Optimal adaptive nonpharmaceutical interventions to mitigate the outbreak of respiratory infections following the COVID-19 pandemic: a deep reinforcement learning study in Hong Kong, China.

BACKGROUND: Long-lasting nonpharmaceutical interventions (NPIs) suppressed the infection of COVID-19 but came at a substantial economic cost and the elevated risk of the outbreak of respiratory infectious diseases (RIDs) following the pandemic. Policymakers need data-driven evidence to guide the relaxation with adaptive NPIs that consider the risk of both COVID-19 and other RIDs outbreaks, as well as the available healthcare resources. METHODS: Combining the COVID-19 data of the sixth wave in Hong Kong between May 31, 2022 and August 28, 2022, 6-year epidemic data of other RIDs (2014-2019), and the healthcare resources data, we constructed compartment models to predict the epidemic curves of RIDs after the COVID-19-targeted NPIs. A deep reinforcement learning (DRL) model was developed to learn the optimal adaptive NPIs strategies to mitigate the outbreak of RIDs after COVID-19-targeted NPIs are lifted with minimal health and economic cost. The performance was validated by simulations of 1000 days starting August 29, 2022. We also extended the model to Beijing context. FINDINGS: Without any NPIs, Hong Kong experienced a major COVID-19 resurgence far exceeding the hospital bed capacity. Simulation results showed that the proposed DRL-based adaptive NPIs successfully suppressed the outbreak of COVID-19 and other RIDs to lower than capacity. DRL carefully controlled the epidemic curve to be close to the full capacity so that herd immunity can be reached in a relatively short period with minimal cost. DRL derived more stringent adaptive NPIs in Beijing. INTERPRETATION: DRL is a feasible method to identify the optimal adaptive NPIs that lead to minimal health and economic cost by facilitating gradual herd immunity of COVID-19 and mitigating the other RIDs outbreaks without overwhelming the hospitals. The insights can be extended to other countries/regions.

Future trajectory of respiratory infections following the COVID-19 pandemic in Hong Kong.

The accumulation of susceptible populations for respiratory infectious diseases (RIDs) when COVID-19-targeted non-pharmaceutical interventions (NPIs) were in place might pose a greater risk of future RID outbreaks. We examined the timing and magnitude of RID resurgence after lifting COVID-19-targeted NPIs and assessed the burdens on the health system. We proposed the Threshold-based Control Method (TCM) to identify data-driven solutions to maintain the resilience of the health system by re-introducing NPIs when the number of severe infections reaches a threshold. There will be outbreaks of all RIDs with staggered peak times after lifting COVID-19-targeted NPIs. Such a large-scale resurgence of RID patients will impose a significant risk of overwhelming the health system. With a strict NPI strategy, a TCM-initiated threshold of 600 severe infections can ensure a sufficient supply of hospital beds for all hospitalized severely infected patients. The proposed TCM identifies effective dynamic NPIs, which facilitate future NPI relaxation policymaking.

The Hub Genes Related to Osteoporosis Were Identified by Bioinformatics Analysis.

Osteoporosis (OP) is commonly encountered, which is a kind of systemic injury of bone mass and microstructure, leading to brittle fractures. With the aging of the population, this disease will pose a more serious impact on medical, social, and economic aspects, especially postmenopausal osteoporosis (PMOP). This study is aimed at figuring out potential therapeutic targets and new biomarkers in OP via bioinformatics tools. After differentially expressed gene (DEG) analysis, we successfully identified 97 upregulated and 172 downregulated DEGs. They are mainly concentrated in actin binding, regulation of cytokine production, muscle cell promotion, chemokine signaling pathway, and cytokine-cytokine receiver interaction. According to the diagram of protein-protein interaction (PPI), we obtained 10 hub genes: CCL5, CXCL10, EGFR, HMOX1, IL12B, CCL7, TBX21, XCL1, PGR, and ITGA1. Expression analysis showed that Egfr, Hmox1, and Pgr were significantly upregulated in estrogen-treated osteoporotic patients, while Ccl5, Cxcl10, Il12b, Ccl7, Tbx21, Xcl1, and Itga1 were significantly downregulated. In addition, the analysis results of Pearson's correlation revealed that CCL7 has a strong positive association with IL12b, TBX21, and CCL5 and so was CCL5 with IL12b. Conversely, EGFR has a strong negative association with XCL1 and CXCL10. In conclusion, this study screened 10 hub genes related to OP based on the GEO database, laying a biological foundation for further research on new biomarkers and potential therapeutic targets in OP.

