Modelling Disease Mitigation at Mass Gatherings: A Case Study of COVID-19 at the 2022 FIFA World Cup. (preprint)

The 2022 FIFA World Cup was the first major multi-continental sporting Mass Gathering Event (MGE) of the post COVID-19 era to allow foreign spectators. Such large-scale MGEs can potentially lead to outbreaks of infectious disease and contribute to the global dissemination of such pathogens. Here we adapt previous work and create a generalisable model framework for assessing the use of disease control strategies at such events, in terms of reducing infections and hospitalisations. This framework utilises a combination of meta-populations based on clusters of people and their vaccination status, Ordinary Differential Equation integration between fixed time events, and Latin Hypercube sampling. We use the FIFA 2022 World Cup as a case study for this framework. Pre-travel screenings of visitors were found to have little effect in reducing COVID-19 infections and hospitalisations. With pre-match screenings of spectators and match staff being more effective. Rapid Antigen (RA) screenings 0.5 days before match day outperformed RT-PCR screenings 1.5 days before match day. A combination of pre-travel RT-PCR and pre-match RA testing proved to be the most successful screening-based regime. However, a policy of ensuring that all visitors had a COVID-19 vaccination (second or booster dose) within a few months before departure proved to be much more efficacious. The State of Qatar abandoned all COVID-19 related travel testing and vaccination requirements over the period of the World Cup. Our work suggests that the State of Qatar may have been correct in abandoning the pre-travel testing of visitors. However, there was a spike in COVID-19 cases and hospitalisations within Qatar over the World Cup. The research outlined here suggests a policy requiring visitors to have had a recent COVID-19 vaccination may have prevented the increase in COVID-19 cases and hospitalisations during the world cup.

Infectious side effects of baricitinib: a big data analysis based on VigiBase (preprint)

Background and aim: baricitinib is an inhibitor of Janus-associated kinase (JAK) subtypes 1 and 2 with various effects on intracellular signaling pathways. Approved for rheumatoid arthritis (RA), and recently used for severe COVID-19, the prevalence of infections associated with baricitinib treatment has not been widely reported. We aimed to analyze the prevalence of infectious side effects in patients treated with baricitinib. Methods: using the WHO global pharmacovigilance database (VigiBase), we carried out an extensive data analysis of approximately 300 patients treated with baricitinib. The prevalence of infectious side effects was our main focus. Patients treated with baricitinib for the indication of severe COVID-19 were excluded.Results: the most prevalent infectious side effect of baricitinib was oral herpes (IC025 of 4.36) and herpes zoster infection (IC025 of 4.159). On the contrary, gastrointestinal viral infections, infectious pleural effusions and various viral pneumonias had low prevalence rates (IC025 values of 0.173; 0.093; and 0.188, respectively). Conclusion: based on a big data analysis, baricitinib was associated with infectious complications where oral herpes and herpes zoster infections shown to be more prevalent than previously reported. The immunomodulatory effect of baricitinib including cytokine effects on immune cells and the inhibition of numerous growth factors and cytokines such as IL-2, suppressing both innate and adaptive immune responses, are the most comprehensive mechanisms behind such side effects. Given the clinical indications for baricitinib (RA) and the current use in severe COVID-19, cautious approach should be taken before introducing baricitinib particularly in immunosuppressed patients.

Estimating Social Contacts in Mass Gatherings for Disease Outbreak Prevention and Management (Case of Hajj Pilgrimage) (preprint)

Background: Most mass gathering events have been suspended due to the SARS-CoV-2 pandemic. However, with vaccination rollout, whether and how to organize some of these mass gathering events arises as part of the pandemic recovery discussions, and this calls for decision support tools. The Hajj, one of the world's largest religious gatherings, was substantively scaled down in 2020 and 2021 and it is still unclear how it will take place in 2022 and subsequent years. Simulating disease transmission dynamics during the Hajj season under different conditions can provide some insights for better decision-making. Most disease risk assessment models require data on the number and nature of possible close contacts between individuals. Methods: : We sought to use integrated agent-based modeling and discrete events simulation techniques to capture risky contacts among the pilgrims and assess different scenarios in one of the Hajj major sites, namely Masjid-Al-Haram. Results: : The simulation results showed that a plethora of risky contacts may occur during the rituals. Also, as the total number of pilgrims increases at each site, the number of risky contacts increases, and physical distancing measures may be challenging to maintain beyond a certain number of pilgrims in the site. Conclusions: : This study presented a simulation tool that can be relevant for the risk assessment of a variety of (respiratory) infectious diseases, in addition to COVID-19 in the Hajj season. This tool can be expanded to include other contributing elements of disease transmission to quantify the risk of the mass gathering events.

