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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]

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]

Throughout the early stages of the COVID-19 pandemic in Mexico (August—December 2020), we closely followed a cohort of n = 100 healthcare workers. These workers were initially seronegative for Immunoglobulin G (IgG) antibodies against SARS-CoV-2, the virus that causes COVID-19, and maintained close contact with patients afflicted by the disease. We explored the database of demographic, physiological and laboratory parameters of the cohort recorded at baseline to identify potential risk factors for infection with SARS-CoV-2 at a follow-up evaluation six months later. Given that susceptibility to infection may be a systemic rather than a local property, we hypothesized that a multivariate statistical analysis, such as MANOVA, may be an appropriate statistical approach. Our results indicate that susceptibility to infection with SARS-CoV-2 is modulated by sex. For men, different physiological states appear to exist that predispose to or protect against infection, whereas for women, we did not find evidence for divergent physiological states. Intriguingly, male participants who remained uninfected throughout the six-month observation period, had values for mean arterial pressure and waist-to-hip ratio that exceeded the normative reference range. We hypothesize that certain risk factors that worsen the outcome of COVID-19 disease, such as being overweight or having high blood pressure, may instead offer some protection against infection with SARS-CoV-2. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has progressed rapidly, with the emergence of new virus variants that pose challenges in treating infected individuals. In Mexico, four epidemic waves have been recorded with varying disease severity. To understand the heterogeneity in clinical presentation over time and the sensitivity and specificity of signs and symptoms in identifying COVID-19 cases, an analysis of the changes in the clinical presentation of the disease was conducted. Aim: To analyze the changes in the clinical presentation of COVID-19 among 3.38 million individuals tested for SARS-CoV-2 at the Mexican Social Security Institute (IMSS) from March 2020 to October 2021 and evaluate the predictivity of signs and symptoms in identifying COVID-19 cases. Methods: A retrospective analysis of clinical presentation patterns of COVID-19 among individuals treated at IMSS was performed, contrasting the signs and symptoms among SARS-CoV-2-positive individuals with those who tested negative for the virus but had respiratory infection symptoms. The sensitivity and specificity of each sign and symptom in identifying SARS-CoV-2 infection were estimated. Results: The set of signs and symptoms reported for COVID-19-suspected patients treated at IMSS were not highly specific for SARS-CoV-2 positivity. The signs and symptoms exhibited variability based on age and epidemic wave. The area under the receiver operating characteristic (ROC) curve was 0.62 when grouping the five main symptoms (headache, dyspnea, fever, arthralgia, and cough). Most of the individual symptoms had ROC values close to 0.5 (16 out of 22 between 0.48 and 0.52), indicating non-specificity. Conclusions: The results highlight the difficulty in making a clinical diagnosis of COVID-19 due to the lack of specificity of signs and symptoms. The variability of clinical presentation over time and among age groups highlights the need for further research to differentiate whether the changes are due to changes in the virus, who is becoming infected, or the population, particularly with respect to prior infection and vaccination status. [ABSTRACT FROM AUTHOR]

The coordinated efforts to stop the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) include massive immunization of the population at a global scale. The humoral immunity against COVID-19 is conferred by neutralizing antibodies (NAbs) that occur during the post-infection period and upon vaccination. Here, we provide robust data showing that potent neutralizing antibodies are induced in convalescent patients of SARS-CoV-2 infection who have been immunized with different types of vaccines, and patients with no previous history of COVID-19 immunized with a mixed vaccination schedule regardless of the previous infection. More importantly, we showed that a heterologous prime-boost in individuals with Ad5-nCoV (Cansino) vaccine induces higher NAbs levels in comparison to a single vaccination scheme alone. [ABSTRACT FROM AUTHOR]

Background: People who inject drugs may be at elevated SARS-CoV-2 risk due to their living conditions and/or exposures when seeking or using drugs. No study to date has reported upon risk factors for SARS-CoV-2 infection among people who inject drugs. Methods and findings: Between October, 2020 and June, 2021, participants aged ≥18 years from San Diego, California, USA and Tijuana, Baja California, Mexico who injected drugs within the last month underwent interviews and testing for SARS-CoV-2 RNA and antibodies. Binomial regressions identified correlates of SARS-CoV-2 seropositivity. Results: Of 386 participants, SARS-CoV-2 seroprevalence was 36.3% (95% CI: 31.5%-41.1%); 92.1% had detectable IgM antibodies. Only 37.5% had previously been tested. Seroprevalence did not differ by country of residence. None tested RNA-positive. Most (89.5%) reported engaging in ≥1 protective behavior [e.g., facemasks (73.5%), social distancing (46.5%), or increasing handwashing/sanitizers (22.8%)]. In a multivariate model controlling for sex, older age, and Hispanic/Latinx/Mexican ethnicity were independently associated with SARS-CoV-2 seropositivity, as was engaging in sex work (AdjRR: 1.63; 95% CI: 1.18–2.27) and having been incarcerated in the past six months (AdjRR: 1.49; 95% CI: 0.97–2.27). Comorbidities and substance using behaviors were not associated with SARS-CoV-2 seropositivity. Conclusions: In this community-based study of people who inject drugs in the San Diego-Tijuana border region, over one third were SARS-CoV-2 seropositive, exceeding estimates from the general population in either city. We found no evidence that substance use behaviors were associated with an elevated risk of SARS-CoV-2 infection, but observed that circumstances in the risk environment, notably sex work and incarceration, were independently associated with higher SARS-CoV-2 seroprevalence. Our findings suggest that a binational policy response to COVID-19 mitigation is warranted beyond the closure of the U.S.-Mexico border. Furthermore, decriminalizing sex work and drug use could reduce the burden of COVID-19 among people who inject drugs. [ABSTRACT FROM AUTHOR]

COVID-19 is a respiratory disease caused by SARS-CoV-2, which has significantly impacted economic and public healthcare systems worldwide. SARS-CoV-2 is highly lethal in older adults (>65 years old) and in cases with underlying medical conditions, including chronic respiratory diseases, immunosuppression, and cardio-metabolic diseases, including severe obesity, diabetes, and hypertension. The course of the COVID-19 pandemic in Mexico has led to many fatal cases in younger patients attributable to cardio-metabolic conditions. Thus, in the present study, we aimed to perform an early spatial epidemiological analysis for the COVID-19 outbreak in Mexico. Firstly, to evaluate how mortality risk from COVID-19 among tested individuals (MRt) is geographically distributed and secondly, to analyze the association of spatial predictors of MRt across different states in Mexico, controlling for the severity of the disease. Among health-related variables, diabetes and obesity were positively associated with COVID-19 fatality. When analyzing Mexico as a whole, we identified that both the percentages of external and internal migration had positive associations with early COVID-19 mortality risk with external migration having the second-highest positive association. As an indirect measure of urbanicity, population density, and overcrowding in households, the physicians-to-population ratio has the highest positive association with MRt. In contrast, the percentage of individuals in the age group between 10 to 39 years had a negative association with MRt. Geographically, Quintana Roo, Baja California, Chihuahua, and Tabasco (until April 2020) had higher MRt and standardized mortality ratios, suggesting that risks in these states were above what was nationally expected. Additionally, the strength of the association between some spatial predictors and the COVID-19 fatality risk varied by zone. [ABSTRACT FROM AUTHOR]