ABSTRACT
The COVID-19 pandemic has highlighted the need to upgrade systems for infectious disease surveillance and forecasting and modeling of the spread of infection, both of which inform evidence-based public health guidance and policies. Here, we discuss requirements for an effective surveillance system to support decision making during a pandemic, drawing on the lessons of COVID-19 in the U.S., while looking to jurisdictions in the U.S. and beyond to learn lessons about the value of specific data types. In this report, we define the range of decisions for which surveillance data are required, the data elements needed to inform these decisions and to calibrate inputs and outputs of transmission-dynamic models, and the types of data needed to inform decisions by state, territorial, local, and tribal health authorities. We define actions needed to ensure that such data will be available and consider the contribution of such efforts to improving health equity.
Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , United States/epidemiology , SARS-CoV-2 , Pandemics , Population Surveillance , Public HealthABSTRACT
When respiratory viruses co-circulate in a population, individuals may be infected with multiple pathogens and experience possible virus-virus interactions, where concurrent or recent prior infection with one virus affects the infection process of another virus. While experimental studies have provided convincing evidence for within-host mechanisms of virus-virus interactions, evaluating evidence for viral interference or potentiation using population-level data has proven more difficult. Recent studies have quantified the prevalence of co-detections using populations drawn from clinical settings. Here, we focus on selection bias issues associated with this study design. We provide a quantitative account of the conditions under which selection bias arises in these studies, review previous attempts to address this bias, and propose unbiased study designs with sample size estimates needed to ascertain viral interference. We show that selection bias is expected in cross-sectional co-detection prevalence studies conducted in clinical settings, except under a strict set of assumptions regarding the relative probabilities of being included in a study limited to individuals with clinical disease under different viral states. Population-wide studies that collect samples from participants irrespective of their clinical status would meanwhile require large sample sizes to be sufficiently powered to detect viral interference, suggesting that a study's timing, inclusion criteria, and the expected magnitude of interference are instrumental in determining feasibility.
Subject(s)
Coinfection , Humans , Coinfection/virology , Coinfection/epidemiology , Virus Diseases/epidemiology , Virus Diseases/virology , Cross-Sectional Studies , Viral Interference , Selection Bias , Prevalence , Viruses/genetics , Viruses/classification , Viruses/isolation & purification , Respiratory Tract Infections/virology , Respiratory Tract Infections/epidemiologyABSTRACT
The SARS-CoV-2 BA.2.86 lineage, and its sublineage JN.1 in particular, achieved widespread transmission in the US during winter 2023-24. However, the increase in infections was not accompanied by increases in COVID-19 hospitalizations and mortality commensurate with prior waves. To understand shifts in COVID-19 epidemiology associated with JN.1 emergence, we compared characteristics and clinical outcomes of time-matched cases infected with BA.2.86- derived lineages (predominantly representing JN.1) versus co-circulating XBB-derived lineages in December, 2023 and January, 2024. Cases infected with BA.2.86-derived lineages received greater numbers of COVID-19 vaccine doses, including XBB.1.5-targeted and BA.4/BA.5-targeted boosters, in comparison to cases infected with XBB-derived lineages. Additionally, cases infected with BA.2.86-derived lineages experienced greater numbers of documented prior SARS-CoV-2 infections. These associations of BA.2.86-derived lineages with immune escape were confirmed when comparing cases diagnosed during periods when JN.1 was the predominant circulating lineage to cases diagnosed during November, 2023. Cases infected with BA.2.86-derived lineages, or during periods when JN.1 was the predominant circulating lineage, also experienced lower risk of progression to severe clinical outcomes requiring emergency department consultations or hospital admission. Sensitivity analyses suggested under-ascertainment of prior infections, even if differential between cases infected with BA.2.86-derived lineages and non-BA.2.86 lineages, could not explain this apparent attenuation of severity. Our findings implicate escape from immunity acquired from prior vaccination or infection in the emergence of the JN.1 lineage and suggest infections with this lineage are less likely to experience clinically-severe disease. Monitoring of immune escape and clinical severity in emerging SARS-CoV-2 variants remains a priority to inform responses.
