Receptiveness to monkeypox vaccines and public health communication strategies among gay, bisexual and other men who have sex with men in Singapore: cross-sectional quantitative and qualitative insights.

OBJECTIVES: Monkeypox (mpox) was declared as a global health emergency by the WHO, with most reported cases disproportionately involving gay, bisexual and other men who have sex with men (GBMSM). This study explored the willingness of Singaporean GBMSM to receive mpox vaccines and engage in changes to sexual behaviour, and analysed the factors influencing both decisions. METHODS: An online cross-sectional study was disseminated through community groups and Grindr from September to October 2022 among GBMSM in Singapore, where we surveyed respondents' demographics, stigma associated with mpox, perceived risks of sexually transmitted infections (STIs) and changes to sexual behaviours in response to mpox. Descriptive statistics and multivariable linear and logistics regression analyses, as well as thematic analysis of data, were also conducted. RESULTS: 237 GBMSM community members responded to the survey, with the majority being receptive to vaccine and 67.5% indicating changes to sexual behaviour in view of rising mpox infections. Predictors of vaccine receptivity among GBMSM included self-perceived mpox risk (adjusted OR (aOR)=0.44, 95% CI 0.07, 0.82) and self-perceived STI risk (aOR=0.39, 95% CI 0.03, 0.76). Predictors for changes to sexual behaviour included self-perceived mpox stigma (aOR=1.17, 95% CI 1.08, 1.27), self-perceived mpox risk (aOR=1.22, 95% CI 1.03,1.44), age (aOR=0.96, 95% CI 0.93, 1.00) and race (aOR=0.31, 95% CI 0.10, 0.93). In the thematic analysis, respondents raised concerns about vaccine effectiveness, side effects, cost and privacy. CONCLUSIONS: Our findings suggest that the rise in mpox infections have prompted changes to GBMSM's sexual practices. In general, GBMSM are willing to receive the mpox vaccine but are concerned about the physical and social consequences of uptake. These concerns should be addressed when vaccines are released.

Forecasting upper respiratory tract infection burden using high-dimensional time series data and forecast combinations.

Upper respiratory tract infections (URTIs) represent a large strain on primary health resources. To mitigate URTI transmission and public health burdens, it is important to pre-empt and provide forward guidance on URTI burden, while taking into account various facets which influence URTI transmission. This is so that appropriate public health measures can be taken to mitigate strain on primary care resources. This study describes a new approach to forecasting URTIs which can be used for national public health resource planning. Specifically, using environmental and disease data comprising more than 1000 dimensions, we developed sub-models which optimizes model explainability, in-sample model fit, predictive accuracy and combines many weaker predictors over a 2-month time horizon to generate direct, point forecasts over a 1-8 week ahead forecast horizon. Predictive performance was evaluated using rolling out-of-sample forecast assessment within both periods with/without structural breaks in transmission over the period of 2012-2022. We showed that forecast combinations of 5 other forecasting models had better and more consistent predictive performance than other modelling approaches, over periods with and without structural breaks in transmission dynamics. Furthermore, epidemiological analysis on high dimensional data was enabled using post-selection inference, to show the dynamic association between lower temperature, increases in past relative humidity and absolute humidity and increased URTIs attendance. The methods proposed can be used for outbreak preparedness and guide healthcare resource planning, in both stable periods of transmission and periods where structural breaks in data occur.

Short-term and long-term epidemiological impacts of sustained vector control in various dengue endemic settings: A modelling study.

