Hospital admission, facility‐based isolation, and social distancing: An SEIR model with constrained medical resources

COVID‐19 is a highly contagious disease that has spread to most countries at unprecedented transmission speed. Medical resources and treatments provided by the healthcare system help reduce the mortality rate and spread of COVID‐19 by isolating infectious individuals. We introduce a modified SEIR model that considers individuals access to limited medical resources to characterize the central role of medical resources during the pandemic. We discuss how the three hospital admission policies (hierarchy, mixed, and Fangcang healthcare system) affect the spread of the disease and the number of deaths and infections. We find that the Fangcang system results in the least number of infections, deaths, and occupied beds. When hospital capacity is relatively high or the transmission rate of the mildly infected patient is not ignorable, a mixed system can lead to fewer infections and deaths than a hierarchy system, but greater numbers of occupied beds. This occurs by preventing disease transmission to a great extent. The results are confirmed by our surveys with healthcare workers in major hospitals in Wuhan, China. We also investigate the performance of the three healthcare systems under a social distancing policy. We find that the Fangcang system results in the largest reduction in infections and deaths, especially even when the medical capacity is small. Moreover, we compare a one‐time off policy with a bed trigger policy. We find that a one‐time off policy could achieve the similar performance as bed trigger policy when it is initiated neither too early nor too late.

Risk Factors Associated with the Spatiotemporal Spread of the SARS-CoV-2 Omicron BA.2 Variant - Shanghai Municipality, China, 2022.

What is already known about this topic?: Previous studies have explored the spatial transmission patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and have assessed the associated risk factors. However, none of these studies have quantitatively described the spatiotemporal transmission patterns and risk factors for Omicron BA.2 at the micro (within-city) scale. What is added by this report?: This study highlights the heterogeneous spread of the 2022 Omicron BA.2 epidemic in Shanghai, and identifies associations between different metrics of spatial spread at the subdistrict level and demographic and socioeconomic characteristics of the population, human mobility patterns, and adopted interventions. What are the implications for public health practice?: Disentangling different risk factors might contribute to a deeper understanding of the transmission dynamics and ecology of coronavirus disease 2019 and an effective design of monitoring and management strategies.

Epidemiological characteristics and transmission dynamics of the outbreak caused by the SARS-CoV-2 Omicron variant in Shanghai, China: A descriptive study.

Background: In early March 2022, a major outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant spread rapidly throughout Shanghai, China. Here we aimed to provide a description of the epidemiological characteristics and spatiotemporal transmission dynamics of the Omicron outbreak under the population-based screening and lockdown policies implemented in Shanghai. Methods: We extracted individual information on SARS-CoV-2 infections reported between January 1 and May 31, 2022, and on the timeline of the adopted non-pharmaceutical interventions. The epidemic was divided into three phases: i) sporadic infections (January 1-February 28), ii) local transmission (March 1-March 31), and iii) city-wide lockdown (April 1 to May 31). We described the epidemic spread during these three phases and the subdistrict-level spatiotemporal distribution of the infections. To evaluate the impact on the transmission of SARS-CoV-2 of the adopted targeted interventions in Phase 2 and city-wide lockdown in Phase 3, we estimated the dynamics of the net reproduction number (Rt ). Findings: A surge in imported infections in Phase 1 triggered cryptic local transmission of the Omicron variant in early March, resulting in the largest outbreak in mainland China since the original wave. A total of 626,000 SARS-CoV-2 infections were reported in 99.5% (215/216) of the subdistricts of Shanghai until the end of May. The spatial distribution of the infections was highly heterogeneous, with 37% of the subdistricts accounting for 80% of all infections. A clear trend from the city center towards adjacent suburban and rural areas was observed, with a progressive slowdown of the epidemic spread (from 463 to 244 meters/day) prior to the citywide lockdown. During Phase 2, Rt remained well above 1 despite the implementation of multiple targeted interventions. The citywide lockdown imposed on April 1 led to a marked decrease in transmission, bringing Rt below the epidemic threshold in the entire city on April 14 and ultimately leading to containment of the outbreak. Interpretation: Our results highlight the risk of widespread outbreaks in mainland China, particularly under the heightened pressure of imported infections. The targeted interventions adopted in March 2022 were not capable of halting transmission, and the implementation of a strict, prolonged city-wide lockdown was needed to successfully contain the outbreak, highlighting the challenges for containing Omicron outbreaks. Funding: Key Program of the National Natural Science Foundation of China (82130093); Shanghai Rising-Star Program (22QA1402300).