A Dissemination Model Based on Psychological Theories in Complex Social Networks.

Information spread on social media has been extensively studied through both model-driven theoretical research and data-driven case studies. Recent empirical studies have analyzed the differences and complexity of information dissemination, but theoretical explanations of its characteristics from a modeling perspective are underresearched. To capture the complex patterns of the information dissemination mechanism, we propose a resistant linear threshold (RLT) dissemination model based on psychological theories and empirical findings. In this article, we validate the RLT model on three types of networks and then quantify and compare the dissemination characteristics of the simulation results with those from the empirical results. In addition, we examine the factors affecting dissemination. Finally, we perform two case studies of the 2019 novel Corona Virus Disease (COVID-19)-related information dissemination. The dissemination characteristics derived by the simulations are consistent with the empirical research. These results demonstrate that the RLT model is able to capture the patterns of information dissemination on social media and thus provide model-driven insights into the interpretation of public opinion, rumor control, and marketing strategies on social media.

Role of Asymptomatic COVID-19 Cases in Viral Transmission: Findings From a Hierarchical Community Contact Network Model.

As part of ongoing efforts to contain the coronavirus disease (COVID-19) pandemic, understanding the role of asymptomatic patients in the transmission system is essential for infection control. However, the optimal approach to risk assessment and management of asymptomatic cases remains unclear. This study proposed a Susceptible, Exposed, Infectious, No symptoms, Hospitalized and reported, Recovered, Death (SEINRHD) epidemic propagation model. The model was constructed based on epidemiological characteristics of COVID-19 in China and accounting for the heterogeneity of social contact networks. The early community outbreaks in Wuhan were reconstructed and fitted with the actual data. We used this model to assess epidemic control measures for asymptomatic cases in three dimensions. The impact of asymptomatic cases on epidemic propagation was examined based on the effective reproduction number, abnormally high transmission events, and type and structure of transmission. Management of asymptomatic cases can help flatten the infection curve. Tracing 75% of the asymptomatic cases corresponds to a 32.5% overall reduction in new cases (compared with tracing no asymptomatic cases). Regardless of population-wide measures, household transmission is higher than other types of transmission, accounting for an estimated 50% of all cases. The magnitude of tracing of asymptomatic cases is more important than the timing; when all symptomatic patients were traced, tested, and isolated in a timely manner, the overall epidemic was not sensitive to the time of implementing the measures to trace asymptomatic patients. Disease control and prevention within families should be emphasized during an epidemic. Note to Practitioners-This article addresses the urgent need to assess the risk of another COVID-19 outbreak caused by asymptomatic cases and to find the optimal, most practical approach to asymptomatic case management. Previous studies mostly focused on the clinical and statistical characteristics of asymptomatic cases; few have evaluated the impact of asymptomatic case measures using mathematical modeling at the community scale. This study proposed a Susceptible, Exposed, Infectious, No symptoms, Hospitalized and reported, Recovered, Death (SEINRHD) propagation model based on local community structures and social contact networks, according to the development characteristics and trend of COVID-19 in a Chinese community. The conclusion provides theoretical support for emergency work of relevant departments in different periods of an epidemic. In the early stages of the epidemic, timely detection and isolation of symptomatic patients should be a priority. Where there are surplus resources for epidemic prevention, the authorities should consider increasing the proportion of asymptomatic patients being traced. Epidemic prevention measures among family members should be a primary focus of attention. This combination of strategies can help reduce the rate of viral transmission and result in extinguishing the epidemic.

A Pan-Cancer Analysis on the Systematic Correlation of MutS Homolog 2 (MSH2) to a Malignant Tumor.