As the most widespread viral infection transmitted by the Aedes mosquitoes, dengue has been estimated to cause 51 million febrile disease cases globally each year. Although sustained vector control remains key to reducing the burden of dengue, current understanding of the key factors that explain the observed variation in the short- and long-term vector control effectiveness across different transmission settings remains limited. We used a detailed individual-based model to simulate dengue transmission with and without sustained vector control over a 30-year time frame, under different transmission scenarios. Vector control effectiveness was derived for different time windows within the 30-year intervention period. We then used the extreme gradient boosting algorithm to predict the effectiveness of vector control given the simulation parameters, and the resulting machine learning model was interpreted using Shapley Additive Explanations. According to our simulation outputs, dengue transmission would be nearly eliminated during the early stage of sustained and intensive vector control, but over time incidence would gradually bounce back to the pre-intervention level unless the intervention is implemented at a very high level of intensity. The time point at which intervention ceases to be effective is strongly influenced not only by the intensity of vector control, but also by the pre-intervention transmission intensity and the individual-level heterogeneity in biting risk. Moreover, the impact of many transmission model parameters on the intervention effectiveness is shown to be modified by the intensity of vector control, as well as to vary over time. Our study has identified some of the critical drivers for the difference in the time-varying effectiveness of sustained vector control across different dengue endemic settings, and the insights obtained will be useful to inform future model-based studies that seek to predict the impact of dengue vector control in their local contexts.

Fine-scale estimation of effective reproduction numbers for dengue surveillance.

The effective reproduction number Rt is an epidemiological quantity that provides an instantaneous measure of transmission potential of an infectious disease. While dengue is an increasingly important vector-borne disease, few have used Rt as a measure to inform public health operations and policy for dengue. This study demonstrates the utility of Rt for real time dengue surveillance. Using nationally representative, geo-located dengue case data from Singapore over 2010-2020, we estimated Rt by modifying methods from Bayesian (EpiEstim) and filtering (EpiFilter) approaches, at both the national and local levels. We conducted model assessment of Rt from each proposed method and determined exogenous temporal and spatial drivers for Rt in relation to a wide range of environmental and anthropogenic factors. At the national level, both methods achieved satisfactory model performance (R2EpiEstim = 0.95, R2EpiFilter = 0.97), but disparities in performance were large at finer spatial scales when case counts are low (MASE EpiEstim = 1.23, MASEEpiFilter = 0.59). Impervious surfaces and vegetation with structure dominated by human management (without tree canopy) were positively associated with increased transmission intensity. Vegetation with structure dominated by human management (with tree canopy), on the other hand, was associated with lower dengue transmission intensity. We showed that dengue outbreaks were preceded by sustained periods of high transmissibility, demonstrating the potential of Rt as a dengue surveillance tool for detecting large rises in dengue cases. Real time estimation of Rt at the fine scale can assist public health agencies in identifying high transmission risk areas and facilitating localised outbreak preparedness and response.

Association between ambient air pollutants and upper respiratory tract infection and pneumonia disease burden in Thailand from 2000 to 2022: a high frequency ecological analysis.

BACKGROUND: A pertinent risk factor of upper respiratory tract infections (URTIs) and pneumonia is the exposure to major ambient air pollutants, with short term exposures to different air pollutants being shown to exacerbate several respiratory conditions. METHODS: Here, using disease surveillance data comprising of reported disease case counts at the province level, high frequency ambient air pollutant and climate data in Thailand, we delineated the association between ambient air pollution and URTI/Pneumonia burden in Thailand from 2000 - 2022. We developed mixed-data sampling methods and estimation strategies to account for the high frequency nature of ambient air pollutant concentration data. This was used to evaluate the effects past concentrations of fine particulate matter (PM2.5), sulphur dioxide (SO2), and carbon monoxide (CO) and the number of disease case count, after controlling for the confounding meteorological and disease factors. RESULTS: Across provinces, we found that past increases in CO, SO2, and PM2.5 concentration were associated to changes in URTI and pneumonia case counts, but the direction of their association mixed. The contributive burden of past ambient air pollutants on contemporaneous disease burden was also found to be larger than meteorological factors, and comparable to that of disease related factors. CONCLUSIONS: By developing a novel statistical methodology, we prevented subjective variable selection and discretization bias to detect associations, and provided a robust estimate on the effect of ambient air pollutants on URTI and pneumonia burden over a large spatial scale.

