Deciphering the Molecular Mechanism of Post-Acute Sequelae of COVID-19 through Comorbidity Network Analysis (preprint)

IntroductionThe post-acute sequelae of COVID-19 presents a significant health challenge in the post-pandemic world. Our study aims to analyze longitudinal electronic health records to determine the impact of COVID-19 on disease progression, provide molecular insights into these mechanisms, and identify associated biomarkers. MethodWe included 58,710 patients with COVID-19 records from 01/01/2020 to 31/08/2022 and at least one hospital admission before and after the acute phase of COVID-19 (28 days) as the treatment group. A healthy control group of 174,071 individuals was established for comparison using propensity score matching based on pre-existing diseases (before COVID-19). We built a comorbidity network using Pearson correlation coefficient differences between pairs of pre-existing disease and post-infection disease in both groups. Disease-protein mapping and protein-protein interaction network analysis revealed the impact of COVID-19 on disease trajectories through protein interactions in the human body. ResultsThe disparity in the weight of prevalent disease comorbidity patterns between the treatment and control groups highlights the impact of COVID-19. Certain specific comorbidity patterns show a more pronounced influence by COVID-19. For each comorbidity pattern, overlapping proteins directly associated with pre-existing diseases, post-infection diseases, and COVID-19 help to elucidate the biological mechanism of COVID-19s impact on each comorbidity pattern. Proteins essential for explaining the biological mechanism can be identified based on their weights. ConclusionDisease comorbidity associations influenced by COVID-19, as identified through longitudinal electronic health records and disease-protein mapping, can help elucidate the biological mechanisms of COVID-19, discover intervention methods, and decode the molecular basis of comorbidity associations. This analysis can also yield potential biomarkers and corresponding treatments for specific disease patterns. Ethical approvalEthical approval for this study was granted by the Institutional Review Board of the University of Hong Kong/HA HK West Cluster (UW20-556, UW21-149 and UW21-138). RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed for research articles up to Nov 30, 2022, with no language restrictions, using the terms "Post-Acute Sequelae of COVID-19" OR "PASC" OR "Long COVID" AND "comorbidity" OR "multimorbidity" OR "co-morbidity" OR "multi-morbidity". We found most related papers focus on the comorbidity or multimorbidity patterns among PASC. Some papers focus on the associations between specific diseases and PASC. However, no study investigated the biological mechanism of PASC from the perspective of comorbidity network. Added value of this studyThis study investigated the biological mechanism of PASC based on the comorbidity network including the impact of pre-existing diseases (diseases diagnosed within 730 days before COVID-19) on the development of PASC. We classified pairs of pre-existing disease and post-infection disease (new diseases diagnosed in 28 days to 180 days after COVID-19) as comorbidity associations. Through a comparison of the frequency of comorbidity associations in health people group and patients with COVID-19 infection group, we identified comorbidity patterns that are significantly influenced by COVID-19 infection and constructed a comorbidity network comprising of 117 nodes (representing diseases) and 271 edges (representing comorbidity patterns). These comorbidity patterns suggest COVID-19 patients with these pre-existing diseases have higher risk for post-infection diseases. Through the analysis of the Protein-Protein interaction (PPI) network and associations between diseases and proteins, we identified key proteins in the topological distance of each comorbidity pattern and important biological pathways by GO enrichment analysis. These proteins and biological pathways provide insights into the underlying biological mechanism of PASC. Implications of all the available evidenceThe identification of elevated-risk comorbidity patterns associated with COVID-19 infection is crucial for the effective allocation of medical resources, ensuring prompt care for those in greatest need. Furthermore, it facilitates the recovery process of patients from COVID-19, offering a roadmap for their path back to health. The key proteins identified in our study have the potential to serve as biomarkers and targets for therapeutic intervention, thereby establishing a foundation for the development of new drugs and the repurposing of existing ones. Further research should focus on drug discovery and the development of drug recommendations for patients with COVID-19 infections.

