Estimation of the probable outbreak size of novel coronavirus (COVID-19) in social gathering events and industrial activities

Outbreaks of COVID-19 in indoor places of worship: a systematic review

AIMS

This review aimed to describe what has been published on COVID-19 outbreaks originating from indoor places of worship.

METHODS

A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist by searching PubMed, Scopus and Embase from 1 January 2020 to 29 March 2021. Citation chasing was also performed. Studies with information about COVID-19 outbreaks originating in indoor places of worship of any religion were included.

RESULTS

A total of 9729 records were identified and 36 were selected. The articles reported 119 descriptions of outbreaks linked to churches, mosques, synagogues, and temples, referring to approximately 52-74 unique outbreaks. The outbreaks were mostly located in three major areas: East and Southeast Asia (46%), the USA (27%), Europe (22%). All the outbreaks began in 2020. Mainly, there were no restrictive measures, or such measures were not followed at the time of the outbreak. Choir practices presented the highest attack rate (up to 0.867).

CONCLUSIONS

The lack of preventive measures and the role of singing practices were highlighted. Reports were often lacking contact tracing and sometimes did not report the date of outbreak extinction. Moreover, reports came from few geographical areas. Thus, the impact of transmission in places of worship may be largely underestimated.

Dynamic ventilation certificate for smart universities using artificial intelligence techniques

The issue of room ventilation has recently gained momentum due to the COVID-19 pandemic. Ventilation is in fact of particular relevance in educational environments. Smart University platforms, today widespread, are a good starting point to offer control services of different relevant indicators in universities. This study advances a Ventilation Quality Certificate (VQC) for Smart Universities. The certificate informs the university community of the ventilation status of its buildings and premises. It also supports senior management's decision-making, because it allows assessing preventive measures and actions taken. The VQC algorithm models the adequacy of classroom ventilation according to the number of persons present. The input used is the organisation's existing data relating to CO₂ concentration and number of room occupants. AI techniques, specifically Artificial Neural Networks (ANN), were employed to determine the relationship between the different data sources included. A prototype of value-added services was developed for the Smart University platform of the University of Alicante, which allowed to implement the resulting models, together with the VQC. The prototype is currently being replicated in other universities. The case study allowed us to validate the VQC, demonstrating both its usefulness and the advantage of using pre-existing university services and resources.

Quantitative risk assessment of COVID-19 aerosol transmission indoors: a mechanistic stochastic web application

An increasing body of literature suggests that aerosol inhalation plays a primary role in COVID-19 transmission, particularly in indoor settings. Mechanistic stochastic models can help public health professionals, engineers, and space planners understand the risk of aerosol transmission of COVID-19 to mitigate it. We developed such model and a user-friendly web application to meet the need of accessible risk assessment tools during the COVID-19 pandemic. We built our model based on the Wells-Riley model of respiratory disease transmission, using quanta emission rates obtained from COVID-19 outbreak investigations. In this report, three modelled scenarios were evaluated and compared to epidemiological studies looking at similar settings: classrooms, weddings, and heavy exercise sessions. We found that the risk of long-range aerosol transmission increased 309-332% when people were not wearing masks, and 424-488% when the room was poorly ventilated in addition to no masks being worn across the scenarios. Also, the risk of transmission could be reduced by ∼40-60% with ventilation rates of 5 ACH for 1-4 h exposure events, and ∼70% with ventilation rates of 10 ACH for 4 h exposure events. Relative humidity reduced the risk of infection (inducing viral inactivation) by a maximum of ∼40% in a 4 h exposure event at 70% RH compared to a dryer indoor environment with 25% RH. Our web application has been used by more than 1000 people in 52 countries as of September 1st, 2021. Future work is needed to obtain SARS-CoV-2 dose-response functions for more accurate risk estimates.