Modeling transmission of SARS-CoV-2 Omicron in China.

Having adopted a dynamic zero-COVID strategy to respond to SARS-CoV-2 variants with higher transmissibility since August 2021, China is now considering whether, and for how long, this policy can remain in place. The debate has thus shifted towards the identification of mitigation strategies for minimizing disruption to the healthcare system in the case of a nationwide epidemic. To this aim, we developed an age-structured stochastic compartmental susceptible-latent-infectious-removed-susceptible model of SARS-CoV-2 transmission calibrated on the initial growth phase for the 2022 Omicron outbreak in Shanghai, to project COVID-19 burden (that is, number of cases, patients requiring hospitalization and intensive care, and deaths) under hypothetical mitigation scenarios. The model also considers age-specific vaccine coverage data, vaccine efficacy against different clinical endpoints, waning of immunity, different antiviral therapies and nonpharmaceutical interventions. We find that the level of immunity induced by the March 2022 vaccination campaign would be insufficient to prevent an Omicron wave that would result in exceeding critical care capacity with a projected intensive care unit peak demand of 15.6 times the existing capacity and causing approximately 1.55 million deaths. However, we also estimate that protecting vulnerable individuals by ensuring accessibility to vaccines and antiviral therapies, and maintaining implementation of nonpharmaceutical interventions could be sufficient to prevent overwhelming the healthcare system, suggesting that these factors should be points of emphasis in future mitigation policies.

Data augmentation and machine learning techniques for control strategy development in bio-polymerization process.

Machine learning has been increasingly used in biochemistry. However, in organic chemistry and other experiment-based fields, data collected from real experiments are inadequate and the current coronavirus disease (COVID-19) pandemic has made the situation even worse. Such limited data resources may result in the low performance of modeling and affect the proper development of a control strategy. This paper proposes a feasible machine learning solution to the problem of small sample size in the bio-polymerization process. To avoid overfitting, the variational auto-encoder and generative adversarial network algorithms are used for data augmentation. The random forest and artificial neural network algorithms are implemented in the modeling process. The results prove that data augmentation techniques effectively improve the performance of the regression model. Several machine learning models were compared and the experimental results show that the random forest model with data augmentation by the generative adversarial network technique achieved the best performance in predicting the molecular weight on the training set (with an R2 of 0.94) and on the test set (with an R2 of 0.74), and the coefficient of determination of this model was 0.74.

Global diversity of policy, coverage, and demand of COVID-19 vaccines: a descriptive study.

BACKGROUND: Hundreds of millions of doses of coronavirus disease 2019 (COVID-19) vaccines have been administered globally, but progress on vaccination varies considerably between countries. We aimed to provide an overall picture of COVID-19 vaccination campaigns, including policy, coverage, and demand of COVID-19 vaccines. METHODS: We conducted a descriptive study of vaccination policy and doses administered data obtained from multiple public sources as of 8 February 2022. We used these data to develop coverage indicators and explore associations of vaccine coverage with socioeconomic and healthcare-related factors. We estimated vaccine demand as numbers of doses required to complete vaccination of countries' target populations according to their national immunization program policies. RESULTS: Messenger RNA and adenovirus vectored vaccines were the most commonly used COVID-19 vaccines in high-income countries, while adenovirus vectored vaccines were the most widely used vaccines worldwide (180 countries). One hundred ninety-two countries have authorized vaccines for the general public, with 40.1% (77/192) targeting individuals over 12 years and 32.3% (62/192) targeting those ≥ 5 years. Forty-eight and 151 countries have started additional-dose and booster-dose vaccination programs, respectively. Globally, there have been 162.1 doses administered per 100 individuals in target populations, with marked inter-region and inter-country heterogeneity. Completed vaccination series coverage ranged from 0.1% to more than 95.0% of country target populations, and numbers of doses administered per 100 individuals in target populations ranged from 0.2 to 308.6. Doses administered per 100 individuals in whole populations correlated with healthcare access and quality index (R2 = 0.59), socio-demographic index (R2 = 0.52), and gross domestic product per capita (R2 = 0.61). At least 6.4 billion doses will be required to complete interim vaccination programs-3.3 billion for primary immunization and 3.1 billion for additional/booster programs. Globally, 0.53 and 0.74 doses per individual in target populations are needed for primary immunization and additional/booster dose programs, respectively. CONCLUSIONS: There is wide country-level disparity and inequity in COVID-19 vaccines rollout, suggesting large gaps in immunity, especially in low-income countries.

