Can ship travel contain COVID-19 outbreak after re-opening: a Bayesian meta-analysis.

Large gatherings of people on cruise ships and warships are often at high risk of COVID-19 infections. To assess the transmissibility of SARS-CoV-2 on warships and cruise ships and to quantify the effectiveness of the containment measures, the transmission coefficient (ß), basic reproductive number (R0), and time to deploy containment measures were estimated by the Bayesian Susceptible-Exposed-Infected-Recovered model. A meta-analysis was conducted to predict vaccine protection with or without non-pharmaceutical interventions (NPIs). The analysis showed that implementing NPIs during voyages could reduce the transmission coefficients of SARS-CoV-2 by 50%. Two weeks into the voyage of a cruise that begins with 1 infected passenger out of a total of 3,711 passengers, we estimate there would be 45 (95% CI:25-71), 33 (95% CI:20-52), 18 (95% CI:11-26), 9 (95% CI:6-12), 4 (95% CI:3-5), and 2 (95% CI:2-2) final cases under 0%, 10%, 30%, 50%, 70%, and 90% vaccine protection, respectively, without NPIs. The timeliness of strict NPIs along with implementing strict quarantine and isolation measures is imperative to contain COVID-19 cases in cruise ships. The spread of COVID-19 on ships was predicted to be limited in scenarios corresponding to at least 70% protection from prior vaccination, across all passengers and crew.

Estimation of infection fields and pathways for SARS-CoV-2 and its variant based on the relationship among dose-response function parameter, quantum emission rate, and basic reproductive number

The theoretical model of the relationship among dose-response function parameters, quantum emission rate, and basic reproductive number for SARS-CoV-2 was constructed. Then, using this model, infection fields and pathways for SARS-CoV-2 and its variant were estimated. The parameters of the time activity, the number of contacts by the microenvironments and groups, and the COVID-19 risk from multiple pathways in near and far fields were used. Consequently, in lower transmissibility, droplet spray transmission in the near field was dominant, whereas in higher transmissibility, transmission from inhalation of smaller aerosols in the far field was dominant. Moreover, it was suggested that transmission from droplet spray, indirect contacts, and inhalation of smaller aerosols in the near field and inhalation of smaller aerosols in the far field was dominant for the wild-type strain, while transmission from inhalation of smaller aerosols in the far field were dominant for the Delta variant. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Quantifying Mycobacterium tuberculosis transmission dynamics across global settings: a systematic analysis.

The degree to which individual heterogeneity in the production of secondary cases ("superspreading") affects tuberculosis (TB) transmission has not been systematically studied. We searched for population-based or surveillance studies in which whole genome sequencing was used to estimate TB transmission and the size distributions of putative TB transmission clusters were enumerated. We fit cluster size distribution data to a negative binomial branching process model to jointly infer the transmission parameters $R$ (the reproductive number) and dispersion parameter, $k$, which quantifies the propensity of superspreading in a population (generally, lower values of $k$ ($<1.0$) suggest increased heterogeneity). Of 4,796 citations identified in our initial search, nine studies met inclusion criteria ($n=5$ all TB; $n=4$ drug resistant TB) from eight global settings. Estimated $R$ values (range: 0.10, 0.73) were below 1.0, consistent with declining epidemics in the included settings; estimated $k$ values were well below 1.0 (range: 0.02, 0.48), indicating the presence of substantial individual-level heterogeneity in transmission across all settings. We estimated that a minority of cases (range 2-31%) drive the majority (80%) of ongoing transmission at the population level. Identifying sources of heterogeneity and accounting for them in TB control may have a considerable impact on mitigating TB transmission.

The reproduction rate of severe acute respiratory syndrome coronavirus 2 different variants recently circulated in human: a narrative review.

