Moving From COVID-19 Mathematical Models to Vaccine Design: Theory, Practice and Experiences

This chapter presents the COVID-19 pandemic as a complex problem, which has resulted in multidisciplinary and interdisciplinary research of unprecedented magnitude at the international level. In particular, the important role that mathematical and computational modeling has played throughout the different phases of the evolution of the pandemic is analyzed. Some of the most notorious achievements and difficulties in obtaining results with the use of mathematical models and computational simulations are discussed, and some proposals are presented about relevant and still pending tasks.

In the last days of December 2019, in a seafood market in Wuhan, China, an outbreak of pneumonia of unknown origin was identified and later reported to the World Health Organization (WHO) regional office through the International Health Regulations for its dissemination to WHO member countries. The WHO declared the outbreak of a Public Health Emergency of International Concern (PHEIC) on January 30th, and on March 11th, 2020, the WHO Director-General declared it a pandemic. Today, it has been spread to more than 200 countries. The SARS-CoV-2 pandemic could be considered the health event with the greatest impact worldwide in the last 100 years. As of March 11th, 2021, one year later, more than 17 million cases and 2,615,018 deaths have been registered. The clinical picture caused by SARS-CoV-2 ranges from mild to severe forms. Some require hospitalization and, in some cases, even treatment in Intensive Care Units is needed due to multiorgan disease. Medium and long-term sequelae are still under investigation. The pandemic has affected health services in Mexico and all over the world, not only due to the hospital reconversion for the care of patients with COVID-19 in the face of the emergency, but also because the care of patients with other conditions has been delayed with a negative impact on the physical and mental health of populations. Likewise, it has deranged several social activities such as travel, commerce, and education.

COVID-19 is caused by a single-stranded RNA encapsulated betacoronavirus, known as SARS-CoV-2, implicated in the pandemic that started in China in 2019. Viral replication consists of five stages that culminate in the release of the new virion. The exaggerated inflammatory response of COVID-19 is characterized by an elevation of acute phase reactants such as C-reactive protein and ferritin. It is associated with an unfavorable clinical course, intensified by abnormal activation of the protein complex called the inflammasome. When the immune response does not control the virus, lung tissue damage occurs that leads to the massive release of proinflammatory cytokines, producing acute respiratory failure syndrome. Vascular permeability is increased; interaction with coagulation factors develops disseminated intravascular coagulation and multiorgan failure. Up to 33% of cases can be asymptomatic. Clinical manifestations can be mild or severe and involve various organs and systems. Among the most commonly affected are: respiratory, cardiovascular, renal, and hematological and coagulation systems. Among the most representative laboratory data are: elevation of inflammatory markers (CRP, inflammatory cytokines, tumor necrosis factor), high levels of D-Dimer, elevation of troponin I, lymphopenia, thrombocytopenia, alteration of liver enzymes and kidney function. There are risk factors and comorbidities that contribute to the severity of the clinical picture (mainly cardiovascular and metabolic diseases): diabetes mellitus, high blood pressure, obesity, chronic lung diseases, cancer, and chronic kidney failure. There are also other genetic factors associated with the host’s immunopathogenesis and response to SARS COV-2 infection. There are various imaging methods that allow adequate identification and involvement of the pulmonary and cardiovascular systems with great sensitivity and specificity (computed tomography and echocardiography). The pandemic imposed decisions with very little information regarding what may be useful as a therapeutic strategy. This uncertainty applies to the treatment indicated in the prevention phase, as well as to the different stages of severity of the disease. In many cases, treatments were applied without having gone through a trial phase but only with the theoretical support of its probable benefit. However, over time, controlled studies showed that they did not provide any benefit and that they could even have a deleterious effect. Other therapies still in use have shown contradictory results in the different clinical trials where they were tested. Very few therapeutic options have shown undisputable benefit so far. The only ones that can modify the presentation or course of the disease are vaccines, which have also been developed in record time and in controlled trials, and all those that have been approved showed a decrease in the risk of infection and in the risk of presenting a severe manifestation of the disease.