Global landscape of SARS-CoV-2 genomic surveillance and data sharing.

Genomic surveillance has shaped our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. We performed a global landscape analysis on SARS-CoV-2 genomic surveillance and genomic data using a collection of country-specific data. Here, we characterize increasing circulation of the Alpha variant in early 2021, subsequently replaced by the Delta variant around May 2021. SARS-CoV-2 genomic surveillance and sequencing availability varied markedly across countries, with 45 countries performing a high level of routine genomic surveillance and 96 countries with a high availability of SARS-CoV-2 sequencing. We also observed a marked heterogeneity of sequencing percentage, sequencing technologies, turnaround time and completeness of released metadata across regions and income groups. A total of 37% of countries with explicit reporting on variants shared less than half of their sequences of variants of concern (VOCs) in public repositories. Our findings indicate an urgent need to increase timely and full sharing of sequences, the standardization of metadata files and support for countries with limited sequencing and bioinformatics capacity.

Neutralizing Antibodies Against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Variants Induced by Natural Infection or Vaccination: A Systematic Review and Pooled Analysis.

Recently emerged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants may pose a threat to immunity. A systematic landscape of neutralizing antibodies against emerging variants is needed. We systematically searched for studies that evaluated neutralizing antibody titers induced by previous infection or vaccination against SARS-CoV-2 variants and collected individual data. We identified 106 studies meeting the eligibility criteria. Lineage B.1.351 (beta), P.1 (gamma) and B.1.617.2 (delta) significantly escaped natural infection-mediated neutralization, with an average of 4.1-fold (95% confidence interval [CI]: 3.6-4.7-fold), 1.8-fold (1.4-2.4-fold), and 3.2-fold (2.4-4.1-fold) reduction in live virus neutralization assay, while neutralizing titers against B.1.1.7 (alpha) decreased slightly (1.4-fold [95% CI: 1.2-1.6-fold]). Serum from vaccinees also led to significant reductions in neutralization of B.1.351 across different platforms, with an average of 7.1-fold (95% CI: 5.5-9.0-fold) for nonreplicating vector platform, 4.1-fold (3.7-4.4-fold) for messenger RNA platform, and 2.5-fold (1.7-2.9-fold) for protein subunit platform. Neutralizing antibody levels induced by messenger RNA vaccines against SARS-CoV-2 variants were similar to, or higher, than that derived from naturally infected individuals.

Serological evidence of human infection with SARS-CoV-2: a systematic review and meta-analysis.

