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The coronavirus disease 2019 (COVID-19) continues to have a huge impact on health care and economic systems around the world. The first question to ponder is to understand the flow of COVID-19 in the spatial and temporal dimensions. We collected 7 Omicron clusters outbreaks in China since the outbreak of COVID-19 as of August 2022, selected outbreak cases from different Provinces and cities, and collected variable indicators that affect spillover outcomes, such as distance, migration index, PHSM index, daily reported cases number and so on. First, variables influencing spillover outcome events were assessed and analyzed retrospectively by constructing an infectious disease dynamics model and a classifier model, and secondly, the association between explanatory variables and spillover outcome events was constructed by fitting a logistics function. This study incorporates 7 influencing factors and classifies the spillover risk level into 3 levels. If different outbreak sites could be classified into different levels of spillover, it may reduce the pressure of epidemic prevention in some cities due to the lack of a uniform standard, which might be more conducive to achieving the goal of "dynamic zero".
ABSTRACT
Since the epidemic of the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), many governments have used reverse transcription polymerase chain reaction (RT-PCR) to detect the virus. However, there are fewer measures of CT values information based on RT-PCR results, and the relationship between CT values and factors from consecutive tests is not clear enough. So in this study, we analyzed the connection between CT values and the factors based on cohort data from Delta variant of SARS-CoV-2 in Hunan Province. Previous studies have showed that the mean age of the cases was 33.34 years (±18.72 years), with a female predominance (55.03%, n = 71), and the greatest proportion of clinical symptoms were of the common type (60.47%, n = 78). There were statistical differences between the N and ORF1ab genes in the CT values for the cases. Based on the analysis of the association between CT values and the factors, the lowest CT values were obtained for the unvaccinated, older and clinically symptomatic group at 3-10 days, the maximum peak of viral load occurred. Therefore, it is recommended to use patient information to focus on older, clinically symptomatic, unvaccinated patients and to intervene promptly upon admission.
ABSTRACT
Background: The current outbreak of novel coronavirus disease 2019 has caused a serious disease burden worldwide. Vaccines are an important factor to sustain the epidemic. Although with a relatively high-vaccination worldwide, the decay of vaccine efficacy and the arising of new variants lead us to the challenge of maintaining a sufficient immune barrier to protect the population. Method: A case-contact tracking data in Hunan, China, is used to estimate the contact pattern of cases for scenarios including school, workspace, etc, rather than ordinary susceptible population. Based on the estimated vaccine coverage and efficacy, a multi-group vaccinated-exposed-presymptomatic-symptomatic-asymptomatic-removed model (VEFIAR) with 8 age groups, with each partitioned into 4 vaccination status groups is developed. The optimal dose-wise vaccinating strategy is optimized based on the currently estimated immunity barrier of coverage and efficacy, using the greedy algorithm that minimizes the cumulative cases, population size of hospitalization and fatality respectively in a certain future interval. Parameters of Delta and Omicron variants are used respectively in the optimization. Results: The estimated contact matrices of cases showed a concentration on middle ages, and has compatible magnitudes compared to estimations from contact surveys in other studies. The VEFIAR model is numerically stable. The optimal controled vaccination strategy requires immediate vaccination on the un-vaccinated high-contact population of age 30-39 to reduce the cumulative cases, and is stable with different basic reproduction numbers ( R 0 ). As for minimizing hospitalization and fatality, the optimized strategy requires vaccination on the un-vaccinated of both aged 30-39 of high contact frequency and the vulnerable older. Conclusion: The objective of reducing transmission requires vaccination in age groups of the highest contact frequency, with more priority for un-vaccinated than un-fully or fully vaccinated. The objective of reducing total hospitalization and fatality requires not only to reduce transmission but also to protect the vulnerable older. The priority changes by vaccination progress. For any region, if the local contact pattern is available, then with the vaccination coverage, efficacy, and disease characteristics of relative risks in heterogeneous populations, the optimal dose-wise vaccinating process will be obtained and gives hints for decision-making.