COVID-19 attack ratio among children critically depends on the time to removal and activity levels (preprint)

The attack ratio in a subpopulation is defined as the total number of infections over the total number of individuals in this subpopulation. Using a methodology based on modified age-stratified transmission dynamics model, we estimated the attack ratio of COVID-19 among children (individuals 0-11 years) in Ontario, Canada when a large proportion of individuals eligible for vaccination (age 12 and above) are vaccinated to achieve herd immunity among this subpopulation, or the effective herd immunity with additional physical distancing measures (hence effective herd immunity). We describe the relationship between this attack ratio among children, the time to remove infected individuals from the transmission chain and the children-to-children daily contact rate, while considering the increased transmissibility of virus variants (using the Delta variant as an example). We further illustrate the generality and applicability of the methodology established by performing an analysis of the attack ratio of COVID-19 among children in the Canadian population. The clinical attack ratio, the number of symptomatic infections over the total population can be informed from the attack ratio, and both can be reduced substantially via a combination of higher vaccine coverage in the vaccine eligible population, reduced social mixing among children, and rapid testing and isolation.

The Role of Behavioral Compliance to Non-Pharmaceutical and Pharmaceutical Interventions in the Fight Against COVID-19: Insights From a Behavior-Disease Economic Epidemic Model Coupled With Optimal Control Theory (preprint)

It is imperative that resources are channeled towards programs that are efficient and cost effective in combating the spread of COVID-19, the disease caused by severe acute respiratory syndrome coronavirus 2 (SARSCoV-2). This study proposed and analyzed control strategies for that purpose. We developed a mathematical disease model within an optimal control framework that allows us to investigate the best approach for curbing COVID-19 epidemic. We address the question, what is the role of community compliance in as a measure for COVID-19 control? Analyzing the impact of community compliance of recommended guidelines by health authorities — examples, social distancing, face mask use, and sanitizing — couple with efforts by health authorities in areas of vaccine provision and effective quarantine — showed that the best intervention in addition to implementation of vaccination programs and effective quarantine measures, is the active incorporation of individuals’ collective behaviors, and that resources should also be directed towards community campaigns on the importance of face mask use, social distancing, and frequent sanitizing, and any other collective activities. We also demonstrated that collective behavioral response of individuals influences the disease dynamics; implying recommended health policy should be contextualized.

Assessing Inequities in COVID-19 Vaccine Roll-Out Strategy Programs: A Cross-Country Study Using a Machine Learning Approach (preprint)

Background: After the start of the COVID-19 pandemic and its spread across the world, countries have adopted containment measures to stop its transmission, limit fatalities and relieve hospitals from strain and overwhelming imposed by the virus. Many countries implemented social distancing and lockdown strategies that negatively impacted their economies and the psychological wellbeing of their citizens, even though they contributed to saving lives. Recently approved and available, COVID-19 vaccines can provide a really viable and sustainable option for controlling the pandemic. However, their uptake represents a global challenge, due to vaccine hesitancy logistic-organizational hurdles that have made its distribution stagnant in several developed countries despite several appeal by the media, policy- and decision-makers, and community leaders. Vaccine distribution is a concern also in developing countries, where there is scarcity of doses.Objective: To set up a metric to assess vaccination uptake and identify national socio-economic factors influencing this indicator.Methods: We conducted a cross-country study. We first estimated the vaccination uptake rate across countries by fitting a logistic model to reported daily case numbers. Using the uptake rate, we estimated the vaccine roll-out index. Next, we used Random Forest, an “off-the-shelf” machine learning algorithm, to study the association between vaccination uptake rate and socio-economic factors.Results: We found that the mean vaccine roll-out index is 0.016 (standard deviation 0.016), with a range between 0.0001 (Haiti) and 0.0829 (Mongolia). The top four factors associated with vaccine roll-out index are the median per capita income, human development index, percentage of individuals who have used the internet in the last three months, and health expenditure per capita. Conclusion: The still ongoing COVID-19 pandemic has shed light on the chronic inequality in global health systems. The disparity in vaccine adoption across low- and high-income countries is a global public health challenge. We must pave the way for a universal access to vaccines and other approved treatments, regardless of demographic structures and underlying health conditions. Income disparity remains, instead, an important cause of vaccine inequity, and the tendency toward "vaccine nationalism" and “vaccine apartheid” restricts the functioning of the global vaccine allocation framework and, thus, the ending of the pandemic. Stronger mechanisms are needed to foster countries' political willingness to promote vaccine and drug access equity in a globalized society, where future pandemics and other global health rises can be anticipated.

