A new method for the joint estimation of instantaneous reproductive number and serial interval during epidemics.

Although some methods for estimating the instantaneous reproductive number during epidemics have been developed, the existing frameworks usually require information on the distribution of the serial interval and/or additional contact tracing data. However, in the case of outbreaks of emerging infectious diseases with an unknown natural history or undetermined characteristics, the serial interval and/or contact tracing data are often not available, resulting in inaccurate estimates for this quantity. In the present study, a new framework was specifically designed for joint estimates of the instantaneous reproductive number and serial interval. Concretely, a likelihood function for the two quantities was first introduced. Then, the instantaneous reproductive number and the serial interval were modeled parametrically as a function of time using the interpolation method and a known traditional distribution, respectively. Using the Bayesian information criterion and the Markov Chain Monte Carlo method, we ultimately obtained their estimates and distribution. The simulation study revealed that our estimates of the two quantities were consistent with the ground truth. Seven data sets of historical epidemics were considered and further verified the robust performance of our method. Therefore, to some extent, even if we know only the daily incidence, our method can accurately estimate the instantaneous reproductive number and serial interval to provide crucial information for policymakers to design appropriate prevention and control interventions during epidemics.

Application of a time-delay SIR model with vaccination in COVID-19 prediction and its optimal control strategy

In the classical infectious disease compartment model, the parameters are fixed. In reality, the probability of virus transmission in the process of disease transmission depends on the concentration of virus in the environment, and the concentration depends on the proportion of patients in the environment. Therefore, the probability of virus transmission changes with time. Then how to fit the parameters and get the trend of the parameters changing with time is the key to predict the disease course with the model. In this paper, based on the US COVID-19 epidemic statistics during calibration period, the parameters such as infection rate and recovery rate are fitted by using the linear regression algorithm of machine science, and the laws of these parameters changing with time are obtained. Then a SIR model with time delay and vaccination is proposed, and the optimal control strategy of epidemic situation is analyzed by using the optimal control theory and Pontryagin maximum principle, which proves the effectiveness of the control strategy in restraining the transmission of COVID-19. The numerical simulation results show that the time-varying law of the number of active cases obtained by our model basically conforms to the real changing law of the US COVID-19 epidemic statistics during calibration period. In addition, we have predicted the changes in the number of active cases in the COVID-19 epidemic in the USA over time in the future beyond the calibration cycle, and the predicted results are more in line with the actual epidemic data.

Application of a time-delay SIR model with vaccination in COVID-19 prediction and its optimal control strategy.

In the classical infectious disease compartment model, the parameters are fixed. In reality, the probability of virus transmission in the process of disease transmission depends on the concentration of virus in the environment, and the concentration depends on the proportion of patients in the environment. Therefore, the probability of virus transmission changes with time. Then how to fit the parameters and get the trend of the parameters changing with time is the key to predict the disease course with the model. In this paper, based on the US COVID-19 epidemic statistics during calibration period, the parameters such as infection rate and recovery rate are fitted by using the linear regression algorithm of machine science, and the laws of these parameters changing with time are obtained. Then a SIR model with time delay and vaccination is proposed, and the optimal control strategy of epidemic situation is analyzed by using the optimal control theory and Pontryagin maximum principle, which proves the effectiveness of the control strategy in restraining the transmission of COVID-19. The numerical simulation results show that the time-varying law of the number of active cases obtained by our model basically conforms to the real changing law of the US COVID-19 epidemic statistics during calibration period. In addition, we have predicted the changes in the number of active cases in the COVID-19 epidemic in the USA over time in the future beyond the calibration cycle, and the predicted results are more in line with the actual epidemic data.