Modeling social, economic, and health perspectives for optimal pandemic policy decision-making

While different control strategies in the early stages of the COVID-19 pandemic have helped decrease the number of infections, these strategies have had an adverse economic impact on businesses. Therefore, optimal timing and scale of closure and reopening strategies are required to prevent both different waves of the pandemic and the negative economic impact of control strategies. This paper proposes a novel multi-objective mixed-integer linear programming (MOMILP) formulation, which results in the optimal timing of closure and reopening of states and industries in each state to mitigate the economic and epidemiological impact of a pandemic. The three objectives being pursued include: (i) the epidemiological impact, (ii) the economic impact on the local businesses, and (iii) the economic impact on the trades between industries. The proposed model is implemented on a dataset that includes 11 states, the District of Columbia, and 19 industries in the US. The solved by augmented ε-constraint approach is used to solve the multi-objective model, and a final strategy is selected from the set of Pareto-optimal solutions based on the least cubic distance of the solution from the optimal value of each objective. The Pareto-optimal solutions suggest that for any control decision (state and industry closure or reopening), the economic impact and the epidemiological impact change in the opposite direction, and it is more effective to close most states while keeping the majority of industries open during the planning horizon.

COVID-19 outbreaks analysis in the Valencian Region of Spain in the prelude of the third wave

Introduction

The COVID-19 pandemic has led to unprecedented social and mobility restrictions on a global scale. Since its start in the spring of 2020, numerous scientific papers have been published on the characteristics of the virus, and the healthcare, economic and social consequences of the pandemic. However, in-depth analyses of the evolution of single coronavirus outbreaks have been rarely reported.

Methods

In this paper, we analyze the main properties of all the tracked COVID-19 outbreaks in the Valencian Region between September and December of 2020. Our analysis includes the evaluation of the origin, dynamic evolution, duration, and spatial distribution of the outbreaks.

Results

We find that the duration of the outbreaks follows a power-law distribution: most outbreaks are controlled within 2 weeks of their onset, and only a few last more than 2 months. We do not identify any significant differences in the outbreak properties with respect to the geographical location across the entire region. Finally, we also determine the cluster size distribution of each infection origin through a Bayesian statistical model.

Discussion

We hope that our work will assist in optimizing and planning the resource assignment for future pandemic tracking efforts.

Modeling the effects of vaccination, nucleic acid testing, and face mask wearing interventions against COVID-19 in large sports events

The transmission of SARS-CoV-2 leads to devastating COVID-19 infections around the world, which has affected both human health and the development of industries dependent on social gatherings. Sports events are one of the subgroups facing great challenges. The uncertainty of COVID-19 transmission in large-scale sports events is a great barrier to decision-making with regard to reopening auditoriums. Policymakers and health experts are trying to figure out better policies to balance audience experiences and COVID-19 infection control. In this study, we employed the generalized SEIR model in conjunction with the Wells-Riley model to estimate the effects of vaccination, nucleic acid testing, and face mask wearing on audience infection control during the 2021 Chinese Football Association Super League from 20 April to 5 August. The generalized SEIR modeling showed that if the general population were vaccinated by inactive vaccines at an efficiency of 0.78, the total number of infectious people during this time period would decrease from 43,455 to 6,417. We assumed that the general population had the same odds ratio of entering the sports stadiums and becoming the audience. Their infection probabilities in the stadium were further estimated by the Wells-Riley model. The results showed that if all of the 30,000 seats in the stadium were filled by the audience, 371 audience members would have become infected during the 116 football games in the 2021 season. The independent use of vaccination and nucleic acid testing would have decreased this number to 79 and 118, respectively. The combined use of nucleic acid testing and vaccination or face mask wearing would have decreased this number to 14 and 34, respectively. The combined use of all three strategies could have further decreased this number to 0. According to the modeling results, policymakers can consider the combined use of vaccination, nucleic acid testing, and face mask wearing to protect audiences from infection when holding sports events, which could create a balance between audience experiences and COVID-19 infection control.