ABSTRACT
AbstractAccurate diagnostics are critical in public health to ensure successful disease tracking, prevention, and control. Many of the same characteristics are desirable for diagnostic procedures in both medicine and public health: for example, low cost, high speed, low invasiveness, ease of use and interpretation, day-to-day consistency, and high accuracy. This review lays out five principles that are salient when the goal of diagnosis is to improve the overall health of a population rather than that of a particular patient, and it applies them in two important use cases: pandemic infectious disease and antimicrobial resistance.
Subject(s)
Communicable Diseases , Public Health , Humans , Communicable Diseases/diagnosis , Communicable Diseases/epidemiology , Communicable Disease Control/methods , Public Health Surveillance/methods , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19/prevention & control , PandemicsABSTRACT
Studies have reported that prior-season influenza vaccination is associated with higher risk of clinical influenza infection among vaccinees. This effect might arise from incomplete consideration of within-season waning and recent infection. Using data from the US Flu Vaccine Effectiveness (VE) Network (2011-2012 to 2018-2019 seasons), we found that repeat vaccinees were vaccinated earlier in a season by one week. After accounting for waning VE, repeat vaccinees were still more likely to test positive for A(H3N2) (OR=1.11, 95%CI:1.02-1.21) but not for influenza B or A(H1N1). We found that clinical infection influenced individuals' decision to vaccinate in the following season while protecting against clinical infection of the same (sub)type. However, adjusting for recent clinical infections did not strongly influence the estimated effect of prior-season vaccination. In contrast, we found that adjusting for subclinical infection could theoretically attenuate this effect. Additional investigation is needed to determine the impact of subclinical infections on VE.
ABSTRACT
BACKGROUND: Mainland China experienced a major surge in SARS-CoV-2 infections in December 2022-January 2023, but its impact on mortality was unclear given the underreporting of coronavirus disease 2019 deaths. METHODS: Using obituary data from the Chinese Academy of Engineering (CAE), we estimated the excess death rate among senior CAE members by taking the difference between the observed rate of all-cause death in December 2022-January 2023 and the expected rate for the same months in 2017-2022, by age groups. We used this to extrapolate an estimate of the number of excess deaths in December 2022-January 2023 among urban dwellers in Mainland China. RESULTS: In December 2022-January 2023, we estimated excess death rates of 0.94 per 100 persons (95% confidence interval [CI] = -0.54, 3.16) in CAE members aged 80-84 years, 3.95 (95% CI = 0.50, 7.84) in 85-89 years, 10.35 (95% CI = 3.59, 17.71) in 90-94 years, and 16.88 (95% CI = 0.00, 34.62) in 95 years and older. Using our baseline assumptions, this extrapolated to 917,000 (95% CI = 425,000, 1.45 million) excess deaths among urban dwellers in Mainland China, much higher than the 81,000 in-hospital deaths officially reported from 9 December 2022 to 30 January 2023. CONCLUSIONS: As in many jurisdictions, we estimate that the coronavirus disease 2019 pandemic had a much wider impact on mortality than what was officially documented in Mainland China.
Subject(s)
COVID-19 , Humans , SARS-CoV-2 , Urban Population , China/epidemiologyABSTRACT
Public health surveillance for pathogens presents an optimization problem: we require enough sampling to identify intervention-triggering shifts in pathogen epidemiology, such as new introductions or sudden increases in prevalence, but not so much that costs due to surveillance itself outweigh those from pathogen-associated illness. To determine this optimal sampling frequency, we developed a general mathematical model for the introduction of a new pathogen that, once introduced, increases in prevalence exponentially. Given the relative cost of infection vs. sampling, we derived equations for the expected combined cost per unit time of disease burden and surveillance for a specified sampling frequency, and thus the sampling frequency for which the expected total cost per unit time is lowest.