Understanding knowledge, attitudes and behaviours related to dietary sodium intake in a multi-ethnic population in Singapore.

OBJECTIVE: This study aimed to fill the current gap in the understanding of the knowledge, attitudes and behaviours (KAB) related to dietary Na among adult residents in Singapore. DESIGN: A cross-sectional online survey was conducted between October and December 2020 on 955 participants selected through random sampling. SETTING: The survey was conducted in Singapore. PARTICIPANTS: Participants were recruited from the Singapore Population Health Study Online Panel. RESULTS: Participants' mean age was 46·6 ± 14·1 years old and 58 % of them were females. Most of the participants were Chinese (82·1 %), 10·5 % were Indian and 4·5 % were Malay. Findings from the weighted data showed that most participants were aware of the health impact of high Na consumption. However, many participants were unaware of the recommended intake for salt (68%) and Na (83%), had misconceptions, and were unable to correctly use food labels to assess NA content (69%). Findings also alluded to the presence of knowledge gaps in the sources of Na in their diet. While 59 % of the participants reported to be limiting their consumption of Na, many reported facing barriers such as not knowing how to limit their Na intake. Participants also felt that there were limited options for low-Na foods when eating out and were lacking awareness of low-Na products. CONCLUSIONS: Findings highlighted substantial gaps in participants' knowledge and skills in managing their Na consumption. This suggests the need for more public education and improvements in the food environment.

Estimating direct and spill-over impacts of political elections on COVID-19 transmission using synthetic control methods.

Mass gathering events have been identified as high-risk environments for community transmission of coronavirus disease 2019 (COVID-19). Empirical estimates of their direct and spill-over effects however remain challenging to identify. In this study, we propose the use of a novel synthetic control framework to obtain causal estimates for direct and spill-over impacts of these events. The Sabah state elections in Malaysia were used as an example for our proposed methodology and we investigate the event's spatial and temporal impacts on COVID-19 transmission. Results indicate an estimated (i) 70.0% of COVID-19 case counts within Sabah post-state election were attributable to the election's direct effect; (ii) 64.4% of COVID-19 cases in the rest of Malaysia post-state election were attributable to the election's spill-over effects. Sensitivity analysis was further conducted by examining epidemiological pre-trends, surveillance efforts, varying synthetic control matching characteristics and spill-over specifications. We demonstrate that our estimates are not due to pre-existing epidemiological trends, surveillance efforts, and/or preventive policies. These estimates highlight the potential of mass gatherings in one region to spill-over into an outbreak of national scale. Relaxations of mass gathering restrictions must therefore be carefully considered, even in the context of low community transmission and enforcement of safe distancing guidelines.

Increased Dengue Transmissions in Singapore Attributable to SARS-CoV-2 Social Distancing Measures.

Social distancing (SD) measures aimed at curbing the spread of SARS-CoV-2 remain an important public health intervention. Little is known about the collateral impact of reduced mobility on the risk of other communicable diseases. We used differences in dengue case counts pre- and post implementation of SD measures and exploited heterogeneity in SD treatment effects among different age groups in Singapore to identify the spillover effects of SD measures. SD policy caused an increase of over 37.2% in dengue cases from baseline. Additional measures to preemptively mitigate the risk of other communicable diseases must be considered before the implementation/reimplementation of SARS-CoV-2 SD measures.

Effectiveness of Containment Measures Against COVID-19 in Singapore: Implications for Other National Containment Efforts.