A comparative analysis of SARS-CoV-2 viral load across different altitudes

SARS-CoV-2 has spread throughout the world, including areas located at high or very high altitudes. There is a debate about the role of high altitude hypoxia on viral transmission, incidence, and COVID-19 related mortality. This is the first comparison of SARS-CoV-2 viral load across elevations ranging from 0 to 4300 m. To describe the SARS-CoV-2 viral load across samples coming from 62 cities located at low, moderate, high, and very high altitudes in Ecuador. An observational analysis of viral loads among nasopharyngeal swap samples coming from a cohort of 4929 patients with a RT-qPCR test positive for SARS-CoV-2. The relationship between high and low altitude only considering our sample of 4929 persons is equal in both cases and not significative (p-value 0.19). In the case of low altitude, adding the sex variable to the analysis, it was possible to find a significative difference between men and women (p-value < 0.05). Considering initially sex and then altitude, it was possible to find a significative difference between high and low altitude for men (p-value 0.05). There is not enough evidence to state that viral load is affected directly by altitude range but adding a new variable as sex in the analysis shows that the presence of new variables influences the relationship of altitude range and viral load. There is no evidence that viral loads (Ct and copies/ml) differ at low or high altitude. Using sex as a co-factor, we found that men have higher viral loads than women at low and moderate altitude locations, while living at high altitude, no differences were found. When Ct values were aggregated by low, moderate, and high viral load, we found no significant differences when sex was excluded from the analysis. We conclude that viral load is not directly affected by altitude, but COVID-19 incidence and mortality are rather affected by socio-demographic and idiosyncratic dynamics.

The impact of crowd gatherings on the spread of COVID-19

Crowd gatherings are an important cause of COVID-19 outbreaks. However, how the scale, scene and other factors of gatherings affect the spread of the epidemic remains unclear. A total of 184 gathering events worldwide were collected to construct a database, and 99 of them with a clear gathering scale were used for statistical analysis of the impact of these factors on the disease incidence among the crowd in the study. The results showed that the impact of small-scale (less than 100 people) gathering events on the spread of COVID-19 in the city is also not to be underestimated due to their characteristics of more frequent occurrence and less detection and control. In our dataset, 22.22% of small-scale events have an incidence of more than 0.8. In contrast, the incidence of most large-scale events is less than 0.4. Gathering scenes such as "Meal" and "Family" occur in densely populated private or small public places have the highest incidence. We further designed a model of epidemic transmission triggered by crowd gathering events and simulated the impact of crowd gathering events on the overall epidemic situation in the city. The simulation results showed that the number of patients will be drastically reduced if the scale and the density of crowds gathering are halved. It indicated that crowd gatherings should be strictly controlled on a small scale. In addition, it showed that the model well reproduce the epidemic spread after crowd gathering events better than does the original SIER model and could be applied to epidemic prediction after sudden gathering events.

Short-Term and Long-Term COVID-19 Pandemic Forecasting Revisited with the Emergence of OMICRON Variant in Jordan

Three simple approaches to forecast the COVID-19 epidemic in Jordan were previously proposed by Hussein, et al.: a short-term forecast (STF) based on a linear forecast model with a learning database on the reported cases in the previous 5–40 days, a long-term forecast (LTF) based on a mathematical formula that describes the COVID-19 pandemic situation, and a hybrid forecast (HF), which merges the STF and the LTF models. With the emergence of the OMICRON variant, the LTF failed to forecast the pandemic due to vital reasons related to the infection rate and the speed of the OMICRON variant, which is faster than the previous variants. However, the STF remained suitable for the sudden changes in epi curves because these simple models learn for the previous data of reported cases. In this study, we revisited these models by introducing a simple modification for the LTF and the HF model in order to better forecast the COVID-19 pandemic by considering the OMICRON variant. As another approach, we also tested a time-delay neural network (TDNN) to model the dataset. Interestingly, the new modification was to reuse the same function previously used in the LTF model after changing some parameters related to shift and time-lag. Surprisingly, the mathematical function type was still valid, suggesting this is the best one to be used for such pandemic situations of the same virus family. The TDNN was data-driven, and it was robust and successful in capturing the sudden change in +qPCR cases before and after of emergence of the OMICRON variant.

