe lack of clarity from COI sequence analysis highlights potential challenges of species identification within species complexes. These results provide supporting data for the development of molecular assays for delineation of Anopheles in east Ethiopia.

A novel entomological index, Aedes aegypti Breeding Percentage, reveals the geographical spread of the dengue vector in Singapore and serves as a spatial risk indicator for dengue

BackgroundAedes aegypti is an efficient primary vector of dengue, and has a heterogeneous distribution in Singapore. Aedes albopictus, a poor vector of dengue, is native and ubiquitous on the island. Though dengue risk follows the dispersal of Ae. aegypti, the spatial distribution of the vector is often poorly characterized. Here, based on the ubiquitous presence of Ae. albopictus, we developed a novel entomological index, Ae. aegypti Breeding Percentage (BP), to demonstrate the expansion of Ae. aegypti into new territories that redefined the dengue burden map in Singapore. We also determined the thresholds of BP that render the specific area higher risk of dengue transmission.MethodsWe performed analysis of dengue fever incidence and Aedes mosquito breeding in Singapore by utilizing island-wide dengue cases and vector surveillance data from 2003 to 2013. The percentage of Ae. aegypti breeding among the total Aedes breeding habitats (BP), and the reported number of dengue fever cases in each year were calculated for each residential grid.ResultsThe BP of grids, for every year over the 11-year study period, had a consistent positive correlation with the annual case counts. Our findings suggest that the geographical expansion of Ae. aegypti to previously “non-dengue” areas have contributed substantially to the recent dengue fever incidence in Singapore. Our analysis further indicated that non-endemic areas in Singapore are likely to be at risk of dengue fever outbreaks beyond an Ae. aegypti BP of 20%.ConclusionsOur analyses indicate areas with increasing Ae. aegypti BP are likely to become more vulnerable to dengue outbreaks. We propose the usage of Ae. aegypti BP as a factor for spatial risk stratification of dengue fever in endemic countries. The Ae. aegypti BP could be recommended as an indicator for decision making in vector control efforts, and also be used to monitor the geographical expansion of Ae. aegypti.

Using a One Health Approach to Build an Integrated Surveillance Data System

Objective: Integrate and streamline the collection and analysis of environmental, veterinary, and vector zoonotic data using a One Health approach to data system development.Introduction: Environmental Public Health Zoonotic Disease surveillance includes veternary, environmental, and vector data. Surveillance systems within each sector may appear disparate from each other, although they are actually complimentaly and closely allied. Consolidating and integrating data in to one application can be challenging, but there are commonalities shared by all. The goal of the One Health Integrated Data Sysytem is to standardize data collection, streamline data entry, and integrate these sectors in to one application.Methods: Data Assessment. An assessment of each surveillance function was carried out to evaluate data types and needs.Identify Commonalities. Common data was identified across each of the surveillance areas.Identify Unique Data. Data unique to specific surveillance efforts was identified.Build Data Structure. A back-end data structure was developed that reflected the data needs from each surveillance area.Build Data Entry Interfaces. Data entry interfaces were developed to meet the needs of each surveillance area.Build Data QC. Procedures were developed that run several quality control checks on the data.Build Data Eports. To allow users to carry out more extensive analysis of data, customized data exports were built.Results: This data integration project resulted in:● Reduced time spent entering and managing data● Improved data entry error rates● Increased visibility through automated program metrics● Improved access to data from data usersConclusions: Integrating data and building a data system that reflects the diversity of environmental, veterinary, and vector surveillance data is doable using off-the-shelf database tools. The process of integrating data and building the data structure results in a more intimate understanding of the data revealing opportunities for improving data quality.

Taiwan's Travel and Border Health Measures in Response to Zika.

Zika virus has recently emerged as a worldwide public health concern. Travel and border health measures stand as one of the main strategies and frontline defenses in responding to international epidemics. As of October 31, 2016, Taiwan has reported 13 imported cases, 5 of which were detected through routine entry screening and active monitoring at international airports. This article shares Taiwan's disease surveillance activities at designated points of entry and travel and border health measures in response to Zika. The Taiwan government collaborates with its tourism industry to disseminate information about precautionary measures and encourages tour guides to report suspected individuals or events to activate early response measures. Taiwan also engages in vector control activities at points of entry, including targeting aircraft from countries where vector-borne diseases are endemic, implementing mosquito sweep measures, and collecting vector surveillance data. In future emerging and reemerging disease events, entry surveillance at designated points of entry may enable early detection of diseases of international origin and more rapid activation of public health preparedness activities and international collaboration. Taiwan will continue to maximize border and travel health measures in compliance with IHR (2005) requirements, which rely on continued risk assessment, practical implementation activities, and engagement with all stakeholders.

Vector-control response in a post-flood disaster setting, Honiara, Solomon Islands, 2014.