MutS homolog 2 (MSH2) is a crucial participant in human DNA repair, and lots of the studies functionally associated with it were begun with hereditary nonpolyposis colorectal cancer (HNPCC). MSH2 has also been reported to take part in the progresses of various tumors' formation. With the help of GTEx, CCLE, and TCGA pan-cancer databases, the analysis of MSH2 gene distribution in both tumor tissues and normal control tissues was carried out. Kaplan-Meyer survival plots and COX regression analysis were conducted for the assessment into the MSH2's impact on tumor patients' clinical prognosis. In an investigation to the association of MSH2 expression with immune infiltration level of various tumors and a similar study on tumor immune neoantigens, microsatellite instability was subsequently taken. It was found that high expression of MSH2 is prevalent in most cancers. MSH2's efficacy on clinical prognosis as well as immune infiltration in tumor patients revealed a fact that expression of MSH2 in prostate adenocarcinoma (PRAD), brain lower-grade glioma (LGG), breast-invasive carcinoma (BRCA), and head and neck squamous cell carcinoma (HNSC) posed a significant correlation with the immune cell infiltration level of patients. Likewise as above, MSH2's expression comes in a similar trend with tumor immune neoantigens and microsatellite instability. MSH2's expression in the majority of tumors is a direct factor to the activation of tumor-associated pathways as well as immune-associated pathways. MSH2's early screening or even therapeutic target role for sarcoma (SARC) diagnosis is contributing to the efficiency of early screening and overall survival in SARC patients.

Impacts of Export Restrictions on the Global Personal Protective Equipment Trade Network During COVID-19.

The COVID-19 pandemic has caused a dramatic surge in demand for personal protective equipment (PPE) worldwide. Many countries have imposed export restrictions on PPE to ensure the sufficient domestic supply. The surging demand and export restrictions cause shortage contagions on the global PPE trade network. Here, an integrated network model is developed, which integrates a metapopulation model and a threshold model, to investigate the shortage contagion patterns. The metapopulation model captures disease contagion across countries. The threshold model captures the shortage contagion on the global PPE trade network. Due to the Pareto distribution in global exports, the shortage contagion pattern is mainly determined by the export restriction policies of the top exporters. Export restrictions exacerbate the shortages of PPE and cause the shortage contagion to transmit even faster than the disease contagion. To some extent, export restrictions can provide benefits for self-sufficient countries, at the sacrifice of immediate economic shocks at not-self-sufficient countries. With export restrictions, a large amount of PPE is hoarded instead of being distributed to where it is most needed, particularly at the early stage. Cooperation between countries plays an essential role in preventing global shortages of PPE regardless of the production level. Except for promoting global cooperation, governments and international organizations should take actions to reduce supply chain barriers and work together to increase global PPE production.

Equitable access to COVID-19 vaccines makes a life-saving difference to all countries.

Despite broad agreement on the negative consequences of vaccine inequity, the distribution of COVID-19 vaccines is imbalanced. Access to vaccines in high-income countries (HICs) is far greater than in low- and middle-income countries (LMICs). As a result, there continue to be high rates of COVID-19 infections and deaths in LMICs. In addition, recent mutant COVID-19 outbreaks may counteract advances in epidemic control and economic recovery in HICs. To explore the consequences of vaccine (in)equity in the face of evolving COVID-19 strains, we examine vaccine allocation strategies using a multistrain metapopulation model. Our results show that vaccine inequity provides only limited and short-term benefits to HICs. Sharper disparities in vaccine allocation between HICs and LMICs lead to earlier and larger outbreaks of new waves. Equitable vaccine allocation strategies, in contrast, substantially curb the spread of new strains. For HICs, making immediate and generous vaccine donations to LMICs is a practical pathway to protect everyone.

Reconstruction of the Transmission Chain of COVID-19 Outbreak in Beijing's Xinfadi Market, China.

OBJECTIVES: The aim of the study was to reconstruct the complete transmission chain of the COVID-19 outbreak in Beijing's Xinfadi Market using data from epidemiological investigations, which contributes to reflecting transmission dynamics and transmission risk factors. METHODS: We set up a transmission model, and the model parameters are estimated from the survey data via Markov chain Monte Carlo sampling. Bayesian data augmentation approaches are used to account for uncertainty in the source of infection, unobserved onset, and infection dates. RESULTS: The rate of transmission of COVID-19 within households is 9.2%. Older people are more susceptible to infection. The accuracy of our reconstructed transmission chain was 67.26%. In the gathering place of this outbreak, the Beef and Mutton Trading Hall of Xinfadi market, most of the transmission occurs within 20 m, only 19.61% of the transmission occurs over a wider area (>20 m), with an overall average transmission distance of 13.00 m. The deepest transmission generation is 9. In this outbreak, there were 2 abnormally high transmission events. CONCLUSIONS: The statistical method of reconstruction of transmission trees from incomplete epidemic data provides a valuable tool to help understand the complex transmission factors and provides a practical guideline for investigating the characteristics of the development of epidemics and the formulation of control measures.

