Response of Algal-Bacterial Regrowth Characteristics to the Hypochlorite in Landscape Ponds Replenished with Reclaimed Water

Sodium hypochlorite was widely used as a supplementary disinfectant in reclaimed water (RW) production during the COVID-19 epidemic. It is well known that the chlorination of RW results in a relatively high bacterial regrowth potential in pipeline systems. However, the algal growth and algal-bacterial interactions would be another concern in RW-replenished surface water with light irradiation. In this study, microcosmic experiments were used to explore the impact of hypochlorite on the algae-bacteria community, including the influence of hypochlorite on algal-bacterial regrowth, microbial community structure, and the specific bacteria that can survive chlorination. Results demonstrated that algal growth potential could be promoted after chlorination of the RW, and bacteria abundance increased along with an increase in algal density, which is probably related to DOM decomposition by chlorine oxidation. Additionally, the characteristics of the bacterial community were altered. It is more likely that phytospheric bacteria will survive chlorination. It was discovered that the secondary risks of chlorine disinfection include the growth of algae in addition to bacterial regeneration, which is an extension of the common perception. As a consequence, when chlorinated reclaimed water is used as a supplement for urban landscape ponds, particular attention should be paid to controlling bio-available organic matter induced by reactive chlorine, as well as the algal bloom, to decrease the risk of pathogen transmission.

COVID-19 Activity Risk Calculator as a Gamified Public Health Intervention Tool (preprint)

The Coronavirus disease 2019 (COVID-19) pandemic, caused by the virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has impacted over 200 countries leading to hospitalizations and deaths of millions of people. Public health interventions, such as risk estimators, can reduce the spread of pandemics and epidemics through influencing behavior, which impacts risk of exposure and infection. Current publicly available COVID-19 risk estimation tools have had variable effectiveness during the pandemic due to their dependency on rapidly evolving factors such as community transmission levels and variants. There has also been confusion surrounding certain personal protective strategies such as risk reduction by mask-wearing and vaccination. In order to create a simple easy-to-use tool for estimating different individual risks associated with carrying out daily-life activity, we developed COVID-19 Activity Risk Calculator (CovARC). CovARC is a gamified public health intervention as users can "play with" how different risks associated with COVID-19 can change depending on several different factors when carrying out routine daily activities. Empowering the public to make informed, data-driven decisions about safely engaging in activities may help to reduce COVID-19 levels in the community. In this study, we demonstrate a streamlined, scalable and accurate COVID-19 risk calculation system. Our study also demonstrates the quantitative impact of vaccination and mask-wearing during periods of high case counts. Validation of this impact could inform and support policy decisions regarding case thresholds for mask mandates, and other public health interventions.

Behavioral Intention and Its Predictors toward COVID-19 Booster Vaccination among Chinese Parents: Applying Two Behavioral Theories

The booster vaccination of COVID-19 is being implemented in most parts of the world. This study used behavioral psychology to investigate the predictors of parents' intentions regarding the COVID-19 booster vaccination for their children. This is a cross-sectional study with a self-designed questionnaire based on two behavioral theories-protective motivation theory (PMT) and theory of planned behavior (TPB). A stratified multi-stage sampling procedure was conducted in Nanjing, China, and multivariable regression analyses were applied to examine the parents' intentions. The intention rate was 87.3%. The response efficacy (ORa = 2.238, 95% CI: 1.360–3.682) and response cost (ORa = 0.484, 95% CI: 0.319–0.732) in the PMT, were significant psychological predictors of parents' intentions, and so were the attitude (ORa = 2.619, 95% CI: 1.480–4.636) and behavioral control (ORa = 3.743, 95% CI: 2.165–6.471) in the TPB. The findings of crucial independent predictors in the PMT and TPB constructs inform the evidence-based formulation and implementation of strategies for booster vaccination in children.

Accuracy of US CDC COVID-19 Forecasting Models (preprint)

Accurate predictive modeling of pandemics is essential for optimally distributing resources and setting policy. Dozens of case predictions models have been proposed but their accuracy over time and by model type remains unclear. In this study, we analyze all US CDC COVID-19 forecasting models, by first categorizing them and then calculating their mean absolute percent error, both wave-wise and on the complete timeline. We compare their estimates to government-reported case numbers, one another, as well as two baseline models wherein case counts remain static or follow a simple linear trend. The comparison reveals that more than one-third of models fail to outperform a simple static case baseline and two-thirds fail to outperform a simple linear trend forecast. A wave-by-wave comparison of models revealed that no overall modeling approach was superior to others, including ensemble models, and error in modeling has increased over time during the pandemic. This study raises concerns about hosting these models on official public platforms of health organizations including the US-CDC which risks giving them an official imprimatur and further raising concerns if utilized to formulate policy. By offering a universal evaluation method for pandemic forecasting models, we expect this work to serve as the starting point towards the development of more sophisticated models.