Religiosity and the Spread of COVID-19: A Multinational Comparison

This article considers the relationships between population religiosity and the coronavirus pandemic situation across different countries. Country-level analyses were based on data from the World Values Survey, Worldometer, and International Monetary Fund covering information about internal (beliefs) and external (practices) religiosity, religious fundamentalism, the COVID-19 pandemic, and the economic situation at two time points in 47 countries. Results showed that declared attendance at religious services is related to more COVID-19 infections and deaths, as well as when controlling for gross domestic product per capita and the number of coronavirus tests per 1 million population. This effect remained in the longitudinal perspective (of six months) and extended from external religiosity only, to both internal and external religiosity indices.

Preventing the Growing Transmission of COVID Clusters: An Integration of the Maslow's Hierarchy of Needs in the Risk Chain

PURPOSE

China's success in containing the coronavirus is an ongoing process of identifying loopholes and refining the management in the COVID-19 risk chain. This article discusses the role of personal needs in epidemic control and linked psychological needs with management measures to propose intervention advice on curbing viral transmission in a systematic way.

METHODS

Based on case studies, we showed the integration of the Maslow's Hierarchy of Needs in the COVID-19 risk chain. The analysis combined the micro-view from individual needs and macro influences from governmental measures. The proposed chain of vulnerabilities could help identify critical links of COVID-19 crisis management in case that cascading effects such as super-spread can be intercepted in time.

RESULTS

The article mainly focused on curbing the viral transmission timely whenever cluster of cases resurge. Considering the triggered activities from personal needs may facilitate the spread, minimizing the impact scale while managing the crisis could start with protecting vulnerable population, well governing potential hotspots, and necessary restrictions on group activities. Besides, "individual" protections combined with "institutional" solutions are strongly advocated. The worst scenario would be the governance link slackened or made mistakes, together with delayed identification, plus unprotected way of living and gathering. In order to cut the transmission in time, besides virus-blocking strategies and vaccination approach, screening measures in combination with the satisfaction of personal needs would help identify confirmed cases earlier. Publicizing the model citizen of being responsible could show needs' satisfaction can live with the virus elimination. At the emergency response stage, it is also crucial to secure fewer loopholes in the health system and strengthen the self-protection barrier by all means.

CONCLUSION

China's experience offers a reference for the balance between the resurgence of clustered cases and sustained recovery. As long as the global pandemic continues, its impact on personal activities will not stop, and vice versa. The chain of vulnerabilities integrating psychological needs into the COVID-19 risk management can provide clear clues for cutting further transmission in an efficient and more socially acceptable way.

Risk factors for increased COVID-19 case-fatality in the United States: A county-level analysis during the first wave

The ongoing COVID-19 pandemic is causing significant morbidity and mortality across the US. In this ecological study, we identified county-level variables associated with the COVID-19 case-fatality rate (CFR) using publicly available datasets and a negative binomial generalized linear model. Variables associated with decreased CFR included a greater number of hospitals per 10,000 people, banning religious gatherings, a higher percentage of people living in mobile homes, and a higher percentage of uninsured people. Variables associated with increased CFR included a higher percentage of the population over age 65, a higher percentage of Black or African Americans, a higher asthma prevalence, and a greater number of hospitals in a county. By identifying factors that are associated with COVID-19 CFR in US counties, we hope to help officials target public health interventions and healthcare resources to locations that are at increased risk of COVID-19 fatalities.

"Mass gathering events and COVID-19 transmission in Borriana (Spain): A retrospective cohort study"

OBJECTIVE

Mass gathering events (MGEs) are associated with the transmission of COVID-19. Between 6 and 10 March 2020, several MGEs related to the Falles festival took place in Borriana, a municipality in the province of Castellon (Spain). The aim of this study was to estimate the incidence of COVID-19 and its association with these MGEs, and to quantify the potential risk factors of its occurrence.