Intervertebral bridging ossification after kyphoplasty in a Parkinson's patient with Kummell's disease: A case report.

BACKGROUND: The short-term therapeutic efficacy of kyphoplasty on Kummell's disease is obvious. However, postoperative refracture and adjacent vertebral fracture occur occasionally and are difficult to treat. Parkinson's disease (PD) is a pathological disorder associated with heterotopic ossification. In a patient with PD, an intervertebral bridge was formed in a short period of time after postoperative refracture and adjacent vertebral fracture, providing new stability. CASE SUMMARY: A 78-year-old woman had been suffering from PD for more than 10 years. Three months before operation, she developed lower back pain and discomfort. The visual analog scale (VAS) score was 9 points. Preoperative magnetic resonance imaging indicated collapse of the L2 vertebra. Kyphoplasty was performed and significantly decreased the severity of intractable pain. The patient's VAS score for pain improved from 9 to 2. Fifty days postoperatively, the patient suddenly developed severe back pain, and the VAS score was 9 points. X-ray showed L2 vertebral body collapse, slight forward bone cement displacement, L1 vertebral compression fracture, and severe L1 collapse. The patient was given calcium acetate capsules 0.6 g po qd and alfacalcidol 0.5ug po qd, and bed rest and brace protection were ordered. After conservative treatment for 2 mo, the patient's back pain was alleviated, and the VAS score improved from 9 to 2. Computed tomography at the 7-mo follow-up indicated extensive callus formation around the T12-L2 vertebrae and intervertebral bridging ossification, providing new stability. CONCLUSION: Kyphoplasty is currently a conventional treatment for Kummell's disease, with definite short-term effects. However, complications still occur in the long term, and these complications are difficult to address; thus, the treatment needs to be selected carefully. To avoid refracture, an interlaced structure of bone cement with trabeculae should be created to the greatest extent possible during the injection of bone cement. Surgical intervention may not be urgently needed when a patient with PD experiences refracture and adjacent vertebral fracture, as a strong bridge may help stabilize the vertebrae and relieve pain.

Data science approaches to confronting the COVID-19 pandemic: a narrative review.

During the COVID-19 pandemic, more than ever, data science has become a powerful weapon in combating an infectious disease epidemic and arguably any future infectious disease epidemic. Computer scientists, data scientists, physicists and mathematicians have joined public health professionals and virologists to confront the largest pandemic in the century by capitalizing on the large-scale 'big data' generated and harnessed for combating the COVID-19 pandemic. In this paper, we review the newly born data science approaches to confronting COVID-19, including the estimation of epidemiological parameters, digital contact tracing, diagnosis, policy-making, resource allocation, risk assessment, mental health surveillance, social media analytics, drug repurposing and drug development. We compare the new approaches with conventional epidemiological studies, discuss lessons we learned from the COVID-19 pandemic, and highlight opportunities and challenges of data science approaches to confronting future infectious disease epidemics. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.

Mobility in China, 2020: a tale of four phases.

2020 was an unprecedented year, with rapid and drastic changes in human mobility due to the COVID-19 pandemic. To understand the variation in commuting patterns among the Chinese population across stable and unstable periods, we used nationwide mobility data from 318 million mobile phone users in China to examine the extreme fluctuations of population movements in 2020, ranging from the Lunar New Year travel season (chunyun), to the exceptional calm of COVID-19 lockdown, and then to the recovery period. We observed that cross-city movements, which increased substantially in chunyun and then dropped sharply during the lockdown, are primarily dependent on travel distance and the socio-economic development of cities. Following the Lunar New Year holiday, national mobility remained low until mid-February, and COVID-19 interventions delayed more than 72.89 million people returning to large cities. Mobility network analysis revealed clusters of highly connected cities, conforming to the social-economic division of urban agglomerations in China. While the mass migration back to large cities was delayed, smaller cities connected more densely to form new clusters. During the recovery period after travel restrictions were lifted, the netflows of over 55% city pairs reversed in direction compared to before the lockdown. These findings offer the most comprehensive picture of Chinese mobility at fine resolution across various scenarios in China and are of critical importance for decision making regarding future public-health-emergency response, transportation planning and regional economic development, among others.