Supply chain loss from easing COVID-19 restrictions: an evolutionary epidemiological-economic modeling study (preprint)

Since the start of the COVID-19 pandemic, many firms have been shifting their supply chains away from countries with stringent control measures to mitigate supply chain disruption. Nowadays, the global economy is reopening from the COVID-19 pandemic at various paces in different countries. Understanding how the global supply network evolves during and after the pandemic is necessary for determining the timing of reopening. By harnessing the real-world and real-time global human movement and the latest macroeconomic data, we propose an evolutionary epidemiological-economic model to explore the evolutionary dynamics of the global supply network under various global reopening scenarios. We find that the delay in full reopening in highly restrictive countries has limited public health benefits in the long run but leads to significant supply chain loss to less restrictive ones. Longer duration of stringent control measures leads to lower supply chain recovery in five years. The recovery rate varies across production sectors, depending on the characteristics of production, the degree of self-reliance, and the location of production hubs. This research presents the first data-driven evidence of supply chain loss due to the timing of reopening and sheds light on the post-pandemic supply chain reformation and recovery. Our results provide data-driven evidence that supports the reopening in countries with high vaccine coverage.

Comparisons of the risk of myopericarditis between COVID-19 patients and individuals receiving COVID-19 vaccines: a population-based study (preprint)

Background Both COVID-19 infection and COVID-19 vaccines have been associated with the development of myopericarditis. The objective of this study is to 1) analyze the rates of myopericarditis after COVID-19 infection and COVID-19 vaccination in Hong Kong and 2) compare to the background rates, and 3) compare the rates of myopericarditis after COVID-19 vaccination to those reported in other countries. Methods This was a population-based cohort study from Hong Kong, China. Patients with positive RT-PCR test for COVID-19 between 1 st January 2020 and 30 th June 2021 or individuals who received COVID-19 vaccination until 31 st August were included. The main exposures were COVID-19 positivity or COVID-19 vaccination. The primary outcome was myopericarditis. Results This study included 11441 COVID-19 patients from Hong Kong, of whom four suffered from myopericarditis (rate per million: 350; 95% confidence interval [CI]: 140-900). The rate was higher than the pre-COVID-19 background rate in 2020 (rate per million: 61, 95% CI: 55-67) with a rate ratio of 5.73 (95% CI: 2.23-14.73. Compared to background rates, the rate of myopericarditis among vaccinated subjects in Hong Kong was substantially lower (rate per million: 8.6; 95% CI: 6.4-11.6) with a rate ratio of 0.14 (95% CI: 0.10-0.19). The rates of myocarditis after vaccination in Hong Kong are comparable to those vaccinated in the United States, Israel, and the United Kingdom. Conclusions COVID-19 infection is associated with a higher rate of myopericarditis whereas COVID-19 vaccination is associated with a lower rate of myopericarditis compared to the background.

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

Nonpharmaceutical interventions (NPIs) for contact suppression have been widely used worldwide, which impose harmful burdens on the population 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 million 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 is split into 4,905 500m x 500m 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 Googles 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 proposed 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 efficacious of common NPIs and the proposed targeted interventions are evaluated by extensive 100 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.

Impacts of export restrictions on the global personal protective equipment trade network during COVID-19 (preprint)

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, we develop an integrated network model, 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. Results show that, the shortage contagion patterns are mainly decided by top exporters. Export restrictions exacerbate the shortages of PPE and cause the shortage contagion to transmit even faster than the disease contagion. Besides, export restrictions lead to ineffective and inefficient allocation of PPE around the world, which has no benefits for the world to fight against the pandemic.

Renin-angiotensin system blockers and mortality in COVID-19: a territory-wide study from Hong Kong (preprint)

Aims Renin–angiotensin system blockers such as angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) may increase the risk of adverse outcomes in COVID-19. In this study, the relationships between ACEI/ARB use and COVID-19 related mortality were examined. Methods Consecutive patients diagnosed with COVID-19 by RT-PCR at the Hong Kong Hospital Authority between 1 st January and 28 th July 2020 were included. Results This study included 2774 patients. The mortality rate of the COVID-19 positive group was 1.5% (n=42). Those who died had a higher median age (82.3[76.5-89.5] vs. 42.9[28.2-59.5] years old; P<0.0001), more likely to have baseline comorbidities of cardiovascular disease, diabetes mellitus, hypertension, and chronic kidney disease (P<0.0001). They were more frequently prescribed ACEI/ARBs at baseline, and steroids, lopinavir/ritonavir, ribavirin and hydroxychloroquine during admission (P<0.0001). They also had a higher white cell count, higher neutrophil count, lower platelet count, prolonged prothrombin time and activated partial thromboplastin time, higher D-dimer, troponin, lactate dehydrogenase, creatinine, alanine transaminase, aspartate transaminase and alkaline phosphatase (P<0.0001). Multivariate Cox regression showed that age, cardiovascular disease, renal disease, diabetes mellitus, the use of ACEIs/ARBs and diuretics, and various laboratory tests remained significant predictors of mortality. Conclusions We report that an association between ACEIs/ARBs with COVID-19 related mortality even after adjusting for cardiovascular and other comorbidities, as well as medication use. Patients with greater comorbidity burden and laboratory markers reflecting deranged clotting, renal and liver function, and increased tissue inflammation, and ACEI/ARB use have a higher mortality risk. Key Points We report that an association between ACEIs/ARBs with COVID-19 related mortality even after adjusting for cardiovascular and other comorbidities, as well as medication use. Patients with greater comorbidity burden and laboratory markers reflecting deranged clotting, renal and liver function, and increased tissue inflammation, and ACEI/ARB use have a higher mortality risk.

