Risk prediction models for type 2 diabetes using either fasting plasma glucose or HbA1c in Chinese, Malay, and Indians: Results from three multi-ethnic Singapore cohorts.

AIMS: To assess three well-established type 2 diabetes (T2D) risk prediction models based on fasting plasma glucose (FPG) in Chinese, Malays, and Indians, and to develop simplified risk models based on either FPG or HbA1c. METHODS: We used a prospective multiethnic Singapore cohort to evaluate the established models and develop simplified models. 6,217 participants without T2D at baseline were included, with an average follow-up duration of 8.3 years. The simplified risk models were validated in two independent multiethnic Singapore cohorts (N = 12,720). RESULTS: The established risk models had moderate-to-good discrimination (area under the receiver operating characteristic curves, AUCs 0.762 - 0.828) but a lack of fit (P-values < 0.05). Simplified risk models that included fewer predictors (age, BMI, systolic blood pressure, triglycerides, and HbA1c or FPG) showed good discrimination in all cohorts (AUCs ≥ 0.810), and sufficiently captured differences between the ethnic groups. While recalibration improved fit the simplified models in validation cohorts, there remained evidence of miscalibration in Chinese (p ≤ 0.012). CONCLUSIONS: Simplified risk models including HbA1c or FPG had good discrimination in predicting incidence of T2D in three major Asian ethnic groups. Risk functions with HbA1c performed as well as those with FPG.

The societal cost of modifiable risk factors in Singapore.

BACKGROUND: Singapore is one of the most rapidly ageing populations in the world. Nearly half of all disease burdens in Singapore are attributable to modifiable risk factors. This indicates that many illnesses are preventable by modifying behaviours such as increasing physical activity levels or maintaining a healthy diet. Prior cost-of-illness studies have estimated the cost of selected modifiable risk factors. However, no local study has compared costs between groups of modifiable risks. This study aims to estimate the societal cost attributable to a comprehensive list of modifiable risks in Singapore. METHODS: Our study builds on the comparative risk assessment framework from the Global Burden of Disease (GBD) 2019 study. A top-down prevalence-based cost-of-illness approach was undertaken to estimate the societal cost of modifiable risks in 2019. These include healthcare costs from inpatient hospitalisation and productivity losses from absenteeism and premature mortality. RESULTS: Metabolic risks had the highest total cost of US$1.62 billion (95% uncertainty interval [UI] US$1.51-1.84 billion), followed by lifestyle risks of US$1.40 billion (95% UI US$1.36-1.66 billion) and substance risks of US$1.15 billion (95% UI US$1.10-1.24 billion). Across the risk factors, the costs were driven by productivity losses, heavily skewed towards the older working-age group and among males. Most of the costs were driven by cardiovascular diseases. CONCLUSION: This study provides evidence of the high societal cost of modifiable risks and highlights the importance of developing holistic public health promotion programmes. As modifiable risks often do not occur in isolation, implementing effective population-based programmes targeting multiple modifiable risks has a strong potential to manage the cost of the rising disease burden in Singapore.

Lifetime risk, life expectancy, and years of life lost to type 2 diabetes in 23 high-income jurisdictions: a multinational, population-based study.