Subject(s)
Disease Outbreaks , Public Health SurveillanceABSTRACT
BACKGROUND: JYNNEOSTM vaccine has been used as post-exposure prophylaxis (PEP) during a mpox outbreak in New York City (NYC). Data on effectiveness are limited. METHODS: Effectiveness of a single dose of JYNNEOSTM vaccine administered subcutaneously ≤ 14 days as PEP for preventing mpox disease was assessed among individuals exposed to case-patients from May 22, 2022-August 24, 2022. Individuals were evaluated for mpox through 21 days post-exposure. An observational study was conducted emulating a sequence of nested "target" randomized trials starting each day after exposure. Results were adjusted for exposure risk and race/ethnicity. Analyses were conducted separately based on last (PEPL) and first (PEPF) exposure date. We evaluated the potential to overestimate PEP effectiveness when using conventional analytic methods due to exposed individuals developing illness before they can obtain PEP (immortal time bias) compared to the target trial. RESULTS: Median time from last exposure to symptom onset (incubation period) among cases that did not receive PEPL was 7 days (range 1-16). Time to PEPL receipt was 7 days (range 0-14). Among 549 individuals, adjusted PEPL and PEPF effectiveness was 19 % (95 % Confidence Interval [CI], -54 % to 57 %) and -7% (95 % CI, -144 % to 53 %) using the target trial emulation, respectively, and 78 % (95 % CI, 50 % to 91 %) and 73 % (95 % CI, 31 % to 91 %) using conventional analysis. CONCLUSIONS: Determining PEP effectiveness using real-world data during an outbreak is challenging. Time to PEP in NYC coupled with the observed incubation period resulted in overestimated PEP effectiveness using a conventional method. The target trial emulation, while yielding wide confidence intervals due to small sample size, avoided immortal time bias. While results from these evaluations cannot be used as reliable estimates of PEP effectiveness, we present important methodologic considerations for future evaluations.
Subject(s)
Mpox (monkeypox) , Vaccines , Humans , Disease Outbreaks/prevention & control , New York City/epidemiology , Post-Exposure Prophylaxis/methods , Randomized Controlled Trials as TopicABSTRACT
BACKGROUND: Observational studies are used for estimating vaccine effectiveness under real-world conditions. The practical performance of two common approaches-cohort and test-negative designs-need to be compared for COVID-19 vaccines. METHODS: We compared the cohort and test-negative designs to estimate the effectiveness of the BNT162b2 vaccine against COVID-19 outcomes using nationwide data from the United States Department of Veterans Affairs. Specifically, we (1) explicitly emulated a target trial using follow-up data and evaluated the potential for confounding using negative controls and benchmarking to a randomized trial, (2) performed case-control sampling of the cohort to confirm empirically that the same estimate is obtained, (3) further restricted the sampling to person-days with a test, and (4) implemented additional features of a test-negative design. We also compared their performance in limited datasets. RESULTS: Estimated BNT162b2 vaccine effectiveness was similar under all four designs. Empirical results suggested limited residual confounding by healthcare-seeking behavior. Analyses in limited datasets showed evidence of residual confounding, with estimates biased downward in the cohort design and upward in the test-negative design. CONCLUSION: Vaccine effectiveness estimates under a cohort design with explicit target trial emulation and a test-negative design were similar when using rich information from the VA healthcare system, but diverged in opposite directions when using a limited dataset. In settings like ours with sufficient information on confounders and other key variables, the cohort design with explicit target trial emulation may be preferable as a principled approach that allows estimation of absolute risks and facilitates interpretation of effect estimates.