Construction of infectious bronchitis virsus endoribonucleasensp15 defective recombinant virus by reverse genetic system

Infectious Bronchitis Virus (1BV) belongs to the y coronavirus, however, the function of IBV encoded endoribonuclease (non - structural protein 15, nsp15) has not been determined yet. To explore the function of nsp15 in the process of IBV replication, we mutated the IBV nsp15 endonuclease core residue His238 to Ala, constructed the nsp15-defective recombinant virus rIBV-nsp15-H238A, via in vitro ligation and recombination technology. Plaque assay and TCID50 were performed to measure virus titer, virus plaque size and growth curve. The IBV Beaudette-R genome was cloned as 5 fragments in vectors, BsaI or BsmBI restriction sites were added to the end of each fragment. After plasmid amplification, the cDNA fragments were obtained by enzymatic digestion, followed with in vitro ligation and transcription. Full - length genomic RNA was electroporated into Vero cells, together with N transcript, to rescue the recombinant viruses rIBV and rIBV - nsp15- H238A. Plaque assay was performed to detect and compare the viral titer and plaque size of these two recombinant viruses. Results showed that the virus titer of rIBV -nsp15 -H238A was 2.71x106PFU/mL, 3 times lower than that of rIBV (9.4x106PFU/mL). The plaque size of rIBV-nsp15-H238A was much smaller than that of rIBV, indicating that rIBV-nsp15-11238A replicates and spreads slower than rIBV. The growth curve of rIBV-nsp15-H238A was slower than that of rIBV. Our study demonstrates that nsp15 I-1238 is the key amino acid and plays an important role in the replication and spread of IBV. The construction of nsp15 defective recombinant virus provides a powerful tool for the study of the function of nsp15.

SARS-CoV-2 transmission in opposition-controlled Northwest Syria: modeling pandemic responses during political conflict.

INTRODUCTION: Ten years of conflict has displaced more than half of Northwest Syria's (NWS) population and decimated the health system, water and sanitation, and public health infrastructure vital for infectious disease control. The first NWS COVID-19 case was declared on July 9, 2020, but impact estimations in this region are minimal. With the rollout of vaccination and emergence of the B.1.617.2 (Delta) variant, we aimed to estimate the COVID-19 trajectory in NWS and the potential effects of vaccine coverage and hospital occupancy. METHODS: We conducted a mixed-method study, primarily including modeling projections of COVID-19 transmission scenarios with vaccination strategies using an age-structured, compartmental susceptible-exposed-infectious-recovered (SEIR) model, supported by data from 20 semi-structured interviews with frontline health workers to help contextualize interpretation of modeling results. RESULTS: Modeling suggested that existing low stringency non-pharmaceutical interventions (NPIs) minimally affected COVID-19 transmission. Maintaining existing NPIs after the Delta variant introduction is predicted to result in a second COVID-19 wave, overwhelming hospital capacity and resulting in a fourfold increased death toll. Simulations with up to 60% vaccination coverage by June 2022 predict that a second wave is not preventable with current NPIs. However, 60% vaccination coverage by June 2022 combined with 50% coverage of mask-wearing and handwashing should reduce the number of hospital beds and ventilators needed below current capacity levels. In the worst-case scenario of a more transmissible and lethal variant emerging by January 2022, the third wave is predicted. CONCLUSION: Total COVID-19 attributable deaths are expected to remain relatively low owing largely to a young population. Given the negative socioeconomic consequences of restrictive NPIs, such as border or school closures for an already deeply challenged population and their relative ineffectiveness in this context, policymakers and international partners should instead focus on increasing COVID-19 vaccination coverage as rapidly as possible and encouraging mask-wearing.

Heterologous vaccination interventions to reduce pandemic morbidity and mortality: Modeling the US winter 2020 COVID-19 wave.

