Lindsay T. Keegan

BACKGROUND: Diphtheria, once a major cause of childhood morbidity and mortality, all but disappeared following introduction of diphtheria vaccine. Recent outbreaks highlight the risk diphtheria poses when civil unrest interrupts vaccination and healthcare access. Lack of interest over the last century resulted in knowledge gaps about diphtheria's epidemiology, transmission, and control. METHODS: We conducted 9 distinct systematic reviews on PubMed and Scopus (March-May 2018). We pooled and analyzed extracted data to fill in these key knowledge gaps. RESULTS: We identified 6934 articles, reviewed 781 full texts, and included 266. From this, we estimate that the median incubation period is 1.4 days. On average, untreated cases are colonized for 18.5 days (95% credible interval [CrI], 17.7-19.4 days), and 95% clear Corynebacterium diphtheriae within 48 days (95% CrI, 46-51 days). Asymptomatic carriers cause 76% (95% confidence interval, 59%-87%) fewer cases over the course of infection than symptomatic cases. The basic reproductive number is 1.7-4.3. Receipt of 3 doses of diphtheria toxoid vaccine is 87% (95% CrI, 68%-97%) effective against symptomatic disease and reduces transmission by 60% (95% CrI, 51%-68%). Vaccinated individuals can become colonized and transmit; consequently, vaccination alone can only interrupt transmission in 28% of outbreak settings, making isolation and antibiotics essential. While antibiotics reduce the duration of infection, they must be paired with diphtheria antitoxin to limit morbidity. CONCLUSIONS: Appropriate tools to confront diphtheria exist; however, accurate understanding of the unique characteristics is crucial and lifesaving treatments must be made widely available. This comprehensive update provides clinical and public health guidance for diphtheria-specific preparedness and response.

After a period of rapidly declining U.S. COVID-19 incidence during January-March 2021, increases occurred in several jurisdictions (1,2) despite the rapid rollout of a large-scale vaccination program. This increase coincided with the spread of more transmissible variants of SARS-CoV-2, the virus that causes COVID-19, including B.1.1.7 (1,3) and relaxation of COVID-19 prevention strategies such as those for businesses, large-scale gatherings, and educational activities. To provide long-term projections of potential trends in COVID-19 cases, hospitalizations, and deaths, COVID-19 Scenario Modeling Hub teams used a multiple-model approach comprising six models to assess the potential course of COVID-19 in the United States across four scenarios with different vaccination coverage rates and effectiveness estimates and strength and implementation of nonpharmaceutical interventions (NPIs) (public health policies, such as physical distancing and masking) over a 6-month period (April-September 2021) using data available through March 27, 2021 (4). Among the four scenarios, an accelerated decline in NPI adherence (which encapsulates NPI mandates and population behavior) was shown to undermine vaccination-related gains over the subsequent 2-3 months and, in combination with increased transmissibility of new variants, could lead to surges in cases, hospitalizations, and deaths. A sharp decline in cases was projected by July 2021, with a faster decline in the high-vaccination scenarios. High vaccination rates and compliance with public health prevention measures are essential to control the COVID-19 pandemic and to prevent surges in hospitalizations and deaths in the coming months.

Dengue hemorrhagic fever (DHF), a severe manifestation of dengue viral infection that can cause severe bleeding, organ impairment, and even death, affects between 15,000 and 105,000 people each year in Thailand. While all Thai provinces experience at least one DHF case most years, the distribution of cases shifts regionally from year to year. Accurately forecasting where DHF outbreaks occur before the dengue season could help public health officials prioritize public health activities. We develop statistical models that use biologically plausible covariates, observed by April each year, to forecast the cumulative DHF incidence for the remainder of the year. We perform cross-validation during the training phase (2000-2009) to select the covariates for these models. A parsimonious model based on preseason incidence outperforms the 10-y median for 65% of province-level annual forecasts, reduces the mean absolute error by 19%, and successfully forecasts outbreaks (area under the receiver operating characteristic curve = 0.84) over the testing period (2010-2014). We find that functions of past incidence contribute most strongly to model performance, whereas the importance of environmental covariates varies regionally. This work illustrates that accurate forecasts of dengue risk are possible in a policy-relevant timeframe.

Coronavirus disease 2019 (COVID-19) has caused strain on health systems worldwide due to its high mortality rate and the large portion of cases requiring critical care and mechanical ventilation. During these uncertain times, public health decision makers, from city health departments to federal agencies, sought the use of epidemiological models for decision support in allocating resources, developing non-pharmaceutical interventions, and characterizing the dynamics of COVID-19 in their jurisdictions. In response, we developed a flexible scenario modeling pipeline that could quickly tailor models for decision makers seeking to compare projections of epidemic trajectories and healthcare impacts from multiple intervention scenarios in different locations. Here, we present the components and configurable features of the COVID Scenario Pipeline, with a vignette detailing its current use. We also present model limitations and active areas of development to meet ever-changing decision maker needs.

Long-term care facilities (LTCFs) bear disproportionate burden of COVID-19 and are prioritized for vaccine deployment. LTCF outbreaks could continue occurring during vaccine rollout due to incomplete population coverage, and the effect of vaccines on viral transmission are currently unknown. Declining adherence to non-pharmaceutical interventions (NPIs) against within-facility transmission could therefore limit the effectiveness of vaccination. We built a stochastic model to simulate outbreaks in LTCF populations with differing vaccination coverage and NPI adherence to evaluate their interacting effects. Vaccination combined with strong NPI adherence produced the least morbidity and mortality. Healthcare worker vaccination improved outcomes in unvaccinated LTCF residents but was less impactful with declining NPI adherence. To prevent further illness and deaths, there is a continued need for NPIs in LTCFs during vaccine rollout.