The US COVID-19 and Influenza Scenario Modeling Hubs: Delivering long-term projections to guide policy.

Between December 2020 and April 2023, the COVID-19 Scenario Modeling Hub (SMH) generated operational multi-month projections of COVID-19 burden in the US to guide pandemic planning and decision-making in the context of high uncertainty. This effort was born out of an attempt to coordinate, synthesize and effectively use the unprecedented amount of predictive modeling that emerged throughout the COVID-19 pandemic. Here we describe the history of this massive collective research effort, the process of convening and maintaining an open modeling hub active over multiple years, and attempt to provide a blueprint for future efforts.

Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty.

Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.

Inter-rater reliability of the infectious disease modeling reproducibility checklist (IDMRC) as applied to COVID-19 computational modeling research.

Background: Infectious disease computational modeling studies have been widely published during the coronavirus disease 2019 (COVID-19) pandemic, yet they have limited reproducibility. Developed through an iterative testing process with multiple reviewers, the Infectious Disease Modeling Reproducibility Checklist (IDMRC) enumerates the minimal elements necessary to support reproducible infectious disease computational modeling publications. The primary objective of this study was to assess the reliability of the IDMRC and to identify which reproducibility elements were unreported in a sample of COVID-19 computational modeling publications.

Informing pandemic response in the face of uncertainty. .

Our ability to forecast epidemics more than a few weeks into the future is constrained by the complexity of disease systems, our limited ability to measure the current state of an epidemic, and uncertainties in how human action will affect transmission. Realistic longer-term projections (spanning more than a few weeks) may, however, be possible under defined scenarios that specify the future state of critical epidemic drivers, with the additional benefit that such scenarios can be used to anticipate the comparative effect of control measures. Since December 2020, the U.

Inter-rater reliability of the Infectious Disease Modeling Reproducibility Checklist (IDMRC) as applied to COVID-19 computational modeling research.

Background: Infectious disease computational modeling studies have been widely published during the coronavirus disease 2019 (COVID-19) pandemic, yet they have limited reproducibility. Developed through an iterative testing process with multiple reviewers, the Infectious Disease Modeling Reproducibility Checklist (IDMRC) enumerates the minimal elements necessary to support reproducible infectious disease computational modeling publications. The primary objective of this study was to assess the reliability of the IDMRC and to identify which reproducibility elements were unreported in a sample of COVID-19 computational modeling publications.

An implementation framework to improve the transparency and reproducibility of computational models of infectious diseases.

Computational models of infectious diseases have become valuable tools for research and the public health response against epidemic threats. The reproducibility of computational models has been limited, undermining the scientific process and possibly trust in modeling results and related response strategies, such as vaccination. We translated published reproducibility guidelines from a wide range of scientific disciplines into an implementation framework for improving reproducibility of infectious disease computational models.

COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support.

Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed.

Detecting critical slowing down in high-dimensional epidemiological systems.

Despite medical advances, the emergence and re-emergence of infectious diseases continue to pose a public health threat. Low-dimensional epidemiological models predict that epidemic transitions are preceded by the phenomenon of critical slowing down (CSD). This has raised the possibility of anticipating disease (re-)emergence using CSD-based early-warning signals (EWS), which are statistical moments estimated from time series data.

Seasonal and interannual risks of dengue introduction from South-East Asia into China, 2005-2015.

Due to worldwide increased human mobility, air-transportation data and mathematical models have been widely used to measure risks of global dispersal of pathogens. However, the seasonal and interannual risks of pathogens importation and onward transmission from endemic countries have rarely been quantified and validated. We constructed a modelling framework, integrating air travel, epidemiological, demographical, entomological and meteorological data, to measure the seasonal probability of dengue introduction from endemic countries.

Project Tycho 2.0: a repository to improve the integration and reuse of data for global population health.

Objective: In 2013, we released Project Tycho, an open-access database comprising 3.6 million counts of infectious disease cases and deaths reported for over a century by public health surveillance in the United States. Our objective is to describe how Project Tycho version 1 (v1) data has been used to create new knowledge and technology and to present improvements made in the newly released version 2.

Exploring scenarios of chikungunya mitigation with a data-driven agent-based model of the 2014-2016 outbreak in Colombia.

New epidemics of infectious diseases can emerge any time, as illustrated by the emergence of chikungunya virus (CHIKV) and Zika virus (ZIKV) in Latin America. During new epidemics, public health officials face difficult decisions regarding spatial targeting of interventions to optimally allocate limited resources. We used a large-scale, data-driven, agent-based simulation model (ABM) to explore CHIKV mitigation strategies, including strategies based on previous DENV outbreaks.