BACKGROUND: Diabetes is a major public health issue. Because lifetime risk, life expectancy, and years of life lost are meaningful metrics for clinical decision making, we aimed to estimate these measures for type 2 diabetes in the high-income setting. METHODS: For this multinational, population-based study, we sourced data from 24 databases for 23 jurisdictions (either whole countries or regions of a country): Australia; Austria; Canada; Denmark; Finland; France; Germany; Hong Kong; Hungary; Israel; Italy; Japan; Latvia; Lithuania; the Netherlands; Norway; Scotland; Singapore; South Korea; Spain; Taiwan; the UK; and the USA. Our main outcomes were lifetime risk of type 2 diabetes, life expectancy in people with and without type 2 diabetes, and years of life lost to type 2 diabetes. We modelled the incidence and mortality of type 2 diabetes in people with and without type 2 diabetes in sex-stratified, age-adjusted, and calendar year-adjusted Poisson models for each jurisdiction. Using incidence and mortality, we constructed life tables for people of both sexes aged 20-100 years for each jurisdiction and at two timepoints 5 years apart in the period 2005-19 where possible. Life expectancy from a given age was computed as the area under the survival curves and lifetime lost was calculated as the difference between the expected lifetime of people with versus without type 2 diabetes at a given age. Lifetime risk was calculated as the proportion of each cohort who developed type 2 diabetes between the ages of 20 years and 100 years. We estimated 95% CIs using parametric bootstrapping. FINDINGS: Across all study cohorts from the 23 jurisdictions (total person-years 1 577 234 194), there were 5 119 585 incident cases of type 2 diabetes, 4 007 064 deaths in those with type 2 diabetes, and 11 854 043 deaths in those without type 2 diabetes. The lifetime risk of type 2 diabetes ranged from 16·3% (95% CI 15·6-17·0) for Scottish women to 59·6% (58·5-60·8) for Singaporean men. Lifetime risk declined with time in 11 of the 15 jurisdictions for which two timepoints were studied. Among people with type 2 diabetes, the highest life expectancies were found for both sexes in Japan in 2017-18, where life expectancy at age 20 years was 59·2 years (95% CI 59·2-59·3) for men and 64·1 years (64·0-64·2) for women. The lowest life expectancy at age 20 years with type 2 diabetes was observed in 2013-14 in Lithuania (43·7 years [42·7-44·6]) for men and in 2010-11 in Latvia (54·2 years [53·4-54·9]) for women. Life expectancy in people with type 2 diabetes increased with time for both sexes in all jurisdictions, except for Spain and Scotland. The life expectancy gap between those with and without type 2 diabetes declined substantially in Latvia from 2010-11 to 2015-16 and in the USA from 2009-10 to 2014-15. Years of life lost to type 2 diabetes ranged from 2·5 years (Latvia; 2015-16) to 12·9 years (Israel Clalit Health Services; 2015-16) for 20-year-old men and from 3·1 years (Finland; 2011-12) to 11·2 years (Israel Clalit Health Services; 2010-11 and 2015-16) for 20-year-old women. With time, the expected number of years of life lost to type 2 diabetes decreased in some jurisdictions and increased in others. The greatest decrease in years of life lost to type 2 diabetes occurred in the USA between 2009-10 and 2014-15 for 20-year-old men (a decrease of 2·7 years). INTERPRETATION: Despite declining lifetime risk and improvements in life expectancy for those with type 2 diabetes in many high-income jurisdictions, the burden of type 2 diabetes remains substantial. Public health strategies might benefit from tailored approaches to continue to improve health outcomes for people with diabetes. FUNDING: US Centers for Disease Control and Prevention and Diabetes Australia.

Geographical clusters of dengue outbreak in Singapore during the Covid-19 nationwide lockdown of 2020.

Dengue, a mosquito-transmitted viral disease, has posed a public health challenge to Singaporean residents over the years. In 2020, Singapore experienced an unprecedented dengue outbreak. We collected a dataset of geographical dengue clusters reported by the National Environment Agency (NEA) from 15 February to 9 July in 2020, covering the nationwide lockdown associated with Covid-19 during the period from 7 April to 1 June. NEA regularly updates the dengue clusters during which an infected person may be tagged to one cluster based on the most probable infection location (residential apartment or workplace address), which is further matched to fine-grained spatial units with an average coverage of about 1.35 km2. Such dengue cluster dataset helps not only reveal the dengue transmission patterns, but also reflect the effects of lockdown on dengue spreading dynamics. The resulting data records are released in simple formats for easy access to facilitate studies on dengue epidemics.

Analysis of COVID-19 Incidence and Severity Among Adults Vaccinated With 2-Dose mRNA COVID-19 or Inactivated SARS-CoV-2 Vaccines With and Without Boosters in Singapore.