Identifying Sequential Complication and Mortality Patterns in Diabetes Mellitus: Comparisons of Machine Learning Methodologies (preprint)

Background: Diabetes mellitus-related complications adversely affect the quality of life. Better risk-stratified care through mining of sequential complication patterns is needed to enable early detection and prevention. Methods: Univariable and multivariate logistic regression was used to identify significant variables that can predict mortality. A sequence analysis method termed Prefixspan was applied to identify the most common couple, triple, quadruple, quintuple and sextuple sequential complication patterns in the directed comorbidity pathology network. A knowledge enhanced CPT+ (KCPT+) sequence prediction model is developed to predict the next possible outcome along the progression trajectories of diabetes-related complications. Findings: A total of 14,144 diabetic patients (51% males) were included. Acute myocardial infarction (AMI) without known ischaemic heart disease (IHD) (odds ratio [OR]: 2.8, 95% CI: [2.3, 3.4]), peripheral vascular disease (OR: 2.3, 95% CI: [1.9, 2.8]), dementia (OR: 2.1, 95% CI: [1.8, 2.4]), and IHD with AMI (OR: 2.4, 95% CI: [2.1, 2.6]) are the most important multivariate predictors of mortality. KCPT+ shows high accuracy in predicting mortality (F1 score 0.90, ACU 0.88), osteoporosis (F1 score 0.86, AUC 0.82), ophthalmological complications (F1 score 0.82, AUC 0.82), IHD with AMI (F1 score 0.81, AUC 0.85) and neurological complications (F1 score 0.81, AUC 0.83) with a particular prior complication sequence. Interpretation: Sequence analysis identifies the most common pattern characteristics of disease-related complications efficiently. The proposed sequence prediction model is accurate and enables clinicians to diagnose the next complication earlier, provide better risk-stratified care, and devise efficient treatment strategies for diabetes mellitus patients.

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

The emergence of coronavirus disease 2019 (COVID-19) has infected more than 37 million people worldwide. The control responses varied across countries with different outcomes in terms of epidemic size and social disruption. In this study, we presented 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% (IQR 53-95), and the number of deceased cases by 76% (IQR 58-96) by the end of 2020, respectively. Among all the NPIs, social distancing for the entire population and the protection for the elderly in the 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.

Role of asymptomatic COVID-19 cases in viral transmission: Findings from a hierarchical community contact network model (preprint)

Background: As part of on-going efforts to contain the COVID-19 pandemic, understanding the role of asymptomatic patients in the transmission system is essential to infection control. However, optimal approach to risk assessment and management of asymptomatic cases remains unclear. Methods: This study involved a SEINRHD epidemic propagation model, constructed based on epidemiological characteristics of COVID-19 in China, accounting for the heterogeneity of social network. We assessed epidemic control measures for asymptomatic cases on three dimensions. 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. Results: Management of asymptomatic cases can help flatten the infection curve. Tracking 75% of asymptomatic cases corresponds to an overall reduction in new cases by 34.3% (compared to tracking no asymptomatic cases). Regardless of population-wide measures, family transmission is higher than other types of transmission, accounting for an estimated 50% of all cases. Conclusions: Asymptomatic case tracking has significant effect on epidemic progression. When timely and strong measures are taken for symptomatic cases, the overall epidemic is not sensitive to the implementation time of the measures for asymptomatic cases.

