Risk-prediction nomogram for congenital heart disease in offspring of Chinese pregnant women.

BACKGROUND: The identification and assessment of environmental risks are crucial for the primary prevention of congenital heart disease (CHD). We were aimed to establish a nomogram model for CHD in the offspring of pregnant women and validate it using a large CHD database in Northwest China. METHODS: A survey was conducted among 29,204 women with infants born between 2010 and 2013 in Shaanxi province, Northwest China. Participants were randomly assigned to the training set and to the validation set at a ratio of 7:3. The importance of predictive variables was assessed using random forest. A multivariate logistic regression model was used to construct the nomogram for the prediction of CHD. RESULTS: Multivariate analyses revealed that the gravidity, preterm birth history, family history of birth defects, infection, taking medicine, tobacco exposure, pesticide exposure and singleton/twin pregnancy were significant predictive risk factors for CHD in the offspring of pregnant women. The area under the receiver operating characteristic curve for the prediction model was 0.716 (95% CI: 0.671, 0.760) in the training set and 0.714 (95% CI: 0.630, 0.798) in the validation set, indicating moderate discrimination. The prediction model exhibited good calibration (Hosmer-Lemeshow χ2 = 1.529, P = 0.910). CONCLUSIONS: We developed and validated a predictive nomogram for CHD in offspring of Chinese pregnant women, facilitating the early prenatal assessment of the risk of CHD and aiding in health education.

Modelling the impact of treatment adherence on the transmission of HIV drug resistance.

INTRODUCTION: A lower adherence rate (percentage of individuals taking drugs as prescribed) to ART may increase the risk of emergence and transmission of HIV drug resistance, decrease treatment efficacy, and increase mortality rate. Exploring the impact of ART adherence on the transmission of drug resistance could provide insights in controlling the HIV epidemic. METHODS: We proposed a dynamic transmission model incorporating the CD4 cell count-dependent rates of diagnosis, treatment and adherence with transmitted drug resistance (TDR) and acquired drug resistance. This model was calibrated and validated by 2008-2018 HIV/AIDS surveillance data and prevalence of TDR among newly diagnosed treatment-naive individuals from Guangxi, China, respectively. We aimed to identify the impact of adherence on drug resistance and deaths during expanding ART. RESULTS: In the base case (ART at 90% adherence and 79% coverage), we projected the cumulative total new infections, new drug-resistant infections, and HIV-related deaths between 2022 and 2050 would be 420 539, 34 751 and 321 671. Increasing coverage to 95% would reduce the above total new infections (deaths) by 18.85% (15.75%). Reducing adherence to below 57.08% (40.84%) would offset these benefits of increasing coverage to 95% in reducing infections (deaths). Every 10% decrease in adherence would need 5.07% (3.62%) increase in coverage to avoid an increase in infections (deaths). Increasing coverage to 95% with 90% (80%) adherence would increase the above drug-resistant infections by 11.66% (32.98%). CONCLUSIONS: A decrease in adherence might offset the benefits of ART expansion and exacerbate the transmission of drug resistance. Ensuring treated patients' adherence might be as important as expanding ART to untreated individuals.

A multi-strain model with asymptomatic transmission: Application to COVID-19 in the US.

COVID-19, induced by the SARS-CoV-2 infection, has caused an unprecedented pandemic in the world. New variants of the virus have emerged and dominated the virus population. In this paper, we develop a multi-strain model with asymptomatic transmission to study how the asymptomatic or pre-symptomatic infection influences the transmission between different strains and control strategies that aim to mitigate the pandemic. Both analytical and numerical results reveal that the competitive exclusion principle still holds for the model with the asymptomatic transmission. By fitting the model to the COVID-19 case and viral variant data in the US, we show that the omicron variants are more transmissible but less fatal than the previously circulating variants. The basic reproduction number for the omicron variants is estimated to be 11.15, larger than that for the previous variants. Using mask mandate as an example of non-pharmaceutical interventions, we show that implementing it before the prevalence peak can significantly lower and postpone the peak. The time of lifting the mask mandate can affect the emergence and frequency of subsequent waves. Lifting before the peak will result in an earlier and much higher subsequent wave. Caution should also be taken to lift the restriction when a large portion of the population remains susceptible. The methods and results obtained her e may be applied to the study of the dynamics of other infectious diseases with asymptomatic transmission using other control measures.