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Background The epidemiological characteristics and transmissibility of Coronavirus Disease 2019 (COVID-19) may undergo changes due to the mutation of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) strains. The purpose of this study is to compare the differences in the outbreaks of the different strains with regards to aspects such as epidemiological characteristics, transmissibility, and difficulties in prevention and control. Methods COVID-19 data from outbreaks of pre-Delta strains, the Delta variant and Omicron variant, were obtained from the Chinese Center for Disease Control and Prevention (CDC). Case data were collected from China's direct-reporting system, and the data concerning outbreaks were collected by on-site epidemiological investigators and collated by the authors of this paper. Indicators such as the effective reproduction number (Reff), time-dependent reproduction number (Rt), rate of decrease in transmissibility (RDT), and duration from the illness onset date to the diagnosed date (DID)/reported date (DIR) were used to compare differences in transmissibility between pre-Delta strains, Delta variants and Omicron variants. Non-parametric tests (namely the Kruskal-Wallis H and Mean-Whitney U tests) were used to compare differences in epidemiological characteristics and transmissibility between outbreaks of different strains. P < 0.05 indicated that the difference was statistically significant. Results Mainland China has maintained a “dynamic zero-out strategy” since the first case was reported, and clusters of outbreaks have occurred intermittently. The strains causing outbreaks in mainland China have gone through three stages: the outbreak of pre-Delta strains, the outbreak of the Delta variant, and outbreaks involving the superposition of Delta and Omicron variant strains. Each outbreak of pre-Delta strains went through two stages: a rising stage and a falling stage, Each outbreak of the Delta variant and Omicron variant went through three stages: a rising stage, a platform stage and a falling stage. The maximum Reff value of Omicron variant outbreaks was highest (median: 6.7;ranged from 5.3 to 8.0) and the differences were statistically significant. The RDT value of outbreaks involving pre-Delta strains was smallest (median: 91.4%;[IQR]: 87.30–94.27%), and the differences were statistically significant. The DID and DIR for all strains was mostly in a range of 0–2 days, with more than 75%. The range of duration for outbreaks of pre-Delta strains was the largest (median: 20 days, ranging from 1 to 61 days), and the differences were statistically significant. Conclusion With the evolution of the virus, the transmissibility of the variants has increased. The transmissibility of the Omicron variant is higher than that of both the pre-Delta strains and the Delta variant, and is more difficult to suppress. These findings provide us with get a more clear and precise picture of the transmissibility of the different variants in the real world, in accordance with the findings of previous studies. Reff is more suitable than Rt for assessing the transmissibility of the disease during an epidemic outbreak.
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Introduction: The aim of this study was to construct an assessment method for cross-regional transmission of coronavirus disease 2019 (COVID-19) and to provide recommendations for optimizing measures such as interregional population movements. Methods: Taking Xi'an City as the example subject of this study's analysis, a Cross-Regional-Gravitational-Dynamic model was constructed to simulate the epidemic in each district of Xi'an under three scenarios of controlled population movement (Scenario 1: no intensive intervention; Scenario 2: blocking Yanta District on December 18 and blocking the whole region on December 23; and Scenario 3: blocking the whole region on December 23). This study then evaluated the effects of such simulated population control measures. Results: The cumulative number of cases for the three scenarios was 8,901,425, 178, and 474, respectively, and the duration of the epidemic was 175, 18, and 22 days, respectively. The real world prevention and control measures in Xi'an reduced the cumulative number of cases for its outbreak by 99.98% in comparison to the simulated response in Scenario 1; in contrast, the simulated prevention and control strategies set in Scenarios 2 (91.26%) and 3 (76.73%) reduced cases even further than the real world measures used in Xi'an. Discussion: The constructed model can effectively simulate an outbreak across regions. Timely implementation of two-way containment and control measures in areas where spillover is likely to occur is key to stopping cross-regional transmission.
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INTRODUCTION: Vaccination booster shots are completely necessary for controlling breakthrough infections of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China. The study aims to estimate effectiveness of booster vaccines for high-risk populations (HRPs). METHODS: A vaccinated Susceptible-Exposed-Symptomatic-Asymptomatic-Recovered/Removed (SEIAR) model was developed to simulate scenarios of effective reproduction number (R eff ) from 4 to 6. Total number of infectious and asymptomatic cases were used to evaluated vaccination effectiveness. RESULTS: Our model showed that we could not prevent outbreaks when covering 80% of HRPs with booster unless R eff =4.0 or the booster vaccine had efficacy against infectivity and susceptibility of more than 90%. The results were consistent when the outcome index was confirmed cases or asymptomatic cases. CONCLUSIONS: An ideal coronavirus disease 2019 (COVID-19) booster vaccination strategy for HRPs would be expected to reach the initial goal to control the transmission of the Delta variant in China. Accordingly, the recommendation for the COVID-19 booster vaccine should be implemented in HRPs who are already vaccinated and could prevent transmission to other groups.