Condition-specific mortality risk can explain differences in COVID-19 case fatality ratios around the globe

Objectives With COVID-19 infections resulting in death according to a hierarchy of risks, with age and pre-existing health conditions enhancing disease severity, the objective of this study is to estimate the condition-specific case fatality ratio (CFR) for different subpopulations in Italy. Study design The design of the study was to estimate the ‘pre-existing comorbidity’-conditional CFR to eventually explain the mortality risk variability reported around in different countries. Methods We use the available information on pre-existing health conditions identified for deceased patients ‘positive with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)’ in Italy. We (i) estimated the total number of deaths for different pre-existing health conditions categories and (ii) calculated a conditional CFR based upon the number of comorbidities before SARS-CoV-2 infection. Results Our results show a 0.6% conditional CFR for a population with zero pre-existing pathology, increasing to 13.9% for a population diagnosed with one and more pre-existing health conditions. Conclusions Condition-specific mortality risks are important to be evaluated during the COVID-19 pandemic, with potential elements to explain the CFR variability around the globe. A careful postmortem examination of deceased cases to differentiate death ‘caused by COVID-19’ from death ‘positive with SARS-CoV-2’ is therefore urgently needed and will likely improve our understanding of the COVID-19 mortality risk and virus pathogenicity.Marie Skłodowska-Curie grant agreement No 79249

The Impact of Serotype Cross-Protection on Vaccine Trials: DENVax as a Case Study

There is a growing public health need for effective preventive interventions against dengue, and a safe, effective and affordable dengue vaccine against the four serotypes would be a significant achievement for disease prevention and control. Two tetravalent dengue vaccines, Dengvaxia (CYD-TDV—Sanofi Pasteur) and DENVax (TAK 003—Takeda Pharmaceutical Company), have now completed phase 3 clinical trials. Although Dengvaxia resulted in serious adverse events and had to be restricted to individuals with prior dengue infections, DENVax has shown, at first glance, some encouraging results. Using the available data for the TAK 003 trial, we estimate, via the Bayesian approach, vaccine efficacy (VE) of the post-vaccination surveillance periods of 12 and 18 months. Although better measurement over a long time was expected for the second part of the post-vaccination surveillance, variation in serotype-specific efficacy needs careful consideration. Besides observing that individual serostatus prior to vaccination is determinant of DENVax vaccine efficacy, such as for Dengvaxia, we also noted, after comparing the VE estimations for 12- and 18-month periods, that vaccine efficacy is decreasing over time. The comparison of efficacies over time is informative and very important, and brings up the discussion of the role of temporary cross-immunity in dengue vaccine trials and the impact of serostatus prior to vaccination in the context of dengue fever epidemiology.Marie Skłodowska-Curie grant agreement No 79249

Spatially Extended SHAR Epidemiological Framework of Infectious Disease Transmission

Mathematical models play an important role in epidemiology. The inclusion of a spatial component in epidemiological models is especially important to understand and address many relevant ecological and public health questions, e.g., when wanting to differentiate transmission patterns across geographical regions or when considering spatially heterogeneous intervention measures. However, the introduction of spatial effects can have significant consequences on the observed model dynamics and hence must be carefully analyzed and interpreted. Cellular automata epidemiological models typically rely on simplified computational grids but can provide valuable insight into the spatial dynamics of transmission within a population by suitably accounting for the connections between individuals in the considered community. In this paper, we describe a stochastic cellular automata disease model based on an extension of the traditional Susceptible-Infected-Recovered (SIR) compartmentalization of the population, namely, the Susceptible-Hospitalized-Asymptomatic-Recovered (SHAR) formulation, in which infected individuals either present a severe form of the disease, thus requiring hospitalization, or belong to the so-called mild/asymptomatic class. The critical transmission threshold is derived analytically in the nonspatial SHAR formulation, and this generalizes previously obtained theoretical results for the SIR model. We present simulation results discussing the effect of key model parameters and of spatial correlations on model outputs and propose an algorithm for tracking the evolution of infection clusters within the considered population. Focusing on the role of import and criticality on the overall dynamics, we conclude that the current spatial setting increases the critical transmission threshold in comparison to the nonspatial model

Modelling Holling type II functional response in deterministic and stochastic food chain models with mass conservation

The Rosenzweig-MacArthur predator-prey model is the building block in modeling food chain, food webs and ecosystems. There are a number of hidden assumptions involved in the derivation. For instance the prey population growth is logistic without predation but also with predation. In order to reveal these we will start with modelling a resource-predator-prey system in a closed spatially homogeneous environment. This allows us to keep track of the nutrient flow. With an instantaneous remineralisation of the products excreted in the environment by the populations and dead body mass there is conservation of mass. This allows for a model dimension reduction and yields the mass balance predator-prey model. When furthermore the searching and handling processes are much faster that the population changing rates, the trophic interaction is described by a Holling type II functional response, also assumed in the Rosenzweig-MacArthur model. The derivation uses an extended deterministic model with number of searching and handling predators as model variables where the ratio of the predator/prey body masses is used as a mechanistic time-scale parameter. This extended model is also used as a starting point for the derivation of a stochastic model. We will investigate the stochastic effects of random switching between searching and handling of the predators and predator dying. Prey growth by consumption of ambient resources is still deterministic and therefore the stochastic model is hybrid. The transient dynamics is studied by numerical Monte Carlo simulations and also the quasi-equilibrium distribution for the population quantities is calculated. The body mass of the prey individual is the scaling parameter in the stochastic model formulation. This allows for a quantification of the mean-field approximation criterion for the justification of replacement of the stochastic by a deterministic model.Marie Skłodowska-Curie grant agreement No. 79249