Importance: Assessing booster effectiveness of COVID-19 mRNA vaccine and inactivated SARS-CoV-2 vaccine over longer time intervals and in response to any further SARS-CoV-2 variants is crucial in determining optimal COVID-19 vaccination strategies. Objective: To determine levels of protection against severe COVID-19 and confirmed SARS-CoV-2 infection by types and combinations of vaccine boosters in Singapore during the Omicron wave. Design, Setting, and Participants: This cohort study included Singapore residents aged 30 years or more vaccinated with either at least 2 doses of mRNA COVID-19 vaccines (ie, Pfizer-BioNTech BNT162b2 or Moderna mRNA-1273) or inactivated SARS-CoV-2 vaccines (Sinovac CoronaVac or Sinopharm BBIBP-CorV) as of March 10, 2022. Individuals with a known SARS-CoV-2 infection prior to December 27, 2021, an infection on or before the date of their second vaccine dose, or with reinfection cases were excluded. Exposures: Two or 3 doses of Pfizer-BioNTech BNT162b2, Moderna mRNA-1273, Sinovac CoronaVac, or Sinopharm BBIBP-CorV. Main Outcomes and Measures: Notified infections from December 27, 2021, to March 10, 2022, adjusted for age, sex, race, housing status, and calendar days. Estimated booster effectiveness, defined as the relative incidence-rate reduction of severe disease (supplemental oxygen, intensive care, or death) or confirmed infection following 3-dose vaccination compared with 5 months after second mRNA dose, was determined using binomial regression. Results: Among 2 441 581 eligible individuals (1 279 047 [52.4%] women, 846 110 (34.7%) aged 60 years and older), there were 319 943 (13.1%) confirmed SARS-CoV-2 infections, of which 1513 (0.4%) were severe COVID-19 cases. mRNA booster effectiveness against confirmed infection 15 to 60 days after boosting was estimated to range from 31.7% to 41.3% for the 4 boosting combinations (homologous BNT162b2, homologous mRNA-1273, 2-dose BNT162b2/mRNA-1273 booster, and 2-dose mRNA-1273/BNT162b2 booster). Five months and more after boosting, estimated booster effectiveness against confirmed infection waned, ranging from -2.8% to 14.6%. Against severe COVID-19, estimated mRNA booster effectiveness was 87.4% (95% CI, 83.3%-90.5%) 15 to 60 days after boosting and 87.2% (95% CI, 84.2%-89.7%) 5 to 6 months after boosting, with no significant difference comparing vaccine combinations. Booster effectiveness against severe COVID-19 15 days to 330 days after 3-dose inactivated COVID-19 vaccination, regardless of combination, was estimated to be 69.6% (95% CI, 48.7%-81.9%). Conclusions and Relevance: Booster mRNA vaccine protection against severe COVID-19 was estimated to be durable over 6 months. Three-dose inactivated SARS-CoV-2 vaccination provided greater protection than 2-dose but weaker protection compared with 3-dose mRNA.

Trends in all-cause mortality among people with diagnosed diabetes in high-income settings: a multicountry analysis of aggregate data.