Prediction of occult tumor progression via platelet RNAs in a mouse melanoma model: a potential new platform for early detection of cancer.

BACKGROUND: Cancer screening provides the opportunity to detect cancer early, ideally before symptom onset and metastasis, and offers an increased opportunity for a better prognosis. The ideal biomarkers for cancer screening should discriminate individuals who have not developed invasive cancer yet but are destined to do so from healthy subjects. However, most cancers lack effective screening recommendations. Therefore, further studies on novel screening strategies are urgently required. METHODS: We used a simple suboptimal inoculation melanoma mouse model to obtain 'pre-diagnostic samples' of mice with macroscopic melanomas. High-throughput sequencing and bioinformatic analysis were employed to identify differentially expressed RNAs in platelet signatures of mice injected with a suboptimal number of melanoma cells (eDEGs) compared with mice with macroscopic melanomas and negative controls. Moreover, 36 genes selected from the eDEGs via bioinformatics analysis were verified in a mouse validation cohort via quantitative real-time PCR. LASSO regression was utilized to generate the prediction models with gene expression signatures as the best predictors for occult tumor progression in mice. RESULTS: These RNAs identified from eDEGs of mice injected with a suboptimal number of cancer cells were strongly enriched in pathways related to immune response and regulation. The prediction models generated by 36 gene qPCR verification data showed great diagnostic efficacy and predictive value in our murine validation cohort, and could discriminate mice with occult tumors from control group (area under curve (AUC) of 0.935 (training data) and 0.912 (testing data)) (gene signature including Cd19, Cdkn1a, S100a9, Tap1, and Tnfrsf1b) and also from macroscopic tumor group (AUC of 0.920 (training data) and 0.936 (testing data)) (gene signature including Ccr7, Cd4, Kmt2d, and Ly6e). CONCLUSIONS: Our proof-of-concept study provides evidence for potential clinical relevance of blood platelets as a platform for liquid biopsy-based early detection of cancer.

COVID-19 epidemic in New York City: development of an age group-specific mathematical model to predict the outcome of various vaccination strategies.

BACKGROUND: Since December 14, 2020, New York City (NYC) has started the first batch of COVID-19 vaccines. However, the shortage of vaccines is currently an inevitable problem. Therefore, optimizing the age-specific COVID-19 vaccination is an important issue that needs to be addressed as a priority. OBJECTIVE: Combined with the reported COVID-19 data in NYC, this study aimed to construct a mathematical model with five age groups to estimate the impact of age-specific vaccination on reducing the prevalence of COVID-19. METHODS: We proposed an age-structured mathematical model and estimated the unknown parameters based on the method of Markov Chain Monte Carlo (MCMC). We also calibrated our model by using three different types of reported COVID-19 data in NYC. Moreover, we evaluated the reduced cumulative number of deaths and new infections with different vaccine allocation strategies. RESULTS: Compared with the current vaccination strategy in NYC, if we gradually increased the vaccination coverage rate for only one age groups from March 1, 2021 such that the vaccination coverage rate would reach to 40% by June 1, 2021, then as of June 1, 2021, the cumulative deaths in the 75-100 age group would be reduced the most, about 72 fewer deaths per increased 100,000 vaccinated individuals, and the cumulative new infections in the 0-17 age group would be reduced the most, about 21,591 fewer new infections per increased 100,000 vaccinated individuals. If we gradually increased the vaccination coverage rate for two age groups from March 1, 2021 such that the vaccination coverage rate would reach to 40% by June 1, 2021, then as of June 1, 2021, the cumulative deaths in the 65-100 age group would be reduced the most, about 36 fewer deaths per increased 100,000 vaccinated individuals, and the cumulative new infections in the 0-44 age group would be reduced the most, about 17,515 fewer new infections per increased 100,000 vaccinated individuals. In addition, if we had an additional 100,000 doses of vaccine for 0-17 and 75-100 age groups as of June 1, 2021, then the allocation of 80% to the 0-17 age group and 20% to the 75-100 age group would reduce the maximum numbers of new infections and deaths simultaneously in NYC. CONCLUSIONS: The COVID-19 burden including deaths and new infections would decrease with increasing vaccination coverage rate. Priority vaccination to the elderly and adolescents would minimize both deaths and new infections.