Deterministic and Stochastic Dynamics of COVID-19: The Case Study of Italy and Spain

In December 2019, a severe respiratory syndrome (COVID-19) caused by a new coronavirus (SARS-CoV-2) was identified in China and spread rapidly around the globe. COVID-19 was declared a pandemic by the World Health Organization (WHO) in March 2020. With eventually substantial global underestimation, more than 225 million cases were confirmed by the end of August 2021, counting more than 4.5 million deaths. COVID-19 symptoms range from mild (or no symptoms) to severe illness, with disease severity and death occurring according to a hierarchy of risks, with age and preexisting health conditions enhancing the risks of disease severity manifestation. In this paper, a mathematical model for COVID-19 transmission is proposed and analyzed. The model stratifies the studied population into two groups, older and younger. Applied to the COVID-19 outbreaks in Spain and in Italy, we find the disease-free equilibrium and the basic reproduction number for each case study. A sensitivity analysis to identify the key parameters which influence the basic reproduction number, and hence regulate the transmission dynamics of COVID-19, is also performed. Finally, the model is extended to its stochastic counterpart to encapsulate the variation or uncertainty found in the transmissibility of the disease. We observe the variability of the infectious population finding its distribution at a given time, demonstrating that for small populations, stochasticity will play an important role.Marie Skłodowska-Curie grant agreement No. 79249

The effects of public health measures on severe dengue cases: An optimal control approach

Dengue fever is the most important viral mosquito-borne disease worldwide, with approximately 3.9 billion people at risk of acquiring dengue infection. Measures against mosquito bite combined with vector control programs to reduce mosquito population have been used in endemic countries for several years. Most recently, vaccines have become an important ally to prevent and control disease transmission. Economic costs of dengue control programs vary from region to region and therefore designing an optimal control strategy must be evaluated at different epidemiological contexts. Using a multi-strain vector-host mathematical model, we investigate the impact of different control measures to reduce dengue prevalence. A detailed sensitivity analysis to identify the key parameters influencing disease transmission is followed by an exploratory analysis of the possible solutions for the optimal control problem considering preventive measures to avoid mosquito bites, reduce mosquito population and vaccinate human hosts. The proposed cost functional includes a weighted sum of several efforts (not necessarily quantified as economic costs) for the controls which are evaluated alone and combined. The control system is analyzed using the Pontryagin`s Principle for optimal control where different strategies are compared. Our results have shown that the simultaneous use of intervention measures are highly effective to reduce disease cases, however, the use of a single control measure can be as effective as the use of two or more controls combined. A careful evaluation of the epidemiological scenario is advised before designing strategies for disease prevention and control, allowing an optimal allocation of the public health resources

Reproduction ratio and growth rates: Measures for an unfolding pandemic

The initial exponential growth rate of an epidemic is an important measure that follows directly from data at hand, commonly used to infer the basic reproduction number. As the growth rates λ(t) of tested positive COVID-19 cases have crossed the threshold in many countries, with negative numbers as surrogate for disease transmission deceleration, lock- downs lifting are linked to the behavior of the momentary reproduction numbers r(t), often called R0. Important to note that this concept alone can be easily misinterpreted as it is bound to many internal assumptions of the underlying model and significantly affected by the assumed recovery period. Here we present our experience, as part of the Basque Coun- try Modeling Task Force (BMTF), in monitoring the development of the COVID-19 epidemic, by considering not only the behaviour of r(t) estimated for the new tested positive cases— significantly affected by the increased testing capacities, but also the momentary growth rates for hospitalizations, ICU admissions, deceased and recovered cases, in assisting the Basque Health Managers and the Basque Government during the lockdown lifting mea- sures. Two different data sets, collected and then refined during the COVID-19 responses, are used as an exercise to estimate the momentary growth rates and reproduction numbers over time in the Basque Country, and the implications of using those concepts to make deci- sions about easing lockdown and relaxing social distancing measures are discussed. These results are potentially helpful for task forces around the globe which are now struggling to provide real scientific advice for health managers and governments while the lockdown measures are relaxed.Marie Skłodowska-Curie grant agreement No 79249

Understanding COVID-19 Epidemics: A Multi-Scale Modeling Approach

COVID-19 was declared a pandemic by the World Health Organization in March 2020 and, since then, research on mathematical modeling became imperative and very influential to understand the epidemiological dynamics of disease spreading and control under different scenarios. In this chapter, two different approaches to model the spread of COVID-19 are presented. The model frameworks are described and results are presented in connection with the current epidemiological situation of vaccination roll-out. This chapter is structured as follows. Section 2 presents the stochastic SHARUCD modeling framework developed within a modeling task force created to support public health managers during the COVID-19 crisis. As an extension of the basic SHAR (Susceptible-Hospitalized-Asymptomatic-Recovered) model, the SHARUCD models were parameterized and validated with empirical data for the Basque Country, Spain, and have been used (up until now) to monitor COVID-19 spreading and control over the course of the pandemic. Section 3 introduces the kinetic theory of active particles (KTAP) model for the spread of a disease. With an exploratory analysis, we present a possible way to deal with heterogeneity and multiscale features. Section 4 concludes this work, with a discussion on both models and further research perspectives description