Wastewater surveillance of SARS-CoV-2 and chemical markers in campus dormitories in an evolving COVID - 19 pandemic.

In this study, we report the implementation of a comprehensive wastewater surveillance testing program at a university campus in Singapore to identify Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infected individuals and the usage of pharmaceuticals and personal care products (PPCPs) as well as other emerging contaminants (ECs). This unique co-monitoring program simultaneously measured SARS-CoV-2 with chemical markers/contaminants as the COVID-19 situation evolved from pandemic to endemic stages, following a nationwide mass vaccination drive. SARS-CoV-2 RNA concentrations in wastewater from campus dormitories were measured using real-time reverse transcription-polymerase chain reaction (RT-qPCR) and corroborated with the number of symptomatic COVID-19 cases confirmed with the antigen rapid test (ART). Consistent results were observed where the concentrations of SARS-CoV-2 RNA detected in wastewater increased proportionately with the number of COVID-19 infected individuals residing on campus. Similarly, a wide range of ECs, including disinfectants and antibiotics, were detected through sensitive liquid chromatography with tandem mass spectrometry (LC-MS/MS) techniques to establish PPCPs consumption patterns during various stages of the COVID-19 pandemic in Singapore. Statistical correlation of SARS-CoV-2 RNA was observed with few ECs belonging to disinfectants, PCPs and antibiotics. A high concentration of disinfectants and subsequent positive correlation with the number of reported cases on the university campus indicates that disinfectants could serve as a chemical marker during such unprecedented times.

Explicit characterization of human population connectivity reveals long run persistence of interregional dengue shocks.

Dengue is hyper-endemic in Singapore and Malaysia, and daily movement rates between the two countries are consistently high, allowing inference on the role of local transmission and imported dengue cases. This paper describes a custom built sparse space-time autoregressive (SSTAR) model to infer and forecast contemporaneous and future dengue transmission patterns in Singapore and 16 administrative regions within Malaysia, taking into account connectivity and geographical adjacency between regions as well as climatic factors. A modification to forecast impulse responses is developed for the case of the SSTAR and is used to simulate changes in dengue transmission in neighbouring regions following a disturbance. The results indicate that there are long-term responses of the neighbouring regions to shocks in a region. By computation of variable inclusion probabilities, we found that each region's own past counts were important to describe contemporaneous case counts. In 15 out of 16 regions, other regions case counts were important to describe contemporaneous case counts even after controlling for past local dengue transmissions and exogenous factors. Leave-one-region-out analysis using SSTAR showed that dengue transmission counts could be reconstructed for 13 of 16 regions' counts using external dengue transmissions compared to a climate only approach. Lastly, one to four week ahead forecasts from the SSTAR were more accurate than baseline univariate autoregressions.

Inference on dengue epidemics with Bayesian regime switching models.

Dengue, a mosquito-borne infectious disease caused by the dengue viruses, is present in many parts of the tropical and subtropical regions of the world. All four serotypes of dengue viruses are endemic in Singapore, an equatorial city-state. Frequent outbreaks occur, sometimes leading to national epidemics. However, few studies have attempted to characterize breakpoints which precede large rises in dengue case counts. In this paper, Bayesian regime switching (BRS) models were employed to infer epidemic and endemic regimes of dengue transmissions, each containing regime specific autoregressive processes which drive the growth and decline of dengue cases, estimated using a custom built multi-move Gibbs sampling algorithm. Posterior predictive checks indicate that BRS replicates temporal trends in Dengue transmissions well and nowcast accuracy assessed using a post-hoc classification scheme showed that BRS classification accuracy is robust even under limited data with the AUC-ROC at 0.935. LASSO-based regression and bootstrapping was used to account for plausibly high dimensions of climatic factors affecting Dengue transmissions, which was then estimated using cross-validation to conduct statistical inference on long-run climatic effects on the estimated regimes. BRS estimates epidemic and endemic regimes of dengue in Singapore which are characterized by persistence across time, lasting an average of 20 weeks and 66 weeks respectively, with a low probability of transitioning away from their regimes. Climate analysis with LASSO indicates that long-run climatic effects up to 20 weeks ago do not differentiate epidemic and endemic regimes. Lastly, by fitting BRS to simulated disease data generated from a stochastic Susceptible-Infected-Recovered model, mechanistic links between infectivity and regimes classified using BRS were provided. The model proposed could be applied to other localities and diseases under minimal data requirements where transmission counts over time are collected.

Early dengue outbreak detection modeling based on dengue incidences in Singapore during 2012 to 2017.