The impact of supplementary immunization activities on measles transmission dynamics and implications for measles elimination goals: A mathematical modelling study.

BACKGROUND: Measles has re-emerged globally due to the accumulation of susceptible individuals and immunity gap, which causes challenges in eliminating measles. Routine vaccination and supplementary immunization activities (SIAs) have greatly improved measles control, but the impact of SIAs on the measles transmission dynamics remains unclear as the vaccine-induced immunity wanes. METHODS: We developed a comprehensive measles transmission dynamics model by taking into account population demographics, age-specific contact patterns, seasonality, routine vaccination, SIAs, and the waning vaccine-induced immunity. The model was calibrated by the monthly age-specific cases data from 2005 to 2018 in Jiangsu Province, China, and validated by the dynamic sero-prevalence data. We aimed to investigate the short-term and long-term impact of three-time SIAs during 2009-2012 (9.68 million and 4.25 million children aged 8 months-14 years in March 2009 and September 2010, respectively, and 140,000 children aged 8 months-6 years in March 2012) on the measles disease burden and explored whether additional SIAs could accelerate the measles elimination. RESULTS: We estimated that the cumulative numbers of measles cases from March 2009 to December 2012 (in the short run) and to December 2018 (in the long run) after three-time SIAs (base case) were 6,699 (95% confidence interval [CI]: 2,928-10,469), and 22,411 (15,146-29,675), which averted 45.0% (42.9%-47.0%) and 34.3% (30.7%-37.9%) of 12,226 (4,916-19,537) and 34,274 (21,350-47,199) cases without SIAs, respectively. The fraction of susceptibles for children aged 8-23 months and 2-14 years decreased from 8.3% and 10.8% in March 2009 to 5.8% and 5.8% in April 2012, respectively. However, the fraction of susceptibles aged 15-49 years and above 50 years increased gradually to about 15% in 2018 irrespective of SIAs due to the waning immunity. The measles elimination goal would be reached in 2028, and administrating additional one-off SIAs in September 2022 to children aged 8-23 months, or young adolescents aged 15-19 years could accelerate the elimination one year earlier. CONCLUSIONS: SIAs have greatly reduced the measles incidence and the fraction of susceptibles, but the benefit may wane over time. Under the current interventions, Jiangsu province would reach the measles elimination goal in 2028. Additional SIAs may accelerate the measles elimination one year earlier.

Modelling the prevalence of hepatitis B towards eliminating it as a major public health threat in China.

BACKGROUND: The World Health Organization (WHO) requires reduction in the prevalence of hepatitis B virus (HBV) surface antigen (HBsAg) in children to 0.1% by 2030, a key indicator for eliminating viral hepatitis as a major public health threat. Whether and how China can achieve this target remains unknown, although great achievements have been made. We aimed to predict the decline of HBsAg prevalence in China and identify key developments needed to achieve the target. METHODS: An age- and time-dependent dynamic compartmental model was constructed based on the natural history of HBV infection and the national history and current status of hepatitis B control. The model was run from 2006 to 2040 to predict the decline of HBsAg prevalence under three scenarios including maintaining current interventions (status quo), status quo + peripartum antiviral prophylaxis (PAP, recommended by WHO in 2020), and scaling up current interventions + PAP. RESULTS: Under the status quo, HBsAg prevalence would decrease steadily in all age groups, but the WHO's target of 0.1% prevalence in children aged < 5 years would not be achieved until 2037. The results are robust according to sensitivity analyses. Under the status quo + PAP, the HBsAg prevalence of children aged < 5 years would significantly decrease with the introduction of PAP, and the higher the successful interruption coverage is achieved by PAP, the more significant the decline. However, even if the successful interruption coverage reaches 90% by 2030, the 0.1% prevalence target would not be met until 2031. Under the scaling up current interventions + PAP, combined with scale-up of current interventions, the WHO's 0.1% target would be achieved on time or one year in advance if PAP is introduced and the successful interruption coverage is scaled up to 80% or 90% by 2030, respectively. CONCLUSIONS: It is difficult for China to achieve the WHO's target of 0.1% HBsAg prevalence in children by 2030 by maintaining current interventions. PAP may play an important role to shorten the time to achieve the target. A comprehensive scale-up of available interventions including PAP will ensure that China achieves the target on schedule.