Heterogeneity Between States in the Health and Economic Impact of Measles Immunization in the United States.

Background: Vaccines have been used successfully for disease elimination programs in many countries. Evidence on the impact of vaccination programs can support decision-making among medical practitioners and policy makers to improve immunization rates. We estimated the health and economic impact of measles vaccination for each of the 48 contiguous states and the District of Columbia since 1964.

Spatial clustering of measles vaccination coverage among children in sub-Saharan Africa.

Background: During the past two decades, vaccination programs have greatly reduced global morbidity and mortality due to measles, but recently this progress has stalled. Even in countries that report high vaccination coverage rates, transmission has continued, particularly in spatially clustered subpopulations with low vaccination coverage.

Methods: We examined the spatial heterogeneity of measles vaccination coverage among children aged 12-23 months in ten Sub-Saharan African countries.

Placental Transfer of Dengue Virus (DENV)-Specific Antibodies and Kinetics of DENV Infection-Enhancing Activity in Brazilian Infants.

Background: Maternal-fetal transferred dengue virus (DENV)-specific antibodies have been implicated in the immunopathogenesis of dengue during infancy.

Methods: A prospective birth cohort was established in a dengue-endemic area in the Northeast Region of Brazil. DENV-specific immunoglobulin G (IgG) and DENV-1-4 serotype-specific neutralizing antibody (NAb) levels were assessed in 376 paired maternal and umbilical cord blood samples.

Persistent Chaos of Measles Epidemics in the Prevaccination United States Caused by a Small Change in Seasonal Transmission Patterns.

Epidemics of infectious diseases often occur in predictable limit cycles. Theory suggests these cycles can be disrupted by high amplitude seasonal fluctuations in transmission rates, resulting in deterministic chaos. However, persistent deterministic chaos has never been observed, in part because sufficiently large oscillations in transmission rates are uncommon.

The role of influenza in the epidemiology of pneumonia.

Interactions arising from sequential viral and bacterial infections play important roles in the epidemiological outcome of many respiratory pathogens. Influenza virus has been implicated in the pathogenesis of several respiratory bacterial pathogens commonly associated with pneumonia. Though clinical evidence supporting this interaction is unambiguous, its population-level effects-magnitude, epidemiological impact and variation during pandemic and seasonal outbreaks-remain unclear.

Region-wide synchrony and traveling waves of dengue across eight countries in Southeast Asia.

Dengue is a mosquito-transmitted virus infection that causes epidemics of febrile illness and hemorrhagic fever across the tropics and subtropics worldwide. Annual epidemics are commonly observed, but there is substantial spatiotemporal heterogeneity in intensity. A better understanding of this heterogeneity in dengue transmission could lead to improved epidemic prediction and disease control.

The availability and consistency of dengue surveillance data provided online by the World Health Organization.

Background: The use of high quality disease surveillance data has become increasingly important for public health action against new threats. In response, countries have developed a wide range of disease surveillance systems enabled by technological advancements. The heterogeneity and complexity of country data systems have caused a growing need for international organizations such as the World Health Organization (WHO) to coordinate the standardization, integration, and dissemination of country disease data at the global level for research and policy.

A systematic review of barriers to data sharing in public health.

Background: In the current information age, the use of data has become essential for decision making in public health at the local, national, and global level. Despite a global commitment to the use and sharing of public health data, this can be challenging in reality. No systematic framework or global operational guidelines have been created for data sharing in public health.

Risk of dengue for tourists and teams during the World Cup 2014 in Brazil.

Background: This year, Brazil will host about 600,000 foreign visitors during the 2014 FIFA World Cup. The concern of possible dengue transmission during this event has been raised given the high transmission rates reported in the past by this country.

Methodology/principal Findings: We used dengue incidence rates reported by each host city during previous years (2001-2013) to estimate the risk of dengue during the World Cup for tourists and teams.

Decay and persistence of maternal dengue antibodies among infants in Bangkok.

Abstract. Maternal dengue antibodies are important in determining the optimal age of dengue vaccination, but no study has quantified the heterogeneity of antibody decay and persistence in infants. We used longitudinal regression methods and survival analysis to measure decay and persistence times of serotype-specific neutralizing antibodies in 139 infants in Bangkok.