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In this paper we model the spreading of the SARS-CoV-2 in Mexico by introducing a new stochastic approximation constructed from first principles, where the number of new infected individuals caused by a single infectious individual per unit time (a day), is a random variable of a time-dependent Poisson distribution. The model, structured on the basis of a Latent-Infectious-(Recovered or Deceased) (LI(RD)) compartmental approximation together with a modulation of the mean number of new infections (the Poisson parameters), provides a good tool to study theoretical and real scenarios. [ABSTRACT FROM AUTHOR]

Humanity has suffered throughout its history from different types of pandemics caused by deadly infectious diseases. Mathematical models are very useful tools to understand the dynamics of the behavior and propagation of these. Among these models are those known as SIS, SIR and SEIR. This paper presents a brief explanation of the structure of these models and its use in some pandemics. In particular, some SIR-type models without and with delays are analyzed with special interest, as well as their applications to the current pandemic caused by the SARS-CoV-2 virus. To determine the correspondence between some of the models analyzed and the data of Infectious and Recovered from Mexico reported by the World Health Organization (WHO), simulations are presented in three different time periods between the years 2020-2021. In addition, an unpublished SIR-type mathematical model with three delays is proposed as a main contribution. Finally, a discussion is given on the application of numerical algorithms to identify some parameters of mathematical models, such as ordinary least squares. [ABSTRACT FROM AUTHOR]

Objective. To determine the relative risk of a lethal outcome associated with chronic degenerative conditions in patients with COVID-19. Methods. A cohort study was conducted using electronic medical records belonging to patients who tested positive for COVID-19 on RT-PCR while receiving care as outpatients or inpatients in a social security system facility between March 2020 and March 2021. Two study groups were formed. The exposed group was divided into four subgroups, each of which was diagnosed with one and only one chronic condition (diabetes, hypertension, obesity, or chronic kidney disease); the unexposed group was obtained from the medical records of patients without comorbidities. A total of 1 114 medical records were examined using simple random sampling. Once the minimum sample size was reached, the relative risk was calculated for each chronic condition. Combinations of two, three, and four conditions were created, and each of them was included in the analysis. Results. In the absence of a chronic degenerative condition, the prevalence of a lethal outcome from COVID-19 is 3.8%; in the presence of type 2 diabetes mellitus, 15.8%; in the presence of arterial hypertension, 15.6%; and in the presence of obesity, 15.0%. For diabetes and hypertension combined, the prevalence of a lethal outcome is 54.1%; for diabetes and obesity combined, 36.8%, and for obesity and hypertension combined, 28.1%. Conclusion. In patients with COVID-19, the relative risk of a lethal outcome is 4.17 for those with diabetes, 4.13 for those with hypertension, and 3.96 for those with obesity. For two chronic conditions combined, the relative risk doubles or triples. The relative risk of a lethal outcome is 14.27 for diabetes plus hypertension; 9.73 for diabetes plus obesity, and 7.43 for obesity plus hypertension. Chronic conditions do not present alone; they generally occur together, hence the significance of the relative risks for lethal outcomes presented in this paper. [ABSTRACT FROM AUTHOR]

The current COVID-19 public health crisis, caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), has produced a devastating toll both in terms of human life loss and economic disruption. In this paper we present a machine-learning algorithm capable of identifying whether a given patient (actually infected or suspected to be infected) is more likely to survive than to die, or vice-versa. We train this algorithm with historical data, including medical history, demographic data, as well as COVID-19-related information. This is extracted from a database of confirmed and suspected COVID-19 infections in Mexico, constituting the official COVID-19 data compiled and made publicly available by the Mexican Federal Government. We demonstrate that the proposed method can detect high-risk patients with high accuracy, in each of four identified clinical stages, thus improving hospital capacity planning and timely treatment. Furthermore, we show that our method can be extended to provide optimal estimators for hypothesis-testing techniques commonly-used in biological and medical statistics. We believe that our work could be of use in the context of the current pandemic in assisting medical professionals with real-time assessments so as to determine health care priorities. [ABSTRACT FROM AUTHOR]

This paper proposes structural interventions to organize the working population which could be implemented to avoid a new wave of Covid-19 cases without halting economic activity. We summarize the evidence regarding cyclic schedules of work days followed by days in lockdown. We discuss the possible application of cycles of four days of work followed by three in lockdown for the Mexican context. We also discuss two complimentary interventions for these cycles: continuing to work from home for the sectors for which this is possible and staggered work shifts. This discussion takes into account the high informality context and the scarcity of resources to absorb productivity losses in middle and small companies. We consider the need to implement financial help for people and companies to mitigate lost earnings both in the formal and in the informal work sectors. [ABSTRACT FROM AUTHOR]