BACKGROUND: We hypothesize that comprehensive surveillance of COVID-19 in Singapore has facilitated early case detection and prompt contact tracing and, with community-based measures, contained spread. We assessed the effectiveness of containment measures by estimating transmissibility (effective reproduction number, (Equation is included in full-text article.)) over the course of the outbreak. METHODS: We used a Bayesian data augmentation framework to allocate infectors to infectees with no known infectors and determine serial interval distribution parameters via Markov chain Monte Carlo sampling. We fitted a smoothing spline to the number of secondary cases generated by each infector by respective onset dates to estimate (Equation is included in full-text article.)and evaluated increase in mean number of secondary cases per individual for each day's delay in starting isolation or quarantine. RESULTS: As of April 1, 2020, 1000 COVID-19 cases were reported in Singapore. We estimated a mean serial interval of 4.6 days [95% credible interval (CI) = 4.2, 5.1] with a SD of 3.5 days (95% CI = 3.1, 4.0). The posterior mean (Equation is included in full-text article.)was below one for most of the time, peaking at 1.1 (95% CI = 1.0, 1.3) on week 9 of 2020 due to a spreading event in one of the clusters. Eight hundred twenty-seven (82.7%) of cases infected less than one person on average. Over an interval of 7 days, the incremental mean number of cases generated per individual for each day's delay in starting isolation or quarantine was 0.03 cases (95% CI = 0.02, 0.05). CONCLUSIONS: We estimate that robust surveillance, active case detection, prompt contact tracing, and quarantine of close contacts kept (Equation is included in full-text article.)below one.

Time varying methods to infer extremes in dengue transmission dynamics.

Dengue is an arbovirus affecting global populations. Frequent outbreaks occur, especially in equatorial cities such as Singapore, where year-round tropical climate, large daily influx of travelers and population density provide the ideal conditions for dengue to transmit. Little work has, however, quantified the peaks of dengue outbreaks, when health systems are likely to be most stretched. Nor have methods been developed to infer differences in exogenous factors which lead to the rise and fall of dengue case counts across extreme and non-extreme periods. In this paper, we developed time varying extreme mixture (tvEM) methods to account for the temporal dependence of dengue case counts across extreme and non-extreme periods. This approach permits inference of differences in climatic forcing across non-extreme and extreme periods of dengue case counts, quantification of their temporal dependence as well as estimation of thresholds with associated uncertainties to determine dengue case count extremities. Using tvEM, we found no evidence that weather affects dengue case counts in the near term for non-extreme periods, but that it has non-linear and mixed signals in influencing dengue through tvEM parameters in the extreme periods. Using the most appropriate tvEM specification, we found that a threshold at the 70th (95% credible interval 41.1, 83.8) quantile is optimal, with extreme events of 526.6, 1052.2 and 1183.6 weekly case counts expected at return periods of 5, 50 and 75 years. Weather parameters at a 1% scaled increase was found to decrease the long-run expected case counts, but larger increases would lead to a drastic expected rise from the baseline correspondingly. The tvEM approach can provide valuable inference on the extremes of time series, which in the case of infectious disease notifications, allows public health officials to understand the likely scale of outbreaks in the long run.

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.

Associations of park access, park use and physical activity in parks with wellbeing in an Asian urban environment: a cross-sectional study.

BACKGROUND: Relationships between park access, park use, and wellbeing remain poorly understood. The objectives of this study were to investigate: (1) perceived and objective park access in relation to park use and physical activity in parks; and; (2) perceived and objective park access, park use and physical activity in parks and their associations with wellbeing. METHODS: An interviewer-assisted survey collected data on perceived time to walk to parks, park use time, park physical activity time and wellbeing (using a scale containing nine domains) amongst adult participants of the Singapore Multi-Ethnic Cohort. Geospatial maps of parks and the "walkable" street networks were created for the city-state of Singapore to objectively determine distances to accessible points on park boundaries. Multiple linear regression models estimated the importance of park access to park use and associations of park access and park use with wellbeing, adjusting for potential confounders. RESULTS: Participants' (n = 3435) average age was 48.8 years (SD, 12.8), 44.8% were male and 72.6% were of Chinese ethnicity. Better perceived but not true park access was significantly associated with greater park use. Park access (perceived or true) was not associated with physical activity time in parks. Greater participant park time and physical activity time in parks were associated with higher wellbeing scores (p < 0.001). The differences in wellbeing scores between the reference groups, who spent negligible time in parks, and the highest quartiles of time in parks (10.8 h/month) and physical activity in parks (8.3 h/month) were 3.2 (95% CI 2.1-4.4) and 4.2 (95% CI 4.1-6.3) points out of 100 respectively. These associations were similar for most domains of wellbeing, with clear dose-response relationships. CONCLUSIONS: While perceived park access was strongly associated with park use and well-being, true park access was not, and neither park access measure was associated with park physical activity. Future studies could investigate the influence of park attributes on park use, physical activity in parks and wellbeing. The consistent associations of park use and particularly physical activity in parks with wellbeing suggest that promoting park use, and especially physical activity in parks, is a promising strategy for improving wellbeing in urban settings.