Control measures during the COVID-19 outbreak reduced the transmission of hand, foot, and mouth disease

Control measures during the coronavirus disease 2019 (COVID-19) outbreak may have limited the spread of infectious diseases. This study aimed to analyse the impact of COVID-19 on the spread of hand, foot, and mouth disease (HFMD) in China. A mathematical model was established to fit the reported data of HFMD in six selected cities in mainland China from 2015 to 2020. The absolute difference (AD) and relative difference (RD) between the reported incidence in 2020, and simulated maximum, minimum, or median incidence of HFMD in 2015-2019 were calculated. The incidence and Reff of HFMD have decreased in six selected cities since the outbreak of COVID-19, and in the second half of 2020, the incidence and Reff of HFMD have rebounded. The results show that the total attack rate (TAR) in 2020 was lower than the maximum, minimum, and median TAR fitted in previous years in six selected cities (except Changsha city). For the maximum, median, minimum fitted TAR, the range of RD (%) is 42•20-99•20%, 36•35-98•41% 48•35-96•23% (except Changsha city) respectively. The preventive and control measures of COVID-19 have significantly contributed to the containment of HFMD transmission.

Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery (preprint)

Properties of molecules are indicative of their functions and thus are useful in many applications. With the advances of deep learning methods, computational approaches for predicting molecular properties are gaining increasing momentum. However, there lacks customized and advanced methods and comprehensive tools for this task currently. Here we develop a suite of comprehensive machine learning methods and tools spanning different computational models, molecular representations, and loss functions for molecular property prediction and drug discovery. Specifically, we represent molecules as both graphs and sequences. Built on these representations, we develop novel deep models for learning from molecular graphs and sequences. In order to learn effectively from highly imbalanced datasets, we develop advanced loss functions that optimize areas under precision-recall curves. Altogether, our work not only serves as a comprehensive tool, but also contributes towards developing novel and advanced graph and sequence learning methodologies. Results on both online and offline antibiotics discovery and molecular property prediction tasks show that our methods achieve consistent improvements over prior methods. In particular, our methods achieve #1 ranking in terms of both ROC-AUC and PRC-AUC on the AI Cures Open Challenge for drug discovery related to COVID-19. Our software is released as part of the MoleculeX library under AdvProp.

Forecasting the Spread of COVID-19 under Different Reopening Strategies (preprint)

We combine COVID-19 case data with mobility data to estimate a modified susceptible-infected-recovered (SIR) model in the United States. In contrast to a standard SIR model, we find that the incidence of COVID-19 spread is concave in the number of contagious individuals, as would be expected if people have inter-related social networks. This concave shape has a significant impact on forecasted COVID-19 cases. In particular, our model forecasts that the number of COVID cases would only have an exponential growth for a brief period at the beginning of the contagion event or right after a reopening, but would quickly settle into a prolonged period of time with stable, slightly declining levels of disease spread. This pattern is consistent with observed levels of COVID-19 cases in the US, but inconsistent with standard SIR modeling. We forecast rates of new cases for COVID-19 under different social distancing norms and find that if social distancing is eliminated there will be a massive increase in the cases of COVID-19.

Forecasting the Spread of COVID-19 under Different Reopening Strategies (preprint)

We combine COVID-19 case data with mobility data to estimate a modified susceptible-infected-recovered (SIR) model in the United States. In contrast to a standard SIR model, we find that the incidence of COVID-19 spread is concave in the number of infectious individuals, as would be expected if people have inter-related social networks. This concave shape has a significant impact on forecasted COVID-19 cases. In particular, our model forecasts that the number of COVID-19 cases would only have an exponential growth for a brief period at the beginning of the contagion event or right after a reopening, but would quickly settle into a prolonged period of time with stable, slightly declining levels of disease spread. This pattern is consistent with observed levels of COVID-19 cases in the US, but inconsistent with standard SIR modeling. We forecast rates of new cases for COVID-19 under different social distancing norms and find that if social distancing is eliminated there will be a massive increase in the cases of COVID-19.

Anxiety and Depression Symptoms of Patients in Fangcang Shelter Hospital During the COVID-19 Outbreak in China: a Web-Based Cross-Sectional Study (preprint)