Sustainable targeted interventions to mitigate the COVID-19 pandemic: A big data-driven modeling study in Hong Kong.

Nonpharmaceutical interventions (NPIs) for contact suppression have been widely used worldwide, which impose harmful burdens on the well-being of populations and the local economy. The evaluation of alternative NPIs is needed to confront the pandemic with less disruption. By harnessing human mobility data, we develop an agent-based model that can evaluate the efficacies of NPIs with individualized mobility simulations. Based on the model, we propose data-driven targeted interventions to mitigate the COVID-19 pandemic in Hong Kong without city-wide NPIs. We develop a data-driven agent-based model for 7.55×106 Hong Kong residents to evaluate the efficacies of various NPIs in the first 80 days of the initial outbreak. The entire territory of Hong Kong has been split into 4905 500×500m2 grids. The model can simulate detailed agent interactions based on the demographics data, public facilities and functional buildings, transportation systems, and travel patterns. The general daily human mobility patterns are adopted from Google's Community Mobility Report. The scenario without any NPIs is set as the baseline. By simulating the epidemic progression and human movement at the individual level, we propose model-driven targeted interventions which focus on the surgical testing and quarantine of only a small portion of regions instead of enforcing NPIs in the whole city. The effectiveness of common NPIs and the proposed targeted interventions are evaluated by 100 extensive simulations. The proposed model can inform targeted interventions, which are able to effectively contain the COVID-19 outbreak with much lower disruption of the city. It represents a promising approach to sustainable NPIs to help us revive the economy of the city and the world.

Demand-driven spreading patterns of African swine fever in China.

African swine fever (ASF) is a highly contagious hemorrhagic viral disease of domestic and wild pigs. ASF has led to major economic losses and adverse impacts on livelihoods of stakeholders involved in the pork food system in many European and Asian countries. While the epidemiology of ASF virus (ASFV) is fairly well understood, there is neither any effective treatment nor vaccine. In this paper, we propose a novel method to model the spread of ASFV in China by integrating the data of pork import/export, transportation networks, and pork distribution centers. We first empirically analyze the overall spatiotemporal patterns of ASFV spread and conduct extensive experiments to evaluate the efficacy of a number of geographic distance measures. These empirical analyses of ASFV spread within China indicate that the first occurrence of ASFV has not been purely dependent on the geographical distance from existing infected regions. Instead, the pork supply-demand patterns have played an important role. Predictions based on a new distance measure achieve better performance in predicting ASFV spread among Chinese provinces and thus have the potential to enable the design of more effective control interventions.

Relationship between angiotensin-converting enzyme inhibitors or angiotensin receptor blockers and COVID-19 incidence or severe disease.

BACKGROUND: Angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) may be associated with higher susceptibility of COVID-19 infection and adverse outcomes. We compared ACEI/ARB use and COVID-19 positivity in a case-control design, and severity in COVID-19 positive patients. METHODS: Consecutive patients who attended Hong Kong's public hospitals or outpatient clinics between 1 January and 28 July 2020 for COVID-19 real time-PCR (RT-PCR) tests were included. Baseline demographics, past comorbidities, laboratory tests and use of different medications were compared between COVID-19 positive and negative patients. Severe endpoints for COVID-19 positive patients were 28-day mortality, need for intensive care admission or intubation. RESULTS: This study included 213 788 patients (COVID-19 positive: n = 2774 patients; negative: n = 211 014). In total, 162 COVID-19 positive patients (5.83%) met the severity outcome. The use of ACEI/ARB was significantly higher amongst cases than controls (n = 156/2774, 5.62 vs. n = 6708/211014, 3.17%; P < 0.0001). Significant univariate predictors of COVID-19 positivity and severe COVID-19 disease were older age, higher Charlson score, comorbidities, use of ACEI/ARB, antidiabetic, lipid-lowering, anticoagulant and antiplatelet drugs and laboratory tests (odds ratio >1, P < 0.05). The relationship between the use of ACEI/ARB and COVID-19 positivity or severe disease remained significant after multivariable adjustment. No significant differences in COVID-19 positivity or disease severity between ACEI and ARB use were observed (P > 0.05). CONCLUSION: There was a significant relationship between ACEI/ARB use and COVID-19 positivity and severe disease after adjusting for significant confounders.