BACKGROUND: A rapidly increasing number of serological surveys for antibodies to SARS-CoV-2 have been reported worldwide. We aimed to synthesise, combine, and assess this large corpus of data. METHODS: In this systematic review and meta-analysis, we searched PubMed, Embase, Web of Science, and five preprint servers for articles published in English between Dec 1, 2019, and Dec 22, 2020. Studies evaluating SARS-CoV-2 seroprevalence in humans after the first identified case in the area were included. Studies that only reported serological responses among patients with COVID-19, those using known infection status samples, or any animal experiments were all excluded. All data used for analysis were extracted from included papers. Study quality was assessed using a standardised scale. We estimated age-specific, sex-specific, and race-specific seroprevalence by WHO regions and subpopulations with different levels of exposures, and the ratio of serology-identified infections to virologically confirmed cases. This study is registered with PROSPERO, CRD42020198253. FINDINGS: 16 506 studies were identified in the initial search, 2523 were assessed for eligibility after removal of duplicates and inappropriate titles and abstracts, and 404 serological studies (representing tests in 5 168 360 individuals) were included in the meta-analysis. In the 82 studies of higher quality, close contacts (18·0%, 95% CI 15·7-20·3) and high-risk health-care workers (17·1%, 9·9-24·4) had higher seroprevalence than did low-risk health-care workers (4·2%, 1·5-6·9) and the general population (8·0%, 6·8-9·2). The heterogeneity between included studies was high, with an overall I2 of 99·9% (p<0·0001). Seroprevalence varied greatly across WHO regions, with the lowest seroprevalence of general populations in the Western Pacific region (1·7%, 95% CI 0·0-5·0). The pooled infection-to-case ratio was similar between the region of the Americas (6·9, 95% CI 2·7-17·3) and the European region (8·4, 6·5-10·7), but higher in India (56·5, 28·5-112·0), the only country in the South-East Asia region with data. INTERPRETATION: Antibody-mediated herd immunity is far from being reached in most settings. Estimates of the ratio of serologically detected infections per virologically confirmed cases across WHO regions can help provide insights into the true proportion of the population infected from routine confirmation data. FUNDING: National Science Fund for Distinguished Young Scholars, Key Emergency Project of Shanghai Science and Technology Committee, Program of Shanghai Academic/Technology Research Leader, National Science and Technology Major project of China, the US National Institutes of Health. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Investigating vaccine-induced immunity and its effect in mitigating SARS-CoV-2 epidemics in China.

BACKGROUND: To allow a return to a pre-COVID-19 lifestyle, virtually every country has initiated a vaccination program to mitigate severe disease burden and control transmission. However, it remains to be seen whether herd immunity will be within reach of these programs. METHODS: We developed a compartmental model of SARS-CoV-2 transmission for China, a population with low prior immunity from natural infection. Two vaccination programs were tested and model-based estimates of the immunity level in the population were provided. RESULTS: We found that it is unlikely to reach herd immunity for the Delta variant given the relatively low efficacy of the vaccines used in China throughout 2021 and the lack of prior natural immunity. We estimated that, assuming a vaccine efficacy of 90% against the infection, vaccine-induced herd immunity would require a coverage of 93% or higher of the Chinese population. However, even when vaccine-induced herd immunity is not reached, we estimated that vaccination programs can reduce SARS-CoV-2 infections by 50-62% in case of an all-or-nothing vaccine model and an epidemic starts to unfold on December 1, 2021. CONCLUSIONS: Efforts should be taken to increase population's confidence and willingness to be vaccinated and to develop highly efficacious vaccines for a wide age range.

Infectivity, susceptibility, and risk factors associated with SARS-CoV-2 transmission under intensive contact tracing in Hunan, China.

Several mechanisms driving SARS-CoV-2 transmission remain unclear. Based on individual records of 1178 potential SARS-CoV-2 infectors and their 15,648 contacts in Hunan, China, we estimated key transmission parameters. The mean generation time was estimated to be 5.7 (median: 5.5, IQR: 4.5, 6.8) days, with infectiousness peaking 1.8 days before symptom onset, with 95% of transmission events occurring between 8.8 days before and 9.5 days after symptom onset. Most transmission events occurred during the pre-symptomatic phase (59.2%). SARS-CoV-2 susceptibility to infection increases with age, while transmissibility is not significantly different between age groups and between symptomatic and asymptomatic individuals. Contacts in households and exposure to first-generation cases are associated with higher odds of transmission. Our findings support the hypothesis that children can effectively transmit SARS-CoV-2 and highlight how pre-symptomatic and asymptomatic transmission can hinder control efforts.

Who should be prioritized for COVID-19 vaccination in China? A descriptive study.

