Impact assessment of self-medication on COVID-19 prevalence in Gauteng, South Africa, using an age-structured disease transmission modelling framework (preprint)

Introduction: Self-medication, as a global phenomenon, remains a pressing issue that requires attention worldwide, in particular, the Global South. It has been a hindrance to infectious disease control as it, in most cases dampens interventions put forth by health authorities. It is imperative, therefore, that disease dynamics relating to self-medication are incorporated into mathematical models used to inform infectious disease-related public health interventions and policy. COVID-19-associated self-medication is well documented, and its implications for disease prevention and control are alarming. We investigated the impact of self-medication across different age groups on the transmission dynamics of the disease; the interplay of vaccination and self-medication on the spread of the disease; and the age group with the most tendency for self-medication. We used Gauteng Province, South Africa, as a case study.  Methods: We employed ordinary differential equations (ODEs), formulated within an age-structured compartmental disease modelling framework. Model parameters were estimated using a Markov Chain Monte Carlo (MCMC) estimation scheme. Uncertainty and sensitivity analysis were carried out on model parameters.  Results: The model implied estimates indicates that self-medication is predominant among Age group 15-64 (83.13%), followed by Age group 65+ (44.17%). Age group 0-14 records 33.82%. The mean value of the basic reproduction number, first epidemic peak, and first epidemic peak time are 3.19838, 821536, and 214.988, respectively.  Conclusion: Self-medicationplays a crucial role in combating COVID-19, and that regardless of the levelof effectiveness of instituted vaccination programs, it must be put in check. Appropriate campaign against COVID-19 related self-medication is justified. It is also worth noting that campaigns should target the active population (ages 14-64)

A Stochastic Analysis of a Siqr Epidemic Model With Short and Long-term Prophylaxis (preprint)

This paper aims to incorporate a high order stochastic perturbation into a SIQR epidemic model with transient prophylaxis and lasting prophylaxis. The existence and uniqueness of the global positive solution is proven and a stochastic condition in order to study the extinction of an infectious disease is established. The existence of a stationary distribution for the stochastic epidemic model is investigated as well. Numerical simulations are conducted to support our theoretical results and an example of application with COVID-19 data from Canada is used to estimate the transmission rate and basic reproduction number while constructing a model fitting the data.

Estimation of epidemiological parameters and ascertainment rate from early transmission of COVID-19 across Africa (preprint)

Country reported case counts suggested a slow spread of SARS-CoV-2 in the initial phase of the COVID-19 pandemic in Africa. However, due to inadequate public awareness, unestablished monitoring practices, limited testing, ineffective diagnosis, stigmas attached to being infected with SARS-CoV-2, self-medication, and the use of complementary/alternative medicine that are common among Africans for social, economic, and psychological reasons, there might exist extensive under-ascertainment and therefore an underestimation of the true number of cases, especially at the beginning of the novel epidemic. We developed a compartmentalized epidemiological model based on an augmented susceptible-exposed-infectious-recovered (SEIR) model to track the early epidemics in 54 African countries. Data on the reported cumulative number of cases and daily confirmed cases were used to fit the model for the time period with no or little massive national interventions yet in each country. We estimated that the mean basic reproduction number is 2.02 (SD 0.7), with a range between 1.12 (Zambia) and 3.64 (Nigeria), whereas the mean basic reproduction number for observed cases was estimated to be 0.17 (SD 0.17), with a range between 0 (Sao Tomé and Príncipe, Seychelles, Tanzania, South Sudan, Mozambique, Liberia, Togo) and 0.68 (South Africa). It was estimated that the mean overall report rate is 5.37% (SD 5.71%), with the highest 30.41% in Libya and the lowest 0.02% in Sao Tomé and Príncipe. An average of 5.46% (SD 6.4%) of all infected cases were severe cases and 66.74% (SD 17.28%) were asymptomatic ones, with Libya having the most (39.45%) fraction of severe cases and Togo the most (97.38%) fraction of asymptomatic cases. The estimated low reporting rates in Africa suggested a clear need for improved reporting and surveillance system in these countries.