Control strategies and their effects on the COVID-19 pandemic in 2020 in representative countries.

COVID-19 is the most severe pandemic globally since the 1918 influenza pandemic. Effectively responding to this once-in-a-century global pandemic is a worldwide challenge that the international community needs to jointly face and solve. This study reviews and discusses the key measures taken by major countries in 2020 to fight against COVID-19, such as lockdowns, social distancing, wearing masks, hand hygiene, using Fangcang shelter hospitals, large-scale nucleic acid testing, close-contacts tracking, and pandemic information monitoring, as well as their prevention and control effects. We hope it can help improve the efficiency and effectiveness of pandemic prevention and control in future.

Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong.

Recent studies have reported numerous predictors for adverse outcomes in COVID-19 disease. However, there have been few simple clinical risk scores available for prompt risk stratification. The objective is to develop a simple risk score for predicting severe COVID-19 disease using territory-wide data based on simple clinical and laboratory variables. Consecutive patients admitted to Hong Kong's public hospitals between 1 January and 22 August 2020 and diagnosed with COVID-19, as confirmed by RT-PCR, were included. The primary outcome was composite intensive care unit admission, need for intubation or death with follow-up until 8 September 2020. An external independent cohort from Wuhan was used for model validation. COVID-19 testing was performed in 237,493 patients and 4442 patients (median age 44.8 years old, 95% confidence interval (CI): [28.9, 60.8]); 50% males) were tested positive. Of these, 209 patients (4.8%) met the primary outcome. A risk score including the following components was derived from Cox regression: gender, age, diabetes mellitus, hypertension, atrial fibrillation, heart failure, ischemic heart disease, peripheral vascular disease, stroke, dementia, liver diseases, gastrointestinal bleeding, cancer, increases in neutrophil count, potassium, urea, creatinine, aspartate transaminase, alanine transaminase, bilirubin, D-dimer, high sensitive troponin-I, lactate dehydrogenase, activated partial thromboplastin time, prothrombin time, and C-reactive protein, as well as decreases in lymphocyte count, platelet, hematocrit, albumin, sodium, low-density lipoprotein, high-density lipoprotein, cholesterol, glucose, and base excess. The model based on test results taken on the day of admission demonstrated an excellent predictive value. Incorporation of test results on successive time points did not further improve risk prediction. The derived score system was evaluated with out-of-sample five-cross-validation (AUC: 0.86, 95% CI: 0.82-0.91) and external validation (N = 202, AUC: 0.89, 95% CI: 0.85-0.93). A simple clinical score accurately predicted severe COVID-19 disease, even without including symptoms, blood pressure or oxygen status on presentation, or chest radiograph results.

The impact of non-pharmaceutical interventions on the prevention and control of COVID-19 in New York City.

The emergence of coronavirus disease 2019 (COVID-19) has infected more than 62 million people worldwide. Control responses varied across countries with different outcomes in terms of epidemic size and social disruption. This study presents an age-specific susceptible-exposed-infected-recovery-death model that considers the unique characteristics of COVID-19 to examine the effectiveness of various non-pharmaceutical interventions (NPIs) in New York City (NYC). Numerical experiments from our model show that the control policies implemented in NYC reduced the number of infections by 72% [interquartile range (IQR) 53-95] and the number of deceased cases by 76% (IQR 58-96) by the end of 2020. Among all the NPIs, social distancing for the entire population and protection for the elderly in public facilities is the most effective control measure in reducing severe infections and deceased cases. School closure policy may not work as effectively as one might expect in terms of reducing the number of deceased cases. Our simulation results provide novel insights into the city-specific implementation of NPIs with minimal social disruption considering the locations and population characteristics.