BACKGROUND: Population-level trends in mortality among people with diabetes are inadequately described. We aimed to examine the magnitude and trends in excess all-cause mortality in people with diabetes. METHODS: In this retrospective, multicountry analysis, we collected aggregate data from 19 data sources in 16 high-income countries or jurisdictions (in six data sources in Asia, eight in Europe, one from Australia, and four from North America) for the period from Jan 1, 1995, to Dec 31, 2016, (or a subset of this period) on all-cause mortality in people with diagnosed total or type 2 diabetes. We collected data from administrative sources, health insurance records, registries, and a health survey. We estimated excess mortality using the standardised mortality ratio (SMR). FINDINGS: In our dataset, there were approximately 21 million deaths during 0·5 billion person-years of follow-up among people with diagnosed diabetes. 17 of 19 data sources showed decreases in the age-standardised and sex-standardised mortality in people with diabetes, among which the annual percentage change in mortality ranged from -0·5% (95% CI -0·7 to -0·3) in Hungary to -4·2% (-4·3 to -4·1) in Hong Kong. The largest decreases in mortality were observed in east and southeast Asia, with a change of -4·2% (95% CI -4·3 to -4·1) in Hong Kong, -4·0% (-4·8 to -3·2) in South Korea, -3·5% (-4·0 to -3·0) in Taiwan, and -3·6% (-4·2 to -2·9) in Singapore. The annual estimated change in SMR between people with and without diabetes ranged from -3·0% (95% CI -3·0 to -2·9; US Medicare) to 1·6% (1·4 to 1·7; Lombardy, Italy). Among the 17 data sources with decreasing mortality among people with diabetes, we found a significant SMR increase in five data sources, no significant SMR change in four data sources, and a significant SMR decrease in eight data sources. INTERPRETATION: All-cause mortality in diabetes has decreased in most of the high-income countries we assessed. In eight of 19 data sources analysed, mortality decreased more rapidly in people with diabetes than in those without diabetes. Further longevity gains will require continued improvement in prevention and management of diabetes. FUNDING: US Centers for Disease Control and Prevention, Diabetes Australia Research Program, and Victoria State Government Operational Infrastructure Support Program.

Changes in the prevalence of comorbidity of mental and physical disorders in Singapore between 2010 and 2016.

INTRODUCTION: Few studies have examined the changes in the prevalence of comorbidity of mental and physical disorders in recent years. The present study sought to examine whether the prevalence of comorbidity of mental and physical disorders in Singapore showed any changes between 2010 and 2016. METHODS: We extracted data from two repeated nationally representative cross-sectional surveys conducted among resident adults aged ≥ 18 years in Singapore. Significant changes were tested using pooled multinomial logistic regression analyses. RESULTS: The prevalence of comorbid mental and physical disorders increased significantly from 5.8% in 2010 to 6.7% in 2016. Among those with physical disorders, there were significant increases over time in the prevalence of comorbid generalised anxiety disorder (GAD) (0.1% vs. 0.4%) and obsessive-compulsive disorder (OCD) (1.4% vs. 3.9%) in diabetes mellitus, and alcohol dependence in cardiovascular disorders (0.1% vs. 1.3%). Among those with mental disorders, there were significant increases over time in the prevalence of comorbid diabetes mellitus in OCD (4.1% vs. 10.9%), cancer in major depressive disorder (0.4% vs. 2.4%), and cardiovascular disorders in GAD (0.4% vs. 6.7%) and alcohol dependence (0.9% vs. 11.8%). Significant changes in the overall prevalence of comorbid mental and physical disorders were also observed across age group, education and employment status. CONCLUSION: The prevalence of comorbid mental and physical disorders increased significantly over time. This finding supports the need for more appropriate clinical management with better integration between mental health and general medical care professionals across all aspects of the healthcare system to treat this comorbidity in Singapore.

Association of Systolic and Diastolic Blood Pressure With All-Cause Mortality Among Community-Dwelling Older Adults: A Prospective Observational Study.

OBJECTIVES: To assess the association of systolic blood pressure (SBP) and diastolic blood pressure (DBP) with mortality among older adults in Singapore. METHODS: Association of SBP and DBP measured in 2009 for 4443 older adults (69.5±7.4 years; 60-97 years) participating in a nationally representative study with mortality risk through end-December 2015 was assessed using Cox regression. RESULTS: Higher mortality risk was observed at the lower and upper extremes of SBP and DBP. With SBP of 100-119 mmHg as the reference, multivariable mortality hazard ratios [HRs (95% confidence interval)] were SBP <100 mmHg: 2.41 (1.23-4.72); SBP 160-179 mmHg: 1.51 (1.02-2.22); and SBP ≥180 mmHg: 1.78 (1.12-2.81). With DBP of 70-79 mmHg as the reference, HRs were DBP <50 mmHg: 2.41 (1.28-4.54) and DBP ≥110 mmHg: 2.16 (1.09-4.31). DISCUSSION: Management of high blood pressure among older adults will likely reduce their mortality risk. However, the association of excessively low SBP and DBP values with mortality risk needs further evaluation.