Identifying COVID-19 cases in outpatient settings.

Case identification is an ongoing issue for the COVID-19 epidemic, in particular for outpatient care where physicians must decide which patients to prioritise for further testing. This paper reports tools to classify patients based on symptom profiles based on 236 severe acute respiratory syndrome coronavirus 2 positive cases and 564 controls, accounting for the time course of illness using generalised multivariate logistic regression. Significant symptoms included abdominal pain, cough, diarrhoea, fever, headache, muscle ache, runny nose, sore throat, temperature between 37.5 and 37.9 °C and temperature above 38 °C, but their importance varied by day of illness at assessment. With a high percentile threshold for specificity at 0.95, the baseline model had reasonable sensitivity at 0.67. To further evaluate accuracy of model predictions, leave-one-out cross-validation confirmed high classification accuracy with an area under the receiver operating characteristic curve of 0.92. For the baseline model, sensitivity decreased to 0.56. External validation datasets reported similar result. Our study provides a tool to discern COVID-19 patients from controls using symptoms and day from illness onset with good predictive performance. It could be considered as a framework to complement laboratory testing in order to differentiate COVID-19 from other patients presenting with acute symptoms in outpatient care.

Mapping the cryptic spread of the 2015-2016 global Zika virus epidemic.

BACKGROUND: Zika virus (ZIKV) emerged as a global epidemic in 2015-2016 from Latin America with its true geographical extent remaining unclear due to widely presumed underreporting. The identification of locations with potential and unknown spread of ZIKV is a key yet understudied component for outbreak preparedness. Here, we aim to identify locations at a high risk of cryptic ZIKV spread during 2015-2016 to further the understanding of the global ZIKV epidemiology, which is critical for the mitigation of the risk of future epidemics. METHODS: We developed an importation simulation model to estimate the weekly number of ZIKV infections imported in each susceptible spatial unit (i.e. location that did not report any autochthonous Zika cases during 2015-2016), integrating epidemiological, demographic, and travel data as model inputs. Thereafter, a global risk model was applied to estimate the weekly ZIKV transmissibility during 2015-2016 for each location. Finally, we assessed the risk of onward ZIKV spread following importation in each susceptible spatial unit to identify locations with a high potential for cryptic ZIKV spread during 2015-2016. RESULTS: We have found 24 susceptible spatial units that were likely to have experienced cryptic ZIKV spread during 2015-2016, of which 10 continue to have a high risk estimate within a highly conservative scenario, namely, Luanda in Angola, Banten in Indonesia, Maharashtra in India, Lagos in Nigeria, Taiwan and Guangdong in China, Dakar in Senegal, Maputo in Mozambique, Kinshasa in Congo DRC, and Pool in Congo. Notably, among the 24 susceptible spatial units identified, some have reported their first ZIKV outbreaks since 2017, thus adding to the credibility of our results (derived using 2015-2016 data only). CONCLUSION: Our study has provided valuable insights into the potentially high-risk locations for cryptic ZIKV circulation during the 2015-2016 pandemic and has also laid a foundation for future studies that attempt to further narrow this key knowledge gap. Our modelling framework can be adapted to identify areas with likely unknown spread of other emerging vector-borne diseases, which has important implications for public health readiness especially in resource-limited settings.