Development of a predictive risk model for severe COVID-19 disease using population-based administrative data (preprint)

Background: Recent studies have reported numerous significant predictors for adverse outcomes in COVID-19 disease. However, there have been few simple clinical risk score for prompt risk stratification. The objective is to develop a simple risk score for severe COVID-19 disease using territory-wide healthcare data based on simple clinical and laboratory variables. Methods: Consecutive patients admitted to Hong Kong public hospitals between 1st January and 22nd August 2020 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 8th September 2020. Results: COVID-19 testing was performed in 237493 patients and 4445 patients (median age 44.8 years old, 95% CI: [28.9, 60.8]); 50% male) were tested positive. Of these, 212 patients (4.8%) met the primary outcome. A risk score including the following components was derived from Cox regression: gender, age, hypertension, stroke, diabetes mellitus, ischemic heart disease/heart failure, respiratory disease, renal disease, increases in neutrophil count, monocyte count, sodium, potassium, urea, alanine transaminase, alkaline phosphatase, high sensitive troponin-I, prothrombin time, activated partial thromboplastin time, D-dimer and C-reactive protein, as well as decreases in lymphocyte count, base excess and bicarbonate levels. 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. Conclusions: 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.

Role of Asymptomatic COVID-19 Cases in Viral Transmisssion: Findings from a Hierarchical Community Contact Network Model (preprint)

Background: As part of on-going efforts to contain the 2019 novel coronavirus disease (COVID-19) pandemic, population-wide screening is being performed, identifying an increasing number of asymptomatic cases. Understanding the role of asymptomatic patients in the transmission system is essential to infection control. However, optimal approach to risk assessment and management of asymptomatic cases remains unclear. Methods: This study involved a SEINRHD epidemic propagation model, constructed based on epidemiological characteristics of COVID-19 in China, accounting for the heterogeneity of social network in this population. Computational experiments were performed to assess epidemic control measures for asymptomatic cases on three dimensions. Impact of asymptomatic cases on epidemic propagation was examined based on the effective reproduction number, abnormally high transmission events, type of transmission, and transmission tree structure.Findings: Management of asymptomatic cases can help flatten the infection curve. Specifically, asymptomatic case tracking appears to have significant effect on epidemic progression, whereby tracking 75% of asymptomatic cases corresponds to an overall reduction in new cases of 34·3% (compared to tracking no asymptomatic cases). When timely measures are taken for symptomatic cases and the intensity is strong enough, the overall epidemic is not sensitive to the implementation time of the measures for asymptomatic cases. Finally, regardless of population-wide measures, family transmission is higher than other types of transmission, accounting for an estimated 50% of all new cases.Interpretations: These findings can help assess interventions aimed at asymptomatic case management during the COVID-19 pandemic, helping to control disease spread. In addition, these findings might also help identify the type of transmission and abnormally high transmission events that may exist during the epidemic.Funding: This study was funded by National Natural Science Foundation of China (Nos. 72042018, 91546112, 71621002) and Beijing Municipal Natural Science Foundation (No. L192012).Declaration of Interests: All authors declare no competing interests.

The collective wisdom in the COVID-19 research: comparison and synthesis of epidemiological parameter estimates in preprints and peer-reviewed articles (preprint)

Background Research papers related to COVID-19 have exploded. We aimed to explore the academic value of preprints through comparing with peer-reviewed publications, and synthesize the parameter estimates of the two kinds of literature. Method We collected papers regarding the estimation of four key epidemiological parameters of the COVID-19 in China: the basic reproduction number (R0), incubation period, infectious period, and case-fatality-rate (CFR). PubMed, Google Scholar, medRxiv, bioRxiv, arRxiv, and SSRN were searched by 20 March, 2020. Distributions of parameters and timeliness of preprints and peer-reviewed papers were compared. Further, four parameters were synthesized by bootstrap, and their validity was verified by susceptible-exposed-infectious-recovered-dead-cumulative (SEIRDC) model based on the context of China. Findings 106 papers were included for analysis. The distributions of four parameters in two literature groups were close, despite that the timeliness of preprints was better. Four parameter estimates changed over time. Synthesized estimates of R0 (3.18, 95% CI 2.85-3.53), incubation period (5.44 days, 95% CI 4.98-5.99), infectious period (6.25 days, 95% CI 5.09-7.51), and CFR (4.51%, 95% CI 3.41%-6.29%) were obtained from the whole parameters space, all with p<0.05. Their validity was evaluated by simulated cumulative cases of SEIRDC model, which matched well with the onset cases in China. Interpretation Preprints could reflect the changes of epidemic situation sensitively, and their academic value shouldn't be neglected. Synthesized results of literatures could reduce the uncertainty and be used for epidemic decision making. Funding The National Natural Science Foundation of China and Beijing Municipal Natural Science Foundation.