Studying the mixed transmission in a community with age heterogeneity: COVID-19 as a case study (preprint)

COVID-19 has been prevalent worldwide for about 2 years now and has brought unprecedented challenges to our society. Before vaccines were available, the main disease intervention strategies were non-pharmaceutical. Starting December 2020, in Ontario, Canada, vaccines were approved for administering to vulnerable individuals and gradually expanded to all individuals above the age of 12. As the vaccine coverage reached a satisfactory level among the eligible population, normal social activities resumed and schools reopened starting September 2021. However, when schools reopen for in-person learning, children under the age of 12 are unvaccinated and are at higher risks of contracting the virus. We propose an age-stratified model based on the age and vaccine eligibility of the individuals. We fit our model to the data in Ontario, Canada and obtain a good fitting result. The results show that a relaxed between-group contact rate may trigger future epidemic waves more easily than an increased within-group contact rate. An increasing mixed contact rate of the older group quickly amplifies the daily incidence numbers for both groups whereas an increasing mixed contact rate of the younger group mainly leads to future waves in the younger group alone. The results indicate the importance of accelerating vaccine rollout for younger individuals in mitigating disease spread.

The basic reproduction number of COVID-19 across Africa (preprint)

The pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) took the world by surprise. Following the first outbreak of COVID-19 in December 2019, several models have been developed to study and understand its transmission dynamics. Although the spread of COVID-19 is being slowed down by vaccination and other interventions, there is still a need to have a clear understanding of the evolution of the pandemic across countries, states and communities. To this end, there is a need to have a clearer picture of the initial spread of the disease in different regions. In this project, we used a simple SEIR model and a Bayesian inference framework to estimate the basic reproduction number of COVID-19 across Africa. Our estimates vary between 1.98 (Sudan) and 9.66 (Mauritius), with a median of 3.67 (90% CrI: 3.31 - 4.12). The estimates provided in this paper will help to inform COVID-19 modeling in the respective countries/regions.

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.

Assessing the impact of adherence to Non-pharmaceutical interventions and indirect transmission on the dynamics of COVID-19: a mathematical modelling study (preprint)

Adherence to public health policies such as the non-pharmaceutical interventions implemented against COVID-19 plays a major role in reducing infections and controlling the spread of the diseases. In addition, understanding the transmission dynamics of the disease is also important in order to make and implement efficient public health policies. In this paper, we developed an SEIR-type compartmental model to assess the impact of adherence to COVID-19 non-pharmaceutical interventions and indirect transmission on the dynamics of the disease. Our model considers both direct and indirect transmission routes and stratifies the population into two groups: those that adhere to COVID-19 non-pharmaceutical interventions (NPIs) and those that do not adhere to the NPIs. We compute the control reproduction number and the final epidemic size relation for our model and study the effect of different parameters of the model on these quantities. Our results show that direct transmission has more effect on the reproduction number and final epidemic size, relative to indirect transmission. In addition, we showed that there is a significant benefit in adhering to the COVID-19 NPIs.

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.

COVID-19 transmission in a theme-park (preprint)

BackgroundAs COVID-19 vaccination coverage increases, public health and industries are contemplating re-opening measures of public spaces, including theme-parks. To re-open, theme-parks must provide public health mitigation plans. Questions on implementation of public health mitigation strategies such as park cleaning, COVID-19 testing, and enforcement of social distancing and the wearing of personal protective equipment (PPE) in the park remain. MethodsWe have developed a mathematical model of COVID-19 transmission in a theme-park that considers direct human-human and indirect environment-human transmission of the virus. The model thus tracks the changing infection/disease landscape of all visitors, workers, and environmental reservoirs in a theme park setting. FindingsModels results show that theme-park public health mitigation must include mechanisms that reduce virus contamination of the environment to ensure that workers and visitors are protected from COVID-19 transmission in the park. Thus, cleaning rates and mitigation of human-environment contact increases in importance. ConclusionOur findings have important practical implications in terms of public health as policy- and decision-makers are equipped with a mathematical tool that can guide theme-parks in developing public health mitigation strategies for a safe re-opening.