METHODS

During May and June 2020, a population-based retrospective cohort study was carried out by the Public Health Center of Castelló and the Hospital de la Plana in Vila-real. Participants were obtained from a representative sample of 1663 people with potential exposure at six MGEs. A questionnaire survey was carried out to obtain information about attendance at MGEs and COVID-19 disease. In addition, a serologic survey of antibodies against SARS-Cov-2 was implemented. Inverse probability weighted regression was used in the statistical analysis.

RESULTS

A total of 1338 subjects participated in the questionnaire survey (80.5%), 997 of whom undertook the serologic survey. Five hundred and seventy cases were observed with an attack rate (AR) of 42.6%; average age was 36 years, 62.3% were female, 536 cases were confirmed by laboratory tests, and 514 cases were found with SARS-CoV-2 total antibodies. Considering MGE exposure, AR was 39.2% (496/1264). A dose-response relationship was found between MGE attendance and the disease, (adjusted relative risk [aRR] = 4.11 95% confidence interval [CI]3.25-5.19). Two MGEs with a dinner and dance in the same building had higher risks. Associated risk factors with the incidence were older age, obesity, and upper and middle class versus lower class; current smoking was protective.

CONCLUSIONS

The study suggests the significance of MGEs in the COVID-19 transmission that could explain the subsequent outbreak in Borriana.

COVID-19 Pandemic Development in Jordan-Short-Term and Long-Term Forecasting

In this study, we proposed three simple approaches to forecast COVID-19 reported cases in a Middle Eastern society (Jordan). The first approach was a short-term forecast (STF) model based on a linear forecast model using the previous days as a learning data-base for forecasting. The second approach was a long-term forecast (LTF) model based on a mathematical formula that best described the current pandemic situation in Jordan. Both approaches can be seen as complementary: the STF can cope with sudden daily changes in the pandemic whereas the LTF can be utilized to predict the upcoming waves' occurrence and strength. As such, the third approach was a hybrid forecast (HF) model merging both the STF and the LTF models. The HF was shown to be an efficient forecast model with excellent accuracy. It is evident that the decision to enforce the curfew at an early stage followed by the planned lockdown has been effective in eliminating a serious wave in April 2020. Vaccination has been effective in combating COVID-19 by reducing infection rates. Based on the forecasting results, there is some possibility that Jordan may face a third wave of the pandemic during the Summer of 2021.

Simple mathematical modelling approaches to assessing the transmission risk of SARS-CoV-2 at gatherings

BACKGROUND

Gatherings may contribute significantly to the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). For this reason, public health interventions have sought to constrain unrepeated or recurrent gatherings to curb the coronavirus disease 2019 (COVID-19) pandemic. Unfortunately, the range of different types of gatherings hinders specific guidance from setting limiting parameters (e.g. total size, number of cohorts, the extent of physical distancing).

METHODS

We used a generic modelling framework, based on fundamental probability principles, to derive simple formulas to assess introduction and transmission risks associated with gatherings, as well as the potential efficiency of some testing strategies to mitigate these risks.

RESULTS

Introduction risk can be broadly assessed with the population prevalence and the size of the gathering, while transmission risk at a gathering is mainly driven by the gathering size. For recurrent gatherings, the cohort structure does not have a significant impact on transmission between cohorts. Testing strategies can mitigate risk, but frequency of testing and test performance are factors in finding a balance between detection and false positives.

CONCLUSION

The generality of the modelling framework used here helps to disentangle the various factors affecting transmission risk at gatherings and may be useful for public health decision-making.