Subject(s)
COVID-19 , Vaccines , United States/epidemiology , Humans , COVID-19 Vaccines/therapeutic use , BNT162 Vaccine , Vaccine Efficacy , COVID-19/epidemiology , COVID-19/prevention & controlABSTRACT
Background: Infectious disease models, including individual based models (IBMs), can be used to inform public health response. For these models to be effective, accurate estimates of key parameters describing the natural history of infection and disease are needed. However, obtaining these parameter estimates from epidemiological studies is not always straightforward. We aim to 1) outline challenges to parameter estimation that arise due to common biases found in epidemiologic studies and 2) describe the conditions under which careful consideration in the design and analysis of the study could allow us to obtain a causal estimate of the parameter of interest. In this discussion we do not focus on issues of generalizability and transportability. Methods: Using examples from the COVID-19 pandemic, we first identify different ways of parameterizing IBMs and describe ideal study designs to estimate these parameters. Given real-world limitations, we describe challenges in parameter estimation due to confounding and conditioning on a post-exposure observation. We then describe ideal study designs that can lead to unbiased parameter estimates. We finally discuss additional challenges in estimating progression probabilities and the consequences of these challenges. Results: Causal estimation can only occur if we are able to accurately measure and control for all confounding variables that create non-causal associations between the exposure and outcome of interest, which is sometimes challenging given the nature of the variables we need to measure. In the absence of perfect control, non-causal parameter estimates should still be used, as sometimes they are the best available information we have. Conclusions: Identifying which estimates from epidemiologic studies correspond to the quantities needed to parameterize disease models, and determining whether these parameters have causal interpretations, can inform future study designs and improve inferences from infectious disease models. Understanding the way in which biases can arise in parameter estimation can inform sensitivity analyses or help with interpretation of results if the magnitude and direction of the bias is understood.
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Background: Many severe acute respiratory syndrome coronavirus 2 infections have not been detected, reported, or isolated. For community testing programs to locate the most cases under limited testing resources, we developed and evaluated quantitative approaches for geographic targeting of increased coronavirus disease 2019 testing efforts. Methods: For every week from December 5, 2021, to July 23, 2022, testing and vaccination data were obtained in â¼340 cities/communities in Los Angeles County, and models were developed to predict which cities/communities would have the highest test positivity 2 weeks ahead. A series of counterfactual scenarios were constructed to explore the additional number of cases that could be detected under targeted testing. Results: The simplest model based on most recent test positivity performed nearly as well as the best model based on most recent test positivity and weekly tests per 100 persons in identifying communities that would maximize the average yield of cases per test in the following 2 weeks and almost as well as the perfect knowledge of the actual positivity 2 weeks ahead. In the counterfactual scenario, increasing testing by 1% 2 weeks ahead and allocating all tests to communities with the top 10% of predicted positivity would yield a 2% increase in detected cases. Conclusions: Simple models based on current test positivity can predict which communities may have the highest positivity 2 weeks ahead and hence could be allocated with more testing resources.
ABSTRACT
Host immune responses are a key source of selective pressure driving pathogen evolution. Emergence of many SARS-CoV-2 lineages has been associated with enhancements in their ability to evade population immunity resulting from both vaccination and infection. Here we show diverging trends of escape from vaccine-derived and infection-derived immunity for the emerging XBB/XBB.1.5 Omicron lineage. Among 31,739 patients tested in ambulatory settings in Southern California from December, 2022 to February, 2023, adjusted odds of prior receipt of 2, 3, 4, and ≥5 COVID-19 vaccine doses were 10% (95% confidence interval: 1-18%), 11% (3-19%), 13% (3-21%), and 25% (15-34%) lower, respectively, among cases infected with XBB/XBB.1.5 than among cases infected with other co-circulating lineages. Similarly, prior vaccination was associated with greater point estimates of protection against progression to hospitalization among cases with XBB/XBB.1.5 than among non-XBB/XBB.1.5 cases (70% [30-87%] and 48% [7-71%], respectively, for recipients of ≥4 doses). In contrast, cases infected with XBB/XBB.1.5 had 17% (11-24%) and 40% (19-65%) higher adjusted odds of having experienced 1 and ≥2 prior documented infections, respectively, including with pre-Omicron variants. As immunity acquired from SARS-CoV-2 infection becomes increasingly widespread, fitness costs associated with enhanced vaccine sensitivity in XBB/XBB.1.5 may be offset by increased ability to evade infection-derived host responses.