BACKGROUND: All countries are facing decisions about which population groups to prioritize for access to COVID-19 vaccination after the first vaccine products have been licensed, at which time supply shortages are inevitable. Our objective is to define the key target populations, their size, and priority for a COVID-19 vaccination program in the context of China. METHODS: On the basis of utilitarian and egalitarian principles, we define and estimate the size of tiered target population groups for a phased introduction of COVID-19 vaccination, considering evolving goals as vaccine supplies increase, detailed information on the risk of illness and transmission, and past experience with vaccination during the 2009 influenza pandemic. Using publicly available data, we estimated the size of target population groups, and the number of days needed to vaccinate 70% of the target population. Sensitivity analyses considered higher vaccine coverages and scaled up vaccine delivery relative to the 2009 pandemic. RESULTS: Essential workers, including staff in the healthcare, law enforcement, security, nursing homes, social welfare institutes, community services, energy, food and transportation sectors, and overseas workers/students (49.7 million) could be prioritized for vaccination to maintain essential services in the early phase of a vaccination program. Subsequently, older adults, individuals with underlying health conditions and pregnant women (563.6 million) could be targeted for vaccination to reduce the number of individuals with severe COVID-19 outcomes, including hospitalizations, critical care admissions, and deaths. In later stages, the vaccination program could be further extended to target adults without underlying health conditions and children (784.8 million), in order to reduce symptomatic infections and/or to stop virus transmission. Given 10 million doses administered per day, and a two-dose vaccination schedule, it would take 1 week to vaccinate essential workers but likely up to 7 months to vaccinate 70% of the overall population. CONCLUSIONS: The proposed framework is general but could assist Chinese policy-makers in the design of a vaccination program. Additionally, this exercise could be generalized to inform other national and regional strategies for use of COVID-19 vaccines, especially in low- and middle-income countries.

Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning.

COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute respiratory distress syndrome (ARDS) is one of the common clinical manifestations of severe COVID-19 and it is also responsible for the current shortage of ventilators worldwide. This study aims to analyze the clinical characteristics of COVID-19 ARDS patients and establish a diagnostic system based on artificial intelligence (AI) method to predict the probability of ARDS in COVID-19 patients. We collected clinical data of 659 COVID-19 patients from 11 regions in China. The clinical characteristics of the ARDS group and no-ARDS group of COVID-19 patients were elaborately compared and both traditional machine learning algorithms and deep learning-based method were used to build the prediction models. Results indicated that the median age of ARDS patients was 56.5 years old, which was significantly older than those with non-ARDS by 7.5 years. Male and patients with BMI > 25 were more likely to develop ARDS. The clinical features of ARDS patients included cough (80.3%), polypnea (59.2%), lung consolidation (53.9%), secondary bacterial infection (30.3%), and comorbidities such as hypertension (48.7%). Abnormal biochemical indicators such as lymphocyte count, CK, NLR, AST, LDH, and CRP were all strongly related to the aggravation of ARDS. Furthermore, through various AI methods for modeling and prediction effect evaluation based on the above risk factors, decision tree achieved the best AUC, accuracy, sensitivity and specificity in identifying the mild patients who were easy to develop ARDS, which undoubtedly helped to deliver proper care and optimize use of limited resources.

Global, regional, and national estimates of target population sizes for covid-19 vaccination: descriptive study.

OBJECTIVE: To provide global, regional, and national estimates of target population sizes for coronavirus disease 2019 (covid-19) vaccination to inform country specific immunisation strategies on a global scale. DESIGN: Descriptive study. SETTING: 194 member states of the World Health Organization. POPULATION: Target populations for covid-19 vaccination based on country specific characteristics and vaccine objectives (maintaining essential core societal services; reducing severe covid-19; reducing symptomatic infections and stopping virus transmission). MAIN OUTCOME MEASURE: Size of target populations for covid-19 vaccination. Estimates use country specific data on population sizes stratified by occupation, age, risk factors for covid-19 severity, vaccine acceptance, and global vaccine production. These data were derived from a multipronged search of official websites, media sources, and academic journal articles. RESULTS: Target population sizes for covid-19 vaccination vary markedly by vaccination goal and geographical region. Differences in demographic structure, presence of underlying conditions, and number of essential workers lead to highly variable estimates of target populations at regional and country levels. In particular, Europe has the highest share of essential workers (63.0 million, 8.9%) and people with underlying conditions (265.9 million, 37.4%); these two categories are essential in maintaining societal functions and reducing severe covid-19, respectively. In contrast, South East Asia has the highest share of healthy adults (777.5 million, 58.9%), a key target for reducing community transmission. Vaccine hesitancy will probably impact future covid-19 vaccination programmes; based on a literature review, 68.4% (95% confidence interval 64.2% to 72.6%) of the global population is willing to receive covid-19 vaccination. Therefore, the adult population willing to be vaccinated is estimated at 3.7 billion (95% confidence interval 3.2 to 4.1 billion). CONCLUSIONS: The distribution of target groups at country and regional levels highlights the importance of designing an equitable and efficient plan for vaccine prioritisation and allocation. Each country should evaluate different strategies and allocation schemes based on local epidemiology, underlying population health, projections of available vaccine doses, and preference for vaccination strategies that favour direct or indirect benefits.