Modelling the potential role of saliva use during masturbation in the transmission of Neisseria gonorrhoeae at multiple anatomical sites.

Background Neisseria gonorrhoeae can be cultured from saliva in men with pharyngeal gonorrhoea and could theoretically be transmitted from the pharynx to the urethra when saliva is used as a lubricant for masturbation. In this work, we proposed that saliva use during masturbation may be a potential transmission route of gonorrhoea. Methods We analysed the transmission of Neisseria gonorrhoeae at the oropharynx, urethra and anorectum with mathematical models among men who have sex with men using data from six different studies. Model 1 included transmission routes (oral sex, anal sex, rimming, kissing, and three sequential sex practices). In Model 2, we added saliva use during solo masturbation and mutual masturbation to model 1. Results Model 2 could replicate single site infection at the oropharynx, urethra and anorectum and multi-site infection across six different datasets. However, the calibration of Model 2 was not significantly different from Model 1 across four datasets. Model 2 generated an incidence of gonorrhoea from masturbation of between 5.2% (95% CI: 3.2-10.1) to 10.6% (95% CI: 5.8-17.3) across six data sets. Model 2 also estimated that about one in four cases of urethral gonorrhoea might arise from solo masturbation and mutual masturbation. Conclusions Our models raise the possibility that saliva use during masturbation may play a role in transmitting gonorrhoea. This is an important area to explore because it contributes to the knowledge base about gonorrhoea transmission.

Modelling the contribution that different sexual practices involving the oropharynx and saliva have on Neisseria gonorrhoeae infections at multiple anatomical sites in men who have sex with men.

BACKGROUND: The spectrum of sexual practices that transmit Neisseria gonorrhoeae in men who have sex with men (MSM) is controversial. No studies have modelled potential Neisseria gonorrhoeae transmission when one sexual practice follows another in the same sexual encounter ('sequential sexual practices'). Our aim was to test what sequential practices were necessary to replicate the high proportion of MSM who have more than one anatomical site infected with gonorrhoea ('multisite infection'). METHODS: To test our aim, we developed eight compartmental models. We first used a baseline model (model 1) that included no sequential sexual practices. We then added three possible sequential transmission routes to model 1: (1) oral sex followed by anal sex (or vice versa) (model 2); (2) using saliva as a lubricant for penile-anal sex (model 3) and (3) oral sex followed by oral-anal sex (rimming) or vice versa (model 4). The next four models (models 5-8) used combinations of the three transmission routes. RESULTS: The baseline model could only replicate infection at the single anatomical site and underestimated multisite infection. When we added the three transmission routes to the baseline model, oral sex, followed by anal sex or vice versa, could replicate the prevalence of multisite infection. The other two transmission routes alone or together could not replicate multisite infection without the inclusion of oral sex followed by anal sex or vice versa. CONCLUSIONS: Our gonorrhoea model suggests sexual practices that involve oral followed by anal sex (or vice versa) may be important for explaining the high proportion of multisite infection.

Inferencing superspreading potential using zero-truncated negative binomial model: exemplification with COVID-19.