COVID-19 remains a stark health threat worldwide, in part because of minimal levels of targeted vaccination outside high-income countries and highly transmissible variants causing infection in vaccinated individuals. Decades of theoretical and experimental data suggest that nonspecific effects of non-COVID-19 vaccines may help bolster population immunological resilience to new pathogens. These routine vaccinations can stimulate heterologous cross-protective effects, which modulate nontargeted infections. For example, immunization with Bacillus Calmette-Guérin, inactivated influenza vaccine, oral polio vaccine, and other vaccines have been associated with some protection from SARS-CoV-2 infection and amelioration of COVID-19 disease. If heterologous vaccine interventions (HVIs) are to be seriously considered by policy makers as bridging or boosting interventions in pandemic settings to augment nonpharmaceutical interventions and specific vaccination efforts, evidence is needed to determine their optimal implementation. Using the COVID-19 International Modeling Consortium mathematical model, we show that logistically realistic HVIs with low (5 to 15%) effectiveness could have reduced COVID-19 cases, hospitalization, and mortality in the United States fall/winter 2020 wave. Similar to other mass drug administration campaigns (e.g., for malaria), HVI impact is highly dependent on both age targeting and intervention timing in relation to incidence, with maximal benefit accruing from implementation across the widest age cohort when the pandemic reproduction number is >1.0. Optimal HVI logistics therefore differ from optimal rollout parameters for specific COVID-19 immunizations. These results may be generalizable beyond COVID-19 and the US to indicate how even minimally effective heterologous immunization campaigns could reduce the burden of future viral pandemics.

The Effect of the COVID-19 Vaccine on Daily Cases and Deaths Based on Global Vaccine Data.

BACKGROUND: Coronavirus disease 2019 (COVID-19), a global pandemic, has caused over 216 million cases and 4.50 million deaths as of 30 August 2021. Vaccines can be regarded as one of the most powerful weapons to eliminate the pandemic, but the impact of vaccines on daily COVID-19 cases and deaths by country is unclear. This study aimed to investigate the correlation between vaccines and daily newly confirmed cases and deaths of COVID-19 in each country worldwide. METHODS: Daily data on firstly vaccinated people, fully vaccinated people, new cases and new deaths of COVID-19 were collected from 187 countries. First, we used a generalized additive model (GAM) to analyze the association between daily vaccinated people and daily new cases and deaths of COVID-19. Second, a random effects meta-analysis was conducted to calculate the global pooled results. RESULTS: In total, 187 countries and regions were included in the study. During the study period, 1,011,918,763 doses of vaccine were administered, 540,623,907 people received at least one dose of vaccine, and 230,501,824 people received two doses. For the relationship between vaccination and daily increasing cases of COVID-19, the results showed that daily increasing cases of COVID-19 would be reduced by 24.43% [95% CI: 18.89, 29.59] and 7.50% [95% CI: 6.18, 8.80] with 10,000 fully vaccinated people per day and at least one dose of vaccine, respectively. Daily increasing deaths of COVID-19 would be reduced by 13.32% [95% CI: 3.81, 21.89] and 2.02% [95% CI: 0.18, 4.16] with 10,000 fully vaccinated people per day and at least one dose of vaccine, respectively. CONCLUSIONS: These findings showed that vaccination can effectively reduce the new cases and deaths of COVID-19, but vaccines are not distributed fairly worldwide. There is an urgent need to accelerate the speed of vaccination and promote its fair distribution across countries.

Potential global impacts of alternative dosing regimen and rollout options for the ChAdOx1 nCoV-19 vaccine.

The high efficacy, low cost, and long shelf-life of the ChAdOx1 nCoV-19 vaccine positions it well for use in in diverse socioeconomic settings. Using data from clinical trials, an individual-based model was constructed to predict its 6-month population-level impact. Probabilistic sensitivity analyses evaluated the importance of epidemiological, demographic and logistical factors on vaccine effectiveness. Rollout at various levels of availability and delivery speed, conditional on vaccine efficacy profiles (efficacy of each dose and interval between doses) were explored in representative countries. We highlight how expedient vaccine delivery to high-risk groups is critical in mitigating COVID-19 disease and mortality. In scenarios where the availability of vaccine is insufficient for high-risk groups to receive two doses, administration of a single dose of is optimal, even when vaccine efficacy after one dose is just 75% of the two doses. These findings can help inform allocation strategies particularly in areas constrained by availability.