The Impact of Non-Pharmaceutical Interventions on the Second Wave of COVID-19: Insights from an Artificial Intelligence-Based, Cross-Country Study (preprint)

Objective: To determine the relative influences of various non-pharmaceutical interventions (NPIs) put in place during the first wave of COVID-19 across countries on the spreading rate of COVID-19 during the second wave, taking into account national-level climatic, environmental, clinical, health, economic, pollution, social, and demographic factors. Methods: We first estimate the growth of the first and second wave across countries by fitting a logistic model to reported daily case numbers, up to the first and second epidemic peaks. Using the growth rate, we estimate the basic and effective reproduction number (second wave) Re across countries. Next, we use Random Forest, an “off-the-shelf” machine learning algorithm, to study the association between the growth rate of the second wave of COVID-19 and NPIs as well as pre-existing country characteristics (climatic, environmental, clinical, health, economic, pollution, social, and demographic factors). Lastly, we compare the growth rate of the first and second waves of COVID-19. Findings: Our findings reveal that the mean R0 and Re were respectively 2.02 (S.D 1.09) and 1.07 (S.D. 0.41). R0 has the highest value in Israel (R0 = 6.93) and lowest in Senegal (R0 = 1.13) whereas Re (second wave) had the highest value in Mexico ( Re = 3.08) and lowest in Bangladesh (Re = 1.07). The top three factors associated with the growth of the second wave are body mass index, the number of days that the government sets restrictions on requiring facial coverings outside the home at all times regardless of location or presence of other people in some areas, and restrictions on gatherings of 10 people or less. We found a statistically significant difference between the means of the first and second waves. Conclusion: Artificial intelligence techniques can enable scholars as well as public health decision- and policy-makers to estimate the effectiveness of public health policies and mitigation strategies to counteract the toll of the outbreak in terms of infections and deaths, enforcing and implementing “smart” interventions, which are as efficacious as drastic and stringent ones.

Higher Rates of COVID-19 but Less Severe Infections Reported for Patients on Il-4/Il-13 Blocker: A Big Data Analysis of the WHO Vigibase (preprint)

Background: Dupilumab (Dupixent®) is a monoclonal antibody that inhibits IL-4 and IL-13 signaling used for the treatment of allergic diseases. Whilst biological therapy is generally associated with an increased risk of infectious disease, prior studies have suggested Dupilumab may be protective. Objective: We investigated the link between Dupilumab therapy and SARS-CoV-2 infection.Methods: We carryied out a comprehensive data-mining and disproportionality analysis of the WHO global pharmacovigilance database. One asymptomatic COVID-19 case, 106 cases of symptomatic COVID-19, and 2 cases of severe COVID-19 pneumonia were found. Results: Dupilumab treated patients were at higher risk of COVID-19 (with an IC0.25 of 3.05), even though infections were less severe (IC0.25 of -1.71). The risk of developing COVID-19 was significant both among males and females (with an IC0.25 of 0.24 and 0.58, respectively). The risk of developing COVID-19 was significant in the age-group of 45-64 years (with an IC0.25 of 0.17). Limitations: Limitations include: the heterogeneous nature of the database sources. Furthermore, a direct causal relationship cannot be inferred from the current investigation.Conclusion: Dupilumab use was found to reduce COVID-19 related severity. Further studies are needed to better understand the immunological mechanisms and clinical implications of these findings.