SARS-COV-2 is responsible for the current worldwide pandemic, which started on December 2019 in Wuhan, China. On March 2020 World Health Organization announced COVID-19 as the new pandemic. Some SARS-COV-2 variants have increased transmissibility, cause more severe disease (e.g., increased hospitalizations or deaths), are resistant to antibodies produced by the previous infection or vaccination, and there is more difficulty in treatment and diagnosis of them. World Health Organization considered them as SARS-CoV-2 variants of concern. The introductory reproduction rate (R0) is an epidemiologic index of the transmissibility of the virus, defined as the average number of persons infected by the virus after known contact with an infectious person in a susceptible population. An R0 > 1 means that the virus is spreading exponentially, and R0 < 1, means that the outbreak is subsiding. In various studies, the estimated R and VOC growth rates were reported to be greater than the ancestral strains. However, it was also a low level of concordance between the estimated Rt of the same variant in different studies. It is because the R of a variant not only dependent on the biological and intrinsic factors of the virus but also several parameters can affect the R0, including the duration of contagiousness and the likelihood of infection per contact. Evaluation of changes in SARS-CoV-2 has shown that the rate of human-to-human transmission of this virus has increased. Like other viruses with non-human sources which succeeded in surviving in the human population, SARS-CoV-2 has gradually adapted to the human population, and its ability to transmit from human to human has increased. Of course, due to the continuous changes in this virus, it is crucial to survey the rate of transmission of the virus over time.

A Predictive Model for R0(SARS-COV2)

Due to the rapid spread of the COVID-19, scientists are constantly monitoring the evolution of the number of infections in a region. In particular, the basic reproductive number (R0) is studied, because it indicates if the number of cases will increase and the infection will last, or if it will decrease and stability will be reached. The present contribution is focused on forecasting this ratio, based on the extreme gradient boosting tree approach. Gradient reinforcement trees are used. Using public data of the COVID-19 outbreak in the Caribbean and some countries, this value is computed. © 2022 IEEE.

Risk Assessment of Large-Scale Sports Events in the Context of COVID-19

This paper assesses the potential risks of epidemic situation and public opinion during the Beijing Winter Olympic Games by analyzing the epidemic situation and public opinion of the Tokyo Olympic Games. The results show that there is a strong time-lag correlation between the COVID-19 epidemic and the public opinion of the Tokyo Olympics. For the epidemic situation, the multi-agent modeling method is used at the city level to simulate the possible spread of diseases in the city where the event was held. At the Olympic village level, the modified the SEIR transmission model is modified to simulate the virus transmission in the Olympic Village during the Beijing Winter Olympic Games. At the end, the risk analysis of the Beijing Winter Olympic Games is carried out based on the time series prediction model. © 2022, Editorial Board of Journal of the University of Electronic Science and Technology of China. All right reserved.

Reliability of Early Estimates of the Basic Reproduction Number of COVID-19: A Systematic Review and Meta-Analysis.

OBJECTIVE: This systematic review estimated the pooled R0 for early COVID-19 outbreaks and identified the impact of study-related factors such as methods, study location and study period on the estimated R0. METHODS: We searched electronic databases for human studies published in English between 1 December 2019 and 30 September 2020 with no restriction on country/region. Two investigators independently performed the data extraction of the studies selected for inclusion during full-text screening. The primary outcome, R0, was analysed by random-effects meta-analysis using the restricted maximum likelihood method. RESULTS: We identified 26,425 studies through our search and included 151 articles in the systematic review, among which 81 were included in the meta-analysis. The estimates of R0 from studies included in the meta-analysis ranged from 0.4 to 12.58. The pooled R0 for COVID-19 was estimated to be 2.66 (95% CI, 2.41-2.94). The results showed heterogeneity among studies and strong evidence of a small-study effect. CONCLUSIONS: The high heterogeneity in studies makes the use of the R0 for basic epidemic planning difficult and presents a huge problem for risk assessment and data synthesis. Consensus on the use of R0 for outbreak assessment is needed, and its use for assessing epidemic risk is not recommended.

Isolation in the control of epidemic.

Among many epidemic prevention measures, isolation is an important method to control the spread of infectious disease. Scholars rarely study the impact of isolation on disease dissemination from a quantitative perspective. In this paper, we introduce an isolation ratio and establish the corresponding model. The basic reproductive number and its biological explanation are given. The stability conditions of the disease-free and endemic equilibria are obtained by analyzing its distribution of characteristic values. It is shown that the isolation ratio has an important influence on the basic reproductive number and the stability conditions. Taking the COVID-19 in Wuhan as an example, isolating more than 68% of the population can control the spread of the epidemic. This method can provide precise epidemic prevention strategies for government departments. Numerical simulations verify the effectiveness of the results.

