Meteorological, PM2.5 and PM10 factors on SARS-COV-2 transmission: The case of southern regions in Chile.

There are several determinants of a population's health, including meteorological factors and air pollution. For example, it is well known that low temperatures and air pollution increase mortality rates in infant and elderly populations. With the emergence of SARS-COV-2, it is important to understand what factors contribute to its mitigation and control. There is some research in this area which shows scientific evidence on the virus's behavior in the face of these variables. This research aims to quantify the impact of climatic factors and environmental pollution on SARS-COV-2 specifically the effect on the number of new infections in different areas of Chile. At the local level, historical information available from the Department of Statistics and Health Information, the Chilean National Air Quality Information System, the Chilean Meteorological Directorate, and other databases will allow the generation of panel data suitable for the analysis. The results show the significant effect of pollution and climate variables measured in lags and will allow us to explain the behavior of the pandemic by identifying the relevant factors affecting health, using heteroskedastic models, which in turn will serve as a contribution to the generation of more effective and timely public policies for the control of the pandemic.

COVID-19: A Comparative Study of Contagions Peaks in Cities from Europe and the Americas.

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a group of viruses that provoke illnesses ranging from the common cold to more serious illnesses such as pneumonia. COVID-19 started in China and spread rapidly from a single city to an entire country in just 30 days and to the rest of the world in no more than 3 months. Several studies have tried to model the behavior of COVID-19 in diverse regions, based on differential equations of the SIR and stochastic SIR type, and their extensions. In this article, a statistical analysis of daily confirmed COVID-19 cases reported in eleven different cities in Europe and America is conducted. Log-linear models are proposed to model the rise or drop in the number of positive cases reported daily. A classification analysis of the estimated slopes is performed, allowing a comparison of the eleven cities at different epidemic peaks. By rescaling the curves, similar behaviors among rises and drops in different cities are found, independent of socioeconomic conditions, type of quarantine measures taken, whether more or less restrictive. The log-linear model appears to be suitable for modeling the incidence of COVID-19 both in rises and drops.

How far are we from predicting multi-drug interactions during treatment for COVID-19 infection?

Seriously ill patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and hospitalized in intensive care units (ICUs) are commonly given a combination of drugs, a process known as multi-drug treatment. After extracting data on drug-drug interactions with clinical relevance from available online platforms, we hypothesize that an overall interaction map can be generated for all drugs administered. Furthermore, by combining this approach with simulations of cellular biochemical pathways, we may be able to explain the general clinical outcome. Finally, we postulate that by applying this strategy retrospectively to a cohort of patients hospitalized in ICU, a prediction of the timing of developing acute kidney injury (AKI) could be made. Whether or not this approach can be extended to other diseases is uncertain. Still, we believe it represents a valuable pharmacological insight to help improve clinical outcomes for severely ill patients.

Studies on the basic reproduction number in stochastic epidemic models with random perturbations.

In this paper, we discuss the basic reproduction number of stochastic epidemic models with random perturbations. We define the basic reproduction number in epidemic models by using the integral of a function or survival function. We study the systems of stochastic differential equations for SIR, SIS, and SEIR models and their stability analysis. Some results on deterministic epidemic models are also obtained. We give the numerical conditions for which the disease-free equilibrium point is asymptotically stable.