Using excess deaths and testing statistics to improve estimates of COVID-19 mortalities

Decisive Conditions for Strategic Vaccination against SARS-CoV-2

While vaccines against SARS-CoV-2 are being administered, in most countries it may still take months until their supply can meet demand. The majority of available vaccines elicits strong immune responses when administered as prime-boost regimens. Since the immunological response to the first ("prime") injection may provide already a substantial reduction in infectiousness and protection against severe disease, it may be more effective-under certain immunological and epidemiological conditions-to vaccinate as many people as possible with only one shot, instead of administering a person a second ("boost") shot. Such a vaccination campaign may help to more effectively slow down the spread of SARS-CoV-2, reduce hospitalizations, and reduce fatalities, which is our objective. Yet, the conditions which make single-dose vaccination favorable over prime-boost administrations are not well understood. By combining epidemiological modeling, random sampling techniques, and decision tree learning, we find that single-dose vaccination is robustly favored over prime-boost vaccination campaigns, even for low single-dose efficacies. For realistic scenarios and assumptions for SARS-CoV-2, recent data on new variants included, we show that the difference between prime-boost and single-shot waning rates is the only discriminative threshold, falling in the narrow range of 0.01-0.02 day-1 below which single-dose vaccination should be considered.

Effects of the COVID-19 Pandemic on Mental Health in Peru: Psychological Distress

This pandemic has been classified as a “psychological pandemic” that produces anxiety, depression, post-traumatic stress disorder, and sleep disorders. As the mental health effects of the Coronavirus Disease 2019 (COVID-19), caused by SARS-CoV-2, continue to unfold, there are still large knowledge gaps about the variables that predispose individuals to, or protect individuals against the disease. However, there are few publications on the effects of the COVID-19 pandemic on the mental health of citizens in Latin American countries. In this study, the effects that COVID-19 had on citizens of Peru have been described. For this, 1699 questionnaires, collected between 2 April and 2 September 2020, were analyzed. Descriptive, bivariate analysis was performed with odds ratio (OR) calculations and a data mining methodology. Sociodemographic variables (from the General Health Questionnaire), health conditions and perception, symptoms, and variables related to contact and preventive measures regarding COVID-19 were analyzed. As compared to other countries, less affectation of mental health and increased use of preventive measures were observed. It has been suggested that the country’s precarious health system and poverty rates prior to the pandemic may justify higher mortality figures in Peru than in other Latin American countries, despite prompt action for its containment and compliance with the protective measures. Psychological distress had a greater incidence in women, young people, people without a partner, and people without university studies. The most significant conditioning variables were self-perceived health status, headache or muscle pain over the past 14 days, level of studies, and age. The extensive use of preventive measures against COVID-19 is in line with the strict legislative measures taken, and this is, in turn, in line with other countries when looking at the lower effect on mental health, but contrary when focusing on the high lethality identified. The need to include the economy or availability and quality of healthcare in future studies arises, as well as the suitability to analyze the cause for differences between countries.

A statistical model of COVID-19 testing in populations: effects of sampling bias and testing errors

We develop a statistical model for the testing of disease prevalence in a population. The model assumes a binary test result, positive or negative, but allows for biases in sample selection and both type I (false positive) and type II (false negative) testing errors. Our model also incorporates multiple test types and is able to distinguish between retesting and exclusion after testing. Our quantitative framework allows us to directly interpret testing results as a function of errors and biases. By applying our testing model to COVID-19 testing data and actual case data from specific jurisdictions, we are able to estimate and provide uncertainty quantification of indices that are crucial in a pandemic, such as disease prevalence and fatality ratios.