EPIDEMIOLOGIC ASPECTS IN PREVENTION OF THE NEW CORONAVIRUS INFECTION (COVID-19) (LITERATURE REVIEW)

At the end of 2019 the mankind had to face a new coronavirus infection with higher virulence which resulted in its rapid spread all over the world and in an ultimate pandemic. Initially a new virus which causes COVID-19 was called 2019-nCoV but it soon acquired its well-known name, SARS-CoV-2. We can positively state that this new coronavirus infection will remain in the history of world public healthcare as a disease that caused a collapse in rendering medical aid. Undoubtedly, this new coronavirus infection has changed customary lifestyle of the overall world population. This review can be considered problematic in its essence and focuses on examining contemporary trends in the official epidemiologic situation in the world regarding the new coronavirus infection (SARS-CoV-2). Having analyzed several foreign and domestic documents, the authors revealed a necessity to enhance levels and quality of COVID-19 epidemiologic diagnostics. There is a suggestion being considered at the moment on including additional clinical and diagnostic activities aimed at preventing further spread of the new coronavirus infection. We should note that data on COVID-19-related mortality and morbidity are renewed every day and every hour. Given that, it seems rather difficult to keep in line with the latest trends in COVID-19 prevention and epidemiologic diagnostics. However, the authors made an attempt to possibly collect all the latest data on epidemiological peculiarities related to the clinical course of the new coronavirus infection. The authors have a hope that this review will be useful for epidemiologists when they detect new cases of the disease as well as for lecturers at medical higher educational establishments when they train students and resident physicians. © Butaev T.M., Tsirikhova A.S., Kabaloeva D.V., Kudukhova D.O., 2022

Parameter identifiability and optimal control of an SARS-CoV-2 model early in the pandemic.

We fit an SARS-CoV-2 model to US data of COVID-19 cases and deaths. We conclude that the model is not structurally identifiable. We make the model identifiable by prefixing some of the parameters from external information. Practical identifiability of the model through Monte Carlo simulations reveals that two of the parameters may not be practically identifiable. With thus identified parameters, we set up an optimal control problem with social distancing and isolation as control variables. We investigate two scenarios: the controls are applied for the entire duration and the controls are applied only for the period of time. Our results show that if the controls are applied early in the epidemic, the reduction in the infected classes is at least an order of magnitude higher compared to when controls are applied with 2-week delay. Further, removing the controls before the pandemic ends leads to rebound of the infected classes.

Fractional-order coronavirus models with vaccination strategies impacted on Saudi Arabia’s infections

Several newly nonlinear models for describing dynamics of COVID-19 pandemic have been proposed and investigated in literature recently. In light of these models, we attempt to reveal the role of fractional calculus in describing the growth of COVID-19 dynamics implemented on Saudi Arabia’s society over 107 days;from 17 Dec 2020 to 31 March 2021. Above is achieved by operating two fractional-order differential operators, Caputo and the Caputo-Fabrizio operators, instead of the classical one. One of expanded SEIR models is utilized for achieving our purpose. With the help of using the Generalized Euler Method (GEM) and Adams-Bashforth Method (ABM), the numerical simulations are performed respectively in view of the Caputo and Caputo-Fabrizio operators. Accordance with said, the stability analysis of the two proposed fractional-order models is discussed and explored in view of obtaining the equilibrium points, determining the reproductive number (R0) and computing the elasticity indices of R0. Several numerical comparisons reveal that the fractional-order COVID-19 models proposed in this work are better than that of classical one when such comparisons are performed between them and some real data collected from Saudi Arabia’s society. This inference together with the cases predictions that could easily deduced from the proposed fractional-order models can allow primary decision makers and influencers to set the right plans and logic strategies that should be followed to face this pandemic. © 2022 the Author(s), licensee AIMS Press.

The Global dynamics of a SIR model considering competitions among multiple strains in patchy environments.

Pandemics, such as Covid-19 and AIDS, tend to be highly contagious and have the characteristics of global spread and existence of multiple virus strains. To analyze the competition among different strains, a high dimensional SIR model studying multiple strains' competition in patchy environments is introduced in this work. By introducing the basic reproductive number of different strains, we found global stability conditions of disease-free equilibrium and persistence conditions of the model. The competition exclusion conditions of that model are also given. This work gives some insights into the properties of the multiple strain patchy model and all of the analysis methods used in this work could be used in other related high dimension systems.