Revealing regional disparities in the transmission potential of SARS-CoV-2 from interventions in Southeast Asia.

SARS-CoV-2 is a new pathogen responsible for the coronavirus disease 2019 (COVID-19) outbreak. Southeast Asia was the first region to be affected outside China, and although COVID-19 cases have been reported in all countries of Southeast Asia, both the policies and epidemic trajectories differ substantially, potentially due to marked differences in social distancing measures that have been implemented by governments in the region. This paper studies the across-country relationships between social distancing and each population's response to policy, the subsequent effects of these responses to the transmissibility and epidemic trajectories of SARS-CoV-2. The analysis couples COVID-19 case counts with real-time mobility data across Southeast Asia to estimate the effects of host population response to social distancing policy and the subsequent effects on the transmissibility and epidemic trajectories of SARS-CoV-2. A novel inference strategy for the time-varying reproduction number is developed to allow explicit inference of the effects of social distancing on the transmissibility of SARS-CoV-2 through a regression structure. This framework replicates the observed epidemic trajectories across most Southeast Asian countries, provides estimates of the effects of social distancing on the transmissibility of disease and can simulate epidemic histories conditional on changes in the degree of intervention scenarios and compliance within Southeast Asia.

Importations of COVID-19 into African countries and risk of onward spread.

BACKGROUND: The emergence of a novel coronavirus (SARS-CoV-2) in Wuhan, China, at the end of 2019 has caused widespread transmission around the world. As new epicentres in Europe and America have arisen, of particular concern is the increased number of imported coronavirus disease 2019 (COVID-19) cases in Africa, where the impact of the pandemic could be more severe. We aim to estimate the number of COVID-19 cases imported from 12 major epicentres in Europe and America to each African country, as well as the probability of reaching 10,000 cases in total by the end of March, April, May, and June following viral introduction. METHODS: We used the reported number of cases imported from the 12 major epicentres in Europe and America to Singapore, as well as flight data, to estimate the number of imported cases in each African country. Under the assumption that Singapore has detected all the imported cases, the estimates for Africa were thus conservative. We then propagated the uncertainty in the imported case count estimates to simulate the onward spread of the virus, until 10,000 cases are reached or the end of June, whichever is earlier. Specifically, 1,000 simulations were run separately under four different combinations of parameter values to test the sensitivity of our results. RESULTS: We estimated Morocco, Algeria, South Africa, Egypt, Tunisia, and Nigeria as having the largest number of COVID-19 cases imported from the 12 major epicentres. Based on our 1,000 simulation runs, Morocco and Algeria's estimated probability of reaching 10,000 cases by end of March was close to 100% under all scenarios. In particular, we identified countries with less than 1,000 cases in total reported by end of June whilst the estimated probability of reaching 10,000 cases by then was higher than 50% even under the most optimistic scenario. CONCLUSIONS: Our study highlights particular countries that are likely to reach (or have reached) 10,000 cases far earlier than the reported data suggest, calling for the prioritization of resources to mitigate the further spread of the epidemic.

Revealing two dynamic dengue epidemic clusters in Thailand.

BACKGROUND: Thailand is home to around 69 million individuals. Dengue is hyper-endemic and all 4 serotypes are in active circulation in the country. Dengue outbreaks occur almost annually within Thailand in at least one province but the spatio-temporal and environmental interface of these outbreaks has not been studied. METHODS: We develop Bayesian regime switching (BRS) models to characterize outbreaks, their persistence and infer their likelihood of occurrence across time for each administrative province where dengue case counts are collected. BRS was compared against two other classification tools and their agreement is assessed. We further examine how these spatio-temporal clusters of outbreak clusters arise by comparing reported dengue case counts, urban population, urban land cover, climate and flight volumes on the province level. RESULTS: Two dynamic dengue epidemic clusters were found nationally. One cluster consists of 47 provinces and is highly outbreak prone. Provinces with a large number of case counts, urban population, urban land cover and incoming flight passengers are associated to the epidemic prone cluster of dengue. Climate has an effect on determining the probability of outbreaks over time within provinces, but have less influence on whether provinces belong to the epidemic prone cluster. BRS found high agreement with other classification tools. CONCLUSIONS: Importation and urbanization drives the risk of outbreaks across regions strongly. In provinces estimated to have high epidemic persistence, more resource allocation to vector control should be applied to those localities as heightened transmission counts are likely to occur over a longer period of time. Clustering of epidemic and non-epidemic prone areas also highlights the need for prioritization of resource allocation for disease mitigation over provinces in Thailand.