Risk factors for increased COVID-19 case-fatality in the United States: A county-level analysis during the first wave

The ongoing COVID-19 pandemic is causing significant morbidity and mortality across the US. In this ecological study, we identified county-level variables associated with the COVID-19 case-fatality rate (CFR) using publicly available datasets and a negative binomial generalized linear model. Variables associated with decreased CFR included a greater number of hospitals per 10,000 people, banning religious gatherings, a higher percentage of people living in mobile homes, and a higher percentage of uninsured people. Variables associated with increased CFR included a higher percentage of the population over age 65, a higher percentage of Black or African Americans, a higher asthma prevalence, and a greater number of hospitals in a county. By identifying factors that are associated with COVID-19 CFR in US counties, we hope to help officials target public health interventions and healthcare resources to locations that are at increased risk of COVID-19 fatalities.

Mathematical modeling and a month ahead forecast of the coronavirus disease 2019 (COVID-19) pandemic: an Indian scenario

India, the second-most populous country in the world is witnessing a daily surge in the COVID-19 infected cases. India is currently among the worst-hit nations worldwide due to the COVID-19 pandemic and ranks just behind Brazil and the USA. The prediction of the future course of the pandemic is thus of utmost importance in order to prevent further worsening of the situation. In this paper, we develop models for the past trajectory (March 01, 2020-July 25, 2020) and also make a month-long (July 26, 2020-August 24, 2020) forecast of the future evolution of the COVID-19 pandemic in India by using an autoregressive integrated moving average (ARIMA) model. We determine the most optimal ARIMA model (ARIMA(7,2,2)) based on the statistical parameters viz. root-mean-squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and the coefficient of determination ( R 2 ). Subsequently, the developed model is used to obtain a one month-long forecast for the cumulative cases, active cases, recoveries, and the number of fatalities. According to our forecasting results, India is likely to have 3800,989 cumulative infected cases, 1634,142 cumulative active cases, 2110,697 cumulative recoveries, and 56,150 cumulative deaths by August 24, 2020, if the current trend of the pandemic continues to prevail. The implications of these forecasts are that in the upcoming month, the infection rate of COVID-19 in India is going to escalate, while the rate of recovery and the case-fatality rate is likely to reduce. In order to avert these possible scenarios, the administration and health-care personnel need to formulate and implement robust control measures, while the general public needs to be more responsible and strictly adhere to the established and newly formulated guidelines in order to slow down the spread of the pandemic and prevent it from transforming into a catastrophe.

The Effect of Strict State Measures on the Epidemiologic Curve of COVID-19 Infection in the Context of a Developing Country: A Simulation from Jordan

COVID-19 has posed an unprecedented global public health threat and caused a significant number of severe cases that necessitated long hospitalization and overwhelmed health services in the most affected countries. In response, governments initiated a series of non-pharmaceutical interventions (NPIs) that led to severe economic and social impacts. The effect of these intervention measures on the spread of the COVID-19 pandemic are not well investigated within developing country settings. This study simulated the trajectories of the COVID-19 pandemic curve in Jordan between February and May and assessed the effect of Jordan's strict NPI measures on the spread of COVID-19. A modified susceptible, exposed, infected, and recovered (SEIR) epidemic model was utilized. The compartments in the proposed model categorized the Jordanian population into six deterministic compartments: suspected, exposed, infectious pre-symptomatic, infectious with mild symptoms, infectious with moderate to severe symptoms, and recovered. The GLEAMviz client simulator was used to run the simulation model. Epidemic curves were plotted for estimated COVID-19 cases in the simulation model, and compared against the reported cases. The simulation model estimated the highest number of total daily new COVID-19 cases, in the pre-symptomatic compartmental state, to be 65 cases, with an epidemic curve growing to its peak in 49 days and terminating in a duration of 83 days, and a total simulated cumulative case count of 1048 cases. The curve representing the number of actual reported cases in Jordan showed a good pattern compatibility to that in the mild and moderate to severe compartmental states. The reproduction number under the NPIs was reduced from 5.6 to less than one. NPIs in Jordan seem to be effective in controlling the COVID-19 epidemic and reducing the reproduction rate. Early strict intervention measures showed evidence of containing and suppressing the disease.