BACKGROUND: In infectious disease transmission dynamics, the high heterogeneity in individual infectiousness indicates that few index cases generate large numbers of secondary cases, which is commonly known as superspreading events. The heterogeneity in transmission can be measured by describing the distribution of the number of secondary cases as a negative binomial (NB) distribution with dispersion parameter, k. However, such inference framework usually neglects the under-ascertainment of sporadic cases, which are those without known epidemiological link and considered as independent clusters of size one, and this may potentially bias the estimates. METHODS: In this study, we adopt a zero-truncated likelihood-based framework to estimate k. We evaluate the estimation performance by using stochastic simulations, and compare it with the baseline non-truncated version. We exemplify the analytical framework with three contact tracing datasets of COVID-19. RESULTS: We demonstrate that the estimation bias exists when the under-ascertainment of index cases with 0 secondary case occurs, and the zero-truncated inference overcomes this problem and yields a less biased estimator of k. We find that the k of COVID-19 is inferred at 0.32 (95%CI: 0.15, 0.64), which appears slightly smaller than many previous estimates. We provide the simulation codes applying the inference framework in this study. CONCLUSIONS: The zero-truncated framework is recommended for less biased transmission heterogeneity estimates. These findings highlight the importance of individual-specific case management strategies to mitigate COVID-19 pandemic by lowering the transmission risks of potential super-spreaders with priority.

Modelling the impact of universal influenza vaccines on seasonal influenza with different subtypes.

Several candidates of universal influenza vaccine (UIV) have entered phase III clinical trials, which are expected to improve the willingness and coverage of the population substantially. The impact of UIV on the seasonal influenza epidemic in low influenza vaccination coverage regions like China remains unclear. We proposed a new compartmental model involving the transmission of different influenza subtypes to evaluate the effects of UIV. We calibrated the model by weekly surveillance data of influenza in Xi'an City, Shaanxi Province, China, during 2010/11-2018/19 influenza seasons. We calculated the percentage of averted infections under 2-month (September to October) and 6-month (September to the next February) vaccination patterns with varied UIV effectiveness and coverage in each influenza season, compared with no UIV scenario. A total of 195 766 influenza-like illness (ILI) cases were reported during the nine influenza seasons (2010/11-2018/19), of which the highest ILI cases were among age group 0-4 (59.60%) years old, followed by 5-14 (25.22%), 25-59 (8.19%), 15-24 (3.75%) and ⩾60 (3.37%) years old. The influenza-positive rate for all age groups among ILI cases was 17.51%, which is highest among 5-14 (23.75%) age group and followed by 25-59 (16.44%), 15-24 (16.42%), 0-4 (14.66%) and ⩾60 (13.98%) age groups, respectively. Our model showed that UIV might greatly avert influenza infections irrespective of subtypes in each influenza season. For example, in the 2018/19 influenza season, 2-month vaccination pattern with low UIV effectiveness (50%) and coverage (10%), and high UIV effectiveness (75%) and coverage (30%) could avert 41.6% (95% CI 27.8-55.4%) and 83.4% (80.9-85.9%) of influenza infections, respectively; 6-month vaccination pattern with low and high UIV effectiveness and coverage could avert 32.0% (15.9-48.2%) and 74.2% (69.7-78.7%) of influenza infections, respectively. It would need 11.4% (7.9-15.0%) of coverage to reduce half of the influenza infections for 2-month vaccination pattern with low UIV effectiveness and 8.5% (5.0-11.2%) of coverage with high UIV effectiveness, while it would need 15.5% (8.9-20.7%) of coverage for 6-month vaccination pattern with low UIV effectiveness and 11.2% (6.5-15.0%) of coverage with high UIV effectiveness. We conclude that UIV could significantly reduce the influenza infections even for low UIV effectiveness and coverage. The 2-month vaccination pattern could avert more influenza infections than the 6-month vaccination pattern irrespective of influenza subtype and UIV effectiveness and coverage.

Effects of New York's Executive Order on Face Mask Use on COVID-19 Infections and Mortality: A Modeling Study.

