Search

Background Coronavirus Disease 2019 (COVID-19) continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Here, we present projections of COVID-19 hospitalizations and deaths in the United States for the next 2 years under 2 plausible assumptions about immune escape (20% per year and 50% per year) and 3 possible CDC recommendations for the use of annually reformulated vaccines (no recommendation, vaccination for those aged 65 years and over, vaccination for all eligible age groups based on FDA approval). Methods and findings The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023 and April 15, 2025 under 6 scenarios representing the intersection of considered levels of immune escape and vaccination. Annually reformulated vaccines are assumed to be 65% effective against symptomatic infection with strains circulating on June 15 of each year and to become available on September 1. Age- and state-specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. State and national projections from 8 modeling teams were ensembled to produce projections for each scenario and expected reductions in disease outcomes due to vaccination over the projection period. From April 15, 2023 to April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November to January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% projection interval (PI) [1,438,000, 4,270,000]) hospitalizations and 209,000 (90% PI [139,000, 461,000]) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% confidence interval (CI) [104,000, 355,000]) fewer hospitalizations and 33,000 (95% CI [12,000, 54,000]) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI [29,000, 69,000]) fewer deaths. Conclusions COVID-19 is projected to be a significant public health threat over the coming 2 years. Broad vaccination has the potential to substantially reduce the burden of this disease, saving tens of thousands of lives each year., Author(s): Sung-mok Jung 1, Sara L. Loo 2, Emily Howerton 3, Lucie Contamin 4, Claire P. Smith 2, Erica C. Carcelén 2, Katie Yan 3, Samantha J. Bents 5, John [...]

Correction for “An open challenge to advance probabilistic forecasting for dengue epidemics,” by Michael A. Johansson, Karyn M. Apfeldorf, Scott Dobson, Jason Devita, Anna L. Buczak, Benjamin Baugher, Linda J. Moniz, Thomas Bagley, Steven M. Babin, Erhan Guven, Teresa K. Yamana, Jeffrey Shaman, Terry Moschou, Nick Lothian, Aaron Lane, Grant Osborne, Gao Jiang, Logan C. Brooks, David C. Farrow, Sangwon Hyun, Ryan J. Tibshirani, Roni Rosenfeld, Justin Lessler, Nicholas G. Reich, Derek A. T. Cummings, Stephen A. Lauer, Sean M. Moore, Hannah E. Clapham, Rachel Lowe, Trevor C. Bailey, Markel García-Díez, Marilia Sá Carvalho, Xavier Rodó, Tridip Sardar, Richard Paul, Evan L. Ray, Krzysztof Sakrejda, Alexandria C. Brown, Xi Meng, Osonde Osoba, Raffaele Vardavas, David Manheim, Melinda Moore, Dhananjai M. Rao, Travis C. Porco, Sarah Ackley, Fengchen Liu, Lee Worden, Matteo Convertino, Yang Liu, Abraham Reddy, Eloy Ortiz, Jorge Rivero, Humberto Brito, Alicia Juarrero, Leah R. Johnson, Robert B. Gramacy, Jeremy M. Cohen, Erin A. Mordecai, Courtney C. Murdock, Jason R. Rohr, Sadie J. Ryan, Anna M. Stewart-Ibarra, Daniel P. Weikel, Antarpreet Jutla, Rakibul Khan, Marissa Poultney, Rita R. Colwell, Brenda Rivera-García, Christopher M. Barker, Jesse E. Bell, Matthew Biggerstaff, David Swerdlow, Luis Mier-y-Teran-Romero, Brett M. Forshey, Juli Trtanj, Jason Asher, Matt Clay, Harold S. Margolis, Andrew M. Hebbeler, Dylan George, and Jean-Paul Chretien, which was first published November 11, 2019; 10.1073/pnas.1909865116 (Proc. Natl. Acad. Sci. U.S.A. 116, 24268–24274). The authors note that the following statement should be added to the Acknowledgments: “E.A.M. was supported by NIH Grant R35GM133439.