Subject(s)
COVID-19 , Vaccines , Humans , SARS-CoV-2/genetics , COVID-19 Vaccines , COVID-19/prevention & controlABSTRACT
While some studies have previously estimated lives saved by COVID-19 vaccination, we estimate how many deaths could have been averted by vaccination in the US but were not because of a failure to vaccinate. We used a simple method based on a nationally representative dataset to estimate the preventable deaths among unvaccinated individuals in the US from May 30, 2021 to September 3, 2022 adjusted for the effects of age and time. We estimated that at least 232,000 deaths could have been prevented among unvaccinated adults during the 15 months had they been vaccinated with at least a primary series. While uncertainties exist regarding the exact number of preventable deaths and more granular data are needed on other factors causing differences in death rates between the vaccinated and unvaccinated groups to inform these estimates, this method is a rapid assessment on vaccine-preventable deaths due to SARS-CoV-2 that has crucial public health implications. The same rapid method can be used for future public health emergencies.
Subject(s)
COVID-19 , Adult , United States/epidemiology , Humans , COVID-19/prevention & control , COVID-19 Vaccines , SARS-CoV-2 , Vaccination , Public HealthABSTRACT
Studies have reported that prior-season influenza vaccination is associated with higher risk of clinical influenza infection among vaccinees. This effect might arise from incomplete consideration of within-season waning and recent infection. Using data from the US Flu Vaccine Effectiveness (VE) Network (2011-2012 to 2018-2019 seasons), we found that repeat vaccinees were vaccinated earlier in a season by one week. After accounting for waning VE, repeat vaccinees were still more likely to test positive for A(H3N2) (OR=1.11, 95%CI:1.02-1.21) but not for influenza B or A(H1N1). We found that clinical infection influences individuals' decision to vaccinate in the following season while protecting against clinical infection of the same (sub)type. However, adjusting for recent clinical infections did not strongly influence the estimated effect of prior-season vaccination. In contrast, we found that adjusting for subclinical infection could theoretically attenuate this effect. Additional investigation is needed to determine the impact of subclinical infections on VE.
ABSTRACT
The COVID-19 epidemic in the United States has been characterized by two stark disparities. COVID-19 burden has been unequally distributed among racial and ethnic groups and at the same time the mortality rates have been sharply higher among older age groups. These disparities have led some to suggest that inequalities could be reduced by vaccinating front-line workers before vaccinating older individuals, as older individuals in the US are disproportionately Non-Hispanic White. We compare the performance of two distribution policies, one allocating vaccines to front-line workers and another to older individuals aged 65-74-year-old. We estimate both the number of lives saved and the number of years of life saved under each of the policies, overall and in every race/ethnicity groups, in the United States and every state. We show that prioritizing COVID-19 vaccines for 65-74-year-olds saves both more lives and more years of life than allocating vaccines front-line workers in each racial/ethnic group, in the United States as a whole and in nearly every state. When evaluating fairness of vaccine allocation policies, the overall benefit to impact of each population subgroup should be considered, not only the proportion of doses that is distributed to each subgroup. Further work can identify prioritization schemes that perform better on multiple equity metrics.
ABSTRACT
Vaccine allocation decisions during emerging pandemics have proven to be challenging due to competing ethical, practical, and political considerations. Complicating decision making, policy makers need to consider vaccine allocation strategies that balance needs both within and between populations. When vaccine stockpiles are limited, doses should be allocated in locations to maximize their impact. Using a susceptible-exposed-infectious-recovered (SEIR) model we examine optimal vaccine allocation decisions across two populations considering the impact of characteristics of the population (e.g., size, underlying immunity, heterogeneous risk structure, interaction), vaccine (e.g., vaccine efficacy), pathogen (e.g., transmissibility), and delivery (e.g., varying speed and timing of rollout). Across a wide range of characteristics considered, we find that vaccine allocation proportional to population size (i.e., pro-rata allocation) performs either better or comparably to nonproportional allocation strategies in minimizing the cumulative number of infections. These results may argue in favor of sharing of vaccines between locations in the context of an epidemic caused by an emerging pathogen, where many epidemiologic characteristics may not be known.