Emerging and Re-Emerging Infectious Diseases: Humankind’s Companions and Competitors

Infectious disease outbreaks had a significant impact on shaping the societies and cultures throughout human history [...]

Protecting Public Health and Preserving the Financial Viability of North Carolina’s Critical Health Care Facilities during Infectious Disease Outbreaks

While hospitals’ primary emphasis during the COVID-19 pandemic has been on ensuring sufficient health-related resource capacity (e.g., ICU beds, ventilators) to serve admitted patients, the impacts of the pandemic on the financial viability of hospitals has also become a critical concern. Data from the period March 2020-Janaury 2021 suggest that the halt to elective and non-emergency inpatient procedures, combined with a reduction in emergency room procedures, led to losses equal to 6.5% of revenue from inpatient procedures, or about $825 million. This study finds that societal measures to reduce the community transmission rates have a larger impact on available healthcare capacity and hospital financial losses than hospital-level decisions. This study illustrates the tradeoffs between hospital capacity, quality of care, and financial risk faced by health care facilities throughout the U.S. as a result of COVID-19, providing potential insights for many hospitals seeking to navigate these uncertain scenarios through adaptive decision-making.

Sample Size Calculations for Variant Surveillance in the Presence of Biological and Systematic Biases

As demonstrated during the SARS-CoV-2 pandemic, detecting and tracking the emergence and spread of pathogen variants is an important component of monitoring infectious disease outbreaks. Pathogen genome sequencing has emerged as the primary tool for variant characterization, so it is important to consider the number of sequences needed when designing surveillance programs or studies, both to ensure accurate conclusions and to optimize use of limited resources. However, current approaches to calculating sample size for variant monitoring often do not account for the biological and logistical processes that can bias which infections are detected and which samples are ultimately selected for sequencing. In this manuscript, we introduce a framework that models the full process from infection detection to variant characterization and demonstrate how to use this framework to calculate appropriate sample sizes for sequencing-based surveillance studies. We consider both cross-sectional and continuous sampling, and we have implemented our method in a publicly available tool that allows users to estimate necessary sample sizes given a specific aim (e.g., variant detection or measuring variant prevalence) and sampling method. Our framework is designed to be easy to use, while also flexible enough to be adapted to other pathogens and surveillance scenarios.

Global dynamics for a Filippov system with media effects

<abstract><p>In the process of spreading infectious diseases, the media accelerates the dissemination of information, and people have a deeper understanding of the disease, which will significantly change their behavior and reduce the disease transmission; it is very beneficial for people to prevent and control diseases effectively. We propose a Filippov epidemic model with nonlinear incidence to describe media's influence in the epidemic transmission process. Our proposed model extends existing models by introducing a threshold strategy to describe the effects of media coverage once the number of infected individuals exceeds a threshold. Meanwhile, we perform the stability of the equilibriua, boundary equilibrium bifurcation, and global dynamics. The system shows complex dynamical behaviors and eventually stabilizes at the equilibrium points of the subsystem or pseudo equilibrium. In addition, numerical simulation results show that choosing appropriate thresholds and control intensity can stop infectious disease outbreaks, and media coverage can reduce the burden of disease outbreaks and shorten the duration of disease eruptions.</p></abstract>

The Interaction of Vitamin D and Corticosteroids: A Mortality Analysis of 26,508 Veterans Who Tested Positive for SARS-CoV-2

This data-based cohort consisted of 26,508 (7%) United States veterans out of the 399,290 who tested positive for SARS-CoV-2 from 1 March to 10 September 2020. We aimed to assess the interaction of post-index vitamin D (Vit D) and corticosteroid (CRT) use on 30-day mortality among hospitalized and non-hospitalized patients with coronavirus disease 2019 (COVID-19). Combination Vit D and CRT drug use was assessed according to four multinomial pairs (−|+, −|−, +|+, +|−). Respective categorical effects were computed on a log-binomial scale as adjusted relative risk (aRR). Approximately 6% of veterans who tested positive for SARS-CoV-2 died within 30 days of their index date. Among hospitalized patients, a significantly decreased aRR was observed for the use of Vit D in the absence of CRTs relative to patients who received CRTs but not Vit D (aRR = 0.30; multiplicity corrected, p = 0.0004). Among patients receiving systemically administered CRTs (e.g., dexamethasone), the use of Vit D was associated with fewer deaths in hospitalized patients (aRR = 0.51) compared with non-hospitalized patients (aRR = 2.5) (P-for-Interaction = 0.0071). Evaluating the effect of modification of these compounds in the context of hospitalization may aid in the management of COVID-19 and provide a better understanding of the pathophysiological mechanisms underlying this and future infectious disease outbreaks.