The duration and nature of immunity generated in response to SARS-CoV-2 infection is unknown. Many public health responses and modeled scenarios for COVID-19 outbreaks caused by SARSCoV-2 assume that infection results in an immune response that protects individuals from future infections or illness for some amount of time. The timescale of protection is a critical determinant of the future impact of the pathogen. The presence or absence of protective immunity due to infection or vaccination (when available) will affect future transmission and illness severity. The dynamics of immunity and nature of protection are relevant to discussions surrounding therapeutic use of convalescent sera as well as efforts to identify individuals with protective immunity. Here, we review the scientific literature on antibody immunity to coronaviruses, including SARS-CoV-2 as well as the related SARS-CoV-1, MERS-CoV and human endemic coronaviruses (HCoVs). We reviewed 1281 abstracts and identified 322 manuscripts relevant to 5 areas of focus: 1) antibody kinetics, 2) correlates of protection, 3) immunopathogenesis, 4) antigenic diversity and cross-reactivity, and 5) population seroprevalence. While studies of SARS-CoV-2 are necessary to determine immune responses to it, evidence from other coronaviruses can provide clues and guide future research.

Many public health responses and modeled scenarios for COVID-19 outbreaks caused by SARS-CoV-2 assume that infection results in an immune response that protects individuals from future infections or illness for some amount of time. The presence or absence of protective immunity due to infection or vaccination (when available) will affect future transmission and illness severity. Here, we review the scientific literature on antibody immunity to coronaviruses, including SARS-CoV-2 as well as the related SARS-CoV, MERS-CoV and endemic human coronaviruses (HCoVs). We reviewed 2,452 abstracts and identified 491 manuscripts relevant to 5 areas of focus: 1) antibody kinetics, 2) correlates of protection, 3) immunopathogenesis, 4) antigenic diversity and cross-reactivity, and 5) population seroprevalence. While further studies of SARS-CoV-2 are necessary to determine immune responses, evidence from other coronaviruses can provide clues and guide future research.

Background Cholera surveillance relies on clinical diagnosis of acute watery diarrhea. Suspected cholera case definitions have high sensitivity but low specificity, challenging our ability to characterize cholera burden and epidemiology. Our objective was to estimate the proportion of clinically suspected cholera that are true Vibrio cholerae infections and identify factors that explain variation in positivity. Methods and findings We conducted a systematic review of studies that tested [greater than or equal to]10 suspected cholera cases for V. cholerae O1/O139 using culture, PCR, and/or a rapid diagnostic test. We searched PubMed, Embase, Scopus, and Google Scholar for studies that sampled at least one suspected case between January 1, 2000 and April 19, 2023, to reflect contemporary patterns in V. cholerae positivity. We estimated diagnostic test sensitivity and specificity using a latent class meta-analysis. We estimated V. cholerae positivity using a random-effects meta-analysis, adjusting for test performance. We included 119 studies from 30 countries. V. cholerae positivity was lower in studies with representative sampling and in studies that set minimum ages in suspected case definitions. After adjusting for test performance, on average, 52% (95% credible interval (CrI): 24%, 80%) of suspected cases represented true V. cholerae infections. After adjusting for test performance and study methodology, the odds of a suspected case having a true infection were 5.71 (odds ratio 95% CrI: 1.53, 15.43) times higher when surveillance was initiated in response to an outbreak than in non-outbreak settings. Variation across studies was high, and a limitation of our approach was that we were unable to explain all the heterogeneity with study-level attributes, including diagnostic test used, setting, and case definitions. Conclusions In this study, we found that burden estimates based on suspected cases alone may overestimate the incidence of medically attended cholera by 2-fold. However, accounting for cases missed by traditional clinical surveillance is key to unbiased cholera burden estimates. Given the substantial variability in positivity between settings, extrapolations from suspected to confirmed cases, which is necessary to estimate cholera incidence rates without exhaustive testing, should be based on local data., Author(s): Kirsten E. Wiens 1,2, Hanmeng Xu 1, Kaiyue Zou 1, John Mwaba 3,4,5, Justin Lessler 1,6,7, Espoir Bwenge Malembaka 1,8, Maya N. Demby 1, Godfrey Bwire 9, Firdausi Qadri [...]