Identifying main and interaction effects of risk factors to predict intensive care admission in patients hospitalized with COVID-19: a retrospective cohort study in Hong Kong (preprint)

Background: The coronavirus disease 2019 (COVID-19) has become a pandemic, placing significant burdens on the healthcare systems. In this study, we tested the hypothesis that a machine learning approach incorporating hidden nonlinear interactions can improve prediction for Intensive care unit (ICU) admission. Methods: Consecutive patients admitted to public hospitals between 1st January and 24th May 2020 in Hong Kong with COVID-19 diagnosed by RT-PCR were included. The primary endpoint was ICU admission. Results: This study included 1043 patients (median age 35 (IQR: 32-37; 54% male). Nineteen patients were admitted to ICU (median hospital length of stay (LOS): 30 days, median ICU LOS: 16 days). ICU patients were more likely to be prescribed angiotensin converting enzyme inhibitors/angiotensin receptor blockers, anti-retroviral drugs lopinavir/ritonavir and remdesivir, ribavirin, steroids, interferon-beta and hydroxychloroquine. Significant predictors of ICU admission were older age, male sex, prior coronary artery disease, respiratory diseases, diabetes, hypertension and chronic kidney disease, and activated partial thromboplastin time, red cell count, white cell count, albumin and serum sodium. A tree-based machine learning model identified most informative characteristics and hidden interactions that can predict ICU admission. These were: low red cells with 1) male, 2) older age, 3) low albumin, 4) low sodium or 5) prolonged APTT. A five-fold cross validation confirms superior performance of this model over baseline models including XGBoost, LightGBM, random forests, and multivariate logistic regression. Conclusions: A machine learning model including baseline risk factors and their hidden interactions can accurately predict ICU admission in COVID-19.

Effect of heterogeneous risk perception on information diffusion, behavior change, and disease transmission (preprint)

Motivated by the importance of individual differences in risk perception and behavior change in people's responses to infectious disease outbreaks (particularly the ongoing COVID-19 pandemic), we propose a heterogeneous Disease-Behavior-Information (hDBI) transmission model, in which people's risk of getting infected is influenced by information diffusion, behavior change, and disease transmission. We use both a mean-field approximation and Monte Carlo simulations to analyze the dynamics of the model. Information diffusion influences behavior change by allowing people to be aware of the disease and adopt self-protection, and subsequently affects disease transmission by changing the actual infection rate. Results show that (a) awareness plays a central role in epidemic prevention; (b) a reasonable fraction of "over-reacting" nodes are needed in epidemic prevention; (c) R0 has different effects on epidemic outbreak for cases with and without asymptomatic infection; (d) social influence on behavior change can remarkably decrease the epidemic outbreak size. This research indicates that the media and opinion leaders should not understate the transmissibility and severity of diseases to ensure that people could become aware of the disease and adopt self-protection to protect themselves and the whole population.

Healthcare-resource-adjusted vulnerabilities towards the 2019-nCoV epidemic across China (preprint)

We integrate the human movement and healthcare resource data to identify cities with high vulnerability towards the 2019-nCoV epidemic with respect to available health resources. The results inform public health responses in multiple ways.

Incorporating Human Movement Data to Improve Epidemiological Estimates for 2019-nCoV (preprint)

Estimating the key epidemiological features of the novel coronavirus (2019-nCoV) epidemic proves to be challenging, given incompleteness and delays in early data reporting, in particular, the severe under-reporting bias in the epicenter, Wuhan, Hubei Province, China. As a result, the current literature reports widely varying estimates. We developed an alternative geo-stratified debiasing estimation framework by incorporating human mobility with case reporting data in three stratified zones, i.e., Wuhan, Hubei Province excluding Wuhan, and mainland China excluding Hubei. We estimated the latent infection ratio to be around 0.12% (18,556 people) and the basic reproduction number to be 3.24 in Wuhan before the city's lockdown on January 23, 2020. The findings based on this debiasing framework have important implications to prioritization of control and prevention efforts.

Estimating the effective reproduction number of the 2019-nCoV in China (preprint)

We estimate the effective reproduction number for 2019-nCoV based on the daily reported cases from China CDC. The results indicate that 2019-nCoV has a higher effective reproduction number than SARS with a comparable fatality rate.