Disentangling the role of virus infectiousness and awareness-based human behavior during the early phase of the COVID-19 pandemic in the European Union.

In this work, we manage to disentangle the role of virus infectiousness and awareness-based human behavior in the COVID-19 pandemic. Using Bayesian inference, we quantify the uncertainty of a state-space model whose propagator is based on an unusual SEIR-type model since it incorporates the effective population fraction as a parameter. Within the Markov Chain Monte Carlo (MCMC) algorithm, Unscented Kalman Filter (UKF) may be used to evaluate the likelihood approximately. UKF is a suitable strategy in many cases, but it is not well-suited to deal with non-negativity restrictions on the state variables. To overcome this difficulty, we modify the UKF, conveniently truncating Gaussian distributions, which allows us to deal with such restrictions. We use official infection notification records to analyze the first 22 weeks of infection spread in each of the 27 countries of the European Union (EU). It is known that such records are the primary source of information to assess the early evolution of the pandemic and, at the same time, usually suffer underreporting and backlogs. Our model explicitly accounts for uncertainty in the dynamic model parameters, the dynamic model adequacy, and the infection observation process. We argue that this modeling paradigm allows us to disentangle the role of the contact rate, the effective population fraction, and the infection observation probability across time and space with an imperfect first principles model. Our findings agree with phylogenetic evidence showing little variability in the contact rate, or virus infectiousness, across EU countries during the early phase of the pandemic, highlighting the advantage of incorporating the effective population fraction into pandemic modeling for heterogeneity in both human behavior and reporting. Finally, to evaluate the consistency of our data assimilation method, we performed a forecast that adequately fits the actual data. Statement of significance: Data-driven and model-based epidemiological studies aimed at learning the number of people infected early during a pandemic should explicitly consider the behavior-induced effective population effect. Indeed, the non-isolated, or effective, fraction of the population during the early phase of the pandemic is time-varying, and first-principles modeling with quantified uncertainty is imperative for an adequate analysis across time and space. We argue that, although good inference results may be obtained using the classical SEIR type model, the model posed in this work has allowed us to disentangle the role of virus infectiousness and awareness-based human behavior during the early phase of the COVID-19 pandemic in the European Union from official infection notification records.

An optimal control model to design strategies for reducing the spread of the Ebola virus disease.

In this work, we formulate an epidemiological model for studying the spread of Ebola virus disease in a considered territory. This model includes the effect of various control measures, such as: vaccination, education campaigns, early detection campaigns, increase of sanitary measures in hospital, quarantine of infected individuals and restriction of movement between geographical areas. Using optimal control theory, we determine an optimal control strategy which aims to reduce the number of infected individuals, according to some operative restrictions (e.g., economical, logistic, etc.). Furthermore, we study the existence and uniqueness of the optimal control. Finally, we illustrate the interest of the obtained results by considering numerical experiments based on real data.

Be-CoDiS: A Mathematical Model to Predict the Risk of Human Diseases Spread Between Countries--Validation and Application to the 2014-2015 Ebola Virus Disease Epidemic.

Ebola virus disease is a lethal human and primate disease that currently requires a particular attention from the international health authorities due to important outbreaks in some Western African countries and isolated cases in the UK, the USA and Spain. Regarding the emergency of this situation, there is a need for the development of decision tools, such as mathematical models, to assist the authorities to focus their efforts in important factors to eradicate Ebola. In this work, we propose a novel deterministic spatial-temporal model, called Between-Countries Disease Spread (Be-CoDiS), to study the evolution of human diseases within and between countries. The main interesting characteristics of Be-CoDiS are the consideration of the movement of people between countries, the control measure effects and the use of time-dependent coefficients adapted to each country. First, we focus on the mathematical formulation of each component of the model and explain how its parameters and inputs are obtained. Then, in order to validate our approach, we consider two numerical experiments regarding the 2014-2015 Ebola epidemic. The first one studies the ability of the model in predicting the EVD evolution between countries starting from the index cases in Guinea in December 2013. The second one consists of forecasting the evolution of the epidemic by using some recent data. The results obtained with Be-CoDiS are compared to real data and other model outputs found in the literature. Finally, a brief parameter sensitivity analysis is done. A free MATLAB version of Be-CoDiS is available at: http://www.mat.ucm.es/momat/software.htm.

Mathematical modelling of thermoregulation processes for premature infants in closed convectively heated incubators.

The low-weight newborns and especially the premature infants have difficulty in maintaining their temperature in the range considered to be normal. Several studies revealed the importance of thermal environment and moisture to increase the survival rate of newborns. This work models the process of heat exchange and energy balance in premature newborns during the first hours of life in a closed incubator. In addition, a control problem was proposed and solved in order to maintain thermal stability of premature newborns to increase their rate of survival and weight. For this purpose, we propose an algorithm to control the temperature inside the incubator. It takes into account the measurements of the body temperature of a premature newborn which are recorded continuously. We show that using this model the temperature of a premature newborn inside the incubator can be kept in a thermal stability range.

A model for real thermal control in high-pressure treatment of foods.

A model for the simulation of thermal exchanges in a complete high-pressure equipment was developed. Good agreement between simulated and experimental time-temperature profiles was found during different processes of pressurization and depressurization. The model allows study of the effect of different variables to improve thermal control in the treatments performed. This work involved an important advance in optimization and regulation of high-pressure processes in the food industry.

Enfermedad de Parkinson: ciclo alcalosis metabólica-acidosis metabólica-alcalosis respiratoria, tratamiento con acetazolamida / Parkinson disease: alkalosis metabolic cycle - metabolic acidosis - respiratory alkalosi; treatment with acetazolamide

El estudio de los gases arteriales y de electrólitos efectuado en 3 oportunidades con un intervalo de cuatro horas diurnas en dos pacientes con diagnóstico de enfermedad de Parkinson confirmó que el equilibrio ácido-base y electrolitico sufría cambiantes valores. En base a esos resultados y con el propósito de interferir lo que se había denominado ciclo alcalosis metabólica acidosis metabólica-alcalosis respiratoria (AM-AcM-AIR), supuesto agente fisiopatogénico, los pacientes se medicaron con acetazolamida. La mejoria clínica confirmaria tal hipótesis, hipótesis ya verosimil como resultado de haber tratado a otro paciente con cloruro de amonio (AU)

Enfermedad de Parkinson: ciclo alcalosis metabólica-acidosis metabólica-alcalosis respiratoria, tratamiento con acetazolamida / Parkinson disease: alkalosis metabolic cycle - metabolic acidosis - respiratory alkalosi; treatment with acetazolamide

El estudio de los gases arteriales y de electrólitos efectuado en 3 oportunidades con un intervalo de cuatro horas diurnas en dos pacientes con diagnóstico de enfermedad de Parkinson confirmó que el equilibrio ácido-base y electrolitico sufría cambiantes valores. En base a esos resultados y con el propósito de interferir lo que se había denominado ciclo alcalosis metabólica acidosis metabólica-alcalosis respiratoria (AM-AcM-AIR), supuesto agente fisiopatogénico, los pacientes se medicaron con acetazolamida. La mejoria clínica confirmaria tal hipótesis, hipótesis ya verosimil como resultado de haber tratado a otro paciente con cloruro de amonio