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue.

In 2015 and 2016, Zika virus (ZIKV) swept through dengue virus (DENV) endemic areas of Latin America. These viruses are of the same family, share a vector and may interact competitively or synergistically through human immune responses. We examine dengue incidence from Brazil and Colombia before, during, and after the Zika epidemic. We find evidence that dengue incidence was atypically low in 2017 in both countries. We investigate whether subnational Zika incidence is associated with changes in dengue incidence and find mixed results. Using simulations with multiple assumptions of interactions between DENV and ZIKV, we find cross-protection suppresses incidence of dengue following Zika outbreaks and low periods of dengue incidence are followed by resurgence. Our simulations suggest correlations in DENV and ZIKV reproduction numbers could complicate associations between ZIKV incidence and post-ZIKV DENV incidence and that periods of low dengue incidence are followed by large increases in dengue incidence.

As with many pathogens, most dengue infections are subclinical and therefore unobserved 1 . Coupled with limited understanding of the dynamic behaviour of potential serological markers of infection, this observational problem has wide-ranging implications, including hampering our understanding of individual- and population-level correlates of infection and disease risk and how these change over time, between assay interpretations and with cohort design. Here we develop a framework that simultaneously characterizes antibody dynamics and identifies subclinical infections via Bayesian augmentation from detailed cohort data (3,451 individuals with blood draws every 91 days, 143,548 haemagglutination inhibition assay titre measurements)2,3. We identify 1,149 infections (95% confidence interval, 1,135-1,163) that were not detected by active surveillance and estimate that 65% of infections are subclinical. After infection, individuals develop a stable set point antibody load after one year that places them within or outside a risk window. Individuals with pre-existing titres of ≤1:40 develop haemorrhagic fever 7.4 (95% confidence interval, 2.5-8.2) times more often than naive individuals compared to 0.0 times for individuals with titres >1:40 (95% confidence interval: 0.0-1.3). Plaque reduction neutralization test titres ≤1:100 were similarly associated with severe disease. Across the population, variability in the size of epidemics results in large-scale temporal changes in infection and disease risk that correlate poorly with age.

Globally, >100 million cases and >2.6 million deaths had been attributed to coronavirus disease (COVID-19) as of March 14, 2021 (2). Most cases have been reported in Europe and the [...]

AbstractLarge protracted outbreaks of hepatitis E virus (HEV) have been documented in displaced populations in Africa over the past decade though data are limited outside these exceptional settings. Serological studies can provide insights useful for improving surveillance and disease control. We conducted an age-stratified serological survey using samples previously collected for another research study from 206 residents of an internally displaced person camp in Juba, South Sudan. We tested serum for anti-HEV antibodies (IgM and IgG) and estimated the prevalence of recent and historical exposure to the virus. Using data on individuals' serostatus, camp arrival date, and state of origin, we used catalytic transmission models to estimate the relative risk of HEV infection in the camp compared with that in the participants' home states. The age-adjusted seroprevalence of anti-HEV IgG was 71% (95% confidence interval = 63-78), and 4% had evidence of recent exposure (IgM). We estimated HEV exposure rates to be more than 2-fold (hazard ratio = 2.3, 95% credible interval = 0.3-5.8) higher in the camp than in the participants' home states, although this difference was not statistically significant. HEV transmission may be higher than previously appreciated, even in the absence of reported cases. Improved surveillance in similar settings is needed to understand the burden of disease and minimize epidemic impact through early detection and response.