Asymptomatic and pre-symptoms transmission of covid-19 in heterogeneous epidemic network

The diversity of the spread pattern of the Corona virus is one of the most important reasons for the seriousness of the virus. Therefore, in this paper, we present a fractional mathematical SEIAS model that studies many ways of spreading (asymptomatic and pre-symptoms transmission) with the hypothesis of the spread of the virus in a heterogeneous network of individuals. The system consists of nonlinear equations which formed in fractional order. And it turns out that the system has two equilibrium positions (free and endemic positions). We also calculated the disease prevalence threshold (ℛ" ) within the network. The condition for the existence of the epidemiological situation has been determined. The stability of the free equilibrium position has been studied. The numerical part has been added to explain the proved theorems of the system in addition to clarifying the role of the heterogeneous network on the value of the virus spread threshold within the network. Keywords: Complex Networks, Novel Coronavirus (COVID-19), Asymptomatic and Pre-Symptoms Transmission, Basic. © 2022 NSP Natural Sciences Publishing Cor.

The impact of vaccination on the spread of COVID-19: Studying by a mathematical model.

The global spread of COVID-19 has not been effectively controlled, posing a huge threat to public health and the development of the global economy. Currently, a number of vaccines have been approved for use and vaccination campaigns have already started in several countries. This paper designs a mathematical model considering the impact of vaccination to study the spread dynamics of COVID-19. Some basic properties of the model are analyzed. The basic reproductive number ℜ 1 of the model is obtained, and the conditions for the existence of endemic equilibria are provided. There exist two endemic equilibria when ℜ 1 < 1 under certain conditions, which will lead to backward bifurcation. The stability of equilibria are analyzed, and the condition for the backward bifurcation is given. Due to the existence of backward bifurcation, even if ℜ 1 < 1 , COVID-19 may remain prevalent. Sensitivity analysis and simulations show that improving vaccine efficacy can control the spread of COVID-19 faster, while increasing the vaccination rate can reduce and postpone the peak of infection to a greater extent. However, in reality, the improvement of vaccine efficacy cannot be realized in a short time, and relying only on increasing the vaccination rate cannot quickly achieve the control of COVID-19. Therefore, relying only on vaccination may not completely and quickly control COVID-19. Some non-pharmaceutical interventions should continue to be enforced to combat the virus. According to the sensitivity analysis, we improve the model by including some non-pharmaceutical interventions. Combining the sensitivity analysis with the simulation of the improved model, we conclude that together with vaccination, reducing the contact rate of people and increasing the isolation rate of infected individuals will greatly reduce the number of infections and shorten the time of COVID-19 spread. The analysis and simulations in this paper can provide some useful suggestions for the prevention and control of COVID-19.

Epidemic size, trend and spatiotemporal mapping of SARS-CoV-2 using geographical information system in Alborz Province, Iran.

BACKGROUND: The first confirmed cases of COVID-19 in Iran were reported in Qom city. Subsequently, the neighboring provinces and gradually all 31 provinces of Iran were involved. This study aimed to investigate the case fatility rate, basic reproductive number in different period of epidemic, projection of daily and cumulative incidence cases and also spatiotemporal mapping of SARS-CoV-2 in Alborz province, Iran. METHODS: A confirmed case of COVID-19 infection was defined as a case with a positive result of viral nucleic acid testing in respiratory specimens. Serial interval (SI) was fitted by gamma distribution and considered the likelihood-based R0 using a branching process with Poisson likelihood. Seven days average of cases, deaths, doubling times and CFRs used to draw smooth charts. kernel density tool in Arc GIS (Esri) software has been employed to compute hot spot area of the study site. RESULTS: The maximum-likelihood value of R0 was 2.88 (95%, CI: 2.57-3.23) in the early 14 days of epidemic. The case fatility rate for Alborz province (Iran) on March 10, was 8.33% (95%, CI:6.3-11), and by April 20, it had an increasing trend and reached 12.9% (95%,CI:11.5-14.4). The doubling time has been increasing from about two days and then reached about 97 days on April 20, 2020, which shows the slowdown in the spread rate of the disease. Also, from March 26 to April 2, 2020 the whole Geographical area of Karj city was almost affected by SARS-CoV-2. CONCLUSIONS: The R0 of COVID-19 in Alborz province was substantially high at the beginning of the epidemic, but with preventive measures and public education and GIS based monitoring of the cases,it has been reduced to 1.19 within two months. This reduction highpoints the attainment of preventive measures in place, however we must be ready for any second epidemic waves during the next months.

Association between temperature and COVID-19 transmission in 153 countries.