We know that SARS-Cov2 produces the new COVID-19 disease, which is one of the most dangerous pandemics of modern times. This pandemic has critical health and economic consequences, and even the health services of the large, powerful nations may be saturated. Thus, forecasting the number of infected persons in any country is essential for controlling the situation. In the literature, different forecasting methods have been published, attempting to solve the problem. However, a simple and accurate forecasting method is required for its implementation in any part of the world. This paper presents a precise and straightforward forecasting method named SVR-ESAR (Support Vector regression hybridized with the classical Exponential smoothing and ARIMA). We applied this method to the infected time series in four scenarios, which we have taken for the Github repository: the Whole World, China, the US, and Mexico. We compared our results with those of the literature showing the proposed method has the best accuracy. [ABSTRACT FROM AUTHOR]

Objectives: To determine the efficacy of Hydroxychloroquine vs. Hydroxychloroquine + Nitazoxanide in reducing the need for invasive mechanical ventilatory support for patients with COVID-19. Hydroxychloroquine is currently being used in multiple trials with varying doses in an attempt to treat COVID-19. Nitazoxanide has powerful antiviral effects and proven efficacy against a range of viruses including SARS and MERS. Dual therapy by combining appropriate doses of these two medications with diverse activities against COVID-19 is expected to be better than monotherapy with hydroxychloroquine.Trial Design: This is a single centre, randomized, controlled, single blinded, 2 arm (ratio 1:1) parallel group trial.Participants: 86 COVID-19 positive patients that are being treated at the Health Institute of the State of Mexico (ISEM) in Toluca, State of Mexico will be recruited from May 14 to December 31, 2020.Inclusion Criteria: 1)Age older than 18 years.2)Hospitalised COVID-19 PCR test positive patients.3)Within the first 72 hours after performing the PCR test.4)Presence of risk factors for complications (at least one): over 60 years, history of diabetes mellitus, hypertension, and morbid obesity.Exclusion Criteria: 1)Patients with corrected QT interval (QTc) greater than 500ms at hospital admission.2)Patients who have inherent contraindications to each drug.3)Patients who are unable to consent.4)Patients who have previously received chloroquine.5)Patients already intubated. Elimination criteria: 1)Patients whose clinical follow-up is lost or who decide not to continue in the study INTERVENTION AND COMPARATOR: The two management alternatives will be: Control - Hydroxychloroquine 200 mg taken orally every 12 hours for 7 days. Dual therapy - Hydroxychloroquine 400 mg taken orally every 12 hours for two days and then 200 mg taken orally every 12 hours for four days + Nitazoxanide 500 mg orally every 6 hours taken with food, for seven days.Main Outcomes: Primary: Mechanical ventilation requirement assessed at one week. Percentage of COVID-19 positive patients who require mechanical ventilation . All patients will be monitored till hospital discharge or death.Randomisation: Patients will be randomly allocated using allocation papers and opaque sealed envelopes to either receive the placebo or the dual therapy intervention treatment in a 1:1 ratio until we have recruited the required number of patients for each group.Blinding (masking): Trial participants will be blinded.Numbers To Be Randomised (sample Size): 86 participants will be randomized to each group, with 43 in the control group and 43 in the dual therapy group.Trial Status: Protocol version: 2, recruitment will begin on May 14 until sample size is reached , with the analysis deadline of December 31st 2020.Trial Registration: ClinicalTrials.gov Identifier: NCT04341493. Date of trial registration: April 10, 2020 FULL PROTOCOL: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest of expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol. [ABSTRACT FROM AUTHOR]

Since December 2019, the novel coronavirus (SARS-CoV-2) and its associated illness COVID-19 have rapidly spread worldwide. The Mexican government has implemented public safety measures to minimize the spread of the virus. In this paper, we used statistical models in two stages to estimate the total number of coronavirus (COVID-19) cases per day at the state and national levels in Mexico. In this paper, we propose two types of models. First, a polynomial model of the growth for the first part of the outbreak until the inflection point of the pandemic curve and then a second nonlinear growth model used to estimate the middle and the end of the outbreak. Model selection was performed using Vuong's test. The proposed models showed overall fit similar to predictive models (e.g., time series and machine learning); however, the interpretation of parameters is simpler for decisionmakers, and the residuals follow the expected distribution when fitting the models without autocorrelation being an issue. [ABSTRACT FROM AUTHOR]