Social Media COVID-19 Information and Vaccine Decision: A Latent Class Analysis (preprint)

Aims: To identify the impact of the information consumption modalities related to the COVID-19 pandemic and its vaccine, on the vaccination decision among the social media users. Also to study the relationships between vaccination attitudes, and latent subgroups, socio-demographic variables, fear of COVID-19 and perceived stress. Method: A total of 723 subjects (male: n = 353; 48.8%; female: n = 370; 51.2%), aged 31.08 ± 10.77, participated in our survey prepared online on the Google Forms application via the platforms Twitter and Facebook. Results: Five latent classes were identified by the analysis: Class 1 (mixed consumers), class 2 (the largest consumers of social media), class 3 (consumers of official information), class 4 (low consumers of information on the vaccine) and class 5 (social media consumers information verifiers). Also, the subgroup that is knowledgeable about COVID-19 pandemic and its vaccine, and who consumes the most information about the vaccine from official sources, is the one with the highest vaccine acceptance rate. In addition, the hesitant attitude towards the COVID-19 vaccine was linked to gender and mask wearing, while refusal behavior was linked to age, female gender, education level, mask wearing, and fear of COVID-19. Conclusion: The results of the study suggest that specific interventions on social media are needed, to reduce hesitancy rates, and the refusal of vaccination, which is crucial in this period of prevailing of COVID-19 virus.

Development of an Early Alert System for an Additional Wave of COVID-19 Cases using a Recurrent Neural Network with Long Short-Term Memory (preprint)

The impact of the still ongoing “Coronavirus Disease 2019” (COVID-19) pandemic has been and is still vast, affecting not only global human health and stretching healthcare facilities, but also profoundly disrupting societal and economic systems worldwide. The nature of the way the virus spreads causes cases to come in further recurring waves. This is due a complex array of biological, societal and environmental factors, including the novel nature of the emerging pathogen. Other parameters explaining the epidemic trend consisting of recurring waves are logistic-organizational challenges in the implementation of the vaccine roll-out, scarcity of doses and human resources, seasonality, meteorological drivers, and community heterogeneity, as well as cycles of strengthening and easing/lifting of the mitigation interventions. Therefore, it is crucial to be able to have an early alert system to identify when another wave of cases is about to occur. The availability of a variety of newly developed indicators allows for the exploration of multi-feature prediction models for case data. Ten indicators were selected as features for our prediction model. The model chosen is a Recurrent Neural Network with Long Short-Term Memory. This paper documents the development of an early alert/detection system that functions by predicting future daily confirmed cases based on a series of features that include mobility and stringency indices, and epidemiological parameters. The model is trained on the intermittent period in between the first and the second wave, in all of the South African provinces.

How the COVID-19 pandemic is shaping research in Africa: inequalities in scholarly output and collaborations and new opportunities for scientific leadership (preprint)

Background Scientometrics enables scholars to assess and visualize emerging research trends and hot-spots in the scientific literature from a quantitative standpoint. In the last decades, Africa has nearly doubled its absolute count of scholarly output, even though its share in global knowledge production has dramatically decreased. This limited contribution of African scholars to the global research output is in part impacted by the availability of adequate infrastructures and research collaborative networks. The still ongoing COVID-19 pandemic has profoundly impacted the way scholarly research is conducted, published and disseminated. However, the COVID-19 related research focus, the scientific productivity and the research collaborative network of African researchers during the ongoing COVID-19 pandemic remain to be elucidated yet. Methods This study aimed to clarify the COVID-19 research patterns among African researchers and estimate the strength of collaborations and partnerships between African researchers and scholars from the rest of the world during the COVID-19 pandemic, collecting data from electronic scholarly databases such as Web of Sciences (WoS), PubMed/MEDLINE and African Journals OnLine (AJOL), the largest and prominent platform of African-published scholarly journals. Results In the present bibliometric study, we found that COVID-19 related collaboration patterns varied among African regions, being shaped and driven by historical, social, cultural, linguistic, and even religious determinants. For instance, most of the scholarly partnerships occurred with formerly colonial countries (like European or North-American countries). In other cases, scholarly ties of North African countries were above all with the Kingdom of Saudi Arabia. In terms of amount of publications, South Africa and Egypt were among the most productive countries. Discussion Bibliometrics and, in particular, scientometrics can help scholars identify research areas of particular interest, as well as emerging topics, such as the COVID-19 pandemic. With a specific focus on the still ongoing viral outbreak, they can assist decision- and policy-makers in allocating funding and economic-financial, logistic, organizational, and human resources, based on the specific gaps and needs of a given country or research area. Conclusions In conclusion, the ongoing COVID-19 pandemic has exerting a subtle, complex impact on research and publishing patterns in African countries. On the one hand, it has distorted and even amplified existing inequalities and disparities in terms of amount of scholarly output, share of global knowledge, and patterns of collaborations. On the other hand, COVID-19 provided new opportunities for research collaborations.

