An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia hit a major city in China, Wuhan, December 2019 and subsequently reached other provinces/regions of China and other countries. We present estimates of the basic reproduction number, R0, of 2019-nCoV in the early phase of the outbreak. Accounting for the impact of the variations in disease reporting rate, we modelled the epidemic curve of 2019-nCoV cases time series, in mainland China from January 10 to January 24, 2020, through the exponential growth. With the estimated intrinsic growth rate (γ), we estimated R0 by using the serial intervals (SI) of two other well-known coronavirus diseases, MERS and SARS, as approximations for the true unknown SI. The early outbreak data largely follows the exponential growth. We estimated that the mean R0 ranges from 2.24 (95%CI: 1.96-2.55) to 3.58 (95%CI: 2.89-4.39) associated with 8-fold to 2-fold increase in the reporting rate. We demonstrated that changes in reporting rate substantially affect estimates of R0. The mean estimate of R0 for the 2019-nCoV ranges from 2.24 to 3.58, and is significantly larger than 1. Our findings indicate the potential of 2019-nCoV to cause outbreaks.

Background: In December 2019, an outbreak of respiratory illness caused by a novel coronavirus (2019-nCoV) emerged in Wuhan, China and has swiftly spread to other parts of China and a number of foreign countries. The 2019-nCoV cases might have been under-reported roughly from 1 to 15 January 2020, and thus we estimated the number of unreported cases and the basic reproduction number, R₀, of 2019-nCoV. Methods: We modelled the epidemic curve of 2019-nCoV cases, in mainland China from 1 December 2019 to 24 January 2020 through the exponential growth. The number of unreported cases was determined by the maximum likelihood estimation. We used the serial intervals (SI) of infection caused by two other well-known coronaviruses (CoV), Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) CoVs, as approximations of the unknown SI for 2019-nCoV to estimate R₀. Results: We confirmed that the initial growth phase followed an exponential growth pattern. The under-reporting was likely to have resulted in 469 (95% CI: 403–540) unreported cases from 1 to 15 January 2020. The reporting rate after 17 January 2020 was likely to have increased 21-fold (95% CI: 18–25) in comparison to the situation from 1 to 17 January 2020 on average. We estimated the R₀ of 2019-nCoV at 2.56 (95% CI: 2.49–2.63). Conclusion: The under-reporting was likely to have occurred during the first half of January 2020 and should be considered in future investigation.

Aims: We estimated the association between outdoor light at night at the residence and risk of coronary heart disease (CHD) within a prospective cohort of older adults in Hong Kong. Methods and results: Over a median of 11 years of follow-up, we identified 3772 incident CHD hospitalizations and 1695 CHD deaths. Annual levels of outdoor light at night at participants’ residential addresses were estimated using time-varying satellite data for a composite of persistent night-time illumination at ∼1 km2 scale. We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of the association between outdoor light at night at the residence and risk of CHD. The association between light at night and incident CHD hospitalization and mortality exhibited a monotonic exposure-response function. An interquartile range (IQR) (60.0 nW/cm2/sr) increase in outdoor light at night was associated with an HR of 1.11 (95% CI: 1.03, 1.18) for CHD hospitalizations and 1.10 (95% CI: 1.00, 1.22) for CHD deaths after adjusting for both individual and area-level risk factors. The association did not vary across strata of hypothesized risk factors. Conclusion: Among older adults, outdoor light at night at the residence was associated with a higher risk of CHD hospitalizations and deaths. We caution against causal interpretation of these novel findings. Future studies with more detailed information on exposure, individual adaptive behaviours, and potential mediators are warranted to further examine the relationship between light at night and CHD risk.

Backgrounds: An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia hit a major city of China, Wuhan, December 2019 and subsequently reached other provinces/regions of China and countries. We present estimates of the basic reproduction number,R₀, of 2019-nCoV in the early phase of the outbreak.Methods: Accounting for the impact of the variations in disease reporting rate, we modelled the epidemic curve of 2019-nCoV cases time series, in mainland China from January 10 to January 24, 2020, through the exponential growth. With the estimated intrinsic growth rate (γ), we estimatedR₀by using the serial intervals (SI) of two other well-known coronavirus diseases, MERS and SARS, as approximations for the true unknown SI.Findings: The early outbreak data largely follows the exponential growth. We estimated that the meanR₀ranges from 2.24 (95%CI: 1.96-2.55) to 3.58 (95%CI: 2.89-4.39) associated with 8-fold to 2-fold increase in the reporting rate. We demonstrated that changes in reporting rate substantially affect estimates ofR₀.Conclusion: The mean estimate ofR₀for the 2019-nCoV ranges from 2.24 to 3.58, and significantly larger than 1. Our findings indicate the potential of 2019-nCoV to cause outbreaks.

Background: Concerning the changes in the prevalence of neurodevelopmental disorders (NDDs), we estimate the prevalence of attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), intellectual disorder (ID), and learning disability (LD) among US children and adolescents aged 3–17 years in 2019 and 2020.Methods: The study includes 14,983 US children and adolescents aged 3–17 years in 2019 and 2020 from the National Health Interview Survey (NHIS). Parents were interviewed about whether their children ever and/or currently had NDDs diagnosed. Prevalence estimates of NDDs were calculated with a survey-based weighting scheme. Logistic regression models were used to estimate the associations between NDDs prevalence and subgroups.Results: The weighted prevalence of ADHD, ASD, ID, and LD was 8.5% (95% CI: 7.9–9.2%), 2.9% (95% CI: 2.6–3.4%), 1.4% (95% CI: 1.2–1.7%), and 6.4% (95% CI: 5.8–7.0%), respectively. A higher prevalence of ADHD, ASD, ID, and LD was observed in boys, those who ever had anxiety or depression symptoms, those with lower family income, those living in a rented house, ever been bullied, and ever lived with anyone mentally ill.Conclusion: The study found the prevalence of ADHD, ASD, ID, and LD was different by demographics, comorbidity/mental problems, household/parental characteristics, and stressful life events.