There is growing evidence on the effect of face mask use in controlling the spread of COVID-19. However, few studies have examined the effect of local face mask policies on the pandemic. In this study, we developed a dynamic compartmental model of COVID-19 transmission in New York City (NYC), which was the epicenter of the COVID-19 pandemic in the USA. We used data on daily and cumulative COVID-19 infections and deaths from the NYC Department of Health and Mental Hygiene to calibrate and validate our model. We then used the model to assess the effect of the executive order on face mask use on infections and deaths due to COVID-19 in NYC. Our results showed that the executive order on face mask use was estimated to avert 99,517 (95% CIs 72,723-126,312) COVID-19 infections and 7978 (5692-10,265) deaths in NYC. If the executive order was implemented 1 week earlier (on April 10), the averted infections and deaths would be 111,475 (81,593-141,356) and 9017 (6446-11,589), respectively. If the executive order was implemented 2 weeks earlier (on April 3 when the Centers for Disease Control and Prevention recommended face mask use), the averted infections and deaths would be 128,598 (94,373-162,824) and 10,515 (7540-13,489), respectively. Our study provides public health practitioners and policymakers with evidence on the importance of implementing face mask policies in local areas as early as possible to control the spread of COVID-19 and reduce mortality.

Dynamics of a new HIV model with the activation status of infected cells.

The activation status can dictate the fate of an HIV-infected CD4+ T cell. Infected cells with a low level of activation remain latent and do not produce virus, while cells with a higher level of activation are more productive and thus likely to transfer more virions to uninfected cells during cell-to-cell transmission. How the activation status of infected cells affects HIV dynamics under antiretroviral therapy remains unclear. We develop a new mathematical model that structures the population of infected cells continuously according to their activation status. The effectiveness of antiretroviral drugs in blocking cell-to-cell viral transmission decreases as the level of activation of infected cells increases because the more virions are transferred from infected to uninfected cells during cell-to-cell transmission, the less effectively the treatment is able to inhibit the transmission. The basic reproduction number [Formula: see text] of the model is shown to determine the existence and stability of the equilibria. Using the principal spectral theory and comparison principle, we show that the infection-free equilibrium is locally and globally asymptotically stable when [Formula: see text] is less than one. By constructing Lyapunov functional, we prove that the infected equilibrium is globally asymptotically stable when [Formula: see text] is greater than one. Numerical investigation shows that even when treatment can completely block cell-free virus infection, virus can still persist due to cell-to-cell transmission. The random switch between infected cells with different activation levels can also contribute to the replenishment of the latent reservoir, which is considered as a major barrier to viral eradication. This study provides a new modeling framework to study the observations, such as the low viral load persistence, extremely slow decay of latently infected cells and transient viral load measurements above the detection limit, in HIV-infected patients during suppressive antiretroviral therapy.

Evaluating the independent influence of sexual transmission on HBV infection in China: a modeling study.

BACKGROUND: The long-term impact of sexual transmission on the hepatitis B virus (HBV) infection in China remains unclear. This study aims to estimate the independent influence of sexual transmission on HBV infection. METHODS: Based on the natural history of HBV infection and three national serosurvey data of hepatitis B in China, we developed an age- and sex-specific discrete model to describe the transmission dynamics of HBV. The initial conditions of the model were determined according to the age- and sex-specific national serosurvey data in 1992. Based on the national survey data of hepatitis B in 1992 and 2006, by using the Markov Chain Monte Carlo (MCMC) method, we estimated the age- and sex-specific seroclearance rates of hepatitis B surface antigen (HBsAg) and the horizontal transmission rates as well as their 95% confidence intervals (CI). Then we used the age- and sex-specific national serosurvey data of hepatitis B in 2014 to test the accuracy of our model-based estimation. Finally, we evaluated the independent impact of sexual transmission on HBV infection and discussed the long-term effect of promotion of condom use in China. RESULTS: We estimated that the annual rates of HBsAg seroclearance for males and females aged 1-59 years were respectively 1.04% (95% CI, 0.49-1.59%) and 1.92% (95% CI, 1.11-2.73%). Due to sexual transmission, in 2014, the total number of chronic HBV infections in people aged 0-100 years increased 292,581, of which males increased 189,200 and females increased 103,381. In 2006, the acute HBV infections due to sexual transmission accounted for 24.76% (male: 31.33%, female: 17.94%) and in 2014, which accounted for 34.59% (male: 42.93%, female: 25.73%). However, if the condom usage rate was increased by 10% annually starting in 2019, then compared with current practice, the total number of acute HBV infections from 2019 to 2035 would be reduced by 16.68% (male: 21.49%, female: 11.93%). The HBsAg prevalence in people aged 1-59 years in 2035 would be reduced to 2.01% (male: 2.40%, female: 1.58%). CONCLUSIONS: Sexual transmission has become the predominant route of acute HBV infection in China, especially for men. The promotion of condom use plays a significant role in reducing the cases of acute HBV infection.

