Construction sites as an important driver of dengue transmission: implications for disease control.

BACKGROUND: In 2013 and 2014, Singapore experienced its worst dengue outbreak known-to-date. Mosquito breeding in construction sites stood out as a probable risk factor due to its association with major dengue clusters in both years. We, therefore, investigated the contribution of construction sites to dengue transmission in Singapore, highlighting three case studies of large construction site-associated dengue clusters recorded during 2013-16. METHODS: The study included two components; a statistical analysis of cluster records from 2013 to 2016, and case studies of three biggest construction site-associated clusters. We explored the odds of construction site-associated clusters growing into major clusters and determined whether clusters seeded in construction sites demonstrated a higher tendency to expand into major clusters. DENV strains obtained from dengue patients residing in three major clusters were genotyped to determine whether the same strains expanded into the surroundings of construction sites. RESULTS: Despite less than 5% of total recorded clusters being construction site-associated, the odds of such clusters expanding into major clusters were 17.4 (2013), 9.2 (2014), 3.3 (2015) and 4.3 (2016) times higher than non-construction site clusters. Aedes premise index and average larvae count per habitat were also higher in construction sites than residential premises during the study period. The majority of cases in clusters associated with construction sites were residents living in the surroundings. Virus genotype data from three case study sites revealed a transmission link between the construction sites and the surrounding residential areas. CONCLUSIONS: Significantly high case burden and the probability of cluster expansion due to virus spill-over into surrounding areas suggested that construction sites play an important role as a driver of sustained dengue transmission. Our results emphasise that the management of construction-site associated dengue clusters should not be limited to the implicated construction sites, but be extended to the surrounding premises to prevent further transmission.

Mapping dengue risk in Singapore using Random Forest.

BACKGROUND: Singapore experiences endemic dengue, with 2013 being the largest outbreak year known to date, culminating in 22,170 cases. Given the limited resources available, and that vector control is the key approach for prevention in Singapore, it is important that public health professionals know where resources should be invested in. This study aims to stratify the spatial risk of dengue transmission in Singapore for effective deployment of resources. METHODOLOGY/PRINCIPAL FINDINGS: Random Forest was used to predict the risk rank of dengue transmission in 1km2 grids, with dengue, population, entomological and environmental data. The predicted risk ranks are categorized and mapped to four color-coded risk groups for easy operation application. The risk maps were evaluated with dengue case and cluster data. Risk maps produced by Random Forest have high accuracy. More than 80% of the observed risk ranks fell within the 80% prediction interval. The observed and predicted risk ranks were highly correlated ([Formula: see text]≥0.86, P <0.01). Furthermore, the predicted risk levels were in excellent agreement with case density, a weighted Kappa coefficient of more than 0.80 (P <0.01). Close to 90% of the dengue clusters occur in high risk areas, and the odds of cluster forming in high risk areas were higher than in low risk areas. CONCLUSIONS: This study demonstrates the potential of Random Forest and its strong predictive capability in stratifying the spatial risk of dengue transmission in Singapore. Dengue risk map produced using Random Forest has high accuracy, and is a good surveillance tool to guide vector control operations.

Co-distribution and co-infection of chikungunya and dengue viruses.

BACKGROUND: Chikungunya and dengue infections are spatio-temporally related. The current review aims to determine the geographic limits of chikungunya, dengue and the principal mosquito vectors for both viruses and to synthesise current epidemiological understanding of their co-distribution. METHODS: Three biomedical databases (PubMed, Scopus and Web of Science) were searched from their inception until May 2015 for studies that reported concurrent detection of chikungunya and dengue viruses in the same patient. Additionally, data from WHO, CDC and Healthmap alerts were extracted to create up-to-date global distribution maps for both dengue and chikungunya. RESULTS: Evidence for chikungunya-dengue co-infection has been found in Angola, Gabon, India, Madagascar, Malaysia, Myanmar, Nigeria, Saint Martin, Singapore, Sri Lanka, Tanzania, Thailand and Yemen; these constitute only 13 out of the 98 countries/territories where both chikungunya and dengue epidemic/endemic transmission have been reported. CONCLUSIONS: Understanding the true extent of chikungunya-dengue co-infection is hampered by current diagnosis largely based on their similar symptoms. Heightened awareness of chikungunya among the public and public health practitioners in the advent of the ongoing outbreak in the Americas can be expected to improve diagnostic rigour. Maps generated from the newly compiled lists of the geographic distribution of both pathogens and vectors represent the current geographical limits of chikungunya and dengue, as well as the countries/territories at risk of future incursion by both viruses. These describe regions of co-endemicity in which lab-based diagnosis of suspected cases is of higher priority.