The close quartering and exposed living conditions in evacuation centres and the potential increase in vector density after flooding in Solomon Islands resulted in an increased risk of exposure for the occupants to vectorborne diseases. In April 2014, Solomon Islands experienced a flash flooding event that affected many areas and displaced a large number of people. In the capital, Honiara, nearly 10 000 people were housed in emergency evacuation centres at the peak of the post-flood emergency. At the time of the floods, the number of dengue cases was increasing, following a record outbreak in 2013. The National Vector Borne Disease Control Programme with the assistance of the World Health Organization implemented an emergency vector-control response plan to provide protection to the at-risk populations in the evacuation centres. The National Surveillance Unit also activated an early warning disease surveillance system to monitor communicable diseases, including dengue and malaria. Timely and strategic application of the emergency interventions probably prevented an increase in dengue and malaria cases in the affected areas. Rapid and appropriate precautionary vector-control measures applied in a post-natural disaster setting can prevent and mitigate vectorborne disease incidences. Collecting vector surveillance data allows better analysis of vector-control operations' effectiveness.

Re-Model the Relation of Vector Indices, Meteorological Factors and Dengue Fever

Background: Dengue is the most rapidly expanding and spreading mosquito-borne viral disease in tropical and subtropical countries. In Taiwan, dengue incidence clustered in Southern part, especially Kaohsiung in the past decade. Aim: The spatial and temporal patterns of dengue transmission in Taiwan from 2005 to 2012 were examined to investigate the occurrence of dengue fever (DF) patients and its association with immature and adult mosquito indices, and its interaction with meteorological factors and household density. Methods: Three databases were spatially and temporally linked, including the comprehensive chart records of DF cases and vector surveillance data in Kaohsiung, as well as the meteorological and environmental information from 2005 to 2012. A case-crossover study design was used to explore the effects of mosquito indices and weather on the risks of DF, and conditional logistic regression was applied to estimate the odds ratios (OR). Results: Results showed immature mosquito indices had significant positive association with DF in the medium and high household density areas (e.g., adjusted ORs of Breteau index were 1.04, 95% CI=[1.02, 1.06] and 1.06, CI=[1.04, 1.08] respectively), while adult mosquito index was significant to all low/med/high household densities (adjusted ORs of Aedes aegypti index were 1.29, CI=[1.23,1.36]; 1.49, CI=[1.37,1.61] and 1.3, CI=[1.21,1.39] respectively). Meanwhile, combination with 2-week lag rainfall, 2-month lag rainfall, 2-week lag temperature and relative humidity, resulted better prediction of DF incidence. Conclusion: Meteorological conditions affect DF occurrence in a nonlinear way, and a single time-point rainfall variable is insufficient to fit it. Our study suggested that short-lag (last 2 weeks) conditions of moderate rainfall, moderate temperature and high humidity, in combination with a long-lag heavy rainfall were related to higher probability of DF incidence. BI and CI are useful predictors for DF occurrence in medium and high household density areas, but not in the low density areas.

Three-Month Real-Time Dengue Forecast Models: An Early Warning System for Outbreak Alerts and Policy Decision Support in Singapore.

Background:With its tropical rainforest climate, rapid urbanization, and changing demography and ecology, Singapore experiences endemic dengue; the last large outbreak in 2013 culminated in 22,170 cases. In the absence of a vaccine on the market, vector control is the key approach for prevention.Objectives:We sought to forecast the evolution of dengue epidemics in Singapore to provide early warning of outbreaks and to facilitate the public health response to moderate an impending outbreak.Methods:We developed a set of statistical models using least absolute shrinkage and selection operator (LASSO) methods to forecast the weekly incidence of dengue notifications over a 3-month time horizon. This forecasting tool used a variety of data streams and was updated weekly, including recent case data, meteorological data, vector surveillance data, and population-based national statistics. The forecasting methodology was compared with alternative approaches that have been proposed to model dengue case data (seasonal autoregressive integrated moving average and step-down linear regression) by fielding them on the 2013 dengue epidemic, the largest on record in Singapore.Results:Operationally useful forecasts were obtained at a 3-month lag using the LASSO-derived models. Based on the mean average percentage error, the LASSO approach provided more accurate forecasts than the other methods we assessed. We demonstrate its utility in Singapore’s dengue control program by providing a forecast of the 2013 outbreak for advance preparation of outbreak response.Conclusions:Statistical models built using machine learning methods such as LASSO have the potential to markedly improve forecasting techniques for recurrent infectious disease outbreaks such as dengue.Citation:Shi Y, Liu X, Kok SY, Rajarethinam J, Liang S, Yap G, Chong CS, Lee KS, Tan SS, Chin CK, Lo A, Kong W, Ng LC, Cook AR. 2016. Three-month real-time dengue forecast models: an early warning system for outbreak alerts and policy decision support in Singapore. Environ Health Perspect 124:1369–1375; http://dx.doi.org/10.1289/ehp.1509981