To better understand the impact of national and global efforts to contain the Ebola virus disease epidemic of 2014–15 in Liberia, we provide a detailed timeline of the major interventions and relate them to the epidemic curve. In addition to personal experience in the response, we systematically reviewed situation reports from the Liberian government, UN, CDC, WHO, UNICEF, IFRC, USAID, and local and international news reports to create the timeline. We extracted data on the timing and nature of activities and compared them to the timeline of the epidemic curve using the reproduction number—the estimate of the average number of new cases caused by a single case. Interventions were organized around five major strategies, with the majority of resources directed to the creation of treatment beds. We conclude that no single intervention stopped the epidemic; rather, the interventions likely had reinforcing effects, and some were less likely than others to have made a major impact. We find that the epidemic’s turning coincided with a reorganization of the response in August–September 2014, the emergence of community leadership in control efforts, and changing beliefs and practices in the population. Ebola Treatment Units were important for Ebola treatment, but the vast majority of these treatment centre beds became available after the epidemic curve began declining. Similarly, the United Nations Mission for Ebola Emergency Response was launched after the epidemic curve had already turned. These findings have significant policy implications for future epidemics and suggest that much of the decline in the epidemic curve was driven by critical behaviour changes within local communities, rather than by international efforts that came after the epidemic had turned. Future global interventions in epidemic response should focus on building community capabilities, strengthening local ownership, and dramatically reducing delays in the response.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has demonstrated the need for epidemiologic models in public health decision-making. Modeling has been critical to planning outbreak responses since [...]

First discovered in 1947, Zika virus (ZIKV) infection remained a little-known tropical disease until 2015, when its apparent association with a considerable increase in the incidence of microcephaly in Brazil raised alarms worldwide. There is limited information on the key factors that determine the extent of the global threat from ZIKV infection and resulting complications. Here, we review what is known about the epidemiology, natural history, and public health effects of ZIKV infection, the empirical basis for this knowledge, and the critical knowledge gaps that need to be filled.

Despite some notable successes in the control of infectious diseases, transmissible pathogens still pose an enormous threat to human and animal health. The ecological and evolutionary dynamics of infections play out on a wide range of interconnected temporal, organizational, and spatial scales, which span hours to months, cells to ecosystems, and local to global spread. Moreover, some pathogens are directly transmitted between individuals of a single species, whereas others circulate among multiple hosts, need arthropod vectors, or can survive in environmental reservoirs. Many factors, including increasing antimicrobial resistance, increased human connectivity and changeable human behavior, elevate prevention and control from matters of national policy to international challenge. In the face of this complexity, mathematical models offer valuable tools for synthesizing information to understand epidemiological patterns, and for developing quantitative evidence for decision-making in global health.

Dengue virus has traditionally caused substantial morbidity and mortality among children less than 15 years of age in Southeast Asia. Over the last 2 decades, a significant increase in the mean age of cases has been reported, and a once pediatric disease now causes substantial burden among the adult population. An age-stratified serological study (n = 1,736) was conducted in 2010 among schoolchildren in the Mueang Rayong district of Thailand, where a similar study had been conducted in 1980/1981. Serotype-specific forces of infection (λ(t)) and basic reproductive numbers (R0) of dengue were estimated for the periods 1969-1980 and 1993-2010. Despite a significant increase in the age at exposure and a decrease in λ(t) from 0.038/year to 0.019/year, R0 changed only from 3.3 to 3.2. Significant heterogeneity was observed across subdistricts and schools, with R0 ranging between 1.7 and 6.8. These findings are consistent with the idea that the observed age shift might be a consequence of the demographic transition in Thailand. Changes in critical vaccination fractions, estimated by using R0, have not accompanied the increase in age at exposure. These results have implications for dengue control interventions because multiple countries in Southeast Asia are undergoing similar demographic transitions. It is likely that dengue will never again be a disease exclusively of children.