Big Data- and Artificial Intelligence-Based Hot-Spot Analysis of COVID-19: Gauteng, South Africa, as a case study (preprint)

“Coronavirus Disease 2019” (COVID-19) related data contain many complexities that must be taken into account when extracting information to guide public health decision- and policy-makers. In generalising the spread of a virus over a large area, such as a province, it must be assumed that the transmission occurs as a stochastic process. This statistically random spread of a virus through a population is the core of the majority of Susceptible-Infectious-Recovered-Deceased (SIRD) models and is dependent on factors such as number of infected cases, infection rate, level of social interactions, susceptible population and total population. However, the spread of COVID-19 and, therefore, the data representing the virus progression do not always conform to a stochastic model. In this paper, we have focused on the most influential non-stochastic dynamics of COVID-19, hot-spots, utilizing artificial intelligence (AI) based geo-localization and clustering analyses, taking Gauteng (South Africa) as a case study.

Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa (preprint)

COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated, and are using clinical public health (CPH) strategies to control the pandemic. The emergence of Variants of Concern (VOC), unequal access to the vaccine supply and locally specific logistical and vaccine delivery parameters, add complexity to national CPH strategies and amplify the urgent need for effective CPH policies. Big Data and Artificial Intelligence Machine Learning techniques and collaborations can be instrumental in an accurate, timely, locally nuanced analysis of multiple data sources to inform CPH decision-making, vaccination strategies and their staged roll-out. The Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) has been established to develop and employ machine learning techniques to design CPH strategies in Africa, which requires ongoing collaboration, testing and development to maximize the equity and effectiveness of COVID-19 related CPH interventions.

The Impact of Public Health Interventions on Delaying and Mitigating against Replacement by SARS-CoV-2 Variants of Concern (preprint)

Background. Public Health interventions have succeeded marginally to flatten the COVID-19 epidemic curve of the most recent wave in many regions. Several highly transmissible COVID-19 Variants of Concern (VOCs) have emerged internationally, with variable virulence and as-yet unclear antigenicity. Growing public fatigue highlights the importance of accurately quantifying the speed at which VOCs can replace the resident strain, to inform precise decision-making in terms of both timing and scale of interventions.Methods. Using a simple Susceptible-Infected-Removed (SIR) model, we derive a formula calculating the duration for a VOC to increase its frequency from one level to another. We evaluate the impact of interventions on VOC-related variables.Findings. After introduction of a VOC with a higher transmissibility than the resident strain, its prevalence increases exponentially, leading to eventual replacement by the VOC. Even if the outbreak caused by the resident strain has previously been brought under control, the epidemic is eventually going to increase exponentially, possibly after a transient declining phase. The initial VOC frequency, epidemic mean generation time, and reproduction number of the resident strain all contribute to determining the speed of VOC replacement and the resulting epidemic growth.Interpretation. Following the introduction of a VOC, the risk of a sudden surge of the total cases is hidden behind a transient decline. Maintaining or enhancing Public Health interventions are vital to slowing down the VOC replacement, curbing the epidemic growth, and increasing VOC doubling time.