Elevated glucose level leads to rapid COVID-19 progression and high fatality.

OBJECTIVES: We aimed to identify high-risk factors for disease progression and fatality for coronavirus disease 2019 (COVID-19) patients. METHODS: We enrolled 2433 COVID-19 patients and used LASSO regression and multivariable cause-specific Cox proportional hazard models to identify the risk factors for disease progression and fatality. RESULTS: The median time for progression from mild-to-moderate, moderate-to-severe, severe-to-critical, and critical-to-death were 3.0 (interquartile range: 1.8-5.5), 3.0 (1.0-7.0), 3.0 (1.0-8.0), and 6.5 (4.0-16.3) days, respectively. Among 1,758 mild or moderate patients at admission, 474 (27.0%) progressed to a severe or critical stage. Age above 60 years, elevated levels of blood glucose, respiratory rate, fever, chest tightness, c-reaction protein, lactate dehydrogenase, direct bilirubin, and low albumin and lymphocyte count were significant risk factors for progression. Of 675 severe or critical patients at admission, 41 (6.1%) died. Age above 74 years, elevated levels of blood glucose, fibrinogen and creatine kinase-MB, and low plateleta count were significant risk factors for fatality. Patients with elevated blood glucose level were 58% more likely to progress and 3.22 times more likely to die of COVID-19. CONCLUSIONS: Older age, elevated glucose level, and clinical indicators related to systemic inflammatory responses and multiple organ failures, predict both the disease progression and the fatality of COVID-19 patients.

Investigating the relationship between reopening the economy and implementing control measures during the COVID-19 pandemic.

OBJECTIVES: The COVID-19 pandemic has resulted in an enormous burden on population health and the economy around the world. Although most cities in the United States have reopened their economies from previous lockdowns, it was not clear how the magnitude of different control measures-such as face mask use and social distancing-may affect the timing of reopening the economy for a local region. This study aimed to investigate the relationship between reopening dates and control measures and identify the conditions under which a city can be reopened safely. STUDY DESIGN: This was a mathematical modeling study. METHODS: We developed a dynamic compartment model to capture the transmission dynamics of COVID-19 in New York City. We estimated model parameters from local COVID-19 data. We conducted three sets of policy simulations to investigate how different reopening dates and magnitudes of control measures would affect the COVID-19 epidemic. RESULTS: The model estimated that maintaining social contact at 80% of the prepandemic level and a 50% face mask usage would prevent a major surge of COVID-19 after reopening. If social distancing were completely relaxed after reopening, face mask usage would need to be maintained at nearly 80% to prevent a major surge. CONCLUSIONS: Adherence to social distancing and increased face mask usage are keys to prevent a major surge after a city reopens its economy. The findings from our study can help policymakers identify the conditions under which a city can be reopened safely.

Evaluation of work resumption strategies after COVID-19 reopening in the Chinese city of Shenzhen: a mathematical modeling study.

OBJECTIVES: As China is facing a potential second wave of the epidemic, we reviewed and evaluated the intervention measures implemented in a major metropolitan city, Shenzhen, during the early phase of Wuhan lockdown. STUDY DESIGN: Based on the classic SEITR model and combined with population mobility, a compartmental model was constructed to simulate the transmission of COVID-19 and disease progression in the Shenzhen population. METHODS: Based on published epidemiological data on COVID-19 and population mobility data from Baidu Qianxi, we constructed a compartmental model to evaluate the impact of work and traffic resumption on the epidemic in Shenzhen in various scenarios. RESULTS: Imported cases account for most (58.6%) of the early reported cases in Shenzhen. We demonstrated that with strict inflow population control and a high level of mask usage after work resumption, various resumptions resulted in only an insignificant difference in the number of cumulative infections. Shenzhen may experience this second wave of infections approximately two weeks after the traffic resumption if the incidence risk in Hubei is high at the moment of resumption. CONCLUSION: Regardless of the work resumption strategy adopted in Shenzhen, the risk of a resurgence of COVID-19 after its reopening was limited. The strict control of imported cases and extensive use of facial masks play a key role in COVID-19 prevention.