Transitions between social network profiles and their relation with all-cause mortality among older adults.

BACKGROUND: The importance of social network in mortality among older adults has been acknowledged. However, existing studies typically overlook the interplay among social network indicators, and seldom consider the change in social network over time. We take a person-centered approach to identify transitions between social network profiles, and examine the linkage of such transitions with subsequent mortality risk. METHODS: Data came from 2738 older Singaporeans enrolled in a longitudinal study. Latent transition analysis examined nine social network indicators assessed at two time points, two years apart to reveal the corresponding transitions between social network profiles. Adjusted Cox regression then associated the identified transitions with all-cause mortality risk in the subsequent four years. RESULTS: We observed three diverse social network profiles (that featured diversified social interaction and involved both household and non-household members) and three relatively restricted social network profiles (that focused on household or non-household members). Compared to sustained diverse profiles over time, sustained restricted profiles or a transition from diverse to restricted profiles entailed increased mortality risk. The mortality risk following a restricted-to-diverse profile transition could be as low as that associated with sustained diverse profiles. CONCLUSIONS: Dynamics of social network profiles have implications for mortality risk in later life. To promote longevity, initiatives are needed to help older people to maintain or shift to diverse social network profiles.

Gender differences in years of remaining life by living arrangement among older Singaporeans.

Living arrangements of older adults have often been studied as a measure of the support available to them. Given the rapidly ageing and low fertility context of Singapore where the prevalence of older adults living alone and without children is expected to increase, we construct multistate life tables to estimate the number of years that older persons can expect to live in different living arrangements at a population level (population-based) as well as based on their initial living arrangement (status-based). We focus particularly on whether there are gender differences in the expected years of life in different living arrangement states. We use the Panel on Health and Ageing of Singaporean Elderly, a 2009 nationally representative survey of 4990 Singaporeans aged 60 years and older, with follow-up surveys in 2011 and 2015. In calculating the probabilities of transition between different states, we control for number of children, housing type, and time-varying ADL limitations. We find that at age 60, women can expect to spend more than twice the proportion (18%) of their remaining lives living alone compared to men (7%). Status-based estimates indicate that the proportion of remaining years living with a child is higher for women initially living alone, with a spouse only or already with a child, compared to males. Our results indicate that while older women are more likely to live alone compared to their male counterparts, older women living alone are also more likely to transition to living with children. Our research sheds light on the importance of expanding research on life expectancy beyond health, to consider analysis using other forms of social stratification, particularly gender differences in states of living arrangement. SUPPLEMENTARY INFORMATION: The online version of this article contains supplementary material available at 10.1007/s10433-020-00594-3.

Loneliness and health expectancy among older adults: A longitudinal population-based study.

BACKGROUND: The population health impact of loneliness remains unknown. We quantified the impact of loneliness on total life expectancy (TLE) and health expectancy (the duration of remaining life lived in different health states) among older adults, aged ≥60 years. DESIGN: Multistate life table analysis of a nationally representative longitudinal survey. SETTING: Singapore. PARTICIPANTS: Survey participants (n = 3449) interviewed in 2009, 2011-12, and 2015. MEASUREMENTS: Health states were defined using self-rated health (SRH) status and activity of daily living (ADL)/instrumental ADL (IADL) status. Participants with somewhat or very unhealthy SRH were considered as unhealthy. Those reporting health-related difficulty with any ADL/IADL were considered to have limitation in ADLs/IADLs. TLE and health expectancy (healthy and unhealthy life expectancy (HLE and UHLE) in the context of SRH, and active and inactive life expectancy (ALE and IALE) in the context of ADLs/IADLs) were estimated using the multistate life table method with a microsimulation approach, considering loneliness as time varying. RESULTS: At age 60, 70, and 80, those sometimes lonely or mostly lonely generally had shorter TLE, HLE and ALE, similar UHLE and IALE, and a higher proportion of remaining life with unhealthy SRH or with ADL/IADL limitations versus those never lonely. For example, at the age of 60, those sometimes lonely versus never lonely had shorter TLE (by 5.4 [95% Confidence Interval: 3.4-7.9] years), shorter HLE (by 5.9 [4.1-8.6] years), similar UHLE (difference: 0.6 [-0.7-1.7] years), and higher proportion of remaining life with unhealthy SRH (by 6.2 [1.2-10.8] percentage points). For those mostly lonely versus never lonely, TLE was shorter by 3.6 (0.7-6.6) years, HLE was shorter by 4.8 (2.3-8.2) years, UHLE was similar (difference: 1.2 [-0.1-4.0] years), and proportion of remaining life with unhealthy SRH was higher by 7.2 (2.1-18.1) percentage points. CONCLUSION: Identification and management of loneliness may increase years of life with healthy SRH and without limitation in ADLs/IADLs among older adults.