Disease burden and clinical severity of the first pandemic wave of COVID-19 in Wuhan, China.

The novel coronavirus disease 2019 (COVID-19) was first reported in Wuhan, China, where the initial wave of intense community transmissions was cut short by interventions. Using multiple data sources, here we estimate the disease burden and clinical severity by age of COVID-19 in Wuhan from December 1, 2019 to March 31, 2020. Our estimates account for the sensitivity of the laboratory assays, prospective community screenings, and healthcare seeking behaviors. Rates of symptomatic cases, medical consultations, hospitalizations and deaths were estimated at 796 (95% CI: 703-977), 489 (472-509), 370 (358-384), and 36.2 (35.0-37.3) per 100,000 persons, respectively. The COVID-19 outbreak in Wuhan had a higher burden than the 2009 influenza pandemic or seasonal influenza in terms of hospitalization and mortality rates, and clinical severity was similar to that of the 1918 influenza pandemic. Our comparison puts the COVID-19 pandemic into context and could be helpful to guide intervention strategies and preparedness for the potential resurgence of COVID-19.

Global, regional, and national estimates of target population sizes for COVID-19 vaccination.

Background COVID-19 vaccine prioritization and allocation strategies that maximize health benefit through efficient use of limited resources are urgently needed. We aimed to provide global, regional, and national estimates of target population sizes for COVID-19 vaccination to inform country-specific immunization strategies on a global scale. Methods Based on a previous study of international allocation for pandemic COVID-19 vaccines, we classified the entire world population into eleven priority groups. Information on priority groups was derived from a multi-pronged search of official websites, media sources and academic journal articles. The sizes of different priority groups were projected for 194 countries globally. Results Overall, the size of COVID-19 vaccine recipient population varied markedly by goals of the vaccination program and geography. The general population aged <60 years without any underlying condition accounts for the majority of the total population (5.2 billion people, 68%), followed by 2.3 billion individuals at risk of severe disease, and 46.9 million essential workers which are critical to maintaining a functional society. Differences in the demographic structure, presence of underlying conditions, and number of essential workers led to highly variable estimates of target populations both at the WHO region and country level. In particular, Europe has the highest share of essential workers (6.8%) and the highest share of individuals with underlying conditions (37.8%), two priority categories to maintain societal functions and reduce severe burden. In contrast, Africa has the highest share of healthy adults, school-age individuals, and infants (77.6%), which are the key groups to target to reduce community transmission. Interpretation The sizeable distribution of target groups on a country and regional bases underlines the importance of equitable and efficient vaccine prioritization and allocation globally. The direct and indirect benefits of COVID-19 vaccination should be balanced by considering local differences in demography and health.

Health-seeking behaviors of patients with acute respiratory infections during the outbreak of novel coronavirus disease 2019 in Wuhan, China.

We conducted two surveys to evaluate the health-seeking behaviors of individuals with acute respiratory infections (ARI) during the COVID-19 outbreak in Wuhan, China. Among 351 participants reporting ARI (10.3%, 351/3,411), 36.5% sought medical assistance. Children were more likely to seek medical assistance than other age-groups (66.1% vs. 28.0%-35.1%). This population-based study demonstrates that the majority of patients with ARI symptoms did not seek medical assistance during the COVID-19 outbreak in Wuhan. These findings may be used to refine the estimates of disease burden and clinical severity of COVID-19 and to plan for health resources allocation.