Can self-imposed prevention measures mitigate the COVID-19 epidemic?

Yuming Guo and colleagues discuss the research by Teslya et al that highlights the importance of personal preventative measures in avoiding a second wave of the COVID-19 epidemic.

Predicting postmortem interval based on microbial community sequences and machine learning algorithms.

Microbes play an essential role in the decomposition process but were poorly understood in their succession and behaviour. Previous researches have shown that microbes show predictable behaviour that starts at death and changes during the decomposition process. Research of such behaviour enhances the understanding of decomposition and benefits estimating the postmortem interval (PMI) in forensic investigations, which is critical but faces multiple challenges. In this study, we combined microbial community characterization, microbiome sequencing from different organs (i.e. brain, heart and cecum) and machine learning algorithms [random forest (RF), support vector machine (SVM) and artificial neural network (ANN)] to investigate microbial succession pattern during corpse decomposition and estimate PMI in a mouse corpse system. Microbial communities exhibited significant differences between the death point and advanced decay stages. Enterococcus faecalis, Anaerosalibacter bizertensis, Lactobacillus reuteri, and so forth were identified as the most informative species in the decomposition process. Furthermore, the ANN model combined with the postmortem microbial data set from the cecum, which was the best combination among all candidates, yielded a mean absolute error of 1.5 ± 0.8 h within 24-h decomposition and 14.5 ± 4.4 h within 15-day decomposition. This integrated model can serve as a reliable and accurate technology in PMI estimation.

Estimation of the impact of changing drug-use trend on HIV, hepatitis C and syphilis epidemics among people who use synthetic drug-only, polydrug and heroin-only during 2005-2035 in China: modelling study.

OBJECTIVE: The rapid expansion of the recreational drug market becomes a global health concern. It is worrying that the bacterial and viral infection epidemics linking to drug use may worsen accordingly. This study aimed to estimate the impacts of changing trend and behaviours of using heroin only, synthetic drug (SD) only and polydrug (using SD and heroin concurrently) on HIV, hepatitis C virus (HCV) and syphilis epidemics among people who use drugs in China by 2035. METHODS: We constructed a compartmental model to estimate HIV, HCV and syphilis epidemics in the dynamic drug-use trend by three scenarios: SD-only use, heroin-only use and polydrug use based on Monte Carlo simulations. The parameters for the model were collected from a comprehensive literature search. RESULTS: Our model estimated that polydrug use led to the highest HIV and HCV prevalence among three drug-use patterns. The prevalences were projected to increase from 10.9% (95% CI 10.2% to 11.5%) and 61.7% (95% CI 59.4% to 62.5%) in 2005 to 19.0% (95% CI 17.3% to 20.7%) and 69.1% (95% CI 67.3% to 69.5%), respectively, in 2035 among people using polydrug. Similarly, HIV and HCV prevalence in the SD-only group were projected to increase from 0.4% (95% CI 0.3% to 0.4%) and 19.5% (95% CI 19.4% to 21.7%) to 1.8% (95% CI 1.4 to 2.1%) and 33.7% (95% CI 33.2% to 34.9%) in 2005-2035. Conversely, HIV prevalence in the heroin-only group was projected to decrease from 8.0% (95% CI 7.6% to 8.1%) to 2.2% (95% CI 2.0% to 2.3%) in 2005-2035. Syphilis prevalence was estimated to remain unchanged in all population groups within this time frame. It was projected that the proportion of HIV transmitted by sexual transmission will increase compared with unsafe injection transmission in all people who use drugs from 2005 to 2035. CONCLUSION: Our modelling suggests that polydrug use is projected to lead to the highest HIV and HCV disease burden by 2035, and the proportion of HIV transmitted by sexual transmission will increase. Current HIV intervention among people using heroin seems effective according to our estimation.