Risk scorecard to minimize impact of COVID-19 when reopening.

BACKGROUND: We present a novel approach for exiting coronavirus disease 2019 (COVID-19) lockdowns using a 'risk scorecard' to prioritize activities to resume whilst allowing safe reopening. METHODS: We modelled cases generated in the community/week, incorporating parameters for social distancing, contact tracing and imported cases. We set thresholds for cases and analysed the effect of varying parameters. An online tool to facilitate country-specific use including the modification of parameters (https://sshsphdemos.shinyapps.io/covid_riskbudget/) enables visualization of effects of parameter changes and trade-offs. Local outbreak investigation data from Singapore illustrate this. RESULTS: Setting a threshold of 0.9 mean number of secondary cases arising from a case to keep R < 1, we showed that opening all activities excluding high-risk ones (e.g. nightclubs) allows cases to remain within threshold; while opening high-risk activities would exceed the threshold and result in escalating cases. An 80% reduction in imported cases per week (141 to 29) reduced steady-state cases by 30% (295 to 205). One-off surges in cases (due to superspreading) had no effect on the steady state if the R remains <1. Increasing the effectiveness of contact tracing (probability of a community case being isolated when infectious) by 33% (0.6 to 0.8) reduced cases by 22% (295 to 231). Cases grew exponentially if the product of the mean number of secondary cases arising from a case and (1-probability of case being isolated) was >1. CONCLUSIONS: Countries can utilize a 'risk scorecard' to balance relaxations for travel and domestic activity depending on factors that reduce disease impact, including hospital/ICU capacity, contact tracing, quarantine and vaccination. The tool enabled visualization of the combinations of imported cases and activity levels on the case numbers and the trade-offs required. For vaccination, a reduction factor should be applied both for likelihood of an infected case being present and a close contact getting infected.

Spatial-temporal potential exposure risk analytics and urban sustainability impacts related to COVID-19 mitigation: A perspective from car mobility behaviour.

Coronavirus disease-2019 (COVID-19) poses a significant threat to the population and urban sustainability worldwide. The surge mitigation is complicated and associates many factors, including the pandemic status, policy, socioeconomics and resident behaviours. Modelling and analytics with spatial-temporal big urban data are required to assist the mitigation of the pandemic. This study proposes a novel perspective to analyse the spatial-temporal potential exposure risk of residents by capturing human behaviours based on spatial-temporal car park availability data. Near real-time data from 1,904 residential car parks in Singapore, a classical megacity, are collected to analyse car mobility and its spatial-temporal heat map. The implementation of the circuit breaker, a COVID-19 measure, in Singapore has reduced the mobility and heat (daily frequency of mobility) significantly at about 30.0%. It contributes to a 44.3%-55.4% reduction in the transportation-related air emissions under two scenarios of travelling distance reductions. Urban sustainability impacts in both environment and economy are discussed. The spatial-temporal potential exposure risk mapping with space-time interactions is further investigated via an extended Bayesian spatial-temporal regression model. The maximal reduction rate of the defined potential exposure risk lowers to 37.6% by comparison with its peak value. The big data analytics of changes in car mobility behaviour and the resultant potential exposure risks can provide insights to assist in (a) designing a flexible circuit breaker exit strategy, (b) precise management via identifying and tracing hotspots on the mobility heat map, and (c) making timely decisions by fitting curves dynamically in different phases of COVID-19 mitigation. The proposed method has the potential to be used by decision-makers worldwide with available data to make flexible regulations and planning.