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.

Could the new COVID-19 mutant strain undermine vaccination efforts? A mathematical modelling approach for estimating the spread of the UK mutant strain using Ontario, Canada, as a case study (preprint)

BackgroundInfections represent highly dynamic processes, characterized by evolutionary changes and events that involve both the pathogen and the host. Among infectious agents, viruses, such as the "Severe Acute Respiratory Syndrome-related Coronavirus type 2" (SARS-CoV-2), the infectious agent responsible for the currently ongoing "Coronavirus disease 2019" (COVID-2019) pandemic, have a particularly high mutation rate. Taking into account the mutational landscape of an infectious agent, it is important to shed light on its evolution capability over time. As new, more infectious strains of COVID-19 emerge around the world, it is imperative to estimate when these new strains may overtake the wild-type strain in different populations. Therefore, we developed a general-purpose framework to estimate the time at which a mutant variant is able to takeover a wild-type strain during an emerging infectious diseases outbreak. In this study, we used COVID-19 as a case-study, but the model is adaptable to any emerging pathogens. Methods and findingsWe devise a two-strain mathematical framework, to model a wild- and a mutant-type viral population and fit cumulative case data to parameterize the model, using Ontario as a case study. We found that, in the context of under-reporting and the current case levels, a variant strain is unlikely to dominate until March/April 2021. Current non-pharmaceutical interventions in Ontario need to be kept in place longer even with vaccination in order to prevent another outbreak. The spread of a variant strain in Ontario will mostly likely be observed by a widened peak of the daily reported cases. If vaccine efficacy is maintained across strains, then it is still possible to have an immune population by end of 2021. ConclusionsOur findings have important practical implications in terms of public health as policy-and decision-makers are equipped with a mathematical tool that can enable the estimation of the take-over of a mutant strain of an emerging infectious disease.

Modeling the potential impact of indirect transmission on COVID-19 epidemic (preprint)

The spread of SARS-CoV-2 through direct transmission (person-to-person) has been the focus of most studies on the dynamics of COVID-19. The efficacy of social distancing and mask usage at reducing the risk of direct transmission of COVID-19 has been studied by many researchers. Little or no attention is given to indirect transmission of the virus through shared items, commonly touch surfaces and door handles. The impact of the persistence of SARS-CoV-2 on hard surfaces and in the environment, on the dynamics of COVID-19 remain largely unknown. Also, the current increase in the number of cases despite the strict non-pharmaceutical interventions suggests a need to study the indirect transmission of COVID-19 while incorporating testing of infected individuals as a preventive measure. Assessing the impact of indirect transmission of the virus may improve our understanding of the overall dynamics of COVID-19. We developed a novel deterministic susceptible-exposedinfected- removed-virus-death compartmental model to study the impact of indirect transmission pathway on the spread of COVID-19, the sources of infection, and prevention/control. We fitted the model to the cumulative number of confirmed cases at episode date in Toronto, Canada using a Markov Chain Monte Carlo optimization algorithm. We studied the effect of indirect transmission on the epidemic peak, peak time, epidemic final size and the effective reproduction number, based on different initial conditions and at different stages. Our findings revealed an increase in cases with indirect transmission. Our work highlights the importance of implementing additional preventive and control measures involving cleaning of surfaces, fumigation, and disinfection to lower the spread of COVID-19, especially in public areas like the grocery stores, malls and so on. We conclude that indirect transmission of SARS-CoV- 2 has a significant effect on the dynamics of COVID-19, and there is need to consider this transmission route for effective mitigation, prevention and control of COVID-19 epidemic.