In this text, we look at several mathematical models that have been constructed during the present pandemic to address different issues of importance to public health policies about epidemic scenarios and their causes. We start by briefly reviewing the most basic properties of the classic Kermack-McKendrick model and then proceed to look at some generalizations and their applications to contact structures, co-circulation of viral infections, growth patterns of epidemic curves, characterization of probability distributions and passage times necessary to parametrize the compartments that are added to the basic Kermack-McKendrick model. All of these examples revolve around the idea that a system of differential equations is constructed from a specific epidemiological problem, has as a central and main theoretical and conceptual support the epidemiological, medical, and biological context that motivates its construction and analysis.

By the end of 2019, a local pneumonia outbreak in Wuhan, China, was determined to be caused by a novel form of coronavirus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which, since then, has spread worldwide thus becoming the most important public health issue of the beginning of the 21st century. As a response, governments and health organizations started collecting data about this disease for analyzing it, trying to draw conclusions that could lead to a better understanding of it, and eventually to alleviate its effects. Through the analysis of the available data, our area of interest has to do with finding possible correlations among the variables related to COVID-19 cases which could give some insights. For example, which factors make a given patient to present an aggravated form of this illness, or even a higher risk of dying? Does the level of poverty make some people prone to get ill? What about the place where people live in? These and other questions can be answered based on the analysis of the available data. In this chapter, we give a brief introduction to Data Science (DS), a field of Artificial Intelligence (AI), and present some data analysis using publicly available COVID-19 data sets provided by the Mexican government. This allows us to show how AI tools and techniques can help us to better understand some aspects of this kind of situation, and in this way, hopefully helping health officials and providers to create better health policies and services and succeeding in their goal: saving lives.

Social inequalities have become an essential component of the health situation analysis. Health statistics are incomplete if its basic indicators are not linked to socioeconomic, sociodemographic, or sociocultural strata. The evaluation of health systems’ performance must include global indicators along with their distribution in social strata. The COVID-19 pandemic has not affected all countries to the same extent. Global statistics of both, confirmed cases and deaths portray wide differences among countries. Recent reports show wide differences associated with stratification criteria such as skin color or ethnic groups, sex, age, education, socioeconomic condition, and geographic area. The web of interactions between individual and contextual factors also influences the development of the new pandemic. This chapter lays the conceptual basis for measuring social inequalities in health and presents a basic set of the most common indices. It underscores the fact that a metric choice is not only technical, but to a great extent depends on ethical and political considerations. We document social inequalities in several health indicators associated with COVID-19 using public data or information retrieved from recently published papers. The first section of this chapter lays the conceptual foundations for measuring health inequalities. The second one exposes selected results and findings in different settings, and a brief analysis of the application of the inequality indices.

Cuban protocols to treat patients with COVID-19 include interferon alpha 2B and anti-inflammatory therapy at different moments of the progression of the disease. Here, we present the results obtained using a mathematical model to study the immunopathology associated with COVID-19. Model simulations reproduce the clinical observations for the antiviral and anti-inflammatory therapies and provide explanations for their efficacy from an immunological point of view. In addition, we present new data and statistical analysis of the clinical use of itolizumab, a humanized anti-CD6 antibody that reduces the secretion of multiple inflammatory cytokines. Authors concluded that the timely use of itolizumab can reduce the probability of death while its late prescription might not significantly reduce COVID-19 morbidity and mortality.

A year after the COVID-19 pandemic started, a global effort to develop vaccines and make them available to the public, has prompted a turning point in the history of vaccine development. The post-COVID era has accelerated the efforts to bring novel platform vaccine technologies such as nucleic acid or viral-vectored vaccines, which were proved to offer safety, efficacy, and speed in development and production, but have restricted records in the clinic. To date, five candidate vaccines have been approved for emergency use by different regulatory agencies across the world, after demonstrating their robust immunogenicity response and efficacy against SARS-COV-2 infection. We summarize and analyse the progression of those vaccines with major research development and results in peer-reviewed journals.