Inhibition of anti-viral stress granule formation by coronavirus endoribonuclease nsp15 ensures efficient virus replication.

Cytoplasmic stress granules (SGs) are generally triggered by stress-induced translation arrest for storing mRNAs. Recently, it has been shown that SGs exert anti-viral functions due to their involvement in protein synthesis shut off and recruitment of innate immune signaling intermediates. The largest RNA viruses, coronaviruses, impose great threat to public safety and animal health; however, the significance of SGs in coronavirus infection is largely unknown. Infectious Bronchitis Virus (IBV) is the first identified coronavirus in 1930s and has been prevalent in poultry farm for many years. In this study, we provided evidence that IBV overcomes the host antiviral response by inhibiting SGs formation via the virus-encoded endoribonuclease nsp15. By immunofluorescence analysis, we observed that IBV infection not only did not trigger SGs formation in approximately 80% of the infected cells, but also impaired the formation of SGs triggered by heat shock, sodium arsenite, or NaCl stimuli. We further demonstrated that the intrinsic endoribonuclease activity of nsp15 was responsible for the interference of SGs formation. In fact, nsp15-defective recombinant IBV (rIBV-nsp15-H238A) greatly induced the formation of SGs, along with accumulation of dsRNA and activation of PKR, whereas wild type IBV failed to do so. Consequently, infection with rIBV-nsp15-H238A strongly triggered transcription of IFN-ß which in turn greatly affected rIBV-nsp15-H238A replication. Further analysis showed that SGs function as an antiviral hub, as demonstrated by the attenuated IRF3-IFN response and increased production of IBV in SG-defective cells. Additional evidence includes the aggregation of pattern recognition receptors (PRRs) and signaling intermediates to the IBV-induced SGs. Collectively, our data demonstrate that the endoribonuclease nsp15 of IBV interferes with the formation of antiviral hub SGs by regulating the accumulation of viral dsRNA and by antagonizing the activation of PKR, eventually ensuring productive virus replication. We further demonstrated that nsp15s from PEDV, TGEV, SARS-CoV, and SARS-CoV-2 harbor the conserved function to interfere with the formation of chemically-induced SGs. Thus, we speculate that coronaviruses employ similar nsp15-mediated mechanisms to antagonize the host anti-viral SGs formation to ensure efficient virus replication.

Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) is an emerging worldwide threat to public health. While chest computed tomography (CT) plays an indispensable role in its diagnosis, the quantification and localization of lesions cannot be accurately assessed manually. We employed deep learning-based software to aid in detection, localization and quantification of COVID-19 pneumonia. METHODS: A total of 2460 RT-PCR tested SARS-CoV-2-positive patients (1250 men and 1210 women; mean age, 57.7 ± 14.0 years (age range, 11-93 years) were retrospectively identified from Huoshenshan Hospital in Wuhan from February 11 to March 16, 2020. Basic clinical characteristics were reviewed. The uAI Intelligent Assistant Analysis System was used to assess the CT scans. RESULTS: CT scans of 2215 patients (90%) showed multiple lesions of which 36 (1%) and 50 patients (2%) had left and right lung infections, respectively (> 50% of each affected lung's volume), while 27 (1%) had total lung infection (> 50% of the total volume of both lungs). Overall, 298 (12%), 778 (32%) and 1300 (53%) patients exhibited pure ground glass opacities (GGOs), GGOs with sub-solid lesions and GGOs with both sub-solid and solid lesions, respectively. Moreover, 2305 (94%) and 71 (3%) patients presented primarily with GGOs and sub-solid lesions, respectively. Elderly patients (≥ 60 years) were more likely to exhibit sub-solid lesions. The generalized linear mixed model showed that the dorsal segment of the right lower lobe was the favoured site of COVID-19 pneumonia. CONCLUSION: Chest CT combined with analysis by the uAI Intelligent Assistant Analysis System can accurately evaluate pneumonia in COVID-19 patients.