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.

An abundant dysfunctional apolipoprotein A1 in human atheroma.

Recent studies have indicated that high-density lipoproteins (HDLs) and their major structural protein, apolipoprotein A1 (apoA1), recovered from human atheroma are dysfunctional and are extensively oxidized by myeloperoxidase (MPO). In vitro oxidation of either apoA1 or HDL particles by MPO impairs their cholesterol acceptor function. Here, using phage display affinity maturation, we developed a high-affinity monoclonal antibody that specifically recognizes both apoA1 and HDL that have been modified by the MPO-H2O2-Cl(-) system. An oxindolyl alanine (2-OH-Trp) moiety at Trp72 of apoA1 is the immunogenic epitope. Mutagenesis studies confirmed a critical role for apoA1 Trp72 in MPO-mediated inhibition of the ATP-binding cassette transporter A1 (ABCA1)-dependent cholesterol acceptor activity of apoA1 in vitro and in vivo. ApoA1 containing a 2-OH-Trp72 group (oxTrp72-apoA1) is in low abundance within the circulation but accounts for 20% of the apoA1 in atherosclerosis-laden arteries. OxTrp72-apoA1 recovered from human atheroma or plasma is lipid poor, virtually devoid of cholesterol acceptor activity and demonstrated both a potent proinflammatory activity on endothelial cells and an impaired HDL biogenesis activity in vivo. Elevated oxTrp72-apoA1 levels in subjects presenting to a cardiology clinic (n = 627) were associated with increased cardiovascular disease risk. Circulating oxTrp72-apoA1 levels may serve as a way to monitor a proatherogenic process in the artery wall.

Recombinant CD63/ME491/neuroglandular/NKI/C-3 antigen inhibits growth of established tumors in transgenic mice.

Attempts to vaccinate against tumors can be hindered by the induction of immunological tolerance to the target Ag as a result of Ag expression on normal tissues. In this study, we find that transgenic mice expressing the melanoma-associated Ag CD63/ME491/neuroglandular/NKI/C-3 on their normal tissues do, in fact, exhibit immunological tolerance to the Ag, recapitulating the conditions in cancer patients. In these mice, growth of murine melanoma cells expressing the Ag after gene transfer was inhibited by immunization with Ag-expressing recombinant vaccinia virus combined with IL-2, but not by immunization with the protein alone, anti-idiotypic Abs, or irradiated tumor cells. The effect of the recombinant virus was demonstrated both for nonestablished and established tumors. Infiltration with both CD4(+) and CD8(+) T lymphocytes was significantly more extensive in tumors from experimental mice than in tumors from control mice. MHC class I-positive, but not class I-negative, tumors were inhibited by the vaccine, suggesting that MHC class I-restricted T lymphocytes play a role in the antitumor effects. Abs did not appear to be involved in the vaccine effects. CD63 was immunogenic in 2 of 13 melanoma patients, pointing to the potential of this Ag, combined with IL-2, as a vaccine for melanoma patients.

Immunization procedures for anti-idiotypic antibody induction in mice and rats.

Anti-idiotypic antibodies (Ab2) mimicking antigens (Ags)-defined by antibodies (Ab1) directed to tumors or pathogens have elicited Ag-specific humoral, cellular and/or protective immunity in experimental animals and in humans. In immunizations of rodents with Ab1, factors such as animal species, form of Ab1 and choice of adjuvant are crucial for the successful induction of Ab2 as candidate vaccines against tumors and pathogens. Here we survey the outcome of 362 fusion events (each event representing one animal), using nine immunization schedules in mice and seven schedules in rats and including 10 different Ab1 directed against human tumor- and immunodeficiency virus (HIV-1)-associated Ags. Ab1 IgG or F(ab')2 were administered uncoupled or coupled to keyhole limpet hemocyanin (KLH). As adjuvants, complete and incomplete Freund's adjuvant (CFA/IFA), lipid A, aluminum hydroxide, TiterMax or vaccinia virus were used. In syngeneic immunizations with murine Ab1 in mice, F(ab')2 coupled to KLH and emulsified in CFA/IFA preferentially induced Ab2 mimicking tumor or HIV-1 associated epitopes. In xenogeneic immunizations with mouse Ab1 in rats, various forms of Ab1 and adjuvants successfully induced Ab2 mimicking tumor Ags.