Quantifying the Shift in Social Contact Patterns in Response to Non-Pharmaceutical Interventions (preprint)

Background: Social contact mixing plays a critical role in influencing the transmission routes of infectious diseases. Quantifying social contact mixing patterns and their variations in a rapidly evolving pandemic intervened by changing public health measures is key for retroactive evaluation and proactive assessment of the effectiveness of different age- and setting-specific interventions. Methods: We performed an analysis of the intervention escalation phase of the COVID-19 epidemic in Ontario, Canada. Specifically, we integrated social contact patterns derived from empirical data with a disease transmission model, that enabled the usage of age-stratified COVID-19 incidence data to infer age-specific susceptibility, daily contact mixing patterns in workplace, household, school, and community settings; and transmission acquired in these settings under different physical distancing measures. Findings: We quantified the age- and setting (household, workplace, community, and school)-specific mixing patterns and their evolution during the escalation of public health interventions in Ontario, Canada. We estimated a reduction in the average individual contact rate from 12·27 to 6·58 per day, with an increase in household contacts, following the implementation of control measures. We estimated increasing trends by age in both the susceptibility to infection by SARS-CoV-2 and the proportion of symptomatic individuals diagnosed. Interpretation: Inferring the age- and setting-specific social contact mixing and key age-stratified epidemiological parameters, in the presence of evolving control measures, is critical to inform decision- and policy-making for the current COVID-19 pandemic.Funding Statement: This project has been partially supported by the Canadian Institute of Health Research (CIHR) 2019 Novel Coronavirus (COVID-19) rapid research program.Declaration of Interests: The authors declare that they have no conflict of interest.

Interrupted time series analysis of the implementation of social distancing policy, its lifting and the mandate of wearing face masks in Iran to mitigate against COVID-19 (preprint)

BackgroundCOVID-19 was first reported in Wuhan, China, and has spread rapidly around the world. The purpose of this study was to investigate the effects of implementing social distancing policy, and the impact of its lifting, with the resumption of social contacts and activities, as well as the effects of mandating face masks on the temporal trend of new COVID-19 cases in Iran. Methods We employed the interrupted time series analysis (ITSA), which is a very valuable method that can be used to evaluate the impact of the implementation of various policies in the health sector to help health policy-makers make effective decisions. Daily data were collected from the Ministry of Health and Medical Education and the World Health Organization from 954 public hospitals and health center settings. Data were extracted 14 days before and after the implementation of each policy. Results were computed with their 95% confidence interval (CI) and p-values equal to or less than 0.05 were considered as statistically significant. All data were analyzed using the open-source software R Version 3.6.1 using the “nlme” and “car” packages.ResultsThe slope of changes in new confirmed cases following the implementation of the social distancing policy decreased by 118.79 (P <0.001). With the resumption of social and economic activities in all provinces except for Tehran, initially the number of new daily confirmed cases was 3300, which was statistically significant (P <0.001). The slope of changes due to the implementation of this policy was 47.89 (P <0.001). A similar trend was detected with the resumption of social and economic activities in Tehran. With the implementation of the policy of mandatory use of masks, the slope of changes showed a decrease of 25.84 (P <0.001). Conclusion Given the absence of effective drugs and vaccines against COVID-19, policy-makers have implemented non-pharmacological interventions to reduce the transmission of the disease and prevent more deaths. Social distancing may be unsustainable in the long-term, while wearing masks is both a cost-effective and efficacious measure to curb disease transmission.

The Psychological Impact of COVID-19 Pandemic on the Population of Bahrain (preprint)

Background and Aim: The pandemic of COVID-19 is a global crisis that is considered a stressful event directly and indirectly (via prophylactic measures taken) for people in any society. It can have an impact on mental health resulting in a plethora of symptoms. Method: This study measures the psychological impact, demonstrated by the symptoms of depression, anxiety, and stress. An online semi-structured questionnaire has been used with all participants, and with the measure The Arabic version of The Depression Anxiety and Stress Scale -21 (DASS-21). The study design was cross-sectional. Which was conducted in April-May 2020. The sample was (n=1115) from Bahrain’s population, (1081 Bahraini) and (33 non-Bahraini), aged 18 and above, 701 females, most of them were graduated and employed. Results showed 30% were with depressive symptoms, 18.2% have exhibited symptoms, and 30.8% reported stress symptoms. Females were higher than males in depressive and anxiety symptoms. While no gender differences in stress symptoms. The younger age group showed more distress across the board with symptoms reported decreasing with age. Students were also noticed to be the group reporting the highest symptoms, together with people with the lowest income.Conclusion The study has demonstrated a high psychological impact on the population of Bahrain with around a third of the population demonstrating some level of distress.