The WHO characterized coronavirus disease 2019 (COVID-19) as a global pandemic. The influence of temperature on COVID-19 remains unclear. The objective of this study was to investigate the correlation between temperature and daily newly confirmed COVID-19 cases by different climate regions and temperature levels worldwide. Daily data on average temperature (AT), maximum temperature (MAXT), minimum temperature (MINT), and new COVID-19 cases were collected from 153 countries and 31 provinces of mainland China. We used the spline function method to preliminarily explore the relationship between R0 and temperature. The generalized additive model (GAM) was used to analyze the association between temperature and daily new cases of COVID-19, and a random effects meta-analysis was conducted to calculate the pooled results in different regions in the second stage. Our findings revealed that temperature was positively related to daily new cases at low temperature but negatively related to daily new cases at high temperature. When the temperature was below the smoothing plot peak, in the temperate zone or at a low temperature level (e.g., <25th percentiles), the RRs were 1.09 (95% CI: 1.04, 1.15), 1.10 (95% CI: 1.05, 1.15), and 1.14 (95% CI: 1.06, 1.23) associated with a 1°C increase in AT, respectively. Whereas temperature was above the smoothing plot peak, in a tropical zone or at a high temperature level (e.g., >75th percentiles), the RRs were 0.79 (95% CI: 0.68, 0.93), 0.60 (95% CI: 0.43, 0.83), and 0.48 (95% CI: 0.28, 0.81) associated with a 1°C increase in AT, respectively. The results were confirmed to be similar regarding MINT, MAXT, and sensitivity analysis. These findings provide preliminary evidence for the prevention and control of COVID-19 in different regions and temperature levels.

Impact of level five lockdown on the incidence of COVID-19: lessons learned from South Africa.

INTRODUCTION: the level five (L5) lockdown was a very stringent social distancing measure taken to reduce the spread of COVID-19 infections. This study assessed the impact of the L5 lockdown and its association with the incidence of COVID-19 cases in South Africa (SA). METHODS: data was obtained from the National Department of Health (NDoH) from the 5th March to the 30th April 2020. A basic reproductive number (R0) and a serial interval were used to calculate estimated cases (EC). A double exponential smoothing model was used to forecast the number of cases during the L5 lockdown period. A Poisson regression model was fitted to describe the association between L5 lockdown status and incident cases. RESULTS: a total of 5,737 laboratory-confirmed cases (LCC) were reported by 30th April 2020, 4,785 (83%) occurred during L5 lockdown. Our model forecasted 30,629 cases of COVID-19 assuming L5 lockdown was not imposed. High incidence rates of COVID-19 were recorded in KwaZulu-Natal and Mpumalanga Provinces during the L5 lockdown compared to the other provinces. Nationally, the incident rate of COVID-19 was 68.00% higher in L5 lockdown than pre-lockdown for LCC. CONCLUSION: the L5 lockdown was very effective in reducing the incidence of COVID-19 cases. However, the incident rates of LCC and EC were higher nationally, and in some provinces during the L5 lockdown.

Coronavirus Disease 2019 (COVID-19) Transmission in the United States Before Versus After Relaxation of Statewide Social Distancing Measures.

BACKGROUND: Weeks after issuing social distancing orders to suppress severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and reduce growth in cases of severe coronavirus disease 2019 (COVID-19), all US states and the District of Columbia partially or fully relaxed these measures. METHODS: We identified all statewide social distancing measures that were implemented and/or relaxed in the United States between 10 March and 15 July 2020, triangulating data from state government and third-party sources. Using segmented linear regression, we estimated the extent to which relaxation of social distancing affected epidemic control, as indicated by the time-varying, state-specific effective reproduction number (Rt). RESULTS: In the 8 weeks prior to relaxation, mean Rt declined by 0.012 units per day (95% confidence interval [CI], -.013 to -.012), and 46/51 jurisdictions achieved Rt < 1.0 by the date of relaxation. After relaxation of social distancing, Rt reversed course and began increasing by 0.007 units per day (95% CI, .006-.007), reaching a mean Rt of 1.16. Eight weeks later, the mean Rt was 1.16 and only 9/51 jurisdictions were maintaining an Rt < 1.0. Parallel models showed similar reversals in the growth of COVID-19 cases and deaths. Indicators often used to motivate relaxation at the time of relaxation (eg, test positivity rate <5%) predicted greater postrelaxation epidemic growth. CONCLUSIONS: We detected an immediate and significant reversal in SARS-CoV-2 epidemic suppression after relaxation of social distancing measures across the United States. Premature relaxation of social distancing measures undermined the country's ability to control the disease burden associated with COVID-19.