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.

Real-time monitoring the transmission potential of COVID-19 in Singapore, March 2020.

Background: As of March 31, 2020 the ongoing COVID-19 epidemic that started in China in December 2019 is now generating local transmission around the world. The geographic heterogeneity and associated intervention strategies highlight the need to monitor in real time the transmission potential of COVID-19. Singapore provides a unique case example for monitoring transmission, as there have been multiple disease clusters, yet transmission remains relatively continued. Methods: Here we estimate the effective reproduction number, Rt, of COVID-19 in Singapore from the publicly available daily case series of imported and autochthonous cases by date of symptoms onset, after adjusting the local cases for reporting delays as of March 17, 2020. We also derive the reproduction number from the distribution of cluster sizes using a branching process analysis that accounts for truncation of case counts. Results: The local incidence curve displays sub-exponential growth dynamics, with the reproduction number following a declining trend and reaching an estimate at 0.7 (95% CI: 0.3, 1.0) during the first transmission wave by February 14, 2020 while the overall R based on the cluster size distribution as of March 17, 2020 was estimated at 0.6 (95% CI: 0.4, 1.02). The overall mean reporting delay was estimated at 6.4 days (95% CI: 5.8, 6.9), but it was shorter among imported cases compared to local cases (mean 4.3 vs. 7.6 days, Wilcoxon test, p<0.001). Conclusion: The trajectory of the reproduction number in Singapore underscores the significant effects of successful containment efforts in Singapore, but it also suggests the need to sustain social distancing and active case finding efforts to stomp out all active chains of transmission.

Real-time monitoring the transmission potential of COVID-19 in Singapore, March 2020.

BACKGROUND: As of March 31, 2020, the ongoing COVID-19 epidemic that started in China in December 2019 is now generating local transmission around the world. The geographic heterogeneity and associated intervention strategies highlight the need to monitor in real time the transmission potential of COVID-19. Singapore provides a unique case example for monitoring transmission, as there have been multiple disease clusters, yet transmission remains relatively continued. METHODS: Here we estimate the effective reproduction number, Rt, of COVID-19 in Singapore from the publicly available daily case series of imported and autochthonous cases by date of symptoms onset, after adjusting the local cases for reporting delays as of March 17, 2020. We also derive the reproduction number from the distribution of cluster sizes using a branching process analysis that accounts for truncation of case counts. RESULTS: The local incidence curve displays sub-exponential growth dynamics, with the reproduction number following a declining trend and reaching an estimate at 0.7 (95% CI 0.3, 1.0) during the first transmission wave by February 14, 2020, while the overall R based on the cluster size distribution as of March 17, 2020, was estimated at 0.6 (95% CI 0.4, 1.02). The overall mean reporting delay was estimated at 6.4 days (95% CI 5.8, 6.9), but it was shorter among imported cases compared to local cases (mean 4.3 vs. 7.6 days, Wilcoxon test, p < 0.001). CONCLUSION: The trajectory of the reproduction number in Singapore underscores the significant effects of successful containment efforts in Singapore, but it also suggests the need to sustain social distancing and active case finding efforts to stomp out all active chains of transmission.

Decreased Influenza Incidence under COVID-19 Control Measures, Singapore.

We compared indicators of influenza activity in 2020 before and after public health measures were taken to reduce coronavirus disease (COVID-19) with the corresponding indicators from 3 preceding years. Influenza activity declined substantially, suggesting that the measures taken for COVID-19 were effective in reducing spread of other viral respiratory diseases.

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.