Dengue has been as an endemic with year-round presence in Singapore. In the recent years 2013, 2014, and 2016, there were several severe dengue outbreaks, posing serious threat to the public health. To proactively control and mitigate the disease spread, early warnings of dengue outbreaks, at which there are rapid and large-scale spread of dengue incidences, are extremely helpful. In this study, a two-step framework is proposed to predict dengue outbreaks and it is evaluated based on the dengue incidences in Singapore during 2012 to 2017. First, a generalized additive model (GAM) is trained based on the weekly dengue incidence data during 2006 to 2011. The proposed GAM is a one-week-ahead forecasting model, and it inherently accounts for the possible correlation among the historical incidence data, making the residuals approximately normally distributed. Then, an exponentially weighted moving average (EWMA) control chart is proposed to sequentially monitor the weekly residuals during 2012 to 2017. Our investigation shows that the proposed two-step framework is able to give persistent signals at the early stage of the outbreaks in 2013, 2014, and 2016, which provides early alerts of outbreaks and wins time for the early interventions and the preparation of necessary public health resources. In addition, extensive simulations show that the proposed method is comparable to other potential outbreak detection methods and it is robust to the underlying data-generating mechanisms.

Correction: Alternative strategies for mosquito-borne arbovirus control.

[This corrects the article DOI: 10.1371/journal.pntd.0006822.].

Alternative strategies for mosquito-borne arbovirus control.

BACKGROUND: Mosquito-borne viruses-such as Zika, chikungunya, dengue fever, and yellow fever, among others-are of global importance. Although vaccine development for prevention of mosquito-borne arbovirus infections has been a focus, mitigation strategies continue to rely on vector control. However, vector control has failed to prevent recent epidemics and arrest expanding geographic distribution of key arboviruses, such as dengue. As a consequence, there has been increasing necessity to further optimize current strategies within integrated approaches and advance development of alternative, innovative strategies for the control of mosquito-borne arboviruses. METHODS AND FINDINGS: This review, intended as a general overview, is one of a series being generated by the Worldwide Insecticide resistance Network (WIN). The alternative strategies discussed reflect those that are currently under evaluation for public health value by the World Health Organization (WHO) and represent strategies of focus by globally recognized public health stakeholders as potential insecticide resistance (IR)-mitigating strategies. Conditions where these alternative strategies could offer greatest public health value in consideration of mitigating IR will be dependent on the anticipated mechanism of action. Arguably, the most pressing need for endorsement of the strategies described here will be the epidemiological evidence of a public health impact. CONCLUSIONS: As the burden of mosquito-borne arboviruses, predominately those transmitted by Aedes aegypti and A. albopictus, continues to grow at a global scale, new vector-control tools and integrated strategies will be required to meet public health demands. Decisions regarding implementation of alternative strategies will depend on key ecoepidemiological parameters that each is intended to optimally impact toward driving down arbovirus transmission.

Molecular epidemiological investigation of Plasmodium knowlesi in humans and macaques in Singapore.

Singapore reported its first locally acquired human Plasmodium knowlesi infection in 2007, involving a soldier who had undergone training in a forested area where long-tailed macaques are frequently seen. Comprehensive disease surveillance and monitoring system that was set up after the initial case detected four additional human P. knowlesi cases in 2007 and one in 2008. All involved military personnel who had undergone training in the forested area, and none had traveled out of Singapore 1 month before the onset of symptoms. Screening for malaria parasites on blood obtained from long-tailed macaques revealed that wild monkeys (n=3) caught from the forested area were infected with P. knowlesi, whereas peri-domestic monkeys (n=10) caught from a nature reserve park were not infected with any malaria parasites. Phylogenetic analysis of the nonrepeat region of the P. knowlesi csp genes showed that the sequences obtained from the human cases were not distinct from those obtained from wild monkeys. Further, certain genotypes were shared between samples from humans and macaques. Our findings provide evidence that long-tailed macaques are the natural hosts of P. knowlesi in Singapore and the human cases acquired their infection in the same vicinity where these monkeys are found. Further, the risk of acquiring P. knowlesi infection among the general population of Singapore is small as evident from the absence of P. knowlesi in peri-domestic monkeys.

Evaluation of a “fogging” canister for indoor elimination of adult Aedes aegypti.

Aedes aegypti is the primary vector for dengue transmission in Singapore. In response to dengue transmission, ultra-low volume (ULV) spraying of insecticides in apartments in transmission areas is used to supplement source-reduction effort. This study seeks to determine the efficacy of Mozzie Zap, a “fogger” in an aerosol canister, which contains 1.7% cypermethrin and 3.4% piperonyl butoxide (PBO, a pesticide synergist). The product, which has the unique continual release mechanism, was tested using two canisters in a two-bedroom apartment block. Caged female adult mosquitoes and magnesium oxide slides were positioned prior to the release of the chemicals. All bioassays and slides were exposed for 30 minutes before removal. Subsequent residual activity tests were carried out using WHO contact bioassay cones on horizontal (floor) and vertical (wall) surfaces. Mozzie Zap’s fog produced fog of effective droplet size (average VMD=24.32 μm), and achieved 100% mortality against Ae. aegypti. However, no residual activity on wall surfaces (3.1% on Day 2) was observed.