Evaluating smoking control policies in the e-cigarette era: a modelling study.

BACKGROUND: In jurisdictions in which electronic cigarettes are currently prohibited, policy makers must weigh the potentially lower risk compared with conventional cigarettes against the risk of initiation of e-cigarettes among non-smokers. METHODS: We simulated a synthetic population over a 50-year time horizon with an open cohort model using data from Singapore, a country where e-cigarettes are currently prohibited, and data from the USA, the UK and Japan. Using the smoking prevalence and the quality-adjusted life year gained calculated, we compared tobacco control policies without e-cigarettes-namely, raising the minimum legal age (MLA), introducing a smoke-free generation (SFG) and tax rises on tobacco consumption-with policies legalising e-cigarettes, either taking a laissez-faire approach or under some form of restriction. We also evaluated combinations of these policies. RESULTS: Regardless of the country informing the transition probabilities to and from e-cigarette use in Singapore, a laissez-faire e-cigarette policy could reduce the smoking prevalence in the short term, but it is not as effective as other policies in the long term. The most effective single policies evaluated were SFG and aggressive tax rises; the most effective combination of policies considered was MLA plus moderate tax rises and e-cigarettes on prescription. CONCLUSION: Policy makers in jurisdictions in which e-cigarettes are not yet established may be advised not to prioritise e-cigarettes in their tobacco end-game strategy, unless their use can be restricted to current smokers seeking to quit.

Cost-Effectiveness Analysis for Influenza Vaccination Coverage and Timing in Tropical and Subtropical Climate Settings: A Modeling Study.

BACKGROUND: The lack of seasonality in influenza epidemics in the tropics makes the application of well-established temperate zone national vaccination plans challenging. OBJECTIVES: We developed an individual-based simulation model to study optimal vaccination scheduling and assess cost-effectiveness of these vaccination schedules in scenarios of no influenza seasonality and the seasonality regimes of Singapore, Taipei, and Tokyo. METHODS: The simulation models heterogeneities in human contact networks, levels of protective antibodies following infection, the effectiveness of the influenza vaccine, and seasonality. Using a no intervention baseline, we consider 3 alternative vaccination strategies: (1) annual vaccination for a percentage of the elderly, (2) biannual vaccination for a percentage of the elderly, and (3) annual vaccination for all elderly and a fraction of the remaining population. We considered 5 vaccination uptake rates for each strategy and modeled the estimated costs, quality-adjusted life years, and incremental cost-effectiveness ratios (ICERs), indicating the cost-effectiveness of each scenario. RESULTS: In Singapore, annual vaccination for a proportion of elderly is largely cost-effective. However, with fixed uptake rates, partial biannual vaccination for the elderly yields a higher ICER than partial annual vaccination for the elderly, resulting in a cost-ineffective ICER. The most optimal strategy is the total vaccination of all the elderly and a proportion of individuals from other age groups, which results in a cost-saving ICER. This finding is consistent across different seasonality regimes. CONCLUSIONS: Tropical countries like Singapore can have comparably cost-effective vaccination strategies as found in countries with winter epidemics. The vaccination of all the elderly and a proportion of other age groups is the most cost-effective strategy, supporting the need for an extensive national influenza vaccination program.