Background The COVID-19 pandemic is a major health crisis has led to adverse mental health consequences in the general public, medical staff, and individual in self isolation. In order to stop transmission of the virus and save lives, Fangcang shelter hospitals were developed and used for the first time in China. However, there is no research on mental health problems in Fangcang shelter hospitals patients during the COVID-19 outbreak. The aim of this study was to survey the prevalence and major influencing factors of anxiety, depression among the hospitalized Coronavirus Disease 2019 (COVID-19) cases in Fangcang shelter hospital.Methods From February 23rd, 2020, to February 26th, 2020, we obtained the information of demographic data, clinical symptoms, and assessed the mental health status, sleep quality by using an online questionnaire including self-rating anxiety scale (SAS), self-rating depressive scale (SDS) and pittsburgh sleep quality index (PSQI) at Jianghan Fangcang shelter hospital. We assessed the prevalence of anxiety, depression symptoms and poor sleep quality via the scores of SAS, SDS and PSQI. We explored the influencing factors of anxiety and depression in COVID-19 patients using multivariable logistic regression models.Results We collected data from 307 COVID-19 patients in Jianghan Fangcang shelter hospital. The prevalence of anxiety, depression symptoms were 18.6% and 13.4%, respectively. Poor Sleep quality, number of current physical symptoms ≥ 2 were independent risk factors for anxiety symptoms (P < 0.05); female, family member confirmed COVID-19, number of current physical symptoms ≥ 2 were independent risk factors for depression symptoms (P < 0.05). PSQI scores were significant positively associate with SAS scores and SDS scores (P ༜ 0.05).Conclusions Anxiety and depression are common among the COVID-19 patients in Fangcang shelter hospital. Those with more current physical symptoms, poor sleep quality are more likely to have anxiety. Females, those with their family members diagnosed with COVID-19, more current physical symptoms are more vulnerable to depression symptom. Our findings can be used to formulate targeted psychological interventions to reduce adverse psychological impacts in Fangcang shelter hospital during the outbreak of epidemic disease in the future.

Basic reproduction number of 2019 Novel Coronavirus Disease in Major Endemic Areas of China: A latent profile analysis (preprint)

Objective: The aim of the study is to analyze the latent class of basic reproduction number (R0) trend of 2019 novel coronavirus disease (COVID-19) in major endemic areas of China. Methods The provinces that reported more than 500 cases of COVID-19 till February 18, 2020 were selected as the major endemic area. The Verhulst model was used to fit the growth rate of cumulative confirmed cases. The R0 of COVID-19 was calculated using the parameters of severe acute respiratory syndrome (SARS) and COVID-19, respectively. The latent class of R0 was analyzed using a latent profile analysis model. Results The median R0 calculated from SARS and COVID-19 parameters were 1.84 - 3.18 and 1.74 - 2.91, respectively. The R0 calculated from the SARS parameters was greater than that of calculated from the COVID-19 parameters (Z = -4.782 - -4.623, P < 0.01). Both R0 can be divided into three latent classes. The initial value of R0 in class 1 (Shandong Province, Sichuan Province and Chongqing Municipality) was relatively low and decreases slowly. The initial value of R0 in class 2 (Anhui Province, Hunan Province, Jiangxi Province, Henan Province, Zhejiang Province, Guangdong Province and Jiangsu Province) was relatively high and decreases rapidly. Moreover, the initial value of R0 of class 3 (Hubei Province) was between that of class 1 and class 2, but the higher level of R0 lasts longer and decreases slowly. Conclusion The results indicated that overall trend of R0 has been falling with the strengthening of China's comprehensive prevention and control measures for COVID-19, however, presents regional differences.

Elucidating the Mechanisms of Action of Chinese Medicine for the Treatment of COVID-19 via Systems Pharmacology and Virtual Screening Approaches (preprint)

The novel coronavirus disease 2019 (COVID-19) epidemic is threatening public health security in China. Owing to the severity of this epidemic, identifying a proper treatment for COVID-19 is crucial. Although the Quing-Fei-Pai-Du (QFPD) decoction preliminary displayed promising clinical efficacy, its potential biological mechanism has not been explored for further pharmaceutical development and treatment. Through systems pharmacology and virtual screening approaches, we explored the potential mechanism of action and the active ingredients of the QFPD decoction for COVID-19 treatment. A ingredients-targets dataset was generated. ADME/Tox and molecular docking analyses were conducted to screen the potentially-active ingredients. Protein-protein interaction network and the detection algorithm were applied to dynamically identify functionally-relevant protein groupings. Totally, 705 ingredients and 1,489 targets were obtained. Docking revealed that 40 components could be promising active ingredients, 6 of which (Neohesperidin, Naringenin, Liquiritigenin, Apigenin, Isoquercitrin and Herbacetin), were highlighted according to the comprehensive analysis. The enrichment analysis of the highlighted ingredients showed that several significant pathways could be highly related to their mechanisms of action, such as response to oxygen levels, response to oxidative stress, and blood circulation. Overall, our findings suggest the promising effects of the QFPD decoction for COVID-19 treatment. Further experimental and clinical verifications are needed.Authors Xiting Wang and Meng Liu contributed equally to this work.

Analysis on epidemic situation and spatiotemporal changes of COVID-19 in Anhui / 中华预防医学杂志

We used the epidemic data of COVID-19 published on the official website of the municipal health commission in Anhui province. We mapped the spatiotemporal changes of confirmed cases, fitted the epidemic situation by the population growth curve at different stages and took statistical description and analysis of the epidemic situation in Anhui province. It was found that the cumulative incidence of COVID-19 was 156/100 000 by February 18, 2020 and the trend of COVID-19 epidemic declined after February 7, changing from J curve to S curve. The actual number of new cases began to decrease from February 2 to February 4 due to the time of case report and actual onset delayed by 3 to 5 days.